March 2025 arXiv papers — page 188

Marc Munsch, Yuichiro Toma

In this paper, we investigate the size of moments of quadratic character sums averaged over the family of fundamental discriminants. We obtain an asymptotic formula for all integer moments in a restricted range of parameters using a multivariate tauberian theorem. As a consequence, we prove unconditional lower bounds for all even integer moments of quadratic

Francesco Cazzaro, Justin Kleindienst, Sofia Marquez Gomez, Ariadna Quattoni

In recent years, the need for natural language interfaces to knowledge graphs has become increasingly important since they enable easy and efficient access to the information contained in them. In particular, property graphs (PGs) have seen increased adoption as a means of representing complex structured information. Despite their growing popularity in indus

Amal Alphonse, Ana Djurdjevac, Emil Engström, Eskil Hansen

Parabolic equations on evolving domains model a multitude of applications including various industrial processes such as the molding of heated materials. Such equations are numerically challenging as they require large-scale computations and the usage of parallel hardware. Domain decomposition is a common choice of numerical method for stationary domains, as

Yung-Ching Chang, Chan-Chuan Liu, Wan-Ping Chan, Yu-Long Lin

Glioblastoma (GBM) is an aggressive and fatal tumor. The infiltrative spread of GBM cells hinders the gross total resection. The residual GBM cells are significantly associated with survival and recurrence. Therefore, a theranostic method that can enhance the contrast between residual GBM and normal astrocyte (AS) cells as well as selectively eradicate GBM c

Rumi Allbert, Makai L. Allbert

We present PhiloBERTA, a cross-lingual transformer model that measures semantic relationships between ancient Greek and Latin lexicons. Through analysis of selected term pairs from classical texts, we use contextual embeddings and angular similarity metrics to identify precise semantic alignments. Our results show that etymologically related pairs demonstrat

Mark Russinovich, Ahmed Salem

We introduce the Context Compliance Attack (CCA), a novel, optimization-free method for bypassing AI safety mechanisms. Unlike current approaches -- which rely on complex prompt engineering and computationally intensive optimization -- CCA exploits a fundamental architectural vulnerability inherent in many deployed AI systems. By subtly manipulating conversa

Thomas Mildner, Daniel Fidel, Evropi Stefanidi, Pawel W. Wozniak

Dark patterns are deceptive strategies that recent work in human-computer interaction (HCI) has captured throughout digital domains, including social networking sites (SNSs). While research has identified difficulties among people to recognise dark patterns effectively, few studies consider vulnerable populations and their experience in this regard, includin

P. Kamphuis, P. Serra, D. Kleiner, R. -J. Dettmar

The MeerKAT Fornax Survey (MFS) is a large survey project mapping the HI in the Fornax cluster. Most of the cluster members detected in HI show significant signs of interaction with the intra-cluster medium or other galaxies. The galaxy ESO 358-60 however stands out as its large HI disk appears regular and undisturbed. A possible explanation for this undistu

Lin Nie, Yi-Qing Guo, Si-Ming Liu

Long-term observations of the Galactic center by Fermi and HESS have revealed a novel phenomenon: the high-energy gamma-ray spectrum from the Galactic center exhibits a double power-law structure. In this study, we propose a new explanation for this phenomenon. We suggest that the low-energy (GeV) power-law spectrum originates from interactions between trapp

Matteo Ceccarello, Andrea Pietracaprina, Geppino Pucci, Francesco Visonà

The $k$-center problem requires the selection of $k$ points (centers) from a given metric pointset $W$ so to minimize the maximum distance of any point of $W$ from the closest center. This paper focuses on a fair variant of the problem, known as \emph {fair center}, where each input point belongs to some category and each category may contribute a limited nu

Nikolaj Kühne Jakobsen

The quality of software products tends to correlate with the quality of the abstractions adopted early in the design process. Acknowledging this tendency has led to the development of various tools and methodologies for modeling systems thoroughly before implementing them. However, creating effective abstract models of domain problems is difficult, especiall

Eirini Chavli, Götz Pfeiffer

The exceptional complex reflection groups of rank 2 are partitioned into three families. We construct explicit matrix models for the Hecke algebras associated to the maximal groups in the tetrahedral and octahedral family, and use them to verify the BMM symmetrising trace conjecture for all groups in these two families, providing evidence that a similar stra

Nir Gavrielov, Santiago Oviedo-Casado, Alex Retzker

Exploring the noise spectrum impacting a qubit and extending its coherence duration are fundamental components of quantum technologies. In this study, we introduce parametric spectroscopy, a method that merges parametric modulation of a qubit's energy gap with dynamical decoupling sequences. The parametric modulation provides high sensitivity to extensive re

Vadim V. Bobylev

The most complete sample of galactic maser sources and radio stars with trigonometric parallaxes, proper motions and radial velocities measured by the VLBI method has been compiled based on literature data. These sources are associated with young stars located in high mass star forming regions. The rotation parameters of the Galaxy have been determined based

Chunle Huang

In this paper, we will show that under certain conditions, associated to any fixed distortion function $g$, the distortion risk measure of a sum of two counter-monotonic risks can be expressed as the sum of two related distortion risk measures of the marginals involved, one associated to the original distortion function $g$ and the other associated to the du

Guanghao Zhang, Tao Zhong, Yan Xia, Mushui Liu

While previous multimodal slow-thinking methods have demonstrated remarkable success in single-image understanding scenarios, their effectiveness becomes fundamentally constrained when extended to more complex multi-image comprehension tasks. This limitation stems from their predominant reliance on text-based intermediate reasoning processes. While for human

Adele Ravagnani, Fabrizio Lillo

Devising models of the limit order book that realistically reproduce the market response to exogenous trades is extremely challenging and fundamental in order to test trading strategies. We propose a novel explainable model for small tick assets, the Non-Markovian Zero Intelligence, which is a variant of the well-known Zero Intelligence model. The main modif

Adrian William Romero Jorge, Taesoo Song, Qi Zhou, Elena Bratkovskaya

We investigate dilepton production in heavy-ion, proton-proton, and proton-nucleus collisions from low energies of 1 AGeV (SIS) to ultra-relativistic energies (LHC) using the Parton-Hadron-String Dynamics (PHSD) transport approach. PHSD is a microscopic, non-equilibrium approach that integrates hadronic and partonic degrees of freedom, providing a comprehens

Lorenzo Scarciglia, Antonio Paolillo, Daniele Palossi

Palm-sized autonomous nano-drones, i.e., sub-50g in weight, recently entered the drone racing scenario, where they are tasked to avoid obstacles and navigate as fast as possible through gates. However, in contrast with their bigger counterparts, i.e., kg-scale drones, nano-drones expose three orders of magnitude less onboard memory and compute power, demandi

Hiroko Shinnaga, Miyako Oyadomari, Hiroshi Imai, Tomoaki Oyama

We achieved the first VLBI detections of the ground vibrational state ($v=0$) $^{28}$SiO (hereafter, SiO) and $^{29}$SiO masers of the $J=1\rightarrow 0$ rotational transitions, towards the 25 \Msun ~red supergiant (RSG) star, VY Canis Majoris (VY CMa), taking advantage of the high sensitivity of the VLBI Exploration of Radio Astrometry (VERA) telescopes tha

En-Jui Chang

Collective coherent (CC) errors are inevitable, as every physical qubit undergoes free evolution under its kinetic Hamiltonian. These errors can be more damaging than stochastic Pauli errors because they affect all qubits coherently, resulting in high-weight errors that standard quantum error-correcting (QEC) codes struggle to correct. In quantum memories an

Bowen Pang, Kai Li, Feifan Wang

The increasing adoption of large language models (LLMs) necessitates inference serving systems that can deliver both high throughput and low latency. Deploying LLMs with hundreds of billions of parameters on memory-constrained GPUs exposes significant limitations in static batching methods. Current inference serving systems often treat batch sizes as fixed h

Anudeep Vurity, Emanuela Marasco, Raghavendra Ramachandra, Jongwoo Park

Finger photo Presentation Attack Detection (PAD) can significantly strengthen smartphone device security. However, these algorithms are trained to detect certain types of attacks. Furthermore, they are designed to operate on images acquired by specific capture devices, leading to poor generalization and a lack of robustness in handling the evolving nature of

Chengqi Zheng, Haiyan Yin, Jianda Chen, Terence Ng

Continual Reinforcement Learning (CRL) is essential for developing agents that can learn, adapt, and accumulate knowledge over time. However, a fundamental challenge persists as agents must strike a delicate balance between plasticity, which enables rapid skill acquisition, and stability, which ensures long-term knowledge retention while preventing catastrop

Johanna P. Müller, Robert Wright, Thomas G. Day, Lorenzo Venturini

Accurate analysis of prenatal ultrasound (US) is essential for early detection of developmental anomalies. However, operator dependency and technical limitations (e.g. intrinsic artefacts and effects, setting errors) can complicate image interpretation and the assessment of diagnostic uncertainty. We present L-FUSION (Laplacian Fetal US Segmentation with Int

Yuning Wu, Jiahao Mei, Ming Yan, Chenliang Li

Recent advancements in large language models (LLMs) have significantly enhanced text generation capabilities, yet evaluating their performance in generative writing remains a challenge. Existing benchmarks primarily focus on generic text generation or limited in writing tasks, failing to capture the diverse requirements of high-quality written contents acros

Gianluca Passarelli, Angelo Russomanno, Procolo Lucignano

Boundary time crystals exhibit measurement-induced phase transitions in their steady-state entanglement, with critical behavior that depends on the particular unraveling of the Lindblad dynamics. In this work, we investigate another key measure of quantum complexity -- nonstabilizerness (or ``magic'') -- and show that it follows a markedly different pattern.

Xuenan Xu, Jiahao Mei, Chenliang Li, Yuning Wu

The rapid advancement of large language models (LLMs) and artificial intelligence-generated content (AIGC) has accelerated AI-native applications, such as AI-based storybooks that automate engaging story production for children. However, challenges remain in improving story attractiveness, enriching storytelling expressiveness, and developing open-source eva

Biao Dong, Bin Cao, Qinyu Zhang

This work is concerned with integrated sensing, communication, and computation (ISCC) in uplink orthogonal frequency division multiplexing (OFDM) systems, wherein multiple devices perform target sensing and over-the-air computation (AirComp) simultaneously. We aim to minimize the computational mean squared error (MSE) by jointly optimizing the transmitting v

Arif Ali Khan, Boshuai Ye, Muhammad Azeem Akbar, Javed Ali Khan

The rise of quantum computing has driven the need for quantum software engineering, yet its programming landscape remains largely unexplored in empirical research. As quantum technologies advance toward industrial adoption, understanding programming aspects is crucial to addressing software development challenges. This study analyzes 6,935 quantum software p

Soroush H. Zargarbashi, Aleksandar Bojchevski

Conformal prediction (CP) converts any model's output to prediction sets with a guarantee to cover the true label with (adjustable) high probability. Robust CP extends this guarantee to worst-case (adversarial) inputs. Existing baselines achieve robustness by bounding randomly smoothed conformity scores. In practice, they need expensive Monte-Carlo (MC) samp

Mohit Prashant, Arvind Easwaran, Suman Das, Michael Yuhas

An issue concerning the use of deep reinforcement learning (RL) agents is whether they can be trusted to perform reliably when deployed, as training environments may not reflect real-life environments. Anticipating instances outside their training scope, learning-enabled systems are often equipped with out-of-distribution (OOD) detectors that alert when a tr

Yibin Wang, Yuhang Zang, Hao Li, Cheng Jin

Recent advances in human preference alignment have significantly improved multimodal generation and understanding. A key approach is to train reward models that provide supervision signals for preference optimization. However, existing reward models are often task-specific, limiting their adaptability across diverse visual applications. We also argue that a

Wankai Li, Yixuan Wang, Xing Li, Tao Yang

Resonance-enhanced multiphoton ionization (REMPI) in potassium atom is investigated within the strong coupling regime using photoelectron momentum imaging techniques. The kinetic energy distribution of the ionized electrons reveals the eigenenergies of the dressed states, which exhibit Autler-Townes (AT) splitting. This splitting is proportional to the laser

Leonardo Becchetti, Nazaria Solferino

We explore the political and ideological positioning of ChatGPT, a leading large language model (LLM), by comparing its responses to political economy questions from the European Social Survey (ESS). The questions concern environmental sustainability, civil rights, income inequality, and government size. ChatGPT's self-assessed placement on a left-right poli

Julien Posso, Hugo Kieffer, Nicolas Menga, Omar Hlimi

This study introduces a lightweight U-Net model optimized for real-time semantic segmentation of aerial images, targeting the efficient utilization of Commercial Off-The-Shelf (COTS) embedded computing platforms. We maintain the accuracy of the U-Net on a real-world dataset while significantly reducing the model's parameters and Multiply-Accumulate (MAC) ope

Divya Ahuja, Abhishek Banerjee, Surjeet Kour

We study the noncommutative base change of an entwining structure $(A,C,\psi)$ by a Grothendieck category $\mathfrak S$, using two module like categories. These are the categories of entwined comodule objects and entwined contramodule objects in $\mathfrak S$ over the entwining structure $(A,C,\psi)$. We consider criteria for maps between these noncommutativ

Anaïs Rat, Magali Tournus

The goal of the present paper is to explore the long-time behavior of the growth-fragmentation equation formulated in the case of equal mitosis and variability in growth rate, under fairly general assumptions on the coefficients. The first results concern the monotonicity of the Malthus parameter with respect to the coefficients. Existence of a solution to t

Shuo Jiang, Haonan Li, Ruochen Ren, Yanmin Zhou

Cutting-edge robot learning techniques including foundation models and imitation learning from humans all pose huge demands on large-scale and high-quality datasets which constitute one of the bottleneck in the general intelligent robot fields. This paper presents the Kaiwu multimodal dataset to address the missing real-world synchronized multimodal data pro

Prama Adhya, Sachin M. B. Gautham, Tarak K Patra, Manish Kaushal

Precise control over the flow behavior of liquids is a critical problem and is demanding for multifaceted applications. Introducing surface-engineered nanoparticles into the liquid can tune the flow behavior. However, the extent of tunability depends on the compatibility of the surface groups on nanoparticles and the liquid matrix. Herein, we report a strate

Kalle Kujanpää, Daulet Baimukashev, Farzeen Munir, Shoaib Azam

Learning to perform accurate and rich simulations of human driving behaviors from data for autonomous vehicle testing remains challenging due to human driving styles' high diversity and variance. We address this challenge by proposing a novel approach that leverages contrastive learning to extract a dictionary of driving styles from pre-existing human drivin

Ruoxuan Zhang, Hongxia Xie, Yi Yao, Jian-Yu Jiang-Lin

Recipe image generation is an important challenge in food computing, with applications from culinary education to interactive recipe platforms. However, there is currently no real-world dataset that comprehensively connects recipe goals, sequential steps, and corresponding images. To address this, we introduce RecipeGen, the first real-world goal-step-image

Jungbae Park, Heonseok Jang

E-commerce search optimization has evolved to include a wider range of metrics that reflect user engagement and business objectives. Modern search frameworks now incorporate advanced quality features, such as sales counts and document-query relevance, to better align search results with these goals. Traditional methods typically focus on click-through rate (

Léa Orsini, Emmanuel Lesaffre, Guosheng Yin, Caroline Brard

The difference in restricted mean survival time (RMST) is a clinically meaningful measure to quantify treatment effect in randomized controlled trials, especially when the proportional hazards assumption does not hold. Several frequentist methods exist to estimate RMST adjusted for covariates based on modeling and integrating the survival function. A more na

Baris Yilmaz, Erdem Akagündüz, Salih Tileylioglu

This study explores the use of deep learning for predicting the time averaged shear wave velocity in the top 30 m of the subsurface ($V_{s30}$) at strong motion recording stations in T\"urkiye. $V_{s30}$ is a key parameter in site characterization and, as a result for seismic hazard assessment. However, it is often unavailable due to the lack of direct measu

Yifan Liu, Yu Fang, Zhouhan Lin

Video-to-speech (V2S) synthesis, the task of generating speech directly from silent video input, is inherently more challenging than other speech synthesis tasks due to the need to accurately reconstruct both speech content and speaker characteristics from visual cues alone. Recently, audio-visual pre-training has eliminated the need for additional acoustic

Mazen Alamir

Reconstructing high derivatives of noisy measurements is an important step in many control, identification and diagnosis problems. In this paper, a heuristic is proposed to address this challenging issue. The framework is based on a dictionary of identified models indexed by the bandwidth, the noise level and the required degrees of derivation. Each model in

Coralie Aubert, Guilhem Scheiblin, Anne Paul, Hélène Pauchet

In the framework of the MACIV project, a consortium of French laboratories has deployed a temporary seismic network of 100 broadband stations in the French Massif Central (FMC) for 3-4 years (2023-2027). The project aims at imaging the crust and upper mantle of the FMC to better assess the sources of volcanism, and the impacts of the Variscan inheritance or

Julián Méndez, Marc Satkowski

In this position paper, we propose researching the combination of Augmented Reality (AR) and Artificial Intelligence (AI) to support conversations, inspired by the interfaces of dialogue systems commonly found in videogames. AR-capable devices are becoming more powerful and conventional in looks, as seen in head-mounted displays (HMDs) like the Snapchat Spec

Chang-Han Rhee, Jeeho Ryu, Insuk Seo

Kesten's stochastic recurrent equation is a classical subject of research in probability theory and its applications. Recently, it has garnered attention as a model for stochastic gradient descent with a quadratic objective function and the emergence of heavy-tailed dynamics in machine learning. This context calls for analysis of its asymptotic behavior unde

Mingyang Guo, Klas Markström

Let $n,k$ be positive integers such that $n\geq k$ and $G$ be a tripartite graph with parts $A,B,C$ such that $|A|=|B|=|C|=n$. Denote the edge densities of $G[A,B]$, $G[A,C]$ and $G[B,C]$ by $\alpha$, $\beta$ and $\gamma$, respectively. In this paper, we study edge density conditions for the existence of $k$ vertex-disjoint triangles in a tripartite graph. F

Gulpi Qorik Oktagalu Pratamasunu, Guoqing Hao, Kazuhiro Fukui

This paper proposes Separability Membrane, a robust 3D active contour for extracting a surface from 3D point cloud object. Our approach defines the surface of a 3D object as the boundary that maximizes the separability of point features, such as intensity, color, or local density, between its inner and outer regions based on Fisher's ratio. Separability Memb

Johanna Ockenfels, Yoshio Okamoto, Patrick Schnider

Assume that you have lost your puppy on an embedded graph. You can walk around on the graph and the puppy will run towards you at infinite speed, always locally minimizing the distance to your current position. Is it always possible for you to reunite with the puppy? We show that if the embedded graph is an orthogonal straight-line embedding the answer is ye

Andreas Nienkötter, Sandro Vega-Pons, Xiaoyi Jiang

Robustness in terms of outliers is an important topic and has been formally studied for a variety of problems in machine learning and computer vision. Generalized median computation is a special instance of consensus learning and a common approach to finding prototypes. Related research can be found in numerous problem domains with a broad range of applicati

Bill Cassidy, Christian McBride, Connah Kendrick, Neil D. Reeves

The growing rate of chronic wound occurrence, especially in patients with diabetes, has become a concerning trend in recent years. Chronic wounds are difficult and costly to treat, and have become a serious burden on health care systems worldwide. Chronic wounds can have devastating consequences for the patient, with infection often leading to reduced qualit

Jinghao Zhang, Yuting Liu, Wenjie Wang, Qiang Liu

Personalized text generation aims to infer users' writing style preferences from their historical texts and generate outputs that faithfully reflect these stylistic characteristics. Existing solutions primarily adopt two paradigms: retrieval-augmented generation (RAG) and parameter-efficient fine-tuning (PEFT). While these approaches have advanced the field,

Guoxiu He, Xin Song, Aixin Sun

As real-world knowledge evolves, the information embedded within large language models (LLMs) can become outdated, inadequate, or erroneous. Model editing has emerged as a prominent approach for updating LLMs' knowledge with minimal computational costs and parameter changes. This approach typically identifies and adjusts specific model parameters associated

Chen Hong, Xiangbin Teng, Yu Li, Shen-Mou Hsu

Verbal communication transmits information across diverse linguistic levels, with neural synchronization (NS) between speakers and listeners emerging as a putative mechanism underlying successful exchange. However, the specific speech features driving this synchronization and how language-specific versus universal characteristics facilitate information trans

J. Chagoya, M. Sabido, A. Silva-García

In this work we propose a new 2+1 theory of gravity. We start with a modification of Chern-Simons 2+1 gravity. By introducing a vector field $\Phi$ to the usual composition of the vierbein and connection, we derive a new action that includes the Einstein-Hilbert term, a cosmological constant and a polynomial term for the vector field. This allows us to write

Xiaokang Zhang, Bhargav Rallabandi

We study the interaction between a pair of particles suspended in a uniform oscillatory flow. The time-averaged behavior of particles under these conditions, driven by inertial and viscous effects, is explored through a theoretical framework relying on small oscillation amplitude. We approximate the oscillatory flow in terms of dual multipole expansions, wit

Sangyeup Kim, Nayeon Kim, Yinhua Piao, Sun Kim

Molecular language modeling tasks such as molecule captioning have been recognized for their potential to further understand molecular properties that can aid drug discovery or material synthesis based on chemical reactions. Unlike the common use of molecule graphs in predicting molecular properties, most methods in molecular language modeling rely heavily o

Yunkai Gao, Jiaming Guo, Fan Wu, Rui Zhang

Offline reinforcement learning (RL) aims to optimize a policy by using pre-collected datasets, to maximize cumulative rewards. However, offline reinforcement learning suffers challenges due to the distributional shift between the learned and behavior policies, leading to errors when computing Q-values for out-of-distribution (OOD) actions. To mitigate this i

Orestis Tsirakis, Konstantinos Fysarakis, Vasileios Mavroeidis, Ioannis Papaefstathiou

Modern cybersecurity threats are growing in complexity, targeting increasingly intricate & interconnected systems. To effectively defend against these evolving threats, security teams utilize automation & orchestration to enhance response efficiency and consistency. In that sense, cybersecurity playbooks are key enablers, providing a structured, reusable, an

Qingjie Wu, Beixiong Zheng, Guangchi Zhang, Derrick Wing Kwan Ng

Compared to traditional electromagnetic stealth (ES) materials, which are effective only within specific frequencies and orientations, intelligent reflecting surface (IRS) technology introduces a novel paradigm for achieving dynamic and adaptive ES by adapting its reflection pattern in real time to neutralize radar probing signals echoed back from the target

Zining Chen, Zhicheng Zhao, Fei Su, Xiaoqin Zhang

Zero-shot Composed Image Retrieval (ZS-CIR) aims to retrieve the target image based on a reference image and a text description without requiring in-distribution triplets for training. One prevalent approach follows the vision-language pretraining paradigm that employs a mapping network to transfer the image embedding to a pseudo-word token in the text embed

Hairu Wang, Yuan Feng, Xike Xie, S Kevin Zhou

Although Large Language Models achieve strong success in many tasks, they still suffer from hallucinations and knowledge deficiencies in real-world applications. Many knowledge graph-based retrieval-augmented generation (KG-RAG) methods enhance the quality and credibility of LLMs by leveraging structure and semantic information in KGs as external knowledge b

Ok Song An, Jin U Kang, Yong Jin Kim, Ui Ri Mun

We propose an idea to build a bridge between reheating and late-time observations in quintessential inflation by backtracking the evolution of the inflaton field from the present time to the end of reheating. This idea is implemented when the potential gradient is negligible compared to the Hubble friction, rendering the inflaton field frozen, till the prese

Rajnish Kumar, Tapas Tripura, Souvik Chakraborty, Sitikantha Roy

Electromyography (EMG)--based computational musculoskeletal modeling is a non-invasive method for studying musculotendon function, human movement, and neuromuscular control, providing estimates of internal variables like muscle forces and joint torques. However, EMG signals from deeper muscles are often challenging to measure by placing the surface EMG elect

Pranshav Gajjar, Vijay K. Shah

Despite the transformative impact of Large Language Models (LLMs) across critical domains such as healthcare, customer service, and business marketing, their integration into Open Radio Access Networks (O-RAN) remains limited. This gap is primarily due to the absence of domain-specific foundational models, with existing solutions often relying on general-pur

Dana O. Byrne, Stephanie M. Ribet, Karen C. Bustillo, Colin Ophus

Neutral atoms emitted from liquid metal ion sources are an often-overlooked source of contamination and damage in focused ion beam microscopy. Beyond ions and single atoms, these sources also emit atom clusters. While most studies have investigated charged clusters, here we demonstrate that neutral clusters are also emitted. These neutral clusters bypass the

Meng-Zhe Yang, Shih-Ping Lai, Janik Karoly, Kate Pattle

We acquired 450 {\mu}m and 850 {\mu}m dust continuum polarization observations toward the inner region of the Central Molecular Zone (CMZ) as part of the B-Fields In Star-Forming Region Observations (BISTRO) survey using the POL-2 polarimeter on the James Clerk Maxwell Telescope. These observations encompassed three dense structures: the 20 km s{^{-1}} cloud

Yu Feng, Zheng Liu, Weikai Lin, Zihan Liu

Point clouds are increasingly important in intelligent applications, but frequent off-chip memory traffic in accelerators causes pipeline stalls and leads to high energy consumption. While conventional line buffer techniques can eliminate off-chip traffic, they cannot be directly applied to point clouds due to their inherent computation patterns. To address

Hanzhi Guo, Yixiao Chen, Dongye Xiaonuo, Zeyu Tian

We propose selective-training Gaussian head avatars (STGA) to enhance the details of dynamic head Gaussian. The dynamic head Gaussian model is trained based on the FLAME parameterized model. Each Gaussian splat is embedded within the FLAME mesh to achieve mesh-based animation of the Gaussian model. Before training, our selection strategy calculates the 3D Ga

Wonjong Lee, Joonyeol Sim, Joonkyung Kim, Siwon Jo

We propose a hybrid approach for decentralized multi-robot navigation that ensures both safety and deadlock prevention. Building on a standard control formulation, we add a lightweight deadlock prevention mechanism by forming temporary "roundabouts" (circular reference paths). Each robot relies only on local, peer-to-peer communication and a controller for b

Yiming Jiang, Yanwei Liu, Jinxia Liu, Antonios Argyriou

Holographic-type communication brings an immersive tele-holography experience by delivering holographic contents to users. As the direct representation of holographic contents, hologram videos are naturally three-dimensional representation, which consist of a huge volume of data. Advanced multi-connectivity (MC) millimeter-wave (mmWave) networks are now avai

Yanci Zhang, Han Yu

Federated Learning (FL) is a collaborative machine learning paradigm for enhancing data privacy preservation. Its privacy-preserving nature complicates the explanation of the decision-making processes and the evaluation of the reliability of the generated explanations. In this paper, we propose the Uncertainty-aware eXplainable Federated Learning (UncertainX

Mufan Xu, Gewen Liang, Kehai Chen, Wei Wang

Large language models (LLMs) have achieved remarkable performance on knowledge graph question answering (KGQA) tasks by planning and interacting with knowledge graphs. However, existing methods often confuse tool utilization with knowledge reasoning, harming readability of model outputs and giving rise to hallucinatory tool invocations, which hinder the adva

Jie Li, Kedong Wang, Kai Wang, Xu Zhang

Symplectic integrator plays a pivotal role in the long-term tracking of charged particles within accelerators. To get symplectic maps in accurate simulation of single-particle trajectories, two key components are addressed: precise analytical expressions for arbitrary electromagnetic fields and a robust treatment of the equations of motion. In a source-free

Christopher G. Bailey, Adrian Mena, Tik Lun Leung, Nicholas P. Sloane

The successful development of optoelectronic devices is contingent on a detailed understanding of interactions between light and excited energy states in photoactive materials. In 2D perovskites, excitons are the dominant photogenerated species and their energetic structure plays a pivotal role, governing photon absorption and emission processes. In these ma

Lei Zhu, Yanyu Xu, Huazhu Fu, Xinxing Xu

Unpaired Multi-Modal Learning (UMML) which leverages unpaired multi-modal data to boost model performance on each individual modality has attracted a lot of research interests in medical image analysis. However, existing UMML methods require multi-modal datasets to be fully labeled, which incurs tremendous annotation cost. In this paper, we investigate the u

Justin Yu, Kush Hari, Karim El-Refai, Arnav Dalal

Tracking and manipulating irregularly-shaped, previously unseen objects in dynamic environments is important for robotic applications in manufacturing, assembly, and logistics. Recently introduced Gaussian Splats efficiently model object geometry, but lack persistent state estimation for task-oriented manipulation. We present Persistent Object Gaussian Splat

Jiachun Li, Pengfei Cao, Zhuoran Jin, Yubo Chen

Inference-time scaling techniques have shown promise in enhancing the reasoning capabilities of large language models (LLMs). While recent research has primarily focused on training-time optimization, our work highlights inference-time reward model (RM)-based reasoning as a critical yet overlooked avenue. In this paper, we conduct a systematic analysis of RM

Huaiqian Yi, Xiaofan Wang, Li Zeng, Yifan Liang

The self-amplified spontaneous emission (SASE) mechanism, the fundamental operating principle of numerous free-electron laser (FEL) facilities, is driven by electron beam shot noise and leads to significant fluctuations in the output pulse energy. This study presents a robust method for improving pulse energy stability by incorporating a dispersion element t

Chan Hur, Jeong-hun Hong, Dong-hun Lee, Dabin Kang

In recent text-video retrieval, the use of additional captions from vision-language models has shown promising effects on the performance. However, existing models using additional captions often have struggled to capture the rich semantics, including temporal changes, inherent in the video. In addition, incorrect information caused by generative models can

Fengbin Zhu, Junfeng Li, Liangming Pan, Wenjie Wang

Finance decision-making often relies on in-depth data analysis across various data sources, including financial tables, news articles, stock prices, etc. In this work, we introduce FinTMMBench, the first comprehensive benchmark for evaluating temporal-aware multi-modal Retrieval-Augmented Generation (RAG) systems in finance. Built from heterologous data of N

Giovanni Alberto Ummarino, Alessio Zaccone

It is known that noble metals such as gold, silver and copper are not supercon- 1 ductors, so as magnesium. This is due to the weakness of the electron-phonon interaction 2 which makes them excellent conductors but not superconductors. As it has recently been 3 shown for gold, silver and copper, even for magnesium it is possible that in very particular 4 sit

Qingyuan Zhou, Yuehu Gong, Weidong Yang, Jiaze Li

Novel view synthesis (NVS) and surface reconstruction (SR) are essential tasks in 3D Gaussian Splatting (3D-GS). Despite recent progress, these tasks are often addressed independently, with GS-based rendering methods struggling under diverse light conditions and failing to produce accurate surfaces, while GS-based reconstruction methods frequently compromise

Kangyu Lin, Toshiyuki Ohtsuka

Our recent study (Lin and Ohtsuka, 2024) proposed a new penalty method for solving mathematical programming with complementarity constraints (MPCC). This method first reformulates MPCC as a parameterized nonlinear programming called gap penalty reformulation and then solves a sequence of gap penalty reformulations with an increasing penalty parameter. This s

Zherui Huang, Xing Gao, Guanjie Zheng, Licheng Wen

Traffic simulation, complementing real-world data with a long-tail distribution, allows for effective evaluation and enhancement of the ability of autonomous vehicles to handle accident-prone scenarios. Simulating such safety-critical scenarios is nontrivial, however, from log data that are typically regular scenarios, especially in consideration of dynamic

Simon A. Aytes, Jinheon Baek, Sung Ju Hwang

Recent advances in large language models (LLMs) have enabled strong reasoning capabilities through Chain-of-Thought (CoT) prompting, which elicits step-by-step problem solving, but often at the cost of excessive verbosity in intermediate outputs, leading to increased computational overhead. We propose Sketch-of-Thought (SoT), a prompting framework that integ

Helena Bergold, Arun Kumar Das, Robert Lauff, Manfred Scheucher

We address the problem of computing the minimum number of triangles to separate a set of blue points from a set of red points in $\mathbb{R}^2$. A set of triangles is a \emph{separator} of one color from the other if every point of that color is contained in some triangle and no triangle contains points of both colors. We consider several variants of the pro

Vahram Asatryan, Erik Babasyan, Sevak Mkrtchyan

We study conditions under which integer sequences with independent, identically distributed gaps are asymptotically $k$-complete, meaning that every sufficiently large integer can be represented as the sum of exactly $k$ distinct elements of the sequence, or equivalently whether $k$-fold sumsets with distinct entries from such sequences generate all sufficie

Shuxiang Xu, Hao Wang, Mengwu Huo, Deyuan Hu

Recent discoveries of superconductivity in Ruddlesden-Popper nickelates realize a rare category of superconductors. However, the use of high-pressure diamond anvil cells limits spectroscopic characterization of the density waves and superconducting gaps. Here, we systematically studied the pressure evolution of La$_{4}$Ni$_{3}$O$_{10}$ using ultrafast optica

Wyame Benslimane, Paul Grigas

We propose a new methodology for parameterized constrained robust optimization, an important class of optimization problems under uncertainty, based on learning with a self-supervised penalty-based loss function. Whereas supervised learning requires pre-solved instances for training, our approach leverages a custom loss function derived from the exact penalt

Linqi Yang, Xiongwei Zhao, Qihao Sun, Ke Wang

6-DoF pose estimation is a fundamental task in computer vision with wide-ranging applications in augmented reality and robotics. Existing single RGB-based methods often compromise accuracy due to their reliance on initial pose estimates and susceptibility to rotational ambiguity, while approaches requiring depth sensors or multi-view setups incur significant

Vincent Cohen-Addad, Shaofeng H. -C. Jiang, Qiaoyuan Yang, Yubo Zhang

We study streaming algorithms for proportionally fair clustering, a notion originally suggested by Chierichetti et. al. (2017), in the sliding window model. We show that although there exist efficient streaming algorithms in the insertion-only model, surprisingly no algorithm can achieve finite multiplicative ratio without violating the fairness constraint i

Manoj Kumar Manda, Mitali Sisodia, Plaban Saha, Binayak S. Choudhury

In this paper, we present a hybrid bidirectional controlled quantum communication protocol between two parties, initiated by a Mentor. Initially, the two main parties and the controller do not share a common quantum entanglement; instead, each party shares entanglement separately with the Mentor. The Mentor's actions create entanglement among the two parties

Louis Hanotel, Elena R. Loubenets

In the present paper, based on the general analytical expression [arXiv:2412.03470] for the maximum of the CHSH expectation under local Alice and Bob spin-$s$ measurements in a two-qudit state of dimension $d=2s+1$, $s\geq 1/2$, we analyze whether or not, under spin-$1$ measurements in an arbitrary two-qutrit state, the CHSH inequality is violated. We find a

Mei Ai, Ming Zhu, Nai-ping Yu, Jin-long Xu

We present the high-sensitivity and large-scale atomic hydrogen (HI) observations towards lenticular (S0) galaxy NGC 4111 using the Five-hundred-meter Aperture Spherical Radio Telescope (FAST). The column density map shows that NGC4111 and seven other different types of galaxies share a huge HI gas complex. The data also suggest that NGC 4111 is interacting

Willmer Rafell Quinones Robles, Sakonporn Noree, Jongwoo Kim, Young Sin Ko

Deep learning has shown strong potential in cancer classification from whole-slide images (WSIs), but the need for extensive expert annotations often limits its success. Annotation-free approaches, such as multiple instance learning (MIL) and self-supervised learning (SSL), have emerged as promising alternatives to traditional annotation-based methods. Howev