pwc-archive/papers-with-abstracts huggingface dataset:
Target Sensing With Off-grid Sparse Bayesian Learning for AFDM-ISAC System
Authors: Yirui Luo, Yong Liang Guan, Yao Ge, Chau Yuen
Date: 2025-03-13 00:00:00
Tasks: Integrated sensing and communication, ISAC, parameter estimation

The recently proposed multi-chirp waveform, affine frequency division multiplexing (AFDM), is regarded as a prospective candidate for integrated sensing and communication (ISAC) due to its robust performance in high-mobility scenarios and full diversity achievement in doubly dispersive channels. However, the insufficient Doppler resolution caused by limited transmission duration can reduce the accuracy of parameter estimation. In this paper, we propose a new off-grid target parameter estimation scheme to jointly estimate the range and velocity of the targets for AFDM-ISAC system, where the off-grid Doppler components are incorporated to enhance estimation accuracy. Specifically, we form the sensing model as an off-grid sparse signal recovery problem relying on the virtual delay and Doppler grids defined in the discrete affine Fourier (DAF) domain, where the off-grid components are regarded as hyper-parameters for estimation. We also employ the expectation-maximization (EM) technique via a sparse Bayesian learning (SBL) framework to update hyper-parameters iteratively. Simulation results indicate that our proposed off-grid algorithm outperforms existing algorithms in sensing performance and is highly robust to the AFDM-ISAC high-mobility scenario.
Edge-Fog Computing-Enabled EEG Data Compression via Asymmetrical Variational Discrete Cosine Transform Network
Authors: Xin Zhu, Hongyi Pan, Ahmet Enis Cetin
Date: 2025-03-13 00:00:00
Tasks: Brain Computer Interface, Data Compression, EEG, Variational Inference

The large volume of electroencephalograph (EEG) data produced by brain-computer interface (BCI) systems presents challenges for rapid transmission over bandwidth-limited channels in Internet of Things (IoT) networks. To address the issue, we propose a novel multi-channel asymmetrical variational discrete cosine transform (DCT) network for EEG data compression within an edge-fog computing framework. At the edge level, low-complexity DCT compression units are designed using parallel trainable hard-thresholding and scaling operators to remove redundant data and extract the effective latent space representation. At the fog level, an adaptive filter bank is applied to merge important features from adjacent channels into each individual channel by leveraging inter-channel correlations. Then, the inverse DCT reconstructed multi-head attention is developed to capture both local and global dependencies and reconstruct the original signals. Furthermore, by applying the principles of variational inference, a new evidence lower bound is formulated as the loss function, driving the model to balance compression efficiency and reconstruction accuracy. Experimental results on two public datasets demonstrate that the proposed method achieves superior compression performance without sacrificing any useful information for BCI detection compared with state-of-the-art techniques, indicating a feasible solution for EEG data compression.
RIS-Assisted Joint Sensing and Communications via Fractionally Constrained Fractional Programming
Authors: Yiming Liu, Kareem M. Attiah, Wei Yu
Date: 2025-03-13 00:00:00

This paper studies an uplink dual-functional sensing and communication system aided by a reconfigurable intelligent surface (RIS), whose reflection pattern is optimally configured to trade-off sensing and communication functionalities. Specifically, the Bayesian Cram\'er-Rao lower bound (BCRLB) for estimating the azimuth angle of a sensing user is minimized while ensuring the signal-to-interference-plus-noise ratio constraints for communication users. We show that this problem can be formulated as a novel fractionally constrained fractional programming (FCFP) problem. To deal with this highly nontrivial problem, we extend a quadratic transform technique, originally proposed to handle optimization problems containing ratio structures only in objectives, to the scenario where the constraints also contain ratio structures. First, we consider the case where the fading coefficient is known. Using the quadratic transform, the FCFP problem is turned into a sequence of subproblems that are convex except for the constant-modulus constraints which can be tackled using a penalty-based method. To further reduce the computational complexity, we leverage the constant-modulus conditions and propose a novel linear transform. This new transform enables the FCFP problem to be turned into a sequence of linear programming (LP) subproblems, which can be solved with linear complexity in the dimension of reflecting elements. Then, we consider the case where the fading coefficient is unknown. A modified BCRLB is used to make the problem more tractable, and the proposed quadratic transform-based algorithm is used to solve the problem. Finally, numerical results unveil nontrivial and effective reflection patterns that the RIS can be configured to generate to facilitate both functionalities.
Model-Agnostic Uncertainty Quantification for Fast NFC Tag Identification using RF Fingerprinting
Authors: Dickson Akuoko Sarpong, Adam Kamrath, Rohit Bhusal, Hongzhi Guo
Date: 2025-03-12 00:00:00
Tasks: Conformal Prediction, TAG, Uncertainty Quantification

Near Field Communication (NFC) is widely used in security applications such as door access systems and ID cards. However, clone attacks can replicate digital information, enabling unauthorized access. RF fingerprinting offers a robust defense by extracting unique physical-layer features from NFC cards that cannot be cloned. While RF fingerprinting has been extensively applied to Internet of Things (IoT) device authentication, NFC tags present distinct characteristics that require specialized approaches. This paper focuses on RF fingerprinting for the ISO15693 NFC tag, which is a widely used international standard, by leveraging multi-channel, multi-rate data sampling to enhance accuracy. Deep learning and Random Forest models are employed to identify NFC tags, while uncertainty quantification, particularly Conformal Prediction, accelerates the identification process with high confidence and precision. A software-defined radio (SDR) testbed is developed to transmit customized commands and collect multi-channel multi-rate NFC signals. The multi-channel multi-rate NFC signals are progressively collected to ensure fast and accurate identification. Experimental results demonstrate that the proposed system achieves high accuracy by adaptively utilizing the optimal combination of NFC signals. The developed solution is model-agnostic which can be utilized for any machine learning-based NFC tag identification.
Digital resampling using Farrow structures
Authors: N. V. Bakholdin, V. A. Chernienko, S. A. Bakhurin
Date: 2025-03-12 00:00:00

Farrow filters allow to develop universal oversamplers for fractional delay correction and sampling rate conversion. The use of cubic Lagrangian interpolation in Farrow filter does not allow to provide processing bandwidth more than 0.4 of sampling frequency, and also does not guarantee smoothness of impulse response of the interpolator. In this paper, we propose Farrow filter structures based on 3rd, 5th and 7th order Hermite splines using a high order differentiator. The proposed filters increase the processing bandwidth up to 0.8 of sampling frequency by increasing the order of the polynomial with additional constraints on the continuity of the derivatives at the interpolation nodes.
T-Parameters Based Modeling for Stacked Intelligent Metasurfaces: Tractable and Physically Consistent Model
Authors: Hamad Yahya, Matteo Nerini, Bruno Clerckx, Merouane Debbah
Date: 2025-03-12 00:00:00

This work develops a physically consistent model for stacked intelligent metasurfaces (SIM) using multiport network theory and transfer scattering parameters (T-parameters). Unlike the scattering parameters (S-parameters) model, the developed T-parameters model is simpler and more tractable. Moreover, the T-parameters constraints for lossless reciprocal reconfigurable intelligent surfaces (RISs) are derived. Additionally, a gradient descent algorithm (GDA) is introduced to maximize sum-rate in SIM-aided multiuser scenarios, demonstrating that mutual coupling and feedback between consecutive layers enhance performance. However, increasing SIM layers with a fixed total number of elements typically degrades sum-rate, unless the simplified channel model employing Rayleigh-Sommerfeld diffraction coefficients is utilized.
Once bitten, twice shy: A modeling framework for incorporating heterogeneous mosquito biting into transmission models
Authors: Kyle J. -M. Dahlin, Michael A. Robert, Lauren M. Childs
Date: 2025-03-13 00:00:00

The potential for outbreaks of mosquito-borne disease is linked to the frequency at which mosquitoes feed on blood, also known as the biting rate. But standard models of mosquito-borne disease transmission inherently assume that mosquitoes bite only once per reproductive cycle, an assumption commonly violated in nature. Drivers of multiple biting, like host defensive behaviors or climate factors, also affect the mosquito gonotrophic cycle duration, a quantity used to estimate the biting rate. Here, we present a novel framework for incorporating complex mosquito biting behaviors into transmission models. This framework accounts for heterogeneity in and linkages between biting rates and multiple biting. We provide formulas for the basic offspring number, $\mathcal{N}_0$, and basic reproduction number, $\mathcal{R}_0$, threshold measures for mosquito population and pathogen transmission persistence, respectively. To exhibit its flexibility, we expand on models derived from the framework that arise from empirical, phenomenological, or mechanistic perspectives. Using the GCD as a standard quantity to make comparisons among the models, we show that assumptions about the biting process affect the relationship between GCD and $\mathcal{R}_0$. While under standard assumptions, $\mathcal{R}_0$ is an increasing linear function of the inverse of the GCD, other models of the biting process exhibit saturating or concave relationships. Through sensitivity analysis of the mechanistic model, we determine that parameters related to probing and ingesting success are the most important targets for disease control. This work highlights the importance of incorporating the behavioral dynamics of mosquitoes into transmission models and provides methods for evaluating how individual-level interventions against mosquito biting scale up to determine population-level mosquito-borne disease risk.
Langevin Monte-Carlo Provably Learns Depth Two Neural Nets at Any Size and Data
Authors: Dibyakanti Kumar, Samyak Jha, Anirbit Mukherjee
Date: 2025-03-13 00:00:00

In this work, we will establish that the Langevin Monte-Carlo algorithm can learn depth-2 neural nets of any size and for any data and we give non-asymptotic convergence rates for it. We achieve this via showing that under Total Variation distance and q-Renyi divergence, the iterates of Langevin Monte Carlo converge to the Gibbs distribution of Frobenius norm regularized losses for any of these nets, when using smooth activations and in both classification and regression settings. Most critically, the amount of regularization needed for our results is independent of the size of the net. This result combines several recent observations, like our previous papers showing that two-layer neural loss functions can always be regularized by a certain constant amount such that they satisfy the Villani conditions, and thus their Gibbs measures satisfy a Poincare inequality.
Are Convex Optimization Curves Convex?
Authors: Guy Barzilai, Ohad Shamir, Moslem Zamani
Date: 2025-03-13 00:00:00

In this paper, we study when we might expect the optimization curve induced by gradient descent to be \emph{convex} -- precluding, for example, an initial plateau followed by a sharp decrease, making it difficult to decide when optimization should stop. Although such undesirable behavior can certainly occur when optimizing general functions, might it also occur in the benign and well-studied case of smooth convex functions? As far as we know, this question has not been tackled in previous work. We show, perhaps surprisingly, that the answer crucially depends on the choice of the step size. In particular, for the range of step sizes which are known to result in monotonic convergence to an optimal value, we characterize a regime where the optimization curve will be provably convex, and a regime where the curve can be non-convex. We also extend our results to gradient flow, and to the closely-related but different question of whether the gradient norm decreases monotonically.
Revisiting Multi-Agent Asynchronous Online Optimization with Delays: the Strongly Convex Case
Authors: Lingchan Bao, Tong Wei, Yuanyu Wan
Date: 2025-03-13 00:00:00

We revisit multi-agent asynchronous online optimization with delays, where only one of the agents becomes active for making the decision at each round, and the corresponding feedback is received by all the agents after unknown delays. Although previous studies have established an $O(\sqrt{dT})$ regret bound for this problem, they assume that the maximum delay $d$ is knowable or the arrival order of feedback satisfies a special property, which may not hold in practice. In this paper, we surprisingly find that when the loss functions are strongly convex, these assumptions can be eliminated, and the existing regret bound can be significantly improved to $O(d\log T)$ meanwhile. Specifically, to exploit the strong convexity of functions, we first propose a delayed variant of the classical follow-the-leader algorithm, namely FTDL, which is very simple but requires the full information of functions as feedback. Moreover, to handle the more general case with only the gradient feedback, we develop an approximate variant of FTDL by combining it with surrogate loss functions. Experimental results show that the approximate FTDL outperforms the existing algorithm in the strongly convex case.
OR-LLM-Agent: Automating Modeling and Solving of Operations Research Optimization Problem with Reasoning Large Language Model
Authors: BoWen Zhang, Pengcheng Luo
Date: 2025-03-13 00:00:00
Tasks: AI Agent, Language Modeling, Language Modelling, Large Language Model

Operations Research (OR) has been widely applied in various fields such as resource allocation, production planning, and supply chain management. However, addressing real-world OR problems requires OR experts to perform mathematical modeling and programmers to develop solution algorithms. This traditional method, heavily reliant on experts, is costly and has long development cycles, severely limiting the widespread adoption of OR techniques. Few have considered using Artificial Intelligence (AI) to replace professionals to achieve fully automated solutions for OR problems. We propose OR-LLM-Agent, the first AI agent that enables end-to-end automation for solving real-world OR problems. OR-LLM-Agent leverages the Chain-of-Thought (CoT) reasoning capabilities of Large Language Models (LLMs) to translate natural language problem descriptions into formal mathematical models and automatically generate Gurobi solver code. In OR-LLM-Agent, OR-CodeAgent is designed to automate code execution and repair within a sandbox environment, facilitating the derivation of the final solution. Due to the lack of dedicated benchmark datasets for evaluating the automated solving of OR problems, we construct a benchmark dataset comprising 83 real-world OR problems described in natural language. We conduct comparative experiments with state-of-the-art (SOTA) reasoning LLMs, including GPT-o3-mini, DeepSeek-R1, and Gemini 2.0 Flash Thinking. The OR-LLM-Agent achieved the highest pass rate of 100% and the highest solution accuracy of 85%, demonstrating the feasibility of automated OR problem-solving. Data and code have been publicly available at https://github.com/bwz96sco/or_llm_agent.
Adaptive Moment Estimation Optimization Algorithm Using Projection Gradient for Deep Learning
Authors: Yongqi Li, Xiaowei Zhang
Date: 2025-03-13 00:00:00

Training deep neural networks is challenging. To accelerate training and enhance performance, we propose PadamP, a novel optimization algorithm. PadamP is derived by applying the adaptive estimation of the p-th power of the second-order moments under scale invariance, enhancing projection adaptability by modifying the projection discrimination condition. It is integrated into Adam-type algorithms, accelerating training, boosting performance, and improving generalization in deep learning. Combining projected gradient benefits with adaptive moment estimation, PadamP tackles unconstrained non-convex problems. Convergence for the non-convex case is analyzed, focusing on the decoupling of first-order moment estimation coefficients and second-order moment estimation coefficients. Unlike prior work relying on , our proof generalizes the convergence theorem, enhancing practicality. Experiments using VGG-16 and ResNet-18 on CIFAR-10 and CIFAR-100 show PadamP's effectiveness, with notable performance on CIFAR-10/100, especially for VGG-16. The results demonstrate that PadamP outperforms existing algorithms in terms of convergence speed and generalization ability, making it a valuable addition to the field of deep learning optimization.
A Semantic-Loss Function Modeling Framework With Task-Oriented Machine Learning Perspectives
Authors: Ti Ti Nguyen, Thanh-Dung Le, Vu Nguyen Ha, Hong-Fu Chou, Geoffrey Eappen, Duc-Dung Tran, Hung Nguyen-Kha, Prabhu Thiruvasagam, Luis M. Garces-Socarras, Jorge L. Gonzalez-Rios, Juan C. Merlano-Duncan, Symeon Chatzinotas
Date: 2025-03-12 00:00:00
Tasks: Earth Observation, Semantic Communication

The integration of machine learning (ML) has significantly enhanced the capabilities of Earth Observation (EO) systems by enabling the extraction of actionable insights from complex datasets. However, the performance of data-driven EO applications is heavily influenced by the data collection and transmission processes, where limited satellite bandwidth and latency constraints can hinder the full transmission of original data to the receivers. To address this issue, adopting the concepts of Semantic Communication (SC) offers a promising solution by prioritizing the transmission of essential data semantics over raw information. Implementing SC for EO systems requires a thorough understanding of the impact of data processing and communication channel conditions on semantic loss at the processing center. This work proposes a novel data-fitting framework to empirically model the semantic loss using real-world EO datasets and domain-specific insights. The framework quantifies two primary types of semantic loss: (1) source coding loss, assessed via a data quality indicator measuring the impact of processing on raw source data, and (2) transmission loss, evaluated by comparing practical transmission performance against the Shannon limit. Semantic losses are estimated by evaluating the accuracy of EO applications using four task-oriented ML models, EfficientViT, MobileViT, ResNet50-DINO, and ResNet8-KD, on lossy image datasets under varying channel conditions and compression ratios. These results underpin a framework for efficient semantic-loss modeling in bandwidth-constrained EO scenarios, enabling more reliable and effective operations.
Unveiling Hidden Pivotal Players with GoalNet: A GNN-Based Soccer Player Evaluation System
Authors: Jacky Hao Jiang, Jerry Cai, Anastasios Kyrillidis
Date: 2025-03-12 00:00:00
Tasks: Graph Attention

Soccer analysis tools emphasize metrics such as expected goals, leading to an overrepresentation of attacking players' contributions and overlooking players who facilitate ball control and link attacks. Examples include Rodri from Manchester City and Palhinha who just transferred to Bayern Munich. To address this bias, we aim to identify players with pivotal roles in a soccer team, incorporating both spatial and temporal features. In this work, we introduce a GNN-based framework that assigns individual credit for changes in expected threat (xT), thus capturing overlooked yet vital contributions in soccer. Our pipeline encodes both spatial and temporal features in event-centric graphs, enabling fair attribution of non-scoring actions such as defensive or transitional plays. We incorporate centrality measures into the learned player embeddings, ensuring that ball-retaining defenders and defensive midfielders receive due recognition for their overall impact. Furthermore, we explore diverse GNN variants-including Graph Attention Networks and Transformer-based models-to handle long-range dependencies and evolving match contexts, discussing their relative performance and computational complexity. Experiments on real match data confirm the robustness of our approach in highlighting pivotal roles that traditional attacking metrics typically miss, underscoring the model's utility for more comprehensive soccer analytics.
On the Injective Norm of Sums of Random Tensors and the Moments of Gaussian Chaoses
Authors: Ishaq Aden-Ali
Date: 2025-03-13 00:00:00
Tasks: LEMMA

We prove an upper bound on the expected $\ell_p$ injective norm of sums of subgaussian random tensors. Our proof is simple and does not rely on any explicit geometric or chaining arguments. Instead, it follows from a simple application of the PAC-Bayesian lemma, a tool that has proven effective at controlling the suprema of certain ``smooth'' empirical processes in recent years. Our bound strictly improves a very recent result of Bandeira, Gopi, Jiang, Lucca, and Rothvoss. In the Euclidean case ($p=2$), our bound sharpens a result of Lata{\l}a that was central to proving his estimates on the moments of Gaussian chaoses. As a consequence, we obtain an elementary proof of this fundamental result.
On the contraction properties of Sinkhorn semigroups
Authors: O. Deniz Akyildiz, Pierre Del Moral, Joaquin Miguez
Date: 2025-03-12 00:00:00
Tasks: Density Estimation

We develop a novel semigroup contraction analysis based on Lyapunov techniques to prove the exponential convergence of Sinkhorn equations on weighted Banach spaces. This operator-theoretic framework yields exponential decays of Sinkhorn iterates towards Schr\"odinger bridges with respect to general classes of $\phi$-divergences as well as in weighted Banach spaces. To the best of our knowledge, these are the first results of this type in the literature on entropic transport and the Sinkhorn algorithm. We also illustrate the impact of these results in the context of multivariate linear Gaussian models as well as statistical finite mixture models including Gaussian-kernel density estimation of complex data distributions arising in generative models.