As the feature size of the CMOS technology keeps scaling down, the charge sharing caused by radiation is becoming more and more prominent, and the occurrence possibility of the triple-node upset (TNU) increases significantly. In this paper, we propose a low-overhead and TNU-tolerant latch (LTTL) that leverages three parallel storage cells and an output-level error interceptive module to achieve complete TNU tolerance while minimizing design overhead. The optimized structure eliminates redundant devices and employs a high-speed D-to-Q path, significantly reducing delay-area-power product (DAPP). Even any three nodes of the latch are flipped at the same time, the output of the latch can retain the original value. Simulation results not only confirm the TNU tolerance of the proposed latch but also demonstrate that the latch can provide a 57% reduction in delay, 20% reduction in area, and 62% reduction in DAPP on average compared to state-of-the-art TNU-tolerant latches.

Emotion recognition is essential for improving user experience and interaction quality in human-centered applications. While recent studies have leveraged both event and traditional cameras to enhance eye-based emotion recognition, their practical deployment is hindered by the scarcity of event cameras and the complexity of dual-modality frameworks. Personalization, which is critical for handling individual differences in emotional expression, is also affected by these factors, resulting in reduced performance and adaptation efficiency. To address these challenges, we propose a lightweight and personalized single-eye emotion recognition network, called LPSEER. LPSEER introduces a novel hybrid neural architecture that integrates a convolutional neural network (CNN) and a spiking neural network (SNN) to capture spatiotemporal features from video frames and events, respectively. Additionally, we design a memorybased event feature inference (MEFI) module that recalls event features from video frames, eliminating the reliance on event cameras during inference and personalization while retaining the discriminative advantages of event-based representations. Experimental results demonstrate that LPSEER achieves state-of- the-art recognition accuracy while maintaining the smallest model size and lowest computational cost. Further experiments confirm the strong generalization capabilities and the ability to achieve faster, more accurate personalization. These advantages collectively enable lightweight, accurate, and efficient emotion recognition for real-world human-centered applications.

Spiking neural networks (SNNs) are increasingly applied to event-based data generated by event cameras due to their asynchronous and sparse properties. Event cameras can inherently respond to the changes in the scene, which is a quite desirable property for action recognition tasks. However, existing works of SNNs for event-based action recognition are still limited. To capture the rich dynamics embedded in event streams, we propose the two-stream SNN that consists of spatial spiking stream and motion spiking stream to address event-based action recognition. To effectively build the two-stream SNN, we present a motion feature aggregation strategy and an attention-based two-stream fusion method. The motion feature aggregation strategy accumulates motion information and groups it into distinct channels for input into the SNN, which can alleviate the dilemma of information loss caused by compact representation. The attention-based two-stream fusion method can fuse the spatial and motion features effectively using the channel-wise attention mechanism, which helps our network to achieve better integration of two-stream information. Extensive experimental results on three event-based action recognition datasets show our proposed two-stream SNN achieves competitive performance with much fewer trainable parameters, which demonstrates the effectiveness of our work in event-based action recognition tasks.

Lysergic acid diethylamide (LSD) has shown remarkable potential in modulating brain functional organization and dynamics. However, the exact mechanisms underlying its effects remain unclear. In this study, we employed a data-driven approach to analyze recurrent functional connectivity patterns in resting-state fMRI data and developed a parameterized feedback inhibition model to characterize excitatory/inhibitory (E/I) balance. The findings demonstrate that LSD enhances global brain synchrony and dynamic complexity. This enhanced synchrony likely stems from LSD's preferential stabilization of a globally synchronized yet functionally non-modular brain state - a pattern showing higher occurrence probability and acts as an "attractor" that recruits transitions from cognitive control networks. Crucially, these phenomena appear underpinned by LSD-induced convergence of excitatory/inhibitory balance across cortical hierarchies, particularly through Sensorimotor (SOM) suppression coupled with transmodal potentiation, where the Sensorimotor cortices emerge as potential regulatory hubs driving this neurochemical rebalancing. These convergent effects are consistent with the emergence of a brain state characterized by weakened sensory anchoring and enhanced cognitive flexibility, where the typical separation between concrete perception and abstract cognition becomes blurred. This neurophysiological remodeling therefore suggests a potential mechanism that could contribute to LSD's hallucinatory effects and its therapeutic potential in mental disorders characterized by rigid thought patterns.

Humans excel at audiovisual speech recognition (AVSR), motivating the development of human-inspired computing for robust and efficient AVSR models. Spiking neural networks (SNNs), mimicking the brain’s information-processing mechanisms, offer a promising foundation. However, research on SNN-based AVSR remains limited, with most audio-visual methods focusing on object or digit recognition. These methods oversimplify multimodal fusion, neglecting modality-specific characteristics and interactions. Additionally, they often rely on future information, increasing recognition latency and limiting real-time applicability. Inspired by human speech perception, this paper proposes a novel human-inspired SNN named HI-AVSNN for AVSR, incorporating three computing characteristics: spike activity, cueing interaction, and causal processing. For cueing interaction, we introduce a Spike-Driven Visual-Cued Speech Processing (sVCSP) scheme, where visual features hierarchically guide speech processing to enhance critical features. For causal processing, we align the temporal dimensions of SNN with audio-visual inputs and apply temporal masking to ensure only past and current information is used. For spike activity, in addition to SNNs, we incorporate event cameras to capture lip movements as spikes, efficiently encoding visual data like the human retina. Experiments on two event-based AVSR datasets demonstrate our method outperforms existing audio-visual SNN fusion techniques, showcasing the effectiveness, robustness, and efficiency achieved through our human-inspired computing.

The brain-inspired Spiking Neural Networks (SNNs) work in an event-driven manner and have an implicit recurrence in neuronal membrane potential to memorize information over time, which are inherently suitable to handle temporal event-based streams. Despite their temporal nature and recent approaches advancements, these methods have predominantly been assessed on event-based classification tasks. In this paper, we explore the utility of SNNs for event-based tracking tasks. Specifically, we propose a brain-inspired adaptive Leaky Integrate-and-Fire neuron (BA-LIF) that can adaptively adjust the membrane time constant according to the inputs, thereby accelerating the leakage of meaningless noise features and reducing the decay of valuable information. SNNs composed of our proposed BA-LIF neurons can achieve high performance without a careful and time-consuming trial-by-error initialization on the membrane time constant. The adaptive capability of our network is further improved by introducing an extra temporal feature aggregator (TFA) that assigns attention weights over the temporal dimension. Extensive experiments on various event-based tracking datasets validate the effectiveness of our proposed method. We further validate the generalization capability of our method by applying it to other event-classification tasks.

Speech applications are expected to be low-power and robust under noisy conditions. An effective Voice Activity Detection (VAD) front-end lowers the computational need. Spiking Neural Networks (SNNs) are known to be biologically plausible and power-efficient. However, SNN-based VADs have yet to achieve noise robustness and often require large models for high performance. This paper introduces a novel SNN-based VAD model, referred to as sVAD, which features an auditory encoder with an SNN-based attention mechanism. Particularly, it provides effective auditory feature representation through SincNet and 1D convolution, and improves noise robustness with attention mechanisms. The classifier utilizes Spiking Recurrent Neural Networks (sRNN) to exploit temporal speech information. Experimental results demonstrate that our sVAD achieves remarkable noise robustness and meanwhile maintains low power consumption and a small footprint, making it a promising solution for real-world VAD applications.

Peptide drugs are becoming star drug agents with high efficiency and selectivity which open up new therapeutic avenues for various diseases. However, the sensitivity to hydrolase and the relatively short half-life have severely hindered their development. In this study, a new generation artificial intelligence-based system for accurate prediction of peptide half-life was proposed, which realized the half-life prediction of both natural and modified peptides and successfully bridged the evaluation possibility between two important species (human, mouse) and two organs (blood, intestine). To achieve this, enzymatic cleavage descriptors were integrated with traditional peptide descriptors to construct a better representation. Then, robust models with accurate performance were established by comparing traditional machine learning and transfer learning, systematically. Results indicated that enzymatic cleavage features could certainly enhance model performance. The deep learning model integrating transfer learning significantly improved predictive accuracy, achieving remarkable R2 values: 0.84 for natural peptides and 0.90 for modified peptides in human blood, 0.984 for natural peptides and 0.93 for modified peptides in mouse blood, and 0.94 for modified peptides in mouse intestine on the test set, respectively. These models not only successfully composed the above-mentioned system but also improved by approximately 15% in terms of correlation compared to related works. This study is expected to provide powerful solutions for peptide half-life evaluation and boost peptide drug development.

Spiking neural networks (SNNs) have increasingly drawn massive research attention due to biological interpretability and efficient computation. Recent achievements are devoted to utilizing the surrogate gradient (SG) method to avoid the dilemma of non-differentiability of spiking activity to directly train SNNs by backpropagation. However, the fixed width of the SG leads to gradient vanishing and mismatch problems, thus limiting the performance of directly trained SNNs. In this work, we propose a novel perspective to unlock the width limitation of SG, called the learnable surrogate gradient (LSG) method. The LSG method modulates the width of SG according to the change of the distribution of the membrane potentials, which is identified to be related to the decay factors based on our theoretical analysis. Then we introduce the trainable decay factors to implement the LSG method, which can optimize the width of SG automatically during training to avoid the gradient vanishing and mismatch problems caused by the limited width of SG. We evaluate the proposed LSG method on both image and neuromorphic datasets. Experimental results show that the LSG method can effectively alleviate the blocking of gradient propagation caused by the limited width of SG when training deep SNNs directly. Meanwhile, the LSG method can help SNNs achieve competitive performance on both latency and accuracy.

Debugging is a distinct subject in programming that is both comprehensive and challenging for novice programmers. However, instructors have limited opportunities to gain insights into the difficulties students encountered in isolated debugging processes. While qualitative studies have identified debugging strategies that novice programmers use and how they relate to theoretical debugging frameworks, limited larger scale quantitative analyses have been conducted to investigate how students’ debugging behaviors observed in log data align with the identified strategies and how they relate to successful debugging. In this study, we used submission log data to understand how the existing debugging strategies are employed by students in an introductory CS course when solving homework problems. We identified strategies from existing debugging literature that can be observed with trace data and extracted features to reveal how efficient debugging is associated with debugging strategy usage. Our findings both align with and contradict past assumptions from previous studies by suggesting that minor code edition can be a beneficial strategy and that width and depth aggregations of the same debugging behavior can reveal opposite effects on debugging efficiency.

This study focuses on a topology identification problem of weighted networks with different connection strength, where binary time series generated by propagation dynamics are utilized. An influence probability matrix reflecting the weight of connection is proposed to quantify the influence of other nodes on one node as it transfers from susceptible state to infected state. Further, maximum likelihood estimate and expectation–maximization algorithm are used to obtain the influence probability matrix. A threshold method and a weight-based-identification algorithm are provided to identify connection strength. The robustness against fault data and conflicting results of the same connection is mitigated by introducing a confidence factor. Several Monte-Carlo simulations demonstrate the high identification accuracy of our methods under different network models.

Human brain can effectively integrate visual and auditory information. Dynamic Vision Sensor (DVS) and Dynamic Audio Sensor (DAS) are event-based sensors imitating the mechanism of human retina and cochlea. Since the sensors record the visual and auditory input as asynchronous discrete events, they are inherently suitable to cooperate with the spiking neural network (SNN). Existing works of SNNs for processing events mainly focus on unimodality, however, audiovisual multimodal SNNs are still limited. In this paper, we propose an end-to-end event-based multimodal spiking neural network. The network consists of visual and auditory unimodal subnetworks and a novel attention-based cross-modal subnetwork for fusion. The attention mechanism measures the significance of each modality and allocates the weights to two modalities. We evaluate our proposed multimodal network on an event-based audiovisual joint dataset (MNIST-DVS and N-TIDIGITS datasets). Experimental results show the performance improvement of this multimodal network and the effectiveness of our proposed attention mechanism.

Spiking neural networks (SNNs) are bio-inspired neural networks with asynchronous discrete and sparse characteristics, which have increasingly manifested their superiority in low energy consumption. Recent research is devoted to utilizing spatio-temporal information to directly train SNNs by backpropagation. However, the binary and non-differentiable properties of spike activities force directly trained SNNs to suffer from serious gradient vanishing and network degradation, which greatly limits the performance of directly trained SNNs and prevents them from going deeper. In this paper, we propose a multi-level firing (MLF) method based on the existing spatio-temporal back propagation (STBP) method, and spiking dormant-suppressed residual network (spiking DS-ResNet). MLF enables more efficient gradient propagation and the incremental expression ability of the neurons. Spiking DS-ResNet can efficiently perform identity mapping of discrete spikes, as well as provide a more suitable connection for gradient propagation in deep SNNs. With the proposed method, our model achieves superior performances on a non-neuromorphic dataset and two neuromorphic datasets with much fewer trainable parameters and demonstrates the great ability to combat the gradient vanishing and degradation problem in deep SNNs.

Recent advances in deep reinforcement learning (DRL) have largely promoted the performance of adaptive traffic signal control (ATSC). Nevertheless, regarding the implementation, most works are cumbersome in terms of storage and computation. This hinders their deployment on scenarios where resources are limited. In this work, we propose TinyLight, the first DRL-based ATSC model that is designed for devices with extremely limited resources. TinyLight first constructs a super-graph to associate a rich set of candidate features with a group of light-weighted network blocks. Then, to diminish the model's resource consumption, we ablate edges in the super-graph automatically with a novel entropy-minimized objective function. This enables TinyLight to work on a standalone microcontroller with merely 2KB RAM and 32KB ROM. We evaluate TinyLight on multiple road networks with real-world traffic demands. Experiments show that even with extremely limited resources, TinyLight still achieves competitive performance. The source code and appendix of this work can be found at https://bit.ly/38hH8t8.

A quality headline with a high click-rate should not only summarize the content of an article, but also reflect a style that attracts users. Such demand has drawn rising attention to the task of stylistic headline generation (SHG). An intuitive method is to first generate plain headlines leveraged by document-headline parallel data then transfer them to a target style. However, this inevitably suffers from error propagation. Therefore, to unify the two sub-tasks and explicitly decompose style-relevant attributes and summarize content, we propose an end-to-end stage-wise SHG model containing the style generation component and the content insertion component, where the former generates stylistic-relevant intermediate outputs and the latter receives these outputs then inserts the summarized content. The intermediate outputs are observable, making the style generation easy to control. Our system is comprehensively evaluated by both quantitative and qualitative metrics, and it achieves state-of-the-art results in SHG over three different stylistic datasets.

Recent work on spiking neural networks (SNNs) has focused on achieving deep architectures. They commonly use backpropagation (BP) to train SNNs directly, which allows SNNs to go deeper and achieve higher performance. However, the BP training procedure is computing intensive and complicated by many trainable parameters. Inspired by global pooling in convolutional neural networks (CNNs), we present the spike probabilistic global pooling (SPGP) method based on a probability function for training deep convolutional SNNs. It aims to remove the difficulty of too many trainable parameters brought by multiple layers in the training process, which can reduce the risk of overfitting and get better performance for deep SNNs (DSNNs). We use the discrete leaky-integrate-fire model and the spatiotemporal BP algorithm for training DSNNs directly. As a result, our model trained with the SPGP method achieves competitive performance compared to the existing DSNNs on image and neuromorphic data sets while minimizing the number of trainable parameters. In addition, the proposed SPGP method shows its effectiveness in performance improvement, convergence, and generalization ability.

Event-based cameras have attracted increasing attention due to their advantages of biologically inspired paradigm and low power consumption. Since event-based cameras record the visual input as asynchronous discrete events, they are inherently suitable to cooperate with the spiking neural network (SNN). Existing works of SNNs for processing events mainly focus on the task of object recognition. However, events from the event-based camera are triggered by dynamic changes, which makes it an ideal choice to capture actions in the visual scene. Inspired by the dorsal stream in visual cortex, we propose a hierarchical SNN architecture for event-based action recognition using motion information. Motion features are extracted and utilized from events to local and finally to global perception for action recognition. To the best of the authors’ knowledge, it is the first attempt of SNN to apply motion information to event-based action recognition. We evaluate our proposed SNN on three event-based action recognition datasets, including our newly published DailyAction-DVS dataset comprising 12 actions collected under diverse recording conditions. Extensive experimental results show the effectiveness of motion information and our proposed SNN architecture for event-based action recognition.

In ad-hoc teamwork, an agent is required to cooperate with unknown teammates without prior coordination. To swiftly adapt to an unknown teammate, most works adopt a type-based approach, which pre-trains the agent with a set of pre-prepared teammate types, then associates the unknown teammate with a particular type. Typically, these types are collected manually. This hampers previous works by both the availability and diversity of types they manage to obtain. To eliminate these limitations, this work addresses to achieve ad-hoc teamwork in a type-free approach. Specifically, we propose the model of Entropy-regularized Deep Recurrent Q-Network (EDRQN) to generate teammates automatically, meanwhile utilize them to pre-train our agent. These teammates are obtained from scratch and are designed to perform the task with various behaviors, therefore their availability and diversity are both ensured. We evaluate our model on several benchmark domains of ad-hoc teamwork. The result shows that even if our model has no access to any pre-prepared teammate types, it still achieves significant performance.

Address event representation (AER) cameras have recently attracted more attention due to the advantages of high temporal resolution and low power consumption, compared with traditional frame-based cameras. Since AER cameras record the visual input as asynchronous discrete events, they are inherently suitable to coordinate with the spiking neural network (SNN), which is biologically plausible and energy-efficient on neuromorphic hardware. However, using SNN to perform the AER object classification is still challenging, due to the lack of effective learning algorithms for this new representation. To tackle this issue, we propose an AER object classification model using a novel segmented probability-maximization (SPA) learning algorithm. Technically, 1) the SPA learning algorithm iteratively maximizes the probability of the classes that samples belong to, in order to improve the reliability of neuron responses and effectiveness of learning; 2) a peak detection (PD) mechanism is introduced in SPA to locate informative time points segment by segment, based on which information within the whole event stream can be fully utilized by the learning. Extensive experimental results show that, compared to state-of-the-art methods, not only our model is more effective, but also it requires less information to reach a certain level of accuracy.

This article proposes an unsupervised address event representation (AER) object recognition approach. The proposed approach consists of a novel multiscale spatio-temporal feature (MuST) representation of input AER events and a spiking neural network (SNN) using spike-timing-dependent plasticity (STDP) for object recognition with MuST. MuST extracts the features contained in both the spatial and temporal information of AER event flow, and forms an informative and compact feature spike representation. We show not only how MuST exploits spikes to convey information more effectively, but also how it benefits the recognition using SNN. The recognition process is performed in an unsupervised manner, which does not need to specify the desired status of every single neuron of SNN, and thus can be flexibly applied in real-world recognition tasks. The experiments are performed on five AER datasets including a new one named GESTURE-DVS. Extensive experimental results show the effectiveness and advantages of the proposed approach.

In the age of the Internet, how to integrate virtual online behavior and real-world offline activity is a key issue in enterprise operations management, and Online-to-Offline (O2O) strategy is a hotly debated method to solve relevant problems. Taking advertisement industry for a case study, issues concerning management information systems (MIS) based on Online-to-Offline strategy are studied and analyzed, which can meet clients' personalized requirements in multidimensional degrees such as time, location, media, manner, and cost. O2O MIS can optimize advertising resources configuration, and by integration and synchronization of digital network resource fragments, the proposed O2O MIS has promising potential in the coming future.