February 2025 arXiv papers — page 8

S. Nageeb Ali, Nicole Immorlica, Meena Jagadeesan, Brendan Lucier

In the digital economy, technological innovations make it cheaper to produce high-quality content. For example, generative AI tools reduce costs for creators who develop content to be distributed online, but can also reduce production costs for the users who consume that content. These innovations can thus lead to disintermediation, since consumers may choos

Christopher Eur, Thomas Lam

Positive geometries are semialgebraic sets equipped with a canonical differential form whose residues mirror the boundary structure of the geometry. Every full-dimensional projective polytope is a positive geometry. Motivated by the canonical forms of polytopes, we construct a canonical form for any tope of an oriented matroid, inside the Orlik--Solomon alge

Yong Fang

Arithmetic codes are usually deemed as the most important means to implement lossless source coding, whose principle is mapping every source symbol to a sub-interval in [0, 1). For every source symbol, the length of its mapping sub-interval is exactly equal to its probability. With this symbol-interval mapping rule, the interval [0,1) will be fully covered a

Yuko Nakagi, Keigo Tada, Sota Yoshino, Shinji Nishimoto

Large language models (LLMs) often exhibit abrupt emergent behavior, whereby new abilities arise at certain points during their training. This phenomenon, commonly referred to as a ''phase transition'', remains poorly understood. In this study, we conduct an integrative analysis of such phase transitions by examining three interconnected perspectives: the si

Zihao Wu, Janko Böhm, Rourou Ma, Johann Usovitsch

We introduce a new version v1.1 of NeatIBP. In this new version, a Kira interface is included. It allows the user to reduce the integration-by-parts (IBP) identity systems generated by NeatIBP using Kira in a highly automated way. This new version also implements the so-called spanning cuts method. It helps to reduce the total computational complexity of IBP

Long Huang, Jianping Yao

Advanced artificial intelligence (AI) algorithms, particularly those based on artificial neural networks, have garnered significant attention for their potential applications in areas such as image recognition and natural language processing. Notably, neural networks make heavy use of matrix-vector multiplication (MVM) operations, causing substantial computi

Zhi-Peng Wang, X. X. Yi, Hai-Jun Wang

The nonlinearity of the conformal group is an essential factor that ruins the global conformal invariance for interacting material fields. In this paper we attempt to track such nonlinearity from spacetime transformations to spinor representations. To this end we rederive the spinor representation by generalizing the linear fractional transformation from two

Christian Hakert, Shuo-Han Chen, Kay Heider, Roland Kühn

Arising disruptive memory technologies continuously make their way into the memory hierarchy at various levels. Racetrack memory is one promising candidate for future memory due to the overall low energy consumption, access latency and high endurance. However, the access dependent shift property of racetrack memory can make it easily a poor candidate, when t

Pascal Ochem, Théo Pierron

A tangram is a word in which every letter occurs an even number of times. Thus it can be cut into parts that can be arranged into two identical words. The \emph{cut number} of a tangram is the minimum number of required cuts in this process. Tangrams with cut number one corresponds to squares. For $k\ge1$, let $t(k)$ denote the minimum size of an alphabet ov

Quang Anh Le, Seung Ki Baek

Indirect reciprocity explains the evolution of cooperation by considering how our cooperative behavior toward someone is reciprocated by someone else who has observed us. A cohesive society has a shared norm that prescribes how to assess observed behavior as well as how to behave toward others based on the assessments, and the eight social norms that are evo

Hwanjun Song, Jeonghwan Choi, Minseok Kim

Retrieval-augmented generation (RAG) enhances LLMs with external knowledge, yet generation remains vulnerable to retrieval-induced noise and uncertain placement of relevant chunks, often causing hallucinations. We present Ext2Gen, an extract-then-generate framework that strengthens LLMs via joint evidence selection and answer generation, dynamically identify

Tianyi Zeng, Tianyi Wang, Zimo Zeng, Feiyang Zhang

Accurate state estimation is fundamental to intelligent vehicles. Wheel load, one of the most important chassis states, serves as an essential input for advanced driver assistance systems (ADAS) and exerts a direct influence on vehicle stability and safety. However, wheel load estimation remains challenging due to the complexity of chassis modeling and the s

Huihui Guo, Huizhang Luo, Huilong Pi, Mingxing Duan

With the advancements in modern intelligent technologies, mobile robots equipped with manipulators are increasingly operating in unstructured environments. These robots can plan sequences of actions for long-horizon tasks based on perceived information. However, in practice, the planned actions often fail due to discrepancies between the perceptual informati

Yasuya Nakayama

We review the theoretical aspects of determining linear rheology using passive microrheology from simulations under periodic boundary conditions (PBC). It is common to impose periodic boundary conditions when evaluating bulk properties by molecular simulation. The Brownian motion of the probe particles is affected by PBCs, and thus their effects must be cons

Yizhou Zhang, Yi-An Ma, Eric Mazumdar

We consider the problem of learning to exploit learning algorithms through repeated interactions in games. Specifically, we focus on the case of repeated two player, finite-action games, in which an optimizer aims to steer a no-regret learner to a Stackelberg equilibrium without knowledge of its payoffs. We first show that this is impossible if the optimizer

Yueyang Li, Lei Chen, Wenhao Dong, Shengyu Gong

Developing interpretable models for neurodevelopmental disorders (NDDs) diagnosis presents significant challenges in effectively encoding, decoding, and integrating multimodal neuroimaging data. While many existing machine learning approaches have shown promise in brain network analysis, they typically suffer from limited interpretability, particularly in ex

Kangjian Wu, Jia Li, Qingxiang Xu

The H$\ddot{{\rm o}}$lder-McCarty inequalities are originally derived in the Hilbert space case and have been generalized via a convex inequality. The main purpose of this paper is to extend this convex inequality to the Hilbert $C^*$-module case, and meanwhile to make some investigations on the H$\ddot{{\rm o}}$lder-McCarty inequalities in the Hilbert $C^*$

Hui Lai, Qi Chen, Junping Zhang, Jian Pu

Accurate localization is essential for the safe and effective navigation of autonomous vehicles, and Simultaneous Localization and Mapping (SLAM) is a cornerstone technology in this context. However, The performance of the SLAM system can deteriorate under challenging conditions such as low light, adverse weather, or obstructions due to sensor degradation. W

Xunhao Lai, Jianqiao Lu, Yao Luo, Yiyuan Ma

Large language models (LLMs) encounter computational challenges during long-sequence inference, especially in the attention pre-filling phase, where the complexity grows quadratically with the prompt length. Previous efforts to mitigate these challenges have relied on fixed sparse attention patterns or identifying sparse attention patterns based on limited c

Keshav Kumar Sharma, Ashutosh Ujjwal, Rohit Saini, Ramesh Karuppannan

Though the common metal electrode-based perovskite solar cells have achieved a power conversion efficiency of >25%, they also play a crucial role in accelerating the degradation of the cells. In this study, we investigated phase transition engineering in Ag electrodes via Cu and Zn alloying, transforming from a cubic to a tetragonal phase. These alloyed elec

Junpeng Wang, Chin-Chia Michael Yeh, Uday Singh Saini, Mahashweta Das

State space models (SSMs) have emerged as an efficient alternative to transformer-based models, offering linear complexity that scales better than transformers. One of the latest advances in SSMs, Mamba, introduces a selective scan mechanism that assigns trainable weights to input tokens, effectively mimicking the attention mechanism. Mamba has also been suc

Zhiquan Tan, Weiran Huang

The interplay of optimizers and architectures in neural networks is complicated and hard to understand why some optimizers work better on some specific architectures. In this paper, we find that the traditionally used sharpness metric does not fully explain the intricate interplay and introduces information-theoretic metrics called entropy gap to better help

Alexandros Gazis, Ioannis Papadongonas, Athanasios Andriopoulos, Constantinos Zioudas

This article provides a comprehensive overview of sensors commonly used in low-cost, low-power systems, focusing on key concepts such as Internet of Things (IoT), Big Data, and smart sensor technologies. It outlines the evolving roles of sensors, emphasizing their characteristics, technological advancements, and the transition toward "smart sensors" with int

Zhaoyang Jia, Bin Li, Jiahao Li, Wenxuan Xie

We introduce a practical real-time neural video codec (NVC) designed to deliver high compression ratio, low latency and broad versatility. In practice, the coding speed of NVCs depends on 1) computational costs, and 2) non-computational operational costs, such as memory I/O and the number of function calls. While most efficient NVCs prioritize reducing compu

Wenhao Li

We study the arithmetic of del Pezzo surfaces $Y$ of degree 2 over a function field, and in particular, the cokernel of the homomorphism from the Picard group to the Galois-invariants of the geometric Picard group $\operatorname{Pic} Y \rightarrow(\operatorname{Pic} \bar{Y})^{G}$. Applying this to a fibration $\pi:X\to S$ in del Pezzo surfaces of degree 2 ov

Weijia Zhang, Fei Xie, Weidong Cai, Chao Ma

Knowledge distillation (KD) aims to transfer the knowledge of a more capable yet cumbersome teacher model to a lightweight student model. In recent years, relation-based KD methods have fallen behind, as their instance-matching counterparts dominate in performance. In this paper, we revive relational KD by identifying and tackling several key issues in relat

Ning Sun, Lei Feng, Pengfei Zhang

When two non-relativistic particles interact resonantly in three dimensions, an infinite tower of three-body bound states emerges, exhibiting a discrete scale invariance. This universal phenomenon, known as the Efimov effect, has garnered extensive attention across various fields, including atomic, nuclear, condensed matter, and particle physics. In this let

Seyed Pouyan Mousavi Davoudi, Amin Gholami Davodi, Alireza Amiri-Margavi, Alireza Shafiee Fard

We introduce a new approach in which several advanced large language models-specifically GPT-4-0125-preview, Meta-LLAMA-3-70B-Instruct, Claude-3-Opus, and Gemini-1.5-Flash-collaborate to both produce and answer intricate, doctoral-level probability problems without relying on any single "correct" reference. Rather than depending on an established ground trut

Yihong Tang, Kehai Chen, Xuefeng Bai, Zhengyu Niu

Large Language Models (LLMs) have made remarkable advances in role-playing dialogue agents, demonstrating their utility in character simulations. However, it remains challenging for these agents to balance character portrayal utility with content safety because this essential character simulation often comes with the risk of generating unsafe content. To add

Bogdan Maxim

The aim of this paper is to state and prove existence and uniqueness results for a general elliptic problem with homogeneous Neumann boundary conditions, often associated with image processing tasks like denoising. The novelty is that we surpass the lack of coercivity of the Euler-Lagrange functional with an innovative technique that has at its core the idea

Soumya Mukherjee, Bharath K. Sriperumbudur

Reproducing Kernel Hilbert Space (RKHS) embedding of probability distributions has proved to be an effective approach, via MMD (maximum mean discrepancy), for nonparametric hypothesis testing problems involving distributions defined over general (non-Euclidean) domains. While a substantial amount of work has been done on this topic, only recently have minima

Shiwali Mohan, Aaron H. Mininger, James R. Kirk, John E. Laird

We present an approach for acquiring grounded representations of words from mixed-initiative, situated interactions with a human instructor. The work focuses on the acquisition of diverse types of knowledge including perceptual, semantic, and procedural knowledge along with learning grounded meanings. Interactive learning allows the agent to control its lear

Zhuo Zhang, Amit Yaron, Dai Akita, Tomoyo Isoguchi Shiramatsu

Understanding how neural networks process complex patterns of information is crucial for advancing both neuroscience and artificial intelligence. To investigate fundamental principles of neural computation, we studied dissociated neuronal cultures, one of the most primitive living neural networks, on high-resolution CMOS microelectrode arrays and tested whet

Sean Kouma, William Edds

With costs and risks increasing for investors and home buyers alike, additional analysis of the housing market is required to help individuals make the right choice. In addition to traditional market analysis, other aspects such as the economic vulnerabilities of the local community must be taken into account to further ensure real estate buyers receive a po

Jun Li, Ziluo Zhang, Zhanglin Hou, Yuto Hosaka

We discuss the locomotion of a thermally driven elastic two-sphere microswimmer with internal feedback control that is realized by the position-dependent friction coefficients. In our model, the two spheres are in equilibrium with independent heat baths having different temperatures, causing a heat flow between the two spheres. We generally show that the ave

Jiaqi Bai, Hongcheng Guo, Zhongyuan Peng, Jian Yang

Large vision-language models show tremendous potential in understanding visual information through human languages. However, they are prone to suffer from object hallucination, i.e., the generated image descriptions contain objects that do not exist in the image. In this paper, we reveal that object hallucination can be attributed to overconfidence in irrele

Yichi Zhang, Bohao Lv, Le Xue, Wenbo Zhang

Deep learning-based medical image segmentation typically requires large amount of labeled data for training, making it less applicable in clinical settings due to high annotation cost. Semi-supervised learning (SSL) has emerged as an appealing strategy due to its less dependence on acquiring abundant annotations from experts compared to fully supervised meth

Heejin Do, Sangwon Ryu, Gary Geunbae Lee

Multi-trait automated essay scoring (AES) systems provide a fine-grained evaluation of an essay's diverse aspects. While they excel in scoring, prior systems fail to explain why specific trait scores are assigned. This lack of transparency leaves instructors and learners unconvinced of the AES outputs, hindering their practical use. To address this, we propo

Eugene Klishevich, Yegor Denisov-Blanch, Simon Obstbaum, Igor Ciobanu

Large Language Models (LLMs) promise to streamline software code reviews, but their ability to produce consistent assessments remains an open question. In this study, we tested four leading LLMs -- GPT-4o mini, GPT-4o, Claude 3.5 Sonnet, and LLaMA 3.2 90B Vision -- on 70 Java commits from both private and public repositories. By setting each model's temperat

S. X. Li, R. G. Ping, C. P. Shen

We present a Monte Carlo simulation-based partial wave analysis of the decay $\Xi_c^+ \to \Xi^-\pi^+\pi^+$ by using the Feynman-Diagram-Calculation framework. The consistency of the input and output parameters implies the reliability of the partial wave analysis method. A robust method of the spin-parity determination of the $\Xi^{*0}$ resonance is introduce

Xinyi Chen, Marta Blangiardo, Connor Gascoigne, Garyfallos Konstantinoudis

Exposure to high ambient temperatures is a significant driver of preventable mortality, with non-linear health effects and elevated risks in specific regions. To capture this complexity and account for spatial dependencies across small areas, we propose a Bayesian framework that integrates non-linear functions with the Besag, York, and Mollie (BYM2) model. A

Jordi Del Castillo, Dan Zhao, Zongrui Pei

Quantum language models are the alternative to classical language models, which borrow concepts and methods from quantum machine learning and computational linguistics. While several quantum natural language processing (QNLP) methods and frameworks exist for text classification and generation, there is a lack of systematic study to compare the performance ac

Keiya Ishiguro, Takafumi Kai, Tatsuo Kobayashi, Yuichi Koga

In this work, we study modular symmetries in type IIB flux landscape by investigating symplectic basis transformations of period vectors on toroidal orbifolds. To fix explicit cycles of a third-cohomology basis regarding the untwisted complex structure modulus, which is necessary to construct the period vectors, we find that the following two symmetries are

Xiwen Liang, Min Lin, Weiqi Ruan, Rongtao Xu

Existing methods for vision-language task planning excel in short-horizon tasks but often fall short in complex, long-horizon planning within dynamic environments. These challenges primarily arise from the difficulty of effectively training models to produce high-quality reasoning processes for long-horizon tasks. To address this, we propose Structured Prefe

Toshiya Namikawa, Irene Abril-Cabezas, Blake D. Sherwin

We present a follow-up study to the method recently proposed by Namikawa and Sherwin (2023) to probe gravitational waves using cross-correlations between two cosmic microwave background (CMB) $B$-modes and a large-scale structure tracer. We first improve on the previous forecast by including the impact of CMB component separation and find that, if the tensor

Ziyi Sun, Chao Ding

The {\Pi}-operator plays an important role in complex analysis, especially in the theory of generalized analytic functions in the sense of Vekua. In this paper, we introduce a generalized {\Pi}-operator in the theory of slice monogenic functions, and some mapping properties of the generalized {\Pi}-operator are also introduced. Further, a left and right inve

Min-Su Kim, Kyoungjun Lee, Ryo Ishikawa, Kyung Song

Interplay of lattice, orbital, and charge degrees of freedom in complex oxide materials has hosted a plethora of exotic quantum phases and physical properties. Recent advances in synthesis of freestanding complex oxide membranes and twisted heterostructures assembled from membranes provide new opportunities for discovery using moir\'e design with local latti

Manjie Hu, Chao Ding, Yifei Shen, Jiani Wang

The theory of generalized partial-slice monogenic functions is considered as a syhthesis of the classical Clifford analysis and the theory of slice monogenic functions. In this paper, we investigate the Cauchy integral formula and the Plemelj formula for generalized partial-slice monogenic functions. Further, we study some properties of the Teodorescu transf

Ankur Sarkar

Let $M$ be a closed, 3-connected, 8-dimensional smooth manifold. In this paper, we compute the concordance inertia group of the product manifold $M\times\mathbb{S}^k$ for $1\leq k\leq 14$ and classify all smooth manifolds homeomorphic to $M\times\mathbb{S}^k,$ up to concordance for $1\leq k\leq 10.$ Moreover, we provide a diffeomorphism classification of smo

Zichen Huang, Hui Chen, Zhongqin Zhang, Hao Zhang

The chromium-based kagome metal CsCr3Sb5 has garnered significant interest due to its strong electron correlations, intertwined orders and potential for unconventional superconductivity under high pressure. The evolution of magnetic and superconducting interactions as the more frequently studied CsV3Sb5 is doped to CsCr3Sb5 remains poorly understood. Here, w

Motoko Fujiwara, Martin Vollmann

Electroweakly interacting stable spin-1 particle in the $(1-10)$ TeV mass range can be a dark matter candidate with rich testability. In particular, one or even two gamma-ray line-like features are expected to be a smoking-gun signature for indirect detection in this scenario. The presence of large Sudakov logarithmic corrections, though, significantly compl

Yubing Tu, Zongyuan Zhang, Wenjian Lu, Tao Han

The quantum states of matter reorganize themselves in response to defects, giving rise to emergent local excitations that imprint unique characteristics of the host states. While magnetic impurities are known to generate Kondo screening in a Fermi liquid and Yu-Shiba-Rusinov (YSR) states in a conventional superconductor, it remains unclear whether they can e

Yuan Li, Cheng Lin, Yuan Liu, Xiaoxiao Long

Diffusion-based 3D generation has made remarkable progress in recent years. However, existing 3D generative models often produce overly dense and unstructured meshes, which stand in stark contrast to the compact, structured, and sharply-edged Computer-Aided Design (CAD) models crafted by human designers. To address this gap, we introduce CADDreamer, a novel

Sean Kouma, Rachel Masters

Indoor positioning systems (IPSs) have gained attention as outdoor navigation becomes prevalent in everyday life. Research is being actively conducted on how indoor smartphone navigation can be accomplished and improved using received signal strength indication (RSSI) and machine learning (ML). IPSs have more use cases that need further exploration, and we a

Zhuoqun Li, Haiyang Yu, Xuanang Chen, Hongyu Lin

Designing solutions for complex engineering challenges is crucial in human production activities. However, previous research in the retrieval-augmented generation (RAG) field has not sufficiently addressed tasks related to the design of complex engineering solutions. To fill this gap, we introduce a new benchmark, SolutionBench, to evaluate a system's abilit

Ben Walters, Yeshwanth Bethi, Taylor Kergan, Binh Nguyen

Neuromorphic engineering aims to advance computing by mimicking the brain's efficient processing, where data is encoded as asynchronous temporal events. This eliminates the need for a synchronisation clock and minimises power consumption when no data is present. However, many benchmarks for neuromorphic algorithms primarily focus on spatial features, neglect

Han-Byul Kim, Duc Hoang, Arnav Kundu, Mohammad Samragh

With the rapid expansion in the scale of large language models (LLMs), enabling efficient distributed inference across multiple computing units has become increasingly critical. However, communication overheads from popular distributed inference techniques such as Tensor Parallelism pose a significant challenge to achieve scalability and low latency. Therefo

Yifei Duan, Raphael Shang, Deng Liang, Yongqiang Cai

Language models can be viewed as functions that embed text into Euclidean space, where the quality of the embedding vectors directly determines model performance, training such neural networks involves various uncertainties. This paper focuses on improving the performance of pre-trained language models in zero-shot settings through a simple and easily implem

Isabelle Quaye, Temi Taylor

We aim to improve the performance of the Quotient Filter at high load factors. Our Graveyard Filter is a variation of the Quotient Filter which incorporates Graveyard Hashing, a technique that uses tombstones to counteract the effects of primary clustering. We summarize our implementation of the graveyard filter and detail approaches to redistributing tombst

Arup Kumar Sarker, Aymen Alsaadi, Alexander James Halpern, Prabhath Tangella

Significant obstacles exist in scientific domains including genetics, climate modeling, and astronomy due to the management, preprocess, and training on complicated data for deep learning. Even while several large-scale solutions offer distributed execution environments, open-source alternatives that integrate scalable runtime tools, deep learning and data f

Bin Shen

In this manuscript, we investigate the exponentially harmonic equation on noncompact forward complete Finsler metric measure spaces. We demonstrate that this Finslerian equation represents a critical point of an exponential energy functional. Furthermore, we establish that any bounded solution to this equation is constant, provided that the mixed weighted Ri

Lu Wei, Zhian Jia, Yufeng Wang, Dagomir Kaszlikowski

The transformer architecture, known for capturing long-range dependencies and intricate patterns, has extended beyond natural language processing. Recently, it has attracted significant attention in quantum information and condensed matter physics. In this work, we propose the \textit{transformer density operator ansatz} for determining the steady states of

Xinyue Wang, Liming Li, Michael Roman, Xi Zhang

With its extreme axial tilt, radiant energy budget and internal heat of Uranus remain among the most intriguing mysteries of our Solar System. Here, we present the global radiant energy budget spanning a complete orbital period, revealing significant seasonal variations driven primarily by the highly variable solar flux. Despite these fluctuations, emitted t

Kazuhiro Sano, Kenshin Nato

Using first-principles calculations, we investigate the melting temperatures $T_{\rm m}$ and formation free energy of carbon compounds with sodalite structures, $X$C$ _6$, $X$C$ _{10}$, and $X$C$ _{12}$, where $X$ is F, Na, Cl, and so on. These compounds are expected to be phonon-mediated superconductors exhibiting high transition temperatures $T_{\rm c}$ of

Ravindran Vishnu, Kalale Chola

Studies on the finite amplitude stability of pipe flows identified a range of different scaling exponents between $\beta\approx -1 $ and $\beta\approx-1.5$, relating $A\sim Re^{\beta}$, where $A$ is the minimum amplitude of disturbance to cause a transition to turbulence and $Re$ is the Reynolds number. The circumstance under which a particular scaling expon

Guanglin Zhou, Sebastiano Barbieri

Generating realistic synthetic electronic health records (EHRs) holds tremendous promise for accelerating healthcare research, facilitating AI model development and enhancing patient privacy. However, existing generative methods typically treat EHRs as flat sequences of discrete medical codes. This approach overlooks two critical aspects: the inherent hierar

Xiao Tan, Pio Ong, Paulo Tabuada, Aaron D. Ames

Cyber-physical systems are prone to sensor attacks that can compromise safety. A common approach to synthesizing controllers robust to sensor attacks is secure state reconstruction (SSR) -- but this is computationally expensive, hindering real-time control. In this paper, we take a safety-critical perspective on mitigating severe sensor attacks, leading to a

Aakanksha Sharma, Samar Shailendra, Rajan Kadel

Generative Artificial Intelligence (GenAI) has the potential to transform higher education by generating human-like content. The advancement in GenAI has revolutionised several aspects of education, especially subject and assessment design. In this era, it is crucial to design assessments that challenge students and cannot be solved using GenAI tools. This m

Chenhao Zhu, Tingting Shi, Liangyu Ding, Zhiyue Zheng

We experimentally demonstrate an unambiguous quantum state discrimination of two qubit states under a non-Hermitian Hamiltonian with parity-time-reversal ($\mathcal{PT}$) symmetry in a single trapped $^{40}$Ca$^+$ ion. We show that any two non-orthogonal states can become orthogonal subjected to time evolution of a $\mathcal{PT}$-symmetric Hamiltonian in bot

Kiranmayee Janardhan, Christy Bobby Thomas

Glioma, a prevalent and heterogeneous tumor originating from the glial cells, can be differentiated as Low Grade Glioma (LGG) and High Grade Glioma (HGG) according to World Health Organization's norms. Classifying gliomas is essential for treatment protocols that depend extensively on subtype differentiation. For non-invasive glioma evaluation, Magnetic Reso

Simone Franchini

We propose a simple model of quantum void where the flow of time is deduced directly from quantum fluctuations and the consequent particle-antiparticle creations. Given a certain number of space-like separated pair creation events, assumed to happen all at the same initial time, we show that past and future can be foliated into a sequence of adapted manifold

Naoyasu Kita, Hayato Miyazaki, Takuya Sato

This paper is concerned with the $L^{2}$-decay estimate of solutions to nonlinear dissipative Schr\"odinger equations with power-type nonlinearity of the order $p$. It is known that the sign of the real part of the dissipation coefficient affects the long-time behavior of solutions, when neither size restriction on the initial data nor strong dissipative con

Shuai Ouyang, Yuzi Yang, Yang Zhang, Aiqiang Zhang

The Jinping Neutrino Experiment (JNE) will utilize approximately 3000 8-inch MCP-PMTs identified as GDB-6082 from North Night Vision Technology to detect neutrinos. To enhance the effective coverage of the JNE detector, mounting a custom-designed light concentrator on each PMT is a practical and economical approach. We measured angular responses of the conce

Edmund Heng, Anthony M. Licata, Oded Yacobi

Periodic elements in finite type Artin--Tits groups are elements some positive power of which is central. We give a dynamical characterisation of periodic elements via their action on the corresponding 2-Calabi--Yau category and on its space of (fusion equivariant) Bridgeland stability conditions. The main theorem is that an element $\beta$ is periodic if an

Enhyeok Jang, Youngmin Kim, Jeewoo Seo, Seungwoo Choi

One of the predominant causes of program distortion in the real quantum computing system may be attributed to the probability deviation caused by thermal relaxation. We introduce Barber (Balancing reAdout Results using Bit-invErted ciRcuits), a method designed to counteract the asymmetric thermal relaxation deviation and improve the reliability of near-term

Tianyi Ma, Yiyue Qian, Zehong Wang, Zheyuan Zhang

As the market for illicit drugs remains extremely profitable, major online platforms have become direct-to-consumer intermediaries for illicit drug trafficking participants. These online activities raise significant social concerns that require immediate actions. Existing approaches to combating this challenge are generally impractical, due to the imbalance

Zhengyi Zhong, Weidong Bao, Ji Wang, Shuai Zhang

Federated Learning is a promising paradigm for privacy-preserving collaborative model training. In practice, it is essential not only to continuously train the model to acquire new knowledge but also to guarantee old knowledge the right to be forgotten (i.e., federated unlearning), especially for privacy-sensitive information or harmful knowledge. However, c

John Duchi, Saminul Haque, Rohith Kuditipudi

Let $\mathcal{Z} = \{Z_1, \dots, Z_n\} \stackrel{\mathrm{i.i.d.}}{\sim} P \subset \mathbb{R}^d$ from a distribution $P$ with mean zero and covariance $\Sigma$. Given a dataset $\mathcal{X}$ such that $d_{\mathrm{ham}}(\mathcal{X}, \mathcal{Z}) \leq \varepsilon n$, we are interested in finding an efficient estimator $\widehat{\Sigma}$ that achieves $\mathrm{e

Shiyong Zhang, Xuebo Zhang, Qianli Dong, Ziyu Wang

In this paper, we propose a systematic framework for fast exploration of complex and large 3-D environments using micro aerial vehicles (MAVs). The key insight is the organic integration of the frontier-based and sampling-based strategies that can achieve rapid global exploration of the environment. Specifically, a field-of-view-based (FOV) frontier detector

Daniel Grainger

Natural capital accounting is important to efforts that attempt to measure the value of nature to decide on how best to trade-off natural resource productive use and conservation. Much work on measuring reciprocal physical stocks and flows of natural resources between nature and the economy has occurred. However, the current open problem of translating natur

Maximilian Holsman, Yukun Huang, Bhuwan Dhingra

Speculative Decoding (SD) enforces strict distributional equivalence to the target model when accepting candidate tokens. While it maintains the target model's generation quality, this strict equivalence limits the speedup achievable by SD and prevents users from trading deviations from the target distribution in exchange for further inference speed gains. T

Po-Wei Tang, Chia-Hsiang Lin, Jian-Kai Huang, Alfredo R. Huete

Due to the intensifying impacts of extreme climate changes, drought forecasting (DF), which aims to predict droughts from historical meteorological data, has become increasingly critical for monitoring and managing water resources. Though drought conditions often exhibit spatial climatic coherence among neighboring regions, benchmark deep learning-based DF m

Ilya D. Shkredov

We continue to study the relationship between the size of the sum of a set and the common energy of its subsets. We find a rather sharp subexponential dependence between the doubling constant of a set $A$ and the minimal common energy taken over all partitions of $A$ into two disjoint subsets. As an application, we give a proof of the well--known arithmetic

Nghi Truong, Phanish Puranam, Ilia Testlin

In human-AI interactions, explanation is widely seen as necessary for enabling trust in AI systems. We argue that trust, however, may be a pre-requisite because explanation is sometimes impossible. We derive this result from a formalization of explanation as a search process through knowledge networks, where explainers must find paths between shared concepts

Jianwei Tan, Jimin Wang, Ji-Feng Zhang

This paper proposes a differentially private recursive least squares algorithm to estimate the parameter of autoregressive systems with exogenous inputs and multi-participants (MP-ARX systems) and protect each participant's sensitive information from potential attackers. We first give a rigorous differential privacy analysis of the algorithm, and establish t

Geoffrey Cruttwell, Marcello Lanfranchi

In differential geometry, the existence of pullbacks is a delicate matter, since the category of smooth manifolds does not admit all of them. When pullbacks are required, often submersions are employed as an ideal class of maps which behaves well under this operation and the tangent bundle functor. This issue is reflected in tangent category theory, which ai

Ke Sun, Shen Chen, Taiping Yao, Ziyin Zhou

Face manipulation techniques have achieved significant advances, presenting serious challenges to security and social trust. Recent works demonstrate that leveraging multimodal models can enhance the generalization and interpretability of face forgery detection. However, existing annotation approaches, whether through human labeling or direct Multimodal Larg

Andrew Eberhardt, Elisa Ferreira, Wentao Luo, Shurui Lin

Ultralight dark matter is an interesting dark matter candidate describing the lightest end of the mass parameter space. This model produces an oscillating granular pattern in halo densities. These fluctuations have the potential to produce a time-varying density along the line of sight creating a small lensing signal for any stars observed through a dark mat

Dinesh Jackson Samuel, Inna Skarga-Bandurova, David Sikolia, Muhammad Awais

AgroLLM is an AI-powered chatbot designed to enhance knowledge-sharing and education in agriculture using Large Language Models (LLMs) and a Retrieval-Augmented Generation (RAG) framework. By using a comprehensive open-source agricultural database, AgroLLM provides accurate, contextually relevant responses while reducing incorrect information retrieval. The

Lu Xu, Ding Wang, Xiaobo Yuan, Dongfa Lan

In this study, we employed the non-equilibrium Green's function method combined with density functional theory to investigate the spin transport properties of the actinide sandwich phthalocyanine molecule U(Pc)2.This study aims to provide beneficial assistance for the development of actinide phthalocyanine molecular spintronic devices.

Yang Shi, Yiping Sun, Jiaolong Du, Xiaocheng Zhong

Approximate Nearest Neighbor Search (ANNS) is essential for modern data-driven applications that require efficient retrieval of top-k results from massive vector databases. Although existing graph-based ANNS algorithms achieve a high recall rate on billion-scale datasets, their slow construction speed and limited scalability hinder their applicability to lar

Dacheng Li, Yunhao Fang, Yukang Chen, Shuo Yang

Video generation models have rapidly progressed, positioning themselves as video world models capable of supporting decision-making applications like robotics and autonomous driving. However, current benchmarks fail to rigorously evaluate these claims, focusing only on general video quality, ignoring important factors to world models such as physics adherenc

Bruno Belzile, Tatiana Wanang-Siyapdjie, Sina Karimi, Rafael Gomes Braga

Mobile robotic systems are increasingly used in various work environments to support productivity. However, deploying robots in workplaces crowded by human workers and interacting with them results in safety challenges and concerns, namely robot-worker collisions and worker distractions in hazardous environments. Moreover, the literature on risk assessment a

Qinghao Yan, P. H. Diamond

In scenarios where a sustained energetic particle source strongly drives toroidal Alfv\'en eigenmodes (TAE), and phase-space transport is insufficient to saturate TAE, this novel theory of TAE-zonal mode (ZM)-turbulence -- self-regulated by cross-scale interactions (including collisionless ZF damping) -- merits consideration. Zonal modes are driven by Reynol

Zhixiang Hu, An Liu, Minjian Zhao

Joint utilization of multiple discrete frequency bands can enhance the accuracy of delay estimation. Although some unique challenges of multiband fusion, such as phase distortion, oscillation phenomena, and high-dimensional search, have been partially addressed, further challenges remain. Specifically, under conditions of low signal-to-noise ratio (SNR), ins

Yuqi Wu, Guangya Wan, Jingjing Li, Shengming Zhao

Large Language Models (LLMs) offer promising opportunities to support mental healthcare workflows, yet they often lack the structured clinical reasoning needed for reliable diagnosis and may struggle to provide the emotionally attuned communication essential for patient trust. Here, we introduce WiseMind, a novel multi-agent framework inspired by the theory

Everardo Rivera-Oliva

In this study, the Riccati equation is resolved using the generalized recursive integrating factor method. By applying a non-linear transformation to the dependent variable $y(x)$ of the Riccati equation, a second-order linear differential equation is derived for a variable $u(x)$ that is related to $y(x)$ through the aforementioned transformation. The secon

Yihan Wang, Fei Xiong, Zhexin Han, Qi Song

Two-tower models are widely adopted in the industrial-scale matching stage across a broad range of application domains, such as content recommendations, advertisement systems, and search engines. This model efficiently handles large-scale candidate item screening by separating user and item representations. However, the decoupling network also leads to a neg

Chi-Sheng Chen, Samuel Yen-Chi Chen, Huan-Hsin Tseng

Electroencephalography (EEG) is widely used in neuroscience and clinical research for analyzing brain activity. While deep learning models such as EEGNet have shown success in decoding EEG signals, they often struggle with data complexity, inter-subject variability, and noise robustness. Recent advancements in quantum machine learning (QML) offer new opportu

Dongki Jung, Jaehoon Choi, Yonghan Lee, Somi Jeong

We introduce the first learning-based dense matching algorithm, termed Equirectangular Projection-Oriented Dense Kernelized Feature Matching (EDM), specifically designed for omnidirectional images. Equirectangular projection (ERP) images, with their large fields of view, are particularly suited for dense matching techniques that aim to establish comprehensiv