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1

Lindgren, Timothy Carl. "Place Blogging: Local Economies of Attention in the Network." Thesis, Boston College, 2009. http://hdl.handle.net/2345/647.

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Thesis advisor: Lad Tobin<br>This study examines the emergence of place blogging as an online genre designed to foster a deeper sense of place and to share local knowledge. Focusing on a period between 2003 and 2006, it spotlights a transitional moment in web culture when the relationship between online life and offline life is undergoing an important shift. The bloggers highlighted in this study offer a ground-level view of how ordinary writers and readers participate in the transition to what Eric Gordon calls "network locality," a condition in which the experience of place is increasingly mediated by networked technologies. Because networked life creates an information-saturated environment in which place must compete with everything else for an increasingly scarce resource--human attention--place bloggers redefine blogging as a way to more deliberately and regularly invest attention in place. To do so, they remediate older genres to create a blogging style that differs from the political and technology blogs that were popular at the time: some draw on nature writing and diary writing (essayistic place bloggers) while others tend to draw more heavily on genres of local journalism (journalistic place bloggers). A rhetorical analysis reveals how genre remediation offers place bloggers a range of strategies for managing the flow of attention between self, place, and audience as they interact around digital objects in the network. These insights offer important contributions to scholarly conversions interested in examining how online forms of rhetoric continue to evolve and how our ideas about place are adapting to life in a networked society<br>Thesis (PhD) — Boston College, 2009<br>Submitted to: Boston College. Graduate School of Arts and Sciences<br>Discipline: English
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Janakiraman, Laxmipreethi. "Deep Directive Attention Network(DDAN) based Sign Language Translation." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/26581.

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Sign language is a visual language. It is an effective way of communication for hearing and speech impaired community. In general, all visual languages are multi-modal which utilizes hand gestures, facial expressions and other non-manual features to effectively consider the linguistics of the language while communicating with others. In the recent few years, due to the advancement of computer vision and the NLP field, Sign Language Recognition(SLR) and Sign Language Translation(SLT) topic has attracted many researchers to find an effective way to translate sign language videos to spoken language sentence. Over a decade many ideations have been published but most of them focused on SLR as a mere gesture recognition problem without considering the linguistic structure. In the literature review, we dive deep in to the understanding of various Senor and vision based approaches which were used in the earlier days followed by Deep learning techniques which are offering state-of-the-art results in the recent days. Applying a mid-level Sign Gloss Representation is a key component of performing a successful SLT. Hence, an effective joint learning of mid-level sign Gloss into the Text translation is crucial to improve the performance. In this dissertation, we propose Deep Directive Attention Network (DDAN)-based sign translation framework that allows aligning key-tokens in sign Gloss with key-words in Text. Directive attention transformer is successfully used in this approach to have better inter-intra modal relationship between Gloss sequences and Text sentences which aids in higher translation accuracy of Sign videos to Text sentences. The proposed DDAN contains the Self-Attention (SA) of each sign Gloss and Text, as well as the Gloss Directive-Attention (DA) of Text. Those two attention units, SA and DA, can be placed and integrated in three different proposed DDAN variants, including DA, SDA and SSDA. We evaluate the translation performance of our Sign2(Gloss+Text) and Gloss2Text approaches on the two challenging benchmark datasets PHOENIX-Weather 2014T and ASLG-PC12. The data statistics were analyzed as the first step. Then, three different model variants were evaluated on the above mentioned datasets. The model variant SSDA outperformed the baseline models in both datasets with higher translation accuracy of Sign videos to Text sentences as well as Gloss sequences to Text sentences . Furthermore, we evaluated on various numbers of encoder and decoder to see the optimal count of layers in which the model outperformed the baselines. The hyper-parameter testing result shows the robustness of the proposed framework. In addition to quantitative analysis results, we also provide the qualitative results of the evaluations which shows the generated text sentences has translation precision close to gold standard text along with evident improvement in the morpho-syntax. Based on all the evaluations and analysis results, we demonstrate that out DDAN-based SLT framework outperforms all the state-of-the-art SLT models and achieve better and higher translation accuracy score.
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3

Sun, Renfei. "Attention Network for Video Based Freezing of Gait Detection." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/28908.

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Freezing of gait (FoG) is a typical symptom of Parkinson's disease (PD), which is a brief, episodic absence or marked reduction despite the patients' intention of walking. It is important to timely identify FoG events for clinical assessments. However, well-trained experts are required to identify FoG events, which is subjective and time-consuming. Therefore, automatic FoG identification methods are highly demanded. In this study, we address this task as a human action detection problem based on vision inputs. Two novel attention based deep learning architectures, namely convolutional 3D attention network (C3DAN) and higher order polynomial transformer (HP-Transformer), are proposed to investigate fine-grained FoG patterns. The C3DAN addresses the FoG detection task by exploring the appearance features in detail to learn an informative region for more effective detection. The network consists of two main parts: Spatial Attention Network (SAN) and 3-dimensional convolutional network (C3D). SAN aims to generate an attention regions from coarse to fine, while C3D extracts discriminative features. Our proposed approach is able to localize attention region without manual annotation and to extract discriminative features in an end-to-end way. The HP-Transformer incorporates pose and appearance feature sequences to formulate fine-grained FoG patterns. In particular, higher order self-attentions are proposed based on higher order polynomials. To this end, linear, bilinear and trilinear transformers are formulated in pursuit of discriminative fine-grained representations. These representations are treated as multiple streams and further fused by a self-attention based fusion strategy for FoG detection. Comprehensive experiments on a large in-house dataset collected during clinical assessments demonstrate the effectiveness of the proposed methods. The two methods both achieved promising results and in particular, the HP-Transformer achieved an AUC of 0.92 in the FoG detection task.
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4

Lehtonen, Sanna Elina. "Self-reported Inattention and Hyperactivity-impulsivity as Predictors of Attention Network Efficiency." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/psych_diss/34.

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Previous research has shown that individuals endorsing inattention and hyperactivity-impulsivity have deficient performance on tasks tapping different aspects of attention. Although there is empirical evidence suggesting that the behavioral domains of inattention and hyperactivity-impulsivity are linked to functioning of independent and separate brain areas and neurotransmitter systems, cognitive characterization of adults presenting with problems within these domains is not complete. The aim for this study was to identify the cognitive correlates of the core behavioral domains that define the diagnosis of AD/HD (i.e., inattention and hyperactivity-impulsivity) in a sample of college students, utilizing a computerized attention task, the Attention Network Test (ANT). Different ANT task components have been found to activate separate brain areas linked to the functioning of alerting, orienting and executive attention, and have the potential to provide an indication of the efficiency of these brain networks. In addition to completing the ANT, the participants filled out questionnaires covering common symptoms of adult AD/HD, anxiety and depression. Hierarchical regression analyses revealed that there were no reliable relationships between self-reported symptoms of current inattention and hyperactivity-impulsivity and ANT performance. Further, self-reported depression and/or anxiety did not seem to impact the efficiency of attention networks to a significant degree in this study sample. Gender proved to be the most consistent predictor of ANT performance. Female gender was related to poorer executive attention efficiency. An exploratory ANCOVA revealed that individuals reporting high levels of impulsivity and emotional lability had poorer executive attention efficiency in comparison to those reporting these behaviors and problems to a lesser extent. Future research is needed in order to further explore the relationship between ANT performance and behavioral expressions of adult AD/HD and other neurological and psychiatric conditions.
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Li, Yuchuan. "Dual-Attention Generative Adversarial Network and Flame and Smoke Analysis." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42774.

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Flame and smoke image processing and analysis could improve performance to detect smoke or fire and identify many complicated fire hazards, eventually to help firefighters to fight fires safely. Deep Learning applied to image processing has been prevailing in recent years among image-related research fields. Fire safety researchers also brought it into their studies due to its leading performance in image-related tasks and statistical analysis. From the perspective of input data type, traditional fire research is based on simple mathematical regressions or empirical correlations relying on sensor data, such as temperature. However, data from advanced vision devices or sensors can be analyzed by applying deep learning beyond auxiliary methods in data processing and analysis. Deep Learning has a bigger capacity in non-linear problems, especially in high-dimensional spaces, such as flame and smoke image processing. We propose a video-based real-time smoke and flame analysis system with deep learning networks and fire safety knowledge. It takes videos of fire as input and produces analysis and prediction for flashover of fire. Our system consists of four modules. The Color2IR Conversion module is made by deep neural networks to convert RGB video frames into InfraRed (IR) frames, which could provide important thermal information of fire. Thermal information is critically important for fire hazard detection. For example, 600 °C marks the start of a flashover. As RGB cameras cannot capture thermal information, we propose an image conversion module from RGB to IR images. The core of this conversion is a new network that we innovatively proposed: Dual-Attention Generative Adversarial Network (DAGAN), and it is trained using a pair of RGB and IR images. Next, Video Semantic Segmentation Module helps extract flame and smoke areas from the scene in the RGB video frames. We innovated to use synthetic RGB video data generated and captured from 3D modeling software for data augmentation. After that, a Video Prediction Module takes the RGB video frames and IR frames as input and produces predictions of the subsequent frames of their scenes. Finally, a Fire Knowledge Analysis Module predicts if flashover is coming or not, based on fire knowledge criteria such as thermal information extracted from IR images, temperature increase rate, the flashover occurrence temperature, and increase rate of lowest temperature. For our contributions and innovations, we introduce a novel network, DAGAN, by applying foreground and background attention mechanisms in the image conversion module to help reduce the hardware device requirement for flashover prediction. Besides, we also make use of combination of thermal information from IR images and segmentation information from RGB images in our system for flame and smoke analysis. We also apply a hybrid design of deep neural networks and a knowledge-based system to achieve high accuracy. Moreover, data augmentation is also applied on the Video Semantic Segmentation Module by introducing synthetic video data for training. The test results of flashover prediction show that our system has leading places quantitative and qualitative in terms of various metrics compared with other existing approaches. It can give a flashover prediction as early as 51 seconds with 94.5% accuracy before it happens.
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6

Singh, J. P., A. Kumar, Nripendra P. Rana, and Y. K. Dwivedi. "Attention-based LSTM network for rumor veracity estimation of tweets." Springer, 2020. http://hdl.handle.net/10454/17942.

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Yes<br>Twitter has become a fertile place for rumors, as information can spread to a large number of people immediately. Rumors can mislead public opinion, weaken social order, decrease the legitimacy of government, and lead to a significant threat to social stability. Therefore, timely detection and debunking rumor are urgently needed. In this work, we proposed an Attention-based Long-Short Term Memory (LSTM) network that uses tweet text with thirteen different linguistic and user features to distinguish rumor and non-rumor tweets. The performance of the proposed Attention-based LSTM model is compared with several conventional machine and deep learning models. The proposed Attention-based LSTM model achieved an F1-score of 0.88 in classifying rumor and non-rumor tweets, which is better than the state-of-the-art results. The proposed system can reduce the impact of rumors on society and weaken the loss of life, money, and build the firm trust of users with social media platforms.
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Popli, Labhesh. "AN ATTENTION BASED DEEP NEURAL NETWORK FOR VISUAL QUESTIONANSWERING SYSTEM." Cleveland State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=csu1579015180507068.

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8

You, Di. "Attributed Multi-Relational Attention Network for Fact-checking URL Recommendation." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-theses/1321.

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To combat fake news, researchers mostly focused on detecting fake news and journalists built and maintained fact-checking sites (e.g., Snopes.com and Politifact.com). However, fake news dissemination has been greatly promoted by social media sites, and these fact-checking sites have not been fully utilized. To overcome these problems and complement existing methods against fake news, in this thesis, we propose a deep-learning based fact-checking URL recommender system to mitigate impact of fake news in social media sites such as Twitter and Facebook. In particular, our proposed framework consists of a multi-relational attentive module and a heterogeneous graph attention network to learn complex/semantic relationship between user-URL pairs, user-user pairs, and URL-URL pairs. Extensive experiments on a real-world dataset show that our proposed framework outperforms seven state-of-the-art recommendation models, achieving at least 3~5.3% improvement.
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9

Moradi, Mahdi. "TIME SERIES FORECASTING USING DUAL-STAGE ATTENTION-BASED RECURRENT NEURAL NETWORK." OpenSIUC, 2020. https://opensiuc.lib.siu.edu/theses/2701.

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AN ABSTRACT OF THE RESEARCH PAPER OFMahdi Moradi, for the Master of Science degree in Computer Science, presented on April 1, 2020, at Southern Illinois University Carbondale.TITLE: TIME SERIES FORECASTING USING DUAL-STAGE ATTENTION-BASED RECURRENT NEURAL NETWORKMAJOR PROFESSOR: Dr. Banafsheh Rekabdar
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10

Hanson, Sarah Elizabeth. "Classification of ADHD Using Heterogeneity Classes and Attention Network Task Timing." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/83610.

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Throughout the 1990s ADHD diagnosis and medication rates have increased rapidly, and this trend continues today. These sharp increases have been met with both public and clinical criticism, detractors stating over-diagnosis is a problem and healthy children are being unnecessarily medicated and labeled as disabled. However, others say that ADHD is being under-diagnosed in some populations. Critics often state that there are multiple factors that introduce subjectivity into the diagnosis process, meaning that a final diagnosis may be influenced by more than the desire to protect a patient's wellbeing. Some of these factors include standardized testing, legislation affecting special education funding, and the diagnostic process. In an effort to circumvent these extraneous factors, this work aims to further develop a potential method of using EEG signals to accurately discriminate between ADHD and non-ADHD children using features that capture spectral and perhaps temporal information from evoked EEG signals. KNN has been shown in prior research to be an effective tool in discriminating between ADHD and non-ADHD, therefore several different KNN models are created using features derived in a variety of fashions. One takes into account the heterogeneity of ADHD, and another one seeks to exploit differences in executive functioning of ADHD and non-ADHD subjects. The results of this classification method vary widely depending on the sample used to train and test the KNN model. With unfiltered Dataset 1 data over the entire ANT1 period, the most accurate EEG channel pair achieved an overall vector classification accuracy of 94%, and the 5th percentile of classification confidence was 80%. These metrics suggest that using KNN of EEG signals taken during the ANT task would be a useful diagnosis tool. However, the most accurate channel pair for unfiltered Dataset 2 data achieved an overall accuracy of 65% and a 5th percentile of classification confidence of 17%. The same method that worked so well for Dataset 1 did not work well for Dataset 2, and no conclusive reason for this difference was identified, although several methods to remove possible sources of noise were used. Using target time linked intervals did appear to marginally improve results in both Dataset 1 and Dataset 2. However, the changes in accuracy of intervals relative to target presentation vary between Dataset 1 and Dataset 2. Separating subjects into heterogeneity classes does appear to result in good (up to 83%) classification accuracy for some classes, but results are poor (about 50%) for other heterogeneity classes. A much larger data set is necessary to determine whether or not the very positive results found with Dataset 1 extend to a wide population.<br>Master of Science
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Wang, Yuchen. "Detection of Opioid Addicts via Attention-based bidirectional Recurrent Neural Network." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1592255095863388.

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12

Kobayashi, Kei. "Relationship between media multitasking and functional connectivity in the dorsal attention network." Kyoto University, 2021. http://hdl.handle.net/2433/261612.

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13

Dixon, Matthew Luke. "Contextual and temporal variability in large-scale functional network interactions underlying attention." Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/62694.

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Attentional mechanisms filter and constrain the flow of information processing so that only the most relevant interoceptive and exteroceptive signals are highlighted for further processing. A variety of brain networks play a role in different facets of attention, including the default network, dorsal attention network, salience network, and frontoparietal control network. The present research used functional magnetic resonance imaging (fMRI) and graph theoretic analyses to examine the extent to which interactions between these large-scale brain networks vary across time and different contexts. We addressed the following questions: (i) is there a fundamental competition between networks involved in attending to perceptual versus conceptual information? (ii) is the frontoparietal control network―the key network implicated in the deliberate control of attention―a domain general system, or does it exhibit a finer level of organization related to perceptual versus conceptual attention? and (iii) how does network configuration vary during different modes of internally-directed attention characterized by conceptual elaboration versus interoceptive awareness? Our findings provide novel insights into these fundamental questions, and provide evidence that network organization dynamically changes across time and context. These findings caution against using resting state data to make general inferences about brain organization.<br>Arts, Faculty of<br>Psychology, Department of<br>Graduate
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Saifullah, Mohammad. "Biologically-Based Interactive Neural Network Models for Visual Attention and Object Recognition." Doctoral thesis, Linköpings universitet, Institutionen för datavetenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-79336.

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The main focus of this thesis is to develop biologically-based computational models for object recognition. A series of models for attention and object recognition were developed in the order of increasing functionality and complexity. These models are based on information processing in the primate brain, and specially inspired from the theory of visual information processing along the two parallel processing pathways of the primate visual cortex. To capture the true essence of incremental, constraint satisfaction style processing in the visual system, interactive neural networks were used for implementing our models. Results from eye-tracking studies on the relevant visual tasks, as well as our hypothesis regarding the information processing in the primate visual system, were implemented in the models and tested with simulations. As a first step, a model based on the ventral pathway was developed to recognize single objects. Through systematic testing, structural and algorithmic parameters of these models were fine tuned for performing their task optimally. In the second step, the model was extended by considering the dorsal pathway, which enables simulation of visual attention as an emergent phenomenon. The extended model was then investigated for visual search tasks. In the last step, we focussed on occluded and overlapped object recognition. A couple of eye-tracking studies were conducted in this regard and on the basis of the results we made some hypotheses regarding information processing in the primate visual system. The models were further advanced on the lines of the presented hypothesis, and simulated on the tasks of occluded and overlapped object recognition. On the basis of the results and analysis of our simulations we have further found that the generalization performance of interactive hierarchical networks improves with the addition of a small amount of Hebbian learning to an otherwise pure error-driven learning. We also concluded that the size of the receptive fields in our networks is an important parameter for the generalization task and depends on the object of interest in the image. Our results show that networks using hard coded feature extraction perform better than the networks that use Hebbian learning for developing feature detectors. We have successfully demonstrated the emergence of visual attention within an interactive network and also the role of context in the search task. Simulation results with occluded and overlapped objects support our extended interactive processing approach, which is a combination of the interactive and top-down approach, to the segmentation-recognition issue. Furthermore, the simulation behavior of our models is in line with known human behavior for similar tasks. In general, the work in this thesis will improve the understanding and performance of biologically-based interactive networks for object recognition and provide a biologically-plausible solution to recognition of occluded and overlapped objects. Moreover, our models provide some suggestions for the underlying neural mechanism and strategies behind biological object recognition.
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Almabruk, Tahani A. A. "Study of Executive Attention Network using EEG Coherency: A Data-Driven Approach." Thesis, Curtin University, 2017. http://hdl.handle.net/20.500.11937/56445.

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Assessing executive attention in human is of paramount importance in understanding the development of critical neural pathways of the brain. In particular, it deals with one’s ability to monitor and resolve cognitive conflicts. In the literature, the efficiency of the executive attention is widely assessed based on the conflict-related changes in the behavioural data. In this thesis, we introduce a new data-driven approach, which exploits Electroencephalography (EEG) coherency to study the topography and efficiency of the executive attention.
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Antezana, Ligia. "Salience and Frontoparietal Network Patterns in Children with Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/83967.

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Autism spectrum disorder (ASD) and attention deficit/hyperactivity disorder (ADHD) have been difficult to differentiate in clinical settings, as these two disorders are phenotypically similar and both exhibit atypical attention and executive functioning. Mischaracterizations between these two disorders can lead to inappropriate medication regimes, significant delays in special services, and personal distress to families and caregivers. There is evidence that ASD and ADHD are biologically different for attentional and executive functioning mechanisms, as only half of individuals with co-occurring ASD and ADHD respond to stimulant medication. Further, neurobehavioral work has supported these biological differences for ASD and ADHD, with both shared and distinct functional connectivity. In specific, two brain networks have been implicated in these disorders: the salience network (SN) and frontoparietal network (FPN). The SN is a network anchored by bilateral anterior insula and the dorsal anterior cingulate cortex and has been implicated in “bottom-up” attentional processes for both internal and external events. The FPN is anchored by lateral prefrontal cortex areas and the parietal lobe and plays a roll in “top-down” executive processes. Functional connectivity subgroups differentiated ASD from ADHD with between SN-FPN connectivity patterns, but not by within-SN or within-FPN connectivity patterns. Further, subgroup differences in ASD+ADHD comorbidity vs. ASD only were found for within-FPN connectivity.<br>Master of Science<br>Autism spectrum disorder (ASD) and attention deficit/hyperactivity disorder (ADHD) have been difficult to differentiate in clinical settings, as these two disorders are similar and both exhibit attention and executive functioning difficulties. ASD and ADHD have shared and distinct functional brain network connectivity related to attention and executive functioning. Two brain networks have been implicated in these disorders: the salience network (SN) and frontoparietal network (FPN). The SN is a network that has been implicated in “bottom-up” attentional processes for both internal and external events. The FPN plays a roll in “top-down” executive processes. This study found that functional connectivity patterns between the SN and FPN differentiated ASD from ADHD. Further, connectivity patterns in children with co-occurring ASD and ADHD were characterized by within-FPN connectivity.
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Anderson, Jeffrey S., Scott M. Treiman, Michael A. Ferguson, et al. "Violence: heightened brain attentional network response is selectively muted in Down syndrome." Springer, 2015. http://hdl.handle.net/10150/610322.

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BACKGROUND: The ability to recognize and respond appropriately to threat is critical to survival, and the neural substrates subserving attention to threat may be probed using depictions of media violence. Whether neural responses to potential threat differ in Down syndrome is not known. METHODS: We performed functional MRI scans of 15 adolescent and adult Down syndrome and 14 typically developing individuals, group matched by age and gender, during 50 min of passive cartoon viewing. Brain activation to auditory and visual features, violence, and presence of the protagonist and antagonist were compared across cartoon segments. fMRI signal from the brain's dorsal attention network was compared to thematic and violent events within the cartoons between Down syndrome and control samples. RESULTS: We found that in typical development, the brain's dorsal attention network was most active during violent scenes in the cartoons and that this was significantly and specifically reduced in Down syndrome. When the antagonist was on screen, there was significantly less activation in the left medial temporal lobe of individuals with Down syndrome. As scenes represented greater relative threat, the disparity between attentional brain activation in Down syndrome and control individuals increased. There was a reduction in the temporal autocorrelation of the dorsal attention network, consistent with a shortened attention span in Down syndrome. Individuals with Down syndrome exhibited significantly reduced activation in primary sensory cortices, and such perceptual impairments may constrain their ability to respond to more complex social cues such as violence. CONCLUSIONS: These findings may indicate a relative deficit in emotive perception of violence in Down syndrome, possibly mediated by impaired sensory perception and hypoactivation of medial temporal structures in response to threats, with relative preservation of activity in pro-social brain regions. These findings indicate that specific genetic differences associated with Down syndrome can modulate the brain's response to violence and other complex emotive ideas.
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Ta, Thi Minh Tam [Verfasser]. "Test-Retest Reliabilität des Attention-Network-Test bei Schizophrenie / Thi Minh Tam Ta." Berlin : Medizinische Fakultät Charité - Universitätsmedizin Berlin, 2013. http://d-nb.info/1037725654/34.

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19

Liu, Ruixu. "Attention Based Temporal Convolutional Neural Network for Real-time 3D Human Pose Reconstruction." University of Dayton / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=dayton157546836015948.

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20

Ahmad, Mohd Riduan. "Interaction of Lightning Flashes with Wireless Communication Networks : Special Attention to Narrow Bipolar Pulses." Doctoral thesis, Uppsala universitet, Elektricitetslära, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-233673.

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In this thesis, the features of electric field signatures of narrow bipolar pulses (NBPs) generated by cloud flashes are investigated and their effects on wireless communication systems are studied. A handful amount of NBPs (14.5%) have been observed to occur as part of cloud-to-ground flashes in South Malaysia. Occurrence of NBPs in Sweden has been reported for the first time in this thesis. The electric field waveform characteristics of NBPs as part of cloud-to-ground flashes were similar to isolated NBPs found in Sweden and South Malaysia and also to those isolated NBPs reported by previous studies from various geographical areas. This is a strong indication that their breakdown mechanisms are similar at any latitudes regardless of geographical areas. A comparative study on the occurrence of NBPs and other forms of lightning flashes across various geographical areas ranging from northern regions to the tropics is presented. As the latitude decreased from Uppsala, Sweden (59.8°N) to South Malaysia (1.5°N), the percentage of NBP emissions relative to the total number of lightning flashes increased significantly from 0.13% to 12%. Occurrences of positive NBPs were more common than negative NBPs at all observed latitudes. However, as latitudes decreased, the negative NBP emissions increased significantly from 20% (Sweden) to 45% (South Malaysia). Factors involving mixed-phase region elevations and vertical extents of thundercloud tops are invoked to explain the observed results. These factors are fundamentally latitude dependent. In this thesis, the interaction between microwave radiations emitted by cloud-to-ground and cloud flashes events and bits transmission in wireless communication networks are also presented. To the best of our knowledge, this is the first time such effects are investigated in the literature. Narrow bipolar pulses were found to be the strongest source of interference that interfered with the bits transmission.
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Garagnani, Max. "Understanding language and attention : brain-based model and neurophysiological experiments." Thesis, University of Cambridge, 2009. https://www.repository.cam.ac.uk/handle/1810/243852.

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This work concerns the investigation of the neuronal mechanisms at the basis of language acquisition and processing, and the complex interactions of language and attention processes in the human brain. In particular, this research was motivated by two sets of existing neurophysiological data which cannot be reconciled on the basis of current psycholinguistic accounts: on the one hand, the N400, a robust index of lexico-semantic processing which emerges at around 400ms after stimulus onset in attention demanding tasks and is larger for senseless materials (meaningless pseudowords) than for matched meaningful stimuli (words); on the other, the more recent results on the Mismatch Negativity (MMN, latency 100-250ms), an early automatic brain response elicited under distraction which is larger to words than to pseudowords. We asked what the mechanisms underlying these differential neurophysiological responses may be, and whether attention and language processes could interact so as to produce the observed brain responses, having opposite magnitude and different latencies. We also asked questions about the functional nature and anatomical characteristics of the cortical representation of linguistic elements. These questions were addressed by combining neurocomputational techniques and neuroimaging (magneto-encephalography, MEG) experimental methods. Firstly, a neurobiologically realistic neural-network model composed of neuron-like elements (graded response units) was implemented, which closely replicates the neuroanatomical and connectivity features of the main areas of the left perisylvian cortex involved in spoken language processing (i.e., the areas controlling speech output – left inferior-prefrontal cortex, including Broca’s area – and the main sensory input – auditory – areas, located in the left superior-temporal lobe, including Wernicke’s area). Secondly, the model was used to simulate early word acquisition processes by means of a Hebbian correlation learning rule (which reflects known synaptic plasticity mechanisms of the neocortex). The network was “taught” to associate pairs of auditory and articulatory activation patterns, simulating activity due to perception and production of the same speech sound: as a result, neuronal word representations distributed over the different cortical areas of the model emerged. Thirdly, the network was stimulated, in its “auditory cortex”, with either one of the words it had learned, or new, unfamiliar pseudoword patterns, while the availability of attentional resources was modulated by changing the level of non-specific, global cortical inhibition. In this way, the model was able to replicate both the MMN and N400 brain responses by means of a single set of neuroscientifically grounded principles, providing the first mechanistic account, at the cortical-circuit level, for these data. Finally, in order to verify the neurophysiological validity of the model, its crucial predictions were tested in a novel MEG experiment investigating how attention processes modulate event-related brain responses to speech stimuli. Neurophysiological responses to the same words and pseudowords were recorded while the same subjects were asked to attend to the spoken input or ignore it. The experimental results confirmed the model’s predictions; in particular, profound variability of magnetic brain responses to pseudowords but relative stability of activation to words as a function of attention emerged. While the results of the simulations demonstrated that distributed cortical representations for words can spontaneously emerge in the cortex as a result of neuroanatomical structure and synaptic plasticity, the experimental results confirm the validity of the model and provide evidence in support of the existence of such memory circuits in the brain. This work is a first step towards a mechanistic account of cognition in which the basic atoms of cognitive processing (e.g., words, objects, faces) are represented in the brain as discrete and distributed action-perception networks that behave as closed, independent systems.
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22

Guedj, Carole. "Modulation noradrénergique de l’attention." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSE1294/document.

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La neuromodulation apporte une extraordinaire richesse à la dynamique des réseaux de neurones. Parmi les neuromodulateurs du système nerveux central, la noradrénaline permettrait de faciliter l'adaptation du comportement face aux variations des contraintes environnementales en modulant l'attention, cette fonction au coeur de la cognition qui nous permet de sélectionner l'information la plus pertinente en fonction de notre but. Ce processus complexe qui opère à chaque instant à la fois dans l'espace et le temps, constitue une étape essentielle dans cette adaptation comportementale. Cependant, à ce jour, les mécanismes par lesquels ce neuromodulateur exerce ses effets sur le cerveau sain demeurent mal connus. Mon travail de thèse a pour objectif d'examiner les déterminants comportementaux et les marqueurs neuronaux de l'effet stimulant des agonistes noradrénergiques. La question posée était : "Comment la noradrénaline agit-elle pour optimiser l'attention?" Pour répondre à cette question, j'ai choisi de combiner la pharmacologie, l'analyse du comportement, et l'imagerie par résonnance magnétique fonctionnelle chez le singe. Un des principaux résultats de mes travaux est que l'administration d'agents noradrénergiques induit une large réorganisation des réseaux cérébraux, qui pourrait être à l'origine de l'optimisation des réponses comportementales observées parallèlement<br>Neuromodulation provides an extraordinary wealth to the dynamics of neural networks. Among the neuromodulators of the central nervous system, noradrenaline would facilitate behavioral adaptation facing variations of environmental constraints by modulating attention, this function at the heart of cognition that allows us to select the most relevant information based our goal. This complex process that operates at every moment both in space and time, is an essential step in this behavioral adaptation. However, to date, the mechanisms by which this neuromodulator exerts its effects on healthy brain remain unknown. My thesis aims to examine the behavioral and neural markers of the boosting effect of noradrenergic agonists. The question asked was: "How does noradrenaline optimize attention?" To answer this question, I chose to combine pharmacology, behavior analysis, and functional Magnetic Resonance Imaging in monkeys. One of the main results of my work is that the administration of noradrenergic agents induced a large-scale brain networks reorganization, which could be responsible for optimizing behavioral responses observed in parallel
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Bopaiah, Jeevith. "A recurrent neural network architecture for biomedical event trigger classification." UKnowledge, 2018. https://uknowledge.uky.edu/cs_etds/73.

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A “biomedical event” is a broad term used to describe the roles and interactions between entities (such as proteins, genes and cells) in a biological system. The task of biomedical event extraction aims at identifying and extracting these events from unstructured texts. An important component in the early stage of the task is biomedical trigger classification which involves identifying and classifying words/phrases that indicate an event. In this thesis, we present our work on biomedical trigger classification developed using the multi-level event extraction dataset. We restrict the scope of our classification to 19 biomedical event types grouped under four broad categories - Anatomical, Molecular, General and Planned. While most of the existing approaches are based on traditional machine learning algorithms which require extensive feature engineering, our model relies on neural networks to implicitly learn important features directly from the text. We use natural language processing techniques to transform the text into vectorized inputs that can be used in a neural network architecture. As per our knowledge, this is the first time neural attention strategies are being explored in the area of biomedical trigger classification. Our best results were obtained from an ensemble of 50 models which produced a micro F-score of 79.82%, an improvement of 1.3% over the previous best score.
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Dronzeková, Michaela. "Analýza polygonálních modelů pomocí neuronových sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2020. http://www.nusl.cz/ntk/nusl-417253.

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This thesis deals with rotation estimation of 3D model of human jaw. It describes and compares methods for direct analysis od 3D models as well as method to analyze model using rasterization. To evaluate perfomance of proposed method, a metric that computes number of cases when prediction was less than 30° from ground truth is used. Proposed method that uses rasterization, takes  three x-ray views of model as an input and processes it with convolutional network. It achieves best preformance, 99% with described metric. Method to directly analyze polygonal model as a sequence uses attention mechanism to do so and was inspired by transformer architecture. A special pooling function was proposed for this network that decreases memory requirements of the network. This method achieves 88%, but does not use rasterization and can process polygonal model directly. It is not as good as rasterization method with x-ray display, byt it is better than rasterization method with model not rendered as x-ray.  The last method uses graph representation of mesh. Graph network had problems with overfitting, that is why it did not get good results and I think this method is not very suitable for analyzing plygonal model.
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Isunza, Navarro Abgeiba Yaroslava. "Evaluation of Attention Mechanisms for Just-In-Time Software Defect Prediction." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-288724.

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Just-In-Time Software Defect Prediction (JIT-DP) focuses on predicting errors in software at change-level with the objective of helping developers identify defects while the development process is still ongoing, and improving the quality of software applications. This work studies deep learning techniques by applying attention mechanisms that have been successful in, among others, Natural Language Processing (NLP) tasks. We introduce two networks named Convolutional Neural Network with Bidirectional Attention (BACNN) and Bidirectional Attention Code Network (BACoN) that employ a bi-directional attention mechanism between the code and message of a software change. Furthermore, we examine BERT [17] and RoBERTa [57] attention architectures for JIT-DP. More specifically, we study the effectiveness of the aforementioned attention-based models to predict defective commits compared to the current state of the art, DeepJIT [37] and TLEL [101]. Our experiments evaluate the models by using software changes from the OpenStack open source project. The results showed that attention-based networks outperformed the baseline models in terms of accuracy in the different evaluation settings. The attention-based models, particularly BERT and RoBERTa architectures, demonstrated promising results in identifying defective software changes and proved to be effective in predicting defects in changes of new software releases.<br>Just-In-Time Defect Prediction (JIT-DP) fokuserar på att förutspå fel i mjukvara vid ändringar i koden, med målet att hjälpa utvecklare att identifiera defekter medan utvecklingsprocessen fortfarande är pågående, och att förbättra kvaliteten hos applikationsprogramvara. Detta arbete studerar djupinlärningstekniker genom att tillämpa attentionmekanismer som har varit framgångsrika inom, bland annat, språkteknologi (NLP). Vi introducerar två nätverk vid namn Convolutional Neural Network with Bidirectional Attention (BACNN), och Bidirectional Attention Code Network (BACoN), som använder en tvåriktad attentionmekanism mellan koden och meddelandet om en mjukvaruändring. Dessutom undersöker vi BERT [17] och RoBERTa [57], attentionarkitekturer för JIT-DP. Mer specifikt studerar vi hur effektivt dessa attentionbaserade modeller kan förutspå defekta ändringar, och jämför dem med de bästa tillgängliga arkitekturerna DeePJIT [37] och TLEL [101]. Våra experiment utvärderar modellerna genom att använda mjukvaruändringar från det öppna källkodsprojektet OpenStack. Våra resultat visar att attentionbaserade nätverk överträffar referensmodellen sett till träffsäkerheten i de olika scenarierna. De attentionbaserade modellerna, framför allt BERT och RoBERTa, demonstrerade lovade resultat när det kommer till att identifiera defekta mjukvaruändringar och visade sig vara effektiva på att förutspå defekter i ändringar av nya mjukvaruversioner.
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Lee, John Boaz T. "Deep Learning on Graph-structured Data." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-dissertations/570.

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In recent years, deep learning has made a significant impact in various fields – helping to push the state-of-the-art forward in many application domains. Convolutional Neural Networks (CNN) have been applied successfully to tasks such as visual object detection, image super-resolution, and video action recognition while Long Short-term Memory (LSTM) and Transformer networks have been used to solve a variety of challenging tasks in natural language processing. However, these popular deep learning architectures (i.e., CNNs, LSTMs, and Transformers) can only handle data that can be represented as grids or sequences. Due to this limitation, many existing deep learning approaches do not generalize to problem domains where the data is represented as graphs – social networks in social network analysis or molecular graphs in chemoinformatics, for instance. The goal of this thesis is to help bridge the gap by studying deep learning solutions that can handle graph data naturally. In particular, we explore deep learning-based approaches in the following areas. 1. Graph Attention. In the real-world, graphs can be both large – with many complex patterns – and noisy which can pose a problem for effective graph mining. An effective way to deal with this issue is to use an attention-based deep learning model. An attention mechanism allows the model to focus on task-relevant parts of the graph which helps the model make better decisions. We introduce a model for graph classification which uses an attention-guided walk to bias exploration towards more task-relevant parts of the graph. For the task of node classification, we study a different model – one with an attention mechanism which allows each node to select the most task-relevant neighborhood to integrate information from. 2. Graph Representation Learning. Graph representation learning seeks to learn a mapping that embeds nodes, and even entire graphs, as points in a low-dimensional continuous space. The function is optimized such that the geometric distance between objects in the embedding space reflect some sort of similarity based on the structure of the original graph(s). We study the problem of learning time-respecting embeddings for nodes in a dynamic network. 3. Brain Network Discovery. One of the fundamental tasks in functional brain analysis is the task of brain network discovery. The brain is a complex structure which is made up of various brain regions, many of which interact with each other. The objective of brain network discovery is two-fold. First, we wish to partition voxels – from a functional Magnetic Resonance Imaging scan – into functionally and spatially cohesive regions (i.e., nodes). Second, we want to identify the relationships (i.e., edges) between the discovered regions. We introduce a deep learning model which learns to construct a group-cohesive partition of voxels from the scans of multiple individuals in the same group. We then introduce a second model which can recover a hierarchical set of brain regions, allowing us to examine the functional organization of the brain at different levels of granularity. Finally, we propose a model for the problem of unified and group-contrasting edge discovery which aims to discover discriminative brain networks that can help us to better distinguish between samples from different classes.
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27

Ossandon, Valdes Tomas. "A prefrontal-temporal network underlying state changes between Stimulus-Driven and Stimulus-Independent Cognition." Phd thesis, Université Claude Bernard - Lyon I, 2010. http://tel.archives-ouvertes.fr/tel-00726306.

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The brain displays moment-to-moment activity fluctuations that reflect various levels of engagement with the outside world. Processing external stimuli is not only associated with increased brain metabolism but also with prominent deactivation in specific structures, collectively known as the default-mode network (DMN). The role of the DMN remains enigmatic partly because its electrophysiological correlates and temporal dynamics are still poorly understood. Using unprecedented wide-spread depth recordings in epileptic patients, undergoing intracranial EEG during pre-surgical evaluation, we reveal that DMN neural populations display task-related suppressions of gamma (60-140 Hz) power and, critically, we show how millisecond temporal profile and amplitude of gamma deactivation tightly correlate with task demands and subject performance. The results show also that during an attentional task, sustained activations in the gamma band power are presented across large cortical networks, while transient activations are mostly specific to occipital and temporal regions. Our findings reveal a pivotal role for broadband gamma modulations in the interplay between activation and deactivation networks mediating efficient goal-directed behavior
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MacNamara, Kailey. "Behavioral and Neural Mechanisms of Social Heterogeneity in Attention Deficit/Hyperactivity Disorder." FIU Digital Commons, 2017. http://digitalcommons.fiu.edu/etd/3390.

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Attention-deficit/hyperactivity disorder (ADHD) is one of the most common child-onset neurodevelopment disorders, affecting 5% of children in the United States (American Psychiatric Association, 2013). Treatment matching in ADHD is difficult and unsatisfactory; the same general treatment algorithm is recommended for all children. It is therefore important to consider the development of specialized treatment programs based on a variety of behavioral and neurological biomarkers. Unfortunately, due to its multi-faceted classification, the heterogeneity of this behavioral disorder is under-investigated (Costa Dias et al., 2015). Scientific research in this area is especially limited as the severity of ADHD goes undiagnosed, children tend to have difficulties remaining still in MRI scanners, and the hyperactivity-impulsivity that is associated with ADHD may cause further challenges when trying to remain motionless in the scanner. Furthermore, tasks such as Facial Emotion Perception Task (FEPT) and Theory of Mind (ToM) have not been used to analyze social and behavioral deficits in children with ADHD. More research needs to be allocated to helping uncover the neural substrates underlying the inattention and hyperactivity traits of this disorder. For this reason, we acquired functional magnetic resonance imaging (fMRI) data from five children with ADHD performing the FEPT and ToM tasks. The results showed the children have an easier and quick time correctly identifying happy emotional states, as compared to the fearful, angry, and neutral conditions. Results from the FEPT task also revealed that the participants were thinking and reasoning more (i.e., taking longer to deduce an ending) when identifying emotions than identifying animals. The ToM task showed that the default mode network (DMN) may not be fully suppressed when the children are choosing the correct cartoon ending, and therefore the children may be having lapses in attention. These findings may assist the current hypothesis that the default mode network has reduced network homogeneity in people with ADHD. Overall, the findings presented in this thesis provide a good diving board into discovering the reason(s) for the social cognition and emotion recognition impairments associated with ADHD, but further research is needed in order to one day pinpoint and ultimately correct the regions(s) of dysfunction.
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Stan, Denis-Emanuel. "News flow and trading activity: A study of investor attention and market predictability." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/203276/1/Denis-Emanuel_Stan_Thesis.pdf.

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This thesis examines the relationship between investors' attention and movements in financial markets. Providing an explanation to the relationship between investor attention and market returns and return volatility, where attention is measured by Google search volume and two indirect price-based measures, investor attention does not contribute to return predictability however significant links to volatility are found. Furthermore, revisiting the joint volume-volatility relationship seeking to investigate the dynamic links of market volatility, trading volume, and investor attention (measured by Google search and Twitter tweet volume), investor attention provides a somewhat significant link for the rate at which investors seek market information.
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Morissette, Laurence. "Auditory Object Segregation: Investigation Using Computer Modelling and Empirical Event-Related Potential Measures." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/37856.

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There are multiple factors that influence auditory steaming. Some, like frequency separation or rate of presentation, have effects that are well understood while others remain contentious. Human behavioural studies and event-related potential (ERP) studies have shown dissociation between a pre-attentive sound segregation process and an attention-dependent process in forming perceptual objects and streams. This thesis first presents a model that synthetises the processes involved in auditory object creation. It includes sensory feature extraction based on research by Bregman (1990), sensory feature binding through an oscillatory neural network based on work by Wang (1995; 1996; 1999; 2005; 2008), work by Itti and Koch (2001a) for the saliency map, and finally, work by Wrigley and Brown (2004) for the architecture of single feature processing streams, the inhibition of return of the activation and the attentional leaky integrate and fire neuron. The model was tested using stimuli and an experimental paradigm used by Carlyon, Cusack, Foxton and Robertson (2001). Several modifications were then implemented to the initial model to bring it closer to psychological and cognitive validity. The second part of the thesis furthers the knowledge available concerning the influence of the time spent attending to a task on streaming. Two deviant detection experiments using triplet stimuli are presented. The first experiment is a follow-up of Thompson, Carlyon and Cusack (2011) and replicated their behavioural findings, showing that the time spent attending to a task enhances streaming, and that deviant detection is easier when one stream is perceived. The ERP results showed double decisions markers indicating that subjects may have made their deviant detection based on the absence of the time delayed deviant and confirmed their decision with its later presence. The second experiment investigated the effect of the time spent attending to the task in presence of a continuity illusion on streaming. It was found that the presence of this illusion prevented streaming in such a way that the pattern of the triplet was strengthened through time instead of separated into two streams, and that the deviant detection was easier the longer the subjects attended to the sound sequence.
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31

Abdel, Rahim Yasser. "Imaging identity : a study of Aljazeera's online news and its representation of Arabness with particular attention to "Arabs in diaspora&quot." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=100306.

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This thesis studies the relations between media image, online news design, and the framing of identity. It scrutinizes current images of Arab identity and their representation in Aljazeera Net in order to examine how Aljazeera Net constructs the 'reality' of Arabs. The dissertation begins by defining Arabness in terms of ethnic, cultural and postcolonial identities. It proposes and assesses the sources of Arab identity and examines Arab identity as a source of meanings for Arabs. Likewise, it evaluates the sources of Arab identity in the Arab diaspora. Through the lens of a remediation approach, the study explores newly emerging practices in the representation of news, and investigates how the design of Aljazeera Net alters the construction of meaning in news representation. The frames that govern the representation of Arab identity determine the complexity of the image of Arabness, and reveal the differences between the acknowledged perspectives and evolving identity of Aljazeera. The study conceives Aljazeera Net as a space for the reciprocal relationship between Aljazeera and Arabs in diaspora, and as a site for the overlapping between the local and the global in media representations. Finally, it considers how Arabs in the North American diaspora, particularly Arab media experts, academics and community leaders, perceive their identity, and how they evaluate Aljazeera as a Pan Arab media.
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Silva, Adriana Ferreira. "Efeito da estimulação transcraniana de corrente contínua com a tarefa neurocognitiva na capacidade atencional e na dor de pacientes com fibromialgia." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2015. http://hdl.handle.net/10183/129673.

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Introdução: Fibromialgia (FM) é uma síndrome que acomete entre 1-6% da população, com maior frequência em mulheres. Costuma cursar com dor crônica, alterações de sono, sintomas depressivos e prejuízo de memória. Seu impacto na vida das pacientes está relacionado às limitações para atividades da vida diária, incluindo as funções executivas. A disfunção das redes neurais envolvidas no sistema de excitabilidade e de inibição tem repercussões cognitivas que comprometem a atenção e o desempenho de atividades laborais. A dor é o sintoma que governa esta síndrome e é capaz de afetar a capacidade atencional de pacientes com FM e prejudicar realizações cotidianas. No entanto, pouco se conhece sobre as vias e os processos envolvidos nesse conjunto de sintomas. Faz-se necessário, portanto, compreender esses processos e buscar estratégias terapêuticas com efeito nesses mecanismos. Dentre os tratamentos, pode-se citar a estimulação transcraniana de corrente continua (ETCC), intervenção com feito modulador da atividade neuronal, cujo potencial beneficio tem sido demonstrado na FM. A integração do efeito excitatório da ETCC ao efeito inibitório das tarefas neurocogntivas em áreas envolvidas no processamento afetivo-motivacional, incluindo a dor crônica, não foi profundamente explorada. O objetivo do presente estudo foi comparar o efeito da ETCC-ativa (a) com ETCC-sham (s) combinada a tarefa neurocognitva inibitória (Go- noGo Task) na dor e capacidade atencional de pacientes com FM. Métodos: Foram selecionadas pacientes com diagnóstico de FM de acordo com o American College of Rheumatology (ACR) 2010. A amostra foi composta por 40 pacientes, subdivididas em dois grupos- ETCC-a ou ETCC-s- num ensaio clínico do tipo cruzado, duas sessões com intervalo de sete dias entre uma intervenção e outra. A estimulação ETCC foi anódica pré-frontal dorsolateral (DLPFC) de 1 mA por 20 minutos. As intervenções utilizadas foram Go- noGo Task (GNG), Attention Network Task (ANT) e ETCC. Resultados: Houve significativa diferença entre os grupos ETCC-a e ETCC-s nos resultados de ANT. ETCC-a combinada a tarefa GNG foi capaz de potencializar a rede de atenção executiva e amenizar a sensação de dor. Em ANT os dados relacionados à orientação foram -14,63 de diferença média, com 95% intervalo de confiança (IC) (de -18,89 a -0,37). Quanto à execução, foi verificada média de 21.00 com 95% de IC (4.11 a 37.89). Em relação ao alerta não houve diferença, apresentando a média de -3,17; com 95% de IC (-3,17 a 4,88). Pacientes com maior nível de catastrofização e dor apresentaram diminuição da atenção executiva em comparação com os demais pacientes do estudo. Conclusão: Os efeitos sobre a rede neuronal induzida por uma tarefa inibitória combinada com ETCC-a apresentou maior desempenho na execução atencional e redução da dor.<br>Introduction: Fibromyalgia (FM) is a syndrome that affects 1-6% of the population, mostly women. It usually courses with chronic pain, sleep disturbance, symptoms of depression and memory loss. Its impact on the female patients’ life is related to the limitations in everyday activities, including executive functions. The dysfunction of the neural networks involved in the excitability and inhibition systems has cognitive repercussions that compromise attention and the performance of work-related activities. Pain is the symptom that rules this syndrome and can affect the attentional capacity of patients with FM and impair daily achievements. However, little is known about the pathways and the processes involved in this set of symptoms. It is, therefore, necessary to understand these processes and look for therapeutic strategies that have an effect on these mechanisms. Among these treatments we can mention Transcranial direct current stimulation (tDCS) an intervention with a modulating effect of neuronal activity, whose potential benefit has been demonstrated in FM. The integration of the excitatory effect of tDCS on the inhibitory effect of neurocognitive tasks in areas involved in the affective-motivational processing, including chronic pain, has not been profoundly explored. The purpose of the present study was to compare the effect of active tDCS(a) with sham tDCS(s) combining the inhibitory neurocognitive task (Go- noGo Task) in pain and in the attentional capacity of patients with FM. Methods: Patients with a diagnosis of FM according to the American College of Rheumatology (ACR) 2010 were selected. The sample was composed by 40 patients, subdivided into two groups - tDCS-a or tDCS-s - in a clinical assay of the cross-matched type, two sessions with a seven-day interval between one intervention and another. The tDCS stimulation was anodyne (DLPFC) of 1mA for 20 minutes. The interventions used were Go- noGo Task (GNG), Attention Network Task (ANT) and tDCS. Results: There was a significant difference betweeen the tDCS-a and tDCS-s groups in the ANT results. tDCS-a combined with the GNG task was able to potentiate the network of executive attention and attenuate the feeling of pain. In ANT the data related to orientation were -14.63 of mean difference, with a 95% confidence interval (CI) (from -18.89 to -0.37). As to execution, a mean of 21.00 was found with 95% CI (4.11 to 37.89). As to the alert, there was no difference, and the mean was -3.17; with 95% CI (-3.17 to 4.88). Patients with a higher level of catastrophization and pain presented reduced executive attention compared to the other patients in the study. Conclusion: The effects on the neuronal network induced by an inhibtory task combined with tDCS-a presented a greater performance in attentional execution and pain reduction.
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33

Carman, Benjamin Andrew. "Translating LaTeX to Coq: A Recurrent Neural Network Approach to Formalizing Natural Language Proofs." Ohio University Honors Tutorial College / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ouhonors161919616626269.

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SACHDEVA, NITIN. "CYBERBULLYING DETECTION ON SOCIAL MEDIA USING DEEP LEARNING MODELS." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18914.

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Application of deep learning models for cyberbullying detection in social media is an upcoming area for both researchers and practitioners for finding, exploring and analysing the extensibility of human-based expressions. Automated cyberbullying detection is typically a classification problem in natural language processing where the intent is to classify each abusive or offensive comment or post or message or image as either bullying or non-bullying. It needs high-level semantic analysis as well. Most of the earlier attempts on cyberbullying detection rely on manual feature extraction methods. Such methods are not only time-consuming and cumbersome, but often fail to correctly capture the meaning of the sentence. This fosters the need to build an intelligent analytic paradigm for detecting cyberbullying in social media data to lower down its hazard with minimal human intervention. Motivated by it, this research utilizes deep learning models for cyberbullying detection in social media as they trivialize the need of explicit feature extraction and are highly skilful, fast and more efficient in retrieval of essential features and patterns by themselves. In our research, we have applied deep learning for cyberbullying detection on textual and non-textual social media content. With high volume and variety of user-generated content on complex social media platforms, the challenges to detect cyberbullying in real-time have amplified. The influx of content makes it challenging to timely regulate online expression. Moreover, the anonymity and context-independence of expressions in online posts can be ambiguous or misleading. Nowadays, cyberbullying, through varied content modalities is also very common. At the same time, cultural diversities, unconventional use of typographical resources and easy availability of native-language keyboards augment to the variety and volume of user- generated content compounding the linguistic challenges in detecting online bullying posts. In an effort to deal with this antagonistic online delinquency referred to as cyberbullying, this research computationally analysed the content, modality and language-use in social media using deep learning models. This research has shown that the use of embeddings with deep learning architectures show better representation learning capabilities and simplify the feature selection process with enhanced classification accuracy as compared to baseline machine learning methods. The goal of the research is to automatically detect cyberbullying on textual, multimodal and mash-up social media content using deep learning models. In our research, we build models for these using deep architectures including capsule network, convolution neural network, multi-layer perceptron, self-attention mechanism, bi-directional gated recurrent unit, long short-term memory & bi-directional long short-term memory using embeddings such as GloVe, fastText and ELMo on social media like Askfm.in, Formspring.me, MySpace, Twitter, YouTube, Instagram and Facebook. The results show superlative performance as compared to SOTA as well.
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Zheng, Xiang. "Three Essays in Fintech and Corporate Finance:." Thesis, Boston College, 2021. http://hdl.handle.net/2345/bc-ir:109077.

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Thesis advisor: Thomas Chemmanur<br>My Ph.D. dissertation consists of three essays. The first essay studies the economic consequence of the current patent screening process on firm performance using a machine-learning approach. Using USPTO patent application data, I apply a machine-learning algorithm to analyze how the current patent examination process in the U.S. can be improved in terms of granting higher quality patents. I make use of the quasi-random assignment of patent applications to examiners to show that screening decisions aided by a machine learning algorithm lead to a 15.5% gain in patent generality. To analyze the economic consequences of current patent screening on both public and private firms, I construct an ex-ante measure of past false acceptance rate for each examiner by exploiting the disagreement in patent screening decisions between the algorithm and current patent examiner. I first show that patents granted by examiners with higher false acceptance rates have lower announcement returns around patent grant news. Moreover, these patents are more likely to expire early. Next, I find that public firms whose patents are granted by such examiners are more likely to get sued in patent litigation cases. Consequently, these firms cut R&amp;D investments and have worse operating performance. Lastly, I find that private firms whose patents are granted by such examiners are less likely to exit successfully by an IPO or an M&amp;A. Overall, this study suggests that the social and economic cost of an inefficient patent screening system is large and can be mitigated with the help of a machine learning algorithm. The second essay studies how investor attention affects various aspects of SEOs. Models of seasoned equity offerings (SEOs) such as Myers and Majluf (1984) assume that all investors in the economy pay immediate attention to SEO announcements and the pricing of SEOs. In this paper, we analyze, theoretically and empirically, the implications of only a fraction of investors in the equity market paying immediate attention to SEO announcements. We first show theoretically that, in the above setting, the announcement effect of an SEO will be positively related to the fraction of investors paying attention to the announcement and that there will be a post-announcement stock-return drift that is negatively related to investor attention. In the second part of the paper, we test the above predictions using the media coverage of firms announcing SEOs as our main proxy for investor attention, and find evidence consistent with the above predictions. In the third part of the paper, we develop and test various hypotheses relating investor attention paid to an issuing firm to various SEO characteristics. We empirically show that institutional investor participation in SEOs, the post-SEO equity market valuation of firms, SEO underpricing, and SEO valuation are all positively related to investor attention. Lastly, we also use the number of SEC EDGAR file downloads as an alternative proxy for investor attention, and our findings are robust to this alternative investor attention measure. The results of our identification tests show that the above results are causal. The third essay studies how the location of a lead underwriter in its network of investment banks affects various aspects of seasoned equity offerings (SEOs). We hypothesize that investment banking networks perform an important economic role in the SEO underwriting process for SEOs, namely, that of information dissemination, where the lead underwriter uses its investment banking network to disseminate information about the SEO firm to institutional investors. Consistent with the above information dissemination role, we show that firms whose SEOs are underwritten by more central lead underwriters are associated with a smaller extent of information asymmetry in the equity market. We then develop testable hypotheses based on the information dissemination role of underwriter networks for the relationship between SEO underwriter centrality and various SEO characteristics, which we test in our empirical analysis. Consistent with the above hypotheses, we find that more central lead SEO underwriters are associated with less negative SEO announcement effects; smaller SEO offer price revisions; smaller SEO discounts and underpricing; higher immediate post-SEO equity valuations for issuing firms; and greater post-SEO long-run stock returns for issuing firms. We also find that SEOs with more central lead underwriters are associated with greater institutional investor participation. Our instrumental variable (IV) analysis using the industry-average bargaining power of underwriters relative to issuers as the instrument shows that the above results are causal. Consistent with greater value creation by more central lead underwriters, we find that more central lead underwriters receive greater compensation<br>Thesis (PhD) — Boston College, 2021<br>Submitted to: Boston College. Carroll School of Management<br>Discipline: Finance
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36

Beuth, Frederik. "Visual attention in primates and for machines - neuronal mechanisms." Universitätsverlag Chemnitz, 2017. https://monarch.qucosa.de/id/qucosa%3A35655.

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Visual attention is an important cognitive concept for the daily life of humans, but still not fully understood. Due to this, it is also rarely utilized in computer vision systems. However, understanding visual attention is challenging as it has many and seemingly-different aspects, both at neuronal and behavioral level. Thus, it is very hard to give a uniform explanation of visual attention that can account for all aspects. To tackle this problem, this thesis has the goal to identify a common set of neuronal mechanisms, which underlie both neuronal and behavioral aspects. The mechanisms are simulated by neuro-computational models, thus, resulting in a single modeling approach to explain a wide range of phenomena at once. In the thesis, the chosen aspects are multiple neurophysiological effects, real-world object localization, and a visual masking paradigm (OSM). In each of the considered fields, the work also advances the current state-of-the-art to better understand this aspect of attention itself. The three chosen aspects highlight that the approach can account for crucial neurophysiological, functional, and behavioral properties, thus the mechanisms might constitute the general neuronal substrate of visual attention in the cortex. As outlook, our work provides for computer vision a deeper understanding and a concrete prototype of attention to incorporate this crucial aspect of human perception in future systems.:1. General introduction 2. The state-of-the-art in modeling visual attention 3. Microcircuit model of attention 4. Object localization with a model of visual attention 5. Object substitution masking 6. General conclusion<br>Visuelle Aufmerksamkeit ist ein wichtiges kognitives Konzept für das tägliche Leben des Menschen. Es ist aber immer noch nicht komplett verstanden, so dass es ein langjähriges Ziel der Neurowissenschaften ist, das Phänomen grundlegend zu durchdringen. Gleichzeitig wird es aufgrund des mangelnden Verständnisses nur selten in maschinellen Sehsystemen in der Informatik eingesetzt. Das Verständnis von visueller Aufmerksamkeit ist jedoch eine komplexe Herausforderung, da Aufmerksamkeit äußerst vielfältige und scheinbar unterschiedliche Aspekte besitzt. Sie verändert multipel sowohl die neuronalen Feuerraten als auch das menschliche Verhalten. Daher ist es sehr schwierig, eine einheitliche Erklärung von visueller Aufmerksamkeit zu finden, welche für alle Aspekte gleichermaßen gilt. Um dieses Problem anzugehen, hat diese Arbeit das Ziel, einen gemeinsamen Satz neuronaler Mechanismen zu identifizieren, welche sowohl den neuronalen als auch den verhaltenstechnischen Aspekten zugrunde liegen. Die Mechanismen werden in neuro-computationalen Modellen simuliert, wodurch ein einzelnes Modellierungsframework entsteht, welches zum ersten Mal viele und verschiedenste Phänomene von visueller Aufmerksamkeit auf einmal erklären kann. Als Aspekte wurden in dieser Dissertation multiple neurophysiologische Effekte, Realwelt Objektlokalisation und ein visuelles Maskierungsparadigma (OSM) gewählt. In jedem dieser betrachteten Felder wird gleichzeitig der State-of-the-Art verbessert, um auch diesen Teilbereich von Aufmerksamkeit selbst besser zu verstehen. Die drei gewählten Gebiete zeigen, dass der Ansatz grundlegende neurophysiologische, funktionale und verhaltensbezogene Eigenschaften von visueller Aufmerksamkeit erklären kann. Da die gefundenen Mechanismen somit ausreichend sind, das Phänomen so umfassend zu erklären, könnten die Mechanismen vielleicht sogar das essentielle neuronale Substrat von visueller Aufmerksamkeit im Cortex darstellen. Für die Informatik stellt die Arbeit damit ein tiefergehendes Verständnis von visueller Aufmerksamkeit dar. Darüber hinaus liefert das Framework mit seinen neuronalen Mechanismen sogar eine Referenzimplementierung um Aufmerksamkeit in zukünftige Systeme integrieren zu können. Aufmerksamkeit könnte laut der vorliegenden Forschung sehr nützlich für diese sein, da es im Gehirn eine Aufgabenspezifische Optimierung des visuellen Systems bereitstellt. Dieser Aspekt menschlicher Wahrnehmung fehlt meist in den aktuellen, starken Computervisionssystemen, so dass eine Integration in aktuelle Systeme deren Leistung sprunghaft erhöhen und eine neue Klasse definieren dürfte.:1. General introduction 2. The state-of-the-art in modeling visual attention 3. Microcircuit model of attention 4. Object localization with a model of visual attention 5. Object substitution masking 6. General conclusion
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37

Blini, Elvio A. "Biases in Visuo-Spatial Attention: from Assessment to Experimental Induction." Doctoral thesis, Università degli studi di Padova, 2016. http://hdl.handle.net/11577/3424480.

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In this work I present several studies, which might appear rather heterogeneous for both experimental questions and methodological approaches, and yet are linked by a common leitmotiv: spatial attention. I will address issues related to the assessment of attentional asymmetries, in the healthy individual as in patients with neurological disorders, their role in various aspects of human cognition, and their neural underpinning, driven by the deep belief that spatial attention plays an important role in various mental processes that are not necessarily confined to perception. What follows is organized into two distinct sections. In the first I will focus on the evaluation of visuospatial asymmetries, starting from the description of a new paradigm particularly suitable for this purpose. In the first chapter I will describe the effects of multitasking in a spatial monitoring test; the main result shows a striking decreasing in detection performance as a function of the introduced memory load. In the second chapter I will apply the same paradigm to a clinical population characterized by a brain lesion affecting the left hemisphere. Despite a standard neuropsychological battery failed to highlight any lateralized attentional deficit, I will show that exploiting concurrent demands might lead to enhanced sensitivity of diagnostic tests and consequently positive effects on patients’ diagnostic and therapeutic management. Finally, in the third chapter I will suggest, in light of preliminary data, that attentional asymmetries also occur along the sagittal axis; I will argue, in particular, that more attentional resources appear to be allocated around peripersonal space, the resulting benefits extending to various tasks (i.e., discrimination tasks). Then, in the second section, I will follow a complementary approach: I will seek to induce attentional shifts in order to evaluate their role in different cognitive tasks. In the fourth and fifth chapters this will be pursued exploiting sensory stimulations: visual optokinetic stimulation and galvanic vestibular stimulation, respectively. In the fourth chapter I will show that spatial attention is highly involved in numerical cognition, this relationship being bidirectional. Specifically, I will show that optokinetic stimulation modulates the occurrence of procedural errors during mental arithmetics, and that calculation itself affects oculomotor behaviour in turn. In the fifth chapter I will examine the effects of galvanic vestibular stimulation, a particularly promising technique for the rehabilitation of lateralized attention disorders, on spatial representations. I will discuss critically a recent account for unilateral spatial neglect, suggesting that vestibular stimulations or disorders might indeed affect the metric representation of space, but not necessarily resulting in spatial unawareness. Finally, in the sixth chapter I will describe an attentional capture phenomenon by intrinsically rewarding distracters. I will seek, in particular, to predict the degree of attentional capture from resting-state functional magnetic resonance imaging data and the related brain connectivity pattern; I will report preliminary data focused on the importance of the cingulate-opercular network, and discuss the results through a parallel with clinical populations characterized by behavioural addictions.<br>In questo lavoro presenterò una serie di ricerche che possono sembrare piuttosto eterogenee per quesiti sperimentali e approcci metodologici, ma sono tuttavia legate da un filo conduttore comune: i costrutti di ragionamento e attenzione spaziale. Affronterò in particolare aspetti legati alla valutazione delle asimmetrie attenzionali, nell'individuo sano come nel paziente con disturbi neurologici, il loro ruolo in vari aspetti della cognizione umana, e i loro substrati neurali, guidato dalla convinzione che l’attenzione spaziale giochi un ruolo importante in svariati processi mentali non necessariamente limitati alla percezione. Quanto segue è stato dunque organizzato in due sezioni distinte. Nella prima mi soffermerò sulla valutazione delle asimmetrie visuospaziali, iniziando dalla descrizione di un nuovo paradigma particolarmente adatto a questo scopo. Nel primo capitolo descriverò gli effetti del doppio compito e del carico attenzionale su un test di monitoraggio spaziale; il risultato principale mostra un netto peggioramento nella prestazione al compito di detezione spaziale in funzione del carico di memoria introdotto. Nel secondo capitolo applicherò lo stesso paradigma ad una popolazione clinica contraddistinta da lesione cerebrale dell’emisfero sinistro. Nonostante una valutazione neuropsicologica standard non evidenziasse alcun deficit lateralizzato dell’attenzione, mostrerò che sfruttare un compito accessorio può portare ad una spiccata maggiore sensibilità dei test diagnostici, con evidenti ricadute benefiche sull'iter clinico e terapeutico dei pazienti. Infine, nel terzo capitolo suggerirò, tramite dati preliminari, che asimmetrie attenzionali possono essere individuate, nell'individuo sano, anche lungo l’asse sagittale; argomenterò, in particolare, che attorno allo spazio peripersonale sembrano essere generalmente concentrate più risorse attentive, e che i benefici conseguenti si estendono a compiti di varia natura (ad esempio compiti di discriminazione). Passerò dunque alla seconda sezione, in cui, seguendo una logica inversa, indurrò degli spostamenti nel focus attentivo in modo da valutarne il ruolo in compiti di varia natura. Nei capitoli quarto e quinto sfrutterò delle stimolazioni sensoriali: la stimolazione visiva optocinetica e la stimolazione galvanico vestibolare, rispettivamente. Nel quarto capitolo mostrerò che l’attenzione spaziale è coinvolta nella cognizione numerica, con cui intrattiene rapporti bidirezionali. Nello specifico mostrerò da un lato che la stimolazione optocinetica può modulare l’occorrenza di errori procedurali nel calcolo mentale, dall'altro che il calcolo stesso ha degli effetti sull'attenzione spaziale e in particolare sul comportamento oculomotorio. Nel quinto capitolo esaminerò gli effetti della stimolazione galvanica vestibolare, una tecnica particolarmente promettente per la riabilitazione dei disturbi attentivi lateralizzati, sulle rappresentazioni mentali dello spazio. Discuterò in modo critico un recente modello della negligenza spaziale unilaterale, suggerendo che stimolazioni e disturbi vestibolari possano sì avere ripercussioni sulle rappresentazioni metriche dello spazio, ma senza comportare necessariamente inattenzione per lo spazio stesso. Infine, nel sesto capitolo descriverò gli effetti di cattura dell’attenzione visuospaziale che stimoli distrattori intrinsecamente motivanti possono esercitare nell'adulto sano. Cercherò, in particolare, di predire l’entità di questa cattura attenzionale partendo da immagini di risonanza magnetica funzionale a riposo: riporterò dati preliminari focalizzati sull'importanza del circuito cingolo-opercolare, effettuando un parallelismo con popolazioni cliniche caratterizzate da comportamenti di dipendenza.
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38

Tu, Guoyun. "Image Captioning On General Data And Fashion Data : An Attribute-Image-Combined Attention-Based Network for Image Captioning on Mutli-Object Images and Single-Object Images." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-282925.

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Image captioning is a crucial field across computer vision and natural language processing. It could be widely applied to high-volume web images, such as conveying image content to visually impaired users. Many methods are adopted in this area such as attention-based methods, semantic-concept based models. These achieve excellent performance on general image datasets such as the MS COCO dataset. However, it is still left unexplored on single-object images.In this paper, we propose a new attribute-information-combined attention- based network (AIC-AB Net). At each time step, attribute information is added as a supplementary of visual information. For sequential word generation, spatial attention determines specific regions of images to pass the decoder. The sentinel gate decides whether to attend to the image or to the visual sentinel (what the decoder already knows, including the attribute information). Text attribute information is synchronously fed in to help image recognition and reduce uncertainty.We build a new fashion dataset consisting of fashion images to establish a benchmark for single-object images. This fashion dataset consists of 144,422 images from 24,649 fashion products, with one description sentence for each image. Our method is tested on the MS COCO dataset and the proposed Fashion dataset. The results show the superior performance of the proposed model on both multi-object images and single-object images. Our AIC-AB net outperforms the state-of-the-art network, Adaptive Attention Network by 0.017, 0.095, and 0.095 (CIDEr Score) on the COCO dataset, Fashion dataset (Bestsellers), and Fashion dataset (all vendors), respectively. The results also reveal the complement of attention architecture and attribute information.<br>Bildtextning är ett avgörande fält för datorsyn och behandling av naturligt språk. Det kan tillämpas i stor utsträckning på högvolyms webbbilder, som att överföra bildinnehåll till synskadade användare. Många metoder antas inom detta område såsom uppmärksamhetsbaserade metoder, semantiska konceptbaserade modeller. Dessa uppnår utmärkt prestanda på allmänna bilddatamängder som MS COCO-dataset. Det lämnas dock fortfarande outforskat på bilder med ett objekt.I denna uppsats föreslår vi ett nytt attribut-information-kombinerat uppmärksamhetsbaserat nätverk (AIC-AB Net). I varje tidsteg läggs attributinformation till som ett komplement till visuell information. För sekventiell ordgenerering bestämmer rumslig uppmärksamhet specifika regioner av bilder som ska passera avkodaren. Sentinelgrinden bestämmer om den ska ta hand om bilden eller den visuella vaktposten (vad avkodaren redan vet, inklusive attributinformation). Text attributinformation matas synkront för att hjälpa bildigenkänning och minska osäkerheten.Vi bygger en ny modedataset bestående av modebilder för att skapa ett riktmärke för bilder med en objekt. Denna modedataset består av 144 422 bilder från 24 649 modeprodukter, med en beskrivningsmening för varje bild. Vår metod testas på MS COCO dataset och den föreslagna Fashion dataset. Resultaten visar den överlägsna prestandan hos den föreslagna modellen på både bilder med flera objekt och enbildsbilder. Vårt AIC-AB-nät överträffar det senaste nätverket Adaptive Attention Network med 0,017, 0,095 och 0,095 (CIDEr Score) i COCO-datasetet, modedataset (bästsäljare) respektive modedatasetet (alla leverantörer). Resultaten avslöjar också komplementet till uppmärksamhetsarkitektur och attributinformation.
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39

Lee, Christine Anne. "PERSON-CENTERED ANALYSIS OF ADHD COMORBIDITIES AND DIFFERENTIAL CHARACTERISTICS AND OUTCOMES." UKnowledge, 2018. https://uknowledge.uky.edu/psychology_etds/147.

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Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent and impairing childhood disorders (5%; American Psychiatric Association, 2013), yet it is often studied in isolation. Such an approach is at odds with the clinical reality, where ADHD has a high comorbidity with oppositional defiant disorder, anxiety, and depression (Jensen, Martin, & Cantwell, 1997). Based on the possible presentations of ADHD with both externalizing and internalizing symptoms, there may be differences in associated characteristics, areas of impairment, and resulting assessment interventions. Therefore, the present study investigated how ADHD comorbidities manifested in a population of 233 elementary age children and how these profiles varied in already established characteristics (i.e., traits, social behaviors) and areas of deficit for children with ADHD (i.e., social functioning, academics, narrative comprehension). Characteristics and outcomes were examined using rating scales, behavior observations, laboratory tasks, and grades. Based on latent profile analyses, different patterns of comorbidity were identified using both parent and teacher ratings of ADHD. Based on parent and teacher report, those with high ADHD/ODD symptoms had more negative characteristics and outcomes. Network analyses corroborated these results, showing that internalizing symptoms were less relevant for associated characteristics and outcomes compared to ADHD and ODD symptoms. Overall, these results suggest that ADHD comorbidities may be primarily driven by ADHD and ODD symptoms, with this profile displaying more severe negative characteristics and outcomes.
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40

Dimitriadis, Spyridon. "Multi-task regression QSAR/QSPR prediction utilizing text-based Transformer Neural Network and single-task using feature-based models." Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177186.

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With the recent advantages of machine learning in cheminformatics, the drug discovery process has been accelerated; providing a high impact in the field of medicine and public health. Molecular property and activity prediction are key elements in the early stages of drug discovery by helping prioritize the experiments and reduce the experimental work. In this thesis, a novel approach for multi-task regression using a text-based Transformer model is introduced and thoroughly explored for training on a number of properties or activities simultaneously. This multi-task regression with Transformer based model is inspired by the field of Natural Language Processing (NLP) which uses prefix tokens to distinguish between each task. In order to investigate our architecture two data categories are used; 133 biological activities from ExCAPE database and three physical chemistry properties from MoleculeNet benchmark datasets. The Transformer model consists of the embedding layer with positional encoding, a number of encoder layers, and a Feedforward Neural Network (FNN) to turn it into a regression problem. The molecules are represented as a string of characters using the Simplified Molecular-Input Line-Entry System (SMILES) which is a ’chemistry language’ with its own syntax. In addition, the effect of Transfer Learning is explored by experimenting with two pretrained Transformer models, pretrained on 1.5 million and on 100 million molecules. The text-base Transformer models are compared with a feature-based Support Vector Regression (SVR) with the Tanimoto kernel where the input molecules are encoded as Extended Connectivity Fingerprint (ECFP), which are calculated features. The results have shown that Transfer Learning is crucial for improving the performance on both property and activity predictions. On bioactivity tasks, the larger pretrained Transformer on 100 million molecules achieved comparable performance to the feature-based SVR model; however, overall SVR performed better on the majority of the bioactivity tasks. On the other hand, on physicochemistry property tasks, the larger pretrained Transformer outperformed SVR on all three tasks. Concluding, the multi-task regression architecture with the prefix token had comparable performance with the traditional feature-based approach on predicting different molecular properties or activities. Lastly, using the larger pretrained models trained on a wide chemical space can play a key role in improving the performance of Transformer models on these tasks.
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Frigotto, Silvana Maria. "Mudança social e os impactos na rede de atenção, apoio, cuidado e proteção da mulher." Faculdades EST, 2014. http://tede.est.edu.br/tede/tde_busca/arquivo.php?codArquivo=540.

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A transformação social envolve gênero, a importância da mulher nas redes de apoio, atenção, cuidado e proteção, as mudanças na educação de meninos e meninas para a erradicação da violência contra a mulher e a viabilização da intervenção e fortalecimento dos laços no interior da rede pela ação do operador de rede. Desvela-se a importância do empoderamento das mulheres por meio da educação e da geração de renda que impactam a rede social particular e que favorecem no desempenho do papel de cuidadoras, educadoras e articuladoras da vida. Contribuições necessárias para que a realidade possa ser transformada numa sociedade mais humana e justa e que esse futuro almejado seja mais imediato para a mulher e rede que demanda atenção social. O enfoque dado é de rede ou mais precisamente da rede de apoio, atenção, cuidado e proteção que é atendida pela mulher e que envolve condições disposicionais características femininas, as quais se apresentam de forma simultânea ou articuladas, culminando na oferta, pela mulher, de serviços imprescindíveis, sem ônus e de demanda com atendimento espontâneo e imediato. A inclusão educacional e produtiva da mulher impacta a sua rede de apoio, atenção, cuidado e proteção. Há um espraiamento da força, da energia e das conquistas dessa mulher para a rede e desdobramentos para a sociedade. As mulheres em geral, especialmente, aquelas em/com risco social têm direito a políticas públicas específicas, benefícios de ação afirmativa, numa concepção de direitos humanos e de dignidade da pessoa humana. Ao final da investigação conclui-se que: 1) Através de um pedido de ajuda ou de uma queixa a mulher tem o direito de ser contemplada com o suporte da rede secundária; 2) A mulher e os demais indivíduos da rede com a ajuda de um operador de rede podem mobilizar as redes no sentido de deflagrar fatores de proteção e de prevenção por meio de outras redes na reconstrução de vínculos da rede primária; 3) O contexto vulnerável ou de risco se intensifica e é de difícil reversão quando se mantém de forma duradoura ou ininterrupta por longo tempo; 4) A postura de assistencialismo desrespeita, humilha e predispõe à apatia, à inércia, à asfixia da iniciativa, da autonomia e do protagonismo dos assistidos, gerando clientelismo indesejado; 5) As redes esfaceladas podem ser reconstruídas por meio do suporte de rede substitutiva: a rede secundária; 6) Pode-se ajudar as mulheres que se encontram em situação de atenção a recompor, de uma forma ou de outra, seus eus destruídos ou fragilizados através das redes secundárias pela intervenção terapêutica e comunitária do trabalho das redes desenvolvido pelo operador de rede, que envolve o acolhimento, o empoderamento e a autonomia. Assume-se com isso uma postura de ganha-ganha social, num sentido mais imediato em favor da mulher, mas ao final o bônus da mudança social ficará com as gerações futuras.<br>Social transformation involves gender, the importance of the woman in the networks of support, attention, care and protection, the changes in the education of boys and girls to eradicate the violence against women and making viable the intervention and strengthening of the ties inside the network through the action of the network operator. The paper reveals the importance of the empowerment of the women through education and through income generation, which impact the private social network and which strengthen the fulfillment of the role of caregivers, educators and life articulators. These are necessary contributions so that the reality can be transformed into a more human and just society and that this longed for future can be more immediate for the woman and the network which demands social attention. The focus presented is the network or, more precisely, the support, attention, care and protection network, which is tended by the woman and involves dispositional conditions that are characteristically feminine, which present themselves in a simultaneous or articulated way, culminating in the offering, by the woman, of indispensable services, without charges and in demand, with spontaneous and immediate service. The educational and productive inclusion of the woman impacts her support, attention, care and protection network. There is a spreading of the force, energy and conquests of this woman to the network and ramifications to society. Women in general, especially those at or in social risk, have the right to specific public policies, benefits of affirmative action, in a conception of human rights and of dignity of the human person. At the end of the research the conclusion is that: 1) Through a request for help or of a complaint, the woman has the right to be contemplated with support from the secondary network; 2) The woman and other individuals of the network with the help of a network operator can mobilize the networks in the sense of triggering protection and prevention factors through the other networks in the reconstruction of the ties of the primary network; 3) The vulnerable context or one of risk is intensified and difficult to reverse when it is maintained in a longstanding or uninterrupted way for a long time; 4) The posture of welfarism disrespects, humiliates and predisposes to apathy, inertia, asphyxiation of initiative, of autonomy and protagonism of those assisted, generating an undesired clientilism; 5) The dismantled networks can be reconstructed through the support of a substitute network: the secondary network; 6) The women who are in situations demanding attention can be helped to recompose, in one way or another, their destroyed or weakened egos through the secondary networks through the therapeutic and community intervention of the work of the networks developed by the network operator, which involves welcoming, empowerment and autonomy. Thus, a social win-win posture is assumed, in a more immediate sense in favor of the woman, but at the end the bonus of the social change will remain with the future generations.
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42

Brorsson, David. "Training attention with video games : How playing and training with video games impact attentional networks." Thesis, Högskolan i Skövde, Institutionen för biovetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-19464.

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Video games as an entertainment form are very popular. Understanding what video games do to us in a long-term and short-term manner is therefore of interest. Attention is a widely studied field and research into how attentional networks are affected by video is a research field on the rise. Here, I will be investigating how video game play affects our attentional networks and if it is possible for elderly individuals to train their attentional networks with video games. Video game players have high performance in reaction time and accuracy in different attentional, working memory, and cognitive control tasks. As the difficulty of video games increase video game players seem to more efficiently utilize their attentional networks. Whilst some articles cannot replicate findings in other articles this irregularity might be explained with by level of difficulty or load during task performance. Studies see group differences only when the task difficulty is high. Therefore, an important part of video game research is to find an effective and replicable standard for video game research. Measuring video game play with EEG shows that players better can forgo distracting stimuli in central and peripheral view and discriminating stimuli giving video game players more confidence when making decisions. Video game players also seem to have more efficient processes and functional connectivity in attentional networks but utilizing these networks more as non-video game players as mental load increases. Not only does video game players have more efficient attentional networks, but attentional benefits from video games is also something that can be trained with those who do not play video games. Suggesting that older individuals can utilize video games to train attentional networks.
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43

Maynard, David Charles. "Paying Attention to the Alien: Reevaluating Composition Studies' Construction of Human Agency in Light of Secret Government Surveillance." University of Findlay / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=findlay149381947498726.

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44

Mignot, Coralie. "Modulation des activations cérébrales par des odeurs subliminales : une étude en IRM fonctionnelle." Thesis, Strasbourg, 2019. http://www.theses.fr/2019STRAJ023/document.

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Certaines études ont montré que des odeurs subliminales – odeurs d'intensité très faible activant le système olfactif mais non perçues consciemment – peuvent impacter le comportement alimentaire. Cependant, les mécanismes sensoriels et cognitifs impliqués dans le traitement des odeurs subliminales demeurent mal connus. Ce travail de thèse avait pour but d'explorer les activations cérébrales induites par des odeurs subliminales au moyen de l'Imagerie par Résonance Magnétique fonctionnelle. Durant les acquisitions IRM, les participants sont exposés à leur insu à deux odeurs présentées à intensité subliminale puis supraliminale. Quatre réseaux cérébraux mis en évidence par Analyse en Composantes Indépendantes s’avèrent spécifiques de la condition subliminale. Ces réseaux ne sont pas propres au traitement des odeurs et semblent liés à des processus attentionnels et de contrôle exécutif. La modulation de leur activité par des odeurs subliminales apporte des éléments nouveaux pour comprendre l’impact de ces odeurs sur le comportement, et suggère des applications possibles d'utilisation de ces odeurs pour réguler le comportement alimentaire<br>Some studies showed that subliminal odours – odours of very low intensity which activate the olfactory system but are not consciously perceived – can impact food behaviours. However, the sensory and cognitive mechanisms involved in subliminal odours processing remain poorly known. This work aims exploring cerebral activity induced by subliminal odours by the means of functional Magnetic Resonance Imaging. During MRI acquisitions, participants were unknowingly exposed to two odours presented at subliminal intensity and then at supraliminal intensity. Four cerebral networks highlighted by Independent Component Analysis (ICA) prove to be specific to the subliminal condition. These networks are not particular to olfactory processing and seem to be linked to attentional and executive control processes. The modulation of their activity by subliminal odours brings new elements to understand the impact of these odours on behaviour, and suggests possible applications for using these odours to regulate food behaviour
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45

Kajimura, Shogo. "Mind wandering regulation by non-invasive brain stimulation." 京都大学 (Kyoto University), 2017. http://hdl.handle.net/2433/225352.

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Dougal, Duane K. "Improving the Quality of Neural Machine Translation Using Terminology Injection." BYU ScholarsArchive, 2018. https://scholarsarchive.byu.edu/etd/7025.

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Most organizations use an increasing number of domain- or organization-specific words and phrases. A translation process, whether human or automated, must also be able to accurately and efficiently use these specific multilingual terminology collections. However, comparatively little has been done to explore the use of vetted terminology as an input to machine translation (MT) for improved results. In fact, no single established process currently exists to integrate terminology into MT as a general practice, and especially no established process for neural machine translation (NMT) exists to ensure that the translation of individual terms is consistent with an approved terminology collection. The use of tokenization as a method of injecting terminology and of evaluating terminology injection is the focus of this thesis. I use the attention mechanism prevalent in state-of-the-art NMT systems to produce the desired results. Attention vectors play an important part of this method to correctly identify semantic entities and to align the tokens that represent them. My methods presented in this thesis use these attention vectors to align the source tokens in the sentence to be translated with the target tokens in the final translation output. Then, supplied terminology is injected, where these alignments correctly identify semantic entities. My methods demonstrate significant improvement to the state-of-the-art results for NMT using terminology injection.
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Neligh, Nate Leigh. "Essays on Network Formation and Attention." Thesis, 2018. https://doi.org/10.7916/D8N603HZ.

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This dissertation tackles two important developing topics in economics: network formation and the allocation of attention. First, it examine the idea that the timing of entry into the network is a crucial determinant of a node’s final centrality. We propose a model of strategic network growth which makes novel predictions about the forward-looking behaviors of players. In particular, the model predicts that agents entering the network at specific times will become central “vie for dominance”. In a laboratory experiment, we find that players do exhibit “vying for dominance” behavior, but do not always do so at the predicted critical times. A model of heterogeneous risk aversion best fits the observed deviations from initial predictions. Timing determines whether players have the opportunity to become attempt to become dominant, but individual characteristics determine whether players exploit that opportunity. This dissertation also examines models of rational inattention, in which decision-makers rationally evaluate the trade-off between the costs and the benefits of information acquisition. We provide results on recovering the implicit attention cost function by looking at the relationship between incentives and performance. We conduct laboratory experiments consisting of simple perceptual tasks with fine-grained variation in the level of potential rewards. We find that most subjects exhibit monotonicity in performance with respect to potential rewards, and there is mixed evidence on continuity and convexity of costs. We also perform a model selection exercise and find that subjects’ behavior is generally most consistent with a small but diverse subset of cost functions commonly assumed in the literature.
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Lin, Ting-An, and 林庭安. "Deep Supporting Attention in Memory Network." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/8cdcyb.

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碩士<br>國立交通大學<br>電機工程學系<br>106<br>Deep learning has been successfully developing for different natural language processing tasks such as speech recognition, machine translation, dialogue system, language understanding, reading comprehension, image caption, image comprehension, and question answering where the temporal information in natural language can be learned by recurrent neural network (RNN) or long short-term memory (LSTM). There are twofold limitations in standard RNN or LSTM. First, temporal information in LSTM is stored in an internal memory. This model is too limited to store abundant information in long and rich history data. Second, the temporal information without attention is basically a loose and insufficient representation for natural language. Accordingly, this dissertation presents a series of new attention mechanisms for memory-augmented neural networks where deep supporting attention are proposed and incorporated in memory networks which provide external memory for information storage. In general, attention over an observed sample or natural language is run by spotting or locating the region or position of interest for pattern classification. Such an attention parameter is a latent variable, which was indirectly estimated by minimizing the classification loss. Using this attention, the target information may not be correctly identified. Therefore, in addition to minimizing the classification error, we directly attend the region of interest by minimizing the reconstruction error due to support data. This solution can be formulated by variational inference. In this study, the tasks or scenarios based on object recognition, question answering and image caption are introduced to illustrate various attention solutions to deep learning. In particular, we focus on the solutions to sequence-to-sequence learning in an end-to-end memory network. In the task of object recognition, the attention mechanism is developed to learn the location and identify of an object in a noisy and shifted observation sample. Our idea is to learn how to attend through the so-called supporting attention where the support information is available. This attention mechanism corresponds to learning for translation invariance. This idea is not only developed for object recognition but also for question answering. Basically, the task of question answering is to build a model that can read a story and answer the query related to the story. However, in many cases, some sentences in a story are not helpful for finding the answer. To deal with this issue, we adopt the supporting sentences to learn the way of attention in sequence-to-sequence learning for question answering based on memory network. This model is capable of reading the story, building the memories, attending the informative sentences and embedding the information into a fixed-dimensional context vector. Context vector is token for reconstructing the supporting sentences, and then augmented with query vector for retrieving the associated answer. In this procedure, the encoder and decoder are driven by LSTMs and the input memories are obtained by word embedding. In addition, image caption aims to find the best natural sentence to describe an input image. We propose two attention methods to image caption based on memory network. The first method is proposed by adjusting the supporting attention in question answering to work for image caption. Using this method, an input image is first encoded by convolutional layer. Convolutional weights are trained by using ImageNet dataset. A high-dimensional feature vector is encoded. This vector is then attended multiple times to sequentially calculate the hidden codes to produce different words in the transcription. In the implementation, the support data are automatically acquired by using the other attention method. The supporting attention sequentially zooms in different objects of an image for text generation. The second method is developed by combining a self-attention and a word-to-image attention which are complementary for image caption. Again, an image is encoded into a number of feature maps as an input memory by a convolutional layer. Objects in an image are represented by memory slots. Self-attention is performed to attend the pairs of objects of an image which are helpful for generating natural language. The word-to-image attention is applied to attend the object from individual word. A desirable text transcription does not only reflect the individual objects in lexical level but also characterize the relations of objects in syntactic level. This method further incorporates a residual scheme to allow single attention mechanism in sequential learning at each word. The experiments on object recognition, question answering and image caption are evaluated by using noisy/shifted/cluttered MNIST dataset, bAbI dataset and MS-COCO dataset, respectively. The evaluation on object recognition is conducted in presence of different distortions. The evaluation on question answering using bAbI task is performed over 20 kinds of questions in different styles. The evaluation on image caption using MS-COCO task is reported by BLEU score when comparing with the reference captions. We report a number of experiments to demonstrate the effectiveness of supporting attention and hybrid attention in end-to-end memory network.
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Huang, Po-Chih, and 黃柏智. "Target Attention Network for Targeted Sentiment Analysis." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/bawxc6.

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碩士<br>國立臺灣大學<br>資訊工程學研究所<br>106<br>We propose a novel target attention network (TAN) to identify the sentiment of opinion targets in a review or a twitter. Unlike previous works which represent the target in an averaging manner, we apply target attention to focus on more relevant parts of the target, which shows benefits for later sentiment classification. We also introduce a novel target-aware position embedding, which directly models the location relation between the target and its context to provide distinct information from semantic meanings for sequence encoder. Extensive experiments demonstrate the robustness of our findings. The experimental results show that our model consistently outperforms the state-of-the-art methods on four public benchmarks.
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Undy, JW. "Acute effects of caffeine on behavioural and ERP indices of attention in healthy, low consumers of caffeine." Thesis, 2018. https://eprints.utas.edu.au/31168/1/Undy_whole_thesis.pdf.

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Caffeine is commonly used to enhance attentional processes. However, contention remains regarding the extent to which each attentional mechanism is affected by caffeine, and whether caffeine enhances attention over-and-above improvements in general arousal and sustained attention. Subsequently, the present study examined the acute effects of caffeine on behavioural (reaction time & accuracy) and electrophysiological (N1 ERP amplitude) measures of attention. During two separate sessions (separated by 7-14 days), twenty (14 female & 6 male) healthy, low consumers of caffeine (<150mg/day) completed an Attentional Network Task prior to ingesting either caffeine (200mg) or placebo, and again 30-minutes following ingestion. While a partial effect of caffeine upon the alerting and executive control networks was found, results of the present study suggested improvements in reaction time and accuracy following caffeine predominantly reflected a maintenance of general arousal and tonic alertness. It was concluded that caffeine primarily enhanced attentional processing by preventing fatigue and sustaining attention.
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