pwc-archive/papers-with-abstracts huggingface dataset:
Hybrid Deep Learning Model for Multiple Cache Side Channel Attacks Detection: A Comparative Analysis
Authors: Tejal Joshi, Aarya Kawalay, Anvi Jamkhande, Amit Joshi
Date: 2025-01-28 00:00:00

Cache side channel attacks are a sophisticated and persistent threat that exploit vulnerabilities in modern processors to extract sensitive information. These attacks leverage weaknesses in shared computational resources, particularly the last level cache, to infer patterns in data access and execution flows, often bypassing traditional security defenses. Such attacks are especially dangerous as they can be executed remotely without requiring physical access to the victim's device. This study focuses on a specific class of these threats: fingerprinting attacks, where an adversary monitors and analyzes the behavior of co-located processes via cache side channels. This can potentially reveal confidential information, such as encryption keys or user activity patterns. A comprehensive threat model illustrates how attackers sharing computational resources with target systems exploit these side channels to compromise sensitive data. To mitigate such risks, a hybrid deep learning model is proposed for detecting cache side channel attacks. Its performance is compared with five widely used deep learning models: Multi-Layer Perceptron, Convolutional Neural Network, Simple Recurrent Neural Network, Long Short-Term Memory, and Gated Recurrent Unit. The experimental results demonstrate that the hybrid model achieves a detection rate of up to 99.96%. These findings highlight the limitations of existing models, the need for enhanced defensive mechanisms, and directions for future research to secure sensitive data against evolving side channel threats.
Toward Relative Positional Encoding in Spiking Transformers
Authors: Changze Lv, Yansen Wang, Dongqi Han, Yifei Shen, Xiaoqing Zheng, Xuanjing Huang, Dongsheng Li
Date: 2025-01-28 00:00:00
Tasks: image-classification, Image Classification, text-classification, Text Classification, Time Series Forecasting

Spiking neural networks (SNNs) are bio-inspired networks that mimic how neurons in the brain communicate through discrete spikes, which have great potential in various tasks due to their energy efficiency and temporal processing capabilities. SNNs with self-attention mechanisms (spiking Transformers) have recently shown great advancements in various tasks, and inspired by traditional Transformers, several studies have demonstrated that spiking absolute positional encoding can help capture sequential relationships for input data, enhancing the capabilities of spiking Transformers for tasks such as sequential modeling and image classification. However, how to incorporate relative positional information into SNNs remains a challenge. In this paper, we introduce several strategies to approximate relative positional encoding (RPE) in spiking Transformers while preserving the binary nature of spikes. Firstly, we formally prove that encoding relative distances with Gray Code ensures that the binary representations of positional indices maintain a constant Hamming distance whenever their decimal values differ by a power of two, and we propose Gray-PE based on this property. In addition, we propose another RPE method called Log-PE, which combines the logarithmic form of the relative distance matrix directly into the spiking attention map. Furthermore, we extend our RPE methods to a two-dimensional form, making them suitable for processing image patches. We evaluate our RPE methods on various tasks, including time series forecasting, text classification, and patch-based image classification, and the experimental results demonstrate a satisfying performance gain by incorporating our RPE methods across many architectures.
Stochastic Population Update Provably Needs An Archive in Evolutionary Multi-objective Optimization
Authors: Shengjie Ren, Zimin Liang, Miqing Li, Chao Qian
Date: 2025-01-28 00:00:00
Tasks: Evolutionary Algorithms

Evolutionary algorithms (EAs) have been widely applied to multi-objective optimization, due to their nature of population-based search. Population update, a key component in multi-objective EAs (MOEAs), is usually performed in a greedy, deterministic manner. However, recent studies have questioned this practice and shown that stochastic population update (SPU), which allows inferior solutions have a chance to be preserved, can help MOEAs jump out of local optima more easily. While introducing randomness in the population update process boosts the exploration of MOEAs, there is a drawback that the population may not always preserve the very best solutions found, thus entailing a large population. Intuitively, a possible solution to this issue is to introduce an archive that stores the best solutions ever found. In this paper, we theoretically show that using an archive can allow a small population and accelerate the search of SPU-based MOEAs substantially. Specifically, we analyze the expected running time of two well-established MOEAs, SMS-EMOA and NSGA-II, with SPU for solving a commonly studied bi-objective problem OneJumpZeroJump, and prove that using an archive can bring (even exponential) speedups. The comparison between SMS-EMOA and NSGA-II also suggests that the $(\mu+\mu)$ update mode may be more suitable for SPU than the $(\mu+1)$ update mode. Furthermore, our derived running time bounds for using SPU alone are significantly tighter than previously known ones. Our theoretical findings are also empirically validated on a well-known practical problem, the multi-objective traveling salesperson problem. We hope this work may provide theoretical support to explore different ideas of designing algorithms in evolutionary multi-objective optimization.
Advanced Physics-Informed Neural Network with Residuals for Solving Complex Integral Equations
Authors: Mahdi Movahedian Moghaddam, Kourosh Parand, Saeed Reza Kheradpisheh
Date: 2025-01-22 00:00:00

In this paper, we present the Residual Integral Solver Network (RISN), a novel neural network architecture designed to solve a wide range of integral and integro-differential equations, including one-dimensional, multi-dimensional, ordinary and partial integro-differential, systems, fractional types, and Helmholtz-type integral equations involving oscillatory kernels. RISN integrates residual connections with high-accuracy numerical methods such as Gaussian quadrature and fractional derivative operational matrices, enabling it to achieve higher accuracy and stability than traditional Physics-Informed Neural Networks (PINN). The residual connections help mitigate vanishing gradient issues, allowing RISN to handle deeper networks and more complex kernels, particularly in multi-dimensional problems. Through extensive experiments, we demonstrate that RISN consistently outperforms not only classical PINNs but also advanced variants such as Auxiliary PINN (A-PINN) and Self-Adaptive PINN (SA-PINN), achieving significantly lower Mean Absolute Errors (MAE) across various types of equations. These results highlight RISN's robustness and efficiency in solving challenging integral and integro-differential problems, making it a valuable tool for real-world applications where traditional methods often struggle.
Synthetic Data Generation by Supervised Neural Gas Network for Physiological Emotion Recognition Data
Authors: S. Muhammad Hossein Mousavi
Date: 2025-01-19 00:00:00
Tasks: EEG, Emotion Recognition, Synthetic Data Generation

Data scarcity remains a significant challenge in the field of emotion recognition using physiological signals, as acquiring comprehensive and diverse datasets is often prevented by privacy concerns and logistical constraints. This limitation restricts the development and generalization of robust emotion recognition models, making the need for effective synthetic data generation methods more critical. Emotion recognition from physiological signals such as EEG, ECG, and GSR plays a pivotal role in enhancing human-computer interaction and understanding human affective states. Utilizing these signals, this study introduces an innovative approach to synthetic data generation using a Supervised Neural Gas (SNG) network, which has demonstrated noteworthy speed advantages over established models like Conditional VAE, Conditional GAN, diffusion model, and Variational LSTM. The Neural Gas network, known for its adaptability in organizing data based on topological and feature-space proximity, provides a robust framework for generating real-world-like synthetic datasets that preserve the intrinsic patterns of physiological emotion data. Our implementation of the SNG efficiently processes the input data, creating synthetic instances that closely mimic the original data distributions, as demonstrated through comparative accuracy assessments. In experiments, while our approach did not universally outperform all models, it achieved superior performance against most of the evaluated models and offered significant improvements in processing time. These outcomes underscore the potential of using SNG networks for fast, efficient, and effective synthetic data generation in emotion recognition applications.
Explore Activation Sparsity in Recurrent LLMs for Energy-Efficient Neuromorphic Computing
Authors: Ivan Knunyants, Maryam Tavakol, Manolis Sifalakis, Yingfu Xu, Amirreza Yousefzadeh, Guangzhi Tang
Date: 2025-01-09 00:00:00
Tasks: Zero-Shot Learning

The recent rise of Large Language Models (LLMs) has revolutionized the deep learning field. However, the desire to deploy LLMs on edge devices introduces energy efficiency and latency challenges. Recurrent LLM (R-LLM) architectures have proven effective in mitigating the quadratic complexity of self-attention, making them a potential paradigm for computing on-edge neuromorphic processors. In this work, we propose a low-cost, training-free algorithm to sparsify R-LLMs' activations to enhance energy efficiency on neuromorphic hardware. Our approach capitalizes on the inherent structure of these models, rendering them well-suited for energy-constrained environments. Although primarily designed for R-LLMs, this method can be generalized to other LLM architectures, such as transformers, as demonstrated on the OPT model, achieving comparable sparsity and efficiency improvements. Empirical studies illustrate that our method significantly reduces computational demands while maintaining competitive accuracy across multiple zero-shot learning benchmarks. Additionally, hardware simulations with the SENECA neuromorphic processor underscore notable energy savings and latency improvements. These results pave the way for low-power, real-time neuromorphic deployment of LLMs and demonstrate the feasibility of training-free on-chip adaptation using activation sparsity.
Runtime Analysis of Evolutionary Algorithms for Multiparty Multiobjective Optimization
Authors: Yuetong Sun, Peilan Xu, Wenjian Luo
Date: 2025-01-09 00:00:00
Tasks: Evolutionary Algorithms, Multiobjective Optimization

In scenarios where multiple decision-makers operate within a common decision space, each focusing on their own multi-objective optimization problem (e.g., bargaining games), the problem can be modeled as a multi-party multi-objective optimization problem (MPMOP). While numerous evolutionary algorithms have been proposed to solve MPMOPs, most results remain empirical. This paper presents the first theoretical analysis of the expected runtime of evolutionary algorithms on bi-party multi-objective optimization problems (BPMOPs). Our findings demonstrate that employing traditional multi-objective optimization algorithms to solve MPMOPs is both time-consuming and inefficient, as the resulting population contains many solutions that fail to achieve consensus among decision-makers. An alternative approach involves decision-makers individually solving their respective optimization problems and seeking consensus only in the final stage. While feasible for pseudo-Boolean optimization problems, this method may fail to guarantee approximate performance for one party in NP-hard problems. Finally, We propose coevolutionary multi-party multi-objective optimizers (CoEMPMO) for pseudo-Boolean optimization and shortest path problems within a multi-party multi-objective context, which maintains a common solution set among all parties through coevolution. Theoretical and experimental results demonstrate that the proposed \( \text{CoEMPMO}_{\text{random}} \) outperforms previous algorithms in terms of the expected lower bound on runtime for pseudo-Boolean optimization problems. Additionally, \( \text{CoEMPMO}_{\text{cons}}^{\text{SP}} \) achieves better efficiency and precision in solving shortest path problems compared to existing algorithms.
The empirical median for estimating the common mean of heteroscedastic random variables
Authors: Sirine Louati
Date: 2025-01-28 00:00:00

We study the problem of mean estimation in the heteroscedastic setting. In particular, we consider symmetric random variables having the same location parameter and different and unknown scale parameters. Our goal is then to estimate their unknown common location parameter. It is an elementary topic but yet a not very well-studied one since we always make the assumption that the random variables are independent and identically distributed. In this paper, we study the median estimator and we establish upper and lower bounds on its estimation error that are of the same order and that generalize and improve recent results of Devroye et al. and Xia.
Consistent support recovery for high-dimensional diffusions
Authors: Dmytro Marushkevych, Francisco Pina, Mark Podolskij
Date: 2025-01-28 00:00:00
Tasks: Variable Selection

Statistical inference for stochastic processes has advanced significantly due to applications in diverse fields, but challenges remain in high-dimensional settings where parameters are allowed to grow with the sample size. This paper analyzes a d-dimensional ergodic diffusion process under sparsity constraints, focusing on the adaptive Lasso estimator, which improves variable selection and bias over the standard Lasso. We derive conditions under which the adaptive Lasso achieves support recovery property and asymptotic normality for the drift parameter, with a focus on linear models. Explicit parameter relationships guide tuning for optimal performance, and a marginal estimator is proposed for p>>d scenarios under partial orthogonality assumption. Numerical studies confirm the adaptive Lasso's superiority over standard Lasso and MLE in accuracy and support recovery, providing robust solutions for high-dimensional stochastic processes.
Exploratory Mean-Variance Portfolio Optimization with Regime-Switching Market Dynamics
Authors: Yuling Max Chen, Bin Li, David Saunders
Date: 2025-01-28 00:00:00
Tasks: Portfolio Optimization, Reinforcement Learning (RL)

Considering the continuous-time Mean-Variance (MV) portfolio optimization problem, we study a regime-switching market setting and apply reinforcement learning (RL) techniques to assist informed exploration within the control space. We introduce and solve the Exploratory Mean Variance with Regime Switching (EMVRS) problem. We also present a Policy Improvement Theorem. Further, we recognize that the widely applied Temporal Difference (TD) learning is not adequate for the EMVRS context, hence we consider Orthogonality Condition (OC) learning, leveraging the martingale property of the induced optimal value function from the analytical solution to EMVRS. We design a RL algorithm that has more meaningful parameterization using the market parameters and propose an updating scheme for each parameter. Our empirical results demonstrate the superiority of OC learning over TD learning with a clear convergence of the market parameters towards their corresponding ``grounding true" values in a simulated market scenario. In a real market data study, EMVRS with OC learning outperforms its counterparts with the highest mean and reasonably low volatility of the annualized portfolio returns.
Ancestral Inference and Learning for Branching Processes in Random Environments
Authors: Xiaoran Jiang, Anand N. Vidyashankar
Date: 2025-01-27 00:00:00

Ancestral inference for branching processes in random environments involves determining the ancestor distribution parameters using the population sizes of descendant generations. In this paper, we introduce a new methodology for ancestral inference utilizing the generalized method of moments. We demonstrate that the estimator's behavior is critically influenced by the coefficient of variation of the environment sequence. Furthermore, despite the process's evolution being heavily dependent on the offspring means of various generations, we show that the joint limiting distribution of the ancestor and offspring estimators of the mean, under appropriate centering and scaling, decouple and converge to independent Gaussian random variables when the ratio of the number of generations to the logarithm of the number of replicates converges to zero. Additionally, we provide estimators for the limiting variance and illustrate our findings through numerical experiments and data from Polymerase Chain Reaction experiments and COVID-19 data.
On characterizing optimal learning trajectories in a class of learning problems
Authors: Getachew K Befekadu
Date: 2025-01-27 00:00:00

In this brief paper, we provide a mathematical framework that exploits the relationship between the maximum principle and dynamic programming for characterizing optimal learning trajectories in a class of learning problem, which is related to point estimations for modeling of high-dimensional nonlinear functions. Here, such characterization for the optimal learning trajectories is associated with the solution of an optimal control problem for a weakly-controlled gradient system with small parameters, whose time-evolution is guided by a model training dataset and its perturbed version, while the optimization problem consists of a cost functional that summarizes how to gauge the quality/performance of the estimated model parameters at a certain fixed final time w.r.t. a model validating dataset. Moreover, using a successive Galerkin approximation method, we provide an algorithmic recipe how to construct the corresponding optimal learning trajectories leading to the optimal estimated model parameters for such a class of learning problem.
Enhanced Retrieval of Long Documents: Leveraging Fine-Grained Block Representations with Large Language Models
Authors: Minghan Li, Eric Gaussier, Guodong Zhou
Date: 2025-01-28 00:00:00
Tasks: Information Retrieval, RAG, Retrieval, Retrieval-augmented Generation

In recent years, large language models (LLMs) have demonstrated exceptional power in various domains, including information retrieval. Most of the previous practices involve leveraging these models to create a single embedding for each query, each passage, or each document individually, a strategy exemplified and used by the Retrieval-Augmented Generation (RAG) framework. While this method has proven effective, we argue that it falls short in fully capturing the nuanced intricacies of document-level texts due to its reliance on a relatively coarse-grained representation. To address this limitation, we introduce a novel, fine-grained approach aimed at enhancing the accuracy of relevance scoring for long documents. Our methodology firstly segments a long document into blocks, each of which is embedded using an LLM, for matching with the query representation. When calculating the relevance score, we aggregate the query-block relevance scores through a weighted sum method, yielding a comprehensive score for the query with the entire document. Despite its apparent simplicity, our experimental findings reveal that this approach outperforms standard representation methods and achieves a significant reduction in embedding generation latency. Moreover, by carefully optimizing pairwise loss functions, superior performances have been achieved.
Document Screenshot Retrievers are Vulnerable to Pixel Poisoning Attacks
Authors: Shengyao Zhuang, Ekaterina Khramtsova, Xueguang Ma, Bevan Koopman, Jimmy Lin, Guido Zuccon
Date: 2025-01-28 00:00:00
Tasks: Language Modeling, Language Modelling, Retrieval

Recent advancements in dense retrieval have introduced vision-language model (VLM)-based retrievers, such as DSE and ColPali, which leverage document screenshots embedded as vectors to enable effective search and offer a simplified pipeline over traditional text-only methods. In this study, we propose three pixel poisoning attack methods designed to compromise VLM-based retrievers and evaluate their effectiveness under various attack settings and parameter configurations. Our empirical results demonstrate that injecting even a single adversarial screenshot into the retrieval corpus can significantly disrupt search results, poisoning the top-10 retrieved documents for 41.9% of queries in the case of DSE and 26.4% for ColPali. These vulnerability rates notably exceed those observed with equivalent attacks on text-only retrievers. Moreover, when targeting a small set of known queries, the attack success rate raises, achieving complete success in certain cases. By exposing the vulnerabilities inherent in vision-language models, this work highlights the potential risks associated with their deployment.
Secure Federated Graph-Filtering for Recommender Systems
Authors: Julien Nicolas, César Sabater, Mohamed Maouche, Sonia Ben Mokhtar, Mark Coates
Date: 2025-01-28 00:00:00
Tasks: Privacy Preserving, Recommendation Systems

Recommender systems often rely on graph-based filters, such as normalized item-item adjacency matrices and low-pass filters. While effective, the centralized computation of these components raises concerns about privacy, security, and the ethical use of user data. This work proposes two decentralized frameworks for securely computing these critical graph components without centralizing sensitive information. The first approach leverages lightweight Multi-Party Computation and distributed singular vector computations to privately compute key graph filters. The second extends this framework by incorporating low-rank approximations, enabling a trade-off between communication efficiency and predictive performance. Empirical evaluations on benchmark datasets demonstrate that the proposed methods achieve comparable accuracy to centralized state-of-the-art systems while ensuring data confidentiality and maintaining low communication costs. Our results highlight the potential for privacy-preserving decentralized architectures to bridge the gap between utility and user data protection in modern recommender systems.