October 2023 arXiv papers — page 159

Zihan Yu, Liang He, Zhen Wu, Xinyu Dai

Chain-of-Thought (CoT), a step-wise and coherent reasoning chain, shows its impressive strength when used as a prompting strategy for large language models (LLM). Recent years, the prominent effect of CoT prompting has attracted emerging research. However, there still lacks of a systematic summary about key factors of CoT prompting and comprehensive guide fo

Toma Oregel-Chaumont, Antonio Sunjerga, Pasan Hettiarachchi, Vernon Cooray

X-rays have been observed in natural downward cloud-to-ground lightning for over twenty years and in rocket-triggered lightning for slightly less. In both cases, this energetic radiation has been detected during the stepped and dart leader phases of downward negative flashes. More recently, X-rays have also been reported during the dart leader phase of upwar

Payam Mohammadi, Claudia Garrido Martins

Immersive technologies (ImT), like Virtual Reality (VR), have several potential applications in the construction industry. However, the absence of a cost-benefit analysis discourages construction decision-makers from implementing these technologies. In this study, we proposed a primary model for conducting a cost-benefit analysis for implementing virtual rea

Zili Chen, Hongyu Wang, Shimin Yu, Yu Wang

In this paper, the electrical characteristics of the Gaseous Electronics Conference (GEC) reference cell with impedance matching are investigated through a two-dimensional electrostatic implicit Particle-in-Cell/Monte Carlo Collision (PIC/MCC) model in an axisymmetric coordinate system. The coupling between the complex reactor geometry and the external circu

Shashank Jere, Karim Said, Lizhong Zheng, Lingjia Liu

Deep learning has seen a rapid adoption in a variety of wireless communications applications, including at the physical layer. While it has delivered impressive performance in tasks such as channel equalization and receive processing/symbol detection, it leaves much to be desired when it comes to explaining this superior performance. In this work, we investi

Jiazhi Li, Mahyar Khayatkhoei, Jiageng Zhu, Hanchen Xie

Ensuring a neural network is not relying on protected attributes (e.g., race, sex, age) for predictions is crucial in advancing fair and trustworthy AI. While several promising methods for removing attribute bias in neural networks have been proposed, their limitations remain under-explored. In this work, we mathematically and empirically reveal an important

Zhi-Yong Wang, Hing Cheung So

Applying half-quadratic optimization to loss functions can yield the corresponding regularizers, while these regularizers are usually not sparsity-inducing regularizers (SIRs). To solve this problem, we devise a framework to generate an SIR with closed-form proximity operator. Besides, we specify our framework using several commonly-used loss functions, and

Wenli Xiao, Tairan He, John Dolan, Guanya Shi

A critical goal of autonomy and artificial intelligence is enabling autonomous robots to rapidly adapt in dynamic and uncertain environments. Classic adaptive control and safe control provide stability and safety guarantees but are limited to specific system classes. In contrast, policy adaptation based on reinforcement learning (RL) offers versatility and g

Zhi-Yong Wang, Hing Cheung So

M-estmators including the Welsch and Cauchy have been widely adopted for robustness against outliers, but they also down-weigh the uncontaminated data. To address this issue, we devise a framework to generate a class of nonconvex functions which only down-weigh outlier-corrupted observations. Our framework is then applied to the Welsch, Cauchy and $\ell_p$-n

Sergio Mundo, Richard Mushotzky

We present results from the first dedicated study in the time domain of the hard X-ray variability behavior of blazars on long timescales based on $\sim$13 years of continuous hard X-ray data in the 14-195 keV band. We use monthly-binned data from the recent 157-month Swift-BAT catalog to characterize the hard X-ray variability of 127 blazars and search for

Weixiang Yan, Yuchen Tian, Yunzhe Li, Qian Chen

Recent code translation techniques exploit neural machine translation models to translate source code from one programming language to another to satisfy production compatibility or to improve efficiency of codebase maintenance. Most existing code translation datasets only focus on a single pair of popular programming languages. To advance research on code t

Fernanda Pérez-Verdugo, Samuel Banks, Shiladitya Banerjee

Pulsatory activity patterns, driven by mechanochemical feedback, are prevalent in many biological systems. Here we present a theoretical framework to elucidate the mechanical origin and regulation of pulsatile activity patterns within multicellular tissues. We show that a simple mechanical feedback at the level of individual cells - activation of contractili

Yueling Zeng, Li-C. Wang

With the availability of large language models, there is a growing interest for semiconductor chip design companies to leverage the technologies. For those companies, deployment of a new methodology must include two important considerations: confidentiality and scalability. In this context, this work tackles the problem of knowledge graph construction from h

Defu Cao, Furong Jia, Sercan O Arik, Tomas Pfister

The past decade has witnessed significant advances in time series modeling with deep learning. While achieving state-of-the-art results, the best-performing architectures vary highly across applications and domains. Meanwhile, for natural language processing, the Generative Pre-trained Transformer (GPT) has demonstrated impressive performance via training on

Shalanika Dayarathna, Peter Smith, Rajitha Senanayake, Jamie Evans

Orthogonal time frequency space (OTFS) modulation has recently been identified as a suitable waveform for joint radar and communication systems. Focusing on the effect of data modulation on the radar sensing performance, we derive the ambiguity function (AF) of the OTFS waveform and characterize the radar global accuracy. We evaluate the behavior of the AF w

Zheng Zhang, Liang Zhao

Deep learning has shown remarkable success in the field of clustering recently. However, how to transfer a trained clustering model on a source domain to a target domain by leveraging the acquired knowledge to guide the clustering process remains challenging. Existing deep clustering methods often lack generalizability to new domains because they typically l

Paul G. Schmidt, Amnon J. Meir

Ever since the public release of ChatGPT in November 2022, serious concerns have been raised about the impact and potentially dire consequences of the increasingly widespread use of generative AI tools for purposes of scientific writing and publishing. We document the ongoing discussion in the science community with a review of news articles, editorials, and

Zheng Zhang, Chen Zheng, Da Tang, Ke Sun

This paper introduces a multifaceted methodology for fine-tuning and evaluating large language models (LLMs) for specialized monetization tasks. The goal is to balance general language proficiency with domain-specific skills. The methodology has three main components: 1) Carefully blending in-domain and general-purpose data during fine-tuning to achieve an o

Yuntong Hu, Zheng Zhang, Liang Zhao

Large language models (LLMs) have achieved impressive performance on many natural language processing tasks. However, their capabilities on graph-structured data remain relatively unexplored. In this paper, we conduct a series of experiments benchmarking leading LLMs on diverse graph prediction tasks spanning node, edge, and graph levels. We aim to assess wh

Georgios Papas

We establish an effective version of Siegel's lower bounds for class numbers of imaginary quadratic fields in certain cures in $Y(1)^n$. Our proof goes through the G-functions method of Yves Andr\'e.

Zheng Zhang, Hossein Amiri, Zhenke Liu, Andreas Züfle

Identifying anomalous human spatial trajectory patterns can indicate dynamic changes in mobility behavior with applications in domains like infectious disease monitoring and elderly care. Recent advancements in large language models (LLMs) have demonstrated their ability to reason in a manner akin to humans. This presents significant potential for analyzing

Eungyeup Kim, Mingjie Sun, Christina Baek, Aditi Raghunathan

Recently, Miller et al. (2021) and Baek et al. (2022) empirically demonstrated strong linear correlations between in-distribution (ID) versus out-of-distribution (OOD) accuracy and agreement. These trends, coined accuracy-on-the-line (ACL) and agreement-on-the-line (AGL), enable OOD model selection and performance estimation without labeled data. However, th

Shengxi Shou, Che-Kai Liu, Sanggeon Yun, Zishen Wan

In this work, we propose SEE-MCAM, scalable and compact multi-bit CAM (MCAM) designs that utilize the three-terminal ferroelectric FET (FeFET) as the proxy. By exploiting the multi-level-cell characteristics of FeFETs, our proposed SEE-MCAM designs enable multi-bit associative search functions and achieve better energy efficiency and performance than existin

Yoseob Yoon, Zheyu Lu, Can Uzundal, Ruishi Qi

Phononic engineering at gigahertz (GHz) frequencies form the foundation of microwave acoustic filters, acousto-optic modulators, and quantum transducers. Terahertz (THz) phononic engineering could lead to acoustic filters and modulators at higher bandwidth and speed, as well as quantum circuits operating at higher temperatures. Despite its potential, methods

Sangyun Lee, Andrew J. Woods, P. F. S. Rosa, S. M. Thomas

UTe2 is a newly discovered unconventional superconductor, where electron Cooper pairs combine into a spin-triplet ground state. Here we report the specific heat C(H,T) of a high-quality single crystal of UTe2 with a single specific heat anomaly at the superconducting transition temperature T_c {\approx} 2 K and a small zero-field residual Sommerfeld coeffici

Md Abdullah-Al Kaiser, Akhilesh R. Jaiswal

The high volume of data transmission between the edge sensor and the cloud processor leads to energy and throughput bottlenecks for resource-constrained edge devices focused on computer vision. Hence, researchers are investigating different approaches (e.g., near-sensor processing, in-sensor processing, in-pixel processing) by executing computations closer t

E. Wes Bethel, Mercy G. Amankwah, Jan Balewski, Roel Van Beeumen

The focus of this Visualization Viewpoints article is to provide some background on Quantum Computing (QC), to explore ideas related to how visualization helps in understanding QC, and examine how QC might be useful for visualization with the growth and maturation of both technologies in the future. In a quickly evolving technology landscape, QC is emerging

Takeo Sasai, Minami Takahashi, Masanori Nakamura, Etsushi Yamazaki

This paper presents a linear least squares method for fiber-longitudinal power profile estimation (PPE), which estimates an optical signal power distribution throughout a fiber-optic link at a coherent receiver. The method finds the global optimum in least square estimation of longitudinal power profiles, thus closely matching true optical power profiles and

Sokhna Diarra Mbacke, Florence Clerc, Pascal Germain

Since their inception, Variational Autoencoders (VAEs) have become central in machine learning. Despite their widespread use, numerous questions regarding their theoretical properties remain open. Using PAC-Bayesian theory, this work develops statistical guarantees for VAEs. First, we derive the first PAC-Bayesian bound for posterior distributions conditione

Xiancheng Lin, Hao Chen

Detecting communities in networks and graphs is an important task across many disciplines such as statistics, social science and engineering. There are generally three different kinds of mixing patterns for the case of two communities: assortative mixing, disassortative mixing and core-periphery structure. Modularity optimization is a classical way for fitti

Qian-Ping Gu, Jiajian Leo Liang

Mobility-on-demand (MoD) ridesharing is a promising way to improve the occupancy rate of personal vehicles and reduce traffic congestion and emissions. Maximizing the number of passengers served and maximizing a profit target are major optimization goals in MoD ridesharing. We study the ridesharing with profit constraint problem (labeled as RPC) which consid

Sabrina Pasterski

The Celestial Holography program encompasses recent efforts to understand the flat space hologram in terms of a CFT living on the celestial sphere. A key development instigating these efforts came from understanding how soft limits of scattering encode infinite dimensional symmetry enhancements corresponding to the asymptotic symmetry group of the bulk space

K. Bhandari, R. de A. Capistrano-Filho, S. Majumdar, T. Y. Tanaka

This article presents some controllability and stabilization results for a system of two coupled linear Schr\"odinger equations in the one-dimensional case where the state components are interacting through the Kirchhoff boundary conditions. Considering the system in a bounded domain, the null boundary controllability result is shown. The result is achieved

Yuqi Xiang, Feitong Chen, Qinsi Wang, Yang Gang

The capability to transfer mastered skills to accomplish a range of similar yet novel tasks is crucial for intelligent robots. In this work, we introduce $\textit{Diff-Transfer}$, a novel framework leveraging differentiable physics simulation to efficiently transfer robotic skills. Specifically, $\textit{Diff-Transfer}$ discovers a feasible path within the t

Konstantinos P. Panousis, Sotirios Chatzis

Modern deep networks are highly complex and their inferential outcome very hard to interpret. This is a serious obstacle to their transparent deployment in safety-critical or bias-aware applications. This work contributes to post-hoc interpretability, and specifically Network Dissection. Our goal is to present a framework that makes it easier to discover the

Jiawei Yao, Yingxin Lai, Hongrui Kou, Tong Wu

3D object detection plays a pivotal role in autonomous driving and robotics, demanding precise interpretation of Bird's Eye View (BEV) images. The dynamic nature of real-world environments necessitates the use of dynamic query mechanisms in 3D object detection to adaptively capture and process the complex spatio-temporal relationships present in these scenes

Fajri Koto, Nurul Aisyah, Haonan Li, Timothy Baldwin

Although large language models (LLMs) are often pre-trained on large-scale multilingual texts, their reasoning abilities and real-world knowledge are mainly evaluated based on English datasets. Assessing LLM capabilities beyond English is increasingly vital but hindered due to the lack of suitable datasets. In this work, we introduce IndoMMLU, the first mult

Niyaz R. Beysengulov, Johannes Pollanen, Øyvind S. Schøyen, Stian D. Bilek

The generation and evolution of entanglement in quantum many-body systems is an active area of research that spans multiple fields, from quantum information science to the simulation of quantum many-body systems encountered in condensed matter, subatomic physics, and quantum chemistry. Motivated by recent experiments exploring quantum information processing

Alonso Castillo-Ramirez, Luguis de los Santos Baños

Given a finite set $A$ and a group homomorphism $\phi : H \to G$, a $\phi$-cellular automaton is a function $\mathcal{T} : A^G \to A^H$ that is continuous with respect to the prodiscrete topologies and $\phi$-equivariant in the sense that $h \cdot \mathcal{T}(x) = \mathcal{T}( \phi(h) \cdot x)$, for all $x \in A^G, h \in H$, where $\cdot$ denotes the shift a

Mila AI4Science, :, Alex Hernandez-Garcia, Alexandre Duval

The discovery of novel solid-state materials, such as electrocatalysts, super-ionic conductors, or photovoltaic materials, plays a critical role in addressing various global challenges. It has, for instance, the potential to significantly improve the efficiency of renewable energy production and storage, thereby making substantial contributions to climate cr

Michael Howes

Monte Carlo significance tests are a general tool that produce p-values by generating samples from the null distribution. However, Monte Carlo tests are limited to null hypothesis which we can exactly sample from. Markov chain Monte Carlo (MCMC) significance tests are a way to produce statistical valid p-values for null hypothesis we can only approximately s

Hong-fu Chou

For efficient modulation and error control coding, the deliberate flipping approach imposes the run-length-limited(RLL) constraint by bit error before recording. From the read side, a high coding rate limits the correcting capability of RLL bit error. In this paper, we study the low-density parity-check (LDPC) coding for RLL constrained recording system base

Jingwei Dong, Kaikai Pan, Sergio Pequito, Peyman Mohajerin Esfahani

This paper studies the problem of fault detection and estimation (FDE) for linear time-invariant (LTI) systems with a particular focus on frequency content information of faults, possibly as multiple disjoint continuum ranges, and under both disturbances and stochastic noise. To ensure the worst-case fault sensitivity in the considered frequency ranges and m

Jiacheng Liu, Ramakanth Pasunuru, Hannaneh Hajishirzi, Yejin Choi

Extensive work has shown that the performance and interpretability of commonsense reasoning can be improved via knowledge-augmented reasoning methods, where the knowledge that underpins the reasoning process is explicitly verbalized and utilized. However, existing implementations, including "chain-of-thought" and its variants, fall short in capturing the int

Elijah Pelofske

We consider the hypothetical quantum network case where Alice wishes to transmit one qubit of information (specifically a pure quantum state) to $M$ parties, where $M$ is some large number. The remote receivers locally perform single qubit quantum state tomography on the transmitted qubits in order to compute the quantum state within some error rate (depende

Yushu Shi, Michael Martens

In modern scientific research, the objective is often to identify which variables are associated with an outcome among a large class of potential predictors. This goal can be achieved by selecting variables in a manner that controls the the false discovery rate (FDR), the proportion of irrelevant predictors among the selections. Knockoff filtering is a cutti

Lei You, Hei Victor Cheng

This study addresses the challenge of inaccurate gradients in computing the empirical Fisher Information Matrix during neural network pruning. We introduce SWAP, a formulation of Entropic Wasserstein regression (EWR) for pruning, capitalizing on the geometric properties of the optimal transport problem. The ``swap'' of the commonly used linear regression wit

Jeffrey Uhlmann

We critique the formulation of Arrow's no-dictator condition to show that it does not correspond to the accepted informal/intuitive interpretation. This has implications for the theorem's scope of applicability.

Brendon G. Anderson, Samuel Pfrommer, Somayeh Sojoudi

The reliable deployment of neural networks in control systems requires rigorous robustness guarantees. In this paper, we obtain tight robustness certificates over convex attack sets for min-max representations of ReLU neural networks by developing a convex reformulation of the nonconvex certification problem. This is done by "lifting" the problem to an infin

Zihan Zhou, Ruiying Liu, Tianshu Yu

Diffusion-based generative models in SE(3)-invariant space have demonstrated promising performance in molecular conformation generation, but typically require solving stochastic differential equations (SDEs) with thousands of update steps. Till now, it remains unclear how to effectively accelerate this procedure explicitly in SE(3)-invariant space, which gre

Avinash Madasu, Anahita Bhiwandiwalla, Vasudev Lal

Foundational multimodal models pre-trained on large scale image-text pairs or video-text pairs or both have shown strong generalization abilities on downstream tasks. However unlike image-text models, pretraining video-text models is always not feasible due to the difficulty in collecting large-scale clean and aligned data, and exponential computational cost

E. P. Mattos, A. Vidiella-Barranco

In this letter, we present a simple and versatile scheme for enhancing the nonclassical properties of light states using only linear optics and photodetectors. By combining a coherent state $|\alpha\rangle$ and an arbitrary pure state of light $|\phi\rangle$ (excluding coherent states) at two beam splitters, we show that the amplitude $\alpha$ of the coheren

George Nousias, Konstantinos Delibasis, Ilias Maglogiannis

Estimating the homography matrix between images captured under radically different camera poses and zoom factors is a complex challenge. Traditional methods rely on the Random Sample Consensus (RANSAC) algorithm, which requires pairs of homologous points, pre-matched based on local image feature vectors. Sampling consensus is a core step in many Artificial I

Homa Nikbakht, Michèle Wigger, Shlomo Shamai, Jean-Marie Gorce

To answer the call for a new theoretical framework to simultaneously accommodate random user activity and heterogeneous delay traffic in Internet of Things (IoT) systems, in this paper we propose coding schemes and information-theoretic converse results for the transmission of heterogeneous delay traffic over interference networks with random user activity a

Weihe Zhai, Arkaitz Zubiaga

The fusion of language models (LMs) and knowledge graphs (KGs) is widely used in commonsense question answering, but generating faithful explanations remains challenging. Current methods often overlook path decoding faithfulness, leading to divergence between graph encoder outputs and model predictions. We identify confounding effects and LM-KG misalignment

Masud Mansuripur

The fundamental processes of absorption, stimulated and spontaneous emission, and elastic as well as inelastic scattering involving light and atoms, molecules, and nano-particles have been studied for decades using both classical and quantum theories. While providing an overview of the subject, this paper presents a streamlined approach to studying atom-phot

Richard Gumbel, Christian Ross, A. S. Umar

We employ a microscopic approach that examines the impact of isospin dynamics on the process of low energy nuclear fusion along an isotope chain and dependence on deformation. Our method utilizes the density constrained time-dependent Hartree-Fock theory (DC-TDHF), where isoscalar and isovector characteristics of the energy density functional (EDF) are exami

Rima Chatterjee, John B. Etnyre, Hyunki Min, Anubhav Mukherjee

In this paper we give necessary and sufficient conditions for a knot type to admit non-loose Legendrian and transverse representatives in some overtwisted contact structure, classify all non-loose rational unknots in lens spaces, and discuss conditions under which non-looseness is preserved under cabling.

Emanuele Citera, Francesco De Pretis

We study the stochastic structure of cryptocurrency rates of returns as compared to stock returns by focusing on the associated cross-sectional distributions. We build two datasets. The first comprises forty-six major cryptocurrencies, and the second includes all the companies listed in the S&P 500. We collect individual data from January 2017 until December

Kailai Wang

The debate on whether young Americans are becoming less reliant on automobiles is still ongoing. This research compares driver's license acquisition patterns between Millennials and their succeeding Generation Z during late adolescence. It also examines factors influencing teenagers' decisions to obtain driver's licenses. The findings suggest that the declin

M. P. Dussan, A. P. Franco Filho, R. S. Santos

In this paper we introduce a $\theta$-family of spacelike surfaces in the Lorentz-Minkowski space R^4_1 based in two complex valued functions $a(w), \mu(w)$, which when they are holomorphic we will be dealing with a family of spacelike minimal surfaces. The $\theta$-family is such that it connects spacelike minimal surfaces in R^3_1 to spacelike minimal surf

Marjan Petreski, Stefan Tanevski

The objective of the paper is to understand the role of workers bargaining for the labor share in transition economies. We rely on a share-capital schedule, whereby workers bargaining power is represented as a move off the schedule. Quantitative indicators of bargaining power are amended with own-constructed qualitative indices from textual information descr

Yeonsu Jung, Thomas Plumb-Reyes, Hao-Yu Greg Lin, L. Mahadevan

Random packings of stiff rods are self-supporting mechanical structures stabilized by long range interactions induced by contacts. To understand the geometrical and topological complexity of the packings, we first deploy X-ray computerized tomography to unveil the structure of the packing. This allows us to directly visualize the spatial variations in densit

Ali Jaafar, Tarek Khalil

The effect of magnetic STM-tip on electronic, magnetic and electronic transport properties through the molecule junction STM-tip-Co/CoPc/Co(111), has been investigated by mean of ab initio electronic structure calculations. The spin transition has been studied by varying the distance (passing from the tunneling regime to the contact regime) between the tip a

Samet Hicsonmez, Nermin Samet, Fidan Samet, Oguz Bakir

In this paper, we explore a new domain for video-to-video translation. Motivated by the availability of animation movies that are adopted from illustrated books for children, we aim to stylize these videos with the style of the original illustrations. Current state-of-the-art video-to-video translation models rely on having a video sequence or a single style

Ziliang Wang, Xiaohong Zhang, Kechi Zhang, Ze Shi Li

Individual objects, whether users or services, within a specific region often exhibit similar network states due to their shared origin from the same city or autonomous system (AS). Despite this regional network similarity, many existing techniques overlook its potential, resulting in subpar performance arising from challenges such as data sparsity and label

Nina Shvetsova, Anna Kukleva, Xudong Hong, Christian Rupprecht

Instructional videos are a common source for learning text-video or even multimodal representations by leveraging subtitles extracted with automatic speech recognition systems (ASR) from the audio signal in the videos. However, in contrast to human-annotated captions, both speech and subtitles naturally differ from the visual content of the videos and thus p

Alireza Arbabi, Ardeshir Shojaeinasab, Behnam Bahrak, Homayoun Najjaran

This manuscript presents an exhaustive review of blockchain-based mixing services, aiming to fill the existing gap between academic innovations and real-world implementations. Starting with an identification of the core functionalities and techniques employed by mixing services, the paper delves into detailed explanations of these operational mechanisms. It

Sam Grierson, William J Buchanan, Craig Thomson, Baraq Ghaleb

Within a trust infrastructure, a private key is often used to digitally sign a transaction, which can be verified with an associated public key. Using PKI (Public Key Infrastructure), a trusted entity can produce a digital signature, verifying the authenticity of the public key. However, what happens when external entities are not trusted to verify the publi

Shan Ye

A simple experiment was conducted to test the ability of the Chinese-based generative artificial intelligence (AI) platform, Wenxin Yige, to render images of urban street views of different countries. The study found that images generated by this AI platform may contain continental-level stereotypes in terms of showing the level of economic development and m

Erin Lanus, Charles J. Colbourn, Gail-Joon Ahn

Attribute-based methods, such as attribute-based access control and attribute-based encryption, make decisions based on attributes possessed by a subject rather than the subject's identity. While this allows for anonymous authorization -- determining that a subject is authorized without knowing the identity of the subject -- it does not guarantee anonymity.

Lucas N. Kirsten, Cláudio R. Jung

Cell detection and tracking are paramount for bio-analysis. Recent approaches rely on the tracking-by-model evolution paradigm, which usually consists of training end-to-end deep learning models to detect and track the cells on the frames with promising results. However, such methods require extensive amounts of annotated data, which is time-consuming to obt

Kento Tomita, Yuri Shimane, Koki Ho

This paper delves into the predictive sensor tasking algorithm for the multi-observer, multi-target sensor setting, leveraging the Extended Information Filter (EIF). Conventional predictive formulations suffer from the curse of dimensionality due to the dependence of the performance metric on the target-observer assignment history. This paper exploits the EI

Ali Behrouz, Farnoosh Hashemi

Finding dense subgraphs of a large network is a fundamental problem in graph mining that has been studied extensively both for its theoretical richness and its many practical applications over the last five decades. However, most existing studies have focused on graphs with a single type of connection. In applications such as biological, social, and transpor

Ines Zelch, Matthias Hagen, Martin Potthast

How will generative AI pay for itself? Unless charging users for access, selling advertising is the only alternative. Especially in the multi-billion dollar web search market with ads as the main source of revenue, the introduction of a subscription model seems unlikely. The recent disruption of search by generative large language models could thus ultimatel

Michael Morrow

We introduce the $\textit{Macaulay2}$ package $\texttt{OIGroebnerBases}$ for working with OI-modules over Noetherian polynomial OI-algebras. The main methods implement OI-analogues of Buchberger's algorithm and Schreyer's theorem to compute Gr\"obner bases, syzygies and free resolutions of submodules of free OI-modules.

E. A. Jagla

We perform molecular dynamics simulations of homogeneous athermal systems of poly-disperse soft discs under shear. For purely repulsive interactions between particles, and under a confining external pressure, a monotonous flow curve (strain rate vs. stress) starting at a critical yield stress is obtained, with deformation distributing uniformly in the system

Chenhan Yuan, Hoda Eldardiry

Temporal knowledge graphs (TKGs) have shown promise for reasoning tasks by incorporating a temporal dimension to represent how facts evolve over time. However, existing TKG reasoning (TKGR) models lack explainability due to their black-box nature. Recent work has attempted to address this through customized model architectures that generate reasoning paths,

Andrei Shelopugin, Alexander Sirotkin

One of the key problems in the field of soccer analytics is predicting how a player performance changes when transitioning from one league to another. One potential solution to address this issue lies in the evaluation of the respective league strength. This article endeavors to compute club ratings of the first and second European and South American leagues

Massimo Robberto, Mario Gennaro, Nicola Da Rio, Giovanni Maria Strampelli

We performed a HST/WFC3-IR imaging survey of the young stellar cluster NGC 2024 in three filters probing the 1.4~$\mu$m H$_2$O absorption feature, characteristic of the population of low mass and sub-stellar mass objects down to a few Jupyter masses. We detect 812 point sources, 550 of them in all 3 filters with signal to noise greater than 5. Using a distan

Reemon Spector

The problem of selecting a model given a set of candidates remains a challenging one that pervades many scientific fields. We employ techniques from the theory of Lie groups to analyse the symmetries in differential equation models of population growth, with the aim of informing the model selection problem. To illustrate the use of Lie symmetries in model se

James Goppert, Li-Yu Lin, Kartik Pant, Benjamin Perseghetti

Strapdown inertial navigation systems (SINS) are ubiquitious in robotics and engineering since they can estimate a rigid body pose using onboard kinematic measurements without knowledge of the dynamics of the vehicle to which they are attached. While recent work has focused on the closed-form evolution of the estimation error for SINS, which is critical for

Rupesh Chafle, Vishal Panwar, Kaushik Das, Somnath Bhowmick

Multiscale modelling is a new paradigm that has emerged in recent times to study the well-known problem of the process-structure-property relationship in the area of materials science and engineering. For obtaining the desired performance for materials of strategic importance, such as superalloys, it is essential to bridge different length and time scales in

MohammadTaghi Hajiaghayi, Mohammad Mahdavi, Keivan Rezaei, Suho Shin

We present a study on a repeated delegated choice problem, which is the first to consider an online learning variant of Kleinberg and Kleinberg, EC'18. In this model, a principal interacts repeatedly with an agent who possesses an exogenous set of solutions to search for efficient ones. Each solution can yield varying utility for both the principal and the a

L. G. Barbosa, C. C. Barros

In this work, we study spin-0 particles in a spacetime whose structure is determined by a homogeneous magnetic field and a cosmological constant. For this purpose, we take into account a framework based on the Bonnor-Melvin solution with the inclusion of the cosmological constant. We write the Klein-Gordon equation, solve it, and determine the Landau levels.

Rebekka V. Woldseth, J. Andreas Bærentzen, Ole Sigmund

This paper presents an alternative approach to dehomogenisation of elastic Rank-N laminate structures based on the computer graphics discipline of phasor noise. The proposed methodology offers an improvement of existing methods, where high-quality single-scale designs can be obtained efficiently without the utilisation of any least-squares problem or pre-tra

Krutika Sarode, Shashidhar Reddy Javaji, Vishal Kalakonnavar

This research focuses on utilizing natural language processing techniques to predict stock price fluctuations, with a specific interest in early detection of economic, political, social, and technological changes that can be leveraged for capturing market opportunities. The proposed approach includes the identification of salient facts and events from news a

Alfredo Rates, Ad Lagendijk, Aurèle J. L. Adam, Wilbert L. IJzerman

Wavefront shaping is a technique to study and control light transport inside scattering media. Wavefront shaping is considered to be applicable to any complex material, yet in most previous studies, the only sample geometries that are studied are slabs or wave-guides. In this paper, we study how macroscopic changes in the sample shape affect light scattering

Shashidhar Reddy Javaji, Krutika Sarode

Recommender systems have emerged as a crucial component of the modern web ecosystem. The effectiveness and accuracy of such systems are critical for providing users with personalized recommendations that meet their specific interests and needs. In this paper, we introduce a novel model that utilizes a Graph Neural Network (GNN) in conjunction with sentence t

Masud Mansuripur

A single electromagnetic plane-wave propagating in free space possesses neither spin nor orbital angular momentum. Both types of angular momentum arise from interference between pairs of plane-waves having the same temporal frequency {\omega} but differing k-vectors k_1 and k_2. While it is fairly straightforward to evaluate a wavepacket's spin and orbital a

Jingyin Huang, Mahan Mj

We develop a framework for common commensurators of discrete subgroups of lattices in isometry groups of CAT(0) spaces. We show that the Greenberg-Shalom hypothesis about discreteness of common commensurators of Zariski dense subgroups and lattices fails in this generality, even if one imposes strong finiteness conditions. We analyze some examples due to Bur

Gabriele Tolomei, Cesare Campagnano, Fabrizio Silvestri, Giovanni Trappolini

In this paper, we present a groundbreaking paradigm for human-computer interaction that revolutionizes the traditional notion of an operating system. Within this innovative framework, user requests issued to the machine are handled by an interconnected ecosystem of generative AI models that seamlessly integrate with or even replace traditional software appli

Yuheng Qiu, Chen Wang, Can Xu, Yutian Chen

Inertial odometry (IO) using strap-down inertial measurement units (IMUs) is critical in many robotic applications where precise orientation and position tracking are essential. Prior kinematic motion model-based IO methods often use a simplified linearized IMU noise model and thus usually encounter difficulties in modeling non-deterministic errors arising f

Xuan-Quang Bui, Nguyen Van Minh

This paper is concerned with the roughness of exponential dichotomies under unbounded perturbations of a class of linear partial functional differential equations \begin{equation}\label{pfde-000-1star} u'(t)=Au(t)+Bu_t, \end{equation} where $A$ is a linear operator on a Banach space $\mathbb{X}$ and $B$ is a linear operator from $C([-r,0],\mathbb{X})$ into $

Ke Xiao, Erik Learned-Miller, Evangelos Kalogerakis, James Priest

Mitral regurgitation (MR) is a heart valve disease with potentially fatal consequences that can only be forestalled through timely diagnosis and treatment. Traditional diagnosis methods are expensive, labor-intensive and require clinical expertise, posing a barrier to screening for MR. To overcome this impediment, we propose a new semi-supervised model for M

Haoze Wu, Clark Barrett, Nina Narodytska

The demonstrated code-understanding capability of LLMs raises the question of whether they can be used for automated program verification, a task that demands high-level abstract reasoning about program properties that is challenging for verification tools. We propose a general methodology to combine the power of LLMs and automated reasoners for automated pr

Yue Jiang, Eldon Schoop, Amanda Swearngin, Jeffrey Nichols

Multimodal Vision-Language Models (VLMs) enable powerful applications from their fused understanding of images and language, but many perform poorly on UI tasks due to the lack of UI training data. In this paper, we adapt a recipe for generating paired text-image training data for VLMs to the UI domain by combining existing pixel-based methods with a Large L

Jules Berman, Benjamin Peherstorfer

Training neural networks sequentially in time to approximate solution fields of time-dependent partial differential equations can be beneficial for preserving causality and other physics properties; however, the sequential-in-time training is numerically challenging because training errors quickly accumulate and amplify over time. This work introduces Neural

Aria Halavati

We prove that if an N-vortex pair nearly minimizes the Yang-Mills-Higgs energy, then it is second order close to a minimizer. First we use new weighted inequalities in two dimensions and compactness arguments to show stability for sections with some regularity. Second we define a selection principle using a penalized functional and by elliptic regularity and

Zixuan Liu, Gaurush Hiranandani, Kun Qian, Eddie W. Huang

Developing text mining approaches to mine aspects from customer reviews has been well-studied due to its importance in understanding customer needs and product attributes. In contrast, it remains unclear how to predict the future emerging aspects of a new product that currently has little review information. This task, which we named product aspect forecasti