pwc-archive/papers-with-abstracts huggingface dataset:
Unveiling Biases in AI: ChatGPT's Political Economy Perspectives and Human Comparisons
Authors: Leonardo Becchetti, Nazaria Solferino
Date: 2025-03-07 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

We explore the political and ideological positioning of ChatGPT, a leading large language model (LLM), by comparing its responses to political economy questions from the European Social Survey (ESS). The questions concern environmental sustainability, civil rights, income inequality, and government size. ChatGPT's self-assessed placement on a left-right political spectrum is compared to the ideological stances of individuals providing similar answers in the ESS dataset. Results highlight a significant left-oriented bias in ChatGPT's answers, particularly on environmental and civil rights topics, diverging from its same self-declared center-left stance. These findings underscore the need for transparency in AI systems to prevent potential ideological influences on users. We conclude by discussing the implications for AI governance, debiasing strategies, and educational use.
Improving Motor Imagery EEG Classification Based on Channel Selection Using a Deep Learning Architecture
Authors: T Mwata-Velu, JG Avina-Cervantes, J Ruiz-Pinales
Date: 2022-07-01 00:00:00
Proceeding: Mathematics 2022 7
Tasks: channel selection, EEG, Motor Imagery

Recently, motor imagery EEG signals have been widely applied in Brain–Computer Interfaces (BCI). These signals are typically observed in the first motor cortex of the brain, resulting from the imagination of body limb movements. For non-invasive BCI systems, it is not apparent how to locate the electrodes, optimizing the accuracy for a given task. This study proposes a comparative analysis of channel signals exploiting the Deep Learning (DL) technique and a public dataset to locate the most discriminant channels. EEG channels are usually selected based on the function and nomenclature of electrode location from international standards. Instead, the most suitable configuration for a given paradigm must be determined by analyzing the proper selection of the channels. Therefore, an EEGNet network was implemented to classify signals from different channel location using the accuracy metric. Achieved results were then contrasted with results from the state-of-the-art. As a result, the proposed method improved BCI classification accuracy.
PhiloBERTA: A Transformer-Based Cross-Lingual Analysis of Greek and Latin Lexicons
Authors: Rumi A. Allbert, Makai L. Allbert
Date: 2025-03-07 00:00:00

We present PhiloBERTA, a cross-lingual transformer model that measures semantic relationships between ancient Greek and Latin lexicons. Through analysis of selected term pairs from classical texts, we use contextual embeddings and angular similarity metrics to identify precise semantic alignments. Our results show that etymologically related pairs demonstrate significantly higher similarity scores, particularly for abstract philosophical concepts such as epist\=em\=e (scientia) and dikaiosyn\=e (iustitia). Statistical analysis reveals consistent patterns in these relationships (p = 0.012), with etymologically related pairs showing remarkably stable semantic preservation compared to control pairs. These findings establish a quantitative framework for examining how philosophical concepts moved between Greek and Latin traditions, offering new methods for classical philological research.
Agentic AI for Behavior-Driven Development Testing Using Large Language Models
Authors: Ciprian Paduraru, Miruna Zavelca, Alin Stefanescu
Date: 2025-02-23 00:00:00
Proceeding: Proceedings of the 17th International Conference on Agents and Artificial Intelligence - (Volume 2) 2025 2
Tasks: Computational Efficiency, Test Case Creation

Behavior-driven development (BDD) testing significantly improves communication and collaboration between developers, testers and business stakeholders, and ensures that software functionality meets business requirements. However, the benefits of BDD are often overshadowed by the complexity of writing test cases, making it difficult for non-technical stakeholders. To address this challenge, we propose BDDTestAIGen, a framework that uses Large Language Models (LLMs), Natural Language Processing (NLP) techniques, human-in-the-loop and Agentic AI methods to automate BDD test creation. This approach aims to reduce manual effort and effectively involve all project stakeholders. By fine-tuning an open-source LLM, we improve domain-specific customization, data privacy and cost efficiency. Our research shows that small models provide a balance between computational efficiency and ease of use. Contributions include the innovative integration of NLP and LLMs into BDD test automation, an adaptable open-source framework, evaluation against industry-relevant scenarios, and a discussion of the limitations, challenges and future directions in this area.
A functional approach for curve alignment and shape analysis
Authors: Issam-Ali Moindjié, Cédric Beaulac, Marie-Hélène Descary
Date: 2025-03-07 00:00:00

The shape $\tilde{\mathbf{X}}$ of a random planar curve $\mathbf{X}$ is what remains after removing deformation effects such as scaling, rotation, translation, and parametrization. Previous studies in statistical shape analysis have focused on analyzing $\tilde{\bf X}$ through discrete observations of the curve ${\bf X}$. While this approach has some computational advantages, it overlooks the continuous nature of both ${\bf X}$ and its shape $\tilde{\bf X}$. It also ignores potential dependencies among the deformation variables and their effect on $\tilde{ \bf X}$, which may result in information loss and reduced interpretability. In this paper, we introduce a novel framework for analyzing $\bf X$ in the context of Functional Data Analysis (FDA). Basis expansion techniques are employed to derive analytic solutions for estimating the deformation variables such as rotation and reparametrization, thereby achieving shape alignment. The generative model of $\bf X$ is then investigated using a joint-principal component analysis approach. Numerical experiments on simulated data and the \textit{MPEG-7} database demonstrate that our new approach successfully identifies the deformation parameters and captures the underlying distribution of planar curves in situations where traditional FDA methods fail to do so.
Middleman Bias in Advertising: Aligning Relevance of Keyphrase Recommendations with Search
Authors: Soumik Dey, Wei zhang, Hansi Wu, Bingfeng Dong, Binbin Li
Date: 2025-01-31 00:00:00

E-commerce sellers are recommended keyphrases based on their inventory on which they advertise to increase buyer engagement (clicks/sales). Keyphrases must be pertinent to items; otherwise, it can result in seller dissatisfaction and poor targeting -- towards that end relevance filters are employed. In this work, we describe the shortcomings of training relevance filter models on biased click/sales signals. We re-conceptualize advertiser keyphrase relevance as interaction between two dynamical systems -- Advertising which produces the keyphrases and Search which acts as a middleman to reach buyers. We discuss the bias of search relevance systems (middleman bias) and the need to align advertiser keyphrases with search relevance signals. We also compare the performance of cross encoders and bi-encoders in modeling this alignment and the scalability of such a solution for sellers at eBay.
Balcony: A Lightweight Approach to Dynamic Inference of Generative Language Models
Authors: Benyamin Jamialahmadi, Parsa Kavehzadeh, Mehdi Rezagholizadeh, Parsa Farinneya, Hossein Rajabzadeh, Aref Jafari, Boxing Chen, Marzieh Tahaei
Date: 2025-03-06 00:00:00

Deploying large language models (LLMs) in real-world applications is often hindered by strict computational and latency constraints. While dynamic inference offers the flexibility to adjust model behavior based on varying resource budgets, existing methods are frequently limited by hardware inefficiencies or performance degradation. In this paper, we introduce Balcony, a simple yet highly effective framework for depth-based dynamic inference. By freezing the pretrained LLM and inserting additional transformer layers at selected exit points, Balcony maintains the full model's performance while enabling real-time adaptation to different computational budgets. These additional layers are trained using a straightforward self-distillation loss, aligning the sub-model outputs with those of the full model. This approach requires significantly fewer training tokens and tunable parameters, drastically reducing computational costs compared to prior methods. When applied to the LLaMA3-8B model, using only 0.2% of the original pretraining data, Balcony achieves minimal performance degradation while enabling significant speedups. Remarkably, we show that Balcony outperforms state-of-the-art methods such as Flextron and Layerskip as well as other leading compression techniques on multiple models and at various scales, across a variety of benchmarks.
VideoPainter: Any-length Video Inpainting and Editing with Plug-and-Play Context Control
Authors: Yuxuan Bian, Zhaoyang Zhang, Xuan Ju, Mingdeng Cao, Liangbin Xie, Ying Shan, Qiang Xu
Date: 2025-03-07 00:00:00
Tasks: Image Inpainting, Optical Flow Estimation, Text-to-Video Editing, Video Editing, Video Inpainting

Video inpainting, which aims to restore corrupted video content, has experienced substantial progress. Despite these advances, existing methods, whether propagating unmasked region pixels through optical flow and receptive field priors, or extending image-inpainting models temporally, face challenges in generating fully masked objects or balancing the competing objectives of background context preservation and foreground generation in one model, respectively. To address these limitations, we propose a novel dual-stream paradigm VideoPainter that incorporates an efficient context encoder (comprising only 6% of the backbone parameters) to process masked videos and inject backbone-aware background contextual cues to any pre-trained video DiT, producing semantically consistent content in a plug-and-play manner. This architectural separation significantly reduces the model's learning complexity while enabling nuanced integration of crucial background context. We also introduce a novel target region ID resampling technique that enables any-length video inpainting, greatly enhancing our practical applicability. Additionally, we establish a scalable dataset pipeline leveraging current vision understanding models, contributing VPData and VPBench to facilitate segmentation-based inpainting training and assessment, the largest video inpainting dataset and benchmark to date with over 390K diverse clips. Using inpainting as a pipeline basis, we also explore downstream applications including video editing and video editing pair data generation, demonstrating competitive performance and significant practical potential. Extensive experiments demonstrate VideoPainter's superior performance in both any-length video inpainting and editing, across eight key metrics, including video quality, mask region preservation, and textual coherence.
IMPACT: A Large-scale Integrated Multimodal Patent Analysis and Creation Dataset for Design Patents
Authors: Homaira Huda Shomee, Zhu Wang, Sathya N. Ravi, Sourav Medya
Date: 2024-12-10 00:00:00
Proceeding: NeurIPS 2024 12
Tasks: Cross-Modal Retrieval, Image Classification, Image Retrieval, Patent classification, Question Answering, Retrieval, Visual Question Answering, Visual Question Answering (VQA), Zero-Shot Cross-Modal Retrieval

In this paper, we introduce IMPACT (Integrated Multimodal Patent Analysis and Creation Dataset for Design Patents), a large-scale multimodal patent dataset with detailed captions for design patent figures. Our dataset includes half a million design patents comprising 3.61 million figures along with captions from patents granted by the United States Patent and Trademark Office (USPTO) over a 16-year period from 2007 to 2022. We incorporate the metadata of each patent application with elaborate captions that are coherent with multiple viewpoints of designs. Even though patents themselves contain a variety of design figures, titles, and descriptions of viewpoints, we find that they lack detailed descriptions that are necessary to perform multimodal tasks such as classification and retrieval. IMPACT closes this gap thereby providing researchers with necessary ingredients to instantiate a variety of multimodal tasks. Our dataset has a huge potential for novel design inspiration and can be used with advanced computer vision models in tandem. We perform preliminary evaluations on the dataset on the popular patent analysis tasks such as classification and retrieval. Our results indicate that integrating images with generated captions significantly improves the performance of different models on the corresponding tasks. Given that design patents offer various benefits for modeling novel tasks, we propose two standard computer vision tasks that have not been investigated in analyzing patents as future directions using IMPACT as a benchmark viz., 3D Image Construction and Visual Question Answering (VQA). To facilitate research in these directions, we make our IMPACT dataset and the code/models used in this work publicly available at https://github.com/AI4Patents/IMPACT.
Vision-aware Multimodal Prompt Tuning for Uploadable Multi-source Few-shot Domain Adaptation
Authors: Kuanghong Liu, Jin Wang, Kangjian He, Dan Xu, Xuejie Zhang
Date: 2025-03-08 00:00:00
Tasks: Domain Adaptation

Conventional multi-source domain few-shot adaptation (MFDA) faces the challenge of further reducing the load on edge-side devices in low-resource scenarios. Considering the native language-supervised advantage of CLIP and the plug-and-play nature of prompt to transfer CLIP efficiently, this paper introduces an uploadable multi-source few-shot domain adaptation (UMFDA) schema. It belongs to a decentralized edge collaborative learning in the edge-side models that must maintain a low computational load. And only a limited amount of annotations in source domain data is provided, with most of the data being unannotated. Further, this paper proposes a vision-aware multimodal prompt tuning framework (VAMP) under the decentralized schema, where the vision-aware prompt guides the text domain-specific prompt to maintain semantic discriminability and perceive the domain information. The cross-modal semantic and domain distribution alignment losses optimize each edge-side model, while text classifier consistency and semantic diversity losses promote collaborative learning among edge-side models. Extensive experiments were conducted on OfficeHome and DomainNet datasets to demonstrate the effectiveness of the proposed VAMP in the UMFDA, which outperformed the previous prompt tuning methods.
Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement
Authors: Yuqi Liu, Bohao Peng, Zhisheng Zhong, Zihao Yue, Fanbin Lu, Bei Yu, Jiaya Jia
Date: 2025-03-09 00:00:00
Tasks: Domain Generalization, Object Detection, Open Vocabulary Object Detection, Open Vocabulary Semantic Segmentation, Reasoning Segmentation, Referring Expression Segmentation, Segmentation, Zero-shot Generalization, Zero Shot Segmentation

Traditional methods for reasoning segmentation rely on supervised fine-tuning with categorical labels and simple descriptions, limiting its out-of-domain generalization and lacking explicit reasoning processes. To address these limitations, we propose Seg-Zero, a novel framework that demonstrates remarkable generalizability and derives explicit chain-of-thought reasoning through cognitive reinforcement. Seg-Zero introduces a decoupled architecture consisting of a reasoning model and a segmentation model. The reasoning model interprets user intentions, generates explicit reasoning chains, and produces positional prompts, which are subsequently used by the segmentation model to generate precious pixel-level masks. We design a sophisticated reward mechanism that integrates both format and accuracy rewards to effectively guide optimization directions. Trained exclusively via reinforcement learning with GRPO and without explicit reasoning data, Seg-Zero achieves robust zero-shot generalization and exhibits emergent test-time reasoning capabilities. Experiments show that Seg-Zero-7B achieves a zero-shot performance of 57.5 on the ReasonSeg benchmark, surpassing the prior LISA-7B by 18\%. This significant improvement highlights Seg-Zero's ability to generalize across domains while presenting an explicit reasoning process. Code is available at https://github.com/dvlab-research/Seg-Zero.
A Novel Dual-robot Accurate Calibration Method Using Convex Optimization and Lie Derivative
Authors: Cheng Jiang, Wen-long Li, Wen-pan Li, Dong-fang Wang, Li-jun Zhu, Wei Xu, Huan Zhao, Han Ding
Date: 2023-12-18 00:00:00
Proceeding: IEEE Transactions on Robotics 2023 12

Calibrating unknown transformation relationships is an essential task for multi-robot cooperative systems. Traditional linear methods are inadequate to decouple and simultaneously solve the unknown matrices due to their intercoupling. This paper proposes a novel dual-robot accurate calibration method that uses convex optimization and Lie derivative to solve the dual-robot calibration problem simultaneously. The key idea is that a convex optimization model based on dual-robot transformation chain is established using Lie representation of SE(3). The Jacobian matrix of the established optimization model is explicitly derived using the corresponding Lie derivative of SE(3). To balance the influence of the magnitudes of the rotational and translational optimization variables, a weight coefficient is defined. Due to the closure and smoothness of Lie group, the optimization model can be solved simultaneously using Newton-like iterative methods without additional orthogonalization processing. The performance of the proposed method is verified through simulation and actual calibration experiments. The results show that the proposed method outperforms the previous calibration methods in terms of accuracy and stability. The actual experiments are used to compare the proposed method with two existing calibration methods, and the mean measurement error of a certified ceramic sphere is reduced from 0.9205mm and 0.5363mm to 0.4381mm, respectively.
From Reusing to Forecasting: Accelerating Diffusion Models with TaylorSeers
Authors: Jiacheng Liu, Chang Zou, Yuanhuiyi Lyu, Junjie Chen, Linfeng Zhang
Date: 2025-03-10 00:00:00

Diffusion Transformers (DiT) have revolutionized high-fidelity image and video synthesis, yet their computational demands remain prohibitive for real-time applications. To solve this problem, feature caching has been proposed to accelerate diffusion models by caching the features in the previous timesteps and then reusing them in the following timesteps. However, at timesteps with significant intervals, the feature similarity in diffusion models decreases substantially, leading to a pronounced increase in errors introduced by feature caching, significantly harming the generation quality. To solve this problem, we propose TaylorSeer, which firstly shows that features of diffusion models at future timesteps can be predicted based on their values at previous timesteps. Based on the fact that features change slowly and continuously across timesteps, TaylorSeer employs a differential method to approximate the higher-order derivatives of features and predict features in future timesteps with Taylor series expansion. Extensive experiments demonstrate its significant effectiveness in both image and video synthesis, especially in high acceleration ratios. For instance, it achieves an almost lossless acceleration of 4.99$\times$ on FLUX and 5.00$\times$ on HunyuanVideo without additional training. On DiT, it achieves $3.41$ lower FID compared with previous SOTA at $4.53$$\times$ acceleration. %Our code is provided in the supplementary materials and will be made publicly available on GitHub. Our codes have been released in Github:https://github.com/Shenyi-Z/TaylorSeer
MSA-GCN: Exploiting Multi-Scale Temporal Dynamics With Adaptive Graph Convolution for Skeleton-Based Action Recognition
Authors: Kowovi Comivi Alowonou, Ji-Hyeong Han
Date: 2024-12-19 00:00:00
Proceeding: IEEE Access 2024 12
Tasks: Action Recognition, Skeleton Based Action Recognition

Graph convolutional networks (GCNs) have been widely used and have achieved remarkable results in skeleton-based action recognition. We note that existing GCN-based approaches rely on local context information of the skeleton joints to construct adaptive graphs for feature aggregation, limiting their ability to understand actions that involve coordinated movements across various parts of the body. An adaptive graph built upon the global context information of the joints can help move beyond this limitation. Therefore, in this paper, we propose a novel approach to skeleton-based action recognition named Multi-stage Adaptive Graph Convolution Network (MSA-GCN). It consists of two modules: Multi-stage Adaptive Graph Convolution (MSA-GC) and Temporal Multi-Scale Transformer (TMST). These two modules work together to capture complex spatial and temporal patterns within skeleton data effectively. Specifically, MSA-GC explores both local and global context information of the joints across all sequences to construct the adaptive graph and facilitates the understanding of complex and nuanced relationships between joints. On the other hand, the TMST module integrates a Gated Multi-stage Temporal Convolution (GMSTC) with a Temporal Multi-Head Self-Attention (TMHSA) to capture global temporal features and accommodate both long-term and short-term dependencies within action sequences. Through extensive experiments on multiple benchmark datasets, including NTU RGB+D 60, NTU RGB+D 120, and Northwestern-UCLA, MSA-GCN achieves state-of-the-art performance and verifies its effectiveness in skeleton-based action recognition.
Consistent Image Layout Editing with Diffusion Models
Authors: Tao Xia, Yudi Zhang, Ting Liu Lei Zhang
Date: 2025-03-09 00:00:00
Tasks: Image Generation

Despite the great success of large-scale text-to-image diffusion models in image generation and image editing, existing methods still struggle to edit the layout of real images. Although a few works have been proposed to tackle this problem, they either fail to adjust the layout of images, or have difficulty in preserving visual appearance of objects after the layout adjustment. To bridge this gap, this paper proposes a novel image layout editing method that can not only re-arrange a real image to a specified layout, but also can ensure the visual appearance of the objects consistent with their appearance before editing. Concretely, the proposed method consists of two key components. Firstly, a multi-concept learning scheme is used to learn the concepts of different objects from a single image, which is crucial for keeping visual consistency in the layout editing. Secondly, it leverages the semantic consistency within intermediate features of diffusion models to project the appearance information of objects to the desired regions directly. Besides, a novel initialization noise design is adopted to facilitate the process of re-arranging the layout. Extensive experiments demonstrate that the proposed method outperforms previous works in both layout alignment and visual consistency for the task of image layout editing