pwc-archive/papers-with-abstracts huggingface dataset:
Importance Sampling via Score-based Generative Models
Authors: Heasung Kim, Taekyun Lee, Hyeji Kim, Gustavo de Veciana
Date: 2025-02-07 00:00:00
Tasks: Data Augmentation

Importance sampling, which involves sampling from a probability density function (PDF) proportional to the product of an importance weight function and a base PDF, is a powerful technique with applications in variance reduction, biased or customized sampling, data augmentation, and beyond. Inspired by the growing availability of score-based generative models (SGMs), we propose an entirely training-free Importance sampling framework that relies solely on an SGM for the base PDF. Our key innovation is realizing the importance sampling process as a backward diffusion process, expressed in terms of the score function of the base PDF and the specified importance weight function--both readily available--eliminating the need for any additional training. We conduct a thorough analysis demonstrating the method's scalability and effectiveness across diverse datasets and tasks, including importance sampling for industrial and natural images with neural importance weight functions. The training-free aspect of our method is particularly compelling in real-world scenarios where a single base distribution underlies multiple biased sampling tasks, each requiring a different importance weight function. To the best of our knowledge our approach is the first importance sampling framework to achieve this.
CMoE: Fast Carving of Mixture-of-Experts for Efficient LLM Inference
Authors: Zehua Pei, Lancheng Zou, Hui-Ling Zhen, Xianzhi Yu, Wulong Liu, Sinno Jialin Pan, Mingxuan Yuan, Bei Yu
Date: 2025-02-06 00:00:00
Tasks: Mixture-of-Experts

Large language models (LLMs) achieve impressive performance by scaling model parameters, but this comes with significant inference overhead. Feed-forward networks (FFNs), which dominate LLM parameters, exhibit high activation sparsity in hidden neurons. To exploit this, researchers have proposed using a mixture-of-experts (MoE) architecture, where only a subset of parameters is activated. However, existing approaches often require extensive training data and resources, limiting their practicality. We propose CMoE (Carved MoE), a novel framework to efficiently carve MoE models from dense models. CMoE achieves remarkable performance through efficient expert grouping and lightweight adaptation. First, neurons are grouped into shared and routed experts based on activation rates. Next, we construct a routing mechanism without training from scratch, incorporating a differentiable routing process and load balancing. Using modest data, CMoE produces a well-designed, usable MoE from a 7B dense model within five minutes. With lightweight fine-tuning, it achieves high-performance recovery in under an hour. We make our code publicly available at https://github.com/JarvisPei/CMoE.
Artificial Intelligence Approaches for Anti-Addiction Drug Discovery
Authors: Dong Chen, Jian Jiang, Zhe Su, Guo-Wei Wei
Date: 2025-02-05 00:00:00
Tasks: Drug Discovery

Drug addiction is a complex and pervasive global challenge that continues to pose significant public health concerns. Traditional approaches to anti-addiction drug discovery have struggled to deliver effective therapeutics, facing high attrition rates, long development timelines, and inefficiencies in processing large-scale data. Artificial intelligence (AI) has emerged as a transformative solution to address these issues. Using advanced algorithms, AI is revolutionizing drug discovery by enhancing the speed and precision of key processes. This review explores the transformative role of AI in the pipeline for anti-addiction drug discovery, including data collection, target identification, and compound optimization. By highlighting the potential of AI to overcome traditional barriers, this review systematically examines how AI addresses critical gaps in anti-addiction research, emphasizing its potential to revolutionize drug discovery and development, overcome challenges, and advance more effective therapeutic strategies.
Temporal knowledge graph question answering via subgraph reasoning
Authors: Ziyang Chen, Xiang Zhao, Jinzhi Liao, Xinyi Li, Evangelos Kanoulas
Date: 2022-05-30 00:00:00
Proceeding: KBS 2022 5
Tasks: Graph Question Answering, Knowledge Graphs, Question Answering

Knowledge graph question answering (KGQA) has recently received a lot of attention and many innovative methods have been proposed in this area, but few have been developed for temporal KGQA. Most of the existing temporal KGQA methods focus on semantic or temporal level matching and lack the ability to reason about time constraints. In this paper we propose a subgraph-based model for answering complex questions over temporal knowledge graphs (TKG), inspired by human cognition. Our method, called SubGraph Temporal Reasoning (SubGTR), consists of three main modules: implicit knowledge extraction, relevant facts search, and subgraph logic reasoning. First, the question is reformulated using background knowledge stored in the temporal knowledge graph to acquire explicit time constraints. Then, the TKG is being searched to identify relevant entities and obtain an initial scoring of them. Finally the time constraints are quantified and applied using temporal logic to reach to the final answer. To evaluate our model we experiment against temporal QA benchmarks. We observe that existing benchmarks contain many pseudo-temporal questions, and we propose Complex-CronQuestions, which a filtered version of CronQuestions and which can better demonstrate the model’s inference ability for complex temporal questions. Experimental results show that SubGTR achieves state-of-the-art performance on both CronQuestions and Complex-CronQuestions. Moreover, our model shows better performance in handling the entity cold-start problem compared to existing temporal KGQA methods.
Leveraging band diversity for feature selection in EO data
Authors: Sadia Hussain, Brejesh lall
Date: 2025-02-07 00:00:00
Tasks: Diversity, Earth Observation, feature selection, Point Processes

Hyperspectral imaging (HSI) is a powerful earth observation technology that captures and processes information across a wide spectrum of wavelengths. Hyperspectral imaging provides comprehensive and detailed spectral data that is invaluable for a wide range of reconstruction problems. However due to complexity in analysis it often becomes difficult to handle this data. To address the challenge of handling large number of bands in reconstructing high quality HSI, we propose to form groups of bands. In this position paper we propose a method of selecting diverse bands using determinantal point processes in correlated bands. To address the issue of overlapping bands that may arise from grouping, we use spectral angle mapper analysis. This analysis can be fed to any Machine learning model to enable detailed analysis and monitoring with high precision and accuracy.
Differentiable Mobile Display Photometric Stereo
Authors: Gawoon Ban, Hyeongjun Kim, Seokjun Choi, Seungwoo Yoon, Seung-Hwan Baek
Date: 2025-02-07 00:00:00

Display photometric stereo uses a display as a programmable light source to illuminate a scene with diverse illumination conditions. Recently, differentiable display photometric stereo (DDPS) demonstrated improved normal reconstruction accuracy by using learned display patterns. However, DDPS faced limitations in practicality, requiring a fixed desktop imaging setup using a polarization camera and a desktop-scale monitor. In this paper, we propose a more practical physics-based photometric stereo, differentiable mobile display photometric stereo (DMDPS), that leverages a mobile phone consisting of a display and a camera. We overcome the limitations of using a mobile device by developing a mobile app and method that simultaneously displays patterns and captures high-quality HDR images. Using this technique, we capture real-world 3D-printed objects and learn display patterns via a differentiable learning process. We demonstrate the effectiveness of DMDPS on both a 3D printed dataset and a first dataset of fallen leaves. The leaf dataset contains reconstructed surface normals and albedos of fallen leaves that may enable future research beyond computer graphics and vision. We believe that DMDPS takes a step forward for practical physics-based photometric stereo.
IoV-Oriented Integrated Sensing, Computation, and Communication: System Design and Resource Allocation
Authors: Junhui Zhao, Ruixing Ren, Dan Zou, Qingmiao Zhang, and Wei Xu, Senior Member
Date: 2024-10-01 00:00:00
Proceeding: journal 2024 10
Tasks: Deep Reinforcement Learning, Edge-computing, Integrated sensing and communication, ISAC

—In future mobile communication systems, the deep
integration of communication, sensing, and computation has become a new trend. This article paper designs an integrated sensing, computation, and communication (ISCC) system for internet
of vehicles (IoV) by leveraging mobile edge computing (MEC)
and integrated sensing and communication (ISAC). Specifically,
a collaborative sensing data fusion architecture is proposed, where
vehicles collaborate with road side unit (RSU) to address the issue
of limited sensing range for a single vehicle. Furthermore, the
randomness of collaborative sensing tasks arrival in real-world
traffic scenarios is simulated, with communication, sensing, and
computation being modeled respectively. The joint optimization of
wireless and edge computing resources in the ISCC system aims at
maximizing the completion rate of collaborative sensing tasks while
ensuring system service delay. To address the mixed-integer nonlinear optimization problem, a resource allocation scheme based
on deep reinforcement learning (DRL) is developed, which enables adaptive allocation of wireless and edge computing resources
through online learning. Simulation results show that agent of DRL
can learn a smart resource allocation strategy from the interactive
environment, and achieve better performance than conventional
resource allocation schemes.
ARR: Question Answering with Large Language Models via Analyzing, Retrieving, and Reasoning
Authors: Yuwei Yin, Giuseppe Carenini
Date: 2025-02-07 00:00:00
Tasks: Multiple-choice, Question Answering

Large language models (LLMs) have demonstrated impressive capabilities on complex evaluation benchmarks, many of which are formulated as question-answering (QA) tasks. Enhancing the performance of LLMs in QA contexts is becoming increasingly vital for advancing their development and applicability. This paper introduces ARR, an intuitive, effective, and general QA solving method that explicitly incorporates three key steps: analyzing the intent of the question, retrieving relevant information, and reasoning step by step. Notably, this paper is the first to introduce intent analysis in QA, which plays a vital role in ARR. Comprehensive evaluations across 10 diverse QA tasks demonstrate that ARR consistently outperforms the baseline methods. Ablation and case studies further validate the positive contributions of each ARR component. Furthermore, experiments involving variations in prompt design indicate that ARR maintains its effectiveness regardless of the specific prompt formulation. Additionally, extensive evaluations across various model sizes, LLM series, and generation settings solidify the effectiveness, robustness, and generalizability of ARR.
Lost in Edits? A $λ$-Compass for AIGC Provenance
Authors: Wenhao You, Bryan Hooi, Yiwei Wang, Euijin Choo, Ming-Hsuan Yang, Junsong Yuan, Zi Huang, Yujun Cai
Date: 2025-02-05 00:00:00
Tasks: text-guided-image-editing

Recent advancements in diffusion models have driven the growth of text-guided image editing tools, enabling precise and iterative modifications of synthesized content. However, as these tools become increasingly accessible, they also introduce significant risks of misuse, emphasizing the critical need for robust attribution methods to ensure content authenticity and traceability. Despite the creative potential of such tools, they pose significant challenges for attribution, particularly in adversarial settings where edits can be layered to obscure an image's origins. We propose LambdaTracer, a novel latent-space attribution method that robustly identifies and differentiates authentic outputs from manipulated ones without requiring any modifications to generative or editing pipelines. By adaptively calibrating reconstruction losses, LambdaTracer remains effective across diverse iterative editing processes, whether automated through text-guided editing tools such as InstructPix2Pix and ControlNet or performed manually with editing software such as Adobe Photoshop. Extensive experiments reveal that our method consistently outperforms baseline approaches in distinguishing maliciously edited images, providing a practical solution to safeguard ownership, creativity, and credibility in the open, fast-evolving AI ecosystems.
Multi-agent Architecture Search via Agentic Supernet
Authors: Guibin Zhang, Luyang Niu, Junfeng Fang, Kun Wang, Lei Bai, Xiang Wang
Date: 2025-02-06 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Large Language Model (LLM)-empowered multi-agent systems extend the cognitive boundaries of individual agents through disciplined collaboration and interaction, while constructing these systems often requires labor-intensive manual designs. Despite the availability of methods to automate the design of agentic workflows, they typically seek to identify a static, complex, one-size-fits-all system, which, however, fails to dynamically allocate inference resources based on the difficulty and domain of each query. To address this challenge, we shift away from the pursuit of a monolithic agentic system, instead optimizing the \textbf{agentic supernet}, a probabilistic and continuous distribution of agentic architectures. We introduce MaAS, an automated framework that samples query-dependent agentic systems from the supernet, delivering high-quality solutions and tailored resource allocation (\textit{e.g.}, LLM calls, tool calls, token cost). Comprehensive evaluation across six benchmarks demonstrates that MaAS \textbf{(I)} requires only $6\sim45\%$ of the inference costs of existing handcrafted or automated multi-agent systems, \textbf{(II)} surpasses them by $0.54\%\sim11.82\%$, and \textbf{(III)} enjoys superior cross-dataset and cross-LLM-backbone transferability.
QuEST: Stable Training of LLMs with 1-Bit Weights and Activations
Authors: Andrei Panferov, Jiale Chen, Soroush Tabesh, Roberto L. Castro, Mahdi Nikdan, Dan Alistarh
Date: 2025-02-07 00:00:00
Tasks: GPU, Quantization

One approach to reducing the massive costs of large language models (LLMs) is the use of quantized or sparse representations for training or deployment. While post-training compression methods are very popular, the question of obtaining even more accurate compressed models by directly training over such representations, i.e., Quantization-Aware Training (QAT), is still open: for example, a recent study (arXiv:2411.04330v2) put the "optimal" bit-width at which models can be trained using QAT, while staying accuracy-competitive with standard FP16/BF16 precision, at 8-bits weights and activations. We advance this state-of-the-art via a new method called QuEST, which is Pareto-competitive with FP16, i.e., it provides better accuracy at lower model size, while training models with weights and activations in 4-bits or less. Moreover, QuEST allows stable training with 1-bit weights and activations. QuEST achieves this by improving two key aspects of QAT methods: (1) accurate and fast quantization of the (continuous) distributions of weights and activations via Hadamard normalization and MSE-optimal fitting; (2) a new trust gradient estimator based on the idea of explicitly minimizing the error between the noisy gradient computed over quantized states and the "true" (but unknown) full-precision gradient. Experiments on Llama-type architectures show that QuEST induces stable scaling laws across the entire range of hardware-supported precisions, and can be extended to sparse representations. We provide GPU kernel support showing that models produced by QuEST can be executed efficiently. Our code is available at https://github.com/IST-DASLab/QuEST.
Beautiful Images, Toxic Words: Understanding and Addressing Offensive Text in Generated Images
Authors: Aditya Kumar, Tom Blanchard, Adam Dziedzic, Franziska Boenisch
Date: 2025-02-07 00:00:00
Tasks: Text Generation

State-of-the-art visual generation models, such as Diffusion Models (DMs) and Vision Auto-Regressive Models (VARs), produce highly realistic images. While prior work has successfully mitigated Not Safe For Work (NSFW) content in the visual domain, we identify a novel threat: the generation of NSFW text embedded within images. This includes offensive language, such as insults, racial slurs, and sexually explicit terms, posing significant risks to users. We show that all state-of-the-art DMs (e.g., SD3, Flux, DeepFloyd IF) and VARs (e.g., Infinity) are vulnerable to this issue. Through extensive experiments, we demonstrate that existing mitigation techniques, effective for visual content, fail to prevent harmful text generation while substantially degrading benign text generation. As an initial step toward addressing this threat, we explore safety fine-tuning of the text encoder underlying major DM architectures using a customized dataset. Thereby, we suppress NSFW generation while preserving overall image and text generation quality. Finally, to advance research in this area, we introduce ToxicBench, an open-source benchmark for evaluating NSFW text generation in images. ToxicBench provides a curated dataset of harmful prompts, new metrics, and an evaluation pipeline assessing both NSFW-ness and generation quality. Our benchmark aims to guide future efforts in mitigating NSFW text generation in text-to-image models and is available at https://github.com/sprintml/ToxicBench
Rethinking Oversmoothing in Graph Neural Networks: A Rank-Based Perspective
Authors: Kaicheng Zhang, Piero Deidda, Desmond Higham, Francesco Tudisco
Date: 2025-02-07 00:00:00

Oversmoothing is a fundamental challenge in graph neural networks (GNNs): as the number of layers increases, node embeddings become increasingly similar, and model performance drops sharply. Traditionally, oversmoothing has been quantified using metrics that measure the similarity of neighbouring node features, such as the Dirichlet energy. While these metrics are related to oversmoothing, we argue they have critical limitations and fail to reliably capture oversmoothing in realistic scenarios. For instance, they provide meaningful insights only for very deep networks and under somewhat strict conditions on the norm of network weights and feature representations. As an alternative, we propose measuring oversmoothing by examining the numerical or effective rank of the feature representations. We provide theoretical support for this approach, demonstrating that the numerical rank of feature representations converges to one for a broad family of nonlinear activation functions under the assumption of nonnegative trained weights. To the best of our knowledge, this is the first result that proves the occurrence of oversmoothing in the nonlinear setting without assumptions on the boundedness of the weight matrices. Along with the theoretical findings, we provide extensive numerical evaluation across diverse graph architectures. Our results show that rank-based metrics consistently capture oversmoothing, whereas energy-based metrics often fail. Notably, we reveal that a significant drop in the rank aligns closely with performance degradation, even in scenarios where energy metrics remain unchanged.
Development of a machine learning-based design optimization method for crashworthiness analysis
Authors: Aditya Borse, Rutwrik Gualakala, Marcus Stoffel
Date: 2024-03-06 00:00:00
Proceeding: Archives of Mechanics 2024 3
Tasks: Crashworthiness Design, Generative Adversarial Network, Multiobjective Optimization

This article investigates design optimisation in the automotive field using machine learning (ML). A thin-walled crash box under axial impact is studied and the design parameters are optimised for front-impact crash tests. This study is based on geometrically and physically nonlinear shell theory, finite element analysis (FEA), dynamic buckling analysis and design optimisation using ML. An artificial neural network framework consisting of various ML methods is developed. A generative adversarial network is established for data generation and reinforcement learning is implemented to automate exploration of the design parameter. This ML framework is proven to determine optimal parameters under predefined crashworthiness constraints.
Distributed Control of Multi-Energy Storage Systems for Voltage Regulation in Distribution Networks: A Back-and-Forth Communication Framework
Authors: Peng Yu, Can Wan, Yonghua Song, Yibao Jiang
Date: 2020-09-25 00:00:00
Proceeding: IEEE Transactions on Smart Grid 2020 9

—Distributed energy storage systems (DESSs) are
widely utilized to regulate voltages in active distribution networks
with high penetration of volatile renewable energy. In this
paper, the distributed multi-energy storage systems (MESSs)
are integrated into the active distribution network to enhance
the capability of voltage regulation by exploiting interactions
among multi-energy loads. A novel distributed control strategy
based on back-and-forth communication (BFC) framework is
developed to optimally coordinate multiple MESSs for multitime-step voltage regulation. To achieve the independent control
of MESSs at each bus, the BFC is proposed to determine the
global sensitivities of voltage violation in the whole network
with respect to the power injection at each bus in only one-step
iteration. These sensitivities updated in real time during BFC
quantify the capability of the single MESS to mitigate voltage
fluctuations across all buses, which can be used to individually
inform the operation of each MESS. Distinct from traditional
distributed control methods that achieve voltage regulation in a
single timeslot, the proposed control strategy regulates MESSs
in a multi-time-step manner to alleviate anticipated voltage
violation in advance. The simulation based on a modified 11-
bus radial distribution network demonstrates the effectiveness of
the proposed distributed control strategy
Distributed Voltage Control of Active Distribution Networks With Global Sensitivity
Authors: Peng Yu; Can Wan; Mingyang Sun; Yongzhi Zhou; Yonghua Song
Date: 2022-02-24 00:00:00
Proceeding: IEEE Transactions on Power Systems 2022 2
Tasks: global-optimization, Sensitivity

With the growing penetration of distributed renewable energy, distributed control approaches are widely utilized in voltage control of active distribution networks (ADNs), suffering from limited applicability for control devices or heavy communication burden. This paper develops an innovative distributed voltage control strategy of ADNs with global sensitivities (DVC-GS), which integrates network information from a global optimization view to coordinate energy storages, PV inverters and the OLTC with little communication and computing time. The global sensitivities of voltage violations across all buses with respect to nodal voltage and active/reactive power injection are formulated to quantify the ability of each controllable resource for alleviating voltage violations in the entire ADN. The back-and-forth communication combined with the modified DistFlow model is newly developed for the real-time update of global sensitivities at each bus with high accuracy and no iteration. Then the coordinated control of various devices is implemented on basis of global sensitivities to effectively ensure voltage security of ADNs. Comprehensive simulations based on modified 11-bus and 123-bus systems demonstrate the significant efficiency and superiority of the global sensitivities and DVC-GS.