February 2024 arXiv papers — page 101

Dheeraj R. Pasham, Michal Zajacek, C. J. Nixon, Eric R. Coughlin

An accretion disk formed around a supermassive black hole (SMBH) after it disrupts a star is expected to be initially misaligned with respect to the black hole's equatorial plane. This misalignment induces relativistic torques (the Lense-Thirring effect) on the disk, causing the disk to precess at early times, while at late times the disk aligns with the bla

Jinhu Jiang, Chaoyi Liang, Rongchao Dong, Zhaohui Yang

System-level emulators have been used extensively for system design, debugging and evaluation. They work by providing a system-level virtual machine to support a guest operating system (OS) running on a platform with the same or different native OS that uses the same or different instruction-set architecture. For such system-level emulation, dynamic binary t

Ali Zeynali, Shahin Kamali, Mohammad Hajiesmaili

We present the first learning-augmented data structure for implementing dictionaries with optimal consistency and robustness. Our data structure, named RobustSL, is a skip list augmented by predictions of access frequencies of elements in a data sequence. With proper predictions, RobustSL has optimal consistency (achieves static optimality). At the same time

Wentao Liu, Xiongjun Fang, Jiliang Jing, Jieci Wang

In this paper, we investigate the quasinormal modes (QNMs) of a Lorentz-violating spacetime, factoring in a cosmological constant, within the framework of Einstein-Bumblebee gravity. Our findings reveal that the interaction of spacetime with an anisotropic bumblebee field imparts distinct contributions to the axial and polar sectors of the vector perturbatio

Yaoqiang Pan, Kewei Hu, Tianhao Liu, Chao Chen

Accurate reconstruction of plant models for phenotyping analysis is critical for optimising sustainable agricultural practices in precision agriculture. Traditional laboratory-based phenotyping, while valuable, falls short of understanding how plants grow under uncontrolled conditions. Robotic technologies offer a promising avenue for large-scale, direct phe

Geoffrey Mo, Hsin-Yu Chen

The LIGO-Virgo-KAGRA Collaboration has detected over one hundred compact binary mergers in gravitational waves, but the formation history of these binaries remains an open question. Finding the host galaxies of these mergers will provide critical information that reveals how these binaries were formed. However, without an electromagnetic counterpart, localiz

Zeya Chen, Ruth Schmidt

Designing seamless, frictionless user experiences has long been a dominant trend in both applied behavioral science and artificial intelligence (AI), in which the goal of making desirable actions easy and efficient informs efforts to minimize friction in user experiences. However, in some settings, friction can be genuinely beneficial, such as the insertion

Geneva Ecola, Bill Yen, Ana Banzer Morgado, Bodhi Priyantha

SARLink is a passive satellite backscatter communication system that uses existing spaceborne synthetic aperture radar (SAR) imaging satellites to provide connectivity in remote regions. It achieves orders of magnitude more range than traditional backscatter systems, enabling communication between a passive ground node and a satellite in low earth orbit. The

Yujie Luo, Thomas Christensen, Ognjen Ilic

Active nanophotonic materials that can emulate and adapt between many different spectral profiles -- with high fidelity and over a broad bandwidth -- could have a far-reaching impact, but are challenging to design due to a high-dimensional and complex design space. Here, we show that a metamaterial network of coupled 2D-material nanoresonators in graphene ca

Tomohiro Nishiyama, Yoshihiko Hasegawa

The Heisenberg uncertainty relation, together with Robertson's generalisation, serves as a fundamental concept in quantum mechanics, showing that noncommutative pairs of observables cannot be measured precisely. In this study, we explore the Robertson-type uncertainty relations to demonstrate their effectiveness in establishing a series of thermodynamic unce

Jian Chen, Mingcong Chen, Qingxiang Zhao, Shuai Wang

Traditional rigid endoscopes have challenges in flexibly treating tumors located deep in the brain, and low operability and fixed viewing angles limit its development. This study introduces a novel dual-segment flexible robotic endoscope MicroNeuro, designed to perform biopsies with dexterous surgical manipulation deep in the brain. Taking into account the u

Jonathan Pelliciari, Enrique Mejia, John M. Woods, Yanhong Gu

Single-photon emitters serve as building blocks for many emerging concepts in quantum photonics. The recent identification of bright, tunable, and stable emitters in hexagonal boron nitride (hBN) has opened the door to quantum platforms operating across the infrared to ultraviolet spectrum. While it is widely acknowledged that defects are responsible for sin

He Zhu, Ren Togo, Takahiro Ogawa, Miki Haseyama

We present a novel prompt-based personalized federated learning (pFL) method to address data heterogeneity and privacy concerns in traditional medical visual question answering (VQA) methods. Specifically, we regard medical datasets from different organs as clients and use pFL to train personalized transformer-based VQA models for each client. To address the

Tatyana Benko, Martin Buck, Ilya Amburg, Stephen J. Young

In data science, hypergraphs are natural models for data exhibiting multi-way relations, whereas graphs only capture pairwise. Nonetheless, many proposed hypergraph neural networks effectively reduce hypergraphs to undirected graphs via symmetrized matrix representations, potentially losing important information. We propose an alternative approach to hypergr

Yifu Ding, Serena Patel, Dharik Mallapragada, Robert James Stoner

Coal power plants will need to be phased out and face stranded asset risks under the net-zero energy system transition. Repurposing coal power plants could recoup profits and reduce carbon emissions using the existing infrastructure and grid connections. This paper investigates a retrofitting strategy that turns coal power plants into thermal energy storage

Chawin Sitawarin, Norman Mu, David Wagner, Alexandre Araujo

Large Language Models (LLMs) have surged in popularity in recent months, but they have demonstrated concerning capabilities to generate harmful content when manipulated. While techniques like safety fine-tuning aim to minimize harmful use, recent works have shown that LLMs remain vulnerable to attacks that elicit toxic responses. In this work, we introduce t

Tong Han

This report provides the detailed description of the synthetic 33-bus microgrid (MG), including its structure, dynamic models, and time-series parameters of loads and generations. The network structure is adapted from the IEEE 33-bus distribution network, with additional converter-interfaced renewable energy resources and energy storage systems. Time-series

David Hunn, Willie Harrison

A new method is explored for analyzing the performance of coset codes over the binary erasure wiretap channel (BEWC) by decomposing the code over subspaces of the code space. This technique leads to an improved algorithm for calculating equivocation loss. It also provides a continuous-valued function for equivocation loss, permitting proofs of local optimali

Anton Kapustin

A simple model of charge transport is provided by a classical particle in a smooth random potential and a dissipative coupling to the environment in the form of Markovian noise and friction. The corresponding Non-Equilibrium Steady State (NESS) can be determined analytically when both the disorder and dissipation are weak. We use it to illuminate some founda

David Noever, Forrest McKee

This investigation reveals a novel exploit derived from PNG image file formats, specifically their alpha transparency layer, and its potential to fool multiple AI vision systems. Our method uses this alpha layer as a clandestine channel invisible to human observers but fully actionable by AI image processors. The scope tested for the vulnerability spans repr

Brian Hu Zhang, Ioannis Anagnostides, Gabriele Farina, Tuomas Sandholm

Recent breakthrough results by Dagan, Daskalakis, Fishelson and Golowich [2023] and Peng and Rubinstein [2023] established an efficient algorithm attaining at most $\epsilon$ swap regret over extensive-form strategy spaces of dimension $N$ in $N^{\tilde O(1/\epsilon)}$ rounds. On the other extreme, Farina and Pipis [2023] developed an efficient algorithm for

William W. Menasco, Deepisha Solanki

Our main results concern changing an arbitrary plat presentation of a split or composite link to one which is obviously recognizable as being split or composite. Pocket moves, first described in \cite{unlinkviaplats}, are utilized -- a pocket move alters a plat presentation without changing its link type, its bridge index or the double coset. A plat presenta

Noveen Sachdeva, Benjamin Coleman, Wang-Cheng Kang, Jianmo Ni

The training of large language models (LLMs) is expensive. In this paper, we study data-efficient approaches for pre-training LLMs, i.e., techniques that aim to optimize the Pareto frontier of model quality and training resource/data consumption. We seek to understand the tradeoffs associated with data selection routines based on (i) expensive-to-compute dat

Aditya Potukuchi, Shikha Singh

Properties of stable matchings in the popular random-matching-market model have been studied for over 50 years. In a random matching market, each agent has complete preferences drawn uniformly and independently at random. Wilson (1972), Knuth (1976) and Pittel (1989) proved that in balanced random matching markets, the proposers are matched to their $\ln n$t

Ying Su, Tianqing Fang, Huiru Xiao, Weiqi Wang

Commonsense knowledge graph completion is a new challenge for commonsense knowledge graph construction and application. In contrast to factual knowledge graphs such as Freebase and YAGO, commonsense knowledge graphs (CSKGs; e.g., ConceptNet) utilize free-form text to represent named entities, short phrases, and events as their nodes. Such a loose structure r

Yuhui Wang, Shupeng Xu, Liang Feng, Ritesh Agarwal

Topological flat bands (TFBs) are increasingly recognized as an important paradigm to study topological effects in the context of strong correlation physics. As a representative example, recently it has been theoretically proposed that the topological non-triviality offers a unique contribution to flat-band superconductivity, which can potentially lead to a

Changshu Liu, Yang Chen, Reyhaneh Jabbarvand

Large Language Models (LLMs) have been widely used to automate programming tasks. Their capabilities have been evaluated by assessing the quality of generated code through tests or proofs. The extent to which they can reason about code is a critical question revealing important insights about their true capabilities. This paper introduces CodeMind, a framewo

Ketan Suhaas Saichandran

This paper presents a hand shape classification approach employing multiscale template matching. The integration of background subtraction is utilized to derive a binary image of the hand object, enabling the extraction of key features such as centroid and bounding box. The methodology, while simple, demonstrates effectiveness in basic hand shape classificat

Syed T. Mubarrat

This article introduces GeoBotsVR, an easily accessible virtual reality game that combines elements of puzzle-solving with robotics learning and aims to cultivate interest and motivation in robotics, programming, and electronics among individuals with limited experience in these domains. The game allows players to build and customize a two-wheeled mobile rob

Quansen Jiu, Jitao Liu, Yaowei Xie

Whether the global well-posedness of strong solutions of $n$-dimensional compressible isentropic magnetohydrodynamic (MHD for short) equations without magnetic diffusion holds true or not remains an challenging open problem, even for the small initial data. In recent years, stared from the pioneer work by Wu and Wu [Adv. Math. 310 (2017), 759--888], much mor

Erasmo Purificato, Ludovico Boratto, Ernesto William De Luca

The integration of artificial intelligence (AI) into daily life, particularly through information retrieval and recommender systems, has necessitated advanced user modeling and profiling techniques to deliver personalized experiences. These techniques aim to construct accurate user representations based on the rich amounts of data generated through interacti

Hayley J. Macpherson

We study the impact of nearby inhomogeneities on an observer's inference of the Hubble constant. Large-scale structures induce a dependence of cosmological parameters on observer position as well as an anisotropic variance of those parameters across an observer's sky. While the former has been explored quite thoroughly, the latter has not. Incomplete samplin

Amir Mohammad Naderi, Jennifer G. Casey, Mao-Hsiang Huang, Rachelle Victorio

Quantifying cardiovascular parameters like ejection fraction in zebrafish as a host of biological investigations has been extensively studied. Since current manual monitoring techniques are time-consuming and fallible, several image processing frameworks have been proposed to automate the process. Most of these works rely on supervised deep-learning architec

Aditya Kommineni, Kleanthis Avramidis, Richard Leahy, Shrikanth Narayanan

Self-supervised learning has produced impressive results in multimedia domains of audio, vision and speech. This paradigm is equally, if not more, relevant for the domain of biosignals, owing to the scarcity of labelled data in such scenarios. The ability to leverage large-scale unlabelled data to learn robust representations could help improve the performan

Jiacheng Yao, Wei Xu, Zhaohui Yang, Xiaohu You

In this paper, we quantitatively compare these two effective communication schemes, i.e., digital and analog ones, for wireless federated learning (FL) over resource-constrained networks, highlighting their essential differences as well as their respective application scenarios. We first examine both digital and analog transmission methods, together with a u

Wanli Yang, Fei Sun, Xinyu Ma, Xun Liu

Although model editing has shown promise in revising knowledge in Large Language Models (LLMs), its impact on the inherent capabilities of LLMs is often overlooked. In this work, we reveal a critical phenomenon: even a single edit can trigger model collapse, manifesting as significant performance degradation in various benchmark tasks. However, benchmarking

Kleanthis Avramidis, Melinda Y. Chang, Rahul Sharma, Mark S. Borchert

A wide range of neurological and cognitive disorders exhibit distinct behavioral markers aside from their clinical manifestations. Cortical Visual Impairment (CVI) is a prime example of such conditions, resulting from damage to visual pathways in the brain, and adversely impacting low- and high-level visual function. The characteristics impacted by CVI are p

Uttam Dhakal, Aniket Kumar Singh, Suman Devkota, Yogesh Sapkota

This study investigates GPT-4's assessment of its performance in healthcare applications. A simple prompting technique was used to prompt the LLM with questions taken from the United States Medical Licensing Examination (USMLE) questionnaire and it was tasked to evaluate its confidence score before posing the question and after asking the question. The quest

Byung-Hoon Hwang

In this paper, we consider a BGK-type kinetic model relaxing to the isentropic gas dynamics in the hydrodynamic limit. We introduce a linearization of the equation around the global equilibrium. Then we prove the global existence of classical solutions with an exponential convergence rate toward the equilibrium state in the periodic domain when the initial d

S. Go, Y. Tsuzuki, H. Yoneda, Y. Ichikawa

To detect and track structural changes in atomic nuclei, the systematic study of nuclear levels with firm spin-parity assignments is important. While linear polarization measurements have been applied to determine the electromagnetic character of gamma-ray transitions, the applicable range is strongly limited due to the low efficiency of the detection system

Yang Hong, Chakkrit Tantithamthavorn, Jirat Pasuksmit, Patanamon Thongtanunam

Continuous Integration (CI) build failures could significantly impact the software development process and teams, such as delaying the release of new features and reducing developers' productivity. In this work, we report on an empirical study that investigates CI build failures throughout product development at Atlassian. Our quantitative analysis found tha

Jiale Fang, Calvin Yeung, Keisuke Fujii

Recent advances in computer vision have made significant progress in tracking and pose estimation of sports players. However, there have been fewer studies on behavior prediction with pose estimation in sports, in particular, the prediction of soccer fouls is challenging because of the smaller image size of each player and of difficulty in the usage of e.g.,

Chao Wang, Hehe Fan, Ruijie Quan, Yi Yang

Protein research is crucial in various fundamental disciplines, but understanding their intricate structure-function relationships remains challenging. Recent Large Language Models (LLMs) have made significant strides in comprehending task-specific knowledge, suggesting the potential for ChatGPT-like systems specialized in protein to facilitate basic researc

Zhengzhong Yi, Zhipeng Liang, Jiahan Chen, Zicheng Wang

The low coding rate of quantum stabilizer codes results in formidable physical qubit overhead when realizing quantum error correcting in engineering. In this letter, we propose a new class of hypergraph-product code called TGRE-hypergraph-product code. This code has constant coding rate 0.2, which is the highest constant coding rate of quantum stabilizer cod

Jakub Konieczny

We show that the extension of Presburger arithmetic by a quadratic generalised polynomial of a specific form is undecidable.

Gordon W. Semenoff, Riley A. Stewart

The multi-critical behaviour of an approximately scale and conformal invariant quantum field theory, which can be regarded as the deformation of the critical Gross-Neveu model in 3+epsilon dimensions by a nearly marginal parity violating operator, is studied using a large $N$ expansion. When epsilon is greater than a number of order 1/N, the deformation is m

Marcelo H. Alvarenga, Júlio C. Fabris, Hermano Velten, Luiz Filipe Guimarães

The structure of unimodular gravity (UG) is invariant to a subclass of diffeomorphism, the transverse diffeomorphism, due to the unimodular condition ($\sqrt{-g}=\epsilon=cte$). Consequently, there is a freedom to define how the conservation laws of the energy-momentum tensor in unimodular gravity in the cosmological context. One of the main characteristics

Ryan Guide, Eric Pauley, Yohan Beugin, Ryan Sheatsley

Signature-based Intrusion Detection Systems (SIDSs) are traditionally used to detect malicious activity in networks. A notable example of such a system is Snort, which compares network traffic against a series of rules that match known exploits. Current SIDS rules are designed to minimize the amount of legitimate traffic flagged incorrectly, reducing the bur

V. A. Dzuba, V. V. Flambaum, A. J. Mansour

The aim of this paper is to derive limits on various forms of ``new physics'' using atomic experimental data. Interactions with dark energy and dark matter fields can lead to space-time variations of fundamental constants, which can be detected through atomic spectroscopy. In this study, we examine the effects of a varying nuclear mass $m_{N}$ and nuclear ra

Letian Peng, Yuwei Zhang, Zilong Wang, Jayanth Srinivasa

This work aims to build a text embedder that can capture characteristics of texts specified by user instructions. Despite its tremendous potential to deploy user-oriented embeddings, none of previous approaches provides a concrete solution for it. This paper offers a new viewpoint, which treats the instruction as a question about the input text and encodes t

Nobuyuki Ishibashi

A closed string worldsheet of genus $g$ with $n$ punctures can be presented as a contact interaction in which $n$ semi-infinite cylinders are glued together in a specific way via the Strebel differential on it, if $n\geq1,\ 2g-2+n>0$. We construct a string field theory of closed strings such that all the Feynman diagrams are represented by such contact inter

Srdjan Stefanović

We determine the exact diameter of the orthograph related to mutual strong Birkhoff-James orthogonality in arbitrary finite dimensional $C^{*}$-algebra. Additionally, we will estimate the distance between vertices in an arbitrary $C^{*}$-algebra that can be represented as a direct sum.

So Sasaki, Cédric Langbort

Social media platforms have diverse content moderation policies, with many prominent actors hesitant to impose strict regulations. A key reason for this reluctance could be the competitive advantage that comes with lax regulation. A popular platform that starts enforcing content moderation rules may fear that it could lose users to less-regulated alternative

Ke Li, Fan Li

Real-world black-box optimization often involves time-consuming or costly experiments and simulations. Multi-fidelity optimization (MFO) stands out as a cost-effective strategy that balances high-fidelity accuracy with computational efficiency through a hierarchical fidelity approach. This survey presents a systematic exploration of MFO, underpinned by a nov

Eyad Shtaiwi, Ahmed Abdelhadi, Husheng Li, Zhu Han

Sixth-generation (6G) wireless communication systems, as stated in the European 6G flagship project Hexa-X, are anticipated to feature the integration of intelligence, communication, sensing, positioning, and computation. An important aspect of this integration is integrated sensing and communication (ISAC), in which the same waveform is used for both system

Kathleen Baur, Xin Xiong, Erickson Torio, Rose Du

Although Digital Subtraction Angiography (DSA) is the most important imaging for visualizing cerebrovascular anatomy, its interpretation by clinicians remains difficult. This is particularly true when treating arteriovenous malformations (AVMs), where entangled vasculature connecting arteries and veins needs to be carefully identified.The presented method ai

Sedat Ozer, Alain P. Ndigande

This paper proposes a deep learning based solution for multi-modal image alignment regarding UAV-taken images. Many recently proposed state-of-the-art alignment techniques rely on using Lucas-Kanade (LK) based solutions for a successful alignment. However, we show that we can achieve state of the art results without using LK-based methods. Our approach caref

Kurtis Raymond, Fabrice Retière, Harry Lewis, Andrea Capra

Large-area next-generation physics experiments rely on using Silicon Photo-Multiplier (SiPM) devices to detect single photons, which trigger charge avalanches. The noise mechanism of external cross-talk occurs when secondary photons produced during a charge avalanche escape from an SiPM and trigger other devices within a detector system. This work presents m

Haoran Zhao, Andrew Naylor, Shih-Chieh Hsu, Paolo Calafiura

Recent studies have shown promising results for track finding in dense environments using Graph Neural Network (GNN)-based algorithms. However, GNN-based track finding is computationally slow on CPUs, necessitating the use of coprocessors to accelerate the inference time. Additionally, the large input graph size demands a large device memory for efficient co

T. A. Schad, A. Fehlmann, G. I. Dima, J. R. Kuhn

We report commissioning observations of the Si X 1430 nm solar coronal line observed coronagraphically with the Cryogenic Near-Infrared Spectropolarimeter (Cryo-NIRSP) at the National Science Foundation's Daniel K. Inouye Solar Telescope (DKIST). These are the first known spatially resolved observations of this spectral line, which has strong potential as a

Shashwat Singh, Shauli Ravfogel, Jonathan Herzig, Roee Aharoni

Language models often exhibit undesirable behavior, e.g., generating toxic or gender-biased text. In the case of neural language models, an encoding of the undesirable behavior is often present in the model's representations. Thus, one natural (and common) approach to prevent the model from exhibiting undesirable behavior is to steer the model's representati

Xiaowen Li, Jingyu Li, Ming Mei, Jean-Christophe Nave

In this paper we propose the first framework to study Burgers' equation featuring critical fast diffusion in form of $u_t+f(u)_x = (\ln u)_{xx}$. The solution possesses a strong singularity when $u=0$ hence bringing technical challenges. The main purpose of this paper is to investigate the asymptotic stability of viscous shocks, particularly those with shock

Seohyun Lee, Anindya Bijoy Das, Satyavrat Wagle, Christopher G. Brinton

One of the main challenges of decentralized machine learning paradigms such as Federated Learning (FL) is the presence of local non-i.i.d. datasets. Device-to-device transfers (D2D) between distributed devices has been shown to be an effective tool for dealing with this problem and robust to stragglers. In an unsupervised case, however, it is not obvious how

Wolfgang Bietenholz

Researchers working in lattice field theory constitute an established community since the early 1990s, and around the same time the online open-access e-print repository arXiv was created. The fact that this field has a specific arXiv section, hep-lat, which is comprehensively used, provides a unique opportunity for a statistical study of its evolution over

Hilário Alencar, G. Pacelli Bessa, Gregório Silva Neto

In this paper, we prove gap results for complete self-shrinkers of the $r$-mean curvature flow involving a modified second fundamental form. These results extend previous results for self-shrinkers of the mean curvature flow due to Cao-Li and Cheng-Peng. To prove our results we show that, under suitable curvature bounds, proper self-shrinkers are parabolic f

Greg DePaul, Serkan Hoşten, Nilava Metya, Ikenna Nometa

The study of the closest point(s) on a statistical model from a given distribution in the probability simplex with respect to a fixed Wasserstein metric gives rise to a polyhedral norm distance optimization problem. There are two components to the complexity of determining the Wasserstein distance from a data point to a model. One is the combinatorial comple

Johannes Flake, Robert Laugwitz, Sebastian Posur

Given a monoidal adjunction, we show that the right adjoint induces a braided lax monoidal functor between the corresponding Drinfeld centers provided that certain natural transformations, called projection formula morphisms, are invertible. We investigate these induced functors on Drinfeld centers in more detail for the monoidal adjunction of restriction an

Zhixiang Chen, Anji Li, Neng Zhang, Jianguo Chen

Inferring the types of API elements in incomplete code snippets (e.g., those on Q&A forums) is a prepositive step required to work with the code snippets. Existing type inference methods can be mainly categorized as constraint-based or statistically-based. The former imposes higher requirements on code syntax and often suffers from low recall due to the synt

Ole Fredrik Brevig, Adrián Llinares, Kristian Seip

Let $\mathfrak{p}_d(q)$ denote the critical exponent of the Riesz projection from $L^q(\mathbb{T}^d)$ to the Hardy space $H^p(\mathbb{T}^d)$, where $\mathbb{T}$ is the unit circle. We present the state-of-the-art on the conjecture that $\mathfrak{p}_1(q) = 4(1-1/q)$ for $1 \leq q \leq \infty$ and prove that it holds in the endpoint case $q = 1$. We then exte

Tanvir Hossain, Matthew Showers, Mahmudul Hasan, Tamzidul Hoque

Commercial-off-the-shelf (COTS) components are often preferred over custom Integrated Circuits (ICs) to achieve reduced system development time and cost, easy adoption of new technologies, and replaceability. Unfortunately, the integration of COTS components introduces serious security concerns. None of the entities in the COTS IC supply chain are trusted fr

Olivia Macmillan-Scott, Mirco Musolesi

Do large language models (LLMs) display rational reasoning? LLMs have been shown to contain human biases due to the data they have been trained on; whether this is reflected in rational reasoning remains less clear. In this paper, we answer this question by evaluating seven language models using tasks from the cognitive psychology literature. We find that, l

Bartosz Kaczmarski, Sophie Leanza, Renee Zhao, Derek E. Moulton

One of the key problems in active materials is the control of shape through actuation. A fascinating example of such control is the elephant trunk, a long, muscular, and extremely dexterous organ with multiple vital functions. The elephant trunk is an object of fascination for biologists, physicists, and children alike. Its versatility relies on the intricat

Andrea Ottolini

Given a large connected graph $G=(V,E)$, and two vertices $w,\neq v$, let $T_{w,v}$ be the first hitting time to $v$ starting from $w$ for the simple random walk on $G$. We prove a general theorem that guarantees, under some assumptions on $G$, to approximate $\mathbb E[T_{w,v}]$ up to $o(1)$ terms. As a corollary, we derive explicit formulas for the stochas

Yanfei Zhou, Lars Lindemann, Matteo Sesia

This paper presents a new conformal method for generating simultaneous forecasting bands guaranteed to cover the entire path of a new random trajectory with sufficiently high probability. Prompted by the need for dependable uncertainty estimates in motion planning applications where the behavior of diverse objects may be more or less unpredictable, we blend

Hong Qin

Non-thermal advanced fuel fusion trades the requirement of a large amount of recirculating tritium in the system for that of large recirculating power. Phase space engineering technologies utilizing externally injected electromagnetic fields can be applied to meet the challenge of maintaining non-thermal particle distributions at a reasonable cost. The physi

Rui Liu, Jianping Pan

Secure and privacy-preserving data aggregation in the Internet of Vehicles (IoV) continues to be a focal point of interest in both the industry and academia. Aiming at tackling the challenges and solving the remaining limitations of existing works, this paper introduces a novel Schnorr approval-based IoV data aggregation framework based on a two-layered arch

Junna Sugiyama, Tomoki Terasaki, Kana Sakaguri, Bryce Bixler

The Simons Observatory (SO) experiment is a cosmic microwave background (CMB) experiment located in the Atacama Desert, Chile. The SO' s small aperture telescopes (SATs) consist of three telescopes designed for precise CMB polarimetry at large angular scales. Each SAT uses a cryogenic rotating half-wave plate (HWP) as a polarization modulator to mitigate atm

Zhou Zhang, Saman Atapattu, Yizhu Wang, Sumei Sun

This paper presents an optimization approach for cooperative Medium Access Control (MAC) techniques in Vehicular Ad Hoc Networks (VANETs) equipped with Roadside Unit (RSU) to enhance network throughput. Our method employs a distributed cooperative MAC scheme based on Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol, featuring selecti

Bijoy John Mathew, Sanchit Srivastava, Anil Shaji

Non-classical correlations generated within a quantum probe system when it interacts with a large, macroscopic system can signal the presence of quantum features in the latter. Theoretical models have considered how entanglement generated in photosynthetic bacteria can be probed using light that interacts with them. More recently, a tardigrade was entangled

Xinyuan Wang, Liang Wu, Liangjie Hong, Hao Liu

Graph recommendation methods, representing a connected interaction perspective, reformulate user-item interactions as graphs to leverage graph structure and topology to recommend and have proved practical effectiveness at scale. Large language models, representing a textual generative perspective, excel at modeling user languages, understanding behavioral co

Hilário Alencar, Ronaldo Garcia, Gregório Silva Neto

We classify the $O(p)\times O(q)$-invariant constant mean curvature hypersurfaces with singularity at the origin, solving a conjecture of Wu-yi Hsiang.

Zhen Guo, Adriana Meza Soria, Wei Sun, Yikang Shen

We introduce API Pack, a massive multi-programming language dataset containing over one million instruction-API calls for improving the API call generation capabilities of large language models. Our evaluation highlights three key findings: First, fine-tuning on API Pack enables open-source models to outperform GPT-3.5 and GPT-4 in generating code for entire

Aliakbar Nafar, Kristen Brent Venable, Parisa Kordjamshidi

This paper considers the challenges Large Language Models (LLMs) face when reasoning over text that includes information involving uncertainty explicitly quantified via probability values. This type of reasoning is relevant to a variety of contexts ranging from everyday conversations to medical decision-making. Despite improvements in the mathematical reason

Laura Niss, Kevin Vogt-Lowell, Theodoros Tsiligkaridis

Foundations models are presented as generalists that often perform well over a myriad of tasks. Fine-tuning these models, even on limited data, provides an additional boost in task-specific performance but often at the cost of their wider generalization, an effect termed catastrophic forgetting. In this paper, we analyze the relation between task difficulty

Matthew Baker, Tong Jin, Oliver Lorscheid

We develop and study a generalization of commutative rings called bands, along with the corresponding geometric theory of band schemes. Bands generalize both hyperrings, in the sense of Krasner, and partial fields in the sense of Semple and Whittle. They from a ring-like counterpart to the field-like category of idylls introduced by the first and third autho

Phillip Rust, Bowen Shi, Skyler Wang, Necati Cihan Camgöz

A major impediment to the advancement of sign language translation (SLT) is data scarcity. Much of the sign language data currently available on the web cannot be used for training supervised models due to the lack of aligned captions. Furthermore, scaling SLT using large-scale web-scraped datasets bears privacy risks due to the presence of biometric informa

María Camarasa-Gómez, Daniel Hernangómez-Pérez, Ferdinand Evers

The use of electric fields applied across magnetic heterojunctions that lack spatial inversion symmetry has been previously proposed as a non-magnetic mean of controlling localized magnetic moments through spin-orbit torques (SOT). The implementation of this concept at the single-molecule level has remained a challenge, however. Here, we present first-princi

Zhexiong Liu, Jing Zhang, Jiaying Lu, Wenjing Ma

Logic reasoning has been critically needed in problem-solving and decision-making. Although Language Models (LMs) have demonstrated capabilities of handling multiple reasoning tasks (e.g., commonsense reasoning), their ability to reason complex mathematical problems, specifically propositional logic, remains largely underexplored. This lack of exploration ca

Brian Simanek

We resolve some questions posed by Handelman in 1996 concerning log convex integrable functions. In particular, we give a negative answer to a question he posed concerning the integrability of $h^2(x)/h(2x)$ when $h$ is integrable and log convex and $h(n)^{1/n}$ converges to 1.

Russell Tsuchida, Cheng Soon Ong, Dino Sejdinovic

We introduce squared neural Poisson point processes (SNEPPPs) by parameterising the intensity function by the squared norm of a two layer neural network. When the hidden layer is fixed and the second layer has a single neuron, our approach resembles previous uses of squared Gaussian process or kernel methods, but allowing the hidden layer to be learnt allows

Satoshi Yoshida, Shiro Tamiya, Hayata Yamasaki

The essential requirement for fault-tolerant quantum computation (FTQC) is the total protocol design to achieve a fair balance of all the critical factors relevant to its practical realization, such as the space overhead, the threshold, and the modularity. A major obstacle in realizing FTQC with conventional protocols, such as those based on the surface code

A. George Morgan

The generalized Benjamin-Bona-Mahony equation (gBBM) is a model for nonlinear dispersive waves which, in the long-wave limit, is approximately equivalent to the generalized Korteweg-de Vries equation (gKdV). While the long-time behaviour of small solutions to gKdV is well-understood, the corresponding theory for gBBM has progressed little since the 1990s. Us

Haoyu Dong, Nicholas Konz, Hanxue Gu, Maciej A. Mazurowski

Test-time adaptation (TTA) refers to adapting a trained model to a new domain during testing. Existing TTA techniques rely on having multiple test images from the same domain, yet this may be impractical in real-world applications such as medical imaging, where data acquisition is expensive and imaging conditions vary frequently. Here, we approach such a tas

Ali Saheb Pasand, Reza Moravej, Mahdi Biparva, Raika Karimi

In regularization Self-Supervised Learning (SSL) methods for graphs, computational complexity increases with the number of nodes in graphs and embedding dimensions. To mitigate the scalability of non-contrastive graph SSL, we propose a novel approach to reduce the cost of computing the covariance matrix for the pre-training loss function with volume-maximiza

Iasonas Nikolaou, Konstantinos Pelechrinis, Evimaria Terzi

In this work, we develop a graphical model to capture team dynamics. We analyze the model and show how to learn its parameters from data. Using our model we study the phenomenon of team collapse from a computational perspective. We use simulations and real-world experiments to find the main causes of team collapse. We also provide the principles of building

Jessica Quaye, Alicia Parrish, Oana Inel, Charvi Rastogi

With the rise of text-to-image (T2I) generative AI models reaching wide audiences, it is critical to evaluate model robustness against non-obvious attacks to mitigate the generation of offensive images. By focusing on ``implicitly adversarial'' prompts (those that trigger T2I models to generate unsafe images for non-obvious reasons), we isolate a set of diff

Hossein Gazmeh, Yuntao Guo, Xinwu Qian

In this study, we utilize data from over 28,000 public charging stations (PCSs) and 5.5 million points of interest across twenty U.S. metropolitan areas to underscore the importance of considering the availability of opportunities when assessing accessibility to PCSs, rather than relying solely on spatial proximity. Specifically, we conduct comprehensive com

Raul Quiroga-Barranco

We study the relationship between operator algebras, $C^*$ and von Neumann, acting on a Hilbert space and unitary representations of topological groups on the same space. We obtain certain correspondences between both these families of objects. In particular, we prove that the study of Abelian subalgebras of a norm-separable $C^*$-algebra can be, in many asp

Yancheng Wang, Yingzhen Yang

Graph Neural Networks (GNNs) have achieved remarkable success in learning node representations and have shown strong performance in tasks such as node classification. However, recent findings indicate that the presence of noise in real-world graph data can substantially impair the effectiveness of GNNs. To address this challenge, we introduce a robust and in

Jiaying Lai, Yunzhou Liu, Robert B. Scharpf, Rachel Karchin

Most neoplastic tumors originate from a single cell, and their evolution can be genetically traced through lineages characterized by common alterations such as small somatic mutations (SSMs), copy number alterations (CNAs), structural variants (SVs), and aneuploidies. Due to the complexity of these alterations in most tumors and the errors introduced by sequ