October 2025 arXiv papers — page 186

Ka-Yui Au, Kwan-Lok Li, Albert K. H. Kong, Jumpei Takata

X-ray emission is generally believed to be one of the major heating sources for the optical modulation in redback pulsar binaries as we have seen similar phenomena in many low mass X-ray binaries (LMXBs). While, e.g., MeV/GeV gamma-rays from the neutron stars are also possible heating sources, X-ray observations are currently much more sensitive, and therefo

Xiangwei Lv, JinLuan Yang, Wang Lin, Jingyuan Chen

Graph domain adaptation (GDA) has achieved great attention due to its effectiveness in addressing the domain shift between train and test data. A significant bottleneck in existing graph domain adaptation methods is their reliance on source-domain data, which is often unavailable due to privacy or security concerns. This limitation has driven the development

Nishant Balepur, Atrey Desai, Rachel Rudinger

Large language models (LLMs) now give reasoning before answering, excelling in tasks like multiple-choice question answering (MCQA). Yet, a concern is that LLMs do not solve MCQs as intended, as work finds LLMs sans reasoning succeed in MCQA without using the question, i.e., choices-only. Such partial-input success is often linked to trivial shortcuts, but r

Yiqin Lv, Zhiyu Mou, Miao Xu, Jinghao Chen

Generative auto-bidding has demonstrated strong performance in online advertising, yet it often suffers from data scarcity in small-scale settings with limited advertiser participation. While cross-task data sharing is a natural remedy to mitigate this issue, naive approaches often introduce gradient bias due to distribution shifts across different tasks, an

He Chen, Chonghe Jiang, Anthony Man-Cho So

The canonical price-adjustment process, t\^atonnement, typically fails to converge to the exact competitive equilibrium (CE) and requires a high iteration complexity of $\tilde{\mathcal{O}}(1/\epsilon)$ to compute $\epsilon$-CE prices in widely studied linear and quasi-linear Fisher markets. This paper proposes refined price-adjustment processes to overcome

Qiaozhe Zhang, Jun Sun, Ruijie Zhang, Yingzhuang Liu

Sharpness (of the loss minima) is widely believed to be a good indicator of generalization of neural networks. Unfortunately, the correlation between existing sharpness measures and generalization is not as strong as expected, and sometimes even contradiction occurs. To address this problem, a key observation in this paper is: what really matters for general

Xunyang Zhu, Hongfei Ye, Yifei Wang, Taoran Liu

The sizing field defined on a triangular background grid is pivotal for controlling the quality and efficiency of unstructured mesh generation. However, creating an optimal background grid that is geometrically conforming, computationally lightweight, and free from artifacts like banding is a significant challenge. This paper introduces a novel, adaptive bac

Yeor Hafouta

We prove central limit theorems, Berry-Esseen type theorems, almost sure invariance principles, large deviations and Livsic type regularity for partial sums of the form $S_n=\sum_{j=0}^{n-1}f_j(...,X_{j-1},X_j,X_{j+1},...)$, where $(X_j)$ is an inhomogeneous Markov chain satisfying some mixing assumptions and $f_j$ is a sequence of sufficiently regular funct

Zhuoyuan Wang, Takashi Tanaka, Yongxin Chen, Yorie Nakahira

Sampling-based approaches are widely used in systems without analytic models to estimate risk or find optimal control. However, gathering sufficient data in such scenarios can be prohibitively costly. On the other hand, in many situations, low-fidelity models or simulators are available from which samples can be obtained at low cost. In this paper, we propos

Zhengyu Wu, Yinlin Zhu, Xunkai Li, Ziang Qiu

Foundation models have shown remarkable cross-domain generalization in language and vision, inspiring the development of graph foundation models (GFMs). However, existing GFMs typically assume centralized access to multi-domain graphs, which is often infeasible due to privacy and institutional constraints. Federated Graph Foundation Models (FedGFMs) address

Yi-Chi Liao, João Belo, Hee-Seung Moon, Jürgen Steimle

Human-in-the-loop optimization identifies optimal interface designs by iteratively observing user performance. However, it often requires numerous iterations due to the lack of prior information. While recent approaches have accelerated this process by leveraging previous optimization data, collecting user data remains costly and often impractical. We presen

Xuhong Liu, Shuai Shao

We study the connection between quantum secret sharing (QSS) schemes and $k$-uniform states of qubits beyond the equivalence between threshold QSS schemes and AME states. Specifically, we show that $3$-uniformity is a necessary but not sufficient condition for constructing a $3$-homogeneous QSS scheme using states of qubits. To the best of our knowledge, thi

Junhao He, Jiaxu Wang, Jia Li, Mingyuan Sun

Reconstructing Dynamic 3D Gaussian Splatting (3DGS) from low-framerate RGB videos is challenging. This is because large inter-frame motions will increase the uncertainty of the solution space. For example, one pixel in the first frame might have more choices to reach the corresponding pixel in the second frame. Event cameras can asynchronously capture rapid

Shaoze Li, Junhao Wu, Zhibin Deng

Pumped storage hydro (PSH) plants can improve the flexibility of power systems. A well-designed formulation for a PSH model is essential when incorporating the PSH units into a day-ahead unit commitment model. In the literature, the formulation of a PSH model is generally based on the individual PSH unit. This formulation is tight if there is only one PSH un

Alexandre Moreira Nascimento, Gabriel Kenji Godoy Shimanuki, Lúcio Flavio Vismari, João Batista Camargo

Perception failures in autonomous vehicles (AV) remain a major safety concern because they are the basis for many accidents. To study how these failures affect safety, researchers typically inject artificial faults into hardware or software components and observe the outcomes. However, existing fault injection studies often target a single sensor or machine

Diyi Liu, Shuchen Zhu, Guang Hao Low, Lin Lin

Efficient block encoding of many-body Hamiltonians is a central requirement for quantum algorithms in scientific computing, particularly in the early fault-tolerant era. In this work, we introduce new explicit constructions for block encoding second-quantized Hamiltonians that substantially reduce Clifford+T gate complexity and ancilla overhead. By utilizing

Yilun Zhang, Dexing Kong

Large Language Models (LLMs) show promise in medicine but are prone to factual and logical errors, which is unacceptable in this high-stakes field. To address this, we introduce the "Haibu Mathematical-Medical Intelligent Agent" (MMIA), an LLM-driven architecture that ensures reliability through a formally verifiable reasoning process. MMIA recursively break

B. L. Crompvoets, H. Kirk, R. Gutermuth, J. Di Francesco

JWST's exquisite data have opened the doors to new possibilities in detecting broad classes of astronomical objects, but also to new challenges in classifying those objects. In this work, we introduce SESHAT, the Stellar Evolutionary Stage Heuristic Assessment Tool for the identification of Young Stellar Objects, field stars (main sequence through asymptotic

Noah Bergam, Szymon Snoeck, Nakul Verma

Central to the widespread use of t-distributed stochastic neighbor embedding (t-SNE) is the conviction that it produces visualizations whose structure roughly matches that of the input. To the contrary, we prove that (1) the strength of the input clustering, and (2) the extremity of outlier points, cannot be reliably inferred from the t-SNE output. We demons

Chris Engh, P. M. Aronow

We propose a simple yet effective use of LLM-powered AI tools to improve causal estimation. In double machine learning, the accuracy of causal estimates of the effect of a treatment on an outcome in the presence of a high-dimensional confounder depends on the performance of estimators of conditional expectation functions. We show that predictions made by gen

Runyang You, Yongqi Li, Meng Liu, Wenjie Wang

Parallel test-time scaling (TTS) is a pivotal approach for enhancing large language models (LLMs), typically by sampling multiple token-based chains-of-thought in parallel and aggregating outcomes through voting or search. Recent advances in latent reasoning, where intermediate reasoning unfolds in continuous vector spaces, offer a more efficient alternative

Stephan Pfannerer, Joshua P. Swanson

Promotion permutations have recently been associated to each rectangular standard Young tableau by Gaetz--Pechenik--Pfannerer--Striker--Swanson. Here we relate promotion permutations to the Robinson--Schensted (RS) correspondence. More precisely, we show that taking a pair of standard Young tableaux of the same rectangular shape, stacking them, and computing

Tianci Liu, Ran Xu, Tony Yu, Ilgee Hong

Reward modeling lies at the core of reinforcement learning from human feedback (RLHF), yet most existing reward models rely on scalar or pairwise judgments that fail to capture the multifaceted nature of human preferences. Recent studies have explored rubrics-as-rewards (RaR) that uses structured criteria to capture multiple dimensions of response quality. H

Bora Basyildiz, Zhexuan Gong, Sahel Ashhab

The speed of elementary quantum gates sets a limit on the speed at which quantum circuits can be applied and, as a result, the size of the computations that can be performed on a quantum computer. This limitation stems from the fact that present-day quantum hardware systems have finite coherence times that limit the total computation time. The speeds of qubi

Yuang Meng, Xin Jin, Lina Lei, Chun-Le Guo

Ultra-high dynamic range (UHDR) scenes exhibit significant exposure disparities between bright and dark regions. Such conditions are commonly encountered in nighttime scenes with light sources. Even with standard exposure settings, a bimodal intensity distribution with boundary peaks often emerges, making it difficult to preserve both highlight and shadow de

Dezhi Ran, Yuan Cao, Mengzhou Wu, Simin Chen

Large language models (LLMs) have demonstrated remarkable capability in function-level code generation tasks. Unlike isolated functions, real-world applications demand reasoning over the entire software system: developers must orchestrate how different components interact, maintain consistency across states over time, and ensure the application behaves corre

Chengting Yu, Xiaobo Shu, Yadao Wang, Yizhen Zhang

Recursive transformers reuse parameters and iterate over hidden states multiple times, decoupling compute depth from parameter depth. However, under matched compute, recursive models with fewer parameters often lag behind non-recursive counterparts. By probing hidden states, we trace this performance gap to two primary bottlenecks: undifferentiated computati

Yuhi Sakamaki, Shota Goto, Kang Kim, Nobuyuki Matubayasi

The rheological behavior of polymer melts is strongly influenced by parameters such as chain length, chain stiffness, and architecture. In particular, shear thinning, characterized by a power-law decrease in shear viscosity with increasing shear rate, has been widely investigated through molecular dynamics simulations. A central question is the connection be

Fu Chen, Peng Wang, Xiyin Li, Wen Li

Training Large Language Models (LLMs) with Group Relative Policy Optimization (GRPO) encounters a significant challenge: models often fail to produce accurate responses, particularly in small-scale architectures. This limitation not only diminishes performance improvements and undermines the potential of GRPO but also frequently leads to mid-training collaps

Cunli Mao, Xiaofei Gao, Ran Song, Shizhu He

Large language models (LLMs) based Multilingual Knowledge Graph Completion (MKGC) aim to predict missing facts by leveraging LLMs' multilingual understanding capabilities, improving the completeness of multilingual knowledge graphs (KGs). However, existing MKGC research underutilizes the multilingual capabilities of LLMs and ignores the shareability of cross

Rongchao Xu, Kunlin Cai, Lin Jiang, Zhiqing Hong

Location-Based Social Network (LBSN) check-in trajectory data are important for many practical applications, like POI recommendation, advertising, and pandemic intervention. However, the high collection costs and ever-increasing privacy concerns prevent us from accessing large-scale LBSN trajectory data. The recent advances in synthetic data generation provi

Tongyu He, Jiao Li, Xiaobin Zhang, Mikhail Kovalev

We present a comprehensive photometric and spectroscopic analysis of the Algol-type binary \textit{Gaia} DR3 1892576067672499328. We identified the system as a spectroscopic binary based on medium-resolution LAMOST spectra. Combined with \textit{TESS} photometry, we determine an orbital period of \( P = 2.47757 (1) \) days, a low mass ratio of \( q = 0.098 \

Minh-Anh Nguye, Minh-Duc Nguyen, Ha Lan N. T., Kieu Hai Dang

Large language models (LLMs) are increasingly adopted for automating survey paper generation \cite{wang2406autosurvey, liang2025surveyx, yan2025surveyforge,su2025benchmarking,wen2025interactivesurvey}. Existing approaches typically extract content from a large collection of related papers and prompt LLMs to summarize them directly. However, such methods ofte

Ruiling Xu, Yifan Zhang, Qingyun Wang, Carl Edwards

Organic reaction mechanisms are the stepwise elementary reactions by which reactants form intermediates and products, and are fundamental to understanding chemical reactivity and designing new molecules and reactions. Although large language models (LLMs) have shown promise in understanding chemical tasks such as synthesis design, it is unclear to what exten

Changyeon Kim, Haeone Lee, Younggyo Seo, Kimin Lee

Offline reinforcement learning (RL) presents an attractive paradigm for training intelligent agents without expensive online interactions. However, current approaches still struggle with complex, long-horizon sequential decision making. In this work, we introduce DEtached value learning with Action Sequence (DEAS), a simple yet effective offline RL framework

David Wittman

The Fisher information matrix is used widely in astronomy (and presumably other fields) to forecast the precision of future experiments while they are still in the design phase. Although many sources describe the mathematics of the formalism, few sources offer simple examples to help the beginner. This pedagogical document works through a few simple examples

Jian Gao, Mengqi Yuan, Yifei Zeng, Chang Zeng

Gaussian Splatting (GS) enables immersive rendering, but realistic 3D object-scene composition remains challenging. Baked appearance and shadow information in GS radiance fields cause inconsistencies when combining objects and scenes. Addressing this requires relightable object reconstruction and scene lighting estimation. For relightable object reconstructi

Peiyang Liu, Ziqiang Cui, Di Liang, Wei Ye

Retrieval-augmented generation (RAG) enhances Large Language Models (LLMs) by mitigating hallucinations and outdated information issues, yet simultaneously facilitates unauthorized data appropriation at scale. This paper addresses this challenge through two key contributions. First, we introduce RPD, a novel dataset specifically designed for RAG plagiarism d

Luis H. Hatashita, Pranav Nathan, Suhas S. Jain

In this work, important two-phase flow scalings are derived, which enable the quantification of grid-point and time-step requirements as functions of Re, We, and Ca numbers. The adequate grid resolution is determined in the inertia-dominated regime with the aid of high-fidelity simulations of stationary two-phase homogeneous isotropic turbulence by evaluatin

Osamu Hirota

One of the key aspects of Shannon's theory is that it provides guidance for designing the most efficient systems, such as minimizing errors and clarifying the limits of coding. Such theories have made great developments in the 50 years since 1948. It has played a vital role in enabling the development of modern ultra-fast, stable, and highly dependable infor

Kasidit Muenprasitivej, Ye Zhao, Glen Chou

We address the challenge of enabling bipedal robots to traverse rough terrain by developing probabilistically safe planning and control strategies that ensure dynamic feasibility and centroidal robustness under terrain uncertainty. Specifically, we propose a high-level Model Predictive Control (MPC) navigation framework for a bipedal robot with a specified c

Jesica Espino-González, Francisco J. Sevilla, Andrea Valdés-Hernández

The speed of evolution between perfectly distinguishable states is thoroughly analyzed in a closed three-level (qutrit) quantum system. Considering an evolution under an arbitrary time-independent Hamiltonian, we fully characterize the relevant parameters according to whether the corresponding quantum speed limit is given by the Mandelstam-Tamm, the Margolus

Wenyue Chen, Peng Li, Wangguandong Zheng, Chengfeng Zhao

Photorealistic 3D full-body human reconstruction from a single image is a critical yet challenging task for applications in films and video games due to inherent ambiguities and severe self-occlusions. While recent approaches leverage SMPL estimation and SMPL-conditioned image generative models to hallucinate novel views, they suffer from inaccurate 3D prior

Zipeng Guo, Lichen Ma, Xiaolong Fu, Gaojing Zhou

In web data, product images are central to boosting user engagement and advertising efficacy on e-commerce platforms, yet the intrusive elements such as watermarks and promotional text remain major obstacles to delivering clear and appealing product visuals. Although diffusion-based inpainting methods have advanced, they still face challenges in commercial s

Peyang Liu, Xi Wang, Ziqiang Cui, Wei Ye

The rapid proliferation of video content across various platforms has highlighted the urgent need for advanced video retrieval systems. Traditional methods, which primarily depend on directly matching textual queries with video metadata, often fail to bridge the semantic gap between text descriptions and the multifaceted nature of video content. This paper i

Parker Hao Tian, Zahra Yousefijamarani, Alaa Alameldeen

PIM architectures aim to reduce data transfer costs between processors and memory by integrating processing units within memory layers. Prior PIM architectures have shown potential to improve energy efficiency and performance. However, such advantages rely on data proximity to the processing units performing computations. Data movement overheads can degrade

Jiaoyang Li, Junhao Ruan, Shengwei Tang, Saihan Chen

Graph Retrieval-Augmented Generation (Graph RAG) effectively builds a knowledge graph (KG) to connect disparate facts across a large document corpus. However, this broad-view approach often lacks the deep structured reasoning needed for complex multi-hop question answering (QA), leading to incomplete evidence and error accumulation. To address these limitati

Bingzheng Wu, Jianming Wu, Sai Duan, Xin Xu

Water, a ubiquitous and fundamental substance, plays a critical role across a wide range of disciplines from physics and chemistry to biology and engineering. Despite theoretical predictions of several phases of two-dimensional (2D) ice confined between idealized hydrophobic walls, experimental validation has been limited to the square phase, whose structura

Xiaochen Tang, Zhenya Zhang, Miaomiao Zhang, Jie An

In real-time and safety-critical cyber-physical systems (CPSs), control synthesis must guarantee that generated policies meet stringent timing and correctness requirements under uncertain and dynamic conditions. Signal temporal logic (STL) has emerged as a powerful formalism of expressing real-time constraints, with its semantics enabling quantitative assess

You-Ming Liu, Shi-Kai Lin, Pei-Shi Li, Yi-Ran Hao

In photonics, twisted bi-layer systems have demonstrated unprecedented control over light-matter interactions, primarily through the modulation of photonic band structures and the formation of Moir\'e patterns. Meanwhile, magnetic photonic crystals have served as cornerstone platforms for manipulating light propagation, facilitating key applications such as

Shuo Yu, Mingyue Cheng, Daoyu Wang, Qi Liu

The primary form of user-internet engagement is shifting from leveraging implicit feedback signals, such as browsing and clicks, to harnessing the rich explicit feedback provided by textual interactive behaviors. This shift unlocks a rich source of user textual history, presenting a profound opportunity for a deeper form of personalization. However, prevaili

Roman Berens, Trevor Gravely, Alexandru Lupsasca

In this second paper of our series started with \cite{Berens2024}, we investigate linearized gravitational perturbations of a rotating Kerr black hole in a non-asymptotically flat spacetime with (anti-)de Sitter boundary conditions. Here, we explicitly write down the metric components (in both ingoing and outgoing radiation gauge) of the perturbations that c

N. Chen, J. G. Li, H. H. Li

A shell model effective interaction for nuclei beyond the double magic nucleus 78Ni is constructed. First, the single-particle evolutions for valence neutrons above the double magic 78Ni are systematically explored in the N = 51 isotones using the large scale shell model (LSSM) calculations based on the constructed effective interaction. Subsequently, we cal

Alhim Vera, Karen Sanchez, Carlos Hinojosa, Haidar Bin Hamid

Can generative agents be trusted in multimodal environments? Despite advances in large language and vision-language models that enable agents to act autonomously and pursue goals in rich settings, their ability to reason about safety, coherence, and trust across modalities remains limited. We introduce a reproducible simulation framework for evaluating agent

Asaad Abdul-Hamid, Brycen D. Pearl, Hang Woon Lee, Hao Chen

As orbital debris continues to become a higher priority for the space industry, there is a need to explore how partnerships between the public and private space sector may aid in addressing this issue. This research develops a space logistics framework for planning orbital debris remediation missions, providing a quantitative basis for partnerships that are

Chengshuai Zhao, Shu Wan, Paras Sheth, Karan Patwa

The proliferation of online hate speech poses a significant threat to the harmony of the web. While explicit hate is easily recognized through overt slurs, implicit hate speech is often conveyed through sarcasm, irony, stereotypes, or coded language -- making it harder to detect. Existing hate speech detection models, which predominantly rely on surface-leve

Krinos Li, Xianglu Xiao, Shenglong Deng, Lucas He

Large language models (LLMs) are transforming cellular biology by enabling the development of "virtual cells"--computational systems that represent, predict, and reason about cellular states and behaviors. This work provides a comprehensive review of LLMs for virtual cell modeling. We propose a unified taxonomy that organizes existing methods into two paradi

Jianxing Zhang, Xiaodian Chen, Shu Wang, Jiyu Wang

RR Lyrae (RRL) variable stars are cornerstone distance indicators. In particular, double-mode RR Lyrae (RRd) stars enable period--luminosity relations (PLRs) that are less sensitive to metallicity, reducing systematic biases in distance measurements. However, their utility has been limited by a global sample of only $\sim$3,000 objects. We develop an automat

Shuming Yu, Zhentao Fu, Dingkun Qin, Enting Li

In this work, we reveal a curious type of moir\'e effect that selectively modifies the surface states of bulk crystal. We synthesize mixed-dimensional heterostructures consisting of a noble gas monolayer grow on the surface of bulk Bi(111), and determine the electronic structure of the heterostructures using angle-resolved photoemission spectroscopy. We dire

Xiaoxu Ma, Runhao Li, Zhenyu Weng

Deep hashing has been widely adopted for large-scale image retrieval, with numerous strategies proposed to optimize hash function learning. Pairwise-based methods are effective in learning hash functions that preserve local similarity relationships, whereas center-based methods typically achieve superior performance by more effectively capturing global data

Xu Cheng, Yi Wang, Dun Zhou

The present paper investigates the structural stability of bidirectional cyclic negative feedback systems. To address this, we develop a generalized Floquet theory and construct nested invariant cones for the systems. Subsequently, we demonstrate that the Poincar\'{e}-Bendixson property for the limit set persists under $C^1$-perturbations. By applying the ge

K. H. Li, N. Chen, J. G. Li, H. H. Li

Proton-rich nuclei beyond the proton drip line exhibit unique phenomena, such as the Thomas-Ehrman shift (TES), providing valuable insights into nuclear stability and isospin symmetry breaking. The discovery of the lightest new isotope, 21Al, situated beyond the proton drip line, was recently reported in the experiment. In this study, we employ the Gamow she

Raghav Mishra, Ian R. Manchester

We propose enforcing constraints on Model-Based Diffusion by introducing emerging barrier functions inspired by interior point methods. We demonstrate that the standard Model-Based Diffusion algorithm can lead to catastrophic performance degradation in highly constrained environments, even on simple 2D systems due to sample inefficiency in the Monte Carlo ap

Thilo Scharnhorst, Jack Spilecki, John Wright

We show that $n = \Omega(rd/\varepsilon^2)$ copies are necessary to learn a rank $r$ mixed state $\rho \in \mathbb{C}^{d \times d}$ up to error $\varepsilon$ in trace distance. This matches the upper bound of $n = O(rd/\varepsilon^2)$ from prior work, and therefore settles the sample complexity of mixed state tomography. We prove this lower bound by studying

Mack Wesley Cleveland, Andrew Nelson, Ericmoore Jossou

UO2 doped with parts per million Cr2O3 powder is considered a potential near term accident tolerant fuel candidate. Here, the results of decades of industry and academic research into Cr-doped UO2 are analyzed and their shortcomings are critiqued. Focusing on the incorporation mechanisms of Cr into the fuel matrix, we explore a mechanistic understanding of t

Man Hu, Xinyi Wu, Zuofeng Suo, Jinbo Feng

With the rise of advanced reasoning capabilities, large language models (LLMs) are receiving increasing attention. However, although reasoning improves LLMs' performance on downstream tasks, it also introduces new security risks, as adversaries can exploit these capabilities to conduct backdoor attacks. Existing surveys on backdoor attacks and reasoning secu

Joey Chen, Bjørn Kjos-Hanssen, Ivan Koswara, Linus Richter

The automatic complexity of a finite word (string) is an analogue for finite automata of Sipser's distinguishing complexity (1983) and was introduced by Shallit and Wang (2001). For a finite alphabet $\Sigma$ of at least two elements, we consider the non-deterministic automatic complexity given by exactly - yet not necessarily uniquely - accepting automata:

Fei Jiang, Yajie Zhang, Zhipeng Zhang, Youyi Zhao

It is well-known that the Rayleigh--Taylor (abbr. RT) instability can be completely inhibited by the quantum effect stabilization in proper circumstances leading to a cutoff wavelength in the \emph{linear} motion equations. Motivated by the linear theory, we further investigate the {stability} for the \emph{nonlinear} RT problem of quantum Navier--Stokes equ

Justin P. Bergfield

We investigate the thermoelectric response of single-molecule junctions composed of acyclic cross-conjugated molecules, including dendralene analogues and related iso-poly(diacetylene) (iso-PDA) motifs, in which node-possessing repeat units are connected in series. Using many-body quantum transport theory, we show that increasing the number of repeat units l

N. Chen, J. G. Li, K. H. Li, N. Michel

The unbound nucleus 18Na, acting as an intermediate nucleus in the sequential decay of 19Mg, is situated beyond the proton drip line. We employ the coupled-channel Gamow shell model (GSM-CC) to investigate the properties of 18Na, as well as the 17Ne(p, p) cross section. GSM-CC treats the nucleus as an open quantum system and provides a unified framework for

Tangin Amir Smrity, MD Zahin Muntaqim Hasan Muhammad Kafi, Abu Saleh Musa Miah, Najmul Hassan

Induction motors (IMs) are indispensable in industrial and daily life, but they are susceptible to various faults that can lead to overheating, wasted energy consumption, and service failure. Early detection of faults is essential to protect the motor and prolong its lifespan. This paper presents a hybrid method that integrates BYOL with CNNs for classifying

Zhihao Zhou, Yu He, Shixian Liu, Lina Yang

High Bandwidth Memory (HBM), as a key development trend in future memory chip technology, significantly enhances computer performance. At the same time, the thermal challenges arising from its stacked architecture have drawn considerable attention. Most existing studies on HBM thermal management are based on Fourier's law, neglecting the non-Fourier effects

Robinjeet Singh, Avirup Roy, Daniel Becker, Johnathan D. Gard

Arrays of hundreds or thousands of low temperature detectors have been deployed for many experiments, both bolometers for long wavelength applications and calorimeters for shorter wavelength applications. One challenge that is common to many of these arrays is the efficient use of focal plane area to achieve a large fill fraction of absorbers coupled to dete

Shrawan Kumar

Let $G$ be a connected simply-connected simple algebraic group over $\mathbb{C}$ and let $T$ be a maximal torus, $B\supset T$ a Borel subgroup and $K$ a maximal compact subgroup. Then, the product in the (algebraic) based loop group $\Omega(K)$ gives rise to a comultiplication in the topological $T$-equivariant $K$-ring $K_T^{top}(\Omega(K))$. Recall that $\

Bright Lu, James W. Erikson, Bo Xu, Sinica Guo

High-quality factor microresonators are an attractive platform for the study of nonlinear photonics, with diverse applications in communications, sensing, and quantum metrology. The characterization of loss mechanisms and nonlinear properties in a microresonator is a necessity for the development of photonic integrated circuits. Here, we demonstrate a high-q

Yang Yang, Mingjiao Yan, Zongliang Zhang, Miao Zhang

This work develops an elasto-plastic cell-based smoothed finite element method (CSFEM) for geotechnical analysis. The formulation incorporates a smoothed strain field into the standard elasto-plastic framework based on the Mohr-Coulomb criterion and is implemented in ABAQUS through a user-defined element (UEL). A UEL-UMAT data-transfer strategy is introduced

Jifan Zhang, Henry Sleight, Andi Peng, John Schulman

Large language models (LLMs) are increasingly trained from AI constitutions and model specifications that establish behavioral guidelines and ethical principles. However, these specifications face critical challenges, including internal conflicts between principles and insufficient coverage of nuanced scenarios. We present a systematic methodology for stress

Yong Yu, Meiting Yang, Zhengjun Shang, Liangliang Wang

The digitization of historical astronomical plates is essential for preserving century-long observational data. This work presents the development and application of the specialized digitizers at the Shanghai Astronomical Observatory (SHAO), including technical details, international collaborations, and scientific applications on the plates.

Yuhan Sun, Zhiwei Huang, Wanqing Cui, Shaopan Xiong

In AI-powered e-commerce livestreaming, digital avatars require real-time responses to drive engagement, a task for which high-latency Large Reasoning Models (LRMs) are ill-suited. We introduce LiveThinking, a practical two-stage optimization framework to bridge this gap. First, we address computational cost by distilling a 670B teacher LRM into a lightweigh

Bin Xia, Nesar Ramachandra, Azton I. Wells, Salman Habib

We present a multi-modal foundation model for astrophysical galaxy data, designed to map between simulation- and observation-based galactic features. Our encoder-only transformer flexibly ingests scalar quantities (e.g., redshifts, galaxy masses) and vectors (e.g., star formation histories, spectra), supporting multi-task training that includes within-modali

Junyi Qi, Ton Viet Ta

We investigate predator-prey school interactions in aquatic environments using a stochastic differential equation (SDE)-based, particle-level model that incorporates attraction, repulsion, alignment, and environmental noise. Two predation strategies-center attack and nearest attack-are examined to assess their effects on prey survival, predator efficiency, a

Zicheng Yan, Jialiang Tao, Christian Franck, David L. Henann

This work introduces a calibration framework for material parameter identification in isotropic hyperelastic constitutive models. The framework synergizes the Virtual Fields Method (VFM) to define an objective function with a Genetic Algorithm (GA) as the optimization method to facilitate automated calibration. The formulation of the objective function uses

Alan Hammond

Brownian Boost is a one-parameter family of stochastic differential games played on the real line in which players spend at rates of their choosing in an ongoing effort to influence the drift of a randomly diffusing point particle~$X$. One or other player is rewarded, at time infinity, according to whether~$X$ tends to plus or minus infinity. Each player's n

Pranav Sambhu, Om Guin, Madhav Sambhu, Jinho Cha

This study evaluates whether integrating curriculum learning with diffusion-based synthetic augmentation can enhance the detection of difficult pulmonary nodules in chest radiographs, particularly those with low size, brightness, and contrast, which often challenge conventional AI models due to data imbalance and limited annotation. A Faster R-CNN with a Fea

Dan Cristofaro-Gardiner

These are notes to accompany my lectures at the $2024$ "Current Developments in Mathematics" conference hosted by Harvard/MIT. The lectures were about some recent progress in our understanding of two and three dimensional dynamical systems, using in part some tools from low-dimensional topology. In these notes, I try to give a sense for how this works by dis

Louis Beaudoin, Aimé Verrier, Youcef A. Bioud, Mathieu Massicotte

Nuclear magnetic resonance (NMR) spectroscopy is a powerful technique to study local magnetism in a variety of materials. However, the inherently low sensitivity of conventional inductively detected solid state NMR typically requires a large number of spins, reducing its applicability to two-dimensional (2D) materials and nanoscale thin films. To overcome th

Dashen Yan

We develop a gluing theorem for non-degenerate $\mathbb{Z}_{2}$-harmonic $1$-forms on compact manifolds, in which non-degenerate $\mathbb{Z}_{2}$-harmonic $1$-forms on $\mathbb{R}^{n}$ are glued to the regular zeros of a non-degenerate $\mathbb{Z}_{2}$-harmonic $1$-form. As an immediate consequence, viewing an ordinary harmonic $1$-form as a $\mathbb{Z}_{2}$

Yuwen Li, Han Shui

This work develops user-friendly a posteriori error estimates of finite element methods, based on smoothers of linear iterative solvers. The proposed method employs simple smoothers, such as Jacobi or Gauss-Seidel iteration, on an auxiliary finer mesh to process the finite element residual for a posteriori error control. The implementation has linear complex

Lei Li, Chen Wang, Mengchao Wang

The random splitting Langevin Monte Carlo could mitigate the first order bias in Langevin Monte Carlo with little extra work compared other high order schemes. We develop in this work an analysis framework for the sampling error under Wasserstein distance regarding the random splitting Langevin Monte Carlo. First, the sharp local truncation error is obtained

Romeo Ortega, Leyan Fang, Jose Guadalupe Romero

The objective of this note is to share some reflections of the authors regarding the use of sliding mode designs in control systems. We believe the abundant, and ever increasing, appearance of this kind of works on our scientific publications deserves some critical evaluation of their actual role, relevance and pertinence. First, we discuss the procedure fol

Lucas Chen, Shrutheesh Raman Iyer, Zachary Kingston

Sequential robot manipulation tasks require finding collision-free trajectories that satisfy geometric constraints across multiple object interactions in potentially high-dimensional configuration spaces. Solving these problems in real-time and at large scales has remained out of reach due to computational requirements. Recently, GPU-based acceleration has s

A. Nguyen, C. Blake, R. J. Turner, V. Aronica

The peculiar velocities of supernovae and their host galaxies are correlated with the large-scale structure of the Universe, and can be used to constrain the growth rate of structure and test the cosmological model. In this work, we measure the correlation statistics of the large-scale structure traced by the Dark Energy Spectroscopic Instrument Bright Galax

Yuwei Geng, Junqi Yin, Eric C. Cyr, Guannan Zhang

While recent advances in deep learning have shown promising efficiency gains in solving time-dependent partial differential equations (PDEs), matching the accuracy of conventional numerical solvers still remains a challenge. One strategy to improve the accuracy of deep learning-based solutions for time-dependent PDEs is to use the learned solution as the coa

Matt Brigida

Research has shown banks match interest income and expense betas, and thereby obtain net interest income margins which are insensitive to changes in short-term interest rates. The present analysis extends this research in a number of ways. First, we use state-space methods to estimate time-varying betas and test whether they are matched at each time interval

Haoyi Duan, Yunzhi Zhang, Yilun Du, Jiajun Wu

Many video workflows benefit from a mixture of user controls with varying granularity, from exact 4D object trajectories and camera paths to coarse text prompts, while existing video generative models are typically trained for fixed input formats. We develop Ctrl-VI, a video synthesis method that addresses this need and generates samples with high controllab

Xingtao Yang, Zhenghe Guo, Siyun Liang, Zhaohui Yang

This letter investigates the joint sensing problem between unmanned aerial vehicles (UAV) and base stations (BS) in integrated sensing and communication (ISAC) systems with fluid antennas (FA). In this system, the BS enhances its sensing performance through the UAV's perception system. We aim to maximize the communication rate between the BS and UAV while gu

Hai Zhu, Chia-Nan Yeh, Miguel A. Morales, Leslie Greengard

We generalize the interpolative separable density fitting (ISDF) method, used for compressing the four-index electron repulsion integral (ERI) tensor, to incorporate adaptive real space grids for potentially highly localized single-particle basis functions. To do so, we employ a fast adaptive algorithm, the recently-introduced dual-space multilevel kernel-sp

Binzhe Yuan, Xiangyu Zhang, Zeyu Zheng, Yuefeng Zhang

Neural radiance fields (NeRF) have transformed 3D reconstruction and rendering, facilitating photorealistic image synthesis from sparse viewpoints. This work introduces an explicit data reuse neural rendering (EDR-NR) architecture, which reduces frequent external memory accesses (EMAs) and cache misses by exploiting the spatial locality from three phases, in

Heming Wu, Di Wang, Tai Ma, Peng Zhao

Existing pyramid registration networks may accumulate anatomical misalignments and lack an effective mechanism to dynamically determine the number of optimization iterations under varying deformation requirements across images, leading to degraded performance. To solve these limitations, we propose iPEAR. Specifically, iPEAR adopts our proposed Fused Attenti

Jun Muraoka, Daichi Haraguchi, Naoto Inoue, Wataru Shimoda

In layout design for advertisements and web pages, balancing visual appeal and communication efficiency is crucial. This study examines automated text box placement in incomplete layouts, comparing a standard Transformer-based method, a small Vision and Language Model (Phi3.5-vision), a large pretrained VLM (Gemini), and an extended Transformer that processe