March 2025 arXiv papers — page 18

Chengkun Wei, Weixian Li, Chen Gong, Wenzhi Chen

Differentially Private Stochastic Gradient Descent (DP-SGD) is a widely adopted technique for privacy-preserving deep learning. A critical challenge in DP-SGD is selecting the optimal clipping threshold C, which involves balancing the trade-off between clipping bias and noise magnitude, incurring substantial privacy and computing overhead during hyperparamet

Junding Chen, Yunfeng Jiang, Xinan Zhou

We consider correlation functions of two maximal giant gravitons and two light $\frac{1}{2}$-BPS operators in 4d $\mathcal{N}=4$ SYM. Viewed as two-point correlators in the presence of a zero dimensional defect, they can be completely fixed at strong coupling using analytic bootstrap techniques. We determine all infinitely such correlators for arbitrary ligh

Jeongsoo Lee, Daeyong Kwon, Kyohoon Jin, Junnyeong Jeong

Existing RAG benchmarks often overlook query difficulty, leading to inflated performance on simpler questions and unreliable evaluations. A robust benchmark dataset must satisfy three key criteria: quality, diversity, and difficulty, which capturing the complexity of reasoning based on hops and the distribution of supporting evidence. In this paper, we propo

Yunsong Wang, Tianxin Huang, Hanlin Chen, Gim Hee Lee

Recently, the integration of the efficient feed-forward scheme into 3D Gaussian Splatting (3DGS) has been actively explored. However, most existing methods focus on sparse view reconstruction of small regions and cannot produce eligible whole-scene reconstruction results in terms of either quality or efficiency. In this paper, we propose FreeSplat++, which f

Zhengyi Zhao, Shubo Zhang, Zezhong Wang, Bin Liang

Long-context question-answering (LCQA) systems have greatly benefited from the powerful reasoning capabilities of large language models (LLMs), which can be categorized into slow and quick reasoning modes. However, both modes have their limitations. Slow thinking generally leans to explore every possible reasoning path, which leads to heavy overthinking and

Zhuowei Li, Tianchen Zhao, Xiang Xu, Zheng Zhang

Developing a face anti-spoofing model that meets the security requirements of clients worldwide is challenging due to the domain gap between training datasets and diverse end-user test data. Moreover, for security and privacy reasons, it is undesirable for clients to share a large amount of their face data with service providers. In this work, we introduce a

Ashesh Ashesh, Florian Jug

Fluorescence microscopy, while being a key driver for progress in the life sciences, is also subject to technical limitations. To overcome them, computational multiplexing techniques have recently been proposed, which allow multiple cellular structures to be captured in a single image and later be unmixed. Existing image decomposition methods are trained on

Guanqiao Qu, Qian Chen, Xianhao Chen, Kaibin Huang

By provisioning inference offloading services, edge inference drives the rapid growth of AI applications at network edge. However, how to reduce the inference latency remains a significant challenge. To address this issue, we develop a parameter-sharing AI model loading (PartialLoading) framework for multi-user edge inference, which exploits two key insights

Weijian Zheng, Jack Kordas, Tyler J. Skluzacek, Raj Kettimuthu

Many extreme-scale applications require the movement of large quantities of data to, from, and among leadership computing facilities, as well as other scientific facilities and the home institutions of facility users. These applications, particularly when leadership computing facilities are involved, can touch upon edge cases (e.g., terabyte files) that had

Songnian Xu, Dein Wong, Chi Zhang, Wenhao Zhen

Let $G$ be a finite group and $m$ be an integer. We employ the notation $g_i$ to represent elements $(g,i)$ in the Cartesian product $G \times \mathbb{Z}_m$, where $\mathbb{Z}_m$ denotes integers modulo $m$. For given sets $T_{i,j} \subseteq G$ ($i,j \in \mathbb{Z}_m$), we construct the $m$-$Cayley$ $digraph$ $\Gamma = \mathrm{Cay}(G, T_{i,j}: i,j \in \mathb

Hao Tian, Chao Liu, Xiang-Xiang Xue, Dongwei Fan

Low surface density streams are important tracers to study the formation and evolution of the Milky Way. Using the accurate astrometric measurements from Gaia mission, we discover a low surface density stream in the north hemisphere, with length of $\sim110$ degree and width of $1.23$ kpc. The vertical velocity dispersion perpendicular to the stream is aroun

Muhammad Talal Khalid, Ann-Perry Witmer

The potential of large language models (LLMs) to mitigate the time- and cost- related challenges associated with inductive thematic analysis (ITA) has been extensively explored in the literature. However, the use of LLMs to support ITA has often been opportunistic, relying on ad hoc prompt engineering (PE) approaches, thereby undermining the reliability, tra

Yongyi Yang, Jianyang Gao, Wei Hu

Post-training Quantization (PTQ) has become a widely used technique for improving inference efficiency of large language models (LLMs). However, existing PTQ methods generally suffer from crucial limitations such as heavy calibration data requirements and inflexible choice of target number of bits. In this paper, we propose RaanA, a unified PTQ framework tha

Jun Honda

While financial misconduct in advisory services persists despite regulation, the demand-side of market discipline, specifically the timing of investor detection, remains a critical bottleneck. Using approximately 55,700 FINRA BrokerCheck records, we analyze the detection lag between misconduct inception and formal reporting. We document a conditional average

Jiahui Zhang, Yurui Chen, Yanpeng Zhou, Yueming Xu

Recent advances in LVLMs have improved vision-language understanding, but they still struggle with spatial perception, limiting their ability to reason about complex 3D scenes. Unlike previous approaches that incorporate 3D representations into models to improve spatial understanding, we aim to unlock the potential of VLMs by leveraging spatially relevant im

Thomas Barthelmé, Lingfeng Lu

In this article we study topological transitivity of Anosov flows on non-compact 3-manifolds. We provide homological conditions under which the lifts of a transitive Anosov flow to certain infinite covers of the manifold remain transitive. With some deep results in 3-manifold topology, we then deduce that such cover can be obtained for any non-graph manifold

Brian Hyeongseok Kim, Hannah Murray, Isabelle Lee, Jason Byun

Medical institutions are considering the use of LLMs in high-stakes clinical decision-making, such as organ allocation. In such sensitive use cases, evaluating fairness is imperative. However, existing evaluation methods often fall short; benchmarks are too simplistic to capture real-world complexity, and accuracy-based metrics fail to address the absence of

Dzung Tri Tran, Thanh Huy Nguyen, Khiem Hong Phan

Within the framework of the Standard Model Higgs extensions, including the Two-Higgs-Doublet Model with vector-like fermions and the Triplet-Higgs Model, we derive general one-loop contributions to the rare decay process $A \rightarrow Z \gamma \gamma$. The analytical expressions are formulated with Passarino-Veltman scalar functions, which represent the sca

Vivek Iyer, Pinzhen Chen, Ricardo Rei, Alexandra Birch

Cross-lingual open-ended generation - responding in a language different from that of the query - is an important yet understudied problem. This work proposes XL-Instruct, a novel technique for generating high-quality synthetic data, and introduces XL-AlpacaEval, a new benchmark for evaluating cross-lingual generation capabilities of large language models (L

Predrag Cvitanović, Han Liang

We describe spatiotemporally chaotic (or turbulent) field theories discretized over d-dimensional lattices in terms of sums over their multi-periodic orbits. `Chaos theory' is here recast in the language of statistical mechanics, field theory, and solid state physics, with the traditional periodic orbits theory of low-dimensional, temporally chaotic dynamics

Kanishka Ranaweera, Azadeh Ghari Neiat, Xiao Liu, Bipasha Kashyap

Federated learning (FL) has emerged as a promising paradigm in machine learning, enabling collaborative model training across decentralized devices without the need for raw data sharing. In FL, a global model is trained iteratively on local datasets residing on individual devices, each contributing to the model's improvement. However, the heterogeneous natur

Kuntai Cai, Xiaokui Xiao, Yin Yang

Releasing relational databases while preserving privacy is an important research problem with numerous applications. A canonical approach is to generate synthetic data under differential privacy (DP), which provides a strong, rigorous privacy guarantee. The problem is particularly challenging when the data involve not only entities (e.g., represented by reco

Jianing Chen

The Nash Equilibrium (NE), one of the elegant and fundamental concepts in game theory, plays a crucial part within various fields, including engineering and computer science. However, efficiently computing an NE in normal-form games remains a significant challenge, particularly for large-scale problems. In contrast to widely applied simplicial and homotopy m

Hanwool Lee, Dasol Choi, Sooyong Kim, Ilgyun Jeong

Recent advancements in Korean large language models (LLMs) have driven numerous benchmarks and evaluation methods, yet inconsistent protocols cause up to 10 p.p performance gaps across institutions. Overcoming these reproducibility gaps does not mean enforcing a one-size-fits-all evaluation. Rather, effective benchmarking requires diverse experimental approa

Sherry S. L. Yip, Berry L. Han, Holly H. Y. Chan

Starting in February 2024, the HKUST Library further extended the scope of AI literacy to AI utilization, which focuses on fostering student involvement in utilizing state-of-the-art technologies in the projects that initiated by the Library, named "Digital Scholarship (DS) CoLab". A key focus of the DS CoLab scheme has been on cultivating talents and enabli

Marius Tărnăuceanu

A group $G$ is said to have dense normalizers if each non-empty open interval in its subgroup lattice $L(G)$ contains the normalizer of a certain subgroup of $G$. In this note, we find all finite groups satisfying this property. We also classify the finite groups in which $k$ subgroups are not normalizers, for $k=1,2,3,4$.

Henri Mueller, Yechan Kim, Trevor Gee, Mahla Nejati

The global warehousing industry is experiencing rapid growth, with the market size projected to grow at an annual rate of 8.1% from 2024 to 2030 [Grand View Research, 2021]. This expansion has led to a surge in demand for efficient pallet detection and localisation systems. While automation can significantly streamline warehouse operations, the development o

Chen Li, Liang Zhang, Bin Jiang, Hua Guo

The ring polymer molecular dynamics (RPMD) rate theory is an efficient and accurate method for estimating rate coefficients of chemical reactions affected by nuclear quantum effects. The commonly used RPMD treatment of gas-phase bimolecular reactions adopts two dividing surfaces, one at the transition state and another in the reactant asymptote, where partit

Peiyu Chen, Fuling Lin, Weipeng Guan, Peng Lu

Event cameras asynchronously output low-latency event streams, promising for state estimation in high-speed motion and challenging lighting conditions. As opposed to frame-based cameras, the motion-dependent nature of event cameras presents persistent challenges in achieving robust event feature detection and matching. In recent years, learning-based approac

Tianren Zhang, Dai-Bei Yang

Contemporary large language models (LLMs), such as GPT-4 and Llama, have harnessed extensive computational power and diverse text corpora to achieve remarkable proficiency in interpreting and generating domain-specific content, including materials science. To leverage the domain knowledge embedded within these models, we propose a simple yet effective multim

Kayo Kinjo, Rihito Sakurai, Tatsuya Kishimoto, Jun Ohkubo

Tensor networks, particularly the tensor train (TT) format, have emerged as powerful tools for high-dimensional computations in physics and computer science. In solving coupled differential equations, such as those arising from stochastic differential equations (SDEs) via duality relations, ordering the TT cores significantly influences numerical accuracy. I

Zhaoqi Zhang, Chee Yap

In the subdivision approach to robot path planning, we need to subdivide the configuration space of a robot into nice cells to perform various computations. For a rigid spatial robot, this configuration space is $SE(3)=\mathbb{R}^3\times SO(3)$. The subdivision of $\mathbb{R}^3$ is standard but so far, there are no global subdivision schemes for $SO(3)$. We

Derong Kong, Kun Li, Zhiqiang Wang

Given $q\in \mathbb{N}_{\ge 3}$ and a finite set $A\subset\mathbb{Q}$, let $$K(q,A)= \bigg\{\sum_{i=1}^{\infty} \frac{a_i}{q^{i}}:a_i \in A ~\forall i\in \mathbb{N} \bigg\}.$$ For $p\in\mathbb{N}_{\ge 2}$ let $D_p\subset\mathbb{R}$ be the set of all rational numbers having a finite $p$-ary expansion. We show in this paper that for $p \in \mathbb{N}_{\ge 2}$

Ulrich Horst, Huilin Zhang

We analyze a novel class of rough stochastic control problems that allows for a convenient approach to solving pathwise stochastic control problems with both non-anticipative and anticipative controls. We first establish the well-posedness of a class of controlled rough SDEs with affine rough driver and establish the continuity of the solution w.r.t.~the dri

Supriyo Maji, Linran Zhao, Souradip Poddar, David Z. Pan

Layout-dependent effects (LDEs) significantly impact analog circuit performance. Traditionally, designers have relied on symmetric placement of circuit components to mitigate variations caused by LDEs. However, due to non-linear nature of these effects, conventional methods often fall short. We propose an objective-driven, multi-level, multi-agent Q-learning

Dirk HR Spennemann

While it is increasingly evident that the internet is becoming saturated with content created by generated Ai large language models, accurately measuring the scale of this phenomenon has proven challenging. By analyzing the frequency of specific keywords commonly used by ChatGPT, this paper demonstrates that such linguistic markers can effectively be used to

Hao Sun, Jieqi Tu, Revathi Ananthakrishnan, Eunhee Kim

Oncology dose-finding trials are shifting from identifying the maximum tolerated dose (MTD) to determining the optimal biological dose (OBD), driven by the need for efficient methods that consider both toxicity and efficacy. This is particularly important for novel therapies, such as immunotherapies and molecularly targeted therapies, which often exhibit non

Leyun Gao, Cheng-en Liu, Qite Li, Chen Zhou

We propose here a set of new methods involving probing and knocking with muons (PKMu). There is a wealth of rich physics to explore with GeV muon beams. Examples include but not limited to: muon scattering can occur at large angles, providing evidence of potential muon-philic dark matter or dark mediator candidates; muon-electron scattering can be used to de

Yihang Lu, Mahwish Yousaf, Xianwei Meng, Enhong Chen

Imputation of random or non-random missing data is a long-standing research topic and a crucial application for Intelligent Transportation Systems (ITS). However, with the advent of modern communication technologies such as Global Satellite Navigation Systems (GNSS), traffic data collection has introduced new challenges in random missing value imputation and

Xinyu Yao, Aditya Sannabhadti, Holly Wiberg, Karmel S. Shehadeh

Medical knowledge graphs (KGs) are essential for clinical decision support and biomedical research, yet they often exhibit incompleteness due to knowledge gaps and structural limitations in medical coding systems. This issue is particularly evident in treatment mapping, where coding systems such as ICD, Mondo, and ATC lack comprehensive coverage, resulting i

Xianghong Gong, Ziming Shi

We construct a global homotopy formula for $a_q$ domains in a complex manifold. The homotopy operators in the formula will gain $1/2$ derivative in H\"older-Zygmund spaces $\Lambda^{r}$ when the boundaries of the domains are in $\Lambda^{r+3}$ with $r>0$.

Yuxuan Wang, Yueqian Wang, Bo Chen, Tong Wu

The rapid advancement of multi-modal language models (MLLMs) like GPT-4o has propelled the development of Omni language models, designed to process and proactively respond to continuous streams of multi-modal data. Despite their potential, evaluating their real-world interactive capabilities in streaming video contexts remains a formidable challenge. In this

Tingyan Ma, Edwin R. van Dam, Ligong Wang

A graph $G$ is called $k$-factor-critical if $G-S$ has a perfect matching for every $S\subseteq G$ with $|S|=k$. A connected graph $G$ is called $t$-connected if it has more than $t$ vertices and remains connected whenever fewer than $t$ vertices are removed. We give a condition on the number of edges and a condition on the spectral radius for $k$-factor-cri

R. Martínez von Dossow, Luis F. Urrutia

We calculate the effective electromagnetic Lagrangian up to the lowest-order corrections in the derivatives for two fermionic systems of interest in condensed matter physics in the linearized approximation of the tight-binding Hamiltonian near the Fermi level in the Brillouin zone: (i) the $(3+1)$ description of the simplest Weyl semimetal and (ii) the massi

Di Qi, Jian-Guo Liu

We introduce a data assimilation strategy aimed at accurately capturing key non-Gaussian structures in probability distributions using a small ensemble size. A major challenge in statistical forecasting of nonlinearly coupled multiscale systems is mitigating the large errors that arise when computing high-order statistical moments. To address this issue, a h

Tianyang Xu, Xiaoze Liu, Feijie Wu, Xiaoqian Wang

Large Language Models (LLMs) have transformed natural language processing by learning from massive datasets, yet this rapid progress has also drawn legal scrutiny, as the ability to unintentionally generate copyrighted content has already prompted several prominent lawsuits. In this work, we introduce SUV (Selective Unlearning for Verbatim data), a selective

Hugo Guadalupe Reyna Castañeda, María de los Ángeles Sandoval-Romero

In this paper, we show that the conditional expectation of a random variable with finite second moment given a $\sigma$-algebra is the unique critical point of an energy functional in Hilbert space $L^2$. Then, we extend by density the result to every integrable random variable.

Yu Fu, Dennis Bromley, Vidya Setlur

Data-driven storytelling has gained prominence in journalism and other data reporting fields. However, the process of creating these stories remains challenging, often requiring the integration of effective visualizations with compelling narratives to form a cohesive, interactive presentation. To help streamline this process, we present an integrated authori

Xuming He, David Madigan, Bin Yu, Jon Wellner

This project was sponsored by the National Science Foundation and organized by a steering committee and a group of theme leaders. The six-member steering committee, consisting of James Berger, Xuming He, David Madigan, Susan Murphy, Bin Yu, and Jon Wellner, was responsible for the overall planning of the project. This report is designed to be accessible to t

Gabriel Lacerda

Given a compact metric space $X$ and a continuous map $T: X \to X$, the induced hyperspace map $T_\mathcal{K}$ acts on the hyperspace $\mathcal{K}(X)$ of nonempty closed sets of $X$, and the measure-induced map $T_*$ acts on the space of probability measures $\mathcal{M}(X)$. It is proven that a large class of zero-entropy dynamical systems exhibits infinite

Haoyang Wang, Ruishan Guo, Pengtao Ma, Ciyu Ruan

With the increasing complexity of mobile device applications, these devices are evolving toward high agility. This shift imposes new demands on mobile sensing, particularly in achieving high-accuracy and low-latency. Event-based vision has emerged as a disruptive paradigm, offering high temporal resolution and low latency, making it well-suited for high-accu

Kangjie Zhou, Yao Mu, Haoyang Song, Yi Zeng

Robotic navigation in complex environments remains a critical research challenge. Traditional navigation methods focus on optimal trajectory generation within fixed free workspace, therefore struggling in environments lacking viable paths to the goal, such as disaster zones or cluttered warehouses. To address this problem, we propose AINav, an adaptive inter

Yugen Sato, Tomohiro Takagi

Recent advances in large language models (LLMs) have led to the development of multimodal LLMs (MLLMs) in the fields of natural language processing (NLP) and computer vision. Although these models allow for integrated visual and language understanding, they present challenges such as opaque internal processing and the generation of hallucinations and misinfo

Jian Yang

Recently we proposed a state described by the second Landau level (SLL) projection of the antiholomorphic Pfaffian wavefunction as a candidate for the ground state of the 5/2 fractional quantum Hall effect. In this paper we provide a mathematical proof that, when mapped to the lowest Landau level (LLL), the aforementioned state, which we call the SLL PH-Pfaf

Lorelay Mendoza Grijalva, Allisa G. Hastie, Meili Gong, Brenda Rojas Cala

Safe sanitation access is commonly believed to be ubiquitous in high-income countries; however, researchers and community advocates have exposed a glaring lack of access for many low-income communities and communities of color across the U.S. While this disparity has been identified and quantified at a high level, local and household-level implications of sa

Sho Ko, Kunle Olukotun

Long-sequence state-space models (SSMs) such as Hyena and Mamba replace the quadratic complexity of self-attention with more efficient FFT and scan operations. However, modern accelerators like GPUs are poorly suited to these non-GEMM workloads due to rigid execution models and specialization for dense matrix operations. This paper proposes architectural ext

Pei-Kai Huanga, Jun-Xiong Chong, Ming-Tsung Hsu, Fang-Yu Hsu

Face anti-spoofing (FAS) heavily relies on identifying live/spoof discriminative features to counter face presentation attacks. Recently, we proposed LDCformer to successfully incorporate the Learnable Descriptive Convolution (LDC) into ViT, to model long-range dependency of locally descriptive features for FAS. In this paper, we propose three novel training

Zexiang Li, Donna Spiegelman, Molin Wang, Zuoheng Wang

In epidemiology, obtaining accurate individual exposure measurements can be costly and challenging. Thus, these measurements are often subject to error. Regression calibration with a validation study is widely employed as a study design and analysis method to correct for measurement error in the main study due to its broad applicability and simple implementa

Kristina P. Sinaga

In this study, we propose extension of fuzzy c-means (FCM) clustering in multi-view environments. First, we introduce an exponential multi-view FCM (E-MVFCM). E-MVFCM is a centralized MVC with consideration to heat-kernel coefficients (H-KC) and weight factors. Secondly, we propose an exponential bi-level multi-view fuzzy c-means clustering (EB-MVFCM). Diffe

Nicholas Roth, Christopher Hidey, Lucas Spangher, William F. Arnold

In this paper, we propose a novel factored agent architecture designed to overcome the limitations of traditional single-agent systems in agentic AI. Our approach decomposes the agent into two specialized components: (1) a large language model (LLM) that serves as a high level planner and in-context learner, which may use dynamically available information in

Hailong Nan, Zhe Zhou, Min Yang

Host CPU resources are heavily consumed by TCP stack processing, limiting scalability in data centers. Existing offload methods typically address only partial functionality or lack flexibility. This paper introduces PnO (Plug & Offload), an approach to fully offload TCP processing transparently onto off-path SmartNICs (NVIDIA BlueField DPUs). Key to our solu

Behrooz Moosavi Ramezanzadeh

This paper investigates the optimal control of an epidemic governed by a SEIR model with an operational delay in vaccination. We address the mathematical challenge of imposing hard healthcare capacity constraints (e.g., ICU limits) over an infinite time horizon. To rigorously bridge the gap between theoretical constraints and numerical tractability, we emplo

Wei Liu, Qihang Lin, Yangyang Xu

We study first-order methods (FOMs) for solving \emph{composite nonconvex nonsmooth} optimization with linear constraints. Recently, the lower complexity bounds of FOMs on finding an ($\varepsilon,\varepsilon$)-KKT point of the considered problem is established in \cite{liu2025lowercomplexityboundsfirstorder}. However, optimization algorithms that achieve th

Michel Berthier, Nicoletta Prencipe, Edoardo Provenzi

In this paper we deal with the problem of overcoming the intuitive definition of several color perception attributes by replacing them with novel mathematically rigorous ones. Our framework is a quantum-like color perception theory recently developed, which constitutes a radical change of view with respect to the classical CIE models and their color appearan

Yanliang Huang, Sebastian Mair, Zhuoqi Zeng, Matthias Althoff

Autonomous vehicle path planning has reached a stage where safety and regulatory compliance are crucial. This paper presents an approach that integrates a motion planner with a deep reinforcement learning model to predict potential traffic rule violations. Our main innovation is replacing the standard actor network in an actor-critic method with a motion pla

Youjin Sung, Yang Liu

In a recent review, Liu, Pek, & Maydeu-Olivares (2025b) classified reliability coefficients into two types: classical test theory (CTT) reliability and proportional reduction in mean squared error (PRMSE). This article focuses on quantifying the sampling variability of these coefficients under item response theory (IRT) models. While some existing standard e

Yufeng Yang, Yi Zhou, Zhaosong Lu

Distributionally robust optimization (DRO) is a powerful technique to train robust models against data distribution shift. This paper aims to solve regularized nonconvex DRO problems, where the uncertainty set is modeled by a so-called generalized Sinkhorn distance and the loss function is nonconvex and possibly unbounded. Such a distance allows to model unc

Jing-Wen Gao, Xiao-Song Yang

Extending the results of reconstruction of compact metric spaces by inverse limits, we show that if $(X, d), (Y, d)$ are compact metric spaces, then the mapping space $Y^X$ is homotopy equivalent to the inverse limit of an inverse system of finite $T_0$-spaces which depends only on the finite open covers of $X$ and $Y$. Applying our tools, we obtain that if

Michel Berthier, Edoardo Provenzi

In this paper we make a systematic use of the quantum measurement theory to describe perceived colors and analyze some of their fundamental properties. After motivating the naturalness of the quantum measurement approach in the mathematical framework of the color perception theory proposed by the authors in previous papers, we show how to obtain several resu

Wenyi Liu, Huajie Wu, Liuyu Shi, Fangcheng Zhu

In recent years, autonomous unmanned aerial vehicle (UAV) technology has seen rapid advancements, significantly improving operational efficiency and mitigating risks associated with manual tasks in domains such as industrial inspection, agricultural monitoring, and search-and-rescue missions. Despite these developments, existing UAV inspection systems encoun

Ayan Mondal, Priyanka Sinha, Bheema Lingam Chittari

Twisted bilayer graphene (TBG) exhibits flat electronic bands at the so-called magic angle ($\sim 1.1^\circ$), leading to strong electron correlations and emergent quantum phases such as superconductivity and correlated insulating states. However, beyond the magic angle, the band structure generally remains dispersive, diminishing interaction-driven phenomen

Viktor T. Toth

Since their domestication at the dawn of civilization, cats have been known for their uncanny ability to seemingly defy gravity. We conjecture that this innate ability of cats is real: uniquely in the animal kingdom, felis catus, possibly along with a few closely related species, are indeed capable of manipulating their passive gravitational mass. We explore

Sourav Ghosh

Let $L_t$ be a zero Maslov Lagrangian mean curvature flow in $\mathbb{C}^2.$ We show that if the mean curvature stays uniformly bounded along the flow, then the tangent flow at a singular point is unique i.e. the limit of the parabolic rescalings does not depend on the chosen sequence of rescalings.

Caroline Haimerl, Filipe S. Rodrigues, Joseph J. Paton

Because organisms are able to sense its passage, it is perhaps tempting to treat time as a sensory modality, akin to vision or audition. Indeed, certain features of sensory estimation, such as Weber's law, apply to timing and sensation alike (Gibbon, 1977; Pardo-Vazquez et al., 2019). However, from an organismal perspective, time is a derived feature of othe

Hirokuni Miyamoto, Shunsuke Ito, Kenta Suzuki, Singo Tamachi

Fermentative recycling of organic matter is important for a sustainable society, but the functionality of fermented products needs to be adequately evaluated. Here, we clarify the antipathogenic properties for fish of a compost-type feed additive fermented by thermophilic Bacillaceae using non-edible marine resources as raw materials. After prior administrat

Felipe Angeles, Ramón G. Plaza, José Manuel Valdovinos

This work studies the dissipative structure of regularizations of any order of hyperbolic systems of conservation laws in several space dimensions. It is proved that the seminal equivalence theorem by Kawashima and Shizuta (Hokkaido Math. J. 14, 1985, no. 2, 249-275), which relates the strictly dissipative structure of second-order (viscous) systems to a gen

Gianpaolo Piscitelli

In this paper we study the behavior of the second eigenfunction of the anisotropic $p$-Laplace operator \[ - Q_{p}u:=-\textrm{div} \left(F^{p-1}(\nabla u)F_ξ(\nabla u)\right), \] as $p \to 1^+$, where $F$ is a suitable smooth norm of $\mathbb R^{n}$. Moreover, for any regular set $Ω$, we define the second anisotropic Cheeger constant as \begin{equation*} h_{

Chun Huang

We present a novel and somewhat whimsical approach to pulsar hotspot modeling by drawing inspiration from the iconic one-eyed monster, Mike Wazowski, from \emph{Monsters, Inc.}. Utilizing X-ray high-quality timing data from NICER, we apply a Bayesian inference framework to model the X-ray pulse profile of PSR J0437--4715. Our analysis employs a \emph{Wazowsk

Nicolas Chamel

The breaking of translational symmetry in the inner crust of a neutron star leads to the depletion of the neutron superfluid reservoir similarly to cold atomic condensates in optical lattices and in supersolids. This effect is studied in the general framework of the self-consistent time-dependent Hartree-Fock-Bogoliubov (HFB) theory, treating the crust as a

Haiyong Wang, Menghan Wu

Complex Gaussian quadrature rules for oscillatory integral transforms have the advantage that they can achieve optimal asymptotic order. However, their existence for Hankel transform can only be guaranteed when the order of the transform belongs to $[0,1/2]$. In this paper we consider the construction of generalized Gauss-Radau quadrature rules for Hankel tr

Xinyu Wang, Linrui Ma, Jerry Huang, Peng Lu

Recent shifts in the space of large language model (LLM) research have shown an increasing focus on novel architectures to compete with prototypical Transformer-based models that have long dominated this space. Linear recurrent models have proven to be a viable competitor due to their computational efficiency. However, such models still demonstrate a sizable

Aayush Gautam, Susav Shrestha, Narasimha Reddy

Speculative decoding accelerates large language model (LLM) inference by using a smaller draft model to propose tokens, which are then verified by a larger target model. However, selecting an optimal speculation length is critical for maximizing speedup while minimizing wasted computation. We introduce \textit{GammaTune} and \textit{GammaTune+}, training-fre

Xin Liang, Yogesh S Rawat

Clothes-changing person re-identification (CC-ReID) aims to recognize individuals under different clothing scenarios. Current CC-ReID approaches either concentrate on modeling body shape using additional modalities including silhouette, pose, and body mesh, potentially causing the model to overlook other critical biometric traits such as gender, age, and sty

Majid Aalizadeh, Shuo Yang, Suchithra Guntur, Vaishnavi Potluri

In this work, a miniaturized, automated, and cost-effective ELISA device is designed and implemented, without the utilization of conventional techniques such as pipetting or microfluidic valve technologies. The device has dimensions of 24 cm x 19 cm x 14 cm and weighs <3 Kg. The total hardware cost of the device is estimated to be approximately $1,200, which

Casimir Kuzyk, Alexander Dimitrakopoulos, Alireza Nojeh

The scanning electron microscope (SEM) delivers high resolution, high depth of focus and an image quality as if microscopic objects are seen by the naked eye. This makes it not only a powerful scientific instrument, but a tool inherently applicable to nearly all fields of study and curiosity involving the small scale. However, SEMs have remained complex, exp

Anas Berka, Mohamed El Hajji, Raphael Canals, Youssef Es-saady

Aerial and satellite imagery are inherently complementary remote sensing sources, offering high-resolution detail alongside expansive spatial coverage. However, the use of these sources for land cover segmentation introduces several challenges, prompting the development of a variety of segmentation methods. Among these approaches, the DeepLabV3+ architecture

Yuhang Ai, Huanchen Zhai, Johannes Tölle, Garnet Kin-Lic Chan

We propose a general strategy to develop quantum many-body approximations of primitives in linear algebra algorithms. As a practical example, we introduce a coupled-cluster inspired framework to produce approximate Hamiltonian moments, and demonstrate its application in various linear algebra algorithms for ground state estimation. Through numerical examples

Michel Berthier, Edoardo Provenzi

In this paper we provide an overview on the foundation and first results of a very recent quantum theory of color perception, together with novel results about uncertainty relations for chromatic opposition. The major inspiration for this model is the 1974 remarkable work by H.L. Resnikoff, who had the idea to give up the analysis of the space of perceived c

William D. Banks

In space, no one can hear you scream.

Heng Yu, Juze Zhang, Changan Chen, Tiange Xiang

Human interactions in everyday life are inherently social, involving engagements with diverse individuals across various contexts. Modeling these social interactions is fundamental to a wide range of real-world applications. In this paper, we introduce SocialGen, the first unified motion-language model capable of modeling interaction behaviors among varying

Jules Pitcho

We investigate the zero-noise limit for SDE's driven by Brownian motion with a divergence-free drift singular at the initial time and prove that a unique probability measure concentrated on the integral curves of the drift is selected. More precisely, we prove uniqueness of the zero-noise limit for divergence-free drifts in $L^1_{loc}((0,T];BV(\mathbb{T}^d;\

Eugenia Villa, Camilla Quaresmini, Valentina Breschi, Viola Schiaffonati

Algorithmic fairness is an expanding field that addresses a range of discrimination issues associated with algorithmic processes. However, most works in the literature focus on analyzing it only from an ethical perspective, focusing on moral principles and values that should be considered in the design and evaluation of algorithms, while disregarding the epi

Frédéric Ferraty, Han Lin Shang

This paper is concerned with forecasting probability density functions. Density functions are nonnegative and have a constrained integral; thus, they do not constitute a vector space. Implementing unconstrained functional time-series forecasting methods is problematic for such nonlinear and constrained data. A novel forecasting method is developed based on a

Deepeka Garg, Sihan Zeng, Sumitra Ganesh, Leo Ardon

In this paper, we address the challenges of managing Standard Operating Procedures (SOPs), which often suffer from inconsistencies in language, format, and execution, leading to operational inefficiencies. Traditional process modeling demands significant manual effort, domain expertise, and familiarity with complex languages like Business Process Modeling No

Faizuddin Ahmed, Ahmad Al-Badawi, İzzet Sakallı, Sara Kanzi

We investigate the dynamics of test particles, perturbations, and greybody factors within the framework of a Bardeen-like AdS black hole (BH) with a phantom global monopole. This study explores the interactions between nonlinear electrodynamics, the energy scale of symmetry breaking, and space-time topology. We analyze the geodesic motion of null and time-li

Barisha Chowdhury, Md Fazle Rabbi, S. M. Mahedy Hasan, Minhaz F. Zibran

As modern software development increasingly relies on reusable libraries and components, managing dependencies has become critical for ensuring software stability and security. However, challenges such as outdated dependencies, missed releases, and the complexity of interdependent libraries can significantly impact project maintenance. In this paper, we pres

Hilda Hadan, Sabrina Alicia Sgandurra, Leah Zhang-Kennedy, Lennart E. Nacke

Over the last decade, the free-to-play (F2P) game business model has gained popularity in the games industry. We examine the role of deceptive design during a game's transition to F2P and its impacts on players. Our analysis focuses on game mechanics and a Reddit analysis of the Overwatch (OW) series after it transitioned to an F2P model. Our study identifie

Rongjian Liang, Yi-Chen Lu, Wen-Hao Liu, Haoxing Ren

We propose Lib2Vec, a novel self-supervised framework to efficiently learn meaningful vector representations of library cells, enabling ML models to capture essential cell semantics. The framework comprises three key components: (1) an automated method for generating regularity tests to quantitatively evaluate how well cell representations reflect inter-cell

Yuichi Shiozawa

We establish an upper bound of the bottom of the essential spectrum for the generator associated with a regular Dirichlet form in terms of the rates of the volume growth/decay and big jump. Using this bound, we discuss how the bottom of the essential spectrum is affected by the volume growth and coefficient growth.

Mostafa Hassanlou, Hussain Gissy

In this paper, we consider two extensions of Stevic-Sharma operator and find estimations for the essential norm of them from QK(p; q) and H1 into weighted Bloch spaces.