pwc-archive/papers-with-abstracts huggingface dataset:
Sunday Benjamin
Authors: Sunday Benjamin
Date: 2010-10-23 00:00:00
Proceeding: 10-23 2010 10
Tasks: Marketing

Early Life
My name is Sunday Benjamin. I am a young Nigerian student, born on the 23rd of October, 2009, in the town of Ebonyi, located in Ebonyi State, Nigeria. I come from a loving family, and my parents are Mr. and Mrs. Sunday, who have always supported and guided me throughout my life.

I began my educational journey at Balogun Nursery and Primary School, which is situated in Ilorin, Kwara State. I attended this school from the early years of my childhood up to Primary 5. During this time, I learned the foundations of reading, writing, and arithmetic, and I also made many wonderful friends and memories.

In Primary 5, I transferred to a new school, St. Williams Model College, where I continued my studies with dedication and enthusiasm. The transition was a new and exciting chapter for me, as I met new classmates, adapted to a different environment, and continued to grow academically and socially.

As of the year 2024, I am currently in Junior Secondary School 3 (J.S.S. 3). I am working hard and focusing on my studies in order to achieve my dreams and make my family proud. My journey so far has been filled with learning, growth, and the hope of a bright future ahead.

Career
My name is Sunday Benjamin, and I’m truly excited to introduce you to an amazing opportunity that has the potential to transform your life. I recently became a proud member of the FHG Business family in 2024, and it has already been an eye-opening and rewarding experience for me.

FHG Business is a unique 2-in-1 venture that offers not only financial growth but also personal development. One of the things that makes this opportunity so attractive is that you can get started absolutely free—yes, at no cost! Whether you're a student, a stay-at-home parent, an entrepreneur, or someone simply looking for an additional source of income, this business is designed to be accessible and achievable for everyone.

Since joining FHG Business, I’ve discovered the power of community, mentorship, and self-reliance. The support system is incredible, and the tools provided make it easy to learn and grow—even if you have no prior experience in business or sales. I’ve learned valuable skills in marketing, networking, communication, and leadership, and I'm constantly inspired by the success stories of others who started just like I did.

What excites me the most is the chance to help you get started too. This isn't just about making money—it's about building a future, gaining confidence, and creating options for yourself and your family. If you're willing to take a small step today, it could lead to a life-changing journey tomorrow.

So, are you ready to join me and explore what FHG Business has to offer? I’ll be with you every step of the way!
What is the Cost of Differential Privacy for Deep Learning-Based Trajectory Generation?
Authors: Erik Buchholz, Natasha Fernandes, David D. Nguyen, Alsharif Abuadbba, Surya Nepal, Salil S. Kanhere
Date: 2025-06-11 00:00:00

While location trajectories offer valuable insights, they also reveal sensitive personal information. Differential Privacy (DP) offers formal protection, but achieving a favourable utility-privacy trade-off remains challenging. Recent works explore deep learning-based generative models to produce synthetic trajectories. However, current models lack formal privacy guarantees and rely on conditional information derived from real data during generation. This work investigates the utility cost of enforcing DP in such models, addressing three research questions across two datasets and eleven utility metrics. (1) We evaluate how DP-SGD, the standard DP training method for deep learning, affects the utility of state-of-the-art generative models. (2) Since DP-SGD is limited to unconditional models, we propose a novel DP mechanism for conditional generation that provides formal guarantees and assess its impact on utility. (3) We analyse how model types - Diffusion, VAE, and GAN - affect the utility-privacy trade-off. Our results show that DP-SGD significantly impacts performance, although some utility remains if the datasets is sufficiently large. The proposed DP mechanism improves training stability, particularly when combined with DP-SGD, for unstable models such as GANs and on smaller datasets. Diffusion models yield the best utility without guarantees, but with DP-SGD, GANs perform best, indicating that the best non-private model is not necessarily optimal when targeting formal guarantees. In conclusion, DP trajectory generation remains a challenging task, and formal guarantees are currently only feasible with large datasets and in constrained use cases.
Urban1960SatSeg: Unsupervised Semantic Segmentation of Mid-20$^{th}$ century Urban Landscapes with Satellite Imageries
Authors: Tianxiang Hao, Lixian Zhang, Yingjia Zhang, Mengxuan Chen, Jinxiao Zhang, Haohuan Fu
Date: 2025-06-11 00:00:00
Tasks: Segmentation, Self-Supervised Learning, Semantic Segmentation, Unsupervised Semantic Segmentation

Historical satellite imagery, such as mid-20$^{th}$ century Keyhole data, offers rare insights into understanding early urban development and long-term transformation. However, severe quality degradation (e.g., distortion, misalignment, and spectral scarcity) and annotation absence have long hindered semantic segmentation on such historical RS imagery. To bridge this gap and enhance understanding of urban development, we introduce $\textbf{Urban1960SatBench}$, an annotated segmentation dataset based on historical satellite imagery with the earliest observation time among all existing segmentation datasets, along with a benchmark framework for unsupervised segmentation tasks, $\textbf{Urban1960SatUSM}$. First, $\textbf{Urban1960SatBench}$ serves as a novel, expertly annotated semantic segmentation dataset built on mid-20$^{th}$ century Keyhole imagery, covering 1,240 km$^2$ and key urban classes (buildings, roads, farmland, water). As the earliest segmentation dataset of its kind, it provides a pioneering benchmark for historical urban understanding. Second, $\textbf{Urban1960SatUSM}$(Unsupervised Segmentation Model) is a novel unsupervised semantic segmentation framework for historical RS imagery. It employs a confidence-aware alignment mechanism and focal-confidence loss based on a self-supervised learning architecture, which generates robust pseudo-labels and adaptively prioritizes prediction difficulty and label reliability to improve unsupervised segmentation on noisy historical data without manual supervision. Experiments show Urban1960SatUSM significantly outperforms existing unsupervised segmentation methods on Urban1960SatSeg for segmenting historical urban scenes, promising in paving the way for quantitative studies of long-term urban change using modern computer vision. Our benchmark and supplementary material are available at https://github.com/Tianxiang-Hao/Urban1960SatSeg.
Probing Neural Topology of Large Language Models
Authors: Yu Zheng, Yuan Yuan, Yong Li, Paolo Santi
Date: 2025-06-01 00:00:00
Tasks: Functional Connectivity, Graph Matching, Text Generation

Probing large language models (LLMs) has yielded valuable insights into their internal mechanisms by linking neural representations to interpretable semantics. However, how neurons functionally co-activate with each other to give rise to emergent capabilities remains largely unknown, hindering a deeper understanding and safer development of LLMs. In this work, we introduce graph probing, a method for uncovering the functional connectivity topology of LLM neurons and relating it to language generation performance. By analyzing internal neural graphs across diverse LLM families and scales, we discover a universal predictability of next-token prediction performance using only neural topology. This predictability is robust even when retaining just 1% of neuron connections or probing models after only 8 pretraining steps, highlighting the sparsity and early emergence of topological patterns. Further graph matching analysis suggests that, despite significant distinctions in architectures, parameters, and training data, different LLMs develop intricate and consistent neural topological structures that may form the foundation for their language generation abilities. Codes and data for the graph probing toolbox are released at https://github.com/DavyMorgan/llm-graph-probing.
Noise Conditional Variational Score Distillation
Authors: Xinyu Peng, Ziyang Zheng, Yaoming Wang, Han Li, Nuowen Kan, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong
Date: 2025-06-11 00:00:00
Tasks: Conditional Image Generation, Denoising, Image Generation

We propose Noise Conditional Variational Score Distillation (NCVSD), a novel method for distilling pretrained diffusion models into generative denoisers. We achieve this by revealing that the unconditional score function implicitly characterizes the score function of denoising posterior distributions. By integrating this insight into the Variational Score Distillation (VSD) framework, we enable scalable learning of generative denoisers capable of approximating samples from the denoising posterior distribution across a wide range of noise levels. The proposed generative denoisers exhibit desirable properties that allow fast generation while preserve the benefit of iterative refinement: (1) fast one-step generation through sampling from pure Gaussian noise at high noise levels; (2) improved sample quality by scaling the test-time compute with multi-step sampling; and (3) zero-shot probabilistic inference for flexible and controllable sampling. We evaluate NCVSD through extensive experiments, including class-conditional image generation and inverse problem solving. By scaling the test-time compute, our method outperforms teacher diffusion models and is on par with consistency models of larger sizes. Additionally, with significantly fewer NFEs than diffusion-based methods, we achieve record-breaking LPIPS on inverse problems.
Trajectory World Models for Heterogeneous Environments
Authors: Shaofeng Yin, Jialong Wu, Siqiao Huang, Xingjian Su, Xu He, Jianye Hao, Mingsheng Long
Date: 2025-02-03 00:00:00
Tasks: Diversity, Model Predictive Control, Off-policy evaluation

Heterogeneity in sensors and actuators across environments poses a significant challenge to building large-scale pre-trained world models on top of this low-dimensional sensor information. In this work, we explore pre-training world models for heterogeneous environments by addressing key transfer barriers in both data diversity and model flexibility. We introduce UniTraj, a unified dataset comprising over one million trajectories from 80 environments, designed to scale data while preserving critical diversity. Additionally, we propose TrajWorld, a novel architecture capable of flexibly handling varying sensor and actuator information and capturing environment dynamics in-context. Pre-training TrajWorld on UniTraj yields substantial gains in transition prediction, achieves a new state-of-the-art for off-policy evaluation, and also delivers superior online performance of model predictive control. To the best of our knowledge, this work, for the first time, demonstrates the transfer benefits of world models across heterogeneous and complex control environments. Code and data are available at https://github.com/thuml/TrajWorld.
AtmosMJ: Revisiting Gating Mechanism for AI Weather Forecasting Beyond the Year Scale
Authors: Minjong Cheon
Date: 2025-06-11 00:00:00
Tasks: GPU, Weather Forecasting

The advent of Large Weather Models (LWMs) has marked a turning point in data-driven forecasting, with many models now outperforming traditional numerical systems in the medium range. However, achieving stable, long-range autoregressive forecasts beyond a few weeks remains a significant challenge. Prevailing state-of-the-art models that achieve year-long stability, such as SFNO and DLWP-HPX, have relied on transforming input data onto non-standard spatial domains like spherical harmonics or HEALPix meshes. This has led to the prevailing assumption that such representations are necessary to enforce physical consistency and long-term stability. This paper challenges that assumption by investigating whether comparable long-range performance can be achieved on the standard latitude-longitude grid. We introduce AtmosMJ, a deep convolutional network that operates directly on ERA5 data without any spherical remapping. The model's stability is enabled by a novel Gated Residual Fusion (GRF) mechanism, which adaptively moderates feature updates to prevent error accumulation over long recursive simulations. Our results demonstrate that AtmosMJ produces stable and physically plausible forecasts for about 500 days. In quantitative evaluations, it achieves competitive 10-day forecast accuracy against models like Pangu-Weather and GraphCast, all while requiring a remarkably low training budget of 5.7 days on a V100 GPU. Our findings suggest that efficient architectural design, rather than non-standard data representation, can be the key to unlocking stable and computationally efficient long-range weather prediction.
3D-Aware Vision-Language Models Fine-Tuning with Geometric Distillation
Authors: Seonho Lee, Jiho Choi, Inha Kang, Jiwook Kim, Junsung Park, Hyunjung Shim
Date: 2025-06-11 00:00:00
Tasks: Spatial Reasoning

Vision-Language Models (VLMs) have shown remarkable performance on diverse visual and linguistic tasks, yet they remain fundamentally limited in their understanding of 3D spatial structures. We propose Geometric Distillation, a lightweight, annotation-free fine-tuning framework that injects human-inspired geometric cues into pretrained VLMs without modifying their architecture. By distilling (1) sparse correspondences, (2) relative depth relations, and (3) dense cost volumes from off-the-shelf 3D foundation models (e.g., MASt3R, VGGT), our method shapes representations to be geometry-aware while remaining compatible with natural image-text inputs. Through extensive evaluations on 3D vision-language reasoning and 3D perception benchmarks, our method consistently outperforms prior approaches, achieving improved 3D spatial reasoning with significantly lower computational cost. Our work demonstrates a scalable and efficient path to bridge 2D-trained VLMs with 3D understanding, opening up wider use in spatially grounded multimodal tasks.
Revisit What You See: Disclose Language Prior in Vision Tokens for Efficient Guided Decoding of LVLMs
Authors: Beomsik Cho, Jaehyung Kim
Date: 2025-06-11 00:00:00
Tasks: Hallucination, Object Hallucination, Text Generation, Visual Grounding

Large Vision-Language Models (LVLMs) have demonstrated remarkable performance across various multimodal tasks by integrating visual perception with language understanding. However, conventional decoding strategies of LVLMs often fail to successfully utilize visual information, leading to visually ungrounded responses. While various approaches have been proposed to address this limitation, they typically require additional training, multi-step inference procedures, or external model dependencies. This paper introduces ReVisiT, a simple yet effective decoding method that references vision tokens to guide the text generation process in LVLMs. Our approach leverages the semantic information embedded within vision tokens by projecting them into the text token distribution space, and dynamically selecting the most relevant vision token at each decoding step through constrained divergence minimization. This selected vision token is then used to refine the output distribution to better incorporate visual semantics. Experiments on three LVLM hallucination benchmarks with two recent LVLMs demonstrate that ReVisiT consistently enhances visual grounding with minimal computational overhead. Moreover, our method achieves competitive or superior results relative to state-of-the-art baselines while reducing computational costs for up to $2\times$.
Multimodality Adaptive Transformer and Mutual Learning for Unsupervised Domain Adaptation Vehicle Re-Identification
Authors: Xin Zhang, Yunan Ling, Kaige Li, Weimin Shi, Zhong Zho
Date: 2024-09-17 00:00:00
Proceeding: IEEE Transactions on Intelligent Transportation Systems 2024 9
Tasks: Attribute, Domain Adaptation, Pseudo Label, Unsupervised Domain Adaptation, Vehicle Re-Identification

Unsupervised Domain Adaptation Vehicle Re-Identification (UDA vehicle re-ID) aims to enable the model trained in the source domain dataset to adapt to the target domain data and obtain accurate re-identification results, which has received widespread attention due to its practicality in the field of intelligent transportation systems. Most current UDA vehicle re-ID research ignores the mining and utilization of attribute information. Meanwhile, the Convolutional Neural Networks-based (CNN-based) network will cause the loss of fine-grained information, reducing the expression and generalization ability of vehicle features. To alleviate such issues, we are motivated by the Transformer, which can exploit distinguishable attribute information and fuse multimodal features effectively. Therefore, this paper proposes a Multimodality Adaptive Transformer Network (MATNet) to intensify the ability to learn vehicle fine-grained features related to attributes. Moreover, the noise contained in pseudo-labels assigned by cluster algorithms interferes with the performance of the UDA vehicle re-ID method. We also design the Dual Mutual Dynamic Update Pseudo-Label generation strategy (DMDU) to improve the accuracy of pseudo-labels and alleviate error accumulation. The strategy is based on mutual learning, which can effectively utilize the congruous and particular knowledge of the two models to generate pseudo-labels. Extensive experiments on two large-scale public datasets, including VeRi-776 and VehicleID, illustrate that our method outperforms the state-of-the-art methods.
Structured State Space Model Dynamics and Parametrization for Spiking Neural Networks
Authors: Maxime Fabre, Lyubov Dudchenko, Emre Neftci
Date: 2025-06-04 00:00:00
Tasks: speech-recognition, Speech Recognition, State Space Models

Multi-state spiking neurons such as the adaptive leaky integrate-and-fire (AdLIF) neuron offer compelling alternatives to conventional deep learning models thanks to their sparse binary activations, second-order nonlinear recurrent dynamics, and efficient hardware realizations. However, such internal dynamics can cause instabilities during inference and training, often limiting performance and scalability. Meanwhile, state space models (SSMs) excel in long sequence processing using linear state-intrinsic recurrence resembling spiking neurons' subthreshold regime. Here, we establish a mathematical bridge between SSMs and second-order spiking neuron models. Based on structure and parametrization strategies of diagonal SSMs, we propose two novel spiking neuron models. The first extends the AdLIF neuron through timestep training and logarithmic reparametrization to facilitate training and improve final performance. The second additionally brings initialization and structure from complex-state SSMs, broadening the dynamical regime to oscillatory dynamics. Together, our two models achieve beyond or near state-of-the-art (SOTA) performances for reset-based spiking neuron models across both event-based and raw audio speech recognition datasets. We achieve this with a favorable number of parameters and required dynamic memory while maintaining high activity sparsity. Our models demonstrate enhanced scalability in network size and strike a favorable balance between performance and efficiency with respect to SSM models.
Data-Driven Modeling of IRCU Patient Flow in the COVID-19 Pandemic
Authors: Ana Carmen Navas-Ortega, José Antonio Sánchez-Martínez, Paula García-Flores, Concepción Morales-García, Rene Fabregas
Date: 2025-06-11 00:00:00
Tasks: Respiratory Failure

Intermediate Respiratory Care Units (IRCUs) are vital during crises like COVID-19. This study evaluated clinical outcomes and operational dynamics of a new Spanish IRCU with specialized staffing. A prospective cohort study (April-August 2021) included 249 adult patients with COVID-19 respiratory failure (UHVN IRCU, Granada). Data on demographics, Non-Invasive Ventilation (NIV), length of stay (LOS), and outcomes (ICU transfer, exitus, recovery) were analyzed. Patient flow was simulated using a data-calibrated deterministic compartmental model (Ordinary Differential Equations, ODEs) that represented state transitions, and an empirical LOS-based stochastic convolution model that incorporated admission variability. The median age was 51; 31% of patients required NIV. NIV patients were older (median 61 vs 42, p<0.001). Overall, 8% needed ICU transfer; 3% experienced in-IRCU exitus. Notably, no ICU transfers or deaths occurred among 172 non-NIV patients. Of 77 high-risk NIV patients, 68% recovered in IRCU without ICU escalation. The ODE model, based on transition rates between patient states, reflected aggregate outcomes. Both modeling approaches demonstrated system strain during admission surges (partially mitigated by simulated care efficiency improvements via parameter modulation) and yielded consistent peak occupancy estimates. This IRCU, with specialized staffing, effectively managed severe COVID-19. High recovery rates, especially for NIV patients, potentially eased ICU pressure. Dynamic modeling confirmed surge vulnerability but highlighted the benefits of care efficiency from modulated transition parameters. Findings underscore positive outcomes in this IRCU model and support such units in pandemic response.
SDS-Net: Shallow-Deep Synergism-detection Network for infrared small target detection
Authors: Taoran Yue, Xiaojin Lu, Jiaxi Cai, Yuanping Chen, Shibing Chu∗
Date: 2025-06-06 00:00:00
Tasks: Computational Efficiency

Current CNN-based infrared small target detection
 (IRSTD) methods generally overlook the heterogeneity between
 shallow and deep features, leading to inefficient collaboration
 between shallow fine-grained structural information and deep
 high-level semantic representations. Additionally, the dependency
 relationships and fusion mechanisms across different feature
 hierarchies lack systematic modeling, which fails to fully exploit
 the complementarity of multilevel features. These limitations
 hinder IRSTD performance while incurring substantial compu
tational costs. To address these challenges, this paper proposes
 a shallow-deep synergistic detection network (SDS-Net) that
 efficiently models multilevel feature representations to increase
 both the detection accuracy and computational efficiency in
 IRSTD tasks. SDS-Net introduces a dual-branch architecture
 that separately models the structural characteristics and seman
tic properties of features, effectively preserving shallow spatial
 details while capturing deep semantic representations, thereby
 achieving high-precision detection with significantly improved
 inference speed. Furthermore, the network incorporates an
 adaptive feature fusion module to dynamically model cross-layer
 feature correlations, enhancing overall feature collaboration and
 representation capability. Comprehensive experiments on three                    
 public datasets (NUAA-SIRST, NUDT-SIRST, and IRSTD-1K)
 demonstrate that SDS-Net outperforms state-of-the-art IRSTD
 methods while maintaining low computational complexity and
 high inference efficiency, showing superior detection performance
 and broad application prospects. Our code will be made public
 at https://github.com/PhysiLearn/SDS-Net.