pwc-archive/papers-with-abstracts huggingface dataset:
Local Stability and Stabilization of Quadratic-Bilinear Systems using Petersen's Lemma
Authors: Amir Enayati Kafshgarkolaei, Maziar S. Hemati
Date: 2025-03-26 00:00:00
Tasks: LEMMA

Quadratic-bilinear (QB) systems arise in many areas of science and engineering. In this paper, we present a scalable approach for designing locally stabilizing state-feedback control laws and certifying the local stability of QB systems. Sufficient conditions are established for local stability and stabilization based on quadratic Lyapunov functions, which also provide ellipsoidal inner-estimates for the region of attraction and region of stabilizability of an equilibrium point. Our formulation exploits Petersen's Lemma to convert the problem of certifying the sign-definiteness of the Lyapunov condition into a line search over a single scalar parameter. The resulting linear matrix inequality (LMI) conditions scale quadratically with the state dimension for both stability analysis and control synthesis, thus enabling analysis and control of QB systems with hundreds of state variables without resorting to specialized implementations. We demonstrate the approach on three benchmark problems from the existing literature. In all cases, we find our formulation yields comparable approximations of stability domains as determined by other established tools that are otherwise restricted to systems with up to tens of state variables.
Lumina-Image 2.0: A Unified and Efficient Image Generative Framework
Authors: Qi Qin, Le Zhuo, Yi Xin, Ruoyi Du, Zhen Li, Bin Fu, Yiting Lu, Jiakang Yuan, Xinyue Li, Dongyang Liu, Xiangyang Zhu, Manyuan Zhang, Will Beddow, Erwann Millon, Victor Perez, Wenhai Wang, Conghui He, Bo Zhang, Xiaohong Liu, Hongsheng Li, Yu Qiao, Chang Xu, Peng Gao
Date: 2025-03-27 00:00:00
Tasks: Image Generation, Text to Image Generation, Text-to-Image Generation

We introduce Lumina-Image 2.0, an advanced text-to-image generation framework that achieves significant progress compared to previous work, Lumina-Next. Lumina-Image 2.0 is built upon two key principles: (1) Unification - it adopts a unified architecture (Unified Next-DiT) that treats text and image tokens as a joint sequence, enabling natural cross-modal interactions and allowing seamless task expansion. Besides, since high-quality captioners can provide semantically well-aligned text-image training pairs, we introduce a unified captioning system, Unified Captioner (UniCap), specifically designed for T2I generation tasks. UniCap excels at generating comprehensive and accurate captions, accelerating convergence and enhancing prompt adherence. (2) Efficiency - to improve the efficiency of our proposed model, we develop multi-stage progressive training strategies and introduce inference acceleration techniques without compromising image quality. Extensive evaluations on academic benchmarks and public text-to-image arenas show that Lumina-Image 2.0 delivers strong performances even with only 2.6B parameters, highlighting its scalability and design efficiency. We have released our training details, code, and models at https://github.com/Alpha-VLLM/Lumina-Image-2.0.
Joint-Partition Group Attention for skeleton-based action recognition
Authors: Hu Cui, Tessai Hayama
Date: 2024-07-30 00:00:00
Proceeding: Signal Processing 2024 7
Tasks: Action Recognition, Skeleton Based Action Recognition

Skeleton-based action recognition aims to recognize human actions from the coordinates of human joints. By encoding coordinates as joint tokens, previous methods have successfully utilized the self-attention (SA) mechanism to capture the relationship of each pair of joints. However, the attention map generated from the joint Query and joint Key in SA only captures joint-to-joint correlations at a single granularity, which is obviously insufficient for human actions that express semantics in terms of body parts. In this paper, we argue that SA should have a more comprehensive mechanism to capture correlations in joint-to-joint and joint-to-partition patterns for a higher semantic representation of skeleton-based actions. Therefore, we propose Joint-Partition Group Attention (JPGA) to simultaneously capture correlations between joints and body parts of different granularity sizes. Specifically, JPGA integrates the joint tokens according to the joint’s human body partition attributes and produces different body parts tokens (partition-tokens) with different granularities. Then the attention map of JPGA is computed from joint-token and partition-token of different granularity sizes to represent the relationship between joints and body parts. To adaptively partition human body parts at different granularities, we apply the reparameterization trick to adaptively learn the multi-granularity partitioning matrix. Based on JPGA, we construct our Joint-Partition Former (JPFormer), and conduct extensive experiments on NTU-RGB+D, NTU-RRB+D 120, and Northwestern UCLA datasets and achieve state-of-the-art results, which highlights the effectiveness of our design and practice.
A physically-based constitutive model considering mixed dynamic recrystallization for a near-α titanium alloy during hot deformation
Authors: Jie Zhao, Yingming Tu, Binjun Zhou, Kehuan Wang, Gang Liu
Date: 2025-01-17 00:00:00
Proceeding: Journal of Materials Research and Technology 2025 1

The mixed dynamic recrystallization of titanium alloys during hot deformation is rarely described in the existing microstructure evolution models and constitutive models. In this paper, the mechanism of mixed dynamic recrystallization of a near-α titanium alloy rolled sheet during hot tension was analyzed. The used TA15 rolled sheet is mainly composed of equiaxed grains with a lot of deformed structure, which promotes dynamic recrystallization during hot deformation. Continuous dynamic recrystallization is the predominant mechanism of microstructure evolution in the deformation region with the ln Z of about 47.5 and true strain of about 0.3. With the decrease of parameter Z, discontinuous dynamic recrystallization is enhanced. Then a physically-based constitutive model was developed, considering subgrain rotation, recrystallization nucleation, recrystallization grain growth, recrystallization grain transformation into deformation structure and microvoids evolution. The model can well describe the two kinds of mixed dynamic recrystallization: continuous dynamic recrystallization and discontinuous dynamic recrystallization occur successively and simultaneously. Based on the developed model, the process window of hot forming for TA15 titanium alloy components is established, realizing grain size reduction of 20 % and no necking.
Regulation of Dendritic Cell Function by Ermiaosan via the EP4-cAMP-CREB Signaling Pathway
Authors: Jie-Min Ding, Liu Min, Wang Jin, Si-Meng Cheng, Xiang-Wen Meng, Xiao-Yi Jia, Wang Ning
Date: 2025-03-27 00:00:00

Ermiao San (EMS), a traditional Chinese medicine composed of Atractylodes macrocephala and Cortex Phellodendron, has demonstrated therapeutic efficacy in rheumatoid arthritis (RA). Studies suggest that EMS modulates dendritic cell (DC) maturation in adjuvant arthritis (AA) rats, though the precise mechanisms remain unclear. Prostaglandin receptor 4 (EP4) is critical in inflammation and DC function, while cyclic adenosine monophosphate (cAMP) regulates cellular signaling, potentially influencing RA pathogenesis via protein kinase A (PKA) and cAMP response element-binding protein (CREB) activation. EMS exerts protective effects in RA rats by suppressing DC functions, including reduced EP4 mRNA/protein expression, diminished cAMP levels, and impaired CREB phosphorylation. Additionally, serum from EMS-treated rats inhibited antigen uptake by bone marrow-derived DCs (BMDCs), downregulating CD40, CD80, and CD86 expression and altering pro-inflammatory cytokine secretion. Mechanistically, EMS-treated serum suppressed the EP4-cAMP pathway by decreasing EP4 protein expression and CREB activation, alongside reduced intracellular cAMP and PKA levels in BMDCs co-stimulated with PGE2 and TNF-a. These findings indicate that EMS alleviates RA by inhibiting the EP4-cAMP-CREB signaling axis in DCs, providing a scientific rationale for its clinical application in RA treatment.
Does Your Vision-Language Model Get Lost in the Long Video Sampling Dilemma?
Authors: Tianyuan Qu, Longxiang Tang, Bohao Peng, Senqiao Yang, Bei Yu, Jiaya Jia
Date: 2025-03-16 00:00:00
Tasks: Language Modeling, Language Modelling, Video Understanding

The rise of Large Vision-Language Models (LVLMs) has significantly advanced video understanding. However, efficiently processing long videos remains a challenge due to the ``Sampling Dilemma'': low-density sampling risks missing critical information, while high-density sampling introduces redundancy. To address this issue, we introduce LSDBench, the first benchmark designed to evaluate LVLMs on long-video tasks by constructing high Necessary Sampling Density (NSD) questions, where NSD represents the minimum sampling density required to accurately answer a given question. LSDBench focuses on dense, short-duration actions to rigorously assess the sampling strategies employed by LVLMs. To tackle the challenges posed by high-NSD questions, we propose a novel Reasoning-Driven Hierarchical Sampling (RHS) framework, which combines global localization of question-relevant cues with local dense sampling for precise inference. Additionally, we develop a lightweight Semantic-Guided Frame Selector to prioritize informative frames, enabling RHS to achieve comparable or superior performance with significantly fewer sampled frames. Together, our LSDBench and RHS framework address the unique challenges of high-NSD long-video tasks, setting a new standard for evaluating and improving LVLMs in this domain. Our benchmark and evaluation codes has been released at: https://github.com/dvlab-research/LSDBench
Degradation Self-Supervised Learning for Lithium-ion Battery Health Diagnostics
Authors: J. C. Chen
Date: 2025-03-11 00:00:00
Tasks: Self-Supervised Learning

Health evaluation for lithium-ion batteries (LIBs) typically relies on constant charging/discharging protocols, often neglecting scenarios involving dynamic current profiles prevalent in electric vehicles. Conventional health indicators for LIBs also depend on the uniformity of measured data, restricting their adaptability to non-uniform conditions. In this study, a novel training strategy for estimating LIB health based on the paradigm of self-supervised learning is proposed. A multiresolution analysis technique, empirical wavelet transform, is utilized to decompose non-stationary voltage signals in the frequency domain. This allows the removal of ineffective components for the health evaluation model. The transformer neural network serves as the model backbone, and a loss function is designed to describe the capacity degradation behavior with the assumption that the degradation in LIBs across most operating conditions is inevitable and irreversible. The results show that the model can learn the aging characteristics by analyzing sequences of voltage and current profiles obtained at various time intervals from the same LIB cell. The proposed method is successfully applied to the Stanford University LIB aging dataset, derived from electric vehicle real driving profiles. Notably, this approach achieves an average correlation coefficient of 0.9 between the evaluated health index and the degradation of actual capacity, demonstrating its efficacy in capturing LIB health degradation. This research highlights the feasibility of training deep neural networks using unlabeled LIB data, offering cost-efficient means and unleashing the potential of the measured information.
UP-Cycle-SENet: Unpaired Phase-aware Speech Enhancement Using Deep Complex Cycle Adversarial Networks
Authors: Cheolhoon Park, Hyunduck Choi
Date: 2025-01-22 00:00:00
Proceeding: Neurocomputing 2025 1
Tasks: Speech Enhancement

Speech enhancement (SE), which reconstructs intelligible speech by removing noise or interference from noisy speech, plays an important role in many speech applications. Due to the successful introduction of deep learning in SE, a significant performance improvement was recorded compared to the traditional methods. Most spectrogram-based deep SE networks have two main issues. First, many existing methods only focus on estimating the magnitude of the spectrogram while reusing the phase information. Reusing the phase part of a noisy spectrogram results in clear performance limitations, which can become more pronounced when the distortion caused by noise is severe. Second, most deep SE models adopt supervised learning, which requires a large number of paired datasets. Constructing a large dataset that includes clean speech is highly impractical due to the significant effort and cost involved. To address this issue, we propose UP-Cycle-SENet, an end-to-end complex SE network capable of estimating both the magnitude and phase parts of the speech spectrogram under unpaired dataset conditions. The proposed network leverages complex convolutional neural networks and extended modules to efficiently extract features in the complex domain without losing information. Additionally, the introduction of a CNN-based discriminator with non-autoregressive properties makes it suitable for fast training and inference. To effectively validate the benefits of the proposed network, comparative experiments were conducted using public datasets that mix Voice Bank and DEMAND. The experimental results demonstrated that the proposed framework outperforms previous methods in both parallel and non-parallel strategies.
Phishing detection on Ethereum via transaction subgraphs embedding
Authors: Haifeng Lv, Yong Ding
Date: 2023-05-10 00:00:00
Proceeding: IET BlockchainVolume 3, Issue 4: Security and Privacy Issues in Blockchain and its Applications2023 2023 5

With the rapid development of blockchain technology in the financial sector, the security of blockchain is being put to the test due to an increase in phishing fraud. Therefore, it is essential to study more effective measures and better solutions. Graph models have been proven to provide abundant information for downstream assignments. In this study, a graph-based embedding classification method is proposed for phishing detection on Ethereum by modeling its transaction records using subgraphs. Initially, the transaction data of normal addresses and an equal number of confirmed phishing addresses are collected through web crawling. Multiple subgraphs using the collected transaction records are constructed, with each subgraph containing a target address and its nearby transaction network. To extract features of the addresses, a modified Graph2Vec model called imgraph2vec is designed, which considers block height, timestamp, and amount of transactions. Finally, the Extreme Gradient Boosting (XGBoost) algorithm is employed to detect phishing and normal addresses. The experimental results show that the proposed method achieves good performance in phishing detection, indicating the effectiveness of imgraph2vec in feature acquisition of transaction networks compared to existing models.
LandMarkSystem Technical Report
Authors: Zhenxiang Ma, Zhenyu Yang, Miao Tao, Yuanzhen Zhou, Zeyu He, Yuchang Zhang, Rong Fu, Hengjie Li
Date: 2025-03-27 00:00:00
Tasks: 3DGS, 3D Reconstruction, Autonomous Driving, Computational Efficiency, NeRF

3D reconstruction is vital for applications in autonomous driving, virtual reality, augmented reality, and the metaverse. Recent advancements such as Neural Radiance Fields(NeRF) and 3D Gaussian Splatting (3DGS) have transformed the field, yet traditional deep learning frameworks struggle to meet the increasing demands for scene quality and scale. This paper introduces LandMarkSystem, a novel computing framework designed to enhance multi-scale scene reconstruction and rendering. By leveraging a componentized model adaptation layer, LandMarkSystem supports various NeRF and 3DGS structures while optimizing computational efficiency through distributed parallel computing and model parameter offloading. Our system addresses the limitations of existing frameworks, providing dedicated operators for complex 3D sparse computations, thus facilitating efficient training and rapid inference over extensive scenes. Key contributions include a modular architecture, a dynamic loading strategy for limited resources, and proven capabilities across multiple representative algorithms.This comprehensive solution aims to advance the efficiency and effectiveness of 3D reconstruction tasks.To facilitate further research and collaboration, the source code and documentation for the LandMarkSystem project are publicly available in an open-source repository, accessing the repository at: https://github.com/InternLandMark/LandMarkSystem.
vGamba: Attentive State Space Bottleneck for efficient Long-range Dependencies in Visual Recognition
Authors: Yunusa Haruna, Adamu Lawan
Date: 2025-03-27 00:00:00
Tasks: Computational Efficiency, Long-range modeling, Mamba, State Space Models

Capturing long-range dependencies efficiently is essential for visual recognition tasks, yet existing methods face limitations. Convolutional neural networks (CNNs) struggle with restricted receptive fields, while Vision Transformers (ViTs) achieve global context and long-range modeling at a high computational cost. State-space models (SSMs) offer an alternative, but their application in vision remains underexplored. This work introduces vGamba, a hybrid vision backbone that integrates SSMs with attention mechanisms to enhance efficiency and expressiveness. At its core, the Gamba bottleneck block that includes, Gamba Cell, an adaptation of Mamba for 2D spatial structures, alongside a Multi-Head Self-Attention (MHSA) mechanism and a Gated Fusion Module for effective feature representation. The interplay of these components ensures that vGamba leverages the low computational demands of SSMs while maintaining the accuracy of attention mechanisms for modeling long-range dependencies in vision tasks. Additionally, the Fusion module enables seamless interaction between these components. Extensive experiments on classification, detection, and segmentation tasks demonstrate that vGamba achieves a superior trade-off between accuracy and computational efficiency, outperforming several existing models.
Fine-Grained Behavior and Lane Constraints Guided Trajectory Prediction Method
Authors: Wenyi Xiong, Jian Chen, Ziheng Qi
Date: 2025-03-27 00:00:00
Tasks: Autonomous Driving, Decoder, Intent Detection, Prediction, Trajectory Prediction

Trajectory prediction, as a critical component of autonomous driving systems, has attracted the attention of many researchers. Existing prediction algorithms focus on extracting more detailed scene features or selecting more reasonable trajectory destinations. However, in the face of dynamic and evolving future movements of the target vehicle, these algorithms cannot provide a fine-grained and continuous description of future behaviors and lane constraints, which degrades the prediction accuracy. To address this challenge, we present BLNet, a novel dualstream architecture that synergistically integrates behavioral intention recognition and lane constraint modeling through parallel attention mechanisms. The framework generates fine-grained behavior state queries (capturing spatial-temporal movement patterns) and lane queries (encoding lane topology constraints), supervised by two auxiliary losses, respectively. Subsequently, a two-stage decoder first produces trajectory proposals, then performs point-level refinement by jointly incorporating both the continuity of passed lanes and future motion features. Extensive experiments on two large datasets, nuScenes and Argoverse, show that our network exhibits significant performance gains over existing direct regression and goal-based algorithms.
Network Density Analysis of Health Seeking Behavior in Metro Manila: A Retrospective Analysis on COVID-19 Google Trends Data
Authors: Michael T. Lopez II, Cheska Elise Hung, Maria Regina Justina E. Estuar
Date: 2025-03-27 00:00:00

This study examined the temporal aspect of COVID-19-related health-seeking behavior in Metro Manila, National Capital Region, Philippines through a network density analysis of Google Trends data. A total of 15 keywords across five categories (English symptoms, Filipino symptoms, face wearing, quarantine, and new normal) were examined using both 15-day and 30-day rolling windows from March 2020 to March 2021. The methodology involved constructing network graphs using distance correlation coefficients at varying thresholds (0.4, 0.5, 0.6, and 0.8) and analyzing the time-series data of network density and clustering coefficients. Results revealed three key findings: (1) an inverse relationship between the threshold values and network metrics, indicating that higher thresholds provide more meaningful keyword relationships; (2) exceptionally high network connectivity during the initial pandemic months followed by gradual decline; and (3) distinct patterns in keyword relationships, transitioning from policy-focused searches to more symptom-specific queries as the pandemic temporally progressed. The 30-day window analysis showed more stable, but less search activities compared to the 15-day windows, suggesting stronger correlations in immediate search behaviors. These insights are helpful for health communication because it emphasizes the need of a strategic and conscientious information dissemination from the government or the private sector based on the networked search behavior (e.g. prioritizing to inform select symptoms rather than an overview of what the coronavirus is).
Efficient Learning for Entropy-Regularized Markov Decision Processes via Multilevel Monte Carlo
Authors: Matthieu Meunier, Christoph Reisinger, Yufei Zhang
Date: 2025-03-27 00:00:00

Designing efficient learning algorithms with complexity guarantees for Markov decision processes (MDPs) with large or continuous state and action spaces remains a fundamental challenge. We address this challenge for entropy-regularized MDPs with Polish state and action spaces, assuming access to a generative model of the environment. We propose a novel family of multilevel Monte Carlo (MLMC) algorithms that integrate fixed-point iteration with MLMC techniques and a generic stochastic approximation of the Bellman operator. We quantify the precise impact of the chosen approximate Bellman operator on the accuracy of the resulting MLMC estimator. Leveraging this error analysis, we show that using a biased plain MC estimate for the Bellman operator results in quasi-polynomial sample complexity, whereas an unbiased randomized multilevel approximation of the Bellman operator achieves polynomial sample complexity in expectation. Notably, these complexity bounds are independent of the dimensions or cardinalities of the state and action spaces, distinguishing our approach from existing algorithms whose complexities scale with the sizes of these spaces. We validate these theoretical performance guarantees through numerical experiments.