pwc-archive/papers-with-abstracts huggingface dataset:
Machine Learning-Assisted Liquid Crystal Optical Sensor Array Using Cysteine-Functionalized Silver Nanotriangles for Pathogen Detection in Food and Water
Authors: Maryam Mousavizadegan, Morteza Hosseini, Mohammad Mohammadimasoudi, Yiran Guan and Guobao Xu
Date: 2024-12-12 00:00:00
Proceeding: ACS Applied Materials & Interfaces 2024 12

The challenge of rapid identification of bacteria in food and water still persists as a major health problem. To tackle this matter, we have developed a single-probe liquid crystal (LC)-based optical sensing platform for the differentiation of five common bacterial strains, including Bacillus cereus, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, and S. typhimurium, using cysteine-functionalized silver nanotriangles as signal enhancers. Unique optical patterns were generated from the interaction of the samples with the LC interface and captured by using a camera under polarized light. Pattern recognition was carried out based on image analysis and machine learning (ML) calculations. Among the various ML algorithms trained, Support Vector Machines had the best performance and were able to successfully discern the bacteria with 98.89% accuracy. A linear range of 10–106 CFU mL–1 and detection limits of under 10 CFU mL–1 were attained for all of the strains. The proposed method was tested with water, juice, and milk samples, and prediction accuracies of 95.83, 97.92, and 89.58%, respectively, were obtained. The proposed method offers a simple, cost-efficient solution for bacteria recognition.
CLIP-Based Modality Compensation for Visible-Infrared Image Re-Identification
Authors: Gang Hu; Yafei Lv; Jianting Zhang; Qian Wu; Zaidao Wen
Date: 2024-12-25 00:00:00
Proceeding: journal 2024 12
Tasks: Cross-Modal  Person Re-Identification, Cross-Modal Person Re-Identification, Person Re-Identification, Prompt Learning

Visible-infrared image re-identification (VIReID) aims to match objects with the same identity appearing across different modalities. Given the significant differences between visible and infrared images, VIReID poses a formidable challenge. Most existing methods focus on extracting modality-shared features while ignore modality-specific features, which often also contain crucial important discriminative information. In addition, high-level semantic information of the objects, such as shape and appearance, is also crucial for the VIReID task. To further enhance the retrieval performance, we propose a novel one-stage CLIP-based Modality Compensation (CLIP-MC) method for the VIReID task. Our method introduces a new prompt learning paradigm that leverages the semantic understanding capabilities of CLIP to recover missing modality information. CLIP-MC comprises three key modules: Instance Text Prompt Generation (ITPG), Modality Compensation (MC), and Modality Context Learner (MCL). Specifically, the ITPG module facilitates effective alignment and interaction between image tokens and text tokens, enhancing the text encoder's ability to capture detailed visual information from the images. This ensures that the text encoder generates fine-grained descriptions of the images. The MCL module captures the unique information of each modality and generates modality-specific context tokens, which are more flexible compared to fixed text descriptions. Guided by the modality-specific context, the text encoder discovers missing modality information from the images and produces compensated modality features. Finally, the MC module combines the original and compensated modality features to obtain complete modality features that contain more discriminative information. We conduct extensive experiments on three VIReID datasets and compare the performance of our method with other existing approaches to demonstrate its effectiveness and superiority.
Enhancing stroke disease classification through machine learning models by feature selection techniques
Authors: Mahade Hasan, Farhana Yasmin, Xue Yu
Date: 2025-04-01 00:00:00
Tasks: feature selection

Heart disease remains a leading cause of mortality and morbidity worldwide, necessitating the development of accurate and reliable predictive models to facilitate early detection and intervention. While state of the art work has focused on various machine learning approaches for predicting heart disease, but they could not able to achieve remarkable accuracy. In response to this need, we applied nine machine learning algorithms XGBoost, logistic regression, decision tree, random forest, k-nearest neighbors (KNN), support vector machine (SVM), gaussian na\"ive bayes (NB gaussian), adaptive boosting, and linear regression to predict heart disease based on a range of physiological indicators. Our approach involved feature selection techniques to identify the most relevant predictors, aimed at refining the models to enhance both performance and interpretability. The models were trained, incorporating processes such as grid search hyperparameter tuning, and cross-validation to minimize overfitting. Additionally, we have developed a novel voting system with feature selection techniques to advance heart disease classification. Furthermore, we have evaluated the models using key performance metrics including accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (ROC AUC). Among the models, XGBoost demonstrated exceptional performance, achieving 99% accuracy, precision, F1-Score, 98% recall, and 100% ROC AUC. This study offers a promising approach to early heart disease diagnosis and preventive healthcare.
Foreground Focus: Enhancing Coherence and Fidelity in Camouflaged Image Generation
Authors: Pei-Chi Chen, Yi Yao, Chan-Feng Hsu, HongXia Xie, Hung-Jen Chen, Hong-Han Shuai, Wen-Huang Cheng
Date: 2025-04-02 00:00:00
Tasks: Denoising, Image Generation

Camouflaged image generation is emerging as a solution to data scarcity in camouflaged vision perception, offering a cost-effective alternative to data collection and labeling. Recently, the state-of-the-art approach successfully generates camouflaged images using only foreground objects. However, it faces two critical weaknesses: 1) the background knowledge does not integrate effectively with foreground features, resulting in a lack of foreground-background coherence (e.g., color discrepancy); 2) the generation process does not prioritize the fidelity of foreground objects, which leads to distortion, particularly for small objects. To address these issues, we propose a Foreground-Aware Camouflaged Image Generation (FACIG) model. Specifically, we introduce a Foreground-Aware Feature Integration Module (FAFIM) to strengthen the integration between foreground features and background knowledge. In addition, a Foreground-Aware Denoising Loss is designed to enhance foreground reconstruction supervision. Experiments on various datasets show our method outperforms previous methods in overall camouflaged image quality and foreground fidelity.
Deep Reinforcement Learning-based Exploration of Web Applications
Authors: Mohammadreza Abbasnezhad, Amir Jahangard Rafsanjani, Amin Milani Fard
Date: 2023-05-15 00:00:00
Proceeding: International Journal of Information & Communication Technology Research 2023 5
Tasks: Deep Reinforcement Learning, Diversity, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Web application (app) exploration is a crucial part of various analysis and testing techniques. However, the 
current methods are not able to properly explore the state space of web apps. As a result, techniques must be developed 
to guide the exploration in order to get acceptable functionality coverage for web apps. Reinforcement Learning (RL) 
is a machine learning method in which the best way to do a task is learned through trial and error, with the help of 
positive or negative rewards, instead of direct supervision. Deep RL is a recent expansion of RL that makes use of neural 
networks’ learning capabilities. This feature makes Deep RL suitable for exploring the complex state space of web apps. 
However, current methods provide fundamental RL. In this research, we offer DeepEx, a Deep RL-based exploration 
strategy for systematically exploring web apps. Empirically evaluated on seven open-source web apps, DeepEx 
demonstrated a 17% improvement in code coverage and a 16% enhancement in navigational diversity over the state
of-the-art RL-based method. Additionally, it showed a 19% increase in structural diversity. These results confirm the 
superiority of Deep RL over traditional RL methods in web app exploration.
PIM-LLM: A High-Throughput Hybrid PIM Architecture for 1-bit LLMs
Authors: Jinendra Malekar, Peyton Chandarana, Md Hasibul Amin, Mohammed E. Elbtity, Ramtin Zand
Date: 2025-03-31 00:00:00

In this paper, we propose PIM-LLM, a hybrid architecture developed to accelerate 1-bit large language models (LLMs). PIM-LLM leverages analog processing-in-memory (PIM) architectures and digital systolic arrays to accelerate low-precision matrix multiplication (MatMul) operations in projection layers and high-precision MatMul operations in attention heads of 1-bit LLMs, respectively. Our design achieves up to roughly 80x improvement in tokens per second and a 70% increase in tokens per joule compared to conventional hardware accelerators. Additionally, PIM-LLM outperforms previous PIM-based LLM accelerators, setting a new benchmark with at least 2x and 5x improvement in GOPS and GOPS/W, respectively.
TuRTLe: A Unified Evaluation of LLMs for RTL Generation
Authors: Dario Garcia-Gasulla, Gokcen Kestor, Emanuele Parisi, Miquel Albertí-Binimelis, Cristian Gutierrez, Razine Moundir Ghorab, Orlando Montenegro, Bernat Homs, Miquel Moreto
Date: 2025-03-31 00:00:00
Tasks: Code Generation

The rapid advancements in LLMs have driven the adoption of generative AI in various domains, including Electronic Design Automation (EDA). Unlike traditional software development, EDA presents unique challenges, as generated RTL code must not only be syntactically correct and functionally accurate but also synthesizable by hardware generators while meeting performance, power, and area constraints. These additional requirements introduce complexities that existing code-generation benchmarks often fail to capture, limiting their effectiveness in evaluating LLMs for RTL generation. To address this gap, we propose TuRTLe, a unified evaluation framework designed to systematically assess LLMs across key RTL generation tasks. TuRTLe integrates multiple existing benchmarks and automates the evaluation process, enabling a comprehensive assessment of LLM performance in syntax correctness, functional correctness, synthesis, PPA optimization, and exact line completion. Using this framework, we benchmark a diverse set of open LLMs and analyze their strengths and weaknesses in EDA-specific tasks. Our results show that reasoning-based models, such as DeepSeek R1, consistently outperform others across multiple evaluation criteria, but at the cost of increased computational overhead and inference latency. Additionally, base models are better suited in module completion tasks, while instruct-tuned models perform better in specification-to-RTL tasks.
NLS: Natural-Level Synthesis for Hardware Implementation Through GenAI
Authors: Kaiyuan Yang, Huang Ouyang, Xinyi Wang, Bingjie Lu, Yanbo Wang, Charith Abhayaratne, Sizhao Li, Long Jin, Tiantai Deng
Date: 2025-03-28 00:00:00

This paper introduces Natural-Level Synthesis, an innovative approach for generating hardware using generative artificial intelligence on both the system level and component-level. NLS bridges a gap in current hardware development processes, where algorithm and application engineers' involvement typically ends at the requirements stage. With NLS, engineers can participate more deeply in the development, synthesis, and test stages by using Gen-AI models to convert natural language descriptions directly into Hardware Description Language code. This approach not only streamlines hardware development but also improves accessibility, fostering a collaborative workflow between hardware and algorithm engineers. We developed the NLS tool to facilitate natural language-driven HDL synthesis, enabling rapid generation of system-level HDL designs while significantly reducing development complexity. Evaluated through case studies and benchmarks using Performance, Power, and Area metrics, NLS shows its potential to enhance resource efficiency in hardware development. This work provides a extensible, efficient solution for hardware synthesis and establishes a Visual Studio Code Extension to assess Gen-AI-driven HDL generation and system integration, laying a foundation for future AI-enhanced and AI-in-the-loop Electronic Design Automation tools.
Atrial constitutive neural networks
Authors: Mathias Peirlinck, Kevin Linka, Ellen Kuhl
Date: 2025-04-03 00:00:00

This work presents a novel approach for characterizing the mechanical behavior of atrial tissue using constitutive neural networks. Based on experimental biaxial tensile test data of healthy human atria, we automatically discover the most appropriate constitutive material model, thereby overcoming the limitations of traditional, pre-defined models. This approach offers a new perspective on modeling atrial mechanics and is a significant step towards improved simulation and prediction of cardiac health.
Grammar-based Ordinary Differential Equation Discovery
Authors: Karin L. Yu, Eleni Chatzi, Georgios Kissas
Date: 2025-04-03 00:00:00
Tasks: Dimensionality Reduction, Equation Discovery, Symbolic Regression

The understanding and modeling of complex physical phenomena through dynamical systems has historically driven scientific progress, as it provides the tools for predicting the behavior of different systems under diverse conditions through time. The discovery of dynamical systems has been indispensable in engineering, as it allows for the analysis and prediction of complex behaviors for computational modeling, diagnostics, prognostics, and control of engineered systems. Joining recent efforts that harness the power of symbolic regression in this domain, we propose a novel framework for the end-to-end discovery of ordinary differential equations (ODEs), termed Grammar-based ODE Discovery Engine (GODE). The proposed methodology combines formal grammars with dimensionality reduction and stochastic search for efficiently navigating high-dimensional combinatorial spaces. Grammars allow us to seed domain knowledge and structure for both constraining, as well as, exploring the space of candidate expressions. GODE proves to be more sample- and parameter-efficient than state-of-the-art transformer-based models and to discover more accurate and parsimonious ODE expressions than both genetic programming- and other grammar-based methods for more complex inference tasks, such as the discovery of structural dynamics. Thus, we introduce a tool that could play a catalytic role in dynamics discovery tasks, including modeling, system identification, and monitoring tasks.
A User-Tunable Machine Learning Framework for Step-Wise Synthesis Planning
Authors: Shivesh Prakash, Hans-Arno Jacobsen, Viki Kumar Prasad
Date: 2025-04-03 00:00:00

We introduce MHNpath, a machine learning-driven retrosynthetic tool designed for computer-aided synthesis planning. Leveraging modern Hopfield networks and novel comparative metrics, MHNpath efficiently prioritizes reaction templates, improving the scalability and accuracy of retrosynthetic predictions. The tool incorporates a tunable scoring system that allows users to prioritize pathways based on cost, reaction temperature, and toxicity, thereby facilitating the design of greener and cost-effective reaction routes. We demonstrate its effectiveness through case studies involving complex molecules from ChemByDesign, showcasing its ability to predict novel synthetic and enzymatic pathways. Furthermore, we benchmark MHNpath against existing frameworks, replicating experimentally validated "gold-standard" pathways from PaRoutes. Our case studies reveal that the tool can generate shorter, cheaper, moderate-temperature routes employing green solvents, as exemplified by compounds such as dronabinol, arformoterol, and lupinine.
Exploring the Societal and Economic Impacts of Artificial Intelligence: A Scenario Generation Methodology
Authors: Carlos J. Costa, Joao Tiago Aparicio
Date: 2025-03-31 00:00:00

This paper explores artificial intelligence's potential societal and economic impacts (AI) through generating scenarios that assess how AI may influence various sectors. We categorize and analyze key factors affecting AI's integration and adoption by applying an Impact-Uncertainty Matrix. A proposed methodology involves querying academic databases, identifying emerging trends and topics, and categorizing these into an impact uncertainty framework. The paper identifies critical areas where AI may bring significant change and outlines potential future scenarios based on these insights. This research aims to inform policymakers, industry leaders, and researchers on the strategic planning required to address the challenges and opportunities AI presents
Determining Sphere Radius through Pairwise Distances
Authors: Boris Sukhovilov
Date: 2025-04-03 00:00:00

We propose a novel method for determining the radius of a spherical surface based on the distances measured between points on this surface. We consider the most general case of determining the radius when the distances are measured with errors and the sphere has random deviations from its ideal shape. For the solution, we used the minimally necessary four points and an arbitrary N number of points. We provide a new closed form solution for the radius of the sphere through the matrix of pairwise distances. We also determine the standard deviation of the radius estimate caused by measurement errors and deviations of the sphere from its ideal shape. We found optimal configurations of points on the sphere that provide the minimum standard deviation of the radius estimate. This paper describes our solution and provides all the mathematical derivations. We share the implementation of our method as open source code at https://github.com/boris-sukhovilov/Sphere_Radius.
Like Oil and Water: Group Robustness Methods and Poisoning Defenses May Be at Odds
Authors: Michael-Andrei Panaitescu-Liess, Yigitcan Kaya, Sicheng Zhu, Furong Huang, Tudor Dumitras
Date: 2025-04-02 00:00:00
Tasks: Federated Learning

Group robustness has become a major concern in machine learning (ML) as conventional training paradigms were found to produce high error on minority groups. Without explicit group annotations, proposed solutions rely on heuristics that aim to identify and then amplify the minority samples during training. In our work, we first uncover a critical shortcoming of these methods: an inability to distinguish legitimate minority samples from poison samples in the training set. By amplifying poison samples as well, group robustness methods inadvertently boost the success rate of an adversary -- e.g., from $0\%$ without amplification to over $97\%$ with it. Notably, we supplement our empirical evidence with an impossibility result proving this inability of a standard heuristic under some assumptions. Moreover, scrutinizing recent poisoning defenses both in centralized and federated learning, we observe that they rely on similar heuristics to identify which samples should be eliminated as poisons. In consequence, minority samples are eliminated along with poisons, which damages group robustness -- e.g., from $55\%$ without the removal of the minority samples to $41\%$ with it. Finally, as they pursue opposing goals using similar heuristics, our attempt to alleviate the trade-off by combining group robustness methods and poisoning defenses falls short. By exposing this tension, we also hope to highlight how benchmark-driven ML scholarship can obscure the trade-offs among different metrics with potentially detrimental consequences.
One Pic is All it Takes: Poisoning Visual Document Retrieval Augmented Generation with a Single Image
Authors: Ezzeldin Shereen, Dan Ristea, Shae McFadden, Burak Hasircioglu, Vasilios Mavroudis, Chris Hicks
Date: 2025-04-02 00:00:00
Tasks: All, Misinformation, RAG, Retrieval, Retrieval-augmented Generation

Multi-modal retrieval augmented generation (M-RAG) is instrumental for inhibiting hallucinations in large multi-modal models (LMMs) through the use of a factual knowledge base (KB). However, M-RAG introduces new attack vectors for adversaries that aim to disrupt the system by injecting malicious entries into the KB. In this paper, we present the first poisoning attack against M-RAG targeting visual document retrieval applications where the KB contains images of document pages. We propose two attacks, each of which require injecting only a single adversarial image into the KB. Firstly, we propose a universal attack that, for any potential user query, influences the response to cause a denial-of-service (DoS) in the M-RAG system. Secondly, we present a targeted attack against one or a group of user queries, with the goal of spreading targeted misinformation. For both attacks, we use a multi-objective gradient-based adversarial approach to craft the injected image while optimizing for both retrieval and generation. We evaluate our attacks against several visual document retrieval datasets, a diverse set of state-of-the-art retrievers (embedding models) and generators (LMMs), demonstrating the attack effectiveness in both the universal and targeted settings. We additionally present results including commonly used defenses, various attack hyper-parameter settings, ablations, and attack transferability.
On Model Protection in Federated Learning against Eavesdropping Attacks
Authors: Dipankar Maity, Kushal Chakrabarti
Date: 2025-04-02 00:00:00
Tasks: Federated Learning

In this study, we investigate the protection offered by federated learning algorithms against eavesdropping adversaries. In our model, the adversary is capable of intercepting model updates transmitted from clients to the server, enabling it to create its own estimate of the model. Unlike previous research, which predominantly focuses on safeguarding client data, our work shifts attention protecting the client model itself. Through a theoretical analysis, we examine how various factors, such as the probability of client selection, the structure of local objective functions, global aggregation at the server, and the eavesdropper's capabilities, impact the overall level of protection. We further validate our findings through numerical experiments, assessing the protection by evaluating the model accuracy achieved by the adversary. Finally, we compare our results with methods based on differential privacy, underscoring their limitations in this specific context.