This paper presents the first systematic study of evaluating Deep Neural Networks (DNNs) designed to forecast the evolution of stochastic complex systems. We show that traditional evaluation methods like threshold-based classification metrics and error-based scoring rules assess a DNN's ability to replicate the observed ground truth but fail to measure the DNN's learning of the underlying stochastic process. To address this gap, we propose a new evaluation criteria called _Fidelity to Stochastic Process (F2SP)_, representing the DNN's ability to predict the system property _Statistic-GT_—the ground truth of the stochastic process—and introduce an evaluation metric that exclusively assesses F2SP. We formalize F2SP within a stochastic framework and establish criteria for validly measuring it. We formally show that Expected Calibration Error (ECE) satisfies the necessary condition for testing F2SP, unlike traditional evaluation methods. Empirical experiments on synthetic datasets, including wildfire, host-pathogen, and stock market models, demonstrate that ECE uniquely captures F2SP. We further extend our study to real-world wildfire data, highlighting the limitations of conventional evaluation and discuss the practical utility of incorporating F2SP into model assessment. This work offers a new perspective on evaluating DNNs modeling complex systems by emphasizing the importance of capturing underlying the stochastic process.

Graph Neural Networks (GNNs) face a critical limitation known as oversmoothing, where increasing network depth leads to homogenized node representations, severely compromising their expressiveness. We present a novel dynamical systems perspective on this challenge, revealing oversmoothing as an emergent property of GNNs’ convergence to low-dimensional attractor states. Based on this insight, we introduce DYNAMO-GAT, which combines noise-driven covariance analysis with Anti-Hebbian learning to dynamically prune attention weights, effectively preserving distinct attractor states. We provide theoretical guarantees for DYNAMO-GAT’s effectiveness and demonstrate its superior performance on benchmark datasets, consistently outperforming existing methods while requiring fewer computational resources. This work establishes a fundamental connection between dynamical systems theory and GNN behavior, providing both theoretical insights and practical solutions for deep graph learning.

Accurate modeling and prediction of temporal point processes (TPPs) are crucial across domains such as neuroscience, epidemiology, finance, and social media analysis. We introduce the Spiking Dynamic Graph Network (SDGN), which integrates spiking neural networks (SNNs) with local spike-timing-dependent plasticity (STDP) to learn, online and in an event-driven fashion, the evolving spatio-temporal graph underlying a stream of timestamped events. SDGN relies on adaptive time-stepping, surrogate-gradient smoothing, and priority-queue updates to achieve O(log N) complexity per spike, ensuring both stability and efficiency. On synthetic benchmarks and four large-scale real-world datasets (NYC Taxi, 911 dispatches, Reddit posts, and Stack Overflow events), SDGN attains up to 15% higher held-out log-likelihood and 2× faster inference compared to state-of-the-art baselines. Ablation studies quantify the impact of each core component, and we discuss extensions for handling very dense graphs and heavy-tailed inter-event distributions.

We propose Fast Long-range Adaptive Memory for Event (FLAME), a novel scalable architecture that combines neuro-inspired feature extraction with robust structured sequence modeling to efficiently process asynchronous and sparse event camera data. As a departure from conventional input encoding methods, FLAME presents Event Attention Layer, a novel feature extractor that leverages neuromorphic dynamics (Leaky Integrate-and-Fire (LIF)) to directly capture multi-timescale features from event streams. The feature extractor is integrates with a structured state-space model with a novel Event-Aware HiPPO (EA-HiPPO) mechanism that dynamically adapts memory retention based on inter-event intervals to understand relationship across varying temporal scales and event sequences. A Normal Plus Low Rank (NPLR) decomposition reduces the computational complexity of state update from $\mathcal{O}(N^2)$ to $\mathcal{O}(Nr)$, where $N$ represents the dimension of the core state vector and $r$ is the rank of a low-rank component (with $r \ll N$). FLAME demonstrates state-of-the-art accuracy for event-by-event processing on complex event camera datasets.

Recurrent Spiking Neural Networks (RSNNs) have emerged as a computationally efficient and brain-inspired machine learning model. The design of sparse RSNNs with fewer neurons and synapses helps reduce the computational complexity of RSNNs. Traditionally, sparse SNNs are obtained by first training a dense and complex SNN for a target task and, next, eliminating neurons with low activity (activity-based pruning) while maintaining task performance. In contrast, this paper presents a task-agnostic methodology for designing sparse RSNNs by pruning an untrained (arbitrarily initialized) large model. We introduce a novel Lyapunov Noise Pruning (LNP) algorithm that uses graph sparsification methods and utilizes Lyapunov exponents to design a stable sparse RSNN from an untrained RSNN. We show that the LNP can leverage diversity in neuronal timescales to design a sparse Heterogeneous RSNN (HRSNN). Further, we show that the same sparse HRSNN model can be trained for different tasks, such as image classification and time-series prediction. The experimental results show that, in spite of being task-agnostic, LNP increases computational efficiency (fewer neurons and synapses) and prediction performance of RSNNs compared to traditional activity-based pruning of trained dense models.

Spiking Neural Networks (SNNs) have become an essential paradigm in neuroscience and artificial intelligence, providing brain-inspired computation. Recent advances in literature have studied the network representations of deep neural networks. However, there has been little work that studies representations learned by SNNs, especially using unsupervised local learning methods like spike-timing dependent plasticity (STDP). Recent work by [1] has introduced a novel method to compare topological mappings of learned representations called Representation Topology Divergence (RTD). Though useful, this method is engineered particularly for feedforward deep neural networks and cannot be used for recurrent networks like Recurrent SNNs (RSNNs). This paper introduces a novel methodology to use RTD to measure the difference between distributed representations of RSNN models with different learning methods. We propose a novel reformulation of RSNNs using feedforward autoencoder networks with skip connections to help us compute the RTD for recurrent networks. Thus, we investigate the learning capabilities of RSNN trained using STDP and the role of heterogeneity in the synaptic dynamics in learning such representations. We demonstrate that heterogeneous STDP in RSNNs yield distinct representations than their homogeneous and surrogate gradient-based supervised learning counterparts. Our results provide insights into the potential of heterogeneous SNN models, aiding the development of more efficient and biologically plausible hybrid artificial intelligence systems.

We introduce a novel framework, Online Relational Inference (ORI), designed to efficiently identify hidden interaction graphs in evolving multi-agent interacting systems using streaming data. Unlike traditional offline methods that rely on a fixed training set, ORI employs online backpropagation, updating the model with each new data point, thereby allowing it to adapt to changing environments in real-time. A key innovation is the use of an adjacency matrix as a trainable parameter, optimized through a new adaptive learning rate technique called AdaRelation, which adjusts based on the historical sensitivity of the decoder to changes in the interaction graph. Additionally, a data augmentation method named Trajectory Mirror (TM) is introduced to improve generalization by exposing the model to varied trajectory patterns. Experimental results on both synthetic datasets and real-world data (CMU MoCap for human motion) demonstrate that ORI significantly improves the accuracy and adaptability of relational inference in dynamic settings compared to existing methods. This approach is model-agnostic, enabling seamless integration with various neural relational inference (NRI) architectures, and offers a robust solution for real-time applications in complex, evolving systems.

Neuromorphic event-based cameras can unlock the true potential of bio-plausible sensing systems that mimic our human perception. However, efficient spatiotemporal processing algorithms must enable their low-power, low-latency, real-world application. In this talk, we highlight our recent efforts in this direction. Specifically, we talk about how brain-inspired algorithms such as spiking neural networks (SNNs) can approximate spatiotemporal sequences efficiently without requiring complex recurrent structures. Next, we discuss their event-driven formulation for training and inference that can achieve realtime throughput on existing commercial hardware. We also show how a brain-inspired recurrent SNN can be modeled to perform on event-camera data. Finally, we will talk about the potential application of associative memory structures to efficiently build representation for event-based perception.

Energy and data-efficient online time series prediction for predicting evolving dynamical systems are critical in several fields, especially edge AI applications that need to update continuously based on streaming data. However, current Deep Neural Network (DNN)-based supervised online learning models require a large amount of training data and cannot quickly adapt when the underlying system changes. Moreover, these models require continuous retraining with incoming data making them highly inefficient. We present a novel Continuous Learning-based Unsupervised Recurrent Spiking Neural Network Model (CLURSNN), trained with spike timing dependent plasticity (STDP) to solve these issues. CLURSNN makes online predictions by reconstructing the underlying dynamical system using Random Delay Embedding by measuring the membrane potential of neurons in the recurrent layer of the recurrent spiking neural network (RSNN) with the highest betweenness centrality. We also use topological data analysis to propose a novel methodology using the Wasserstein Distance between the persistent homologies of the predicted and observed time series as a loss function. We show that the proposed online time series prediction methodology outperforms state-of-the-art DNN models when predicting an evolving Lorenz63 dynamical system.

Hardware-based Malware Detectors (HMDs) have shown promise in detecting malicious workloads. However, the current HMDs focus solely on the CPU core of a System-on-Chip (SoC) and, therefore, do not exploit the full potential of the hardware telemetry. In this paper, we propose XMD, an HMD that uses an expansive set of telemetry channels extracted from the different subsystems of SoC. XMD exploits the thread-level profiling power of the CPU-core telemetry, and the global profiling power of non-core telemetry channels, to achieve significantly better detection performance than currently used Hardware Performance Counter (HPC) based detectors. We leverage the concept of manifold hypothesis to analytically prove that adding non-core telemetry channels improves the separability of the benign and malware classes, resulting in performance gains. We train and evaluate XMD using hardware telemetries collected from 723 benign applications and 1033 malware samples on a commodity Android Operating System (OS)-based mobile device. XMD improves over currently used HPC-based detectors by 32.91% for the in-distribution test data. XMD achieves the best detection performance of 86.54% with a false positive rate of 2.9%, compared to the detection rate of 80%, offered by the best performing signature-based Anti-Virus(AV) on VirusTotal, on the same set of malware samples.

A key challenge for deployment of artificial intelligence (AI) in real-time safety-critical systems at the edge is to ensure reliable performance even in unreliable environments. This paper will present a broad perspective on how to design AI platforms to achieve this unique goal. First, we will present examples of AI architecture and algorithm that can assist in improving robustness against input perturbations. Next, we will discuss examples of how to make AI platforms robust against hardware induced noise and variation. Finally, we will discuss the concept of using lightweight networks as reliability estimators to generate early warning of potential task failures.

The classification of Internet-of-Things (IoT) devices into different types is of paramount importance, from multiple perspectives, including security and privacy aspects. Recent works have explored machine learning techniques for fingerprinting (or classifying) IoT devices, with promising results. However, the existing works have assumed that the features used for building the machine learning models are readily available or can be easily extracted from the network traffic; in other words, they do not consider the costs associated with feature extraction. In this work, we take a more realistic approach, and argue that feature extraction has a cost, and the costs are different for different features. We also take a step forward from the current practice of considering the misclassification loss as a binary value, and make a case for different losses based on the misclassification performance. Thereby, and more importantly, we introduce the notion of risk for IoT device classification. We define and formulate the problem of cost-aware IoT device classification. This being a combinatorial optimization problem, we develop a novel algorithm to solve it in a fast and effective way using the cross-entropy (CE)-based stochastic optimization technique. Using traffic of real devices, we demonstrate the capability of the CE-based algorithm in selecting features with minimal risk of misclassification while keeping the cost for feature extraction within a specified limit.

This article addresses the noise contamination in spatial filtering of brain responses using a novel signaling game-based approach to the optimal selection of EEG electrodes. The proposed method takes the standard common spatial pattern (CSP) filter as an input and produces an optimal electrode set as output for effective classification of different cognitive tasks. The standard CSP algorithms are highly prone to the inclusion of noise in the EEG data and may select noisy electrodes/signal sources that are redundant for a specific cognitive task which, in turn, may lead to a lower classification accuracy. A lot of literature exists in this area of research, most of which deals with adding the regularization term in the standard CSP algorithm. However, all of these methods lack capturing the uncertainty present in the EEG responses due to intrasession and intersession variations of subjective brain response. The novelty of this article lies in designing the fuzzy signaling game-based approach for optimal electrode selection using an interval type-2 fuzzy set, which can capture both the intrasession and intersession variability of EEG responses acquired from a subject's scalp. Experiments are undertaken over a wide variety of possible cognitive task classification problems which reveal that the proposed method yields superior results in electrode selection with respect to classification accuracy. Statistical tests undertaken using the Friedman test also confirm the superiority of the proposed method over its competitors.

This paper proposes a Fully Spiking Hybrid Neural Network (FSHNN) for energy-efficient and robust object detection in resource-constrained platforms. The network architecture is based on a Spiking Convolutional Neural Network using leaky-integrate-fire neuron models. The model combines unsupervised Spike Time-Dependent Plasticity (STDP) learning with back-propagation (STBP) learning methods and also uses Monte Carlo Dropout to get an estimate of the uncertainty error. FSHNN provides better accuracy compared to DNN based object detectors while being more energy-efficient. It also outperforms these object detectors, when subjected to noisy input data and less labeled training data with a lower uncertainty error.

Human behavior is a complex action which has provoked the thoughts of many people for a long time. However, very little about the cognitive aspect of an individual person's personality is known till the date. In order to differentiate people with significant differences in personality from their brain responses, we must at first be able to classify them based on their thought processes. From the viewpoint of classical ethics, people can broadly be classified into two main classes, namely, Categorical and Consequentialist. In this paper, we conduct several experiments where the subjects experience various ethical dilemmas and their ethical values in response to the presented stimuli are investigated through a question-answer session. The brain responses of the subjects are acquired using electroencephalography, which is then fed to an attention based parallel Convolutional Bi-directional Long Short-Term Memory (AConvBi-LSTM-NN) network with an ultimate aim to classify people into two above mentioned categories. Apart from the application point of view, the novelty of the paper lies in representing the EEG time-series into a sequence of multispectral 2D images which contain the spatial information of the acquired signal. The spectral information, along with the EEG time series (temporal information) are then used to train the proposed convolutional Bi-LSTM Network. The experimental results demonstrate promising results in classifying people based on their ethical values from their brain responses, with high classification accuracy. This provides scope for a new direction of research which can be further explored.

The tremendous progress in nanotechnology over the last century, makes it possible to engineer tiny nanodevices, which they need a nano-communication network to interact. Two solutions are proposed in literature to create a nano-communication system, either by using the classical electromagnetic paradigm with Terahertz band, or using the bio-inspired molecular communication. However, Terahertz is suffering from molecular absorption and scattering losses at nano level, and the achievable throughput of molecular communication is very low. In this paper, we propose a new solution to establish a wired nano-communication. Self-assembled actin-based is a new method that takes advantage of actin filaments self-assembly to create a nano wire between a transmitter and a receiver, and use electrons as information carriers. VPython framework is used in this paper to perform stochastic simulations of the nano wire formation. The algorithms used for the simulations are presented. The stability of the constructed nano wire is analyzed, and the error probability is calculated. Self-assembled actin-based method promises a fast and stable nano-communication system with a very high achievable throughput.

This paper addresses an interesting problem to model common spatial pattern (CSP) using an objective function employed to segregate EEG signals for a given cognitive task into two classes. The novelty of the present research is to include phase information with amplitude of the EEG signals to differentiate class boundaries. A new formulation of CSP is introduced and solved using Lagrange's multiplier method taking phase information of EEG into account. In addition, the proposed CSP is also realized with Schur Decomposition technique instead of using the conventional eigenvalue decomposition to overcome the disadvantage of using the latter one. Experiments undertaken confirm that the proposed phase-sensitive CSP and the CSP with Schur decomposition yield better performance than their non-phase sensitive counterpart by a large margin with respect to classification accuracy with the best results obtained by former CSP algorithm.