pwc-archive/papers-with-abstracts huggingface dataset:
Conjuring Positive Pairs for Efficient Unification of Representation Learning and Image Synthesis
Authors: Imanol G. Estepa, Jesús M. Rodríguez-de-Vera, Ignacio Sarasúa, Bhalaji Nagarajan, Petia Radeva
Date: 2025-03-19 00:00:00
Tasks: Few-Shot Learning, Image Generation, Representation Learning, Self-Supervised Learning, Transfer Learning, Unconditional Image Generation

While representation learning and generative modeling seek to understand visual data, unifying both domains remains unexplored. Recent Unified Self-Supervised Learning (SSL) methods have started to bridge the gap between both paradigms. However, they rely solely on semantic token reconstruction, which requires an external tokenizer during training -- introducing a significant overhead. In this work, we introduce Sorcen, a novel unified SSL framework, incorporating a synergic Contrastive-Reconstruction objective. Our Contrastive objective, "Echo Contrast", leverages the generative capabilities of Sorcen, eliminating the need for additional image crops or augmentations during training. Sorcen "generates" an echo sample in the semantic token space, forming the contrastive positive pair. Sorcen operates exclusively on precomputed tokens, eliminating the need for an online token transformation during training, thereby significantly reducing computational overhead. Extensive experiments on ImageNet-1k demonstrate that Sorcen outperforms the previous Unified SSL SoTA by 0.4%, 1.48 FID, 1.76%, and 1.53% on linear probing, unconditional image generation, few-shot learning, and transfer learning, respectively, while being 60.8% more efficient. Additionally, Sorcen surpasses previous single-crop MIM SoTA in linear probing and achieves SoTA performance in unconditional image generation, highlighting significant improvements and breakthroughs in Unified SSL models.
Reinforcement learning-based motion imitation for physiologically plausible musculoskeletal motor control
Authors: Merkourios Simos, Alberto Silvio Chiappa, Alexander Mathis
Date: 2025-03-18 00:00:00
Tasks: Humanoid Control, Motion Synthesis, Reinforcement Learning (RL)

How do humans move? The quest to understand human motion has broad applications in numerous fields, ranging from computer animation and motion synthesis to neuroscience, human prosthetics and rehabilitation. Although advances in reinforcement learning (RL) have produced impressive results in capturing human motion using simplified humanoids, controlling physiologically accurate models of the body remains an open challenge. In this work, we present a model-free motion imitation framework (KINESIS) to advance the understanding of muscle-based motor control. Using a musculoskeletal model of the lower body with 80 muscle actuators and 20 DoF, we demonstrate that KINESIS achieves strong imitation performance on 1.9 hours of motion capture data, is controllable by natural language through pre-trained text-to-motion generative models, and can be fine-tuned to carry out high-level tasks such as target goal reaching. Importantly, KINESIS generates muscle activity patterns that correlate well with human EMG activity. The physiological plausibility makes KINESIS a promising model for tackling challenging problems in human motor control theory, which we highlight by investigating Bernstein's redundancy problem in the context of locomotion. Code, videos and benchmarks will be available at https://github.com/amathislab/Kinesis.
Has the Paris Agreement Shaped Emission Trends? A Panel VECM Analysis of Energy, Growth, and CO$_2$ in 106 Middle-Income Countries
Authors: Tuhin G. M. Al Mamun, Ehsanullah, Md. Sharif Hassan, Mohammad Bin Amin, Judit Oláh
Date: 2025-03-19 00:00:00

Rising CO$_2$ emissions remain a critical global challenge, particularly in middle-income countries where economic growth drives environmental degradation. This study examines the long-run and short-run relationships between CO$_2$ emissions, energy use, GDP per capita, and population across 106 middle-income countries from 1980 to 2023. Using a Panel Vector Error Correction Model (VECM), we assess the impact of the Paris Agreement (2015) on emissions while conducting cointegration tests to confirm long-run equilibrium relationships. The findings reveal a strong long-run relationship among the variables, with energy use as the dominant driver of emissions, while GDP per capita has a moderate impact. However, the Paris Agreement has not significantly altered emissions trends in middle-income economies. Granger causality tests indicate that energy use strongly causes emissions, but GDP per capita and population do not exhibit significant short-run causal effects. Variance decomposition confirms that energy shocks have the most persistent effects, and impulse response functions (IRFs) show emissions trajectories are primarily shaped by economic activity rather than climate agreements. Robustness checks, including autocorrelation tests, polynomial root stability, and Yamagata-Pesaran slope homogeneity tests, validate model consistency. These results suggest that while global agreements set emissions reduction goals, their effectiveness remains limited without stronger national climate policies, sectoral energy reforms, and financial incentives for clean energy adoption to ensure sustainable economic growth.
Entity-aware Cross-lingual Claim Detection for Automated Fact-checking
Authors: Rrubaa Panchendrarajan, Arkaitz Zubiaga
Date: 2025-03-19 00:00:00
Tasks: Entity Linking, Fact Checking, Misinformation, named-entity-recognition, Named Entity Recognition, Transfer Learning

Identifying claims requiring verification is a critical task in automated fact-checking, especially given the proliferation of misinformation on social media platforms. Despite significant progress in the task, there remain open challenges such as dealing with multilingual and multimodal data prevalent in online discourse. Addressing the multilingual challenge, recent efforts have focused on fine-tuning pre-trained multilingual language models. While these models can handle multiple languages, their ability to effectively transfer cross-lingual knowledge for detecting claims spreading on social media remains under-explored. In this paper, we introduce \textit{EX-Claim}, an entity-aware cross-lingual claim detection model that generalizes well to handle claims written in any language. The model leverages entity information derived from named entity recognition and entity linking techniques to improve the language-level performance of both seen and unseen languages during training. Extensive experiments conducted on three datasets from different social media platforms demonstrate that our proposed model significantly outperforms the baselines, across 27 languages, and achieves the highest rate of knowledge transfer, even with limited training data.
TruthLens:A Training-Free Paradigm for DeepFake Detection
Authors: Ritabrata Chakraborty, Rajatsubhra Chakraborty, Ali Khaleghi Rahimian, Thomas MacDougall
Date: 2025-03-19 00:00:00
Tasks: Binary Classification, DeepFake Detection, Face Swapping, Fake Image Detection, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

The proliferation of synthetic images generated by advanced AI models poses significant challenges in identifying and understanding manipulated visual content. Current fake image detection methods predominantly rely on binary classification models that focus on accuracy while often neglecting interpretability, leaving users without clear insights into why an image is deemed real or fake. To bridge this gap, we introduce TruthLens, a novel training-free framework that reimagines deepfake detection as a visual question-answering (VQA) task. TruthLens utilizes state-of-the-art large vision-language models (LVLMs) to observe and describe visual artifacts and combines this with the reasoning capabilities of large language models (LLMs) like GPT-4 to analyze and aggregate evidence into informed decisions. By adopting a multimodal approach, TruthLens seamlessly integrates visual and semantic reasoning to not only classify images as real or fake but also provide interpretable explanations for its decisions. This transparency enhances trust and provides valuable insights into the artifacts that signal synthetic content. Extensive evaluations demonstrate that TruthLens outperforms conventional methods, achieving high accuracy on challenging datasets while maintaining a strong emphasis on explainability. By reframing deepfake detection as a reasoning-driven process, TruthLens establishes a new paradigm in combating synthetic media, combining cutting-edge performance with interpretability to address the growing threats of visual disinformation.
GlucoLens: Explainable Postprandial Blood Glucose Prediction from Diet and Physical Activity
Authors: Abdullah Mamun, Asiful Arefeen, Susan B. Racette, Dorothy D. Sears, Corrie M. Whisner, Matthew P. Buman, Hassan Ghasemzadeh
Date: 2025-03-05 00:00:00
Tasks: counterfactual

Postprandial hyperglycemia, marked by the blood glucose level exceeding the normal range after meals, is a critical indicator of progression toward type 2 diabetes in prediabetic and healthy individuals. A key metric for understanding blood glucose dynamics after eating is the postprandial area under the curve (PAUC). Predicting PAUC in advance based on a person's diet and activity level and explaining what affects postprandial blood glucose could allow an individual to adjust their lifestyle accordingly to maintain normal glucose levels. In this paper, we propose GlucoLens, an explainable machine learning approach to predict PAUC and hyperglycemia from diet, activity, and recent glucose patterns. We conducted a five-week user study with 10 full-time working individuals to develop and evaluate the computational model. Our machine learning model takes multimodal data including fasting glucose, recent glucose, recent activity, and macronutrient amounts, and provides an interpretable prediction of the postprandial glucose pattern. Our extensive analyses of the collected data revealed that the trained model achieves a normalized root mean squared error (NRMSE) of 0.123. On average, GlucoLense with a Random Forest backbone provides a 16% better result than the baseline models. Additionally, GlucoLens predicts hyperglycemia with an accuracy of 74% and recommends different options to help avoid hyperglycemia through diverse counterfactual explanations. Code available: https://github.com/ab9mamun/GlucoLens.
Spline refinement with differentiable rendering
Authors: Frans Zdyb, Albert Alonso, Julius B. Kirkegaard
Date: 2025-03-15 00:00:00
Tasks: Drug Discovery

Detecting slender, overlapping structures remains a challenge in computational microscopy. While recent coordinate-based approaches improve detection, they often produce less accurate splines than pixel-based methods. We introduce a training-free differentiable rendering approach to spline refinement, achieving both high reliability and sub-pixel accuracy. Our method improves spline quality, enhances robustness to distribution shifts, and shrinks the gap between synthetic and real-world data. Being fully unsupervised, the method is a drop-in replacement for the popular active contour model for spline refinement. Evaluated on C. elegans nematodes, a popular model organism for drug discovery and biomedical research, we demonstrate that our approach combines the strengths of both coordinate- and pixel-based methods.
Enhancing Fault Detection and Isolation in an All-Electric Auxiliary Power Unit (APU) Gas Generator by Utilizing Starter/Generator Signal
Authors: Haotian Mao, Khashayar Khorasani, Yingqing Guo
Date: 2025-03-19 00:00:00
Tasks: All, Diagnostic, Fault Detection, Fault Diagnosis

This study proposes a novel paradigm for enhancing fault detection and isolation (FDI) of gas generators in all-electric auxiliary power unit (APU) by utilizing shaft power information from the starter/generator. First, we conduct a pioneering investigation into the challenges and opportunities for FDI brought about by APU electrification. Our analysis reveals that the electrification of APU opens up new possibilities for utilizing shaft power estimates from starter/generator to improve gas generator FDI. We then provide comprehensive theoretical and analytical evidence demonstrating why, how, and to what extent, the shaft power information from the starter/generator can fundamentally enhance the estimation accuracy of system states and health parameters of the gas generator, while also identifying the key factors influencing these improvements in FDI performance. The effectiveness of the proposed paradigm and its theoretical foundations are validated through extensive Monte Carlo simulations. Furthermore, through comprehensive comparative analysis with state-of-the-art gas generator fault diagnosis methods, our experimental results not only demonstrate the superior performance of the proposed approach but also validate that the diagnostic capabilities of existing advanced FDI techniques can be substantially enhanced by incorporating shaft power information. And the observed performance improvement patterns strongly align with our theoretical analysis, verifying both the effectiveness and guiding significance of our theoretical framework. These research findings provide a unique perspective in answering three fundamental questions: why joint fault diagnosis of the starter/generator and gas generator is essential, how it can be implemented, and what factors determine its effectiveness, thereby opening up promising new avenues for FDI technologies in all-electric APU systems.
Induced Model Matching: Restricted Models Help Train Full-Featured Models
Authors: Usama Muneeb, Mesrob I Ohannessian
Date: 2025-01-15 00:00:00
Proceeding: NeurIPS 2025 1
Tasks: Knowledge Distillation, Language Modeling, Language Modelling

We consider scenarios where a very accurate (often small) predictive model using restricted features is available when training a full-featured (often larger) model. This restricted model may be thought of as side-information'', and can come either from an auxiliary dataset or from the same dataset by forcing the restriction. How can the restricted model be useful to the full model? To answer this, we introduce a methodology called Induced Model Matching (IMM). IMM aligns the context-restricted, or induced, version of the large model with the restricted model. We relate IMM to approaches such as noising, which is implicit in addressing the problem, and reverse knowledge distillation from weak teachers, which is explicit but does not exploit restriction being the nature of the weakness. We show that these prior methods can be thought of as approximations to IMM and can be problematic in terms of consistency. Experimentally, we first motivate IMM using logistic regression as a toy example. We then explore it in language modeling, the application that initially inspired it, and demonstrate it on both LSTM and transformer full models, using bigrams as restricted models. We lastly give a simple RL example, which shows that POMDP policies can help learn better MDP policies. The IMM principle is thus generally applicable in common scenarios where restricted data is cheaper to collect or restricted models are easier to learn.
MTLoc: A Confidence-Based Source-Free Domain Adaptation Approach For Indoor Localization
Authors: Negar Mehregan, Berk Bozkurt, Eric Granger, Mohammadjavad Hajikhani, Mohammadhadi Shateri
Date: 2025-03-18 00:00:00
Tasks: Domain Adaptation, Indoor Localization, Self-Supervised Learning, Source-Free Domain Adaptation, Unsupervised Domain Adaptation

Various deep learning models have been developed for indoor localization based on radio-frequency identification (RFID) tags. However, they often require adaptation to ensure accurate tracking in new target operational domains. To address this challenge, unsupervised domain adaptation (UDA) methods have been proposed to align pre-trained models with data from target environments. However, they rely on large annotated datasets from the initial domain (source). Source data access is limited by privacy, storage, computational, and transfer constraints. Although many source-free domain adaptation (SFDA) methods address these constraints in classification, applying them to regression models for localization remains challenging. Indeed, target datasets for indoor localization are typically small, with few features and samples, and are noisy. Adapting regression models requires high-confidence target pseudo-annotation to avoid over-training. In this paper, a specialized mean-teacher method called MTLoc is proposed for SFDA. MTLoc updates the student network using noisy data and teacher-generated pseudo-labels. The teacher network maintains stability through exponential moving averages. To further ensure robustness, the teacher's pseudo-labels are refined using k-nearest neighbor correction. MTLoc allows for self-supervised learning on target data, facilitating effective adaptation to dynamic and noisy indoor environments. Validated using real-world data from our experimental setup with INLAN Inc., our results show that MTLoc achieves high localization accuracy under challenging conditions, significantly reducing localization error compared to baselines, including the state-of-the-art adversarial UDA approach with access to source data.
Improved Automorphism Ensemble Decoder for Polar Codes
Authors: Seokju Han, Member, IEEE, Bonghoe Kim, Jaehoon Chung, and Jeongseok Ha
Date: 2024-08-08 00:00:00
Proceeding: Conference 2024 8
Tasks: Decoder

In this work, we propose an improved automorphism ensemble (AE) decoder for polar codes. With successive
cancellation (SC) variant automorphisms, multiple decoding
paths in the AE decoder produce their estimates of the transmitted codeword. In the proposed scheme, the decoding results from
the multiple paths are cleverly utilized to generate a new channel
output with which an additional decoding path is established.
Performance evaluations clearly demonstrate that the proposed
decoder achieves significantly improved error-rate performance
as compared to the existing AE decoder for polar codes.
Model Hubs and Beyond: Analyzing Model Popularity, Performance, and Documentation
Authors: Pritam Kadasi, Sriman Reddy, Srivathsa Vamsi Chaturvedula, Rudranshu Sen, Agnish Saha, Soumavo Sikdar, Sayani Sarkar, Suhani Mittal, Rohit Jindal, Mayank Singh
Date: 2025-03-19 00:00:00
Tasks: model, Sentiment Analysis

With the massive surge in ML models on platforms like Hugging Face, users often lose track and struggle to choose the best model for their downstream tasks, frequently relying on model popularity indicated by download counts, likes, or recency. We investigate whether this popularity aligns with actual model performance and how the comprehensiveness of model documentation correlates with both popularity and performance. In our study, we evaluated a comprehensive set of 500 Sentiment Analysis models on Hugging Face. This evaluation involved massive annotation efforts, with human annotators completing nearly 80,000 annotations, alongside extensive model training and evaluation. Our findings reveal that model popularity does not necessarily correlate with performance. Additionally, we identify critical inconsistencies in model card reporting: approximately 80\% of the models analyzed lack detailed information about the model, training, and evaluation processes. Furthermore, about 88\% of model authors overstate their models' performance in the model cards. Based on our findings, we provide a checklist of guidelines for users to choose good models for downstream tasks.
Accurate, transferable, and verifiable machine-learned interatomic potentials for layered materials
Authors: Johnathan D. Georgaras, Akash Ramdas, Chung Hsuan Shan, Elena Halsted, Berwyn, Tianshu Li, Felipe H. da Jornada
Date: 2025-03-19 00:00:00

Twisted layered van-der-Waals materials often exhibit unique electronic and optical properties absent in their non-twisted counterparts. Unfortunately, predicting such properties is hindered by the difficulty in determining the atomic structure in materials displaying large moir\'e domains. Here, we introduce a split machine-learned interatomic potential and dataset curation approach that separates intralayer and interlayer interactions and significantly improves model accuracy -- with a tenfold increase in energy and force prediction accuracy relative to conventional models. We further demonstrate that traditional MLIP validation metrics -- force and energy errors -- are inadequate for moir\'e structures and develop a more holistic, physically-motivated metric based on the distribution of stacking configurations. This metric effectively compares the entirety of large-scale moir\'e domains between two structures instead of relying on conventional measures evaluated on smaller commensurate cells. Finally, we establish that one-dimensional instead of two-dimensional moir\'e structures can serve as efficient surrogate systems for validating MLIPs, allowing for a practical model validation protocol against explicit DFT calculations. Applying our framework to HfS2/GaS bilayers reveals that accurate structural predictions directly translate into reliable electronic properties. Our model-agnostic approach integrates seamlessly with various intralayer and interlayer interaction models, enabling computationally tractable relaxation of moir\'e materials, from bilayer to complex multilayers, with rigorously validated accuracy.
Inteligencia Artificial para la conservación y uso sostenible de la biodiversidad, una visión desde Colombia (Artificial Intelligence for conservation and sustainable use of biodiversity, a view from Colombia)
Authors: Juan Sebastián Cañas, Camila Parra-Guevara, Manuela Montoya-Castrillón, Julieta M Ramírez-Mejía, Gabriel-Alejandro Perilla, Esteban Marentes, Nerieth Leuro, Jose Vladimir Sandoval-Sierra, Sindy Martinez-Callejas, Angélica Díaz, Mario Murcia, Elkin A. Noguera-Urbano, Jose Manuel Ochoa-Quintero, Susana Rodríguez Buriticá, Juan Sebastián Ulloa
Date: 2025-03-17 00:00:00
Tasks: Management

The rise of artificial intelligence (AI) and the aggravating biodiversity crisis have resulted in a research area where AI-based computational methods are being developed to act as allies in conservation, and the sustainable use and management of natural resources. While important general guidelines have been established globally regarding the opportunities and challenges that this interdisciplinary research offers, it is essential to generate local reflections from the specific contexts and realities of each region. Hence, this document aims to analyze the scope of this research area from a perspective focused on Colombia and the Neotropics. In this paper, we summarize the main experiences and debates that took place at the Humboldt Institute between 2023 and 2024 in Colombia. To illustrate the variety of promising opportunities, we present current uses such as automatic species identification from images and recordings, species modeling, and in silico bioprospecting, among others. From the experiences described above, we highlight limitations, challenges, and opportunities for in order to successfully implementate AI in conservation efforts and sustainable management of biological resources in the Neotropics. The result aims to be a guide for researchers, decision makers, and biodiversity managers, facilitating the understanding of how artificial intelligence can be effectively integrated into conservation and sustainable use strategies. Furthermore, it also seeks to open a space for dialogue on the development of policies that promote the responsible and ethical adoption of AI in local contexts, ensuring that its benefits are harnessed without compromising biodiversity or the cultural and ecosystemic values inherent in Colombia and the Neotropics.