pwc-archive/papers-with-abstracts huggingface dataset:
The Sustainable Future is now: a dynamic model to advance investments in PV and Energy Storage
Authors: L. Becchetti, N. Solferino, M. E. Tessitore
Date: 2025-03-10 00:00:00

We examine the relationship among photovoltaic (PV) investments, energy production, and environmental impact using a dynamic optimization model. Our findings show that increasing investment in renewables supports both energy generation and ecological sustainability, with the optimal path depending on policy priorities. Our analysis demonstrates that the economic and technological conditions for a transition to PV energy are already in place, challenging the idea that renewables will only become competitive in the future. We also account for the fact that PV optimality conditions improve over time as storage technology efficiency increases and production costs decrease. In this perspective we find that energy storage may be a more effective policy tool than carbon taxation for cutting emissions, as it faces less political resistance and further strengthens the long-term viability of renewable energy. Policy insights of the paper capture the evolving competitiveness of PV and its role in accelerating the energy transition. They also provide policymakers with strategies to align economic growth with long-term sustainability through renewable energy investments.
Speech Audio Generation from dynamic MRI via a Knowledge Enhanced Conditional Variational Autoencoder
Authors: Yaxuan Li, Han Jiang, Yifei Ma, Shihua Qin, Fangxu Xing
Date: 2025-03-09 00:00:00
Tasks: Audio Generation, Denoising, Variational Inference

Dynamic Magnetic Resonance Imaging (MRI) of the vocal tract has become an increasingly adopted imaging modality for speech motor studies. Beyond image signals, systematic data loss, noise pollution, and audio file corruption can occur due to the unpredictability of the MRI acquisition environment. In such cases, generating audio from images is critical for data recovery in both clinical and research applications. However, this remains challenging due to hardware constraints, acoustic interference, and data corruption. Existing solutions, such as denoising and multi-stage synthesis methods, face limitations in audio fidelity and generalizability. To address these challenges, we propose a Knowledge Enhanced Conditional Variational Autoencoder (KE-CVAE), a novel two-step "knowledge enhancement + variational inference" framework for generating speech audio signals from cine dynamic MRI sequences. This approach introduces two key innovations: (1) integration of unlabeled MRI data for knowledge enhancement, and (2) a variational inference architecture to improve generative modeling capacity. To the best of our knowledge, this is one of the first attempts at synthesizing speech audio directly from dynamic MRI video sequences. The proposed method was trained and evaluated on an open-source dynamic vocal tract MRI dataset recorded during speech. Experimental results demonstrate its effectiveness in generating natural speech waveforms while addressing MRI-specific acoustic challenges, outperforming conventional deep learning-based synthesis approaches.
When Trust Collides: Decoding Human-LLM Cooperation Dynamics through the Prisoner's Dilemma
Authors: Guanxuan Jiang, Shirao Yang, Yuyang Wang, Pan Hui
Date: 2025-03-10 00:00:00
Tasks: AI Agent, Language Modeling, Language Modelling, Large Language Model

As large language models (LLMs) become increasingly capable of autonomous decision-making, they introduce new challenges and opportunities for human-AI cooperation in mixed-motive contexts. While prior research has primarily examined AI in assistive or cooperative roles, little is known about how humans interact with AI agents perceived as independent and strategic actors. This study investigates human cooperative attitudes and behaviors toward LLM agents by engaging 30 participants (15 males, 15 females) in repeated Prisoner's Dilemma games with agents differing in declared identity: purported human, rule-based AI, and LLM agent. Behavioral metrics, including cooperation rate, decision latency, unsolicited cooperative acts and trust restoration tolerance, were analyzed to assess the influence of agent identity and participant gender. Results revealed significant effects of declared agent identity on most cooperation-related behaviors, along with notable gender differences in decision latency. Furthermore, qualitative responses suggest that these behavioral differences were shaped by participants interpretations and expectations of the agents. These findings contribute to our understanding of human adaptation in competitive cooperation with autonomous agents and underscore the importance of agent framing in shaping effective and ethical human-AI interaction.
SRM-Hair: Single Image Head Mesh Reconstruction via 3D Morphable Hair
Authors: Zidu Wang, Jiankuo Zhao, Miao Xu, Xiangyu Zhu, Zhen Lei
Date: 2025-03-08 00:00:00

3D Morphable Models (3DMMs) have played a pivotal role as a fundamental representation or initialization for 3D avatar animation and reconstruction. However, extending 3DMMs to hair remains challenging due to the difficulty of enforcing vertex-level consistent semantic meaning across hair shapes. This paper introduces a novel method, Semantic-consistent Ray Modeling of Hair (SRM-Hair), for making 3D hair morphable and controlled by coefficients. The key contribution lies in semantic-consistent ray modeling, which extracts ordered hair surface vertices and exhibits notable properties such as additivity for hairstyle fusion, adaptability, flipping, and thickness modification. We collect a dataset of over 250 high-fidelity real hair scans paired with 3D face data to serve as a prior for the 3D morphable hair. Based on this, SRM-Hair can reconstruct a hair mesh combined with a 3D head from a single image. Note that SRM-Hair produces an independent hair mesh, facilitating applications in virtual avatar creation, realistic animation, and high-fidelity hair rendering. Both quantitative and qualitative experiments demonstrate that SRM-Hair achieves state-of-the-art performance in 3D mesh reconstruction. Our project is available at https://github.com/wang-zidu/SRM-Hair
Motion Anything: Any to Motion Generation
Authors: Zeyu Zhang, Yiran Wang, Wei Mao, Danning Li, Rui Zhao, Biao Wu, Zirui Song, Bohan Zhuang, Ian Reid, Richard Hartley
Date: 2025-03-10 00:00:00
Tasks: Motion Generation, Motion Synthesis

Conditional motion generation has been extensively studied in computer vision, yet two critical challenges remain. First, while masked autoregressive methods have recently outperformed diffusion-based approaches, existing masking models lack a mechanism to prioritize dynamic frames and body parts based on given conditions. Second, existing methods for different conditioning modalities often fail to integrate multiple modalities effectively, limiting control and coherence in generated motion. To address these challenges, we propose Motion Anything, a multimodal motion generation framework that introduces an Attention-based Mask Modeling approach, enabling fine-grained spatial and temporal control over key frames and actions. Our model adaptively encodes multimodal conditions, including text and music, improving controllability. Additionally, we introduce Text-Motion-Dance (TMD), a new motion dataset consisting of 2,153 pairs of text, music, and dance, making it twice the size of AIST++, thereby filling a critical gap in the community. Extensive experiments demonstrate that Motion Anything surpasses state-of-the-art methods across multiple benchmarks, achieving a 15% improvement in FID on HumanML3D and showing consistent performance gains on AIST++ and TMD. See our project website https://steve-zeyu-zhang.github.io/MotionAnything
DeepNuParc: A Novel Deep Clustering Framework for Fine-scale Parcellation of Brain Nuclei Using Diffusion MRI Tractography
Authors: Haolin He, Ce Zhu, Le Zhang, Yipeng Liu, Xiao Xu, Yuqian Chen, Leo Zekelman, Jarrett Rushmore, Yogesh Rathi, Nikos Makris, Lauren J. O'Donnell, Fan Zhang
Date: 2025-03-10 00:00:00
Tasks: Clustering, Deep Clustering, Diffusion  MRI, Dimensionality Reduction

Brain nuclei are clusters of anatomically distinct neurons that serve as important hubs for processing and relaying information in various neural circuits. Fine-scale parcellation of the brain nuclei is vital for a comprehensive understanding of its anatomico-functional correlations. Diffusion MRI tractography is an advanced imaging technique that can estimate the brain's white matter structural connectivity to potentially reveal the topography of the nuclei of interest for studying its subdivisions. In this work, we present a deep clustering pipeline, namely DeepNuParc, to perform automated, fine-scale parcellation of brain nuclei using diffusion MRI tractography. First, we incorporate a newly proposed deep learning approach to enable accurate segmentation of the nuclei of interest directly on the dMRI data. Next, we design a novel streamline clustering-based structural connectivity feature for a robust representation of voxels within the nuclei. Finally, we improve the popular joint dimensionality reduction and k-means clustering approach to enable nuclei parcellation at a finer scale. We demonstrate DeepNuParc on two important brain structures, i.e. the amygdala and the thalamus, that are known to have multiple anatomically and functionally distinct nuclei subdivisions. Experimental results show that DeepNuParc enables consistent parcellation of the nuclei into multiple parcels across multiple subjects and achieves good correspondence with the widely used coarse-scale atlases. Our codes are available at https://github.com/HarlandZZC/deep_nuclei_parcellation.
TimeLoc: A Unified End-to-End Framework for Precise Timestamp Localization in Long Videos
Authors: Chen-Lin Zhang, Lin Sui, Shuming Liu, Fangzhou Mu, Zhangcheng Wang, Bernard Ghanem
Date: 2025-03-09 00:00:00
Tasks: Action Localization, Boundary Detection, Chunking, Generic Event Boundary Detection, Moment Retrieval, Temporal Action Localization, Temporal Localization, Video Grounding, Video Understanding

Temporal localization in untrimmed videos, which aims to identify specific timestamps, is crucial for video understanding but remains challenging. This task encompasses several subtasks, including temporal action localization, temporal video grounding, moment retrieval, and generic event boundary detection. Existing methods in each subfield are typically designed for specific tasks and lack generalizability across domains. In this paper, we propose TimeLoc, a unified end-to-end framework for timestamp localization that can handle multiple tasks. First, our approach employs a simple yet effective one-stage localization model that supports text queries as input and multiple actions as output. Second, we jointly train the video encoder and localization model in an end-to-end manner. To efficiently process long videos, we introduce temporal chunking, enabling the handling of videos with over 30k frames. Third, we find that fine-tuning pre-trained text encoders with a multi-stage training strategy further enhances text-conditioned localization. TimeLoc achieves state-of-the-art results across multiple benchmarks: +1.3% and +1.9% mAP over previous best methods on THUMOS14 and EPIC-Kitchens-100, +1.1% on Kinetics-GEBD, +2.94% mAP on QVHighlights, and significant improvements in temporal video grounding (+11.5% on TACoS and +6.7% on Charades-STA under R1@0.5). Our code and checkpoints will be released at https://github.com/sming256/TimeLoc.
YOLOE: Real-Time Seeing Anything
Authors: Ao Wang, Lihao Liu, Hui Chen, Zijia Lin, Jungong Han, Guiguang Ding
Date: 2025-03-10 00:00:00
Tasks: 10-shot image generation

Object detection and segmentation are widely employed in computer vision applications, yet conventional models like YOLO series, while efficient and accurate, are limited by predefined categories, hindering adaptability in open scenarios. Recent open-set methods leverage text prompts, visual cues, or prompt-free paradigm to overcome this, but often compromise between performance and efficiency due to high computational demands or deployment complexity. In this work, we introduce YOLOE, which integrates detection and segmentation across diverse open prompt mechanisms within a single highly efficient model, achieving real-time seeing anything. For text prompts, we propose Re-parameterizable Region-Text Alignment (RepRTA) strategy. It refines pretrained textual embeddings via a re-parameterizable lightweight auxiliary network and enhances visual-textual alignment with zero inference and transferring overhead. For visual prompts, we present Semantic-Activated Visual Prompt Encoder (SAVPE). It employs decoupled semantic and activation branches to bring improved visual embedding and accuracy with minimal complexity. For prompt-free scenario, we introduce Lazy Region-Prompt Contrast (LRPC) strategy. It utilizes a built-in large vocabulary and specialized embedding to identify all objects, avoiding costly language model dependency. Extensive experiments show YOLOE's exceptional zero-shot performance and transferability with high inference efficiency and low training cost. Notably, on LVIS, with 3$\times$ less training cost and 1.4$\times$ inference speedup, YOLOE-v8-S surpasses YOLO-Worldv2-S by 3.5 AP. When transferring to COCO, YOLOE-v8-L achieves 0.6 AP$^b$ and 0.4 AP$^m$ gains over closed-set YOLOv8-L with nearly 4$\times$ less training time. Code and models are available at https://github.com/THU-MIG/yoloe.
Detection Avoidance Techniques for Large Language Models
Authors: Sinclair Schneider, Florian Steuber, Joao A. G. Schneider, Gabi Dreo Rodosek
Date: 2025-03-10 00:00:00

The increasing popularity of large language models has not only led to widespread use but has also brought various risks, including the potential for systematically spreading fake news. Consequently, the development of classification systems such as DetectGPT has become vital. These detectors are vulnerable to evasion techniques, as demonstrated in an experimental series: Systematic changes of the generative models' temperature proofed shallow learning-detectors to be the least reliable. Fine-tuning the generative model via reinforcement learning circumvented BERT-based-detectors. Finally, rephrasing led to a >90\% evasion of zero-shot-detectors like DetectGPT, although texts stayed highly similar to the original. A comparison with existing work highlights the better performance of the presented methods. Possible implications for society and further research are discussed.
Cost-Effective Design of Grid-tied Community Microgrid
Authors: Moslem Uddin, Huadong Mo, Daoyi Dong
Date: 2025-03-10 00:00:00
Tasks: Deep Reinforcement Learning, Sensitivity

This study aims to develop a cost-effective microgrid design that optimally balances the economic feasibility, reliability, efficiency, and environmental impact in a grid-tied community microgrid. A multi-objective optimization framework is employed, integrating HOMER Pro for system sizing with deep reinforcement learning (DRL). Sensitivity analyses are conducted to evaluate the system performance under varying load demand and renewable energy fluctuations, while an economic sensitivity assessment examines the impact of electricity prices and capital costs on the Levelized Cost of Energy (LCOE). The proposed microgrid configuration achieves high reliability, satisfying 100% of the load, even under adverse weather conditions. The proposed framework attains an efficiency of 91.99% while maintaining a carbon footprint of 302,747 kg/year, which is approximately 95% lower than that of the grid system. The economic analysis indicates a net present cost (NPC) of $4.83M with a competitive LCOE of $0.208/kWh. In addition, the operation cost is $201,473 per year with a capital investment of $1.42M, rendering it a financially viable alternative to conventional grid-dependent systems.This work can be valuable in identifying effective solutions for supplying reliable and cost-effective power to regional and remote areas.
Emulating Self-attention with Convolution for Efficient Image Super-Resolution
Authors: Dongheon Lee, Seokju Yun, Youngmin Ro
Date: 2025-03-09 00:00:00
Tasks: Computational Efficiency, Image Super-Resolution, Long-range modeling, Super-Resolution

In this paper, we tackle the high computational overhead of transformers for lightweight image super-resolution. (SR). Motivated by the observations of self-attention's inter-layer repetition, we introduce a convolutionized self-attention module named Convolutional Attention (ConvAttn) that emulates self-attention's long-range modeling capability and instance-dependent weighting with a single shared large kernel and dynamic kernels. By utilizing the ConvAttn module, we significantly reduce the reliance on self-attention and its involved memory-bound operations while maintaining the representational capability of transformers. Furthermore, we overcome the challenge of integrating flash attention into the lightweight SR regime, effectively mitigating self-attention's inherent memory bottleneck. We scale up window size to 32$\times$32 with flash attention rather than proposing an intricated self-attention module, significantly improving PSNR by 0.31dB on Urban100$\times$2 while reducing latency and memory usage by 16$\times$ and 12.2$\times$. Building on these approaches, our proposed network, termed Emulating Self-attention with Convolution (ESC), notably improves PSNR by 0.27 dB on Urban100$\times$4 compared to HiT-SRF, reducing the latency and memory usage by 3.7$\times$ and 6.2$\times$, respectively. Extensive experiments demonstrate that our ESC maintains the ability for long-range modeling, data scalability, and the representational power of transformers despite most self-attentions being replaced by the ConvAttn module.
CATANet: Efficient Content-Aware Token Aggregation for Lightweight Image Super-Resolution
Authors: Xin Liu, Jie Liu, Jie Tang, Gangshan Wu
Date: 2025-03-10 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Super-Resolution, Super-Resolution

Transformer-based methods have demonstrated impressive performance in low-level visual tasks such as Image Super-Resolution (SR). However, its computational complexity grows quadratically with the spatial resolution. A series of works attempt to alleviate this problem by dividing Low-Resolution images into local windows, axial stripes, or dilated windows. SR typically leverages the redundancy of images for reconstruction, and this redundancy appears not only in local regions but also in long-range regions. However, these methods limit attention computation to content-agnostic local regions, limiting directly the ability of attention to capture long-range dependency. To address these issues, we propose a lightweight Content-Aware Token Aggregation Network (CATANet). Specifically, we propose an efficient Content-Aware Token Aggregation module for aggregating long-range content-similar tokens, which shares token centers across all image tokens and updates them only during the training phase. Then we utilize intra-group self-attention to enable long-range information interaction. Moreover, we design an inter-group cross-attention to further enhance global information interaction. The experimental results show that, compared with the state-of-the-art cluster-based method SPIN, our method achieves superior performance, with a maximum PSNR improvement of 0.33dB and nearly double the inference speed.
Implicit Reasoning in Transformers is Reasoning through Shortcuts
Authors: Tianhe Lin, Jian Xie, Siyu Yuan, Deqing Yang
Date: 2025-03-10 00:00:00
Tasks: Mathematical Reasoning

Test-time compute is emerging as a new paradigm for enhancing language models' complex multi-step reasoning capabilities, as demonstrated by the success of OpenAI's o1 and o3, as well as DeepSeek's R1. Compared to explicit reasoning in test-time compute, implicit reasoning is more inference-efficient, requiring fewer generated tokens. However, why does the advanced reasoning capability fail to emerge in the implicit reasoning style? In this work, we train GPT-2 from scratch on a curated multi-step mathematical reasoning dataset and conduct analytical experiments to investigate how language models perform implicit reasoning in multi-step tasks. Our findings reveal: 1) Language models can perform step-by-step reasoning and achieve high accuracy in both in-domain and out-of-domain tests via implicit reasoning. However, this capability only emerges when trained on fixed-pattern data. 2) Conversely, implicit reasoning abilities emerging from training on unfixed-pattern data tend to overfit a specific pattern and fail to generalize further. Notably, this limitation is also observed in state-of-the-art large language models. These findings suggest that language models acquire implicit reasoning through shortcut learning, enabling strong performance on tasks with similar patterns while lacking generalization.
REF-VLM: Triplet-Based Referring Paradigm for Unified Visual Decoding
Authors: Yan Tai, Luhao Zhu, Zhiqiang Chen, Ynan Ding, Yiying Dong, Xiaohong Liu, Guodong Guo
Date: 2025-03-10 00:00:00
Tasks: Instruction Following, Keypoint Detection, Multi-Task Learning, Representation Learning, Semantic Segmentation, Triplet

Multimodal Large Language Models (MLLMs) demonstrate robust zero-shot capabilities across diverse vision-language tasks after training on mega-scale datasets. However, dense prediction tasks, such as semantic segmentation and keypoint detection, pose significant challenges for MLLMs when represented solely as text outputs. Simultaneously, current MLLMs utilizing latent embeddings for visual task decoding generally demonstrate limited adaptability to both multi-task learning and multi-granularity scenarios. In this work, we present REF-VLM, an end-to-end framework for unified training of various visual decoding tasks. To address complex visual decoding scenarios, we introduce the Triplet-Based Referring Paradigm (TRP), which explicitly decouples three critical dimensions in visual decoding tasks through a triplet structure: concepts, decoding types, and targets. TRP employs symbolic delimiters to enforce structured representation learning, enhancing the parsability and interpretability of model outputs. Additionally, we construct Visual-Task Instruction Following Dataset (VTInstruct), a large-scale multi-task dataset containing over 100 million multimodal dialogue samples across 25 task types. Beyond text inputs and outputs, VT-Instruct incorporates various visual prompts such as point, box, scribble, and mask, and generates outputs composed of text and visual units like box, keypoint, depth and mask. The combination of different visual prompts and visual units generates a wide variety of task types, expanding the applicability of REF-VLM significantly. Both qualitative and quantitative experiments demonstrate that our REF-VLM outperforms other MLLMs across a variety of standard benchmarks. The code, dataset, and demo available at https://github.com/MacavityT/REF-VLM.