HIERARCHICAL SPATIO-TEMPORAL DYNAMICS OF A CHAOTIC NEURAL NETWORK FOR MULTISTABLE BINOCULAR RIVALRY

The graduation development such as employment and graduate school admission of college students are important tasks. However, mining the factors that can affect the development of graduation remains challenging, because the most important factor “course” is not independent and inequality, which are always ignored by previous researchers. Furthermore, traditional structured methods cannot handle the complex relationships between courses, and attention networks cannot distinguish the weights of compulsory and elective courses with different distributions. Therefore, we present a Graph-based Hierarchical Attention Neural Network Model with Elective Course (GHANN-EC) for the prediction of graduation development in this study. Specifically, we use graph embedding that captures the unstructured relationships between courses and hierarchical attention that assigns the importance of the courses to excavating course information that represent students’ independent interests, and can more accurately understand the relationship between graduation development and academic performance. Experimental results on the real-world datasets show that GHANN-EC outperforms the existing popular approach.

Non-point source pollution from agricultural activities poses a significant threat to water quality by introducing excess nutrients like nitrogen into aquatic ecosystems, leading to issues such as eutrophication and groundwater contamination. In agricultural watersheds, nitrate transport involves intricate physical, chemical, and biological processes influenced by meteorological conditions, hydrological features, and spatial topologies, making accurate short-term predictions challenging. Traditional data-driven deep learning models often fail to incorporate physical constraints and complex spatiotemporal dynamics, limiting their interpretability and predictive accuracy. Here we show a hierarchical transformer and graph neural network model that accurately predicts watershed nitrate concentrations by integrating multi-source data and simulating pollutant migration. The model captures nonlinear multivariate temporal patterns through hierarchical transformers, fuses global meteorological and local hydrological features via neural networks, and models runoff topologies with physically constrained graph neural networks. For predicting the concentration changes of pollutants discharged from watersheds, it outperforms baselines like multi-layer perceptrons, recurrent neural networks, and long short-term memory networks, with state-of-the-art performance in root mean square error, mean absolute error, and R2. Ablation studies confirm the essential roles of multi-source data integration and watershed topological modeling in enhancing performance. This method of directly modeling physical processes by leveraging the characteristics of different neural network architectures opens up a new path for addressing the interpretability problem in neural earth system modeling, apart from the process-guided deep learning and differentiable modelling methods.

When dissimilar images are presented to the two eyes, binocular rivalry (BR) occurs, and perception alternates spontaneously between the images. Although neural correlates of the oscillating perception during BR have been found in multiple sites along the visual pathway, the source of BR dynamics is unclear. Psychophysical and modeling studies suggest that both low- and high-level cortical processes underlie BR dynamics. Previous neuroimaging studies have demonstrated the involvement of high-level regions by showing that frontal and parietal cortices responded time locked to spontaneous perceptual alternation in BR. However, a potential contribution of early visual areas to BR dynamics has been overlooked, because these areas also responded to the physical stimulus alternation mimicking BR. In the present study, instead of focusing on activity during perceptual switches, we highlighted brain activity during suppression periods to investigate a potential link between activity in human early visual areas and BR dynamics. We used a strong interocular suppression paradigm called continuous flash suppression to suppress and fluctuate the visibility of a probe stimulus and measured retinotopic responses to the onset of the invisible probe using functional MRI. There were ∼130-fold differences in the median suppression durations across 12 subjects. The individual differences in suppression durations could be predicted by the amplitudes of the retinotopic activity in extrastriate visual areas (V3 and V4v) evoked by the invisible probe. Weaker responses were associated with longer suppression durations. These results demonstrate that retinotopic representations in early visual areas play a role in the dynamics of perceptual alternations during BR.

A system for approximate number discrimination has been shown to arise in at least two types of hierarchical neural network models—a generative Deep Belief Network (DBN) and a Hierarchical Convolutional Neural Network (HCNN) trained to classify natural objects. Here, we investigate whether the same two network architectures can learn to recognise exact numerosity. A clear difference in performance could be traced to the specificity of the unit responses that emerged in the last hidden layer of each network. In the DBN, the emergence of a layer of monotonic ‘summation units’ was sufficient to produce classification behaviour consistent with the behavioural signature of the approximate number system. In the HCNN, a layer of units uniquely tuned to the transition between particular numerosities effectively encoded a thermometer-like ‘numerosity code’ that ensured near-perfect classification accuracy. The results support the notion that parallel pattern-recognition mechanisms may give rise to exact and approximate number concepts, both of which may contribute to the learning of symbolic numbers and arithmetic.

Prediction of antimicrobial resistance based on whole-genome sequencing data has attracted greater attention due to its rapidity and convenience. Numerous machine learning-based studies have used genetic variants to predict drug resistance in Mycobacterium tuberculosis (MTB), assuming that variants are homogeneous, and most of these studies, however, have ignored the essential correlation between variants and corresponding genes when encoding variants, and used a limited number of variants as prediction input. In this study, taking advantage of genome-wide variants for drug-resistance prediction and inspired by natural language processing, we summarize drug resistance prediction into document classification, in which variants are considered as words, mutated genes in an isolate as sentences, and an isolate as a document. We propose a novel hierarchical attentive neural network model (HANN) that helps discover drug resistance-related genes and variants and acquire more interpretable biological results. It captures the interaction among variants in a mutated gene as well as among mutated genes in an isolate. Our results show that for the four first-line drugs of isoniazid (INH), rifampicin (RIF), ethambutol (EMB) and pyrazinamide (PZA), the HANN achieves the optimal area under the ROC curve of 97.90, 99.05, 96.44 and 95.14% and the optimal sensitivity of 94.63, 96.31, 92.56 and 87.05%, respectively. In addition, without any domain knowledge, the model identifies drug resistance-related genes and variants consistent with those confirmed by previous studies, and more importantly, it discovers one more potential drug-resistance-related gene.

Temporal frequency and contrast tagging bias the type of competition in interocular switch rivalry

The nature of competition underlying perceptual alternations in binocular rivalry remains controversial. Interocular swapping of rivalrous stimuli can result in either slow irregular perceptual alternations that bridge multiple interocular switches or fast regular alternations that are time locked to the stimulus exchanges. We labeled either the inputs to the eyes or the individual rivalrous stimuli using temporal frequency and contrast tagging. Tagging of eye-of-origin signals enhanced the fast regular perceptual alternations associated with eye rivalry, while stimulus tagging shifted perception towards slow irregular alternations characteristic of stimulus rivalry. Thus, the type of competition in binocular rivalry can be biased based on additional cues in the visual inputs. The results are consistent with a model in which the brain combines information across multiple visual features to resolve ambiguities in visual inputs.

Binocular rivalry ensues when different images are presented to the 2 eyes with conscious perception alternating between the possible interpretations. For large rivalry displays, perceptual transitions are initiated at one location and spread to other parts of the visual field, a phenomenon termed "traveling wave." Previous studies investigated the underlying neural mechanisms of the traveling wave and surmised that primary visual cortex might play an important role. We used magnetic resonance imaging and behavioral measures in humans to explore how interindividual differences in observers' subjective experience of the wave are related to anatomical characteristics of cortical regions. We measured wave speed in participants and confirmed the long-term stability of the individual values. Retinotopic mapping was employed to delineate borders of visual areas V1-V3 in order to determine surface area and cortical thickness in those regions. Only the surface areas of V1 and V2, but not V3 showed a correlation with wave speed. For individuals with larger V1/V2 area, the traveling wave needed longer to spread across the same distance in visual space. Our results highlight the role of early visual areas in mediating binocular rivalry and suggest possible mechanisms for the correlation between surface area and the traveling waves.

Analysis on topological features of deterministic hierarchical complex network

When sensory input allows for multiple, competing perceptual interpretations, observers’ perception can fluctuate over time: bistable perception. Imaging studies in humans have revealed transient responses in a right-lateralized network in the frontal-parietal cortex (rFPC) around the time of perceptual transitions between interpretations, potentially reflecting the neural initiation of transitions. We investigated the role of this activity in male human observers, with specific interest in its relation to the temporal structure of transitions, which can be either instantaneous or prolonged by periods during which observers experience a mix of both perceptual interpretations. Using both bistable apparent motion and binocular rivalry we show that transition-related rFPC activity is larger for transitions that last longer, suggesting that rFPC remains active as long as a transition lasts. We also replicate earlier findings that rFPC activity during binocular rivalry transitions exceeds activity during yoked transitions that are simulated using video replay. However, we show that this established finding only holds when perceptual transitions are replayed as instantaneous events. When replay, instead, depicts transitions with the actual durations reported during rivalry, yoked transitions and genuine rivalry transitions elicit equal activity. Together, our results are consistent with the view that at least a component of rFPC activation during bistable perception reflects a response to perceptual transitions, both real and yoked, rather than their cause. This component of activity could reflect the change in sensory experience and task demand that occurs during transitions, which fits well with the known role of these areas in attention and decision-making.

Face-selective neurons are observed in the primate visual pathway and are considered as the basis of face detection in the brain. However, it has been debated as to whether this neuronal selectivity can arise innately or whether it requires training from visual experience. Here, using a hierarchical deep neural network model of the ventral visual stream, we suggest a mechanism in which face-selectivity arises in the complete absence of training. We found that units selective to faces emerge robustly in randomly initialized networks and that these units reproduce many characteristics observed in monkeys. This innate selectivity also enables the untrained network to perform face-detection tasks. Intriguingly, we observed that units selective to various non-face objects can also arise innately in untrained networks. Our results imply that the random feedforward connections in early, untrained deep neural networks may be sufficient for initializing primitive visual selectivity.

This article describes the architecture of a typical mobile health application. Anyoneusingthesystemcansettheirownpersonalizedprivacylevel.Thisarticledescribeshowsocialnetworkingcommunicationscanbemademoresecureandprivateinamulti-cloudenvironment,especiallyformedicalapplications.The purpose of this study is to present a practical approach for extracting individual-level AH from GSV data. We have developed an entirely new hierarchical deep learning method that can identify AH based on global and local visual features. The objective of this study was to present a practical technique for separating individual-level BP from GSV data. To determine AH using global and local visualization features, we created a new hierarchical deep learning system. Thesesystemsbecomemorevulnerabletohackingastechnologyadvances.The research presented in this white paper aims to show the feasibility and utility of artificial intelligence-based hierarchical deep neural network models to protect data in motion. A new aspect of this research is the synergistic formation of multiple clouds using different complex models, which shortens the formation time and increases the accuracy of detecting unidentified attacks. receive. Asaresult, the core cloudmodelconverges much lesstime(6-8)andfarfewerepochsthantheboundarycloudmodel, which takes 35-40epochstotrain.Extensiveevaluationhas demonstrated that theMUSEsystemcantrainhugemerged models faster thanindividuallybuiltunmergedmodelsonthemaincloud.Afteraseriesofruns,wefoundthatthecombinedmodelreducedtrainingtimebyanaverageof26.2%.

Artificial Intelligence in legal research is transforming the legal area in manifold ways. Pendency of court cases is a long-lasting problem in the judiciary due to various reasons such as lack of judges, lack of technology in legal services and the legal loopholes. The judicial system has to be more competent and more reliable in providing justice on time. One of the major causes of pending cases is the lack of legal intelligence to assist the litigants. The study in this paper reviews the challenges faced by judgment prediction system due to lengthy case facts using deep learning model. The Legal Judgment prediction system can help lawyers, judges and civilians to predict the win or loss rate, punishment term and applicable law articles for new cases. Besides, the paper reviews current encoding and decoding architecture with attention mechanism of transformer model that can be used for Legal Judgment Prediction system. Natural Language Processing using deep learning is an exploring field and there is a need for research to evaluate the current state of the art at the intersection of good text processing and feature representation with a deep learning model. This paper aims to develop a systematic review of existing methods used in the legal judgment prediction system and about the Hierarchical Attention Neural network model in detail. This can also be used in other applications such as legal document classification, sentimental analysis, news classification, text translation, medical reports and so on.

In perceptual rivalry such as ambiguous figure perception and binocular rivalry, the conscious percept spontaneously alternates between two stable interpretations of an unchanging stimulus. It is well known that the time intervals of the perceptual alternation follow a gamma distribution (GD), but its implication for the alternation mechanism has not been clarified. We examined quantitatively GDs fitted to alternation intervals, and found that the shape-determining parameter alpha of the GDs took natural numbers. Because a GD determined by a natural number alpha is mathematically obtained from a discrete stochastic process (Poisson process), our result indicates that such a stochastic process underlies perceptual rivalry and that the alpha-time accumulation of the discrete events causes a perceptual alternation.

NEURAL NETWORK BASED REAL-TIME PRODUCTION SCHEDULING FOR INDUSTRIAL PROCESSES