pwc-archive/papers-with-abstracts huggingface dataset:
Southern Newswire Corpus: A Large-Scale Dataset of Mid-Century Wire Articles Beyond the Front Page
Authors: Michael McRae
Date: 2025-02-17 00:00:00
Tasks: Articles, Optical Character Recognition (OCR)

I introduce a new large-scale dataset of historical wire articles from U.S. Southern newspapers, spanning 1960-1975 and covering multiple wire services: The Associated Press, United Press International, Newspaper Enterprise Association. Unlike prior work focusing on front-page content, this dataset captures articles across the entire newspaper, offering broader insight into mid-century Southern coverage. The dataset includes a version that has undergone an LLM-based text cleanup pipeline to reduce OCR noise, enhancing its suitability for quantitative text analysis. Additionally, duplicate versions of articles are retained to enable analysis of editorial differences in language and framing across newspapers. Each article is tagged by wire service, facilitating comparative studies of editorial patterns across agencies. This resource opens new avenues for research in computational social science, digital humanities, and historical linguistics, providing a detailed perspective on how Southern newspapers relayed national and international news during a transformative period in American history. The dataset will be made available upon publication or request for research purposes.
Quantum Logic Operations and Graph Coloring
Authors: Joao Paulo Schwarz Schuler, Luis Otavio Campos Alvares
Date: 2001-11-05 00:00:00
Proceeding: Jornadas Chilenas de Computación 2001 11

This paper introduces notions of quantum computing such as qubit, quantum entanglement and probability amplitudes. Further on introducing physical aspects about quantum computing, this paper proposes a simple method for implementing the logic operations AND and OR based on the logic operation CNot. The source code examples are coded in Quantum Computing Language (QCL). This paper also shows a way to test if a graph is 3-colorable using QCL.
Joint Evaluation of Fairness and Relevance in Recommender Systems with Pareto Frontier
Authors: Theresia Veronika Rampisela, Tuukka Ruotsalo, Maria Maistro, Christina Lioma
Date: 2025-02-17 00:00:00
Tasks: Exposure Fairness, Fairness, Recommendation Systems, Re-Ranking

Fairness and relevance are two important aspects of recommender systems (RSs). Typically, they are evaluated either (i) separately by individual measures of fairness and relevance, or (ii) jointly using a single measure that accounts for fairness with respect to relevance. However, approach (i) often does not provide a reliable joint estimate of the goodness of the models, as it has two different best models: one for fairness and another for relevance. Approach (ii) is also problematic because these measures tend to be ad-hoc and do not relate well to traditional relevance measures, like NDCG. Motivated by this, we present a new approach for jointly evaluating fairness and relevance in RSs: Distance to Pareto Frontier (DPFR). Given some user-item interaction data, we compute their Pareto frontier for a pair of existing relevance and fairness measures, and then use the distance from the frontier as a measure of the jointly achievable fairness and relevance. Our approach is modular and intuitive as it can be computed with existing measures. Experiments with 4 RS models, 3 re-ranking strategies, and 6 datasets show that existing metrics have inconsistent associations with our Pareto-optimal solution, making DPFR a more robust and theoretically well-founded joint measure for assessing fairness and relevance. Our code: https://github.com/theresiavr/DPFR-recsys-evaluation
HADL Framework for Noise Resilient Long-Term Time Series Forecasting
Authors: Aditya Dey, Jonas Kusch, Fadi Al Machot
Date: 2025-02-14 00:00:00
Tasks: Computational Efficiency, Decision Making, Time Series, Time Series Forecasting

Long-term time series forecasting is critical in domains such as finance, economics, and energy, where accurate and reliable predictions over extended horizons drive strategic decision-making. Despite the progress in machine learning-based models, the impact of temporal noise in extended lookback windows remains underexplored, often degrading model performance and computational efficiency. In this paper, we propose a novel framework that addresses these challenges by integrating the Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) to perform noise reduction and extract robust long-term features. These transformations enable the separation of meaningful temporal patterns from noise in both the time and frequency domains. To complement this, we introduce a lightweight low-rank linear prediction layer that not only reduces the influence of residual noise but also improves memory efficiency. Our approach demonstrates competitive robustness to noisy input, significantly reduces computational complexity, and achieves competitive or state-of-the-art forecasting performance across diverse benchmark datasets. Extensive experiments reveal that the proposed framework is particularly effective in scenarios with high noise levels or irregular patterns, making it well suited for real-world forecasting tasks. The code is available in https://github.com/forgee-master/HADL.
The underlying structures of self-attention: symmetry, directionality, and emergent dynamics in Transformer training
Authors: Matteo Saponati, Pascal Sager, Pau Vilimelis Aceituno, Thilo Stadelmann, Benjamin Grewe
Date: 2025-02-15 00:00:00

Self-attention is essential to Transformer architectures, yet how information is embedded in the self-attention matrices and how different objective functions impact this process remains unclear. We present a mathematical framework to analyze self-attention matrices by deriving the structures governing their weight updates. Using this framework, we demonstrate that bidirectional training induces symmetry in the weight matrices, while autoregressive training results in directionality and column dominance. Our theoretical findings are validated across multiple Transformer models - including ModernBERT, GPT, LLaMA3, and Mistral - and input modalities like text, vision, and audio. Finally, we apply these insights by showing that symmetric initialization improves the performance of encoder-only models on language tasks. This mathematical analysis offers a novel theoretical perspective on how information is embedded through self-attention, thereby improving the interpretability of Transformer models.
Investigating Inference-time Scaling for Chain of Multi-modal Thought: A Preliminary Study
Authors: Yujie Lin, Ante Wang, Moye Chen, Jingyao Liu, Hao liu, Jinsong Su, Xinyan Xiao
Date: 2025-02-17 00:00:00

Recently, inference-time scaling of chain-of-thought (CoT) has been demonstrated as a promising approach for addressing multi-modal reasoning tasks. While existing studies have predominantly centered on text-based thinking, the integration of both visual and textual modalities within the reasoning process remains unexplored. In this study, we pioneer the exploration of inference-time scaling with multi-modal thought, aiming to bridge this gap. To provide a comprehensive analysis, we systematically investigate popular sampling-based and tree search-based inference-time scaling methods on 10 challenging tasks spanning various domains. Besides, we uniformly adopt a consistency-enhanced verifier to ensure effective guidance for both methods across different thought paradigms. Results show that multi-modal thought promotes better performance against conventional text-only thought, and blending the two types of thought fosters more diverse thinking. Despite these advantages, multi-modal thoughts necessitate higher token consumption for processing richer visual inputs, which raises concerns in practical applications. We hope that our findings on the merits and drawbacks of this research line will inspire future works in the field.
A Physical Coherence Benchmark for Evaluating Video Generation Models via Optical Flow-guided Frame Prediction
Authors: Yongfan Chen, Xiuwen Zhu, Tianyu Li, Hao Chen, Chunhua Shen
Date: 2025-02-08 00:00:00
Tasks: Model Optimization, Optical Flow Estimation, Video Generation

Recent advances in video generation models demonstrate their potential as world simulators, but they often struggle with videos deviating from physical laws, a key concern overlooked by most text-to-video benchmarks. We introduce a benchmark designed specifically to assess the Physical Coherence of generated videos, PhyCoBench. Our benchmark includes 120 prompts covering 7 categories of physical principles, capturing key physical laws observable in video content. We evaluated four state-of-the-art (SoTA) T2V models on PhyCoBench and conducted manual assessments. Additionally, we propose an automated evaluation model: PhyCoPredictor, a diffusion model that generates optical flow and video frames in a cascade manner. Through a consistency evaluation comparing automated and manual sorting, the experimental results show that PhyCoPredictor currently aligns most closely with human evaluation. Therefore, it can effectively evaluate the physical coherence of videos, providing insights for future model optimization. Our benchmark, which includes physical coherence prompts, automatic evaluation tool PhyCoPredictor, and generated video dataset, will all be released on GitHub shortly.
AKDT: Adaptive Kernel Dilation Transformer for Effective Image Denoising
Authors: Alexandru Brateanu, Raul Balmez, Adrian Avram, Ciprian Orhei
Date: 2025-02-26 00:00:00
Proceeding: VISAPP 2025 2
Tasks: Color Image Denoising, Denoising, Image Denoising

Image denoising is a fundamental yet challenging task, especially when dealing with high-resolution images and complex noise patterns. Most existing methods rely on standard Transformer architectures, which often suffer from high computational complexity and limited adaptability to varying noise levels. In this paper, we introduce the Adaptive Kernel Dilation Transformer (AKDT), a novel Transformer-based model that fully harnesses the power of learnable dilation rates within convolutions. AKDT consists of several layers and custom-designed blocks, including our novel Learnable Dilation Rate (LDR) module, which is utilized to construct a Noise Estimator module (NE). At the core of AKDT, the NE is seamlessly integrated within standard Transformer components to form the Noise-Guided Feed-Forward Network (NG-FFN) and Noise-Guided Multi-Headed Self-Attention (NG-MSA). These noise-modulated Transformer components enable the model to achieve unparalleled denoising performance while significantly reducing computational costs. Extensive experiments across multiple image denoising benchmarks demonstrate that AKDT sets a new state-of-the-art, effectively handling both real and synthetic noise. The source code and pre-trained models are publicly available at https://github.com/albrateanu/AKDT.
Semantica: Decentralized Search using a LLM-Guided Semantic Tree Overlay
Authors: Petru Neague, Quinten Stokkink, Naman Goel, Johan Pouwelse
Date: 2025-02-14 00:00:00

Centralized search engines are key for the Internet, but lead to undesirable concentration of power. Decentralized alternatives fail to offer equal document retrieval accuracy and speed. Nevertheless, Semantic Overlay Networks can come close to the performance of centralized solutions when the semantics of documents are properly captured. This work uses embeddings from Large Language Models to capture semantics and fulfill the promise of Semantic Overlay Networks. Our proposed algorithm, called Semantica, constructs a prefix tree (trie) utilizing document embeddings calculated by a language model. Users connect to each other based on the embeddings of their documents, ensuring that semantically similar users are directly linked. Thereby, this construction makes it more likely for user searches to be answered by the users that they are directly connected to, or by the users they are close to in the network connection graph. The implementation of our algorithm also accommodates the semantic diversity of individual users by spawning "clone" user identifiers in the tree. Our experiments use emulation with a real-world workload to show Semantica's ability to identify and connect to similar users quickly. Semantica finds up to ten times more semantically similar users than current state-of-the-art approaches. At the same time, Semantica can retrieve more than two times the number of relevant documents given the same network load. We also make our code publicly available to facilitate further research in the area.
Enhancing Out-of-Distribution Detection in Medical Imaging with Normalizing Flows
Authors: Dariush Lotfi, Mohammad-Ali Nikouei Mahani, Mohamad Koohi-Moghadam, Kyongtae Ty Bae
Date: 2025-02-17 00:00:00
Tasks: Out-of-Distribution Detection, Out of Distribution (OOD) Detection

Out-of-distribution (OOD) detection is crucial in AI-driven medical imaging to ensure reliability and safety by identifying inputs outside a model's training distribution. Existing methods often require retraining or modifications to pre-trained models, which is impractical for clinical applications. This study introduces a post-hoc normalizing flow-based approach that seamlessly integrates with pre-trained models. By leveraging normalizing flows, it estimates the likelihood of feature vectors extracted from pre-trained models, capturing semantically meaningful representations without relying on pixel-level statistics. The method was evaluated using the MedMNIST benchmark and a newly curated MedOOD dataset simulating clinically relevant distributional shifts. Performance was measured using standard OOD detection metrics (e.g., AUROC, FPR@95, AUPR_IN, AUPR_OUT), with statistical analyses comparing it against ten baseline methods. On MedMNIST, the proposed model achieved an AUROC of 93.80%, outperforming state-of-the-art methods. On MedOOD, it achieved an AUROC of 84.61%, demonstrating superior performance against other methods. Its post-hoc nature ensures compatibility with existing clinical workflows, addressing the limitations of previous approaches. The model and code to build OOD datasets are available at https://github.com/dlotfi/MedOODFlow.
Unveiling Environmental Impacts of Large Language Model Serving: A Functional Unit View
Authors: Yanran Wu, Inez Hua, Yi Ding
Date: 2025-02-16 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Model Selection, Quantization

Large language models (LLMs) offer powerful capabilities but come with significant environmental impact, particularly in carbon emissions. Existing studies benchmark carbon emissions but lack a standardized basis for comparison across different model configurations. To address this, we introduce the concept of functional unit (FU) as a standardized basis and develop FUEL, the first FU-based framework for evaluating LLM serving's environmental impact. Through three case studies, we uncover key insights and trade-offs in reducing carbon emissions by optimizing model size, quantization strategy, and hardware choice, paving the way for more sustainable LLM serving. The code is available at https://github.com/jojacola/FUEL.
A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference
Authors: Kieran Woodward, Eiman Kanjo, Georgios Papandroulidakis, Shady Agwa, Themis Prodromakis
Date: 2025-02-14 00:00:00
Tasks: Edge-computing, Template Matching

In recent years, the development of smart edge computing systems to process information locally is on the rise. Many near-sensor machine learning (ML) approaches have been implemented to introduce accurate and energy efficient template matching operations in resource-constrained edge sensing systems, such as wearables. To introduce novel solutions that can be viable for extreme edge cases, hybrid solutions combining conventional and emerging technologies have started to be proposed. Deep Neural Networks (DNN) optimised for edge application alongside new approaches of computing (both device and architecture -wise) could be a strong candidate in implementing edge ML solutions that aim at competitive accuracy classification while using a fraction of the power of conventional ML solutions. In this work, we are proposing a hybrid software-hardware edge classifier aimed at the extreme edge near-sensor systems. The classifier consists of two parts: (i) an optimised digital tinyML network, working as a front-end feature extractor, and (ii) a back-end RRAM-CMOS analogue content addressable memory (ACAM), working as a final stage template matching system. The combined hybrid system exhibits a competitive trade-off in accuracy versus energy metric with $E_{front-end}$ = $96.23 nJ$ and $E_{back-end}$ = $1.45 nJ$ for each classification operation compared with 78.06$\mu$J for the original teacher model, representing a 792-fold reduction, making it a viable solution for extreme edge applications.
Strassen Multisystolic Array Hardware Architectures
Authors: Trevor E. Pogue, Nicola Nicolici
Date: 2025-02-14 00:00:00

While Strassen's matrix multiplication algorithm reduces the complexity of naive matrix multiplication, general-purpose hardware is not suitable for achieving the algorithm's promised theoretical speedups. This leaves the question of if it could be better exploited in custom hardware architectures designed specifically for executing the algorithm. However, there is limited prior work on this and it is not immediately clear how to derive such architectures or if they can ultimately lead to real improvements. We bridge this gap, presenting and evaluating new systolic array architectures that efficiently translate the theoretical complexity reductions of Strassen's algorithm directly into hardware resource savings. Furthermore, the architectures are multisystolic array designs that can multiply smaller matrices with higher utilization than single-systolic array designs. The proposed designs implemented on FPGA reduce DSP requirements by a factor of $1.14^r$ for $r$ implemented Strassen recursion levels, and otherwise require overall similar soft logic resources when instantiated to support matrix sizes down to 32x32 and 24x24 at 1-2 levels of Strassen recursion, respectively. We evaluate the proposed designs both in isolation and in an end-to-end machine learning accelerator compared to baseline designs and prior works, achieving state-of-the-art performance.
EmbBERT-Q: Breaking Memory Barriers in Embedded NLP
Authors: Riccardo Bravin, Massimo Pavan, Hazem Hesham Yousef Shalby, Fabrizio Pittorino, Manuel Roveri
Date: 2025-02-14 00:00:00
Tasks: Mamba, Quantization

Large Language Models (LLMs) have revolutionized natural language processing, setting new standards across a wide range of applications. However, their relevant memory and computational demands make them impractical for deployment on technologically-constrained tiny devices such as wearable devices and Internet-of-Things units. To address this limitation, we introduce EmbBERT-Q, a novel tiny language model specifically designed for tiny devices with stringent memory constraints. EmbBERT-Q achieves state-of-the-art (SotA) accuracy in Natural Language Processing tasks in this scenario, with a total memory footprint (weights and activations) of just 781 kB, representing a 25x reduction in size with respect to SotA models. By combining architectural innovations with hardware-compatible 8-bit quantization, EmbBERT-Q consistently outperforms several baseline models scaled down to a 2 MB memory budget (i.e., the maximum memory typically available in tiny devices), including heavily compressed versions of BERT and MAMBA. Extensive experimental evaluations on both a selected benchmark dataset, TinyNLP, specifically curated to evaluate Tiny Language Models in NLP tasks and real-world scenarios, and the GLUE benchmark, demonstrate EmbBERT-Q ability to deliver competitive accuracy with respect to existing approaches, achieving an unmatched balance between memory and performance. To ensure the complete and immediate reproducibility of all our results, we release all code, scripts, and model checkpoints at https://github.com/RiccardoBravin/tiny-LLM.
Explaining Necessary Truths
Authors: Gülce Kardeş, Simon DeDeo
Date: 2025-02-16 00:00:00

Knowing the truth is rarely enough -- we also seek out reasons why the fact is true. While much is known about how we explain contingent truths, we understand less about how we explain facts, such as those in mathematics, that are true as a matter of logical necessity. We present a framework, based in computational complexity, where explanations for deductive truths co-emerge with discoveries of simplifying steps during the search process. When such structures are missing, we revert, in turn, to error-based reasons, where a (corrected) mistake can serve as fictitious, but explanatory, contingency-cause: not making the mistake serves as a reason why the truth takes the form it does. We simulate human subjects, using GPT-4o, presented with SAT puzzles of varying complexity and reasonableness, validating our theory and showing how its predictions can be tested in future human studies.
Qubit-Based Framework for Quantum Machine Learning: Bridging Classical Data and Quantum Algorithms
Authors: Bhavna Bose, Saurav Verma
Date: 2025-02-17 00:00:00
Tasks: Quantum Machine Learning

This paper dives into the exciting and rapidly growing field of quantum computing, explaining its core ideas, current progress, and how it could revolutionize the way we solve complex problems. It starts by breaking down the basics, like qubits, quantum circuits, and how principles like superposition and entanglement make quantum computers fundamentally different-and far more powerful for certain tasks-than the classical computers we use today. We also explore how quantum computing deals with complex problems and why it is uniquely suited for challenges classical systems struggle to handle. A big part of this paper focuses on Quantum Machine Learning (QML), where the strengths of quantum computing meet the world of artificial intelligence. By processing massive datasets and optimizing intricate algorithms, quantum systems offer new possibilities for machine learning. We highlight different approaches to combining quantum and classical computing, showing how they can work together to produce faster and more accurate results. Additionally, we explore the tools and platforms available-like TensorFlow Quantum, Qiskit and PennyLane-that are helping researchers and developers bring these theories to life. Of course, quantum computing has its hurdles. Challenges like scaling up hardware, correcting errors, and keeping qubits stable are significant roadblocks. Yet, with rapid advancements in cloud-based platforms and innovative technologies, the potential of quantum computing feels closer than ever. This paper aims to offer readers a clear and comprehensive introduction to quantum computing, its role in machine learning, and the immense possibilities it holds for the future of technology.