pwc-archive/papers-with-abstracts huggingface dataset:
A multi-criteria decision support system to evaluate the effectiveness of training courses on citizens' employability
Authors: Maria C. Bas, Vicente J. Bolos, Alvaro E. Prieto, Roberto Rodriguez-Echeverria, Fernando Sanchez-Figueroa
Date: 2024-12-02 00:00:00
Tasks: Lifelong learning

This study examines the impact of lifelong learning on the professional lives of employed and unemployed individuals. Lifelong learning is a crucial factor in securing employment or enhancing one's existing career prospects. To achieve this objective, this study proposes the implementation of a multi-criteria decision support system for the evaluation of training courses in accordance with their capacity to enhance the employability of the students. The methodology is delineated in four stages. Firstly, a `working life curve' was defined to provide a quantitative description of an individual's working life. Secondly, an analysis based on K-medoids clustering defined a control group for each individual for comparison. Thirdly, the performance of a course according to each of the four predefined criteria was calculated using a t-test to determine the mean performance value of those who took the course. Ultimately, the unweighted TOPSIS method was used to evaluate the efficacy of the various training courses in relation to the four criteria. This approach effectively addresses the challenge of using extensive datasets within a system while facilitating the application of a multi-criteria unweighted TOPSIS method. The results of the multi-criteria TOPSIS method indicated that training courses related to the professional fields of administration and management, hostel and tourism and community and sociocultural services have positive impact on employability and improving the working conditions of citizens. However, courses that demonstrate the greatest effectiveness in ranking are the least demanded by citizens. The results will help policymakers evaluate the effectiveness of each training course offered by the regional government.
Hierarchical Object-Oriented POMDP Planning for Object Rearrangement
Authors: Rajesh Mangannavar, Alan Fern, Prasad Tadepalli
Date: 2024-12-02 00:00:00
Tasks: Object, Object Rearrangement

We present an online planning framework for solving multi-object rearrangement problems in partially observable, multi-room environments. Current object rearrangement solutions, primarily based on Reinforcement Learning or hand-coded planning methods, often lack adaptability to diverse challenges. To address this limitation, we introduce a novel Hierarchical Object-Oriented Partially Observed Markov Decision Process (HOO-POMDP) planning approach. This approach comprises of (a) an object-oriented POMDP planner generating sub-goals, (b) a set of low-level policies for sub-goal achievement, and (c) an abstraction system converting the continuous low-level world into a representation suitable for abstract planning. We evaluate our system on varying numbers of objects, rooms, and problem types in AI2-THOR simulated environments with promising results.
Negative Token Merging: Image-based Adversarial Feature Guidance
Authors: Jaskirat Singh, Lindsey Li, Weijia Shi, Ranjay Krishna, Yejin Choi, Pang Wei Koh, Michael F. Cohen, Stephen Gould, Liang Zheng, Luke Zettlemoyer
Date: 2024-12-02 00:00:00

Text-based adversarial guidance using a negative prompt has emerged as a widely adopted approach to steer diffusion models away from producing undesired concepts. While useful, performing adversarial guidance using text alone can be insufficient to capture complex visual concepts or avoid specific visual elements like copyrighted characters. In this paper, for the first time we explore an alternate modality in this direction by performing adversarial guidance directly using visual features from a reference image or other images in a batch. We introduce negative token merging (NegToMe), a simple but effective training-free approach which performs adversarial guidance through images by selectively pushing apart matching visual features between reference and generated images during the reverse diffusion process. By simply adjusting the used reference, NegToMe enables a diverse range of applications. Notably, when using other images in same batch as reference, we find that NegToMe significantly enhances output diversity (e.g., racial, gender, visual) by guiding features of each image away from others. Similarly, when used w.r.t. copyrighted reference images, NegToMe reduces visual similarity to copyrighted content by 34.57%. NegToMe is simple to implement using just few-lines of code, uses only marginally higher (<4%) inference time and is compatible with different diffusion architectures, including those like Flux, which don't natively support the use of a negative prompt. Code is available at https://negtome.github.io
The "LLM World of Words" English free association norms generated by large language models
Authors: Katherine Abramski, Riccardo Improta, Giulio Rossetti, Massimo Stella
Date: 2024-12-02 00:00:00

Free associations have been extensively used in cognitive psychology and linguistics for studying how conceptual knowledge is organized. Recently, the potential of applying a similar approach for investigating the knowledge encoded in LLMs has emerged, specifically as a method for investigating LLM biases. However, the absence of large-scale LLM-generated free association norms that are comparable with human-generated norms is an obstacle to this new research direction. To address this limitation, we create a new dataset of LLM-generated free association norms modeled after the "Small World of Words" (SWOW) human-generated norms consisting of approximately 12,000 cue words. We prompt three LLMs, namely Mistral, Llama3, and Haiku, with the same cues as those in the SWOW norms to generate three novel comparable datasets, the "LLM World of Words" (LWOW). Using both SWOW and LWOW norms, we construct cognitive network models of semantic memory that represent the conceptual knowledge possessed by humans and LLMs. We demonstrate how these datasets can be used for investigating implicit biases in humans and LLMs, such as the harmful gender stereotypes that are prevalent both in society and LLM outputs.
Explainable fault and severity classification for rolling element bearings using Kolmogorov-Arnold networks
Authors: Spyros Rigas, Michalis Papachristou, Ioannis Sotiropoulos, Georgios Alexandridis
Date: 2024-12-02 00:00:00
Tasks: Classification, Explainable Models, Fault Detection, Fault Diagnosis, feature selection, Kolmogorov-Arnold Networks, severity prediction

Rolling element bearings are critical components of rotating machinery, with their performance directly influencing the efficiency and reliability of industrial systems. At the same time, bearing faults are a leading cause of machinery failures, often resulting in costly downtime, reduced productivity, and, in extreme cases, catastrophic damage. This study presents a methodology that utilizes Kolmogorov-Arnold Networks to address these challenges through automatic feature selection, hyperparameter tuning and interpretable fault analysis within a unified framework. By training shallow network architectures and minimizing the number of selected features, the framework produces lightweight models that deliver explainable results through feature attribution and symbolic representations of their activation functions. Validated on two widely recognized datasets for bearing fault diagnosis, the framework achieved perfect F1-Scores for fault detection and high performance in fault and severity classification tasks, including 100% F1-Scores in most cases. Notably, it demonstrated adaptability by handling diverse fault types, such as imbalance and misalignment, within the same dataset. The symbolic representations enhanced model interpretability, while feature attribution offered insights into the optimal feature types or signals for each studied task. These results highlight the framework's potential for practical applications, such as real-time machinery monitoring, and for scientific research requiring efficient and explainable models.
Long Video Diffusion Generation with Segmented Cross-Attention and Content-Rich Video Data Curation
Authors: Xin Yan, Yuxuan Cai, Qiuyue Wang, Yuan Zhou, Wenhao Huang, Huan Yang
Date: 2024-12-02 00:00:00
Proceeding: CVPR 2025 1
Tasks: Diversity, Video Generation

We introduce Presto, a novel video diffusion model designed to generate 15-second videos with long-range coherence and rich content. Extending video generation methods to maintain scenario diversity over long durations presents significant challenges. To address this, we propose a Segmented Cross-Attention (SCA) strategy, which splits hidden states into segments along the temporal dimension, allowing each segment to cross-attend to a corresponding sub-caption. SCA requires no additional parameters, enabling seamless incorporation into current DiT-based architectures. To facilitate high-quality long video generation, we build the LongTake-HD dataset, consisting of 261k content-rich videos with scenario coherence, annotated with an overall video caption and five progressive sub-captions. Experiments show that our Presto achieves 78.5% on the VBench Semantic Score and 100% on the Dynamic Degree, outperforming existing state-of-the-art video generation methods. This demonstrates that our proposed Presto significantly enhances content richness, maintains long-range coherence, and captures intricate textual details. More details are displayed on our project page: https://presto-video.github.io/.
Multimodal Medical Disease Classification with LLaMA II
Authors: Christian Gapp, Elias Tappeiner, Martin Welk, Rainer Schubert
Date: 2024-12-02 00:00:00
Tasks: Classification, Medical Procedure

Medical patient data is always multimodal. Images, text, age, gender, histopathological data are only few examples for different modalities in this context. Processing and integrating this multimodal data with deep learning based methods is of utmost interest due to its huge potential for medical procedure such as diagnosis and patient treatment planning. In this work we retrain a multimodal transformer-based model for disease classification. To this end we use the text-image pair dataset from OpenI consisting of 2D chest X-rays associated with clinical reports. Our focus is on fusion methods for merging text and vision information extracted from medical datasets. Different architecture structures with a LLaMA II backbone model are tested. Early fusion of modality specific features creates better results with the best model reaching 97.10% mean AUC than late fusion from a deeper level of the architecture (best model: 96.67% mean AUC). Both outperform former classification models tested on the same multimodal dataset. The newly introduced multimodal architecture can be applied to other multimodal datasets with little effort and can be easily adapted for further research, especially, but not limited to, the field of medical AI.
RL2: Reinforce Large Language Model to Assist Safe Reinforcement Learning for Energy Management of Active Distribution Networks
Authors: Xu Yang, Chenhui Lin, Haotian Liu, Wenchuan Wu
Date: 2024-12-02 00:00:00
Tasks: energy management, In-Context Learning, Language Modeling, Language Modelling, Large Language Model, Management, Reinforcement Learning (RL), Safe Reinforcement Learning

As large-scale distributed energy resources are integrated into the active distribution networks (ADNs), effective energy management in ADNs becomes increasingly prominent compared to traditional distribution networks. Although advanced reinforcement learning (RL) methods, which alleviate the burden of complicated modelling and optimization, have greatly improved the efficiency of energy management in ADNs, safety becomes a critical concern for RL applications in real-world problems. Since the design and adjustment of penalty functions, which correspond to operational safety constraints, requires extensive domain knowledge in RL and power system operation, the emerging ADN operators call for a more flexible and customized approach to address the penalty functions so that the operational safety and efficiency can be further enhanced. Empowered with strong comprehension, reasoning, and in-context learning capabilities, large language models (LLMs) provide a promising way to assist safe RL for energy management in ADNs. In this paper, we introduce the LLM to comprehend operational safety requirements in ADNs and generate corresponding penalty functions. In addition, we propose an RL2 mechanism to refine the generated functions iteratively and adaptively through multi-round dialogues, in which the LLM agent adjusts the functions' pattern and parameters based on training and test performance of the downstream RL agent. The proposed method significantly reduces the intervention of the ADN operators. Comprehensive test results demonstrate the effectiveness of the proposed method.
FedAH: Aggregated Head for Personalized Federated Learning
Authors: Pengzhan Zhou, Yuepeng He, Yijun Zhai, Kaixin Gao, Chao Chen, Zhida Qin, Chong Zhang, Songtao Guo
Date: 2024-12-02 00:00:00
Tasks: Federated Learning, Personalized Federated Learning, Privacy Preserving

Recently, Federated Learning (FL) has gained popularity for its privacy-preserving and collaborative learning capabilities. Personalized Federated Learning (PFL), building upon FL, aims to address the issue of statistical heterogeneity and achieve personalization. Personalized-head-based PFL is a common and effective PFL method that splits the model into a feature extractor and a head, where the feature extractor is collaboratively trained and shared, while the head is locally trained and not shared. However, retaining the head locally, although achieving personalization, prevents the model from learning global knowledge in the head, thus affecting the performance of the personalized model. To solve this problem, we propose a novel PFL method called Federated Learning with Aggregated Head (FedAH), which initializes the head with an Aggregated Head at each iteration. The key feature of FedAH is to perform element-level aggregation between the local model head and the global model head to introduce global information from the global model head. To evaluate the effectiveness of FedAH, we conduct extensive experiments on five benchmark datasets in the fields of computer vision and natural language processing. FedAH outperforms ten state-of-the-art FL methods in terms of test accuracy by 2.87%. Additionally, FedAH maintains its advantage even in scenarios where some clients drop out unexpectedly. Our code is open-accessed at https://github.com/heyuepeng/FedAH.
Learning Smooth Distance Functions via Queries
Authors: Akash Kumar, Sanjoy Dasgupta
Date: 2024-12-02 00:00:00
Tasks: Triplet

In this work, we investigate the problem of learning distance functions within the query-based learning framework, where a learner is able to pose triplet queries of the form: ``Is $x_i$ closer to $x_j$ or $x_k$?'' We establish formal guarantees on the query complexity required to learn smooth, but otherwise general, distance functions under two notions of approximation: $\omega$-additive approximation and $(1 + \omega)$-multiplicative approximation. For the additive approximation, we propose a global method whose query complexity is quadratic in the size of a finite cover of the sample space. For the (stronger) multiplicative approximation, we introduce a method that combines global and local approaches, utilizing multiple Mahalanobis distance functions to capture local geometry. This method has a query complexity that scales quadratically with both the size of the cover and the ambient space dimension of the sample space.
MFTF: Mask-free Training-free Object Level Layout Control Diffusion Model
Authors: Shan Yang
Date: 2024-12-02 00:00:00
Tasks: Denoising, Image Generation, Object, Text to Image Generation, Text-to-Image Generation

Text-to-image generation models have revolutionized content creation, but diffusion-based vision-language models still face challenges in precisely controlling the shape, appearance, and positional placement of objects in generated images using text guidance alone. Existing global image editing models rely on additional masks or images as guidance to achieve layout control, often requiring retraining of the model. While local object-editing models allow modifications to object shapes, they lack the capability to control object positions. To address these limitations, we propose the Mask-free Training-free Object-Level Layout Control Diffusion Model (MFTF), which provides precise control over object positions without requiring additional masks or images. The MFTF model supports both single-object and multi-object positional adjustments, such as translation and rotation, while enabling simultaneous layout control and object semantic editing. The MFTF model employs a parallel denoising process for both the source and target diffusion models. During this process, attention masks are dynamically generated from the cross-attention layers of the source diffusion model and applied to queries from the self-attention layers to isolate objects. These queries, generated in the source diffusion model, are then adjusted according to the layout control parameters and re-injected into the self-attention layers of the target diffusion model. This approach ensures accurate and precise positional control of objects. Project source code available at https://github.com/syang-genai/MFTF.
Align-KD: Distilling Cross-Modal Alignment Knowledge for Mobile Vision-Language Model
Authors: Qianhan Feng, Wenshuo Li, Tong Lin, Xinghao Chen
Date: 2024-12-02 00:00:00
Tasks: cross-modal alignment, Knowledge Distillation, Language Modeling, Language Modelling

Vision-Language Models (VLMs) bring powerful understanding and reasoning capabilities to multimodal tasks. Meanwhile, the great need for capable aritificial intelligence on mobile devices also arises, such as the AI assistant software. Some efforts try to migrate VLMs to edge devices to expand their application scope. Simplifying the model structure is a common method, but as the model shrinks, the trade-off between performance and size becomes more and more difficult. Knowledge distillation (KD) can help models improve comprehensive capabilities without increasing size or data volume. However, most of the existing large model distillation techniques only consider applications on single-modal LLMs, or only use teachers to create new data environments for students. None of these methods take into account the distillation of the most important cross-modal alignment knowledge in VLMs. We propose a method called Align-KD to guide the student model to learn the cross-modal matching that occurs at the shallow layer. The teacher also helps student learn the projection of vision token into text embedding space based on the focus of text. Under the guidance of Align-KD, the 1.7B MobileVLM V2 model can learn rich knowledge from the 7B teacher model with light design of training loss, and achieve an average score improvement of 2.0 across 6 benchmarks under two training subsets respectively. Code is available at: https://github.com/fqhank/Align-KD.
FedPAW: Federated Learning with Personalized Aggregation Weights for Urban Vehicle Speed Prediction
Authors: Yuepeng He, Pengzhan Zhou, Yijun Zhai, Fang Qu, Zhida Qin, Mingyan Li, Songtao Guo
Date: 2024-12-02 00:00:00
Tasks: Autonomous Driving, Federated Learning

Vehicle speed prediction is crucial for intelligent transportation systems, promoting more reliable autonomous driving by accurately predicting future vehicle conditions. Due to variations in drivers' driving styles and vehicle types, speed predictions for different target vehicles may significantly differ. Existing methods may not realize personalized vehicle speed prediction while protecting drivers' data privacy. We propose a Federated learning framework with Personalized Aggregation Weights (FedPAW) to overcome these challenges. This method captures client-specific information by measuring the weighted mean squared error between the parameters of local models and global models. The server sends tailored aggregated models to clients instead of a single global model, without incurring additional computational and communication overhead for clients. To evaluate the effectiveness of FedPAW, we collected driving data in urban scenarios using the autonomous driving simulator CARLA, employing an LSTM-based Seq2Seq model with a multi-head attention mechanism to predict the future speed of target vehicles. The results demonstrate that our proposed FedPAW ranks lowest in prediction error within the time horizon of 10 seconds, with a 0.8% reduction in test MAE, compared to eleven representative benchmark baselines. The source code of FedPAW and dataset CarlaVSP are open-accessed at: https://github.com/heyuepeng/PFLlibVSP and https://pan.baidu.com/s/1qs8fxUvSPERV3C9i6pfUIw?pwd=tl3e.
Uncertainty-Aware Artificial Intelligence for Gear Fault Diagnosis in Motor Drives
Authors: Subham Sahoo, Huai Wang, Frede Blaabjerg
Date: 2024-12-02 00:00:00
Tasks: Decision Making, Fault Diagnosis, Uncertainty Quantification

This paper introduces a novel approach to quantify the uncertainties in fault diagnosis of motor drives using Bayesian neural networks (BNN). Conventional data-driven approaches used for fault diagnosis often rely on point-estimate neural networks, which merely provide deterministic outputs and fail to capture the uncertainty associated with the inference process. In contrast, BNNs offer a principled framework to model uncertainty by treating network weights as probability distributions rather than fixed values. It offers several advantages: (a) improved robustness to noisy data, (b) enhanced interpretability of model predictions, and (c) the ability to quantify uncertainty in the decision-making processes. To test the robustness of the proposed BNN, it has been tested under a conservative dataset of gear fault data from an experimental prototype of three fault types at first, and is then incrementally trained on new fault classes and datasets to explore its uncertainty quantification features and model interpretability under noisy data and unseen fault scenarios.
MuLan: Adapting Multilingual Diffusion Models for Hundreds of Languages with Negligible Cost
Authors: Sen Xing, Muyan Zhong, Zeqiang Lai, Liangchen Li, Jiawen Liu, Yaohui Wang, Jifeng Dai, Wenhai Wang
Date: 2024-12-02 00:00:00
Tasks: Image Generation

In this work, we explore a cost-effective framework for multilingual image generation. We find that, unlike models tuned on high-quality images with multilingual annotations, leveraging text encoders pre-trained on widely available, noisy Internet image-text pairs significantly enhances data efficiency in text-to-image (T2I) generation across multiple languages. Based on this insight, we introduce MuLan, Multi-Language adapter, a lightweight language adapter with fewer than 20M parameters, trained alongside a frozen text encoder and image diffusion model. Compared to previous multilingual T2I models, this framework offers: (1) Cost efficiency. Using readily accessible English data and off-the-shelf multilingual text encoders minimizes the training cost; (2) High performance. Achieving comparable generation capabilities in over 110 languages with CLIP similarity scores nearly matching those in English (38.61 for English vs. 37.61 for other languages); and (3) Broad applicability. Seamlessly integrating with compatible community tools like LoRA, LCM, ControlNet, and IP-Adapter, expanding its potential use cases.