pwc-archive/papers-with-abstracts huggingface dataset:
GFSNetwork: Differentiable Feature Selection via Gumbel-Sigmoid Relaxation
Authors: Witold Wydmański, Marek Śmieja
Date: 2025-03-17 00:00:00
Tasks: Computational Efficiency, feature selection

Feature selection in deep learning remains a critical challenge, particularly for high-dimensional tabular data where interpretability and computational efficiency are paramount. We present GFSNetwork, a novel neural architecture that performs differentiable feature selection through temperature-controlled Gumbel-Sigmoid sampling. Unlike traditional methods, where the user has to define the requested number of features, GFSNetwork selects it automatically during an end-to-end process. Moreover, GFSNetwork maintains constant computational overhead regardless of the number of input features. We evaluate GFSNetwork on a series of classification and regression benchmarks, where it consistently outperforms recent methods including DeepLasso, attention maps, as well as traditional feature selectors, while using significantly fewer features. Furthermore, we validate our approach on real-world metagenomic datasets, demonstrating its effectiveness in high-dimensional biological data. Concluding, our method provides a scalable solution that bridges the gap between neural network flexibility and traditional feature selection interpretability. We share our python implementation of GFSNetwork at https://github.com/wwydmanski/GFSNetwork, as well as a PyPi package (gfs_network).
Strain Problems got you in a Twist? Try StrainRelief: A Quantum-Accurate Tool for Ligand Strain Calculations
Authors: Ewan R. S. Wallace, Nathan C. Frey, Joshua A. Rackers
Date: 2025-03-17 00:00:00
Tasks: Drug Design, Drug Discovery

Ligand strain energy, the energy difference between the bound and unbound conformations of a ligand, is an important component of structure-based small molecule drug design. A large majority of observed ligands in protein-small molecule co-crystal structures bind in low-strain conformations, making strain energy a useful filter for structure-based drug design. In this work we present a tool for calculating ligand strain with a high accuracy. StrainRelief uses a MACE Neural Network Potential (NNP), trained on a large database of Density Functional Theory (DFT) calculations to estimate ligand strain of neutral molecules with quantum accuracy. We show that this tool estimates strain energy differences relative to DFT to within 1.4 kcal/mol, more accurately than alternative NNPs. These results highlight the utility of NNPs in drug discovery, and provide a useful tool for drug discovery teams.
Column Property Annotation using Large Language Models
Authors: Keti Korini, Christian Bizer
Date: 2025-01-01 00:00:00
Proceeding: ESWC 2025 1
Tasks: Columns Property Annotation, Column Type Annotation, Data Integration, In-Context Learning, Table annotation

Column property annotation (CPA), also known as column relationship prediction, is the task of predicting the semantic relationship between two columns in a table given a set of candidate relationships. CPA annotations are used in downstream tasks such as data search, data integration, or knowledge graph enrichment. This paper explores the usage of generative large language models (LLMs) for the CPA task. We experiment with different zero-shot prompts for the CPA task which we evaluate using GPT-3.5, GPT-4, and the open-source model SOLAR. We find GPT-3.5 to be quite sensitive to variations of the prompt, while GPT-4 reaches a high performance independent of the variation of the prompt. We further explore the scenario where training data for the CPA task is available and can be used for selecting demonstrations or fine-tuning the model. We show that a fine-tuned GPT-3.5 model outperforms a RoBERTa model that was fine-tuned on the same data by 11% in F1. Comparing in-context learning via demonstrations and fine-tuning shows that the fine-tuned GPT-3.5 performs 9% F1 better than the same model given demonstrations. The fine-tuned GPT-3.5 model also outperforms zero-shot GPT-4 by around 2% F1 for the dataset on which is was fine-tuned, while not generalizing to tasks that require a different vocabulary.
PEBench: A Fictitious Dataset to Benchmark Machine Unlearning for Multimodal Large Language Models
Authors: Zhaopan Xu, Pengfei Zhou, Weidong Tang, Jiaxin Ai, Wangbo Zhao, Xiaojiang Peng, Kai Wang, Yang You, Wenqi Shao, Hongxun Yao, Kaipeng Zhang
Date: 2025-03-16 00:00:00
Tasks: Machine Unlearning, Privacy Preserving, Question Answering, Visual Question Answering

In recent years, Multimodal Large Language Models (MLLMs) have demonstrated remarkable advancements in tasks such as visual question answering, visual understanding, and reasoning. However, this impressive progress relies on vast amounts of data collected from the internet, raising significant concerns about privacy and security. To address these issues, machine unlearning (MU) has emerged as a promising solution, enabling the removal of specific knowledge from an already trained model without requiring retraining from scratch. Although MU for MLLMs has gained attention, current evaluations of its efficacy remain incomplete, and the underlying problem is often poorly defined, which hinders the development of strategies for creating more secure and trustworthy systems. To bridge this gap, we introduce a benchmark, named PEBench, which includes a dataset of personal entities and corresponding general event scenes, designed to comprehensively assess the performance of MU for MLLMs. Through PEBench, we aim to provide a standardized and robust framework to advance research in secure and privacy-preserving multimodal models. We benchmarked 6 MU methods, revealing their strengths and limitations, and shedding light on key challenges and opportunities for MU in MLLMs.
VETRA: A Dataset for Vehicle Tracking in Aerial Imagery - New Challenges for Multi-Object Tracking
Authors: Jens Hellekes, Manuel Mühlhaus, Reza Bahmanyar, Seyed Majid Azimi, Franz Kurz
Date: 2024-09-29 00:00:00
Proceeding: European Conference on Computer Vision (ECCV) 2024 9
Tasks: de-en, Motion Compensation, Multi-Object Tracking, Object Tracking

The informative power of traffic analysis can be enhanced by considering changes in both time and space. Vehicle tracking algorithms applied to drone videos provide a better overview than street-level surveillance cameras. However, existing aerial MOT datasets only address stationary settings, leaving the performance in moving-camera scenarios covering a considerably larger area unknown. To fill this gap, we present VETRA, a dataset for vehicle tracking in aerial imagery introducing heterogeneity in terms of camera movement, frame rate, as well as type, size and number of objects. When dealing with these challenges, state-of-the-art online MOT algorithms experience a decrease in performance compared to other benchmark datasets. The integration of camera motion compensation and an adaptive search radius enables our baseline algorithm to effectively handle the moving field of view and other challenges inherent to VETRA, although potential for further improvement remains. Making the dataset available to the community adds a missing building block for both testing and developing vehicle tracking algorithms for versatile real-world applications.
VETRA can be downloaded here: https://​www.​dlr.​de/​en/​eoc/​vetra
SEAL: Semantic Aware Image Watermarking
Authors: Kasra Arabi, R. Teal Witter, Chinmay Hegde, Niv Cohen
Date: 2025-03-15 00:00:00

Generative models have rapidly evolved to generate realistic outputs. However, their synthetic outputs increasingly challenge the clear distinction between natural and AI-generated content, necessitating robust watermarking techniques. Watermarks are typically expected to preserve the integrity of the target image, withstand removal attempts, and prevent unauthorized replication onto unrelated images. To address this need, recent methods embed persistent watermarks into images produced by diffusion models using the initial noise. Yet, to do so, they either distort the distribution of generated images or rely on searching through a long dictionary of used keys for detection. In this paper, we propose a novel watermarking method that embeds semantic information about the generated image directly into the watermark, enabling a distortion-free watermark that can be verified without requiring a database of key patterns. Instead, the key pattern can be inferred from the semantic embedding of the image using locality-sensitive hashing. Furthermore, conditioning the watermark detection on the original image content improves robustness against forgery attacks. To demonstrate that, we consider two largely overlooked attack strategies: (i) an attacker extracting the initial noise and generating a novel image with the same pattern; (ii) an attacker inserting an unrelated (potentially harmful) object into a watermarked image, possibly while preserving the watermark. We empirically validate our method's increased robustness to these attacks. Taken together, our results suggest that content-aware watermarks can mitigate risks arising from image-generative models.
PCNet: a human pose compensation network based on incremental learning for sports actions estimation
Authors: Jia-Hong Jiang, Nan Xia
Date: 2024-11-11 00:00:00
Proceeding: Complex & Intelligent Systems 2024 11
Tasks: 2D Human Pose Estimation, Incremental Learning, Occlusion Handling, Pose Estimation

Human pose estimation has a wide range of applications. Existing methods perform well in conventional domains, but there
are certain defects when they are applied to sports activities. The first is lack of estimation of the extremity posture, making
it impossible to comprehensively evaluate the movement posture; the second is insufficient occlusion handling. Therefore,
we propose a human pose compensation network based on incremental learning, which obtains shared weights to extract
detailed features under the premise of limited extremity training data. We propose a higher-order feature compensator (HOFcompensator) to embed the attributes of the extremity into the torso and limbs topology structure, building a complete higherorder feature. In addition, to improve the occlusion handling performance, we propose an occlusion feature enhancement
attention mechanism (OFE-attention) that can identify occluded keypoints and enhance attention to occlusion areas. We
design comparative experiments on three public datasets and a self-built sports dataset, achieving the highest mean accuracy
among all comparative methods. In addition, we design a series of ablation analysis and visualization displays to verify that
our method performs best in sports pose estimation
GCBLANE: A graph-enhanced convolutional BiLSTM attention network for improved transcription factor binding site prediction
Authors: Jonas Chris Ferrao, Dickson Dias, Sweta Morajkar, Manisha Gokuldas Fal Dessai
Date: 2025-03-16 00:00:00
Tasks: Graph Neural Network, Transcription Factor Binding Site Prediction

Identifying transcription factor binding sites (TFBS) is crucial for understanding gene regulation, as these sites enable transcription factors (TFs) to bind to DNA and modulate gene expression. Despite advances in high-throughput sequencing, accurately identifying TFBS remains challenging due to the vast genomic data and complex binding patterns. GCBLANE, a graph-enhanced convolutional bidirectional Long Short-Term Memory (LSTM) attention network, is introduced to address this issue. It integrates convolutional, multi-head attention, and recurrent layers with a graph neural network to detect key features for TFBS prediction. On 690 ENCODE ChIP-Seq datasets, GCBLANE achieved an average AUC of 0.943, and on 165 ENCODE datasets, it reached an AUC of 0.9495, outperforming advanced models that utilize multimodal approaches, including DNA shape information. This result underscores GCBLANE's effectiveness compared to other methods. By combining graph-based learning with sequence analysis, GCBLANE significantly advances TFBS prediction.
Adversarial Feature Equilibrium Network for Multimodal Change Detection in Heterogeneous Remote Sensing Images
Authors: Yan Pu, Maoguo Gong, Tongfei Liu, Mingyang Zhang, Tianqi Gao, Fenlong Jiang, Xiaobo Hu
Date: 2024-10-14 00:00:00
Proceeding: IEEE Transactions on Geoscience and Remote Sensing 2024 10
Tasks: Change Detection, Domain Adaptation

Change detection (CD) methods have been crucial in exploring geo-environmental science. With the advancement of remote sensing (RS) technology, multimodal images acquired from different platforms and sensors are widely used for CD tasks. As an emerging task, multimodal CD (MCD) aims to achieve more comprehensive and precise detection of land cover changes through complementary information in multimodal images. However, there are significant differences between modalities, particularly in heterogeneous images. How to deal with modal differences while effectively integrating change information remains a challenge in MCD. In this article, we propose a novel adversarial feature equilibrium network (AFENet), which establishes an additional adversarial optimization to solve the equilibrium problem between modal differences and land cover changes. Our AFENet aligns the features and reduces the modal gap through a multiscale adversarial domain adaptation (MADA) approach. Meanwhile, a divergence-aware contrastive module (DCM) is designed as a regularization term for adversarial optimization. DCM affects the sensitivity of feature extractors by constraining the mutual information between changed and unchanged pixels. In this case, AFENet can maintain the consistency of feature representation while maximizing the discriminability of change targets. The features extracted from AFENet will then be integrated by our multistream feature fusion (MFF) module and utilized to generate change maps. The effectiveness of our approach is demonstrated on two scene-level multimodal RS datasets. Compared with existing methods, our AFENet achieves state-of-the-art (SOTA) performance on both datasets and outperforms the second-best F1 score by 4.64% and 1.1%, respectively.
Neuron-level explainable AI for Alzheimer’s disease assessment from fundus images
Authors: Nooshin Yousefzadeh, Charlie Tran, Adolfo Ramirez-Zamora, Jinghua Chen, Ruogu Fang, My T. Thai
Date: 2024-04-02 00:00:00
Proceeding: Scientific Reports 2024 4
Tasks: Diagnostic

Alzheimer’s Disease (AD) is a progressive neurodegenerative disease and the leading cause of dementia. Early diagnosis is critical for patients to benefit from potential intervention and treatment. The retina has emerged as a plausible diagnostic site for AD detection owing to its anatomical connection with the brain. However, existing AI models for this purpose have yet to provide a rational explanation behind their decisions and have not been able to infer the stage of the disease’s progression. Along this direction, we propose a novel model-agnostic explainable-AI framework, called Granular Neuron Level Explainer (LAVA), an interpretation prototype that probes into intermediate layers of the Convolutional Neural Network (CNN) models to directly assess the continuum of AD from the retinal imaging without the need for longitudinal or clinical evaluations. This innovative approach aims to validate retinal vasculature as a biomarker and diagnostic modality for evaluating Alzheimer’s Disease. Leveraged UK Biobank cognitive tests and vascular morphological features demonstrate significant promise and effectiveness of LAVA in identifying AD stages across the progression continuum.
Spatio-temporal Fourier Transformer (StFT) for Long-term Dynamics Prediction
Authors: Da Long, Shandian Zhe, Samuel Williams, Leonid Oliker, Zhe Bai
Date: 2025-03-14 00:00:00
Tasks: Computational Efficiency

Simulating the long-term dynamics of multi-scale and multi-physics systems poses a significant challenge in understanding complex phenomena across science and engineering. The complexity arises from the intricate interactions between scales and the interplay of diverse physical processes. Neural operators have emerged as promising models for predicting such dynamics due to their flexibility and computational efficiency. However, they often fail to effectively capture multi-scale interactions or quantify the uncertainties inherent in the predictions. These limitations lead to rapid error accumulation, particularly in long-term forecasting of systems characterized by complex and coupled dynamics. To address these challenges, we propose a spatio-temporal Fourier transformer (StFT), in which each transformer block is designed to learn dynamics at a specific scale. By leveraging a structured hierarchy of StFT blocks, the model explicitly captures dynamics across both macro- and micro- spatial scales. Furthermore, a generative residual correction mechanism is integrated to estimate and mitigate predictive uncertainties, enhancing both the accuracy and reliability of long-term forecasts. Evaluations conducted on three benchmark datasets (plasma, fluid, and atmospheric dynamics) demonstrate the advantages of our approach over state-of-the-art ML methods.
Shadow Art Kanji: Inverse Rendering Application
Authors: William Louis Rothman, Yasuyuki Matsushita
Date: 2025-03-15 00:00:00
Tasks: Inverse Rendering

Finding a balance between artistic beauty and machine-generated imagery is always a difficult task. This project seeks to create 3D models that, when illuminated, cast shadows resembling Kanji characters. It aims to combine artistic expression with computational techniques, providing an accurate and efficient approach to visualizing these Japanese characters through shadows.
Muhammed Toheer
Authors: MUHAMMED TOHEER
Date: 2025-03-18 00:00:00
Proceeding: 2024/2025 2025 3
Tasks: Marketing

Abdul rasaq muhammed toheer who is born on / 4/ 2006/ august in ilorin, His parent are Mr. and Mrs. Abdul rasaq who are native of kwara state, but the live in ilorin kwara state, Nigeria. he went to His Zumuratu Nursery and secondary school in ilorin, when he is in pry 4 he left His Zumuratu Nursery and secondary school and went to Centra igboro LGEA school, ilorin and went to ILORIN GRAMMER SCHOOL IGS .After he finished his school he was introduced to a great business know as Network Marketing ([Neolife][1]) as he joined the business he has became an expert freelancer working on Fiverr, Upwork, PeoplePerHour, kwork, Latium and many freelance platform with many project ( Social Media marketer, Programming projects, Digital Marketing and so on) but he sometimes specialized on Digital Marketing service: Google knowledge panel, Tiktok promotion, Instagram promotion Google review removing and adding, TikTok promotion and followers, IMDb page creation, wikipedia page creation, crunchbase and so on. He has been doing this for the past 2 years.

Career

Abdul Rasaq Muhammed Toheer start his career in the years 2024 , Because he join a great and awesome business opportunity call [2]Neolife. And he is also expert in online business that he specialize on Digital marketing, social media marketing, Seo expert, Shopify marketing, dropshipping, promotion and so on. And he is working on some freelancer platform like [Upwork][3], [fiverr][4], [kwork][5], [PeoplePerHour][6], [Latium] [7]and so on
Model-independent upper bounds for the prices of Bermudan options with convex payoffs
Authors: David Hobson, Dominykas Norgilas
Date: 2025-03-17 00:00:00

Suppose $\mu$ and $\nu$ are probability measures on $\mathbb R$ satisfying $\mu \leq_{cx} \nu$. Let $a$ and $b$ be convex functions on $\mathbb R$ with $a \geq b \geq 0$. We are interested in finding \[ \sup_{\mathcal M} \sup_{\tau} \mathbb{E}^{\mathcal M} \left[ a(X) I_{ \{ \tau = 1 \} } + b(Y) I_{ \{ \tau = 2 \} } \right] \] where the first supremum is taken over consistent models $\mathcal M$ (i.e., filtered probability spaces $(\Omega, \mathcal F, \mathbb F, \mathbb P)$) such that $Z=(z,Z_1,Z_2)=(\int_{\mathbb R} x \mu(dx) = \int_{\mathbb R} y \nu(dy), X, Y)$ is a $(\mathbb F,\mathbb P)$ martingale, where $X$ has law $\mu$ and $Y$ has law $\nu$ under $\mathbb P$) and $\tau$ in the second supremum is a $(\mathbb F,\mathbb P)$-stopping time taking values in $\{1,2\}$. Our contributions are first to characterise and simplify the dual problem, and second to completely solve the problem in the symmetric case under the dispersion assumption. A key finding is that the canonical set-up in which the filtration is that generated by $Z$ is not rich enough to define an optimal model and additional randomisation is required. This holds even though the marginal laws $\mu$ and $\nu$ are atom-free. The problem has an interpretation of finding the robust, or model-free, no-arbitrage bound on the price of a Bermudan option with two possible exercise dates, given the prices of co-maturing European options.
Simultaneous prediction of 10m and 100m wind speeds using a model inspired by brain emotional learning
Authors: Alireza Hakimi, Parvin Ghafarian
Date: 2025-02-05 00:00:00
Proceeding: Nature 2025 2

Predicting wind speed simultaneously at multiple heights, particularly at 10 and 100 metres (m), presents unique challenges due to diverse influences. At lower altitudes, wind speed is significantly affected by surface factors including roughness, vegetation, and man-made structures, causing sharp fluctuations, while at higher altitudes, it is primarily influenced by atmospheric conditions, resulting in smoother flow patterns. Traditional models often require separate systems for each altitude, limiting their efficiency and accuracy. This study introduces the brain emotional learning based on basic and functional memories (BELBFM) model, inspired by adaptive emotional learning mechanisms in the mammalian brain, to predict wind speeds at both altitudes simultaneously. Using ERA5 reanalysis data, BELBFM effectively captures the nonlinear dynamics of wind behavior. Evaluation with data from the Persian Gulf demonstrates BELBFM’s high accuracy, enhancing predictive capabilities for applications in renewable energy and structural engineering. This unified model provides a robust and efficient solution for adaptive wind forecasting.