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Dobrev, Stefan, Rastislav Královič, and Dana Pardubská. "Measuring the problem-relevant information in input." RAIRO - Theoretical Informatics and Applications 43, no. 3 (2009): 585–613. http://dx.doi.org/10.1051/ita/2009012.
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Laurı́, D., J. V. Salcedo, M. Martı́nez, and S. Garcı́a-Nieto. "Model predictive control relevant identification: multiple input multiple output against multiple input single output." IET Control Theory & Applications 4, no. 9 (2010): 1756–66. http://dx.doi.org/10.1049/iet-cta.2009.0482.
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Sykacek, P., G. Dorffner, P. Rappelsbeiger, and J. Zeitlhofer. "RELEVANT INPUT FEATURES FOR SLEEP STAGING WITH NEURAL NETWORKS." Journal of Neurosurgical Anesthesiology 9, no. 1 (1997): 91. http://dx.doi.org/10.1097/00008506-199701000-00047.
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Berberian, Nareg, Amanda MacPherson, Eloïse Giraud, Lydia Richardson, and J. P. Thivierge. "Neuronal pattern separation of motion-relevant input in LIP activity." Journal of Neurophysiology 117, no. 2 (2017): 738–55. http://dx.doi.org/10.1152/jn.00145.2016.
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In various regions of the brain, neurons discriminate sensory stimuli by decreasing the similarity between ambiguous input patterns. Here, we examine whether this process of pattern separation may drive the rapid discrimination of visual motion stimuli in the lateral intraparietal area (LIP). Starting with a simple mean-rate population model that captures neuronal activity in LIP, we show that overlapping input patterns can be reformatted dynamically to give rise to separated patterns of neuronal activity. The population model predicts that a key ingredient of pattern separation is the presence of heterogeneity in the response of individual units. Furthermore, the model proposes that pattern separation relies on heterogeneity in the temporal dynamics of neural activity and not merely in the mean firing rates of individual neurons over time. We confirm these predictions in recordings of macaque LIP neurons and show that the accuracy of pattern separation is a strong predictor of behavioral performance. Overall, results propose that LIP relies on neuronal pattern separation to facilitate decision-relevant discrimination of sensory stimuli. NEW & NOTEWORTHY A new hypothesis is proposed on the role of the lateral intraparietal (LIP) region of cortex during rapid decision making. This hypothesis suggests that LIP alters the representation of ambiguous inputs to reduce their overlap, thus improving sensory discrimination. A combination of computational modeling, theoretical analysis, and electrophysiological data shows that the pattern separation hypothesis links neural activity to behavior and offers novel predictions on the role of LIP during sensory discrimination.
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Kautz Osterkamp, L., and L. Longstaff. "American Indian input creates a localculturally-relevant food guide pyramid." Journal of the American Dietetic Association 101, no. 9 (2001): A—102. http://dx.doi.org/10.1016/s0002-8223(01)80344-8.
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Rogers, Keith. "Comprehensible Input FAQs." Journal of Classics Teaching 20, no. 39 (2019): 33–36. http://dx.doi.org/10.1017/s2058631019000059.
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Many teachers in the UK are aware of the issues surrounding a grammar-translation approach to teaching the classical languages but do not see any alternatives. One of the purposes of this edition of the Journal of Classics Teaching is to offer a series of articles which provides an overview of an approach called Comprehensible Input. This brief article has been written to provide an introduction to some of the basic questions and point the reader towards the relevant articles.
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RUGGIERO, JOHN. "IMPACT ASSESSMENT OF INPUT OMISSION ON DEA." International Journal of Information Technology & Decision Making 04, no. 03 (2005): 359–68. http://dx.doi.org/10.1142/s021962200500160x.
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It is well known in the Data Envelopment Analysis literature that proper variable selection is necessary for the reliable measurement of efficiency. Omitting production relevant variables and/or including irrelevant variables will lead to biased measurement. It is also known that the sample size needs to be large relative to the number of inputs and outputs to prevent classification of efficiency by default. In some empirical settings the number of potential relevant variables is large. Careful selection of an appropriate set of variables is necessary for reliable efficiency measurement. This paper looks at the issue of input selection and uses simulation analysis to develop statistical procedures to provide guidelines for input selection.
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Masuda, Naoki. "Simultaneous Rate-Synchrony Codes in Populations of Spiking Neurons." Neural Computation 18, no. 1 (2006): 45–59. http://dx.doi.org/10.1162/089976606774841521.
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Firing rates and synchronous firing are often simultaneously relevant signals, and they independently or cooperatively represent external sensory inputs, cognitive events, and environmental situations such as body position. However, how rates and synchrony comodulate and which aspects of inputs are effectively encoded, particularly in the presence of dynamical inputs, are unanswered questions. We examine theoretically how mixed information in dynamic mean input and noise input is represented by dynamic population firing rates and synchrony. In a subthreshold regime, amplitudes of spatially uncorrelated noise are encoded up to a fairly high input frequency, but this requires both rate and synchrony output channels. In a suprathreshold regime, means and common noise amplitudes can be simultaneously and separately encoded by rates and synchrony, respectively, but the input frequency for which this is possible has a lower limit.
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GÜNDÜÇ, Semra, and Recep ERYİGİT. "Determining the most relevant input parameter set by using extreme learning machine." Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 63, no. 1 (2021): 25–31. http://dx.doi.org/10.33769/aupse.525325.
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In this work, Extreme Learning Machine (ELM) algorithm is used to estimate the GDP per capita. The amount of electricity production, from four different sources, is chosen as input parameters. To find out the most relevant input data for a reasonable estimation of GDP, different sources introduced separately to ELM. By following the coefficient of determination of estimation, by trial and error, results are obtained. The residuals are also given to show that model perform well. Renewable energy sources produce the best results in the estimation of GDP.
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Bórquez Morales, Lilia Sulema, and Martha Guadalupe Hernández Alvarado. "Input, Output, and Intake." Edähi Boletín Científico de Ciencias Sociales y Humanidades del ICSHu 10, no. 19 (2021): 41–45. http://dx.doi.org/10.29057/icshu.v10i19.7869.
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Within the language teaching-learning area, many factors can be identified as impacting the proficiency of the language students achieve. As teachers, we have gone from searching the latest technologies to creating innovative materials that motivate students, passing through the use of resources that integrate skills and curricular designs that help students develop their autonomy. It is in this environment that we consider it relevant to review basic concepts that help us understand how the information we receive needs to be converted into knowledge in order to produce the language being studied. The concepts of input, output, adn intake are reviewed and the relevance of such concepts in Second Language Learning is pointed out in this essay.
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Liu, Nelson F., Kevin Lin, John Hewitt, et al. "Lost in the Middle: How Language Models Use Long Contexts." Transactions of the Association for Computational Linguistics 12 (2024): 157–73. http://dx.doi.org/10.1162/tacl_a_00638.
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Abstract While recent language models have the ability to take long contexts as input, relatively little is known about how well they use longer context. We analyze the performance of language models on two tasks that require identifying relevant information in their input contexts: multi-document question answering and key-value retrieval. We find that performance can degrade significantly when changing the position of relevant information, indicating that current language models do not robustly make use of information in long input contexts. In particular, we observe that performance is often highest when relevant information occurs at the beginning or end of the input context, and significantly degrades when models must access relevant information in the middle of long contexts, even for explicitly long-context models. Our analysis provides a better understanding of how language models use their input context and provides new evaluation protocols for future long-context language models.
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McBride, David J., Claire Fielding, Taksina Newington, et al. "Whole-Genome Sequencing Can Identify Clinically Relevant Variants from a Single Sub-Punch of a Dried Blood Spot Specimen." International Journal of Neonatal Screening 9, no. 3 (2023): 52. http://dx.doi.org/10.3390/ijns9030052.
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The collection of dried blood spots (DBS) facilitates newborn screening for a variety of rare, but very serious conditions in healthcare systems around the world. Sub-punches of varying sizes (1.5–6 mm) can be taken from DBS specimens to use as inputs for a range of biochemical assays. Advances in DNA sequencing workflows allow whole-genome sequencing (WGS) libraries to be generated directly from inputs such as peripheral blood, saliva, and DBS. We compared WGS metrics obtained from libraries generated directly from DBS to those generated from DNA extracted from peripheral blood, the standard input for this type of assay. We explored the flexibility of DBS as an input for WGS by altering the punch number and size as inputs to the assay. We showed that WGS libraries can be successfully generated from a variety of DBS inputs, including a single 3 mm or 6 mm diameter punch, with equivalent data quality observed across a number of key metrics of importance in the detection of gene variants. We observed no difference in the performance of DBS and peripheral-blood-extracted DNA in the detection of likely pathogenic gene variants in samples taken from individuals with cystic fibrosis or phenylketonuria. WGS can be performed directly from DBS and is a powerful method for the rapid discovery of clinically relevant, disease-causing gene variants.
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Moragues, Raul, Juan Aparicio, and Miriam Esteve. "Ranking the Importance of Variables in a Nonparametric Frontier Analysis Using Unsupervised Machine Learning Techniques." Mathematics 11, no. 11 (2023): 2590. http://dx.doi.org/10.3390/math11112590.
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In this paper, we propose and compare new methodologies for ranking the importance of variables in productive processes via an adaptation of OneClass Support Vector Machines. In particular, we adapt two methodologies inspired by the machine learning literature: one involving the random shuffling of values of a variable and another one using the objective value of the dual formulation of the model. Additionally, we motivate the use of these type of algorithms in the production context and compare their performance via a computational experiment. We observe that the methodology based on shuffling the values of a variable outperforms the methodology based on the dual formulation. We observe that the shuffling-based methodology correctly ranks the variables in 94% of the scenarios with one relevant input and one irrelevant input. Moreover, it correctly ranks each variable in at least 65% of replications of a scenario with three relevant inputs and one irrelevant input.
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Demain, C., M. Journée, and C. Bertrand. "Sensitivity of the RMI's MAGIC/Heliosat-2 method to relevant input data." Advances in Science and Research 10, no. 1 (2013): 7–13. http://dx.doi.org/10.5194/asr-10-7-2013.
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Abstract. Appropriate information on solar resources is very important for a variety of technological areas. Based on the potential of retrieving global horizontal irradiance from satellite data, an enhanced version of the Heliosat-2 method has been implemented at the Royal Meteorological Institute of Belgium to estimate surface solar irradiance over Belgium from Meteosat Second Generation at the SEVIRI spatial and temporal resolution. In this contribution, sensitivity of our retrieval scheme to surface albedo, atmospheric aerosol and water vapor contents is investigated. Results indicate that while the use of real-time information instead of climatological values can help to reduce to some extent the RMS error between satellite-retrieved and ground-measured solar irradiance, only the correction of the satellite-derived data with in situ measurements allows to significantly reduce the overall model bias.
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Stroeve, Sybert, and Stan Gielen. "Correlation Between Uncoupled Conductance-Based Integrate-and-Fire Neurons Due to Common and Synchronous Presynaptic Firing." Neural Computation 13, no. 9 (2001): 2005–29. http://dx.doi.org/10.1162/089976601750399281.
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We investigate the firing characteristics of conductance-based integrate- and-fire neurons and the correlation of firing for uncoupled pairs of neurons as a result of common input and synchronous firing of multiple synaptic inputs. Analytical approximations are derived for the moments of the steady state potential and the effective time constant. We show that postsynaptic firing barely depends on the correlation between inhibitory inputs; only the inhibitory firing rate matters. In contrast, both the degree of synchrony and the firing rate of excitatory inputs are relevant. A coefficient of variation CV > 1 can be attained with low inhibitory firing rates and (Poisson-modulated) synchronized excitatory synaptic input, where both the number of presynaptic neurons in synchronous firing assemblies and the synchronous firing rate should be sufficiently large. The correlation in firing of a pair of uncoupled neurons due to common excitatory input is initially increased for increasing firing rates of independent inhibitory inputs but decreases for large inhibitory firing rates. Common inhibitory input to a pair of uncoupled neurons barely induces correlated firing, but amplifies the effect of common excitation. Synchronous firing assemblies in the common input further enhance the correlation and are essential to attain experimentally observed correlation values. Since uncorrelated common input (i.e., common input by neurons, which do not fire in synchrony) cannot induce sufficient postsynaptic correlation, we conclude that lateral couplings are essential to establish clusters of synchronously firing neurons.
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Radke, Tim, Susanne Fuchs, Christian Wilms, Iuliia Polkova, and Marc Rautenhaus. "Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data." Geoscientific Model Development 18, no. 4 (2025): 1017–39. https://doi.org/10.5194/gmd-18-1017-2025.
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Abstract. Detection of atmospheric features in gridded datasets from numerical simulation models is typically done by means of rule-based algorithms. Recently, the feasibility of learning feature detection tasks using supervised learning with convolutional neural networks (CNNs) has been demonstrated. This approach corresponds to semantic segmentation tasks widely investigated in computer vision. However, while in recent studies the performance of CNNs was shown to be comparable to human experts, CNNs are largely treated as a “black box”, and it remains unclear whether they learn the features for physically plausible reasons. Here we build on the recently published “ClimateNet” dataset that contains features of tropical cyclones (TCs) and atmospheric rivers (ARs) as detected by human experts. We adapt the explainable artificial intelligence technique “Layer-wise Relevance Propagation” (LRP) to the semantic segmentation task and investigate which input information CNNs with the Context-Guided Network (CGNet) and U-Net architectures use for feature detection. We find that both CNNs indeed consider plausible patterns in the input fields of atmospheric variables. For instance, relevant patterns include point-shaped extrema in vertically integrated precipitable water (TMQ) and circular wind motion for TCs. For ARs, relevant patterns include elongated bands of high TMQ and eastward winds. Such results help to build trust in the CNN approach. We also demonstrate application of the approach for finding the most relevant input variables (TMQ is found to be most relevant, while surface pressure is rather irrelevant) and evaluating detection robustness when changing the input domain (a CNN trained on global data can also be used for a regional domain, but only partially contained features will likely not be detected). However, LRP in its current form cannot explain shape information used by the CNNs, although our findings suggest that the CNNs make use of both input values and the shape of patterns in the input fields. Also, care needs to be taken regarding the normalization of input values, as LRP cannot explain the contribution of bias neurons, accounting for inputs close to zero. These shortcomings need to be addressed by future work to obtain a more complete explanation of CNNs for geoscientific feature detection.
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Adams, Abdulai. "Analysing the determinants, constraints and opportunities of smallholder farmers access to input markets: evidence from northern Ghana." Journal of Agribusiness and Rural Development 56, no. 2 (2020): 133–43. http://dx.doi.org/10.17306/j.jard.2020.01286.
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Smallholder farmers face multiple constraints in accessing input markets. This study seeks to understand the dynamics that influence input markets in northern Ghana and the opportunities that exist for smallholder farmers to increase their productivity and welfare. Using a random sample of 448 households, the study applied the probit and non-parametric methods in identifying the factors that influence farmers’ access to input markets and the key constraints faced by them. The results show that access to extension services, access to finance, distance to the nearest input market, and input source are significant factors that would be likely to influence farmers’ access to input markets. Lack of finance, poor road network, and low prices of output are the main critically ranked constraints limiting farmers’ access to input markets. Policy initiatives should be geared toward strengthening extension service delivery, farmer education on inputs, improving feeder roads, and encouraging private sector participation in input markets. Available opportunities to leverage on and improve farmers’ access to input markets include the governments’ input subsidy programmes, existing large-scale agricultural projects, private agricultural companies with contract farming models, and extensive network of input dealers and aggregators in the communities. These findings are relevant for farmers, input dealers and policy makers working to improve farmers’ access to input markets.
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Erdogan, Halit Firat, Ayhan Kural, and Can Ozsoy. "Model predictive control of an unmanned aerial vehicle." Aircraft Engineering and Aerospace Technology 89, no. 2 (2017): 193–202. http://dx.doi.org/10.1108/aeat-03-2015-0074.
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Purpose The purpose of this paper is to design a controller for the unmanned aerial vehicle (UAV). Design/methodology/approach In this study, the constrained multivariable multiple-input and multiple-output (MIMO) model predictive controller (MPC) has been designed to control all outputs by manipulating inputs. The aim of the autopilot of UAV is to keep the UAV around trim condition and to track airspeed commands. Findings The purpose of using this control method is to decrease the control effort under the certain constraints and deal with interactions between each output and input while tracking airspeed commands. Originality/value By using constraint, multivariable (four inputs and seven outputs) MPC unlike the relevant literature in this field, the UAV tracked airspeed commands with minimum control effort dealing with interactions between each input and output under disturbances such as wind.
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Ardid, Salva, Jason S. Sherfey, Michelle M. McCarthy, Joachim Hass, Benjamin R. Pittman-Polletta, and Nancy Kopell. "Biased competition in the absence of input bias revealed through corticostriatal computation." Proceedings of the National Academy of Sciences 116, no. 17 (2019): 8564–69. http://dx.doi.org/10.1073/pnas.1812535116.
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Classical accounts of biased competition require an input bias to resolve the competition between neuronal ensembles driving downstream processing. However, flexible and reliable selection of behaviorally relevant ensembles can occur with unbiased stimulation: striatal D1 and D2 spiny projection neurons (SPNs) receive balanced cortical input, yet their activity determines the choice between GO and NO-GO pathways in the basal ganglia. We here present a corticostriatal model identifying three mechanisms that rely on physiological asymmetries to effect rate- and time-coded biased competition in the presence of balanced inputs. First, tonic input strength determines which one of the two SPN phenotypes exhibits a higher mean firing rate. Second, low-strength oscillatory inputs induce higher firing rate in D2 SPNs but higher coherence between D1 SPNs. Third, high-strength inputs oscillating at distinct frequencies can preferentially activate D1 or D2 SPN populations. Of these mechanisms, only the latter accommodates observed rhythmic activity supporting rule-based decision making in prefrontal cortex.
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Harrington, Michael, and Simon Dennis. "INPUT-DRIVEN LANGUAGE LEARNING." Studies in Second Language Acquisition 24, no. 2 (2002): 261–68. http://dx.doi.org/10.1017/s0272263102002103.
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Input-driven models provide an explicit and readily testable account of language learning. Although we share Ellis's view that the statistical structure of the linguistic environment is a crucial and, until recently, relatively neglected variable in language learning, we also recognize that the approach makes three assumptions about cognition and language learning that are not universally shared. The three assumptions concern (a) the language learner as an intuitive statistician, (b) the constraints on what constitute relevant surface cues, and (c) the redescription problem faced by any system that seeks to derive abstract grammatical relations from the frequency of co-occurring surface forms and functions. These are significant assumptions that must be established if input-driven models are to gain wider acceptance. We comment on these issues and briefly describe a distributed, instance-based approach that retains the key features of the input-driven account advocated by Ellis but that also addresses shortcomings of the current approaches.
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Gong, Xuan, Shanglin Li, Yuxiang Bao, et al. "Federated Learning via Input-Output Collaborative Distillation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 20 (2024): 22058–66. http://dx.doi.org/10.1609/aaai.v38i20.30209.
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Federated learning (FL) is a machine learning paradigm in which distributed local nodes collaboratively train a central model without sharing individually held private data. Existing FL methods either iteratively share local model parameters or deploy co-distillation. However, the former is highly susceptible to private data leakage, and the latter design relies on the prerequisites of task-relevant real data. Instead, we propose a data-free FL framework based on local-to-central collaborative distillation with direct input and output space exploitation. Our design eliminates any requirement of recursive local parameter exchange or auxiliary task-relevant data to transfer knowledge, thereby giving direct privacy control to local users. In particular, to cope with the inherent data heterogeneity across locals, our technique learns to distill input on which each local model produces consensual yet unique results to represent each expertise. Our proposed FL framework achieves notable privacy-utility trade-offs with extensive experiments on image classification and segmentation tasks under various real-world heterogeneous federated learning settings on both natural and medical images. Code is available at https://github.com/lsl001006/FedIOD.
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Wang, Yingxue, and Shih-Chii Liu. "Multilayer Processing of Spatiotemporal Spike Patterns in a Neuron with Active Dendrites." Neural Computation 22, no. 8 (2010): 2086–112. http://dx.doi.org/10.1162/neco.2010.06-09-1030.
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With the advent of new experimental evidence showing that dendrites play an active role in processing a neuron's inputs, we revisit the question of a suitable abstraction for the computing function of a neuron in processing spatiotemporal input patterns. Although the integrative role of a neuron in relation to the spatial clustering of synaptic inputs can be described by a two-layer neural network, no corresponding abstraction has yet been described for how a neuron processes temporal input patterns on the dendrites. We address this void using a real-time aVLSI (analog very-large-scale-integrated) dendritic compartmental model, which incorporates two widely studied classes of regenerative event mechanisms: one is mediated by voltage-gated ion channels and the other by transmitter-gated NMDA channels. From this model, we find that the response of a dendritic compartment can be described as a nonlinear sigmoidal function of both the degree of input temporal synchrony and the synaptic input spatial clustering. We propose that a neuron with active dendrites can be modeled as a multilayer network that selectively amplifies responses to relevant spatiotemporal input spike patterns.
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Herranz, Victoria, Diego Napp, and Carmen Perea. "Weight-2 input sequences of $ 1/n $ convolutional codes from linear systems point of view." AIMS Mathematics 8, no. 1 (2022): 713–32. http://dx.doi.org/10.3934/math.2023034.
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<abstract><p>Convolutional codes form an important class of codes that have memory. One natural way to study these codes is by means of input state output representations. In this paper we study the minimum (Hamming) weight among codewords produced by input sequences of weight two. In this paper, we consider rate $ 1/n $ and use the linear system setting called $ (A, B, C, D) $ input-state-space representations of convolutional codes for our analysis. Previous results on this area were recently derived assuming that the matrix $ A $, in the input-state-output representation, is nonsingular. This work completes this thread of research by treating the nontrivial case in which $ A $ is singular. Codewords generated by weight-2 inputs are relevant to determine the effective free distance of Turbo codes.</p></abstract>
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Guse, B., A. Bronstert, M. Rode, B. Tetzlaff, and F. Wendland. "Application of two phosphorus models with different complexities in a mesoscale river catchment." Advances in Geosciences 11 (June 13, 2007): 77–84. http://dx.doi.org/10.5194/adgeo-11-77-2007.
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Abstract. The water balance and phosphorus inputs of surface waters of the Weiße Elster catchment, Germany, have been quantified using the models GROWA/MEPhos and SWAT. A comparison of the model results shows small differences in the mean long-term total runoff for the entire study area. All relevant pathways of phosphorus transport were considered in MEPhos with phosphorus inputs resulting to about 65% from point sources. SWAT focuses on agricultural areas and estimates a phosphorus input of about 60% through erosion. The mean annual phosphorus input from erosion calculated with SWAT is six times higher than the estimation with MEPhos due to the differing model concepts. This shows the uncertainty contributed by the modelling description of phosphorus pathways.
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Pandey, Neeraj, and Akshay Rupnawar. "Idea Generation for New Service Development (NSD): Harnessing the Power of Social Media Platforms." Multidisciplinary Business Review 15, no. 1 (2022): 2–10. http://dx.doi.org/10.35692/07183992.15.1.2.
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Customers frequently use social media (SM) platforms to share and seek information about services. Could the content of customer SM interactions be used for new service development (NSD)? This study analyses: (a) Whether social media input is relevant for NSD? (b) What are the preferred social media platforms for NSD? (c) How social media input can be integrated with the current NSD organisational process? The results highlight the importance of social media input in NSD. Facebook leads the social media pack for relevant NSD input. The SMART roadmap for leveraging the power of social media for NSD has also been suggested.
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Rivera, Daniel E., Shan Zong, and Wei-ming Ling. "A Control-Relevant Multivariable System Identification Methodology Based on Orthogonal Multifrequency Input Perturbations." IFAC Proceedings Volumes 30, no. 11 (1997): 573–78. http://dx.doi.org/10.1016/s1474-6670(17)42906-5.
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Sederberg, Audrey J., Stephanie E. Palmer, and Jason N. MacLean. "Decoding thalamic afferent input using microcircuit spiking activity." Journal of Neurophysiology 113, no. 7 (2015): 2921–33. http://dx.doi.org/10.1152/jn.00885.2014.
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A behavioral response appropriate to a sensory stimulus depends on the collective activity of thousands of interconnected neurons. The majority of cortical connections arise from neighboring neurons, and thus understanding the cortical code requires characterizing information representation at the scale of the cortical microcircuit. Using two-photon calcium imaging, we densely sampled the thalamically evoked response of hundreds of neurons spanning multiple layers and columns in thalamocortical slices of mouse somatosensory cortex. We then used a biologically plausible decoder to characterize the representation of two distinct thalamic inputs, at the level of the microcircuit, to reveal those aspects of the activity pattern that are likely relevant to downstream neurons. Our data suggest a sparse code, distributed across lamina, in which a small population of cells carries stimulus-relevant information. Furthermore, we find that, within this subset of neurons, decoder performance improves when noise correlations are taken into account.
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Sadiq, Mohammed Sanusi, Musa Ahmad Isah, Sanni Ozomata Abdullahi, and Aishat Ammani Aliyu. "Assessment of the Agro-Input Supply Sector in Kogi State, Nigeria." Journal of Agri-Food Science and Technology 3, no. 1 (2022): 10–20. http://dx.doi.org/10.12928/jafost.v3i1.6212.
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Most farmers are experiencing challenges and constraints in accessing agricultural inputs, thus leading to poor and underutilization of agro-inputs and consequently low agricultural productivity in most part of sub-Saharan Africa. This study aimed to assess the agro-input supply sector in Kogi state. A total of 157 input dealers were randomly selected across the twenty-one local government areas in the state. Data were collected using well-structured questionnaire complemented with interview schedule and were analyzed using simple descriptive statistics. Findings reveal that agro-chemicals, fertilizer and feed were the major inputs supplied by most of the retailers and wholesalers that hardly engage in activities that create awareness about their products. Input prices were the most important preference and consideration factor in the purchase of agro-input by customers and form the basis for competition. Casual, family and child labors that proved to be cheaper, commonly accessible and do not require signing of formal employment contract with workers dominated both the retail and wholesale sectors. Poor government support, poor business condition in addition to poor capital base, high transportation cost, price fluctuations, adulteration were the identified major constraints affecting the agripreneurs. The study recommends capacity development on new marketing strategies, registration of businesses with relevant agencies, provision of credit and financial services; formation of formidable, strong and mutual-trust co-operative societies for input supply actors so as to enhance efficiency and effectiveness of the agricultural inputs supply sector in the state.
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Isah, Musa Ahmad, Sanni Ozomata Abdullahi, Aishat Ammani Aliyu, and Sanusi Mohammed Sadiq. "ASSESSMENT OF THE AGRO-INPUT SUPPLY SECTOR IN KOGI STATE, NIGERIA." Agricultural Socio-Economics Journal 23, no. 1 (2023): 59–68. http://dx.doi.org/10.21776/ub.agrise.2023.023.1.8.
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Most farmers are experiencing challenges and constraints in accessing agricultural inputs, thus leading to poor and underutilization of agro inputs and consequently low agricultural productivity in most part of sub-Saharan Africa. This study assessed the agro-input supply sector in Kogi state. A total of 157 input dealers were randomly selected across the twenty-one local government areas in the state. Data were collected using well-structured questionnaire complemented with interview schedule and were analyzed using simple descriptive statistics. Findings reveal that agro-chemicals, fertilizer and feed were the major inputs supplied by most of the retailers and wholesalers that hardly engage in activities that create awareness about their products. Input prices were the most important preference and consideration factor in the purchase of agro-input by customers and form the basis for competition. Casual, family and child labours that proved to be cheaper, commonly accessible and do not require signing of formal employment contract with workers dominated both the retail and wholesale sectors. Poor government support, poor business condition in addition to poor capital base, high transportation cost, price fluctuations, adulteration were the identified major constraints affecting the agripreneurs. The study recommends capacity development on new marketing strategies, registration of businesses with relevant agencies, provision of credit and financial services; formation of formidable, strong and mutual-trust co-operative societies for input supply actors so as to enhance efficiency and effectiveness of the agricultural inputs supply sector in the state.
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McIntosh, Megan D., Gauhar Sabih, Clarke Summers, Tara L. Cavalline, and Brett Q. Tempest. "Quantifying the Effects of Material Input Levels on Jointed Plain Concrete Pavement (JPCP) Performance and Slab Thickness." Construction Materials 4, no. 1 (2024): 251–70. http://dx.doi.org/10.3390/constrmater4010014.
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The mechanistic-empirical pavement design guide (MEPDG) is a commonly accepted design principles guide that aids in jointed plain concrete pavement (JPCP) design and performance analysis. The MEPDG uses three different design parameter input levels, referred to as level one, level two, and level three, providing increasing confidence in the analysis at the lower numbered levels, which use more locally relevant (level two) or project-specific (level one) data. The state-of-the-art pavement ME software (version 2.6.2) uses MEPDG design principles to predict pavement performance. The three performance indicators for JPCP systems (international roughness index (IRI), joint faulting, and transverse cracking) experience significant changes when simulating under a different input level. The IRI and faulting indicator changed by 78 percent when using inputs varying from level one to level three, with the cracking indicator change being more severe at 87 percent. To accommodate the change in performance indicator values between input level one and input level three, increasing the concrete slab thickness is necessary to achieve comparable pavement performance. An increase in the Portland cement concrete (PCC) layer from one inch to two inches is required when input level three simulations are performed, demonstrating the economic and sustainability benefits of using project-specific level one inputs. Understanding the impact of simulation input levels will help to meet design and sustainability goals and improve the lifecycle performance of JPCP systems.
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Clusius, Petri, Carlton Xavier, Lukas Pichelstorfer, et al. "Atmospherically Relevant Chemistry and Aerosol box model – ARCA box (version 1.2)." Geoscientific Model Development 15, no. 18 (2022): 7257–86. http://dx.doi.org/10.5194/gmd-15-7257-2022.
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Abstract. We introduce the Atmospherically Relevant Chemistry and Aerosol box model ARCA box (v.1.2.2). It is a zero-dimensional process model with a focus on atmospheric chemistry and submicron aerosol processes, including cluster formation. A novel feature in the model is its comprehensive graphical user interface, allowing for detailed configuration and documentation of the simulation settings, flexible model input, and output visualization. Additionally, the graphical interface contains tools for module customization and input data acquisition. These properties – customizability, ease of implementation and repeatability – make ARCA an invaluable tool for any atmospheric scientist who needs a view on the complex atmospheric aerosol processes. ARCA is based on previous models (MALTE-BOX, ADiC and ADCHEM), but the code has been fully rewritten and reviewed. The gas-phase chemistry module incorporates the Master Chemical Mechanism (MCMv3.3.1) and Peroxy Radical Autoxidation Mechanism (PRAM) but can use any compatible chemistry scheme. ARCA's aerosol module couples the ACDC (Atmospheric Cluster Dynamics Code) in its particle formation module, and the discrete particle size representation includes the fully stationary and fixed-grid moving average methods. ARCA calculates the gas-particle partitioning of low-volatility organic vapours for any number of compounds included in the chemistry, as well as the Brownian coagulation of the particles. The model has parametrizations for vapour and particle wall losses but accepts user-supplied time- and size-resolved input. ARCA is written in Fortran and Python (user interface and supplementary tools), can be installed on any of the three major operating systems and is licensed under GPLv3.
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Herdt, Leah Rowland, Paige Berroteran, Malini Rajagopalan, Bradley A. Brown, and Jerrod J. Schwartz. "NSCLC Digital PCR Panel Returns Low-Input Sample Results Where Sequencing Fails." Diagnostics 14, no. 3 (2024): 243. http://dx.doi.org/10.3390/diagnostics14030243.
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Molecular diagnostics has drastically improved the survival rate of patients diagnosed with non-small cell lung cancer (NSCLC) over the last 10 years. Despite advancements in molecular testing, targeted therapies, and national guideline recommendations, more than half of NSCLC patients in the United States either never receive testing or patient care is not informed via molecular testing. Here, we sought to explore the relationship between DNA/RNA input, the molecular testing method, and test success rates. On a shared set of low-input reference test materials (n = 3), we ran both a hybrid capture-based, next-generation sequencing (NGS) assay and a multiplexed digital PCR (dPCR) panel. The dPCR panel was highly sensitive and specific for low-input samples in dilution studies ranging from 40 to 1 ng DNA and from 20 to 2.5 ng RNA, while NGS had up to an 86% loss in sensitivity as contrived sample inputs were serially diluted. The dPCR panel also demonstrated a high PPA (>95%) at diluted inputs as low as 15/7.5 ng DNA/RNA on 23 banked clinical samples with the same NGS hybrid capture assay at a high input. These data suggest that digital PCR is an accurate and effective way of identifying clinically relevant NSCLC mutations at low nucleotide input and quality.
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KUMAR, PRIYANK. "IMPACT OF COVID-19 ON THE AIRLINE INDUSTRY." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32294.
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The purpose of the paper is to provide an overview of the issues related to artificial intelligence (AI) applications in the Indian healthcare sector and provide input to policymakers. A qualitative approach has been used in this study to identify government initiatives, opportunities, and challenges for applications of AI and suggest improvements in policy areas relevant to AI in healthcare. The study helps by providing comprehensive inputs for framing policy on AI in healthcare industry in India. The study also highlights that if the proper actions are taken to overcome the various challenges associated with applications of AI in healthcare sector in India by the government, then the healthcare sector will immensely benefit. This article has taken an attempt to provide inputs concerning to policy initiatives, challenges, and recommendations for improving the healthcare system of India using different applications of AI The purpose of the paper is to provide an overview of the issues related to artificial intelligence (AI) applications in the Indian healthcare sector and provide input to policymakers. A qualitative approach has been used in this study to identify government initiatives, opportunities, and challenges for applications of AI and suggest improvements in policy areas relevant to AI in healthcare. The India. The study also highlights that if the proper actions are taken to overcome the various challenges associated with applications of AI in healthcare sector in India by the government, then the healthcare sector will immensely benefit. This article has taken an attempt to provide inputs concerning to policy initiatives, challenges, and recommendations for improving the healthcare system of India using different applications of AI The purpose of the paper is to provide an overview of the issues related to artificial intelligence (AI) applications in the Indian healthcare sector and provide input to policymakers. A qualitative approach has been used in this study to identify government initiatives, opportunities, and challenges for applications of AI and suggest improvements in policy areas relevant to AI in healthcare. The study helps by providing comprehensive inputs for framing policy on AI in healthcare industry in India. The study also highlights that if the proper actions are taken to overcome the various challenges associated with applications of AI in healthcare sector in India by the government, then the healthcare sector will immensely benefit. This article has taken an attempt to provide inputs concerning to policy initiatives, challenges, and recommendations for improving the healthcare system of India using different applications of A The purpose of the paper is to provide an overview of the issues related to artificial intelligence (AI) applications in the Indian healthcare sector and provide input to policymakers. A qualitative approach has been used in this study to identify government initiatives, opportunities, and challenges for applications of AI and suggest improvements in policy areas relevant to AI in healthcare.
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Yu, Yuanhe, Liang Wang, Jinkuo Lin, and Zijun Li. "Optimizing Agricultural Input and Production for Different Types of at-Risk Peasant Households: An Empirical Study of Typical Counties in the Yimeng Mountain Area of Northern China." International Journal of Environmental Research and Public Health 19, no. 21 (2022): 13938. http://dx.doi.org/10.3390/ijerph192113938.
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Using typical counties in the Yimeng Mountain area of northern China as an example, this paper analyzed the household and agricultural input characteristics of different types of peasant households using survey data from 262 farm households. The target minimization of the total absolute deviations (MOTAD) model was applied to determine the optimal combinations in the allocation of agricultural input factors and production for different types of at-risk peasant households to obtain the ideal agricultural income. The relevant results are twofold. (1) The agricultural input behaviors of different types of peasant households vary significantly. The highest levels of agricultural land, labor, and yield-increasing and labor-saving inputs included I part-time peasant households (I PTPH), followed by full-time peasant households (FTPH), while the input levels of II part-time peasant households (II PTPH) and non-agricultural peasant households (NAPH) with higher levels of non-agricultural employment gradually decreased. In general, an increase in peasant households’ part-time employment revealed an inverted U-shaped trend in the agricultural input level, with a trajectory of I PTPH > FTPH > II PTPH > NAPH. (2) The current agricultural inputs and production combinations of different types of peasant households have room for improvement. It is necessary to adjust agricultural inputs and optimize production combinations to obtain target incomes. Overall, all types of peasant households must streamline labor inputs and increase capital inputs, except for I PTPH, for which capital inputs should be reduced. Following optimization, economic crops gradually replace grain crops, and the optimal agricultural incomes of peasant households will be improved. The study results provide practical policy insights for reducing agricultural production risks and improving agricultural production incomes.
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Anghel, Daniel Constantin, Alexandru Ene, and Nadia Belu. "A Matlab Neural Network Application for the Study of Working Conditions." Advanced Materials Research 837 (November 2013): 310–15. http://dx.doi.org/10.4028/www.scientific.net/amr.837.310.
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The paper presents a method based on the neural networks to study of working conditions, for the workstations from the manufacture industry. The neural networks were chosen because they excel in gathering difficult non-linear relationships between the inputs and outputs of a system. The neural network was simulated with Matlab. In this paper, we considered as relevant for the study of working conditions, 6 input parameters: temperature, humidity, noise, luminosity, load and frequency. The neural network designed for the study presented in this paper has 6 input neurons and 3 neurons in the output layer. Some experimental results obtained through simulations, are presented in the final part of the paper.
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Anghel, Daniel Constantin, and Nadia Belu. "Contributions to Ranking an Ergonomic Workstation, Considering the Human Effort and the Microclimate Parameters, Using Neural Networks." Applied Mechanics and Materials 371 (August 2013): 812–16. http://dx.doi.org/10.4028/www.scientific.net/amm.371.812.
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The paper presents a method to use a feed forward neural network in order to rank a working place from the manufacture industry. Neural networks excel in gathering difficult non-linear relationships between the inputs and outputs of a system. The neural network is simulated with a simple simulator: SSNN. In this paper, we considered as relevant for a work place ranking, 6 input parameters: temperature, humidity, noise, luminosity, load and frequency. The neural network designed for the study presented in this paper has 6 input neurons, 13 neurons in the hidden layer and 1 neuron in the output layer. We present also some experimental results obtained through simulations.
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Ceccarelli, S. "Plant breeding technologies relevant to developing countries." BSAP Occasional Publication 16 (1993): 37–46. http://dx.doi.org/10.1017/s0263967x00031050.
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AbstractThe relevance of new and traditional plant breeding technologies is discussed with particular reference to the improvement of sustainable agricultural systems in difficult environments. The focus of the paper is on barley, a crop which is grown as animal food, mostly for small ruminants, on about 17 million ha in developing countries. Barley is also a typical low-input crop being grown largely in agriculturally marginal areas by risk-averse farmers. Differences in straw quality characteristics between varieties of some crops are discussed in relation to their utilization in breeding programmes using either conventional or new technologies. A greater interaction between animal scientists and plant breeders is needed to define appropriate techniques to screen for straw quality. This is considered to be essential to incorporate straw quality characteristics in breeding programmes.
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Banerjee, Sarasij, Pablo Otálora, Mohamed El Mistiri, Owais Khan, José Luis Guzmán, and Daniel E. Rivera. "Control-Relevant Input Signal Design For Integrating Processes: Application to a Microalgae Raceway Reactor." IFAC-PapersOnLine 58, no. 15 (2024): 360–65. http://dx.doi.org/10.1016/j.ifacol.2024.08.555.
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Gaikwad, S. V., and D. E. Rivera. "Control-Relevant Input Signal Design for Multivariable System Identification: Application to High-Purity Distillation." IFAC Proceedings Volumes 29, no. 1 (1996): 6143–48. http://dx.doi.org/10.1016/s1474-6670(17)58666-8.
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Linzhe, Huang, Cao Yuping, Tian Xuemin, and Deng Xiaogang. "A Nonlinear Quality-relevant Process Monitoring Method with Kernel Input-output Canonical Variate Analysis." IFAC-PapersOnLine 48, no. 8 (2015): 611–16. http://dx.doi.org/10.1016/j.ifacol.2015.09.035.
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Filiushov, Vladislav Yu. "The polynomial method of controller synthesis based on the reference and disturbing signals." Analysis and data processing systems, no. 1 (March 25, 2022): 93–108. http://dx.doi.org/10.17212/2782-2001-2022-1-93-108.
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Linear controlled objects with one input and one output (single input - single output, SISO), and objects with multiple inputs and multiple outputs (multi-input – multi-output, MIMO) have different formalized controller synthesis algorithms. At the same time, objects with an unequal number of inputs and outputs, in many cases, are built by the developer intuitively, when changing the existing calculation algorithms for each control object, therefore, the development of a formalized calculation algorithm for this type of objects is relevant. Within the framework of this work, it is proposed to extend the synthesis technique for multichannel objects, which is the polynomial synthesis technique, to objects with a smaller number of inputs compared to the number of outputs, namely, to objects with one input and several outputs (single input – multi-output, SIMO). The reasoning developed in the work is an example of calculating an electromechanical tension control system in the material transportation zone of the production line, which has one input – the voltage supplied to the electric motor and four outputs–- the armature current, the rotation speed of the electric motor shaft, the rotation speed of the roll, the tension in the zone under consideration and the elastic moment. The tension in the considered zone is an adjustable coordinate. The use of the polynomial synthesis method for objects with a non-square matrix function made it possible to place the poles of a closed system in a given position, and the transfer function does not contain zeros according to the assignment. It was also possible to set the disturbance-stimulated zeros of the closed system in such a way that a second-order astatism is obtained.
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Xu, Xiaotong, Judith Fan, and Steven Dow. "Schema and Metadata Guide the Collective Generation of Relevant and Diverse Work." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 8 (October 1, 2020): 178–82. http://dx.doi.org/10.1609/hcomp.v8i1.7479.
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While most crowd work seeks consistent answers, creative domains often seek more diverse input. The typical crowd mechanisms for controlling quality may stifle creativity, yet removing them altogether could just produce noise. Schemas and metadata provide two mechanisms for embedding existing knowledge into task environments. Schemas are expert-derived patterns designed to structure how people think through a problem. Metadata, on the other hand, illustrate a range of creative input that fits within the structure of a schema. To understand the relative effects of schemas and metadata, we conducted a study where crowd workers are asked to generate creative interpretations for a set of placemaking examples. Crowd workers were guided either by schema plus metadata, schema alone, or neither. We found that showing schema along with crowd-produced metadata helped workers contribute interpretations that are both more on-topic and diverse, compared to using the schema alone or no schema. We discuss the implications on how crowds can creatively build on insights shared by others.
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Jacobsen, Thomas, and Erich Schröger. "Input to Verbal Working Memory." Experimental Psychology 51, no. 4 (2004): 231–39. http://dx.doi.org/10.1027/1618-3169.51.4.231.
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Abstract. Working memory uses central sound representations as an informational basis. The central sound representation is the temporally and feature-integrated mental representation that corresponds to phenomenal perception. It is used in (higher-order) mental operations and stored in long-term memory. In the bottom-up processing path, the central sound representation can be probed at the level of auditory sensory memory with the mismatch negativity (MMN) of the event-related potential. The present paper reviews a newly developed MMN paradigm to tap into the processing of speech sound representations. Preattentive vowel categorization based on F1-F2 formant information occurs in speech sounds and complex tones even under conditions of high variability of the auditory input. However, an additional experiment demonstrated the limits of the preattentive categorization of language-relevant information. It tested whether the system categorizes complex tones containing the F1 and F2 formant components of the vowel /a/ differently than six sounds with nonlanguage-like F1-F2 combinations. From the absence of an MMN in this experiment, it is concluded that no adequate vowel representation was constructed. This shows limitations of the capability of preattentive vowel categorization.
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Kasimo, Yulius Yosef, Maria Kristina Ota, and Yuliana M. D’Karmel Kara. "Input hypotheses for developing EFL/ESL learners’ listening comprehension skills." Journal of Research in Instructional 4, no. 2 (2024): 367–79. http://dx.doi.org/10.30862/jri.v4i2.436.
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The objective of this study is to examine the significance of the input hypothesis in enhancing the listening comprehension abilities of EFL/ESL learners. The methodology employed in this study is a critical literature review. This entailed a comprehensive search for relevant articles and other sources, a meticulous reading of the material, the categorization of data, a thorough analysis, and the synthesis of findings. The findings of this study indicate that the input hypothesis plays an essential role in developing ESL/EFL learners' listening comprehension abilities through the acquisition of comprehensible inputs. The activation of linguistic background knowledge enables EFL/ESL learners to develop pronunciation skills, understanding of grammatical accuracy, lexical resources, and meaning. Optimizing the inputs of the listening strategy (cognitive, metacognitive, socio-affective) assists EFL/ESL learners in activating their memory about listening materials. This is achieved through making inferences, elaborations, summaries, translations, observations, assessments, and becoming involved in the community. Inputs from media, audio (visual), and practices can develop EFL/ESL learners' cognition, motivation, stimulating perception, activating prior knowledge. In conclusion, the components of the comprehensible inputs must be integrated and developed in a coordinated manner during the teaching and learning process of listening skills.
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Sun, Ya-Yen, and Ching-Mai Hsu. "The Decomposition Analysis of Tourism Water Footprint in Taiwan: Revealing Decision-Relevant Information." Journal of Travel Research 58, no. 4 (2018): 695–708. http://dx.doi.org/10.1177/0047287518757371.
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Tourism water consumption reflects the dynamics between the visitation volume, economic structure, and water use technology of a destination. This paper presents a structural decomposition analysis that attributes changes of Taiwan’s tourism water footprint into the demand factors of total consumption and purchasing patterns, and production factors of the industry input structure and water use technology. From 2006 to 2011, Taiwan experienced a 48% growth in visitor expenditures and a 74% surge in its water footprint. Diseconomies of scale were observed, with a 1% increase in consumption leading to a 1.5% increase in the tourism water footprint. A strong preference by visitors for water-intensive goods and services and a changing economic structure requiring more water input for tourism establishments and supply chain members contributed to this worrisome pattern. The water requirements received only a minimal offset effect with technological improvements. Decoupling tourism water consumption from economic output is currently unattainable.
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Parodi, Matteo U., Alessio Giardino, Ap van Dongeren, Stuart G. Pearson, Jeremy D. Bricker, and Ad J. H. M. Reniers. "Uncertainties in coastal flood risk assessments in small island developing states." Natural Hazards and Earth System Sciences 20, no. 9 (2020): 2397–414. http://dx.doi.org/10.5194/nhess-20-2397-2020.
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Abstract. Considering the likely increase in coastal flooding in small island developing states (SIDSs) due to climate change, coastal managers at the local and global levels have been developing initiatives aimed at implementing disaster risk reduction (DRR) and adaptation measures. Developing science-based adaptation policies requires accurate coastal flood risk (CFR) assessments, which in the case of insular states are often subject to input uncertainty. We analysed the impact of a number of uncertain inputs on coastal flood damage estimates: (i) significant wave height, (ii) storm surge level and (iii) sea level rise (SLR) contributions to extreme sea levels, as well as the error-driven uncertainty in (iv) bathymetric and (v) topographic datasets, (vi) damage models, and (vii) socioeconomic changes. The methodology was tested through a sensitivity analysis using an ensemble of hydrodynamic models (XBeach and SFINCS) coupled with a direct impact model (Delft-FIAT) for a case study of a number of villages on the islands of São Tomé and Príncipe. Model results indicate that for the current time horizon, depth damage functions (DDFs) and digital elevation models (DEMs) dominate the overall damage estimation uncertainty. When introducing climate and socioeconomic uncertainties to the analysis, SLR projections become the most relevant input for the year 2100 (followed by DEM and DDF). In general, the scarcity of reliable input data leads to considerable predictive uncertainty in CFR assessments in SIDSs. The findings of this research can help to prioritize the allocation of limited resources towards the acquisitions of the most relevant input data for reliable impact estimation.
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Garrido-Charad, Florencia, Tomas Vega-Zuniga, Cristián Gutiérrez-Ibáñez, et al. "“Shepherd’s crook” neurons drive and synchronize the enhancing and suppressive mechanisms of the midbrain stimulus selection network." Proceedings of the National Academy of Sciences 115, no. 32 (2018): E7615—E7623. http://dx.doi.org/10.1073/pnas.1804517115.
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The optic tectum (TeO), or superior colliculus, is a multisensory midbrain center that organizes spatially orienting responses to relevant stimuli. To define the stimulus with the highest priority at each moment, a network of reciprocal connections between the TeO and the isthmi promotes competition between concurrent tectal inputs. In the avian midbrain, the neurons mediating enhancement and suppression of tectal inputs are located in separate isthmic nuclei, facilitating the analysis of the neural processes that mediate competition. A specific subset of radial neurons in the intermediate tectal layers relay retinal inputs to the isthmi, but at present it is unclear whether separate neurons innervate individual nuclei or a single neural type sends a common input to several of them. In this study, we used in vitro neural tracing and cell-filling experiments in chickens to show that single neurons innervate, via axon collaterals, the three nuclei that comprise the isthmotectal network. This demonstrates that the input signals representing the strength of the incoming stimuli are simultaneously relayed to the mechanisms promoting both enhancement and suppression of the input signals. By performing in vivo recordings in anesthetized chicks, we also show that this common input generates synchrony between both antagonistic mechanisms, demonstrating that activity enhancement and suppression are closely coordinated. From a computational point of view, these results suggest that these tectal neurons constitute integrative nodes that combine inputs from different sources to drive in parallel several concurrent neural processes, each performing complementary functions within the network through different firing patterns and connectivity.
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Kim, Seonhoon, Seohyeong Jeong, Eunbyul Kim, Inho Kang, and Nojun Kwak. "Self-supervised Pre-training and Contrastive Representation Learning for Multiple-choice Video QA." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 14 (2021): 13171–79. http://dx.doi.org/10.1609/aaai.v35i14.17556.
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Video Question Answering (VideoQA) requires fine-grained understanding of both video and language modalities to answer the given questions. In this paper, we propose novel training schemes for multiple-choice video question answering with a self-supervised pre-training stage and a supervised contrastive learning in the main stage as an auxiliary learning. In the self-supervised pre-training stage, we transform the original problem format of predicting the correct answer into the one that predicts the relevant question to provide a model with broader contextual inputs without any further dataset or annotation. For contrastive learning in the main stage, we add a masking noise to the input corresponding to the ground-truth answer, and consider the original input of the ground-truth answer as a positive sample, while treating the rest as negative samples. By mapping the positive sample closer to the masked input, we show that the model performance is improved. We further employ locally aligned attention to focus more effectively on the video frames that are particularly relevant to the given corresponding subtitle sentences. We evaluate our proposed model on highly competitive benchmark datasets related to multiple-choice video QA: TVQA, TVQA+, and DramaQA. Experimental results show that our model achieves state-of-the-art performance on all datasets. We also validate our approaches through further analyses.
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Ar Rosyid, Harits, Aulia Yahya Harindra Putra, Muhammad Iqbal Akbar, and Felix Andika Dwiyanto. "Can Multinomial Logistic Regression Predicts Research Group using Text Input?" Knowledge Engineering and Data Science 5, no. 2 (2022): 150. http://dx.doi.org/10.17977/um018v5i22022p150-159.
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While submitting proposals in SISINTA, students often confuse or falsely submit their proposals to the less relevant or incorrect research group. There are 13 research groups for the students to choose from. We proposed a text classification method to help students find the best research group based on the title and/or abstract. The stages in this study include data collection, preprocessing data, classification using Logistic Regression, and evaluation of the results. Three scenarios in research group classification are based on 1) title only, 2) abstract only, and 3) title and abstract. Based on the experiments, research group classification using title-only input is the best overall. This scenario gets the most optimal results with accuracy, precision, recall, and f1-score successively at 63.68%, 64.91%, 63.68%, and 63.46%. This result is sufficient to help students find the best research group based on the text titles. In addition, lecturers can comment more elaborately since the proposals are relevant to the research group’s scope.
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Xia, Yichun, and Yonggang Meng. "Physics-Informed Neural Network (PINN) for Solving Frictional Contact Temperature and Inversely Evaluating Relevant Input Parameters." Lubricants 12, no. 2 (2024): 62. http://dx.doi.org/10.3390/lubricants12020062.
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Ensuring precise prediction, monitoring, and control of frictional contact temperature is imperative for the design and operation of advanced equipment. Currently, the measurement of frictional contact temperature remains a formidable challenge, while the accuracy of simulation results from conventional numerical methods remains uncertain. In this study, a PINN model that incorporates physical information, such as partial differential equation (PDE) and boundary conditions, into neural networks is proposed to solve forward and inverse problems of frictional contact temperature. Compared to the traditional numerical calculation method, the preprocessing of the PINN is more convenient. Another noteworthy characteristic of the PINN is that it can combine data to obtain a more accurate temperature field and solve inverse problems to identify some unknown parameters. The experimental results substantiate that the PINN effectively resolves the forward problems of frictional contact temperature when provided with known input conditions. Additionally, the PINN demonstrates its ability to accurately predict the friction temperature field with an unknown input parameter, which is achieved by incorporating a limited quantity of easily measurable actual temperature data. The PINN can also be employed for the inverse identification of unknown parameters. Finally, the PINN exhibits potential in solving inverse problems associated with frictional contact temperature, even when multiple input parameters are unknown.