December 2024 arXiv papers — page 93

Shahzad Ahmad Butt, Benjamin Reynolds, Veeraraghavan Ramamurthy, Xiao Xiao

Zero-Knowledge Proofs (ZKPs) have emerged as an important cryptographic technique allowing one party (prover) to prove the correctness of a statement to some other party (verifier) and nothing else. ZKPs give rise to user's privacy in many applications such as blockchains, digital voting, and machine learning. Traditionally, ZKPs suffered from poor scalabili

Alex Mallen, Charlie Griffin, Misha Wagner, Alessandro Abate

An AI control protocol is a plan for usefully deploying AI systems that aims to prevent an AI from intentionally causing some unacceptable outcome. This paper investigates how well AI systems can generate and act on their own strategies for subverting control protocols whilst operating statelessly (without shared memory between contexts). To do this, an AI s

Zuguang Li, Wen Wu, Shaohua Wu, Qiaohua Lin

Large models (LMs) have immense potential in Internet of Things (IoT) systems, enabling applications such as intelligent voice assistants, predictive maintenance, and healthcare monitoring. However, training LMs on edge servers raises data privacy concerns, while deploying them directly on IoT devices is constrained by limited computational and memory resour

Xi Cao, Yuan Sun, Jiajun Li, Quzong Gesang

DNN-based language models excel across various NLP tasks but remain highly vulnerable to textual adversarial attacks. While adversarial text generation is crucial for NLP security, explainability, evaluation, and data augmentation, related work remains overwhelmingly English-centric, leaving the problem of constructing high-quality and sustainable adversaria

M. Santiago-García, O. Pusuluk, Ö. E. Müstecaplıoğlu, B. Çakmak

We study a chain of interacting individual quantum systems connected to heat baths at different temperatures on both ends. Starting with the two-system case, we thoroughly investigate the conditions for heat rectification (asymmetric heat conduction), compute thermal conductance, and generalize the results to longer chains. We find that heat rectification in

Zeshun You, Jiebin Yao, Dong Cheng, Zhiwei Wen

The text-to-SQL task aims to convert natural language into Structured Query Language (SQL) without bias. Recently, text-to-SQL methods based on large language models (LLMs) have garnered significant attention. The core of mainstream text-to-SQL frameworks is schema linking, which aligns user queries with relevant tables and columns in the database. Previous

Tong Wu, Yunzhuo Wu, Haoran Chen, Hongyue Xu

Exploring novel systems with perpendicular magnetic anisotropy (PMA) is vital for advancing memory devices. In this study, we report an intriguing PMA system involving an ultrathin Fe layer on an antiferromagnetic (AFM) CoO(001) surface. The measured perpendicular anisotropy field is inversely proportional to the Fe thickness, indicating an interfacial origi

Xuanzhong Chen, Ye Jin, Xiaohao Mao, Lun Wang

Rare diseases, despite their low individual incidence, collectively impact around 300 million people worldwide due to the vast number of diseases. The involvement of multiple organs and systems, and the shortage of specialized doctors with relevant experience, make diagnosing and treating rare diseases more challenging than common diseases. Recently, agents

Hu Caiyu, Tang Lin

In recent years, multiple plane structures of satellite galaxies have been identified in the nearby universe, although their formation mechanisms remain unclear. In this work, we employ the TNG50-1 numerical simulation to classify satellite systems into plane and non-plane structures, based on their geometric and dynamical properties. We focus on comparing t

Long Jin, Han Nong, Liangming Chen, Zhenming Su

The insufficient generalization of adaptive moment estimation (Adam) has hindered its broader application. Recent studies have shown that flat minima in loss landscapes are highly associated with improved generalization. Inspired by the filtering effect of integration operations on high-frequency signals, we propose multiple integral Adam (MIAdam), a novel o

Moxin Li, Yong Zhao, Wenxuan Zhang, Shuaiyi Li

Although large language models (LLMs) store vast amount of knowledge in their parameters, they still have limitations in the memorization and utilization of certain knowledge, leading to undesired behaviors such as generating untruthful and inaccurate responses. This highlights the critical need to understand the knowledge boundary of LLMs, a concept that re

Ruchit Rawal, Victor-Alexandru Pădurean, Sven Apel, Adish Singla

With the recent advances in AI programming assistants such as GitHub Copilot, programming is not limited to classical programming languages anymore--programming tasks can also be expressed and solved by end-users in natural text. Despite the availability of this new programming modality, users still face difficulties with algorithmic understanding and progra

Morgan Himes, Preshanth Jagannathan, Dale Frail, Frank Schinzel

Over 2000 Gamma ray sources identified by the Large Area Telescope (LAT) on NASA's Fermi Gamma-ray Space Telescope are considered unassociated, meaning that they have no known counterparts in any other frequency regime. We have carried out an image-based search for steep spectrum radio sources, with in-band spectral index less than -1.4, within the error reg

Romain Hardy, Sung Eun Kim, Du Hyun Ro, Pranav Rajpurkar

The increasing adoption of AI-generated radiology reports necessitates robust methods for detecting hallucinations--false or unfounded statements that could impact patient care. We present ReXTrust, a novel framework for fine-grained hallucination detection in AI-generated radiology reports. Our approach leverages sequences of hidden states from large vision

Mingquan Feng, Zhijie Chen, Yixin Huang, Yizhou Liu

Optimal control problems (OCPs) involve finding a control function for a dynamical system such that a cost functional is optimized. It is central to physical systems in both academia and industry. In this paper, we propose a novel instance-solution control operator perspective, which solves OCPs in a one-shot manner without direct dependence on the explicit

Bin Dou, Baokun Wang, Yun Zhu, Xiaotong Lin

User targeting, the process of selecting targeted users from a pool of candidates for non-expert marketers, has garnered substantial attention with the advancements in digital marketing. However, existing user targeting methods encounter two significant challenges: (i) Poor cross-domain and cross-scenario transferability and generalization, and (ii) Insuffic

Santiago Arango-Piñeros, María Chara, Asimina S. Hamakiotes, Kiran S. Kedlaya

We establish bounds on a finite separable extension of function fields in terms of the relative class number, thus reducing the problem of classifying extensions with a fixed relative class number to a finite computation. We also solve the relative class number two problem in all cases where the base field has constant field not equal to $\mathbb{F}_2$.

Afsane Ghafari, Ian M. Wanless

We prove that, for all even $n\geq10$, there exists a latin square of order $n$ with at least one transversal, yet all transversals coincide on $ \big\lfloor n/6 \big\rfloor$ entries. These latin squares have at least $ 19 n^2/36 + O(n)$ transversal-free entries. We also prove that for all odd $m\geq 3$, there exists a latin square of order $n=3m$ divided in

Yaofo Chen, Zeng You, Shuhai Zhang, Haokun Li

Transformer-based Large Language Models (LLMs) have exhibited remarkable success in extensive tasks primarily attributed to self-attention mechanism, which requires a token to consider all preceding tokens as its context to compute attention. However, when the context length L becomes very large (e.g., 128K), the amount of potentially redundant information i

Keigo Sakurai, Ren Togo, Takahiro Ogawa, Miki Haseyama

Knowledge Graphs (KGs) represent relationships between entities in a graph structure and have been widely studied as promising tools for realizing recommendations that consider the accurate content information of items. However, traditional KG-based recommendation methods face fundamental challenges: insufficient consideration of temporal information and poo

Aditya Ganeshan, Thibault Groueix, Paul Guerrero, Radomír Měch

Pattern images are everywhere in the digital and physical worlds, and tools to edit them are valuable. But editing pattern images is tricky: desired edits are often programmatic: structure-aware edits that alter the underlying program which generates the pattern. One could attempt to infer this underlying program, but current methods for doing so struggle wi

Xiaowen Gao, Minghui Zhao

The Isometry Theorem for continuous quiver of type $A$ plays an important role in persistent homology. In this paper, we shall generalize Isometry Theorem to continuous quiver of type $\tilde{A}$.

Baoqiang Xia

We investigate the complete integrability of soliton equations with shifted nonlocal reductions under the rapidly decreasing boundary conditions. The illustrative examples we choose are the Ablowitz-Ladik (AL) system and the Ablowitz-Kaup-Newell-Segur (AKNS) system. For this two models with the space and space-time shifted nonlocal reductions, we establish t

Kun Guo, Qiang Ling

Multi-camera 3D object detection aims to detect and localize objects in 3D space using multiple cameras, which has attracted more attention due to its cost-effectiveness trade-off. However, these methods often struggle with the lack of accurate depth estimation caused by the natural weakness of the camera in ranging. Recently, multi-modal fusion and knowledg

Chia-Hsuan Chang, Jui-Tse Tsai, Yi-Hang Tsai, San-Yih Hwang

Topic modeling is widely used for uncovering thematic structures within text corpora, yet traditional models often struggle with specificity and coherence in domain-focused applications. Guided approaches, such as SeededLDA and CorEx, incorporate user-provided seed words to improve relevance but remain labor-intensive and static. Large language models (LLMs)

Jirat Suchato, Sean Wiryadi, Danran Chen, Ava Zhao

We conduct a preliminary analysis of a pairs trading strategy using the Ornstein-Uhlenbeck (OU) process to model stock price spreads. We compare this approach to a naive pairs trading strategy that uses a rolling window to calculate mean and standard deviation parameters. Our findings suggest that the OU model captures signals and trends effectively but unde

Sultan Aitzhan, David M. Ambrose

We consider the Benjamin-Ono equation in the spatially quasiperiodic setting. We establish local well-posedness of the initial value problem with initial data in quasiperiodic Sobolev spaces. This requires developing some of the fundamental properties of Sobolev spaces and the energy method for quasiperiodic functions. We discuss prospects for global existen

Xunkai Li, Zhengyu Wu, Jiayi Wu, Hanwen Cui

With the increasing prevalence of cross-domain Text-Attributed Graph (TAG) Data (e.g., citation networks, recommendation systems, social networks, and ai4science), the integration of Graph Neural Networks (GNNs) and Large Language Models (LLMs) into a unified Model architecture (e.g., LLM as enhancer, LLM as collaborators, LLM as predictor) has emerged as a

Li Zhu, Hongfeng Wu

Let $q=p^e$ be a prime power, let $\ell$ be a prime with $\gcd(\ell,qn)=1$, and let $n$ be a positive integer. While $q$-cyclotomic cosets are usually considered modulo a fixed integer, we consider their behavior along the sequence $n,\ell n,\ell^2n,\ldots$ of moduli. To describe the resulting compatibility among cyclotomic cosets, we introduce the $\ell$-ad

Manuel Lafond, Alitzel López Sánchez, Weidong Luo

In the Cluster Editing problem, sometimes known as (unweighted) Correlation Clustering, we must insert and delete a minimum number of edges to achieve a graph in which every connected component is a clique. Owing to its applications in computational biology, social network analysis, machine learning, and others, this problem has been widely studied for decad

Hanlei Zhang, Qianrui Zhou, Hua Xu, Jianhua Su

Multimodal intent understanding is a significant research area that requires effective leveraging of multiple modalities to analyze human language. Existing methods face two main challenges in this domain. Firstly, they have limitations in capturing the nuanced and high-level semantics underlying complex in-distribution (ID) multimodal intents. Secondly, the

Chengyu Wu, Jiaqing Yang

This paper is concerning the inverse conductive scattering of acoustic waves by a bounded inhomogeneous object with possibly embedded obstacles inside. A new uniqueness theorem is proved that the conductive object is uniquely determined by the fixed frequency far-field measurements, ignoring its contents. Meanwhile, the boundary informations of several relat

Qi Zhang, Xiaoyang Wang, Rong Shi, Xinguo Ren

Lithium is a typical quantum solid, characterized by cubic structures at ambient pressure. As the pressure increases, it forms more complex structures and undergoes a metal-to-semiconductor transformation, complicating theoretical and experimental analyses. We employ the neural canonical transformation approach, an \textit{ab initio} variational method based

Armin Gooran-Shoorakchaly, Sarah Sharif, Yaser Banad

Achieving reliable resistive switching in oxide-based memristive devices requires precise control over conductive filament (CF) formation and behavior, yet the fundamental relationship between oxide material properties and switching uniformity remains incompletely understood. Here, we develop a comprehensive physical model to investigate how electrical and t

Dongya Wu, Xin Li

Powerful deep neural networks are vulnerable to adversarial attacks. To obtain adversarially robust models, researchers have separately developed adversarial training and Jacobian regularization techniques. There are abundant theoretical and empirical studies for adversarial training, but theoretical foundations for Jacobian regularization are still lacking.

Sheng Cheng, Ran Tao, Yuliang Gu, Shenlong Wang

This paper presents the Task-Parameter Nexus (TPN), a learning-based approach for online determination of the (near-)optimal control parameters of model-based controllers (MBCs) for tracking tasks. In TPN, a deep neural network is introduced to predict the control parameters for any given tracking task at runtime, especially when optimal parameters for new t

Jaeseok Yoo, Hojae Han, Youngwon Lee, Jaejin Kim

Code generation with large language models has shown significant promise, especially when employing retrieval-augmented generation (RAG) with few-shot examples. However, selecting effective examples that enhance generation quality remains a challenging task, particularly when the target programming language (PL) is underrepresented. In this study, we present

Fernando Abadie

We show that the existence of a continuous conditional expectation from a Fell bundle to a Fell subbundle implies that the full cross-sectional C*-algebra of the subbundle is contained in the full cross-sectional C*-algebra of the bundle, and moreover there exists a conditional expectation from the latter algebra to the former. A similar fact holds for the r

Zihao Lin, Zichao Wang, Yuanting Pan, Varun Manjunatha

Suggested questions (SQs) provide an effective initial interface for users to engage with their documents in AI-powered reading applications. In practical reading sessions, users have diverse backgrounds and reading goals, yet current SQ features typically ignore such user information, resulting in homogeneous or ineffective questions. We introduce a pipelin

Xuan Shen, Zhao Song, Yufa Zhou, Bo Chen

Diffusion Transformers have emerged as the preeminent models for a wide array of generative tasks, demonstrating superior performance and efficacy across various applications. The promising results come at the cost of slow inference, as each denoising step requires running the whole transformer model with a large amount of parameters. In this paper, we show

Seyedmohamadjavad Motaman, Sarah Sharif, Yaser Banad

Enhancing power efficiency and performance in neuromorphic computing systems is critical for next-generation artificial intelligence applications. We propose the Nanoscale Side-contacted Field Effect Diode (S-FED), a novel solution that significantly lowers power usage and improves circuit speed, facilitating efficient neuron circuit design. Our innovative i

Jiaxu Xing, Ismail Geles, Yunlong Song, Elie Aljalbout

Reinforcement learning (RL) has shown great effectiveness in quadrotor control, enabling specialized policies to develop even human-champion-level performance in single-task scenarios. However, these specialized policies often struggle with novel tasks, requiring a complete retraining of the policy from scratch. To address this limitation, this paper present

Xuan Shen, Zhao Song, Yufa Zhou, Bo Chen

Transformers have emerged as the leading architecture in deep learning, proving to be versatile and highly effective across diverse domains beyond language and image processing. However, their impressive performance often incurs high computational costs due to their substantial model size. This paper focuses on compressing decoder-only transformer-based auto

Fernando Abadie

We prove that if A and B are Fell bundles over the locally compact groups G and H respectively, then the minimal (maximal) tensor product of the C*-algebra of kernels of A with the C*-algebra of kernels of B agrees with the C*-algebra of kernels of the minimal (respectively: maximal) tensor product of A and B.

Yuto Yokoyama, Hirokazu Maruoka, Yoshiyuki Tagawa

Droplet impacts are fundamental to fluid-structure interactions, shaping processes from erosion to bioprinting. While previous scaling laws have provided insights into droplet dynamics, force scaling laws remain insufficiently understood, particularly for soft substrates where both the droplet and substrate deform significantly. Here, we show that droplet im

Jiajun Gu, Zichen Yang, Xintong Lin, Sixun Chen

This project investigates the interplay of technical, market, and statistical factors in predicting stock market performance, with a primary focus on S&P 500 companies. Utilizing a comprehensive dataset spanning multiple years, the analysis constructs advanced financial metrics, such as momentum indicators, volatility measures, and liquidity adjustments. The

Reza Ahmadvand, Sarah Sharif, Yaser Banad

Recent advances in multi-agent systems manipulation have demonstrated a rising demand for the implementation of multi-UAV systems in urban areas which are always subjected to the presence of static and dynamic obstacles. The focus of the presented research is on the introduction of a nature-inspired collision-free control for a multi-UAV system considering o

Tim van Engeland, Lu Yin, Vlado Menkovski

We generalize the formulation of few-shot learning by introducing the concept of an aspect. In the traditional formulation of few-shot learning, there is an underlying assumption that a single "true" label defines the content of each data point. This label serves as a basis for the comparison between the query object and the objects in the support set. Howev

S. Dutta, I. M. Rasulian, M. M. Sheikh-Jabbari, H. Yavartanoo

We study null $p$-branes, $p$-branes with a Carrollian $p+1$-dimensional worldvolume embedded in a generic $D$-dimensional flat Minkowski target space. This theory has a generalized BMS$_{p+1}$ gauge symmetry. By fixing the light-cone gauge, the BMS symmetry is partly fixed, leaving $p$ ``momentum constraints'' alongside $p$-dimensional area-preserving diffe

Walter da C. Freitas, Gerard Favier, Andre L. F. de Almeida

We propose receivers for bistatic sensing and communication that exploit a tensor modeling of the received signals. We consider a hybrid scenario where the sensing link knows the transmitted data to estimate the target parameters while the communication link operates semi-blindly in a direct data decoding approach without channel knowledge. We show that the

Peng Sang, Amritanshu Pandey

Solar PV and battery storage systems have become integral to modern power grids. Therefore, bulk grid models in real-time operation must include their physical behavior accurately for analysis and optimization. AC state estimation is critical to building real-time bulk power systems models. However, current ACSE techniques do not include detailed physics and

Chia-Hsuan Chang, Tien-Yuan Huang, Yi-Hang Tsai, Chia-Ming Chang

Recent works in clustering-based topic models perform well in monolingual topic identification by introducing a pipeline to cluster the contextualized representations. However, the pipeline is suboptimal in identifying topics across languages due to the presence of language-dependent dimensions (LDDs) generated by multilingual language models. To address thi

Yash Patel, Giorgos Tolias, Jiri Matas

This paper addresses supervised deep metric learning for open-set image retrieval, focusing on three key aspects: the loss function, mixup regularization, and model initialization. In deep metric learning, optimizing the retrieval evaluation metric, recall@k, via gradient descent is desirable but challenging due to its non-differentiable nature. To overcome

K. R. Goodearl, B. Huisgen-Zimmermann

Let $K$ be a field, $Q$ a quiver, and $\mathcal{A}$ the ideal of the path algebra $KQ$ that is generated by the arrows of $Q$. We present old and new results about the representation theories of the truncations $KQ/\mathcal{A}^L$, $L \in \mathbb{N}$, tracking their development as $L$ goes to infinity. The goal is to gain a better understanding of the categor

Benjamin Schumacher, Michael D. Westmoreland

In a previous paper, we introduced an axiomatic system for information thermodynamics, deriving an entropy function that includes both thermodynamic and information components. From this function we derived an entropic probability distribution for certain uniform collections of states. Here we extend the concept of entropic probability to more general collec

Takamichi Sano, Otmar Venjakob

We extend the interpolation property of the Lubin-Tate regulator map from [SV24] to Artin characters and show a reciprocity law in the sense of Cherbonnier-Colmez. This allows us to provide a new proof of Kato's explicit reciprocity law for Lubin-Tate formal groups.

Bas Verkennis, Evy van Weelden, Francesca L. Marogna, Maryam Alimardani

Effective cognitive workload management has a major impact on the safety and performance of pilots. Integrating brain-computer interfaces (BCIs) presents an opportunity for real-time workload assessment. Leveraging cognitive workload data from high-fidelity virtual reality (VR) flight simulations allows for dynamic adjustments to training scenarios. While pr

Sai Advaith Maddipatla, Nadav Bojan Sellam, Sanketh Vedula, Ailie Marx

Proteins are dynamic, adopting ensembles of conformations. The nature of this conformational heterogenity is imprinted in the raw electron density measurements obtained from X-ray crystallography experiments. Fitting an ensemble of protein structures to these measurements is a challenging, ill-posed inverse problem. We propose a non-i.i.d. ensemble guidance

Wenda Zhao, Abhishek Goudar, Mingliang Tang, Angela P. Schoellig

Wireless indoor localization has attracted significant research interest due to its high accuracy, low cost, lightweight design, and low power consumption. Specifically, ultra-wideband (UWB) time difference of arrival (TDOA)-based localization has emerged as a scalable positioning solution for mobile robots, consumer electronics, and wearable devices, featur

Varsha Singhania, Shaizeen Aga, Mohamed Assem Ibrahim

Ubiquity of AI makes optimizing GPU power a priority as large GPU-based clusters are often employed to train and serve AI models. An important first step in optimizing GPU power consumption is high-fidelity and fine-grain power measurement of key AI computations on GPUs. To this end, we observe that as GPUs get more powerful, the resulting sub-millisecond to

Enrico C. Domanti, Alejandro Bermudez, Luigi Amico

We study the finite-density phases of a $\mathbb{Z}_2$ lattice gauge theory (LGT) of interconnected loops and dynamical $\mathbb{Z}_2$ charges. The gauge-invariant Wilson terms, accounting for the magnetic flux threading each loop, correspond to simple two-body Ising interactions in this setting. Such terms control the interference of charges tunneling aroun

Abel C. H. Chen

This study examines the performance and structural differences between the two primary standards for Security Credential Management Systems (SCMS) in Vehicular-to-Everything (V2X) communication: the North American IEEE standards and the European ETSI standards. It focuses on comparing their respective Public Key Infrastructure (PKI) architectures, certificat

C. H. Lai, M. -J. Hsieh, N. Puri, Yiing-Rei Chen

The copper metaborate CuB_2O_4(CBO) has been studied within the parametric space of magnetic field (B) and temperature (T), showing a series of distinct phases, including commensurate and incommensurate magnetic orders, as well as magnetic soliton phases. However, no single study has simultaneously demonstrated all these phases. moreover, the existence of ad

Erfan Doroudian, Hamid Taghavifar

This paper presents CLIP-RLDrive, a new reinforcement learning (RL)-based framework for improving the decision-making of autonomous vehicles (AVs) in complex urban driving scenarios, particularly in unsignalized intersections. To achieve this goal, the decisions for AVs are aligned with human-like preferences through Contrastive Language-Image Pretraining (C

Nikola Zubić, Davide Scaramuzza

State Space Models (SSMs) are powerful tools for modeling sequential data in computer vision and time series analysis domains. However, traditional SSMs are limited by fixed, one-dimensional sequential processing, which restricts their ability to model non-local interactions in high-dimensional data. While methods like Mamba and VMamba introduce selective an

Monica Munnangi, Akshay Swaminathan, Jason Alan Fries, Jenelle Jindal

Verifying and attributing factual claims is essential for the safe and effective use of large language models (LLMs) in healthcare. A core component of factuality evaluation is fact decomposition, the process of breaking down complex clinical statements into fine-grained atomic facts for verification. Recent work has proposed fact decomposition, which uses L

Kenichiro Kimura

Beilinson and Deligne proved a weak version of Zagier's conjucture on special values of Dedekind zeta functions assuming the existence of a category of mixed Tate motives which has certain properties. We show that Bloch-Kriz category of mixed Tate motives together with a Hodge realization which we constructed has the required properties.

Li-tao Zhu, Zhongxiang Wang, P. U. Devanand, Alok C. Gupta

We develop a simple method to search for changing-look (CL) active galactic nucleus (AGN) candidates, and conduct a test run. In this method, optical variations of AGNs are monitored and CL-AGNs may appear to have a pattern of being bluer when in brightening flare-like events. Applying this method, previously-classified type 2 AGNs that show the bluer-when-b

Jesús A. De Loera, Gyivan Lopez-Campos, Antonio J. Torres

We study the slices or sections of a convex polytope by affine hyperplanes. We present results on two key problems: First, we provide tight bounds on the maximum number of vertices attainable by a hyperplane slice of $d$-polytope (a sort of upper bound theorem) and discuss a new algorithm to find all sections. Second, we investigate the sequence of numbers o

Nina M. Brown, Bryan VanSaders, Jason M. Kronenfeld, Joseph M. DeSimone

Cohesive granular materials are found in many natural and industrial environments, but experimental platforms for exploring the innate mechanical properties of these materials are often limited by the difficulty of adjusting cohesion strength. Granular particles levitated in an acoustic cavity form a model system to address this. Such particles self-assemble

Jingxiao Luo, Lifu Zhang, Bing-Qiu Chen, Qiyuan Cheng

Linkage between core-collapse supernovae (SNe) and their progenitors is not fully understood and ongoing effort of searching and identifying the progenitors is needed. $\mathrm{SN\,2024abfl}$ is a recent Type II supernova exploded in the nearby star-bursting galaxy $\mathrm{NGC\,2146}$, which is also the host galaxy of $\mathrm{SN\,2018zd}$. From archival Hu

Wuzheng Guo, Qiumin Wang, Shuo Cao, Marek Biesiada

In this Letter, we use the latest results from the Dark Energy Spectroscopic Instrument (DESI) survey to measure the Hubble constant. Baryon acoustic oscillation (BAO) observations released by the DESI survey, allow us to determine $H_0$ from the first principles. Our method is purely data-driven and relies on unanchored luminosity distances reconstructed fr

Daniel Kaplan, Adam Zhang, Joanna Blawat, Rongying Jin

The discovery of novel superconducting materials is a longstanding challenge in materials science, with a wealth of potential for applications in energy, transportation, and computing. Recent advances in artificial intelligence (AI) have enabled expediting the search for new materials by efficiently utilizing vast materials databases. In this study, we devel

Ian Marquette, Damien McLeod, Serena Scapucci, Andreas Vollmer

The vanishing of the Haantjes tensor is an important property that has been linked, for instance, to the existence of separation coordinates and the integrability of systems of hydrodynamic type. We discuss the vanishing of the Haantjes tensor for operator fields that admit a large number of so-called conservation laws. In particular, we investigate Haantjes

Yong-Hui Lin, Hans-Werner Hammer, Ulf-G. Meißner

We review the status of baryon form factors with a special focus on the nucleon electromagnetic form factors which are known best. First, we give an introduction into the dispersive analyses and emphasize the role of unitarity and analyticity in the construction of the isoscalar and isovector spectral functions. Second, we present the state of the art in our

Jacopo Tosca, Marcello Calvanese Strinati, Claudio Conti, Cristiano Ciuti

We investigate the dynamics of multi-mode optical systems driven by two-photon processes and subject to non-local losses, incorporating quantum noise at the Gaussian level. Our findings show that the statistics retrieved from a single Gaussian quantum trajectory exhibits emergent thermal equilibrium governed by an Ising Hamiltonian, encoded in the dissipativ

Jian Wang, Dong-Yu Huang, Xiao-Long Zhou, Ze-Min Shen

The capability to measure the state of a quantum system is vital to a practical quantum network, for applications including distributed quantum computing and long-distance quantum communication. As a thriving platform for quantum information technology, single neutral atoms suffer from low achievable photon scattering rate and shallow trapping potential, whi

Philip Loche, Matej Kanduč, Emanuel Schneck, Roland R. Netz

Water at negative pressures can remain in a metastable state for a surprisingly long time before it reaches equilibrium by cavitation, i.e. by the formation of vapor bubbles. The wide spread of experimentally measured cavitation pressures depending on water purity, surface contact angle and surface quality implicates the relevance of water cavitation in bulk

James Taylor

Let $Ω^d$ be the $d$-dimensional Drinfeld symmetric space for a finite extension $F$ of $\mathbb{Q}_p$. Let $Σ^1$ be a geometrically connected component of the first Drinfeld covering of $Ω^d$ and let $\mathbb{F}$ be the residue field of the unique degree $d+1$ unramified extension of $F$. We show that the natural homomorphism determined by the second Drinfe

Johanna L. Hall, Shiun-Jr Yang, David T. Limmer, Graham R. Fleming

Photosystem II (PSII) can achieve near-unity quantum efficiency of light harvesting in ideal conditions and can dissipate excess light energy as heat to prevent formation of reactive oxygen species under light stress. Understanding how this pigment-protein complex accomplishes these opposing goals is a topic of great interest that has so far been explored pr

Seyfal Sultanov, James P Buban, Robert F Klie

We introduce a Three-Dimensional Convolutional Variational Autoencoder (3D-CVAE) for automated anomaly detection in Electron Energy Loss Spectroscopy Spectrum Imaging (EELS-SI) data. Our approach leverages the full three-dimensional structure of EELS-SI data to detect subtle spectral anomalies while preserving both spatial and spectral correlations across th

Tuka Alhanai, Adam Kasumovic, Mohammad Ghassemi, Aven Zitzelberger

Large Language Models (LLMs) have shown remarkable performance across various tasks, yet significant disparities remain for non-English languages, and especially native African languages. This paper addresses these disparities by creating approximately 1 million human-translated words of new benchmark data in 8 low-resource African languages, covering a popu

Asela Hevapathige, Qing Wang, Ahad N. Zehmakan

Influence maximization is key topic in data mining, with broad applications in social network analysis and viral marketing. In recent years, researchers have increasingly turned to machine learning techniques to address this problem. They have developed methods to learn the underlying diffusion processes in a data-driven manner, which enhances the generaliza

Jessica Lin, Ivan Dudiak, Samuel Hadden, Daniel Tamayo

Observed pileups of planets with period ratios $\approx 1\%$ wide of strong mean motion resonances (MMRs) pose an important puzzle. Early models showed that they can be created through sustained eccentricity damping driving a slow separation of the orbits, but this picture is inconsistent with elevated eccentricities measured through Transit Timing Variation

Pedro Cardoso, Patrícia Gonçalves, Gabriel Nahum

In this article we derive in the hydrodynamic limit a generalized fractional porous medium equation, in the sense that the regional fractional Laplacian is applied to a function of the density given in terms of a power series, instead of a polynomial. The hydrodynamic limit is obtained considering a microscopic dynamics of random particles with long range in

Albert Senen-Cerda

Limitations in measurement instruments can hinder the implementation of some quantum algorithms. Understanding the suboptimality of such measurements with restrictions may then lead to more efficient measurement policies. In this paper, we theoretically examine the suboptimality arising from a Procrustes problem for minimizing the average distance between tw

Geunhee Gwak, Chan Roh, Young-Do Yoon, M. S. Kim

Complete characterization of a multimode optical process has paved the way for understanding complex optical phenomena, leading to the development of novel optical technologies. Until now, however, characterizations have mainly focused on a linear-optical process, despite the plethora of multimode nonlinear-optical processes crucial for photonic technologies

Lauren McEnerney, Joshua Wiscons

We introduce an abstract notion of a 3D-rotation module for a group $G$ that does not require the module to carry a vector space structure, a priori nor a posteriori. We prove that, under an expected irreducibility-like assumption, the only finite $G$ with such a module are those already known from the classical setting: $\operatorname{Alt}(4)$, $\operatorna

Keshav Aggarwal, Sumit Kumar, Chung-Hang Kwan, Wing Hong Leung

Let $f$ be a newform of prime level $p$ with any central character $\chi\, (\bmod\, p)$, and let $g$ be a fixed cusp form or Eisenstein series for $\hbox{SL}_{2}(\mathbb{Z})$. We prove the subconvexity bound: for any $\varepsilon>0$, \begin{align*} L(1/2, \, f \otimes g) \ll p^{1/2-1/524+\varepsilon}, \end{align*} where the implied constant depends on $g$, $

Joseph Bills, Christopher Archibald, Diego Blaylock

In two-player cooperative games, agents can play together effectively when they have accurate assumptions about how their teammate will behave, but may perform poorly when these assumptions are inaccurate. In language games, failure may be due to disagreement in the understanding of either the semantics or pragmatics of an utterance. We model coarse uncertai

Jingde Cheng

Recently, it is often said that the data used for the pre-training of large language models (LLMs) have been exhausted. This paper proposes a solution to the problem: Automated generation of massive reasonable empirical theorems by forward reasoning based on strong relevant logics. In fact, this can be regarded as a part of our approach to the problems of AT

Michele Peruzzi

As the spatial features of multivariate data are increasingly central in researchers' applied problems, there is a growing demand for novel spatially-aware methods that are flexible, easily interpretable, and scalable to large data. We develop inside-out cross-covariance (IOX) models for multivariate spatial likelihood-based inference. IOX leads to valid cro

Meisam Kabiri, Holger Voos

This paper presents a novel approach that integrates 5G Time of Arrival (ToA) measurements into ORB-SLAM3 to enable global localization and enhance mapping capabilities for indoor drone navigation. We extend ORB-SLAM3's optimization pipeline to jointly process ToA data from 5G base stations alongside visual and inertial measurements while estimating system b

Yonghyun Kwon, Jae Kwang Kim, Yumou Qiu

Statistical analysis of voluntary survey data is an important area of research in survey sampling. We consider a unified approach to voluntary survey data analysis under the assumption that the sampling mechanism is ignorable. Generalized entropy calibration is introduced as a unified tool for calibration weighting to control the selection bias. We first est

Mohammed A. Chamma, Victor Pop, Fereshteh Rajabi

Fast radio bursts (FRBs), especially those from repeating sources, exhibit a rich variety of morphologies in their dynamic spectra (or waterfalls). Characterizing these morphologies and spectro-temporal properties is a key strategy in investigating the underlying unknown emission mechanism of FRBs. This type of analysis has been typically accomplished using

Elisa Luo, Liane Young, Grant Ho, M. H. Afifi

Phishing attacks on enterprise employees present one of the most costly and potent threats to organizations. We explore an understudied facet of enterprise phishing attacks: the email relay infrastructure behind successfully delivered phishing emails. We draw on a dataset spanning one year across thousands of enterprises, billions of emails, and over 800,000

Gideon Vos, Liza van Eijk, Zoltan Sarnyai, Mostafa Rahimi Azghadi

Machine Learning is transforming medical research by improving diagnostic accuracy and personalizing treatments. General ML models trained on large datasets identify broad patterns across populations, but their effectiveness is often limited by the diversity of human biology. This has led to interest in subject-specific models that use individual data for mo

C. D. Rodriguez-Camargo, H. O. Gestsson, C. Nation, A. R. Jones

Using second-order perturbation theory in the light-matter interaction, we derive an analytical approximation for the vibronic populations of a diatomic system excited by ultrabroadband frequency entangled photons and evaluate the population dynamics for different degrees of entanglement between photon pairs. Our analytical approach makes the same prediction

Qingyang Zhou, Kangjie Lu, Meng Xu

Causal inconsistency arises when the underlying causal graphs captured by generative models like \textit{Normalizing Flows} (NFs) are inconsistent with those specified in causal models like \textit{Struct Causal Models} (SCMs). This inconsistency can cause unwanted issues including the unfairness problem. Prior works to achieve causal consistency inevitably

Felipe Montealegre-Mora, Carl Boettiger, Carl J. Walters, Christopher L. Cahill

In fishery science, harvest management of size-structured stochastic populations is a long-standing and difficult problem. Rectilinear precautionary policies based on biomass and harvesting reference points have now become a standard approach to this problem. While these standard feedback policies are adapted from analytical or dynamic programming solutions