Relevant bibliographies by topics / Attention network

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  2. Dissertations / Theses
  3. Books
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Academic literature on the topic 'Attention network'

Author: Grafiati
Published: 4 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Attention network"

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Urbanek, Carsten, Nicholetta Weinges-Evers, Judith Bellmann-Strobl, et al. "Attention Network Test reveals alerting network dysfunction in multiple sclerosis." Multiple Sclerosis Journal 16, no. 1 (2009): 93–99. http://dx.doi.org/10.1177/1352458509350308.

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Attention is one of the cognitive domains typically affected in multiple sclerosis. The Attention Network Test was developed to measure the function of the three distinct attentional networks, alerting, orienting, and executive control. The Attention Network Test has been performed in various neuropsychiatric conditions, but not in multiple sclerosis. Our objective was to investigate functions of attentional networks in multiple sclerosis by means of the Attention Network Test. Patients with relapsing—remitting multiple sclerosis (n = 57) and healthy controls (n = 57) matched for age, sex, and education performed the Attention Network Test. Significant differences between patients and controls were detected in the alerting network (p = 0.003), in contrast to the orienting (p = 0.696) and the conflict (p = 0.114) network of visual attention. Mean reaction time in the Attention Network Test was significantly longer in multiple sclerosis patients than in controls (p = 0.032), Multiple sclerosis patients benefited less from alerting cues for conflict resolution compared with healthy controls. The Attention Network Test revealed specific alterations of the attention network in multiple sclerosis patients which were not explained by an overall cognitive slowing.
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Zhuo, Wei, Qianyi Zhan, Yuan Liu, Zhenping Xie, and Jing Lu. "Context Attention Heterogeneous Network Embedding." Computational Intelligence and Neuroscience 2019 (August 21, 2019): 1–15. http://dx.doi.org/10.1155/2019/8106073.

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Network embedding (NE), which maps nodes into a low-dimensional latent Euclidean space to represent effective features of each node in the network, has obtained considerable attention in recent years. Many popular NE methods, such as DeepWalk, Node2vec, and LINE, are capable of handling homogeneous networks. However, nodes are always fully accompanied by heterogeneous information (e.g., text descriptions, node properties, and hashtags) in the real-world network, which remains a great challenge to jointly project the topological structure and different types of information into the fixed-dimensional embedding space due to heterogeneity. Besides, in the unweighted network, how to quantify the strength of edges (tightness of connections between nodes) accurately is also a difficulty faced by existing methods. To bridge the gap, in this paper, we propose CAHNE (context attention heterogeneous network embedding), a novel network embedding method, to accurately determine the learning result. Specifically, we propose the concept of node importance to measure the strength of edges, which can better preserve the context relations of a node in unweighted networks. Moreover, text information is a widely ubiquitous feature in real-world networks, e.g., online social networks and citation networks. On account of the sophisticated interactions between the network structure and text features of nodes, CAHNE learns context embeddings for nodes by introducing the context node sequence, and the attention mechanism is also integrated into our model to better reflect the impact of context nodes on the current node. To corroborate the efficacy of CAHNE, we apply our method and various baseline methods on several real-world datasets. The experimental results show that CAHNE achieves higher quality compared to a number of state-of-the-art network embedding methods on the tasks of network reconstruction, link prediction, node classification, and visualization.
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Hammer, Jiri, Michaela Kajsova, Adam Kalina, et al. "Antagonistic behavior of brain networks mediated by low-frequency oscillations: electrophysiological dynamics during internal–external attention switching." Communications Biology 7 (September 9, 2024): 1105. https://doi.org/10.1038/s42003-024-06732-2.

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Antagonistic activity of brain networks likely plays a fundamental role in how the brain optimizes its performance by efficient allocation of computational resources. A prominent example involves externally/internally oriented attention tasks, implicating two anticorrelated, intrinsic brain networks: the default mode network (DMN) and the dorsal attention network (DAN). To elucidate electrophysiological underpinnings and causal interplay during attention switching, we recorded intracranial EEG (iEEG) from 25 epilepsy patients with electrode contacts localized in the DMN and DAN. We show antagonistic network dynamics of activation-related changes in high-frequency (> 50 Hz) and low-frequency (< 30 Hz) power. The temporal profile of information flow between the networks estimated by functional connectivity suggests that the activated network inhibits the other one, gating its activity by increasing the amplitude of the low-frequency oscillations. Insights about inter-network communication may have profound implications for various brain disorders in which these dynamics are compromised.
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Yang, Yadong, Xiaofeng Wang, Quan Zhao, and Tingting Sui. "Two-Level Attentions and Grouping Attention Convolutional Network for Fine-Grained Image Classification." Applied Sciences 9, no. 9 (2019): 1939. http://dx.doi.org/10.3390/app9091939.

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The focus of fine-grained image classification tasks is to ignore interference information and grasp local features. This challenge is what the visual attention mechanism excels at. Firstly, we have constructed a two-level attention convolutional network, which characterizes the object-level attention and the pixel-level attention. Then, we combine the two kinds of attention through a second-order response transform algorithm. Furthermore, we propose a clustering-based grouping attention model, which implies the part-level attention. The grouping attention method is to stretch all the semantic features, in a deeper convolution layer of the network, into vectors. These vectors are clustered by a vector dot product, and each category represents a special semantic. The grouping attention algorithm implements the functions of group convolution and feature clustering, which can greatly reduce the network parameters and improve the recognition rate and interpretability of the network. Finally, the low-level visual features and high-level semantic information are merged by a multi-level feature fusion method to accurately classify fine-grained images. We have achieved good results without using pre-training networks and fine-tuning techniques.
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Indekeu, J. O. "Special attention network." Physica A: Statistical Mechanics and its Applications 333 (February 2004): 461–64. http://dx.doi.org/10.1016/j.physa.2003.10.081.

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Xue, Lanqing, Xiaopeng Li, and Nevin L. Zhang. "Not All Attention Is Needed: Gated Attention Network for Sequence Data." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 6550–57. http://dx.doi.org/10.1609/aaai.v34i04.6129.

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Although deep neural networks generally have fixed network structures, the concept of dynamic mechanism has drawn more and more attention in recent years. Attention mechanisms compute input-dependent dynamic attention weights for aggregating a sequence of hidden states. Dynamic network configuration in convolutional neural networks (CNNs) selectively activates only part of the network at a time for different inputs. In this paper, we combine the two dynamic mechanisms for text classification tasks. Traditional attention mechanisms attend to the whole sequence of hidden states for an input sentence, while in most cases not all attention is needed especially for long sequences. We propose a novel method called Gated Attention Network (GA-Net) to dynamically select a subset of elements to attend to using an auxiliary network, and compute attention weights to aggregate the selected elements. It avoids a significant amount of unnecessary computation on unattended elements, and allows the model to pay attention to important parts of the sequence. Experiments in various datasets show that the proposed method achieves better performance compared with all baseline models with global or local attention while requiring less computation and achieving better interpretability. It is also promising to extend the idea to more complex attention-based models, such as transformers and seq-to-seq models.
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Hong-Yan Zhang, Hong-Yan Zhang, Xin Wang Hong-Yan Zhang, and Jian-Wei Zhao Xin Wang. "Attention-Based Lightweight Network for PCB Defect Data Augmentation." 電腦學刊 35, no. 6 (2024): 185–99. https://doi.org/10.53106/199115992024123506014.

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<p>Performance of deep learning-based PCB (Printed Circuit Board) surface defect detection networks is often limited by the depth of feature extraction networks and the quality of training data. While significantly increasing network parameters can only slightly enhance system performance, optimizing training data can improve network performance without adding computational overhead. Therefore, this paper proposes a data augmentation network to enhance detection accuracy. First, an autoencoder generator is designed to enhance the feature fitting capability of the latent space. Second, a generative adversarial structural loss function is introduced, and an adversarial training method with different learning rates for the generator and discriminator is employed. Finally, experimental results demonstrate that this method enhances the diversity of PCB defect data and effectively improves the detection network’s recognition accuracy.</p> <p> </p>
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Devaney, Kathryn, Emily Levin, Vaibhav Tripathi, James Higgins, Sara Lazar, and David Somers. "Attention and Default Mode Network Assessments of Meditation Experience during Active Cognition and Rest." Brain Sciences 11, no. 5 (2021): 566. http://dx.doi.org/10.3390/brainsci11050566.

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Meditation experience has previously been shown to improve performance on behavioral assessments of attention, but the neural bases of this improvement are unknown. Two prominent, strongly competing networks exist in the human cortex: a dorsal attention network, that is activated during focused attention, and a default mode network, that is suppressed during attentionally demanding tasks. Prior studies suggest that strong anti-correlations between these networks indicate good brain health. In addition, a third network, a ventral attention network, serves as a “circuit-breaker” that transiently disrupts and redirects focused attention to permit salient stimuli to capture attention. Here, we used functional magnetic resonance imaging to contrast cortical network activation between experienced focused attention Vipassana meditators and matched controls. Participants performed two attention tasks during scanning: a sustained attention task and an attention-capture task. Meditators demonstrated increased magnitude of differential activation in the dorsal attention vs. default mode network in a sustained attention task, relative to controls. In contrast, there were no evident attention network differences between meditators and controls in an attentional reorienting paradigm. A resting state functional connectivity analysis revealed a greater magnitude of anticorrelation between dorsal attention and default mode networks in the meditators as compared to both our local control group and a n = 168 Human Connectome Project dataset. These results demonstrate, with both task- and rest-based fMRI data, increased stability in sustained attention processes without an associated attentional capture cost in meditators. Task and resting-state results, which revealed stronger anticorrelations between dorsal attention and default mode networks in experienced mediators than in controls, are consistent with a brain health benefit of long-term meditation practice.
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Huang, Wanling, Long Zhang, Yaoting Sun, Fangfang Chen, and Kai Wang. "The Prediction Analysis of Autistic and Schizotypal Traits in Attentional Networks." Psychiatry Investigation 18, no. 5 (2021): 417–25. http://dx.doi.org/10.30773/pi.2020.0251.

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Objective Empirical findings confirmed that autistic and schizotypal traits are associated with attentional function as well as include various dimensions. So far, no study has reported which dimension of these traits relates to attentional networks. This study aimed to find out whether there are associations between attentional networks and autistic traits; and between attentional networks and schizotypal traits.Methods A total of 449 volunteers was included in this study, and autism-spectrum quotient (AQ), schizotypal personality questionnaire (SPQ), and attention network test (ANT) were used to measure autistic traits and schizotypal traits. The three independent attentional networks, including alerting network, orienting network, and executive control network, were also measured.Results Autistic traits were associated with the orienting network, whereas schizotypal traits were associated with the orienting network and executive control network. Furthermore, attentional networks could be predicted by specific dimensions of autistic and schizotypal traits. AQ-attention switching [0.104 (-1.175– -0.025), p=0.041] and AQ-attention to detail [-0.097 (-0.798– -0.001), p=0.049] were significant predictors of orienting network and gender were significant predictor of executive network (Beta=0.107; 95% CI=-0.476–10.139; p=0.031). Whereas, schizotypal dimension “interpersonal” was a significant predictor of all three attentional networks [Alerting: 0.147 (-0.010–0.861), p=0.045; Orienting: 0.147 (0.018–0.733), p=0.040; Executive: 0.198 (0.215–1.309), p=0.006].Conclusion This study demonstrated that autistic and schizotypal traits were associated with attentional networks. The specific dimensions of autistic and schizotypal traits could predict attentional networks. Nevertheless, the attentional networks predicted with these two traits were different.
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Wu, Nan, and Chaofan Wang. "Ensemble Graph Attention Networks." Transactions on Machine Learning and Artificial Intelligence 10, no. 3 (2022): 29–41. http://dx.doi.org/10.14738/tmlai.103.12399.

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Graph neural networks have demonstrated its success in many applications on graph-structured data. Many efforts have been devoted to elaborating new network architectures and learning algorithms over the past decade. The exploration of applying ensemble learning techniques to enhance existing graph algorithms have been overlooked. In this work, we propose a simple generic bagging-based ensemble learning strategy which is applicable to any backbone graph models. We then propose two ensemble graph neural network models – Ensemble-GAT and Ensemble-HetGAT by applying the ensemble strategy to the graph attention network (GAT), and a heterogeneous graph attention network (HetGAT). We demonstrate the effectiveness of the proposed ensemble strategy on GAT and HetGAT through comprehensive experiments with four real-world homogeneous graph datasets and three real-world heterogeneous graph datasets on node classification tasks. The proposed Ensemble-GAT and Ensemble-HetGAT outperform the state-of-the-art graph neural network and heterogeneous graph neural network models on most of the benchmark datasets. The proposed ensemble strategy also alleviates the over-smoothing problem in GAT and HetGAT.
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Dissertations / Theses on the topic "Attention network"

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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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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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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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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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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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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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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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Books on the topic "Attention network"

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Neligh, Nate Leigh. Essays on Network Formation and Attention. [publisher not identified], 2018.

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Krannich, Caryl Rae. 101 secrets of highly effective speakers: Controlling fear, commanding attention. Impact, 1998.

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Parker, James N., and Philip M. Parker. Attention defecit disorder: A medical dictionary, bibliography and annotated research guide to Internet references. ICON Health Publications, 2003.

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Parker, Philip M., and James N. Parker. Ritalin: A medical dictionary, bibliography and annotated research guide to Internet references. ICON Health Publications, 2004.

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Parker, Philip M., and James N. Parker. Concerta: A medical dictionary, bibliography, and annotated research guide to Internet references. ICON Health Publications, 2004.

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Gagarina, Larisa, Grigoriy Kuznecov, Evgeniy Portnov, and Anna Doronina. Introduction to information and communication technologies. INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1189946.

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The textbook examines the main milestones in the history of the development of information technologies, computing and computer technology abroad and in Russia. Special attention is paid to the methodology of scientific research in the field of infocommunications. The current sections of the development of telecommunications technologies in the field of multimedia networks and network operating systems are presented. In order to develop practical skills, a laboratory workshop is given.&#x0D; Meets the requirements of the federal state educational standards of higher education of the latest generation.&#x0D; For senior students of technical specialties, postgraduates, researchers, teachers of higher educational institutions, students of advanced training institutes.
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Dolson, David C. Attentive object recognition in the selective tuning network. University of Toronto, Dept. of Computer Science, 1997.

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Cross, Nicola. An attentional what-where vision system using artificial neural networks. typescript, 1999.

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Astrahancevaa, Irina, Sergey Bobkov, Vadim Mizonov, and Sergey Boykov. Modeling of systems. INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1831624.

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The textbook discusses general issues of system modeling, analytical, empirical and simulation approaches to modeling. Typical mathematical schemes used in the analytical approach, methods and tools of simulation modeling of systems are given. Attention is also paid to network and agent-based alternative approaches to modeling.&#x0D; Meets the requirements of the federal state educational standards of higher education of the latest generation.&#x0D; It is intended for undergraduate students studying in the direction of 09.03.02 "Information systems and technologies", whose working curricula include the discipline "Systems Modeling". It is assumed that the training plans also include training courses "Discrete Mathematics", "Mathematical logic and theory of algorithms", "System analysis". The manual will also be useful for master's degree students studying in the direction 09.04.02 "Information systems and technologies" (discipline "Models of information processes and systems").
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Vavrenyuk, Aleksandr, Viktor Makarov, and Stanislav Kutepov. Operating systems. UNIX bases. INFRA-M Academic Publishing LLC., 2016. http://dx.doi.org/10.12737/11186.

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In the manual basics command interfey-are covered&#x0D; са operating systems of UNIX family. Much attention is paid&#x0D; to practical use of teams of system and opportunities of language&#x0D; programming, shell provided by a cover. In a grant vklyu-&#x0D; Chena also some sections devoted to bases administrirova-&#x0D; niya and to network means of OS. At the end of each section there are questions&#x0D; for self-checking, the appendix contains a large number at -&#x0D; mayors of writing of shell-procedures.&#x0D; The manual is addressed to the students studying the modern&#x0D; information technologies according to programs of a bachelor degree, and also all,&#x0D; who wants to master the OS command interface of family independently&#x0D; UNIX in the shortest possible time.&#x0D; The edition can also be used as the short reference book on wasps -&#x0D; new UNIX OS.
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Book chapters on the topic "Attention network"

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Worden, Michael S. "Attention Network Test (ANT)." In Encyclopedia of Clinical Neuropsychology. Springer New York, 2011. http://dx.doi.org/10.1007/978-0-387-79948-3_1268.

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Worden, Michael S. "Attention Network Test (ANT)." In Encyclopedia of Clinical Neuropsychology. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56782-2_1268-2.

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Worden, Michael S. "Attention Network Test (ANT)." In Encyclopedia of Clinical Neuropsychology. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-57111-9_1268.

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Bhise, Naitik, Adam Krzyzak, and Tien D. Bui. "Refining AttnGAN Using Attention on Attention Network." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-23028-8_29.

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Wasserman, Theodore, and Lori Drucker Wasserman. "Models of Attention." In Neural Network Model: Applications and Implications. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-78732-4_2.

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Mash, Lisa E., Raymond M. Klein, and Jeanne Townsend. "Attention Network Tests in ASD." In Encyclopedia of Autism Spectrum Disorders. Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4614-6435-8_102499-1.

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Chen, Lipeng, Daixi Jia, Hang Gao, Fengge Wu, and Junsuo Zhao. "SkaNet: Split Kernel Attention Network." In Artificial Neural Networks and Machine Learning – ICANN 2023. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44192-9_37.

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Zhang, Xueya, Tong Zhang, Wenting Zhao, Zhen Cui, and Jian Yang. "Dual-Attention Graph Convolutional Network." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41299-9_19.

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Li, Shanshan, Qiang Cai, Zhuangzi Li, Haisheng Li, Naiguang Zhang, and Jian Cao. "Attention-Aware Invertible Hashing Network." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34113-8_34.

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Mash, Lisa E., Raymond M. Klein, and Jeanne Townsend. "Attention Network Tests in ASD." In Encyclopedia of Autism Spectrum Disorders. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-91280-6_102499.

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Conference papers on the topic "Attention network"

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Nur Pawestri, Syifa, Hasmawati, and Said Al Faraby. "Question Classification Using Graph Attention Network." In 2024 International Conference on Data Science and Its Applications (ICoDSA). IEEE, 2024. http://dx.doi.org/10.1109/icodsa62899.2024.10652095.

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Zhang, Junpeng. "Multi-Scale Attention Fusion Supervised Network." In 2024 6th International Conference on Frontier Technologies of Information and Computer (ICFTIC). IEEE, 2024. https://doi.org/10.1109/icftic64248.2024.10912882.

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Seal, Sankarsan, and Dipti Prasad Mukherjee. "Revisiting Convolutional Block Attention Module: Attention Enhancing Entropy For Semantic Segmentation of Images." In 2024 International Conference on Computational Intelligence and Network Systems (CINS). IEEE, 2024. https://doi.org/10.1109/cins63881.2024.10864429.

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Wang, Song, Zhenming Zhang, Wei Li, Chen Yin, Yu Ma, and Weiyao Xu. "Dynamic Residual Graph Attention Network for Network Intrusion Detection System." In 2024 Sixth International Conference on Next Generation Data-driven Networks (NGDN). IEEE, 2024. http://dx.doi.org/10.1109/ngdn61651.2024.10744080.

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Xue, Zihan, Haibo Ge, and Yudi Yang. "Siamese Network Target Tracking Algorithm Based on Collaborative Attention Network." In 2024 6th International Conference on Natural Language Processing (ICNLP). IEEE, 2024. http://dx.doi.org/10.1109/icnlp60986.2024.10692407.

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Shi, Min, Yu Huang, Xingquan Zhu, Yufei Tang, Yuan Zhuang, and Jianxun Liu. "GAEN: Graph Attention Evolving Networks." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/213.

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Real-world networked systems often show dynamic properties with continuously evolving network nodes and topology over time. When learning from dynamic networks, it is beneficial to correlate all temporal networks to fully capture the similarity/relevance between nodes. Recent work for dynamic network representation learning typically trains each single network independently and imposes relevance regularization on the network learning at different time steps. Such a snapshot scheme fails to leverage topology similarity between temporal networks for progressive training. In addition to the static node relationships within each network, nodes could show similar variation patterns (e.g., change of local structures) within the temporal network sequence. Both static node structures and temporal variation patterns can be combined to better characterize node affinities for unified embedding learning. In this paper, we propose Graph Attention Evolving Networks (GAEN) for dynamic network embedding with preserved similarities between nodes derived from their temporal variation patterns. Instead of training graph attention weights for each network independently, we allow model weights to share and evolve across all temporal networks based on their respective topology discrepancies. Experiments and validations, on four real-world dynamic graphs, demonstrate that GAEN outperforms the state-of-the-art in both link prediction and node classification tasks.
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Wang, Zhitao, and Wenjie Li. "Hierarchical Diffusion Attention Network." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/531.

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A series of recent studies formulated the diffusion prediction problem as a sequence prediction task and proposed several sequential models based on recurrent neural networks. However, non-sequential properties exist in real diffusion cascades, which do not strictly follow the sequential assumptions of previous work. In this paper, we propose a hierarchical diffusion attention network (HiDAN), which adopts a non-sequential framework and two-level attention mechanisms, for diffusion prediction. At the user level, a dependency attention mechanism is proposed to dynamically capture historical user-to-user dependencies and extract the dependency-aware user information. At the cascade (i.e., sequence) level, a time-aware influence attention is designed to infer possible future user's dependencies on historical users by considering both inherent user importance and time decay effects. Significantly higher effectiveness and efficiency of HiDAN over state-of-the-art sequential models are demonstrated when evaluated on three real diffusion datasets. The further case studies illustrate that HiDAN can accurately capture diffusion dependencies.
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Fang, Songtao, Zhenya Huang, Ming He, et al. "Guided Attention Network for Concept Extraction." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/200.

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Concept extraction aims to find words or phrases describing a concept from massive texts. Recently, researchers propose many neural network-based methods to automatically extract concepts. Although these methods for this task show promising results, they ignore structured information in the raw textual data (e.g., title, topic, and clue words). In this paper, we propose a novel model, named Guided Attention Concept Extraction Network (GACEN), which uses title, topic, and clue words as additional supervision to provide guidance directly. Specifically, GACEN comprises two attention networks, one of them is to gather the relevant title and topic information for each context word in the document. The other one aims to model the implicit connection between informative words (clue words) and concepts. Finally, we aggregate information from two networks as input to Conditional Random Field (CRF) to model dependencies in the output. We collected clue words for three well-studied datasets. Extensive experiments demonstrate that our model outperforms the baseline models with a large margin, especially when the labeled data is insufficient.
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Li, Xiao, Jiaxing Song, and Weidong Liu. "Label-Attentive Hierarchical Attention Network for Text Classification." In ICBDC 2020: 2020 5th International Conference on Big Data and Computing. ACM, 2020. http://dx.doi.org/10.1145/3404687.3404706.

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Zhang, Doudou, Jing Cai, Yanbing Xue, Zan Gao, and Hua Zhang. "Attention Stereo Matching Network." In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021. http://dx.doi.org/10.1109/icpr48806.2021.9412622.

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Reports on the topic "Attention network"

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Ferdaus, Md Meftahul, Mahdi Abdelguerfi, Kendall Niles, Ken Pathak, and Joe Tom. Widened attention-enhanced atrous convolutional network for efficient embedded vision applications under resource constraints. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49459.

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Onboard image analysis enables real-time autonomous capabilities for unmanned platforms including aerial, ground, and aquatic drones. Performing classification on embedded systems, rather than transmitting data, allows rapid perception and decision-making critical for time-sensitive applications such as search and rescue, hazardous environment exploration, and military operations. To fully capitalize on these systems’ potential, specialized deep learning solutions are needed that balance accuracy and computational efficiency for time-sensitive inference. This article introduces the widened attention-enhanced atrous convolution-based efficient network (WACEfNet), a new convolutional neural network designed specifically for real-time visual classification challenges using resource-constrained embedded devices. WACEfNet builds on EfficientNet and integrates innovative width-wise feature processing, atrous convolutions, and attention modules to improve representational power without excessive over-head. Extensive benchmarking confirms state-of-the-art performance from WACEfNet for aerial imaging applications while remaining suitable for embedded deployment. The improvements in accuracy and speed demonstrate the potential of customized deep learning advancements to unlock new capabilities for unmanned aerial vehicles and related embedded systems with tight size, weight, and power constraints. This research offers an optimized framework, combining widened residual learning and attention mechanisms, to meet the unique demands of high-fidelity real-time analytics across a variety of embedded perception paradigms.
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Kranefeld, Robert. Beyond the grid : post-network energy provision in Rwanda. Goethe-Universität, Institut für Humangeographie, 2020. http://dx.doi.org/10.21248/gups.53186.

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In many parts of the world, the centralized grid provides energy to the population only to a limited extent. The electrification for sub-Saharan Africa countries is the lowest in the world, representing half of the world's population withoutelectricity. However, during the last years there has been an increased attention to rural areas in the Global South beyond the centralised grid, especially with respect to improved possibilities of solar power systems. The transition from one dominant form of energy provision to various alternatives includes different dimensions and depends on specific socio-spatial contexts. Energy systems are framed within systems of spatial practices, performed by a variety of involved actors, like consumers, local suppliers, international for-profit companies, international development donors as well as national and regional authorities. As such power systems arealways cause and effect of socio-technical change This study takes the example of Rwanda to analyse the marketization of decentralised energy systems. Based on empirical field work with energy entrepreneurs it combines Post-Colonial Theory with Science and Technology-Studies to theorise the role of energy to the social production of space beyond the grid.
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Soloviev, Volodymyr Mykolayovych, and Viktoriya Volodymyrivna Solovyova. Universal tools of modeling different nature complex systems. ФОП Однорог Т.В., 2018. http://dx.doi.org/10.31812/123456789/2865.

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It is shown that there is а powerful set of tools for the study of self-organization in complex systems, both natural and artificial origin. They characterize the multidimensional nature of complexity - multifractality, irreversibility, non-linearity, recurrence, nonstability, emeregence, etc., and quantitative evaluation of individual dynamical measures of complexity allows for monitoring, predicting and preventing unwanted critical or crisis. Particular attention is paid to measures of network complexity, which are fully applicable to build synergistic network of pedagogical systems.
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Marsden, Eric, Noëlle Laneyrie, Cécile Laugier, and Olivier Chanton. The regulator-regulatee relationship embedded in a coregulatory network. Foundation for an industrial safety culture, 2024. http://dx.doi.org/10.57071/368rrn.

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This document concerns the regulatory oversight and governance of high-hazard industrial activities. A complex set of laws, regulations and institutions contribute to the social control of these activities, reinforcing and serving as a complement to the risk prevention mechanisms put in place by operating companies. This document focuses in particular on the relationships between regulated firms, regulatory authorities and third party intermediaries who play a role in safety oversight (certification bodies, auditors, insurers, professional associations, etc.) and the impact of the quality of these relationships on industrial safety. The scope is the prevention of major accident hazards in different industry sectors (process industry, transport, energy), in France and at an international level. We focus our attention on different forms of “coregulation”, the act of enrolling the entities concerned by regulatory measures in their elaboration and the verification of their compliance, which is believed to improve their appropriation by private actors and thereby produce better oversight than classical command-and-control regulation. We analyze in particular the partial delegation of authority, internal risk control mechanisms and the use of third party intermediaries in the oversight process. This coproduction of regulation by public and private entities is increasingly used in different industry sectors, and leads to a more collaborative and interconnected regulatory process, based on a network of actors rather than a simple regulator-regulatee duopole.
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Deng, Yingjun, ShengJing Liu, Ming Zhao, Feng Zhao, Jun Guo, and Bin Yan. Diet-induced male infertility in mice models: a systematic review and network meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.5.0116.

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Review question / Objective: In order to compare the different high energy diet such as high-fat diet and high sugar diet how to damage the male mice model in metabolize and fertility,and explore a reliable mice model method in the study of obesity with male infertility. P：obesity mice model with male infertility. I: High energy diet such as High-fat or High-sugar diet. C:High-fat diet,High-sugar diet, compared with normal diet in mice model. O:High energy diet induce male mice obesity model and damage their fertility. S: Use network meta-analysis. Condition being studied: The relationship between obesity and male infertility attacth more and more attention at present.So many animal expriments are carried out on this problem,there are enough exprimental article to support this meta analysis.
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Noguere, Gilles, Oscar Cabellos, Denise Neudecker, Andrej Trkov, and Roberto Capote Noy. Summary Report of the IAEA Consultants’ Meeting of the International Nuclear Data Evaluation Network (INDEN) on Actinide Evaluation in the Resonance Region (4). IAEA Nuclear Data Section, 2022. http://dx.doi.org/10.61092/iaea.kw6h-tcge.

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A Consultants’ Meeting on Actinide Evaluation in the Resonance Region (4) of the International Nuclear Data Evaluation Network (INDEN) was held as a hybrid meeting from 1 to 4 November 2021. The meeting was a follow-up of the working group on evaluations in the resonance region of actinide nuclei. On-going evaluation work on 233U, 238U, 235U and 239Pu was discussed. Particular attention was paid to Prompt Fission Neutron Spectra, neutron multiplicities and reference integrals for fission cross sections were proposed for TOF fission data of fissile targets.
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Burge, Laura, and Fiona Marshall. Moving Beyond Institutional Boundaries: A Collaborative Approach to Primary Prevention. Journal of the Australian and New Zealand Student Services Association, 2023. http://dx.doi.org/10.30688/janzssa.2023-2-05.

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Preventing and responding to incidents of sexual assault and sexual harassment on university campuses remains an ongoing challenge for tertiary institutions across Australia and around the world. The growing recognition that universities have an obligation to address sexual harm has led to increased cooperation and collaboration among universities. This paper provides an overview of one such example of sector collaboration—the Victorian Tertiary Primary Prevention Network (TPPN). This community of practice brings together practitioners to share resources, ideas, successes, and challenges in relation to the promotion of safe and respectful university communities, and the prevention of sexual assault and sexual harassment. The paper also highlights transferable elements of the Network, drawing attention to four principles that should be taken into consideration by those seeking to explore or develop similar cross-institutional programs of work.
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Chen, Ziying, Zefei Jiang, Ziyun Guo, Mengchao Wang, Zhen Wang, and Liwei Chen. Comparative efficacy of different types of acupuncture for cancer-related fatigue: a protocol for systematic review and network meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.7.0012.

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Review question / Objective: To evaluate the efficacy and safety of all current acupuncture therapies for the treatment of CRF through network meta-analysis. Condition being studied: Cancer-related fatigue (CRF) has been defined as a distressing, persistent, subjective sense of physical, emotional, and/or cognitive tiredness or exhaustion related to cancer and/or cancer treatment that is not proportional to recent activity and interferes with usual functioning, as one of the most common symptoms in cancer and related therapies, presents a huge challenge to the quality of life for cancer patients. Unlike general fatigue that can be relieved with rest, CRF is more debilitating, more persistent, and manifests itself in various ways, both physically and mentally. The estimated prevalence of CRF varies widely by various fatigue evaluation indicators, types of cancer, and cancer treatments, ranging from 14.03% to 100%, however, the latest systematic review show that it can have a pooled prevalence of up to 52%, this deserves our attention. But there has been no gold standard treatment for CRF.
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Hotsur, Oksana. SOCIAL NETWORKS AND BLOGS AS TOOLS PR-CAMPAIGN IMPLEMENTATIONS. Ivan Franko National University of Lviv, 2021. http://dx.doi.org/10.30970/vjo.2021.50.11110.

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The article deals with the ways in which social networks and the blogosphere influence the formation and implementation of a PR campaign. Examples from the political sphere (election campaigns, initiatives), business (TV brands, traditional and online media) have revealed the opportunities that Facebook, Telegram, Twitter, YouTube and blogs promote in promoting advertising, ideas, campaigns, thoughts, or products. Author blogs created on special websites or online media may not be as much of a tool in PR as an additional tool on social media. It is noted that choosing a blog as the main tool of PR campaign has both positive and negative points. Social networks intervene in the sphere of human life, become a means of communication, promotion, branding. The effectiveness of social networks has been evidenced by such historically significant events as Brexit, the Arab Spring, and the Revolution of Dignity. Special attention was paid to the 2019 presidential election. Based on the analysis of individual PR campaigns, the reasons for successful and unsuccessful campaigns from the point of view of network communication, which provide unlimited multimedia and interactive tools for PR, are highlighted. In fact, these concepts significantly affect the effectiveness of the implementation of PR-campaign, its final effectiveness, which is determined by the achievement of goals. Attention is drawn to the culture of communication during the PR campaign, as well as the concepts of “trolls”, “trolling”, “bots”, “botoin industry”. The social communication component of these concepts is unconditional. Choosing a blog as the main tool of a marketing campaign has both positive and negative aspects. Only a person with great creative potential can run and create a blog. In addition, it takes a long time. In fact, these two points are losing compared to other internet marketing tools. Further research is interesting in two respects. First, a comparison of the dynamics of the effectiveness of PR-campaign tools in Ukraine in 2020 and in the past, in particular, at the dawn of state independence. Secondly, to investigate how/or the concept of PR-campaigns in social networks and blogs is constantly changing.
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Barbosa Martins, Ana, and Sally Sinclair. The global status of sharks, rays, and chimaeras. Edited by Rima Jabado, Alexandra Morata, Rhett Bennett, et al. International Union for Conservation of Nature and Natural Resources, 2024. http://dx.doi.org/10.59216/ssg.gsrsrc.2024.

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In the 20 years since the IUCN Species Survival Commission (SSC) Shark Specialist Group’s (SSG) first status report (Sharks, Rays and Chimaeras: The Status of the Chondrichthyan Fishes), much has changed for sharks, rays and chimaeras. This report updates our understanding, and the scope of information in its 2,000-odd pages reflects the scale of these two decades of change. The breadth of research topics has expanded, mirroring the inclusion of a greater diversity of species, and attention is being trained on the emerging threats and the accelerating global changes to aquatic ecosystems. The diversity of researchers who contribute to science and conservation within the SSG network and beyond, and of the regions they investigate and endeavour to conserve, has grown exponentially: 353 contributors have compiled information for 10 (arbitrary) geopolitical regions, delving into information from 158 countries or jurisdictions with a coastline. The 2005 report heralded a sea change for sharks, rays and chimaeras, whose historical obscurity in policy, conservation and fisheries management was a serious concern as the pace of their declines threatened to outstrip our information for and attention to them. In this report, the increased focus that was called for is now apparent in the scale of work happening across the planet.
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