March 2025 arXiv papers — page 182

Shengjie Xu, Zhijian Shi, Ming Yang, Jingwei Zhang

The intrinsic magnetic topological material MnBi2Te4 has demonstrated great potential to investigate the interplay between topology and magnetism, which opens up new avenues for manipulating non-trivial electronic states and designing quantum devices. However, challenges and controversies remain due to its inevitable n-type antisite defects, hindering the ex

Neha Patel, Arkadeb Pal, C. W. Wang, G. R. Blake

Magnetic systems with noncentrosymmetric crystal structures are renowned for their complex magnetic ordering and diverse and fascinating physical properties. In this report, we provide a comprehensive study of the chiral magnetic system Ni$_2$ScSbO$_6$, which exhibits a robust incommensurate long-range antiferromagnetic spin ordering at a temperature of $T_N

Jiaming Liu, Linghe Kong, Guihai Chen

Segment anything model (SAM) has shown impressive general-purpose segmentation performance on natural images, but its performance on camouflaged object detection (COD) is unsatisfactory. In this paper, we propose SAM-COD that performs camouflaged object detection for RGB-D inputs. While keeping the SAM architecture intact, dual stream adapters are expanded o

Yian Huang, Zhen Huang

We investigate the application of randomized quasi-Monte Carlo (RQMC) methods in random feature approximations for kernel-based learning. Compared to the classical Monte Carlo (MC) approach \citep{rahimi2007random}, RQMC improves the deterministic approximation error bound from $O_P(1/\sqrt{M})$ to $O(1/M)$ (up to logarithmic factors), matching the rate achi

Manan Suri, Nishit Anand, Amisha Bhaskar

The memorization of training data by Large Language Models (LLMs) poses significant risks, including privacy leaks and the regurgitation of copyrighted content. Activation steering, a technique that directly intervenes in model activations, has emerged as a promising approach for manipulating LLMs. In this work, we explore the effectiveness of activation ste

Hongtao Wang, Jiandong Liang, Lei Wang, Shuaizhe Liang

Residual moveout (RMO) provides critical information for travel time tomography. The current industry-standard method for fitting RMO involves scanning high-order polynomial equations. However, this analytical approach does not accurately capture local saltation, leading to low iteration efficiency in tomographic inversion. Supervised learning-based image se

Zhiyu Zhao, Haifeng Zhang

The Control as Inference (CAI) framework has successfully transformed single-agent reinforcement learning (RL) by reframing control tasks as probabilistic inference problems. However, the extension of CAI to multi-agent, general-sum stochastic games (SGs) remains underexplored, particularly in decentralized settings where agents operate independently without

Chengyu Zhou, Qingguo Li

Consistent Hoare, Smyth and Plotkin power domains are introduced and discussed by Yuan and Kou. The consistent algebraic operation $+$ defined by them is a binary partial Scott continuous operation satisfying the requirement: $a+b$ exists whenever there exists a $c$ which is greater than $a$ and $b$. We extend the consistency to be a categorical concept and

Chien-yi Chang, Xin He

This paper explores the legal implications of violating "robots.txt", a technical standard widely used by webmasters to communicate restrictions on automated access to website content. Although historically regarded as a voluntary guideline, the rise of generative AI and large-scale web scraping has amplified the consequences of disregarding "robots.txt" dir

Shengyao Zhuang, Xueguang Ma, Bevan Koopman, Jimmy Lin

In this paper, we introduce Rank-R1, a novel LLM-based reranker that performs reasoning over both the user query and candidate documents before performing the ranking task. Existing document reranking methods based on large language models (LLMs) typically rely on prompting or fine-tuning LLMs to order or label candidate documents according to their relevanc

John D. Clayton

Experimental, theoretical, and numerical studies of adiabatic shear in ductile metals suggest initial defects such as pores or material imperfections increase shear-band susceptibility. Conversely, viscous effects manifesting macroscopically as strain-rate sensitivity inhibit localization. The analytical shear-band process zone model due to D.E. Grady, in tu

F. Marinho

The Deep Underground Neutrino Experiment currently under construction in the US will be a long-baseline neutrino oscillation experiment dedicated to determining the neutrino mass ordering and to measure the CP violation phase in the lepton sector. DUNE will also perform studies of non-beam physics such as atmospheric neutrinos, bursts from supernovae and nuc

Minu J. Bae, Nitish K. Panigrahy, Prajit Dhara, Md Zakir Hossain

Free-space satellite communication has significantly lower photon loss than terrestrial communication via optical fibers. Satellite-based quantum key distribution (QKD) leverages this advantage and provides a promising direction in achieving long-distance QKD. While the technological feasibility of satellite-based QKD has been demonstrated experimentally, op

Yuheng Li, Yuxiang Lai, Maria Thor, Deborah Marshall

Computed tomography (CT) is extensively used for accurate visualization and segmentation of organs and lesions. While deep learning models such as convolutional neural networks (CNNs) and vision transformers (ViTs) have significantly improved CT image analysis, their performance often declines when applied to diverse, real-world clinical data. Although found

Xudong Lu, Haohao Gao, Renshou Wu, Shuai Ren

Large Language Models (LLMs) have become integral to daily life, especially advancing as intelligent assistants through on-device deployment on smartphones. However, existing LLM evaluation benchmarks predominantly focus on objective tasks like mathematics and coding in English, which do not necessarily reflect the practical use cases of on-device LLMs in re

Xinge Ma, Jin Wang, Xuejie Zhang

Federated learning (FL) enables decentralized clients to collaboratively train a global model under the orchestration of a central server without exposing their individual data. However, the iterative exchange of model parameters between the server and clients imposes heavy communication burdens, risks potential privacy leakage, and even precludes collaborat

Xubin Wang, Zhiqing Tang, Jianxiong Guo, Tianhui Meng

The rapid advancement of artificial intelligence (AI) technologies has led to an increasing deployment of AI models on edge and terminal devices, driven by the proliferation of the Internet of Things (IoT) and the need for real-time data processing. This survey comprehensively explores the current state, technical challenges, and future trends of on-device A

Masaru Yajima, Kei Ota, Asako Kanezaki, Rei Kawakami

Achieving zero-shot peg insertion, where inserting an arbitrary peg into an unseen hole without task-specific training, remains a fundamental challenge in robotics. This task demands a highly generalizable perception system capable of detecting potential holes, selecting the correct mating hole from multiple candidates, estimating its precise pose, and execu

Yalong Cao, Yukinobu Toda, Gufang Zhao

Given a regular function $\phi$ on a smooth stack, and a $(-1)$-shifted Lagrangian $M$ on the derived critical locus of $\phi$, under fairly general hypotheses, we construct a pullback map from the Grothendieck group of coherent matrix factorizations of $\phi$ to that of coherent sheaves on $M$. This map satisfies a functoriality property with respect to the

Ying Zhang

Seeking mass patterns is a key to decoding the unknown flavor puzzles in particle physics. Inspired by quark hierarchical masses, the mass matrix can universally be factorized into a family-diagonal phase matrix $K_L^q$ and a real symmetric matrix $M_N^q$ characterized by only two parameters. The factorized structure provides model-independent rules to the m

John D. Clayton

A nonlinear continuum theory is advanced for high-rate mechanics and thermodynamics of liver parenchyma. The homogenized continuum is idealized as a solid-fluid mixture of dense viscoelastic tissue and liquid blood. The solid consists of a matrix material comprising the liver lobules and a collagenous fiber network. Under high loading rates pertinent to impa

Sergio Alvarez

We investigate the classification of quasihomogeneous polynomials in two variables with real coefficients under semialgebraic bi-Lipschitz equivalence in a neighborhood of the origin in ${\mathbb R}^2$. Building on the work of Birbrair, Fernandes, and Panazzolo, our approach is based on reducing the problem to the Lipschitz classification of associated singl

Mingcong Xu, Xiaojin Zhang, Wei Chen, Hai Jin

Federated Learning (FL) enables collaborative training of models across distributed clients without sharing local data, addressing privacy concerns in decentralized systems. However, the gradient-sharing process exposes private data to potential leakage, compromising FL's privacy guarantees in real-world applications. To address this issue, we propose Federa

Yihong Du, Ling Li, Wenjie Ni, Narges Shabgard

Using a reaction-diffusion model with free boundaries in one space dimension for a single population species with density $u(t,x)$ and population range $[g(t), h(t)]$, we demonstrate that the Allee effects can be eliminated if the species maintains its population density at a suitable level at the range boundary by advancing or retreating the fronts. It is p

Xudong Lu, Yinghao Chen, Renshou Wu, Haohao Gao

Recent advancements in Multimodal Large Language Models (MLLMs) have enabled their deployment on mobile devices. However, challenges persist in maintaining strong language capabilities and ensuring hardware compatibility, both of which are crucial for user experience and practical deployment efficiency. In our deployment process, we observe that existing MLL

Martin Bullinger, Vaggos Chatziafratis, Parnian Shahkar

Partitioning a set of $n$ items or agents while maximizing the value of the partition is a fundamental algorithmic task. We study this problem in the specific setting of maximizing social welfare in additively separable hedonic games. Unfortunately, this task faces strong computational boundaries: Extending previous results, we show that approximating welfar

John Franklin Crenshaw, Boris Leistedt, Melissa Lynn Graham, Constantin Payerne

The Vera C. Rubin Observatory will conduct the Legacy Survey of Space and Time (LSST), promising to discover billions of galaxies out to redshift 7, using six photometric bands ($ugrizy$) spanning the near-ultraviolet to the near-infrared. The exact number of and quality of information about these galaxies will depend on survey depth in these six bands, whic

Venkat Sai Suman Lamba Karanam, Sarat Sasank Barla, Byrav Ramamurthy, Derek Weitzel

Although benefits from caching in US HEP are well-known, current caching strategies are not adaptive i.e they do not adapt to changing cache access patterns. Newer developments such as the High-Luminosity - Large Hadron Collider (HL-LHC), Deep Underground Neutrino Experiment (DUNE), a steady move toward streaming readout based Data Acquisition systems (DAQs)

Xiaohao Xu, Feng Xue, Xiang Li, Haowei Li

Depth ambiguity is a fundamental challenge in spatial scene understanding, especially in transparent scenes where single-depth estimates fail to capture full 3D structure. Existing models, limited to deterministic predictions, overlook real-world multi-layer depth. To address this, we introduce a paradigm shift from single-prediction to multi-hypothesis spat

Nobutaka Nakazono

Hirota's discrete KdV equation is a well-known integrable two-dimensional partial difference equation regarded as a discrete analogue of the KdV equation. In this paper, we show that a variation of Hirota's discrete KdV equation with an additional parameter admits two types of exact solutions: discrete Painlev\'e transcendent solutions and periodic solutions

Zhenrong Wang, Qi Zheng, Sihan Ma, Maosheng Ye

With the diversification of human-object interaction (HOI) applications and the success of capturing human meshes, HOI reconstruction has gained widespread attention. Existing mainstream HOI reconstruction methods often rely on explicitly modeling interactions between humans and objects. However, such a way leads to a natural conflict between 3D mesh reconst

Panatchakorn Anantaprayoon, Masahiro Kaneko, Naoaki Okazaki

Self-Correction based on feedback improves the output quality of Large Language Models (LLMs). Moreover, as Self-Correction functions like the slow and conscious System-2 thinking from cognitive psychology's perspective, it can potentially reduce LLMs' social biases. LLMs are sensitive to contextual ambiguities and inconsistencies; therefore, explicitly comm

Seongjun Choi, Youngbum Kim, Nam Woo Kim, Mansun Shin

In this paper, we propose the InfoFusion Controller, an advanced path planning algorithm that integrates both global and local planning strategies to enhance autonomous driving in complex urban environments. The global planner utilizes the informed Theta-Rapidly-exploring Random Tree Star (Informed-TRRT*) algorithm to generate an optimal reference path, whil

Meng Ding, Mingxi Lei, Shaowei Wang, Tianhang Zheng

In this paper, we investigate one of the most fundamental nonconvex learning problems, ReLU regression, in the Differential Privacy (DP) model. Previous studies on private ReLU regression heavily rely on stringent assumptions, such as constant bounded norms for feature vectors and labels. We relax these assumptions to a more standard setting, where data can

Tomoki Hirosawa, Alexander Mook, Maria Azhar

We report the formation of a $\mathbb{Z}_2$ vortex crystal in the tetrahedral antiferromagnetic order on a triangular lattice. The noncoplanar tetrahedral state consists of four sublattices with spins oriented along the faces of a tetrahedron in spin space. The long-range order characterized by a $\mathbb{Z}_2$ topology arises due to the Dzyaloshinskii-Moriy

Manuel A. B. Bache, Jorge Hernandez-Contreras

In this paper, a solution to Zeno's paradox of motion was offered and a possible, based on observations and empirical viewpoint, interpretation. A gedanken experiment was proposed in accordance to our empirical and experimental paradigm, offering solutions from pendular energy and geometric gravity theory, the standard view, and classical physics, as a reply

Robert J. Saskowski

We consider the linearized perturbations of near-horizon extremal Reissner-Nordstr\"om black holes in $d$-dimensional Einstein-Maxwell-Gauss-Bonnet gravity and seven-dimensional third-order Lovelock gravity. We find the solutions for the gravitational perturbations as a function of the higher-derivative coupling coefficients, which we treat nonperturbatively

Michiharu Hyogo, Thomas P. Bickle, Joseph R. Biggs, Adam J. Burgasser

The Disk Detective project, a citizen science initiative, aims to identify circumstellar discs around stars by detecting objects with infrared (IR) excess using data from the Wide-field Infrared Survey Explorer (WISE). In this study, we investigate SIPS J2045-6332, a potential brown dwarf with significant IR excess in WISE and 2MASS bands, initially identifi

Md Azim Khan, Aryya Gangopadhyay, Jianwu Wang, Robert F. Erbacher

Situational awareness applications rely heavily on real-time processing of visual and textual data to provide actionable insights. Vision language models (VLMs) have become essential tools for interpreting complex environments by connecting visual inputs with natural language descriptions. However, these models often face computational challenges, especially

Angie Zhang, Min Kyung Lee

AI expansion has accelerated workplace adoption of new technologies. Yet, it is unclear whether and how knowledge workers are supported and trained to safely use AI. Inadequate training may lead to unrealized benefits if workers abandon tools, or perpetuate biases if workers misinterpret AI-based outcomes. In a workshop with 39 workers from 26 countries spec

Keyao Zhan, Puheng Li, Lei Wu

It was empirically observed in Entezari et al. (2021) that when accounting for the permutation invariance of neural networks, there is likely no loss barrier along the linear interpolation between two SGD solutions -- a phenomenon known as linear mode connectivity (LMC) modulo permutation. This phenomenon has sparked significant attention due to both its the

Sareh Ahmadi, Michelle Rockwell, Megan Stuart, Nicki Rohani

Episodic Future Thinking (EFT) involves vividly imagining personal future events and experiences in detail. It has shown promise as an intervention to reduce delay discounting-the tendency to devalue delayed rewards in favor of immediate gratification- and to promote behavior change in a range of maladaptive health behaviors. We present EFTeacher, an AI chat

Nasim Mirzavand Boroujeni, Jean-Philippe P. Richard, David Sterling, Christopher Wilke

High dose rate brachytherapy (HDR-BT) is an appealing treatment option for superficial cancers that permits the delivery of higher local doses than other radiation modalities without a significant increase in toxicity. In order for HDR-BT to be used in these situations, needles through which the radiation source is passed must be strategically placed in clos

A. Sorgho, L. Verdes-Montenegro, R. Ianjamasimanana, K. M. Hess

Hickson compact groups (HCGs) offer an ideal environment for investigating galaxy transformation as a result of interactions. It has been established that the evolutionary sequence of HCGs is marked by an intermediate stage characterised by a substantial amount of HI in their intragroup medium (IGrM) in the form of tidal tails and bridges (Phase 2), rapidly

Todd A. Brun, Leonard Mlodinow

We show that free QED is equivalent to the continuous-space-and-time limit of Fermi and Bose lattice quantum cellular automata theories derived from quantum random walks satisfying simple symmetry and unitarity conditions. In doing so we define the Fermi and Bose theories in a unified manner using the usual fermion internal space but a boson internal space t

Yasin Sonmez, Hanna Krasowski, Murat Arcak

Imitation learning is a promising approach for training autonomous vehicles (AV) to navigate complex traffic environments by mimicking expert driver behaviors. While existing imitation learning frameworks focus on leveraging expert demonstrations, they often overlook the potential of additional complex driving data from surrounding traffic participants. In t

Calarina Muslimani, Kerrick Johnstonbaugh, Suyog Chandramouli, Serena Booth

Reinforcement learning agents are fundamentally limited by the quality of the reward functions they learn from, yet reward design is often overlooked under the assumption that a well-defined reward is readily available. However, in practice, designing rewards is difficult, and even when specified, evaluating their correctness is equally problematic: how do w

Shan An, Shipeng Dai, Mahrukh Ansari, Yu Liang

Accurate hand pose estimation is vital in robotics, advancing dexterous manipulation in human-computer interaction. Toward this goal, this paper presents ReJSHand (which stands for Refined Joint and Skeleton Features), a cutting-edge network formulated for real-time hand pose estimation and mesh reconstruction. The proposed framework is designed to accuratel

Lianghui Luo

We consider a two-speed branching random walk, which consists of two macroscopic stages with different reproduction laws. We prove that the centered maximum converges in law to a Gumbel variable with a random shift and the extremal process converges in law to a randomly shifted decorated Poisson point process, which can be viewed as a discrete analog for the

Manu Jayadharan, Christina Catlett, Arthur N. Montanari, Niall M. Mangan

Differential-algebraic equations (DAEs) integrate ordinary differential equations (ODEs) with algebraic constraints, providing a fundamental framework for developing models of dynamical systems characterized by timescale separation, conservation laws, and physical constraints. While sparse optimization has revolutionized model development by allowing data-dr

Amirali Sajadi, Kostadin Damevski, Preetha Chatterjee

Context: A deeper understanding of human factors in software engineering (SE) is essential for improving team collaboration, decision-making, and productivity. Communication channels like code reviews and chats provide insights into developers' psychological and emotional states. While large language models excel at text analysis, they often lack transparenc

Eva-Maria Maier, Leonie Maria Tanczer, Lukas Daniel Klausner

Parental control applications, software tools designed to manage and monitor children's online activities, serve as essential safeguards for parents in the digital age. However, their usage has sparked concerns about security and privacy violations inherent in various child monitoring products. Sideloaded software (i. e. apps installed outside official app s

Zixuan Liu, Aaron Honjaya, Yuekai Xu, Yi Zhang

Retinal vessel segmentation is critical for diagnosing ocular conditions, yet current deep learning methods are limited by modality-specific challenges and significant distribution shifts across imaging devices, resolutions, and anatomical regions. In this paper, we propose GrInAdapt, a novel framework for source-free multi-target domain adaptation that leve

Liam P. McGuinness

Quantum information science currently poses a troubling contradiction. It can be summarized as: (1) To factor efficiently, quantum computers must perform exponentially precise energy estimation. (2) Exponentially precise energy estimation is impossible according to both the Heisenberg limit and the Cram\'er-Rao lower bound in quantum metrology. It is surpris

Qi Zhang, Cheng Chuang, Shunan Zhang, Ziqi Zhao

Osteoporotic vertebral compression fractures (OVCFs) are prevalent in the elderly population, typically assessed on computed tomography (CT) scans by evaluating vertebral height loss. This assessment helps determine the fracture's impact on spinal stability and the need for surgical intervention. However, the absence of pre-fracture CT scans and standardized

Yilin Evan Li, Harikrishnan KP, Haidong Lu, Rachel A. Steinhardt

Ultrathin ferroelectric films with out-of-plane polarization and high Curie temperatures are key to miniaturizing electronic devices. Most ferroelectrics employed in devices are proper ferroelectrics, where spontaneous polarization is the primary order parameter. Unfortunately, the Curie temperature of proper ferroelectrics is drastically reduced as the ferr

Ye Tao, Jiawei Zhang, Yahao Shi, Dongqing Zou

Image-based 3D generation has vast applications in robotics and gaming, where high-quality, diverse outputs and consistent 3D representations are crucial. However, existing methods have limitations: 3D diffusion models are limited by dataset scarcity and the absence of strong pre-trained priors, while 2D diffusion-based approaches struggle with geometric con

Yoann L. Launay, Gerasimos I. Rigopoulos, E. Paul S. Shellard

We examine the classical and quantum evolution of inflationary cosmological perturbations from quantum initial conditions, using the on-shell and off-shell contributions to correlators to investigate the signatures of interactions. In particular, we calculate the Keldysh contributions to the leading order bispectrum from past infinity, showing that the squee

Siddarth Narasimhan, Aaron Hao Tan, Daniel Choi, Goldie Nejat

Service robots in human-centered environments such as hospitals, office buildings, and long-term care homes need to navigate while adhering to social norms to ensure the safety and comfortability of the people they are sharing the space with. Furthermore, they need to adapt to new social scenarios that can arise during robot navigation. In this paper, we pre

David Eisenbud, Frank-Olaf Schreyer

Using the connection between hyperelliptic curves, Clifford algebras, and complete intersections $X$ of two quadrics, we describe Ulrich bundles on $X$ and construct some of minimal possible rank.

Alexandre Sanfelici Bazanella

This paper presents a data-driven methodology to estimate the storage function of a passive system. The methodology consists in parametrizing the storage function with a dictionary then running a linear program. Results on a benchmark are presented to illustrate its properties, including its robustness to noise. Various uses of the storage function that do n

Satyavrat Wagle, Akshay Malhotra, Shahab Hamidi-Rad, Aditya Sant

In recent years, machine learning (ML) methods have become increasingly popular in wireless communication systems for several applications. A critical bottleneck for designing ML systems for wireless communications is the availability of realistic wireless channel datasets, which are extremely resource intensive to produce. To this end, the generation of rea

Meiqi Wang, Bin Chen, Trevor Knuth, Christina Cohen

How impulsive solar energetic particle (SEP) events are produced by magnetic-reconnection-driven processes during solar flares remains an outstanding question. Here we report a short-duration SEP event associated with an X-class eruptive flare on July 03, 2021, using a combination of remote sensing observations and in situ measurements. The in situ SEPs were

Siddhant Pandey, Francis Walz, Niranjan Shivaram

We describe an experiment to measure the emitted real-time electric field from an ultrafast third-order nonlinear optical interaction in molecules, using a phase-tracked spectral interferometry scheme. By combining a software lock-in amplification based spectrometer with spectral interferometry, we measure the electric field of the nonlinear optical signal f

Lucius E. J. Bynum, Aahlad Manas Puli, Diego Herrero-Quevedo, Nhi Nguyen

Causal inference and the estimation of causal effects plays a central role in decision-making across many areas, including healthcare and economics. Estimating causal effects typically requires an estimator that is tailored to each problem of interest. But developing estimators can take significant effort for even a single causal inference setting. For examp

Daniele Binosi, Craig D. Roberts, Zhao-Qian Yao

The bulk of visible mass is supposed to emerge from nonperturbative dynamics within quantum chromodynamics (QCD) -- the strong interaction sector of the Standard Model. Following years of development and refinement, continuum and lattice Schwinger function methods have recently joined in revealing the three pillars that support this emergent hadron mass (EHM

Jonas Stelzig

A survey of some results and open questions related to the following algebraic invariants of compact complex manifolds, that can be obtained from differential forms: cohomology groups, Chern classes, rational homotopy groups, and higher operations.

Zihan Zhou, Zizhong Tian, Christine B. Peterson, Le Bao

Accurate assessment of adverse event (AE) incidence is critical in clinical cancer research for drug safety evaluation and regulatory approval. While meta-analysis serves as an essential tool to comprehensively synthesize the evidence across multiple studies, incomplete AE reporting in clinical trials remains a persistent challenge. In particular, AEs occurr

Yihan Zhou, Eric Price, Trung Nguyen

We address the problem of active logistic regression in the realizable setting. It is well known that active learning can require exponentially fewer label queries compared to passive learning, in some cases using $\log \frac{1}{\eps}$ rather than $\poly(1/\eps)$ labels to get error $\eps$ larger than the optimum. We present the first algorithm that is polyn

Samir Abdaljalil, Hasan Kurban, Parichit Sharma, Erchin Serpedin

Large language models (LLMs) are increasingly deployed across diverse domains, yet they are prone to generating factually incorrect outputs - commonly known as "hallucinations." Among existing mitigation strategies, uncertainty-based methods are particularly attractive due to their ease of implementation, independence from external data, and compatibility wi

Zhe Wang, Jiaxin Shi, Nicolas Heess, Arthur Gretton

Autoregressive models (ARMs) have become the workhorse for sequence generation tasks, since many problems can be modeled as next-token prediction. While there appears to be a natural ordering for text (i.e., left-to-right), for many data types, such as graphs, the canonical ordering is less obvious. To address this problem, we introduce a variant of ARM that

Hongwei Yi, Tian Ye, Shitong Shao, Xuancheng Yang

We present MagicInfinite, a novel diffusion Transformer (DiT) framework that overcomes traditional portrait animation limitations, delivering high-fidelity results across diverse character types-realistic humans, full-body figures, and stylized anime characters. It supports varied facial poses, including back-facing views, and animates single or multiple cha

Ming Liu, Wensheng Zhang

As video language models (VLMs) gain more applications in various scenarios, the need for robust and scalable evaluation of their performance becomes increasingly critical. The traditional human expert-based evaluation of VLMs has limitations in consistency and scalability, which sparked interest in automatic methods such as employing VLMs to evaluate VLMs.

Abdullah Al Helal, Jiří Lebl

We prove that the (hermitian) rank of $QP^d$ is bounded from below by the rank of $P^d$ whenever $Q$ is not identically zero and real-analytic in a neighborhood of some point on the zero set of $P$ in $\mathbb{C}^n$ and $P$ is a polynomial of bidegree at most $(1,1)$. This result generalizes the theorem of D'Angelo and the second author which assumed that $P

Anirudh Deb, Carlo Meneghelli, Leonardo Rastelli

A well-developed classification program for 4d $\mathcal{N}=2$ super conformal field theories (SCFTs) leverages Seiberg-Witten geometry on the Coulomb branch of vacua; theories are arranged by increasing $\mathfrak{rank}$, the complex dimension of their Coulomb branch. An alternative organizational scheme focusses on the associated vertex operator algebra (V

Krish Didwania, Ishaan Gakhar, Prakhar Arya, Sanskriti Labroo

Contrast enhancement, a key aspect of image-to-image translation (I2IT), improves visual quality by adjusting intensity differences between pixels. However, many existing methods struggle to preserve fine-grained details, often leading to the loss of low-level features. This paper introduces LapLoss, a novel approach designed for I2IT contrast enhancement, b

Christopher L. Malbon, Sharon Hammes-Schiffer

The nuclear-electronic orbital (NEO) approach incorporates nuclear quantum effects into quantum chemistry calculations by treating specified nuclei quantum mechanically, equivalently to the electrons. Within the NEO framework, excited states are vibronic states representing electronic and nuclear vibrational excitations. The NEO multireference configuration

Hazhar Rahmani, Mukulika Ghosh, Syed Md Hasnayeen

Deception is a common strategy adapted by autonomous systems in adversarial settings. Existing deception methods primarily focus on increasing opacity or misdirecting agents away from their goal or itinerary. In this work, we propose a deception problem aiming to mislead the robot towards a decoy goal through altering sensor events under a constrained budget

Qijun Chen, Shaofan Li

Due to climate change, the extreme wildfire has become one of the most dangerous natural hazards to human civilization. Even though, some wildfires may be initially caused by human activity, but the spread of wildfires is mainly determined by environmental factors, for examples, (1) weather conditions such as temperature, wind direction and intensity, and mo

Talha Bozkus, Urbashi Mitra

Q-learning is a widely used reinforcement learning (RL) algorithm for optimizing wireless networks, but faces challenges with large state-spaces. Recently proposed multi-environment mixed Q-learning (MEMQ) algorithm addresses these challenges by employing multiple Q-learning algorithms across multiple synthetically generated, distinct but structurally relate

Beyza Kalkanli, Tales Imbiriba, Stratis Ioannidis, Deniz Erdogmus

Active learning aims to efficiently build a labeled training set by strategically selecting samples to query labels from annotators. In this sequential process, each sample acquisition influences subsequent selections, causing dependencies among samples in the labeled set. However, these dependencies are overlooked during the model parameter estimation stage

Suraj Poudel, April Horton, Jo Vazquez, Kathleen A. Barger

Widespread galactic winds emanate from the Large Magellanic Cloud (LMC), with the 30 Doradus starburst region generating the fastest and most concentrated gas flows. We report on the gas distribution, kinematics, and ionization conditions of the near-side outflow along 8 down-the-barrel sightlines using UV absorption-line observations from the HST's ULLYSES

Don Danilov, Javier Robledo-Moreno, Kevin J. Sung, Mario Motta

Quantum Selected Configuration Interaction (QSCI) and an extended protocol known as Sample-based Quantum Diagonalization (SQD) have emerged as promising algorithms to solve the electronic Schr\"odinger equation with noisy quantum computers. In QSCI/SQD a quantum circuit is repeatedly prepared on the quantum device, and measured configurations form a subspace

Md Talha Mohsin, Nabid Bin Nasim

Practitioners and researchers trying to strike a balance between accuracy and transparency center Explainable Artificial Intelligence (XAI) at the junction of finance. This paper offers a thorough overview of the changing scene of XAI applications in finance together with domain-specific implementations, methodological developments, and trend mapping of rese

Luke Guerdan, Solon Barocas, Kenneth Holstein, Hanna Wallach

The LLM-as-a-judge paradigm, in which a judge LLM system replaces human raters in rating the outputs of other generative AI (GenAI) systems, plays a critical role in scaling and standardizing GenAI evaluations. To validate such judge systems, evaluators assess human--judge agreement by first collecting multiple human ratings for each item in a validation cor

Haroon Imtiaz, Imran Akhtar, Muhammad R. Hajj

Proper-orthogonal decomposition (POD) based reduced-order models (ROM) of structurally dominant fluid flow can support a wide range of engineering applications. Yet, although they perform well for unsteady laminar flows, their straightforward extension to turbulent flows fails to capture the effects of small scale eddies and often leads to divergent solution

William N. Caballero, Phillip R. Jenkins, David Banks, Matthew Robbins

This research considers Bayesian decision-analytic approaches toward the traversal of an uncertain graph. Namely, a traveler progresses over a graph in which rewards are gained upon a node's first visit and costs are incurred for every edge traversal. The traveler knows the graph's adjacency matrix and his starting position but does not know the rewards and

Franklin Mingzhe Li, Kaitlyn Ng, Bin Zhu, Patrick Carrington

Following recipes while cooking is an important but difficult task for visually impaired individuals. We developed OSCAR (Object Status Context Awareness for Recipes), a novel approach that provides recipe progress tracking and context-aware feedback on the completion of cooking tasks through tracking object statuses. OSCAR leverages both Large-Language Mode

Caitlin Kral, Evan Chance, Ryan Browne, Sanjeena Subedi

While several Gaussian mixture models-based biclustering approaches currently exist in the literature for continuous data, approaches to handle discrete data have not been well researched. A multivariate Poisson-lognormal (MPLN) model-based bi-clustering approach that utilizes a block-diagonal covariance structure is introduced to allow for a more flexible s

Matías Hernández

This article presents an educational proposal based on the computational implementation of a model of interaction between particles of different types, as a tool for the development of STEM (Science, Technology, Engineering, and Mathematics) skills. The mathematical formulation of the model, the equations governing particle interaction, the interaction matri

Daniel Bump, Slava Naprienko

A parametrized Yang-Baxter equation is usually defined to be a map from a group to a set of R-matrices, satisfying the Yang-Baxter commutation relation. These are a mainstay of solvable lattice models. We will show how the parameter space can sometimes be enlarged to a groupoid, and give two examples of such groupoid parametrized Yang-Baxter equations, withi

Lucas Johns, Sherwood Richers, Meng-Ru Wu

Accurate neutrino transport is crucial for reliably modeling explosive astrophysical events like core-collapse supernovae (CCSNe) and neutron star mergers (NSMs). However, in these extremely neutrino-dense systems, flavor oscillations exhibit challenging nonlinear effects rooted in neutrino-neutrino forward scattering. Evidence is quickly accumulating that t

Daniel Guzman-Olivares, Lara Quijano-Sanchez, Federico Liberatore

The rise of generative chat-based Large Language Models (LLMs) over the past two years has spurred a race to develop systems that promise near-human conversational and reasoning experiences. However, recent studies indicate that the language understanding offered by these models remains limited and far from human-like performance, particularly in grasping th

Ravishankar Chatta Subramaniam, Nikhil Borse, Winter Allen, Amogh Sirnoorkar

This second paper in a multi-part series builds on the first, which introduced the Ways of Thinking for Engineering Design-based Physics (WoT4EDP) framework for STEM education in an introductory undergraduate physics course. Here, we apply the framework to analyze transcripts of group discussions and written reports from 14 student teams as they engaged in a

Naïl Khelifa, Ferdia Sherry, Carola-Bibiane Schönlieb

The inherent ill-posed nature of image reconstruction problems, due to limitations in the physical acquisition process, is typically addressed by introducing a regularisation term that incorporates prior knowledge about the underlying image. The iterative framework of Plug-and-Play methods, specifically designed for tackling such inverse problems, achieves s

Elham Torabian, Roman V. Krems

We establish an isomorphism between quantum circuits and a subspace of polyatomic molecules, which suggests that molecules can be used as descriptors of quantum circuits for quantum machine learning. Our numerical results show that the performance of quantum circuits for quantum support vector machines can be characterized by dimensionality-reduced molecular

Mihaela Cătălina Stoian, Eleonora Giunchiglia, Thomas Lukasiewicz

Generative modelling has become the standard approach for synthesising tabular data. However, different use cases demand synthetic data to comply with different requirements to be useful in practice. In this survey, we review deep generative modelling approaches for tabular data from the perspective of five types of requirements: utility of the synthetic dat

Zekai Liang, Zih-Yun Chiu, Florian Richter, Michael C. Yip

Robot pose estimation is a challenging and crucial task for vision-based surgical robotic automation. Typical robotic calibration approaches, however, are not applicable to surgical robots, such as the da Vinci Research Kit (dVRK), due to joint angle measurement errors from cable-drives and the partially visible kinematic chain. Hence, previous works in surg

M. Damasso, O. Morata, S. Kaur, D. Viganò

V1298 Tau is a very young and magnetically active star which hosts a benchmark multi-planetary system to study planet formation and evolutionary history at the earliest stages. We selected V1298 Tau for a first targeted follow-up at radio frequencies with the Karl G. Jansky Very Large Telescope (JVLA), the upgraded Giant Metrewave Radio Telescope (uGMRT), an

Deepak Vungarala, Mohammed E. Elbtity, Sumiya Syed, Sakila Alam

The increasing complexity and scale of Deep Neural Networks (DNNs) necessitate specialized tensor accelerators, such as Tensor Processing Units (TPUs), to meet various computational and energy efficiency requirements. Nevertheless, designing optimal TPU remains challenging due to the high domain expertise level, considerable manual design time, and lack of h