pwc-archive/papers-with-abstracts huggingface dataset:
Quantum Diffusion Model for Quark and Gluon Jet Generation
Authors: Mariia Baidachna, Rey Guadarrama, Gopal Ramesh Dahale, Tom Magorsch, Isabel Pedraza, Konstantin T. Matchev, Katia Matcheva, Kyoungchul Kong, Sergei Gleyzer
Date: 2024-12-30 00:00:00
Tasks: Denoising, Image Generation

Diffusion models have demonstrated remarkable success in image generation, but they are computationally intensive and time-consuming to train. In this paper, we introduce a novel diffusion model that benefits from quantum computing techniques in order to mitigate computational challenges and enhance generative performance within high energy physics data. The fully quantum diffusion model replaces Gaussian noise with random unitary matrices in the forward process and incorporates a variational quantum circuit within the U-Net in the denoising architecture. We run evaluations on the structurally complex quark and gluon jets dataset from the Large Hadron Collider. The results demonstrate that the fully quantum and hybrid models are competitive with a similar classical model for jet generation, highlighting the potential of using quantum techniques for machine learning problems.
Enhanced coarsening of charge density waves induced by electron correlation: Machine-learning enabled large-scale dynamical simulations
Authors: Yang Yang, Chen Cheng, Yunhao Fan, Gia-Wei Chern
Date: 2024-12-30 00:00:00

The phase ordering kinetics of emergent orders in correlated electron systems is a fundamental topic in non-equilibrium physics, yet it remains largely unexplored. The intricate interplay between quasiparticles and emergent order-parameter fields could lead to unusual coarsening dynamics that is beyond the standard theories. However, accurate treatment of both quasiparticles and collective degrees of freedom is a multi-scale challenge in dynamical simulations of correlated electrons. Here we leverage modern machine learning (ML) methods to achieve a linear-scaling algorithm for simulating the coarsening of charge density waves (CDWs), one of the fundamental symmetry breaking phases in functional electron materials. We demonstrate our approach on the square-lattice Hubbard-Holstein model and uncover an intriguing enhancement of CDW coarsening which is related to the screening of on-site potential by electron-electron interactions. Our study provides fresh insights into the role of electron correlations in non-equilibrium dynamics and underscores the promise of ML force-field approaches for advancing multi-scale dynamical modeling of correlated electron systems.
Investigating layer-selective transfer learning of QAOA parameters for Max-Cut problem
Authors: Francesco Aldo Venturelli, Sreetama Das, Filippo Caruso
Date: 2024-12-30 00:00:00
Tasks: Combinatorial Optimization, Transfer Learning, Visual Question Answering (VQA)

Quantum approximate optimization algorithm (QAOA) is a variational quantum algorithm (VQA) ideal for noisy intermediate-scale quantum (NISQ) processors, and is highly successful for solving combinatorial optimization problems (COPs). It has been observed that the optimal variational parameters obtained from one instance of a COP can be transferred to another instance, producing sufficiently satisfactory solutions for the latter. In this context, a suitable method for further improving the solution is to fine-tune a subset of the transferred parameters. We numerically explore the role of optimizing individual QAOA layers in improving the approximate solution of the Max-Cut problem after parameter transfer. We also investigate the trade-off between a good approximation and the required optimization time when optimizing transferred QAOA parameters. These studies show that optimizing a subset of layers can be more effective at a lower time-cost compared to optimizing all layers.
Privacy-Aware Multi-Device Cooperative Edge Inference with Distributed Resource Bidding
Authors: Wenhao Zhuang, Yuyi Mao
Date: 2024-12-30 00:00:00
Tasks: Edge-computing, Feature Compression, Privacy Preserving

Mobile edge computing (MEC) has empowered mobile devices (MDs) in supporting artificial intelligence (AI) applications through collaborative efforts with proximal MEC servers. Unfortunately, despite the great promise of device-edge cooperative AI inference, data privacy becomes an increasing concern. In this paper, we develop a privacy-aware multi-device cooperative edge inference system for classification tasks, which integrates a distributed bidding mechanism for the MEC server's computational resources. Intermediate feature compression is adopted as a principled approach to minimize data privacy leakage. To determine the bidding values and feature compression ratios in a distributed fashion, we formulate a decentralized partially observable Markov decision process (DEC-POMDP) model, for which, a multi-agent deep deterministic policy gradient (MADDPG)-based algorithm is developed. Simulation results demonstrate the effectiveness of the proposed algorithm in privacy-preserving cooperative edge inference. Specifically, given a sufficient level of data privacy protection, the proposed algorithm achieves 0.31-0.95% improvements in classification accuracy compared to the approach being agnostic to the wireless channel conditions. The performance is further enhanced by 1.54-1.67% by considering the difficulties of inference data.
BridgePure: Limited Protection Leakage Can Break Black-Box Data Protection
Authors: Yihan Wang, Yiwei Lu, Xiao-Shan Gao, Gautam Kamath, YaoLiang Yu
Date: 2024-12-30 00:00:00

Availability attacks, or unlearnable examples, are defensive techniques that allow data owners to modify their datasets in ways that prevent unauthorized machine learning models from learning effectively while maintaining the data's intended functionality. It has led to the release of popular black-box tools (e.g., APIs) for users to upload personal data and receive protected counterparts. In this work, we show that such black-box protections can be substantially compromised if a small set of unprotected in-distribution data is available. Specifically, we propose a novel threat model of protection leakage, where an adversary can (1) easily acquire (unprotected, protected) pairs by querying the black-box protections with a small unprotected dataset; and (2) train a diffusion bridge model to build a mapping between unprotected and protected data. This mapping, termed BridgePure, can effectively remove the protection from any previously unseen data within the same distribution. BridgePure demonstrates superior purification performance on classification and style mimicry tasks, exposing critical vulnerabilities in black-box data protection. We suggest that practitioners implement multi-level countermeasures to mitigate such risks.
Learning Epidemiological Dynamics via the Finite Expression Method
Authors: Jianda Du, Senwei Liang, Chunmei Wang
Date: 2024-12-30 00:00:00
Tasks: Decision Making

Modeling and forecasting the spread of infectious diseases is essential for effective public health decision-making. Traditional epidemiological models rely on expert-defined frameworks to describe complex dynamics, while neural networks, despite their predictive power, often lack interpretability due to their ``black-box" nature. This paper introduces the Finite Expression Method, a symbolic learning framework that leverages reinforcement learning to derive explicit mathematical expressions for epidemiological dynamics. Through numerical experiments on both synthetic and real-world datasets, FEX demonstrates high accuracy in modeling and predicting disease spread, while uncovering explicit relationships among epidemiological variables. These results highlight FEX as a powerful tool for infectious disease modeling, combining interpretability with strong predictive performance to support practical applications in public health.
Mind the truncation gap: challenges of learning on dynamic graphs with recurrent architectures
Authors: João Bravo, Jacopo Bono, Pedro Saleiro, Hugo Ferreira, Pedro Bizarro
Date: 2024-12-30 00:00:00

Systems characterized by evolving interactions, prevalent in social, financial, and biological domains, are effectively modeled as continuous-time dynamic graphs (CTDGs). To manage the scale and complexity of these graph datasets, machine learning (ML) approaches have become essential. However, CTDGs pose challenges for ML because traditional static graph methods do not naturally account for event timings. Newer approaches, such as graph recurrent neural networks (GRNNs), are inherently time-aware and offer advantages over static methods for CTDGs. However, GRNNs face another issue: the short truncation of backpropagation-through-time (BPTT), whose impact has not been properly examined until now. In this work, we demonstrate that this truncation can limit the learning of dependencies beyond a single hop, resulting in reduced performance. Through experiments on a novel synthetic task and real-world datasets, we reveal a performance gap between full backpropagation-through-time (F-BPTT) and the truncated backpropagation-through-time (T-BPTT) commonly used to train GRNN models. We term this gap the "truncation gap" and argue that understanding and addressing it is essential as the importance of CTDGs grows, discussing potential future directions for research in this area.
Machine Learning Optimal Ordering in Global Routing Problems in Semiconductors
Authors: Heejin Choi, Minji Lee, Chang Hyeong Lee, Jaeho Yang, Rak-Kyeong Seong
Date: 2024-12-30 00:00:00

In this work, we propose a new method for ordering nets during the process of layer assignment in global routing problems. The global routing problems that we focus on in this work are based on routing problems that occur in the design of substrates in multilayered semiconductor packages. The proposed new method is based on machine learning techniques and we show that the proposed method supersedes conventional net ordering techniques based on heuristic score functions. We perform global routing experiments in multilayered semiconductor package environments in order to illustrate that the routing order based on our new proposed technique outperforms previous methods based on heuristics. Our approach of using machine learning for global routing targets specifically the net ordering step which we show in this work can be significantly improved by deep learning.
Improving Location-based Thermal Emission Side-Channel Analysis Using Iterative Transfer Learning
Authors: Tun-Chieh Lou, Chung-Che Wang, Jyh-Shing Roger Jang, Henian Li, Lang Lin, Norman Chang
Date: 2024-12-30 00:00:00
Tasks: Side Channel Analysis, Transfer Learning

This paper proposes the use of iterative transfer learning applied to deep learning models for side-channel attacks. Currently, most of the side-channel attack methods train a model for each individual byte, without considering the correlation between bytes. However, since the models' parameters for attacking different bytes may be similar, we can leverage transfer learning, meaning that we first train the model for one of the key bytes, then use the trained model as a pretrained model for the remaining bytes. This technique can be applied iteratively, a process known as iterative transfer learning. Experimental results show that when using thermal or power consumption map images as input, and multilayer perceptron or convolutional neural network as the model, our method improves average performance, especially when the amount of data is insufficient.
EdgeRAG: Online-Indexed RAG for Edge Devices
Authors: Korakit Seemakhupt, Sihang Liu, Samira Khan
Date: 2024-12-30 00:00:00
Tasks: RAG, Retrieval, Retrieval-augmented Generation

Deploying Retrieval Augmented Generation (RAG) on resource-constrained edge devices is challenging due to limited memory and processing power. In this work, we propose EdgeRAG which addresses the memory constraint by pruning embeddings within clusters and generating embeddings on-demand during retrieval. To avoid the latency of generating embeddings for large tail clusters, EdgeRAG pre-computes and stores embeddings for these clusters, while adaptively caching remaining embeddings to minimize redundant computations and further optimize latency. The result from BEIR suite shows that EdgeRAG offers significant latency reduction over the baseline IVF index, but with similar generation quality while allowing all of our evaluated datasets to fit into the memory.
Text Classification: Neural Networks VS Machine Learning Models VS Pre-trained Models
Authors: Christos Petridis
Date: 2024-12-30 00:00:00
Tasks: Machine Translation, text-classification, Text Classification, Time Series Analysis

Text classification is a very common task nowadays and there are many efficient methods and algorithms that we can employ to accomplish it. Transformers have revolutionized the field of deep learning, particularly in Natural Language Processing (NLP) and have rapidly expanded to other domains such as computer vision, time-series analysis and more. The transformer model was firstly introduced in the context of machine translation and its architecture relies on self-attention mechanisms to capture complex relationships within data sequences. It is able to handle long-range dependencies more effectively than traditional neural networks (such as Recurrent Neural Networks and Multilayer Perceptrons). In this work, we present a comparison between different techniques to perform text classification. We take into consideration seven pre-trained models, three standard neural networks and three machine learning models. For standard neural networks and machine learning models we also compare two embedding techniques: TF-IDF and GloVe, with the latter consistently outperforming the former. Finally, we demonstrate the results from our experiments where pre-trained models such as BERT and DistilBERT always perform better than standard models/algorithms.
Weber-Fechner Law in Temporal Difference learning derived from Control as Inference
Authors: Keiichiro Takahashi, Taisuke Kobayashi, Tomoya Yamanokuchi, Takamitsu Matsubara
Date: 2024-12-30 00:00:00
Tasks: Reinforcement Learning (RL)

This paper investigates a novel nonlinear update rule based on temporal difference (TD) errors in reinforcement learning (RL). The update rule in the standard RL states that the TD error is linearly proportional to the degree of updates, treating all rewards equally without no bias. On the other hand, the recent biological studies revealed that there are nonlinearities in the TD error and the degree of updates, biasing policies optimistic or pessimistic. Such biases in learning due to nonlinearities are expected to be useful and intentionally leftover features in biological learning. Therefore, this research explores a theoretical framework that can leverage the nonlinearity between the degree of the update and TD errors. To this end, we focus on a control as inference framework, since it is known as a generalized formulation encompassing various RL and optimal control methods. In particular, we investigate the uncomputable nonlinear term needed to be approximately excluded in the derivation of the standard RL from control as inference. By analyzing it, Weber-Fechner law (WFL) is found, namely, perception (a.k.a. the degree of updates) in response to stimulus change (a.k.a. TD error) is attenuated by increase in the stimulus intensity (a.k.a. the value function). To numerically reveal the utilities of WFL on RL, we then propose a practical implementation using a reward-punishment framework and modifying the definition of optimality. Analysis of this implementation reveals that two utilities can be expected i) to increase rewards to a certain level early, and ii) to sufficiently suppress punishment. We finally investigate and discuss the expected utilities through simulations and robot experiments. As a result, the proposed RL algorithm with WFL shows the expected utilities that accelerate the reward-maximizing startup and continue to suppress punishments during learning.
Efficiently Serving LLM Reasoning Programs with Certaindex
Authors: Yichao Fu, Junda Chen, Siqi Zhu, Zheyu Fu, Zhongdongming Dai, Aurick Qiao, Hao Zhang
Date: 2024-12-30 00:00:00
Tasks: Code Generation, Mathematical Problem-Solving, Scheduling

The rapid evolution of large language models (LLMs) has unlocked their capabilities in advanced reasoning tasks like mathematical problem-solving, code generation, and legal analysis. Central to this progress are inference-time reasoning algorithms, which refine outputs by exploring multiple solution paths, at the cost of increasing compute demands and response latencies. Existing serving systems fail to adapt to the scaling behaviors of these algorithms or the varying difficulty of queries, leading to inefficient resource use and unmet latency targets. We present Dynasor, a system that optimizes inference-time compute for LLM reasoning queries. Unlike traditional engines, Dynasor tracks and schedules requests within reasoning queries and uses Certaindex, a proxy that measures statistical reasoning progress based on model certainty, to guide compute allocation dynamically. Dynasor co-adapts scheduling with reasoning progress: it allocates more compute to hard queries, reduces compute for simpler ones, and terminates unpromising queries early, balancing accuracy, latency, and cost. On diverse datasets and algorithms, Dynasor reduces compute by up to 50% in batch processing and sustaining 3.3x higher query rates or 4.7x tighter latency SLOs in online serving.
Verified Lifting of Deep learning Operators
Authors: Qi Zhan, Xing Hu, Xin Xia, Shanping Li
Date: 2024-12-30 00:00:00
Tasks: Deep Learning

Deep learning operators are fundamental components of modern deep learning frameworks. With the growing demand for customized operators, it has become increasingly common for developers to create their own. However, designing and implementing operators is complex and error-prone, due to hardware-specific optimizations and the need for numerical stability. There is a pressing need for tools that can summarize the functionality of both existing and user-defined operators. To address this gap, this work introduces a novel framework for the verified lifting of deep learning operators, which synthesizes high-level mathematical formulas from low-level implementations. Our approach combines symbolic execution, syntax-guided synthesis, and SMT-based verification to produce readable and formally verified mathematical formulas. In synthesis, we employ a combination of top-down and bottom-up strategies to explore the vast search space efficiently; In verification, we design invariant synthesis patterns and leverage SMT solvers to validate the correctness of the derived summaries; In simplification, we use egraph-based techniques with custom rules to restore complex formulas to their natural, intuitive forms. Evaluated on a dataset of deep learning operators implemented in Triton from the real world, our method demonstrates the effectiveness of synthesis and verification compared to existing techniques. This framework bridges the gap between low-level implementations and high-level abstractions, improving understanding and reliability in deep learning operator development.
RobustBlack: Challenging Black-Box Adversarial Attacks on State-of-the-Art Defenses
Authors: Mohamed Djilani, Salah Ghamizi, Maxime Cordy
Date: 2024-12-30 00:00:00
Tasks: Adversarial Robustness

Although adversarial robustness has been extensively studied in white-box settings, recent advances in black-box attacks (including transfer- and query-based approaches) are primarily benchmarked against weak defenses, leaving a significant gap in the evaluation of their effectiveness against more recent and moderate robust models (e.g., those featured in the Robustbench leaderboard). In this paper, we question this lack of attention from black-box attacks to robust models. We establish a framework to evaluate the effectiveness of recent black-box attacks against both top-performing and standard defense mechanisms, on the ImageNet dataset. Our empirical evaluation reveals the following key findings: (1) the most advanced black-box attacks struggle to succeed even against simple adversarially trained models; (2) robust models that are optimized to withstand strong white-box attacks, such as AutoAttack, also exhibits enhanced resilience against black-box attacks; and (3) robustness alignment between the surrogate models and the target model plays a key factor in the success rate of transfer-based attacks
AlignAb: Pareto-Optimal Energy Alignment for Designing Nature-Like Antibodies
Authors: Yibo Wen, Chenwei Xu, Jerry Yao-Chieh Hu, Han Liu
Date: 2024-12-30 00:00:00
Tasks: Language Modeling, Language Modelling

We present a three-stage framework for training deep learning models specializing in antibody sequence-structure co-design. We first pre-train a language model using millions of antibody sequence data. Then, we employ the learned representations to guide the training of a diffusion model for joint optimization over both sequence and structure of antibodies. During the final alignment stage, we optimize the model to favor antibodies with low repulsion and high attraction to the antigen binding site, enhancing the rationality and functionality of the designs. To mitigate conflicting energy preferences, we extend AbDPO (Antibody Direct Preference Optimization) to guide the model towards Pareto optimality under multiple energy-based alignment objectives. Furthermore, we adopt an iterative learning paradigm with temperature scaling, enabling the model to benefit from diverse online datasets without requiring additional data. In practice, our proposed methods achieve high stability and efficiency in producing a better Pareto front of antibody designs compared to top samples generated by baselines and previous alignment techniques. Through extensive experiments, we showcase the superior performance of our methods in generating nature-like antibodies with high binding affinity consistently.
Generalizing in Net-Zero Microgrids: A Study with Federated PPO and TRPO
Authors: Nicolas M Cuadrado Avila, Samuel Horváth, Martin Takáč
Date: 2024-12-30 00:00:00
Tasks: energy management, Federated Learning, Management, Privacy Preserving

This work addresses the challenge of optimal energy management in microgrids through a collaborative and privacy-preserving framework. We propose the FedTRPO methodology, which integrates Federated Learning (FL) and Trust Region Policy Optimization (TRPO) to manage distributed energy resources (DERs) efficiently. Using a customized version of the CityLearn environment and synthetically generated data, we simulate designed net-zero energy scenarios for microgrids composed of multiple buildings. Our approach emphasizes reducing energy costs and carbon emissions while ensuring privacy. Experimental results demonstrate that FedTRPO is comparable with state-of-the-art federated RL methodologies without hyperparameter tunning. The proposed framework highlights the feasibility of collaborative learning for achieving optimal control policies in energy systems, advancing the goals of sustainable and efficient smart grids.