pwc-archive/papers-with-abstracts huggingface dataset:
Contrastive and Transfer Learning for Effective Audio Fingerprinting through a Real-World Evaluation Protocol
Authors: Christos Nikou, Theodoros Giannakopoulos
Date: 2025-07-08 00:00:00
Tasks: Transfer Learning

Recent advances in song identification leverage deep neural networks to learn compact audio fingerprints directly from raw waveforms. While these methods perform well under controlled conditions, their accuracy drops significantly in real-world scenarios where the audio is captured via mobile devices in noisy environments. In this paper, we introduce a novel evaluation protocol designed to better reflect such real-world conditions. We generate three recordings of the same audio, each with increasing levels of noise, captured using a mobile device's microphone. Our results reveal a substantial performance drop for two state-of-the-art CNN-based models under this protocol, compared to previously reported benchmarks. Additionally, we highlight the critical role of the augmentation pipeline during training with contrastive loss. By introduction low pass and high pass filters in the augmentation pipeline we significantly increase the performance of both systems in our proposed evaluation. Furthermore, we develop a transformer-based model with a tailored projection module and demonstrate that transferring knowledge from a semantically relevant domain yields a more robust solution. The transformer architecture outperforms CNN-based models across all noise levels, and query durations. In low noise conditions it achieves 47.99% for 1-sec queries, and 97% for 10-sec queries in finding the correct song, surpassing by 14%, and by 18.5% the second-best performing model, respectively, Under heavy noise levels, we achieve a detection rate 56.5% for 15-second query duration. All experiments are conducted on public large-scale dataset of over 100K songs, with queries matched against a database of 56 million vectors.
CORE: Benchmarking LLMs Code Reasoning Capabilities through Static Analysis Tasks
Authors: Danning Xie, Mingwei Zheng, Xuwei Liu, Jiannan Wang, Chengpeng Wang, Lin Tan, Xiangyu Zhang
Date: 2025-07-03 00:00:00
Tasks: Benchmarking, Code Generation, Program Repair, Vulnerability Detection

Large language models (LLMs) have been widely adopted across diverse software engineering domains, such as code generation, program repair, and vulnerability detection. These applications require understanding beyond surface-level code patterns: value propagation, control flow, and interdependence between program elements. However, existing benchmarks primarily evaluate end-to-end outcomes, such as whether code is correctly repaired or generated, leaving the models ability for program semantic reasoning underexplored. This work presents CoRe, a high-quality, human-verified benchmark designed to evaluate LLMs on fundamental static analysis tasks. CoRe includes 12,553 task instances spanning data dependency, control dependency, and information flow across programs written in C/C++, Java, and Python. To ensure semantic diversity and reasoning complexity, we propose a semantics-aware diverse sampling strategy that selects targets and task instances based on structural coverage and dependency depth. We evaluate 10 mainstream LLMs and show that, while they perform well at identifying dependencies, models still struggle with tasks that require deeper semantic understanding and multi-step reasoning. We further conduct qualitative analyses to uncover key challenges, such as complex control structures and backward dependency patterns, offering insights into improving LLMs code reasoning capabilities.
EduCoder: An Open-Source Annotation System for Education Transcript Data
Authors: Guanzhong Pan, Mei Tan, HyunJi Nam, Lucía Langlois, James Malamut, Liliana Deonizio, Dorottya Demszky
Date: 2025-07-07 00:00:00
Tasks: text annotation

We introduce EduCoder, a domain-specialized tool designed to support utterance-level annotation of educational dialogue. While general-purpose text annotation tools for NLP and qualitative research abound, few address the complexities of coding education dialogue transcripts -- with diverse teacher-student and peer interactions. Common challenges include defining codebooks for complex pedagogical features, supporting both open-ended and categorical coding, and contextualizing utterances with external features, such as the lesson's purpose and the pedagogical value of the instruction. EduCoder is designed to address these challenges by providing a platform for researchers and domain experts to collaboratively define complex codebooks based on observed data. It incorporates both categorical and open-ended annotation types along with contextual materials. Additionally, it offers a side-by-side comparison of multiple annotators' responses, allowing comparison and calibration of annotations with others to improve data reliability. The system is open-source, with a demo video available.
AXLearn: Modular Large Model Training on Heterogeneous Infrastructure
Authors: Mark Lee, Tom Gunter, Chang Lan, John Peebles, Hanzhi Zhou, Kelvin Zou, Sneha Bangalore, Chung-Cheng Chiu, Nan Du, Xianzhi Du, Philipp Dufter, Ruixuan Hou, Haoshuo Huang, Dongseong Hwang, Xiang Kong, Jinhao Lei, Tao Lei, Meng Li, Li Li, Jiarui Lu, Zhiyun Lu, Yiping Ma, David Qiu, Vivek Rathod, Senyu Tong, Zhucheng Tu, Jianyu Wang, Yongqiang Wang, ZiRui Wang, Floris Weers, Sam Wiseman, Guoli Yin, BoWen Zhang, Xiyou Zhou, Danyang Zhuo, Cheng Leong, Ruoming Pang
Date: 2025-07-07 00:00:00
Tasks: Deep Learning

We design and implement AXLearn, a production deep learning system that facilitates scalable and high-performance training of large deep learning models. Compared to other state-of-the-art deep learning systems, AXLearn has a unique focus on modularity and support for heterogeneous hardware infrastructure. AXLearn's internal interfaces between software components follow strict encapsulation, allowing different components to be assembled to facilitate rapid model development and experimentation on heterogeneous compute infrastructure. We introduce a novel method of quantifying modularity via Lines-of-Code (LoC)-complexity, which demonstrates how our system maintains constant complexity as we scale the components in the system, compared to linear or quadratic complexity in other systems. This allows integrating features such as Rotary Position Embeddings (RoPE) into AXLearn across hundred of modules with just 10 lines of code, compared to hundreds as required in other systems. At the same time, AXLearn maintains equivalent performance compared to state-of-the-art training systems. Finally, we share our experience in the development and operation of AXLearn.
Unpatchable Vulnerabilities in Windows 10/11: Security Report 2025
Authors: Vi Nhat Son
Date: 2025-07-10 00:00:00
Proceeding: Independent publication 2025 7
Tasks: Management

This comprehensive security report investigates unpatchable vulnerabilities in Windows 10 and
11, focusing on systemic flaws that resist traditional patching due to their deep integration into
the operating system’s architecture, hardware dependencies, and legacy compatibility requirements. These vulnerabilities, rooted in fundamental design choices and ecosystem constraints,
pose significant challenges to securing millions of Windows devices worldwide. The report examines three critical vulnerabilities: legacy BIOS/UEFI firmware weaknesses, kernel memory
management flaws, and backward compatibility with legacy protocols. It provides a detailed
technical analysis, exploitation vectors, detection challenges, and comprehensive mitigation
strategies. With Windows 10 approaching its end-of-support deadline in October 2025, these
flaws pose heightened risks, necessitating proactive defenses. This report adheres to responsible disclosure principles and aims to support Microsoft’s efforts to strengthen Windows security
in 2025.
Automated Neuron Labelling Enables Generative Steering and Interpretability in Protein Language Models
Authors: Arjun Banerjee, David Martinez, Camille Dang, Ethan Tam
Date: 2025-07-08 00:00:00

Protein language models (PLMs) encode rich biological information, yet their internal neuron representations are poorly understood. We introduce the first automated framework for labeling every neuron in a PLM with biologically grounded natural language descriptions. Unlike prior approaches relying on sparse autoencoders or manual annotation, our method scales to hundreds of thousands of neurons, revealing individual neurons are selectively sensitive to diverse biochemical and structural properties. We then develop a novel neuron activation-guided steering method to generate proteins with desired traits, enabling convergence to target biochemical properties like molecular weight and instability index as well as secondary and tertiary structural motifs, including alpha helices and canonical Zinc Fingers. We finally show that analysis of labeled neurons in different model sizes reveals PLM scaling laws and a structured neuron space distribution.
MS-DPPs: Multi-Source Determinantal Point Processes for Contextual Diversity Refinement of Composite Attributes in Text to Image Retrieval
Authors: Naoya Sogi, Takashi Shibata, Makoto Terao, Masanori Suganuma, Takayuki Okatani
Date: 2025-07-09 00:00:00
Tasks: Diversity, Image Retrieval, Point Processes

Result diversification (RD) is a crucial technique in Text-to-Image Retrieval for enhancing the efficiency of a practical application. Conventional methods focus solely on increasing the diversity metric of image appearances. However, the diversity metric and its desired value vary depending on the application, which limits the applications of RD. This paper proposes a novel task called CDR-CA (Contextual Diversity Refinement of Composite Attributes). CDR-CA aims to refine the diversities of multiple attributes, according to the application's context. To address this task, we propose Multi-Source DPPs, a simple yet strong baseline that extends the Determinantal Point Process (DPP) to multi-sources. We model MS-DPP as a single DPP model with a unified similarity matrix based on a manifold representation. We also introduce Tangent Normalization to reflect contexts. Extensive experiments demonstrate the effectiveness of the proposed method. Our code is publicly available at https://github.com/NEC-N-SOGI/msdpp.
MoFE-Time: Mixture of Frequency Domain Experts for Time-Series Forecasting Models
Authors: YiWen Liu, Chenyu Zhang, Junjie Song, Siqi Chen, Sun Yin, Zihan Wang, Lingming Zeng, Yuji Cao, Junming Jiao
Date: 2025-07-09 00:00:00
Tasks: Mixture-of-Experts, Time Series, Time Series Forecasting

As a prominent data modality task, time series forecasting plays a pivotal role in diverse applications. With the remarkable advancements in Large Language Models (LLMs), the adoption of LLMs as the foundational architecture for time series modeling has gained significant attention. Although existing models achieve some success, they rarely both model time and frequency characteristics in a pretraining-finetuning paradigm leading to suboptimal performance in predictions of complex time series, which requires both modeling periodicity and prior pattern knowledge of signals. We propose MoFE-Time, an innovative time series forecasting model that integrates time and frequency domain features within a Mixture of Experts (MoE) network. Moreover, we use the pretraining-finetuning paradigm as our training framework to effectively transfer prior pattern knowledge across pretraining and finetuning datasets with different periodicity distributions. Our method introduces both frequency and time cells as experts after attention modules and leverages the MoE routing mechanism to construct multidimensional sparse representations of input signals. In experiments on six public benchmarks, MoFE-Time has achieved new state-of-the-art performance, reducing MSE and MAE by 6.95% and 6.02% compared to the representative methods Time-MoE. Beyond the existing evaluation benchmarks, we have developed a proprietary dataset, NEV-sales, derived from real-world business scenarios. Our method achieves outstanding results on this dataset, underscoring the effectiveness of the MoFE-Time model in practical commercial applications.
Speak2Sign3D: A Multi-modal Pipeline for English Speech to American Sign Language Animation
Authors: Kazi Mahathir Rahman, Naveed Imtiaz Nafis, Md. Farhan Sadik, Mohammad Al Rafi, Mehedi Hasan Shahed
Date: 2025-07-09 00:00:00
Tasks: Machine Translation, Sign Language Translation, Translation, Word Embeddings

Helping deaf and hard-of-hearing people communicate more easily is the main goal of Automatic Sign Language Translation. Although most past research has focused on turning sign language into text, doing the reverse, turning spoken English into sign language animations, has been largely overlooked. That's because it involves multiple steps, such as understanding speech, translating it into sign-friendly grammar, and generating natural human motion. In this work, we introduce a complete pipeline that converts English speech into smooth, realistic 3D sign language animations. Our system starts with Whisper to translate spoken English into text. Then, we use a MarianMT machine translation model to translate that text into American Sign Language (ASL) gloss, a simplified version of sign language that captures meaning without grammar. This model performs well, reaching BLEU scores of 0.7714 and 0.8923. To make the gloss translation more accurate, we also use word embeddings such as Word2Vec and FastText to understand word meanings. Finally, we animate the translated gloss using a 3D keypoint-based motion system trained on Sign3D-WLASL, a dataset we created by extracting body, hand, and face key points from real ASL videos in the WLASL dataset. To support the gloss translation stage, we also built a new dataset called BookGlossCorpus-CG, which turns everyday English sentences from the BookCorpus dataset into ASL gloss using grammar rules. Our system stitches everything together by smoothly interpolating between signs to create natural, continuous animations. Unlike previous works like How2Sign and Phoenix-2014T that focus on recognition or use only one type of data, our pipeline brings together audio, text, and motion in a single framework that goes all the way from spoken English to lifelike 3D sign language animation.
PrefixAgent: An LLM-Powered Design Framework for Efficient Prefix Adder Optimization
Authors: Dongsheng Zuo, Jiadong Zhu, Yang Luo, Yuzhe ma
Date: 2025-07-08 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Prefix adders are fundamental arithmetic circuits, but their design space grows exponentially with bit-width, posing significant optimization challenges. Previous works face limitations in performance, generalization, and scalability. To address these challenges, we propose PrefixAgent, a large language model (LLM)-powered framework that enables efficient prefix adder optimization. Specifically, PrefixAgent reformulates the problem into subtasks including backbone synthesis and structure refinement, which effectively reduces the search space. More importantly, this new design perspective enables us to efficiently collect enormous high-quality data and reasoning traces with E-graph, which further results in an effective fine-tuning of LLM. Experimental results show that PrefixAgent synthesizes prefix adders with consistently smaller areas compared to baseline methods, while maintaining scalability and generalization in commercial EDA flows.
Evolutionary and Coevolutionary Multi-Agent Design Choices and Dynamics
Authors: Erik Hemberg, Eric Liu, Lucille Fuller, Stephen Moskal, Una-May O'Reilly
Date: 2025-07-07 00:00:00
Tasks: Evolutionary Algorithms

We investigate two representation alternatives for the controllers of teams of cyber agents. We combine these controller representations with different evolutionary algorithms, one of which introduces a novel LLM-supported mutation operator. Using a cyber security scenario, we evaluate agent learning when one side is trained to compete against a side that does not evolve and when two sides coevolve with each other. This allows us to quantify the relative merits and tradeoffs of representation and algorithm combinations in terms of team performance. Our versions of grammatical evolution algorithms using grammars that allow a controller to be expressed in code-like logic can achieve the best team performance. The scenario also allows us to compare the performance impact and dynamics of coevolution versus evolution under different combinations. Across the algorithms and representations, we observe that coevolution reduces the performance highs and lows of both sides while it induces fluctuations on both sides. In contrast, when only one-side is optimized, performance peaks are higher and is more sustained than when both sides are optimized with coevolution.
Generalized Adaptive Transfer Network: Enhancing Transfer Learning in Reinforcement Learning Across Domains
Authors: Abhishek Verma, Nallarasan V, Balaraman Ravindran
Date: 2025-07-02 00:00:00
Tasks: Atari Games, Chatbot, Computational Efficiency, Deep Learning, Deep Reinforcement Learning, Domain Generalization, MuJoCo, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

Transfer learning in Reinforcement Learning (RL) enables agents to leverage knowledge from source tasks to accelerate learning in target tasks. While prior work, such as the Attend, Adapt, and Transfer (A2T) framework, addresses negative transfer and selective transfer, other critical challenges remain underexplored. This paper introduces the Generalized Adaptive Transfer Network (GATN), a deep RL architecture designed to tackle task generalization across domains, robustness to environmental changes, and computational efficiency in transfer. GATN employs a domain-agnostic representation module, a robustness-aware policy adapter, and an efficient transfer scheduler to achieve these goals. We evaluate GATN on diverse benchmarks, including Atari 2600, MuJoCo, and a custom chatbot dialogue environment, demonstrating superior performance in cross-domain generalization, resilience to dynamic environments, and reduced computational overhead compared to baselines. Our findings suggest GATN is a versatile framework for real-world RL applications, such as adaptive chatbots and robotic control.
GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning
Authors: S M Taslim Uddin Raju, Md. Milon Islam, Md Rezwanul Haque, Hamdi Altaheri, Fakhri Karray
Date: 2025-07-09 00:00:00
Tasks: Caption Generation, Clustering, Graph Neural Network, image-classification, Image Classification, Large Language Model, Multiple Instance Learning

Microscopic assessment of histopathology images is vital for accurate cancer diagnosis and treatment. Whole Slide Image (WSI) classification and captioning have become crucial tasks in computer-aided pathology. However, microscopic WSI face challenges such as redundant patches and unknown patch positions due to subjective pathologist captures. Moreover, generating automatic pathology captions remains a significant challenge. To address these issues, we introduce a novel GNN-ViTCap framework for classification and caption generation from histopathological microscopic images. First, a visual feature extractor generates patch embeddings. Redundant patches are then removed by dynamically clustering these embeddings using deep embedded clustering and selecting representative patches via a scalar dot attention mechanism. We build a graph by connecting each node to its nearest neighbors in the similarity matrix and apply a graph neural network to capture both local and global context. The aggregated image embeddings are projected into the language model's input space through a linear layer and combined with caption tokens to fine-tune a large language model. We validate our method on the BreakHis and PatchGastric datasets. GNN-ViTCap achieves an F1 score of 0.934 and an AUC of 0.963 for classification, along with a BLEU-4 score of 0.811 and a METEOR score of 0.569 for captioning. Experimental results demonstrate that GNN-ViTCap outperforms state of the art approaches, offering a reliable and efficient solution for microscopy based patient diagnosis.
eegFloss: A Python package for refining sleep EEG recordings using machine learning models
Authors: Niloy Sikder, Paul Zerr, Mahdad Jafarzadeh Esfahani, Martin Dresler, Matthias Krauledat
Date: 2025-07-08 00:00:00
Tasks: EEG, Sleep Staging

Electroencephalography (EEG) allows monitoring of brain activity, providing insights into the functional dynamics of various brain regions and their roles in cognitive processes. EEG is a cornerstone in sleep research, serving as the primary modality of polysomnography, the gold standard in the field. However, EEG signals are prone to artifacts caused by both internal (device-specific) factors and external (environmental) interferences. As sleep studies are becoming larger, most rely on automatic sleep staging, a process highly susceptible to artifacts, leading to erroneous sleep scores. This paper addresses this challenge by introducing eegFloss, an open-source Python package to utilize eegUsability, a novel machine learning (ML) model designed to detect segments with artifacts in sleep EEG recordings. eegUsability has been trained and evaluated on manually artifact-labeled EEG data collected from 15 participants over 127 nights using the Zmax headband. It demonstrates solid overall classification performance (F1-score is approximately 0.85, Cohens kappa is 0.78), achieving a high recall rate of approximately 94% in identifying channel-wise usable EEG data, and extends beyond Zmax. Additionally, eegFloss offers features such as automatic time-in-bed detection using another ML model named eegMobility, filtering out certain artifacts, and generating hypnograms and sleep statistics. By addressing a fundamental challenge faced by most sleep studies, eegFloss can enhance the precision and rigor of their analysis as well as the accuracy and reliability of their outcomes.