May 2023 arXiv papers — page 2

Sukru Yaren Gelbal, Mustafa Ridvan Cantas, Bilin Aksun Guvenc, Levent Guvenc

The capability afforded by Vehicle-to-Vehicle communication improves situational awareness and provides advantages for many of the traffic problems caused by reduced visibility or No-Line-of-Sight situations, being useful for both autonomous and non-autonomous driving. Additionally, with the traffic light Signal Phase and Timing and Map Datainformation and o

Haroon Ahmad, Ceren Gürkan

In this work, we propose a novel strategy for the numerical solution of linear convection diffusion equation (CDE) over unfitted domains. In the proposed numerical scheme, strategies from high order Hybridized Discontinuous Galerkin method and eXtended Finite Element method is combined with the level set definition of the boundaries. The proposed scheme and

Dan Edidin, Matthew Satriano

We study the third moment for functions on arbitrary compact Lie groups. We use techniques of representation theory to generalize the notion of band-limited functions in classical Fourier theory to functions on the compact groups $SU(n), SO(n), Sp(n)$. We then prove that for generic band-limited functions the third moment or, its Fourier equivalent, the bisp

Claudia Garcia, Zineb Hassainia, Emeric Roulley

In this work, we analytically study the existence of periodic vortex cap solutions for the homogeneous and incompressible Euler equations on the rotating unit 2-sphere, which was numerically conjectured by Dritschel-Polvani and Kim-Sakajo-Sohn. Such solutions are piecewise constant vorticity distributions, subject to the Gauss constraint and rotating uniform

Jennifer J. Schnur, Nitesh V. Chawla

This tutorial paper provides a general overview of symbolic regression (SR) with specific focus on standards of interpretability. We posit that interpretable modeling, although its definition is still disputed in the literature, is a practical way to support the evaluation of successful information fusion. In order to convey the benefits of SR as a modeling

Sara Venturini, Andrea Cristofari, Francesco Rinaldi, Francesco Tudisco

Clustering (or community detection) on multilayer graphs poses several additional complications with respect to standard graphs as different layers may be characterized by different structures and types of information. One of the major challenges is to establish the extent to which each layer contributes to the cluster assignment in order to effectively take

Muzzamal I. Shaukat, Mario G. Silveirinha

We theoretically investigate the role of chiral-transitions in the quantum friction force that acts on a two-level atom that moves with relative velocity v parallel to a planar metallic surface. We find that the friction force has a component that is sensitive to the handedness of the atomic transition dipole moment. In the particular, we show that the frict

Deepa Krishnaswamy, Dennis Bontempi, Vamsi Thiriveedhi, Davide Punzo

Public imaging datasets are critical for the development and evaluation of automated tools in cancer imaging. Unfortunately, many do not include annotations or image-derived features, complicating their downstream analysis. Artificial intelligence-based annotation tools have been shown to achieve acceptable performance and thus can be used to automatically a

Xinlei Yi, Xiuxian Li, Tao Yang, Lihua Xie

This paper considers distributed online convex optimization with adversarial constraints. In this setting, a network of agents makes decisions at each round, and then only a portion of the loss function and a coordinate block of the constraint function are privately revealed to each agent. The loss and constraint functions are convex and can vary arbitrarily

Wei Xiao, Tsun-Hsuan Wang, Chuang Gan, Daniela Rus

Diffusion model-based approaches have shown promise in data-driven planning, but there are no safety guarantees, thus making it hard to be applied for safety-critical applications. To address these challenges, we propose a new method, called SafeDiffuser, to ensure diffusion probabilistic models satisfy specifications by using a class of control barrier func

Mohd. Tasleem Khan, Oscar Gustafsson

In high sample-rate applications of the least-mean-square (LMS) adaptive filtering algorithm, pipelining or/and block processing is required. As opposed to earlier work, pipelining and block processing are jointly considered to obtain what we refer to as the delayed block LMS (DBLMS) algorithm. Different stochastic analyses for the steady and transient state

B. Zhang, Z. Li, H. Wu, M. Pendharkar

Hybrid superconductor-semiconductor materials are fueling research in mesoscopic physics and quantum technology. Recently demonstrated smooth $\beta$-Sn superconductor shells, due to the increased induced gap, are expanding the available parameter space to new regimes. Fabricated on quasiballistic InSb nanowires, with careful control over the hybrid interfac

Jan Holstermann

In 1989 George Cybenko proved in a landmark paper that wide shallow neural networks can approximate arbitrary continuous functions on a compact set. This universal approximation theorem sparked a lot of follow-up research. Shen, Yang and Zhang determined optimal approximation rates for ReLU-networks in $L^p$-norms with $p \in [1,\infty)$. Kidger and Lyons pr

Ashok Cutkosky, Aaron Defazio, Harsh Mehta

We introduce a technique for tuning the learning rate scale factor of any base optimization algorithm and schedule automatically, which we call \textsc{mechanic}. Our method provides a practical realization of recent theoretical reductions for accomplishing a similar goal in online convex optimization. We rigorously evaluate \textsc{mechanic} on a range of l

Anahita Farhang Ghahfarokhi, Taha Mansouri, Mohammad Reza Sadeghi Moghadam, Nila Bahrambeik

As the number of credit card users has increased, detecting fraud in this domain has become a vital issue. Previous literature has applied various supervised and unsupervised machine learning methods to find an effective fraud detection system. However, some of these methods require an enormous amount of time to achieve reasonable accuracy. In this paper, an

Peter Agbo

This report builds upon work introducing the concept of independent control over current and potential in electrocatalytic systems, as a means of improving control over their product selectivity. Previous work, describing an approach investigating independent control over potentiostat bias and current flow, implemented a biased PV-electrolyzer-type configura

Aekta Aggarwal, Helge Holden, Ganesh Vaidya

In this article, we discuss the error analysis for a certain class of monotone finite volume schemes approximating nonlocal scalar conservation laws, modeling traffic flow and crowd dynamics, without any additional assumptions on monotonicity or linearity of the kernel $\mu$ or the flux $f$. We first prove a novel Kuznetsov-type lemma for this class of PDEs

John Polhill, James Davis, Ken Smith, Eric Swartz

Strongly regular graphs (SRGs) provide a fertile area of exploration in algebraic combinatorics, integrating techniques in graph theory, linear algebra, group theory, finite fields, finite geometry, and number theory. Of particular interest are those SRGs with a large automorphism group. If an automorphism group acts regularly (sharply transitively) on the v

Harender S. Dhattarwal, Rahul Somni, Richard C. Remsing

Solid-state superionic conductors (SSICs) are promising alternatives to liquid electrolytes in batteries and other energy storage technologies. The rational design of SSICs and ultimately their deployment in battery technologies is hindered by the lack of a thorough understanding of their ion conduction mechanisms. In SSICs containing molecular ions, rotatio

L. A. Osorio-Quiroga, G. C. Grime, M. Roberto, R. L. Viana

In tokamaks, modification of the plasma profiles can reduce plasma transport, improving particle confinement. However, this improvement is still not completely understood. In this work, we consider a drift wave test particle model to investigate the influence of the electric and magnetic field profiles on plasma transport. Test particle orbits subjected to E

Tong Li, Zhihao Wang, Liangying Shao, Xuling Zheng

Recently, the text-to-table generation task has attracted increasing attention due to its wide applications. In this aspect, the dominant model formalizes this task as a sequence-to-sequence generation task and serializes each table into a token sequence during training by concatenating all rows in a top-down order. However, it suffers from two serious defec

Nefeli Bountouni, Sotiris Koussouris, Alexandros Vasileiou, Stylianos A. Kazazis

The rapid digitalisation of SMEs, further expedited as a business continuity measure against Covid19 impact, has brought along major cybersecurity challenges, as it creates a fertile landscape for malicious actors, that want to capitalise on the insufficient cybersecurity planning and preparedness of SMEs to conduct low-effort, lucrative attacks. This paper

Mohammad Hasan Ahmadilivani, Mario Barbareschi, Salvatore Barone, Alberto Bosio

Deep Learning, and in particular, Deep Neural Network (DNN) is nowadays widely used in many scenarios, including safety-critical applications such as autonomous driving. In this context, besides energy efficiency and performance, reliability plays a crucial role since a system failure can jeopardize human life. As with any other device, the reliability of ha

Clara Lacroce, Borja Balle, Prakash Panangaden, Guillaume Rabusseau

In this paper, we study the approximate minimization problem of weighted finite automata (WFAs): to compute the best possible approximation of a WFA given a bound on the number of states. By reformulating the problem in terms of Hankel matrices, we leverage classical results on the approximation of Hankel operators, namely the celebrated Adamyan-Arov-Krein (

Robbe De Prins, Guy Van der Sande, Peter Bienstman

Over the last decade, researchers have studied the synergy between quantum computing (QC) and classical machine learning (ML) algorithms. However, measurements in QC often disturb or destroy quantum states, requiring multiple repetitions of data processing to estimate observable values. In particular, this prevents online (i.e., real-time, single-shot) proce

Matthew Jagielski

Canary exposure, introduced in Carlini et al. is frequently used to empirically evaluate, or audit, the privacy of machine learning model training. The goal of this note is to provide some intuition on how to interpret canary exposure, including by relating it to membership inference attacks and differential privacy.

Paul Deroubaix, Takuro Kobashi, Léna Gurriaran, Fouzi Benkhelifa

Urban decarbonization is one of the pillars for strategies to achieve carbon neutrality around the world. However, the current speed of urban decarbonization is insufficient to keep pace with efforts to achieve this goal. Rooftop PVs integrated with electric vehicles (EVs) as battery is a promising technology capable to supply CO2-free, affordable, and dispa

Franz Schreier, J. Lee Grenfell, Fabian Wunderlich, Thomas Trautmann

Atmospheric temperatures are to be estimated from thermal emission spectra of Earth-like exoplanets orbiting M-stars as observed by current and future planned missions. To this end, a line-by-line radiative transfer code is used to generate synthetic thermal infrared (TIR) observations. The range of 'observed' intensities provides a rough hint of the atmosph

Adrián González Casanova, Noemi Kurt, José Luis Pérez

Motivated by the question of the impact of selective advantage in populations with skewed reproduction mechanims, we study a Moran model with selection. We assume that there are two types of individuals, where the reproductive success of one type is larger than the other. The higher reproductive success may stem from either more frequent reproduction, or fro

Stefan Thalhammer, Jean-Baptiste Weibel, Markus Vincze, Jose Garcia-Rodriguez

Object pose estimation is important for object manipulation and scene understanding. In order to improve the general applicability of pose estimators, recent research focuses on providing estimates for novel objects, that is objects unseen during training. Such works use deep template matching strategies to retrieve the closest template connected to a query

Yu Luo, Jason A. Meziere, German D. Samolyuk, Gus L. W. Hart

Machine learning force fields (MLFFs) are an increasingly popular choice for atomistic simulations due to their high fidelity and improvable nature. Here, we propose a hybrid small-cell approach that combines attributes of both offline and active learning to systematically expand a quantum mechanical (QM) database while constructing MLFFs with increasing mod

Junyi Zhu, Ruicong Yao, Matthew B. Blaschko

In Federated Learning (FL) and many other distributed training frameworks, collaborators can hold their private data locally and only share the network weights trained with the local data after multiple iterations. Gradient inversion is a family of privacy attacks that recovers data from its generated gradients. Seemingly, FL can provide a degree of protecti

Jungeum Kim, Veronika Rockova

The success of Bayesian inference with MCMC depends critically on Markov chains rapidly reaching the posterior distribution. Despite the plentitude of inferential theory for posteriors in Bayesian non-parametrics, convergence properties of MCMC algorithms that simulate from such ideal inferential targets are not thoroughly understood. This work focuses on th

Chen Zhao, Feng Mi, Xintao Wu, Kai Jiang

In the problem of online learning for changing environments, data are sequentially received one after another over time, and their distribution assumptions may vary frequently. Although existing methods demonstrate the effectiveness of their learning algorithms by providing a tight bound on either dynamic regret or adaptive regret, most of them completely ig

Massimo Lauria, Jakob Nordström

We consider the graph $k$-colouring problem encoded as a set of polynomial equations in the standard way over $0/1$-valued variables. We prove that there are bounded-degree graphs that do not have legal $k$-colourings but for which the polynomial calculus proof system defined in [Clegg et al '96, Alekhnovich et al '02] requires linear degree, and hence expon

Chunliu Wang, Huiyuan Lai, Malvina Nissim, Johan Bos

Pre-trained language models (PLMs) have achieved great success in NLP and have recently been used for tasks in computational semantics. However, these tasks do not fully benefit from PLMs since meaning representations are not explicitly included in the pre-training stage. We introduce multilingual pre-trained language-meaning models based on Discourse Repres

Jantje Kalin, Sibylle Sievers, Daniel Kalin, Andreas Bauer

A key challenge for the realization of future skyrmion devices comprises the controlled creation, annihilation and detection of these topologically non-trivial magnetic spin textures. In this study, we report an all-optical approach for writing, deleting, and reading skyrmions in the cubic chiral magnet Fe$_{0.25}$Co$_{0.75}$Si based on thermal quenching. Us

Ferenc Bencs, Pjotr Buys

In this paper we investigate the distribution of zeros of the independence polynomial of hypergraphs of maximum degree $\Delta$. For graphs the largest zero-free disk around zero was described by Shearer as having radius $\lambda_s(\Delta)=(\Delta-1)^{\Delta-1}/\Delta^\Delta$. Recently it was shown by Galvin et al. that for hypergraphs the disk of radius $\l

Huiyuan Lai, Antonio Toral, Malvina Nissim

Figures of speech help people express abstract concepts and evoke stronger emotions than literal expressions, thereby making texts more creative and engaging. Due to its pervasive and fundamental character, figurative language understanding has been addressed in Natural Language Processing, but it's highly understudied in a multilingual setting and when cons

Jiayi Xu, Han-Wei Shen

We present VMap, a map-like rectangular space-filling visualization, to perform vertex-centric graph exploration. Existing visualizations have limited support for quality optimization among rectangular aspect ratios, vertex-edge intersection, and data encoding accuracy. To tackle this problem, VMap integrates three novel components: (1) a desired-aspect-rati

Aaron Mishkin, Mert Pilanci

We develop an analytical framework to characterize the set of optimal ReLU neural networks by reformulating the non-convex training problem as a convex program. We show that the global optima of the convex parameterization are given by a polyhedral set and then extend this characterization to the optimal set of the non-convex training objective. Since all st

Angtian Wang, Wufei Ma, Alan Yuille, Adam Kortylewski

Human vision demonstrates higher robustness than current AI algorithms under out-of-distribution scenarios. It has been conjectured such robustness benefits from performing analysis-by-synthesis. Our paper formulates triple vision tasks in a consistent manner using approximate analysis-by-synthesis by render-and-compare algorithms on neural features. In this

Lior Shani, Pim Lueb, Gavin Menning, Mohit Gupta

Quantum devices based on InSb nanowires (NWs) are a prime candidate system for realizing and exploring topologically-protected quantum states and for electrically-controlled spin-based qubits. The influence of disorder on achieving reliable topological regimes has been studied theoretically, highlighting the importance of optimizing both growth and nanofabri

Vlatko Crnković, Renato Huzak, Maja Resman

In this paper we express the Minkowski dimension of spiral trajectories near hyperbolic saddles and semi-hyperbolic singularities in terms of the Minkowski dimension of intersections of such spirals with transversals near these singularities. We apply these results to hyperbolic saddle-loops and hyperbolic $2$-cycles to obtain upper bounds on the cyclicity o

Giancarlo Rossi

This is the first of two companion papers where we prove that the recently discovered non perturbative mechanism capable of giving mass to elementary fermions, in the presence of weak interactions can also generate a mass for the $W$, and can thus be used as a viable alternative to the Higgs scenario. The non perturbative fermion and $W$ masses have the form

Amanda A. Boatswain Jacques, Abdoulaye Baniré Diallo, Etienne Lord

Monitoring land cover using remote sensing is vital for studying environmental changes and ensuring global food security through crop yield forecasting. Specifically, multitemporal remote sensing imagery provides relevant information about the dynamics of a scene, which has proven to lead to better land cover classification results. Nevertheless, few studies

Vincenzo Antonelli, Gianfranco Casnati, Angelo Felice Lopez, Debaditya Raychaudhury

We study varieties $X \subset P^r$ such that is $N_X^*(k)$ is an Ulrich vector bundle for some integer $k$. We first prove that such an $X$ must be a curve. Then we give several examples of curves with $N_X^*(k)$ an Ulrich vector bundle.

Dewen Zeng, Yawen Wu, Xinrong Hu, Xiaowei Xu

This paper presents a new way to identify additional positive pairs for BYOL, a state-of-the-art (SOTA) self-supervised learning framework, to improve its representation learning ability. Unlike conventional BYOL which relies on only one positive pair generated by two augmented views of the same image, we argue that information from different images with the

E. Sanchez-Tovar, E. D. Araya, V. Rosero, P. Hofner

The study of the interaction between ionized jets, molecular outflows and their environments is critical to understanding high-mass star formation, especially because jets and outflows are thought to be key in the transfer of angular momentum outwards from accretion disks. We report a low-spectral resolution VLA survey for hydrogen radio recombination lines,

Peiling Lu, Xin Xu, Chenfei Kang, Botao Yu

Generating music from text descriptions is a user-friendly mode since the text is a relatively easy interface for user engagement. While some approaches utilize texts to control music audio generation, editing musical elements in generated audio is challenging for users. In contrast, symbolic music offers ease of editing, making it more accessible for users

Nick Galatos, Sara Ugolini

We introduce and characterize various gluing constructions for residuated lattices that intersect on a common subreduct, and which are subalgebras, or appropriate subreducts, of the resulting structure. Starting from the 1-sum construction (also known as ordinal sum for residuated structures), where algebras that intersect only in the top element are glued t

Toufique Ahmed, Premkumar Devanbu

Large Language models (LLMs) can be induced to solve non-trivial problems with "few-shot" prompts including illustrative problem-solution examples. Now if the few-shots also include "chain of thought" (CoT) explanations, which are of the form problem-explanation-solution, LLMs will generate a "explained" solution, and perform even better. Recently an excitin

Yizhi Li, Ruibin Yuan, Ge Zhang, Yinghao Ma

Self-supervised learning (SSL) has recently emerged as a promising paradigm for training generalisable models on large-scale data in the fields of vision, text, and speech. Although SSL has been proven effective in speech and audio, its application to music audio has yet to be thoroughly explored. This is partially due to the distinctive challenges associate

Aditya Sundar, Aaron Feinauer, Bryan Kinzer, Joerg Petrasch

A computational framework integrating thermodynamics, kinetics, and mechanical stress calculations is developed to study supercritical CO2 induced corrosion in model Fe-based MA956 and Ni-based H214 alloys. Empirical models parametrized using experimental data show surface oxidation and sub-surface carburization for a wide range of thermodynamic conditions (

R. López-Peña, S. Cordero, E. Nahmad-Achar, O. Castaños

A method for storing quantum information is presented for $3$-level atomic systems interacting dipolarly with a single radiation field. The method involves performing simple local SU(2) rotations on the Hamiltonian. Under equal detuning, these transformations decouple one of the atomic levels from the electromagnetic field for the $\Lambda$- and $V$-configur

Robert M. Corless, David J. Jeffrey, Azar Shakoori

This paper outlines our ideas on how to teach linear algebra in a mechanized mathematical environment, and discusses some of our reasons for thinking that this is a better way to teach linear algebra than the ``old fashioned way''. We discuss some technological tools such as Maple, Matlab, Python, and Jupyter Notebooks, and some choices of topics that are es

Xiao Xu, Bei Li, Chenfei Wu, Shao-Yen Tseng

Two-Tower Vision-Language (VL) models have shown promising improvements on various downstream VL tasks. Although the most advanced work improves performance by building bridges between encoders, it suffers from ineffective layer-by-layer utilization of uni-modal representations and cannot flexibly exploit different levels of uni-modal semantic knowledge. In

Jury I. Radkovski, Sergey M. Sibiryakov

We study the high-energy limit of projectable Ho\v rava gravity using on-shell graviton scattering amplitudes. We compute the tree-level amplitudes using symbolic computer algebra and analyze their properties in the case of collisions with zero total momentum. The amplitudes grow with collision energy in the way consistent with tree-level unitarity. We discu

Boris Šobot

A divisibility relation on ultrafilters is defined as follows: ${\cal F}\hspace{1mm}\widetilde{\mid}\hspace{1mm}{\cal G}$ if and only if every set in $\cal F$ upward closed for divisibility also belongs to $\cal G$. After describing the first $\omega$ levels of this quasiorder, in this paper we generalize the process of determining the basic divisors of an u

Liat Bezalel, Eyal Orgad

Transfer learning for extremely low resource languages is a challenging task as there is no large scale monolingual corpora for pre training or sufficient annotated data for fine tuning. We follow the work of MetaXL which suggests using meta learning for transfer learning from a single source language to an extremely low resource one. We propose an enhanced

Jan de Boer, Victor Godet, Jani Kastikainen, Esko Keski-Vakkuri

Driven quantum systems exhibit a large variety of interesting and sometimes exotic phenomena. Of particular interest are driven conformal field theories (CFTs) which describe quantum many-body systems at criticality. In this paper, we develop both a spacetime and a quantum information geometry perspective on driven 2d CFTs. We show that for a large class of

Christopher R. Schwarze, David S. Simon, Alexander V. Sergienko

Here we introduce interferometric devices by combining optical feedback (cavities) with unbiased multiports, which unlike traditional beam dividers, allow light to reflect back out of the port from which it originated. By replacing the traditional, directionally-biased beam-splitter in a Michelson interferometer with an unbiased multiport, the functional dep

Paul Anderson, Douglas Higinbotham, Sonny Mantry, Xiaochao Zheng

Studies of the Deep Inelastic Scattering (DIS) have provided fundamental information of the nucleon structure for decades. The electron-ion collider (EIC) will be the first collider capable of DIS study with both polarized lepton and polarized hadron beams, providing the possibility of accessing new electroweak structure functions of the nucleon. In this wor

Wonyoung Kim, Garud Iyengar, Assaf Zeevi

We consider Pareto front identification (PFI) for linear bandits (PFILin), i.e., the goal is to identify a set of arms with undominated mean reward vectors when the mean reward vector is a linear function of the context. PFILin includes the best arm identification problem and multi-objective active learning as special cases. The sample complexity of our prop

Roy Laurens, Edo Christianto, Bruce Caulkins, Cliff C. Zou

In many VoIP systems, Voice Activity Detection (VAD) is often used on VoIP traffic to suppress packets of silence in order to reduce the bandwidth consumption of phone calls. Unfortunately, although VoIP traffic is fully encrypted and secured, traffic analysis of this suppression can reveal identifying information about calls made to customer service automat

Manuel Klar, Giacomo Capodaglio, Marta D'Elia, Christian Glusa

Nonlocal models allow for the description of phenomena which cannot be captured by classical partial differential equations. The availability of efficient solvers is one of the main concerns for the use of nonlocal models in real world engineering applications. We present a domain decomposition solver that is inspired by substructuring methods for classical

Vygintas Gontis

Modeling financial markets based on empirical data poses challenges in selecting the most appropriate models. Despite the abundance of empirical data available, researchers often face difficulties in identifying the best-fitting model. Long-range memory and self-similarity estimators, commonly used for this purpose, can yield inconsistent parameter values, a

Jonathan Stern, Drummond Fielding, Zachary Hafen, Kung-Yi Su

Observed accretion rates onto the Milky-Way and other local spirals fall short of that required to sustain star formation for cosmological timescales. A potential avenue for this unseen accretion is an inflow in the volume-filling hot phase ($\sim10^6$ K) of the circumgalactic medium (CGM), as suggested by some cosmological simulations. Using hydrodynamic si

Ilyes Batatia, Mario Geiger, Jose Munoz, Tess Smidt

Reductive Lie Groups, such as the orthogonal groups, the Lorentz group, or the unitary groups, play essential roles across scientific fields as diverse as high energy physics, quantum mechanics, quantum chromodynamics, molecular dynamics, computer vision, and imaging. In this paper, we present a general Equivariant Neural Network architecture capable of resp

Manuel Loparco, Joao Penedones, Kamran Salehi Vaziri, Zimo Sun

We study two-point functions of symmetric traceless local operators in the bulk of de Sitter spacetime. We derive the K\"all\'en-Lehmann spectral decomposition for any spin and show that unitarity implies its spectral densities are nonnegative. In addition, we recover the K\"all\'en-Lehmann decomposition in Minkowski space by taking the flat space limit. Usi

Javiera Jilberto, Samuel J. DePalma, Jason Lo, Hiba Kobeissi

Engineered heart tissues (EHTs) present a potential solution to some of the current challenges in the treatment of heart disease; however, the development of mature, adult-like cardiac tissues remains elusive. Mechanical stimuli have been observed to improve whole-tissue function and cardiomyocyte (CM) maturation, although our ability to fully utilize these

Yuxin Tang, Zhimin Ding, Dimitrije Jankov, Binhang Yuan

The relational data model was designed to facilitate large-scale data management and analytics. We consider the problem of how to differentiate computations expressed relationally. We show experimentally that a relational engine running an auto-differentiated relational algorithm can easily scale to very large datasets, and is competitive with state-of-the-a

Andrew Szot, Unnat Jain, Dhruv Batra, Zsolt Kira

We present the task of "Social Rearrangement", consisting of cooperative everyday tasks like setting up the dinner table, tidying a house or unpacking groceries in a simulated multi-agent environment. In Social Rearrangement, two robots coordinate to complete a long-horizon task, using onboard sensing and egocentric observations, and no privileged informatio

Christopher Langrenez, David R. M. Arvidsson-Shukur, Stephan De Bièvre

The Kirkwood-Dirac (KD) quasiprobability distribution can describe any quantum state with respect to the eigenbases of two observables $A$ and $B$. KD distributions behave similarly to classical joint probability distributions but can assume negative and nonreal values. In recent years, KD distributions have proven instrumental in mapping out nonclassical ph

Gabriel Cuomo, Shuyu Zhang

Coleman's theorem states that continuous internal symmetries cannot be spontaneously broken in two-dimensional quantum field theories (QFTs). In this work we consider surface (i.e. two-dimensional) defects in $d$-dimensional conformal field theories (CFTs) invariant under a continuous internal symmetry group $G$. We study under which conditions it is possibl

Joscha N. Jahns, Laura G. Spitler, Charles R. H. Walker, Carlton M. Baugh

Fast radio bursts (FRBs) are the first cosmological radio sources that vary on millisecond timescales, which makes them a unique probe of the Universe. Many proposed applications of FRBs require associated redshifts. These can only be obtained by localizing FRBs to their host galaxies and subsequently measuring their redshifts. Upcoming FRB surveys will prov

Dominik Hangleiter, Michael J. Gullans

A central challenge in the verification of quantum computers is benchmarking their performance as a whole and demonstrating their computational capabilities. In this work, we find a universal model of quantum computation, Bell sampling, that can be used for both of those tasks and thus provides an ideal stepping stone towards fault-tolerance. In Bell samplin

Federico Castillo, Jean-Philippe Labbé

Consider a real point configuration $\mathbf{A}$ of size $n$ and an integer $r \leq n$. The vertices of the $r$-lineup polytope of $\mathbf{A}$ correspond to the possible orderings of the top $r$ points of the configuration obtained by maximizing a linear functional. The motivation behind the study of lineup polytopes comes from the representability problem

Han-Xiang Xu, Makoto Shimizu, Daniel Guterding, Junya Otsuki

Layered van der Waals ferromagnets, which preserve their magnetic properties down to exfoliated monolayers, are fueling an abundance of fundamental research and nanoscale device demonstration. CrGeTe$_3$ is a prime example for this class of materials. Its temperature-pressure phase diagram features an insulator-to-metal transition and a significant increase

Sarah K. Amer

AI-based text-to-image generation has undergone a significant leap in the production of visually comprehensive and aesthetic imagery over the past year, to the point where differentiating between a man-made piece of art and an AI-generated image is becoming more difficult. Generative Models such as Stable Diffusion, Midjourney and others are expected to affe

Tao Han, Ian M. Lewis, Hongkai Liu, Zhen Liu

We present a comprehensive study on how to distinguish the properties of heavy dijet resonances at hadron colliders. A variety of spins, chiral couplings, charges, and QCD color representations are considered. Distinguishing the different color representations is particularly difficult at hadron colliders. To determine the QCD color structure, we consider a

Yugo Onishi, Liang Fu

We provide a universal tight bound on the energy gap of topological insulators by exploring relationships between topology, quantum geometry, and optical absorption. Applications of our theory to infrared absorption near topological band inversion, magnetic circular dichorism in Chern insulators, and topological gap in moir\'e materials are demonstrated.

Sebastian Hoof, Joerg Jaeckel, Lennert J. Thormaehlen

Axions, if discovered, could serve as a powerful new messenger for studying astrophysical objects. In this study we show how the Sun's spatial and spectral "axion image" can be inverted to infer the radial dependence of solar properties in a model-independent way. In particular, the future helioscope IAXO may allow us to accurately reconstruct the Sun's temp

Brian James Morsony, Ryan De Los Santos, Rubin Hernandez, Joshua Bustamante

To date GW170817, produced by a binary neutron star (BNS) merger, is the only gravitational wave event with an electromagnetic (EM) counterpart. It was associated with a prompt short gamma-ray burst (GRB), an optical kilonova, and the afterglow of a structured, off-axis relativistic jet. We model the prospects for future mergers discovered in gravitational w

Duo Lu, Eric Eaton, Matt Weg, Wei Wang

Road traffic scene reconstruction from videos has been desirable by road safety regulators, city planners, researchers, and autonomous driving technology developers. However, it is expensive and unnecessary to cover every mile of the road with cameras mounted on the road infrastructure. This paper presents a method that can process aerial videos to vehicle t

Nina L. Corvelo Benz, Manuel Gomez Rodriguez

Whenever a binary classifier is used to provide decision support, it typically provides both a label prediction and a confidence value. Then, the decision maker is supposed to use the confidence value to calibrate how much to trust the prediction. In this context, it has been often argued that the confidence value should correspond to a well calibrated estim

Martin Bonkhoff, Simon B. Jäger, Imke Schneider, Axel Pelster

One-dimensional anyonic models of the Hubbard type show intriguing ground-state properties, effectively transmuting between Bose-Einstein and Fermi-Dirac statistics. The simplest model that one can investigate is an anyonic version of the bosonic Josephson junction, the repulsive anyon-Hubbard dimer. In the following we find an exact duality relation to the

Patrick A. Lee, Jacob F. Steiner

We propose using tunneling spectroscopy with a superconducting electrode to probe the collective modes of unconventional superconductors. The modes are predicted to appear as peaks in dI/dV at voltages given by eV = {\omega}i/2 where {\omega}i denotes the mode frequencies. This may prove to be a powerful tool to investigate the pairing symmetry of unconventi

C. M. Cordun, R. Timmerman, G. K. Miley, R. J. van Weeren

It has long been known that luminous, ultra-steep spectrum radio sources are preferentially associated with massive galaxies at high redshifts. Here we describe a pilot project directed at such objects, to demonstrate the feasibility and importance of using LOFAR to study the most distant forming massive galaxies and protoclusters. We have successfully image

Yukei S. Murakami, Adam G. Riess, Benjamin E. Stahl, W. D'Arcy Kenworthy

Recent studies suggest spectroscopic differences explain a fraction of the variation in Type Ia supernova (SN Ia) luminosities after light-curve/color standardization. In this work, (i) we empirically characterize the variations of standardized SN Ia luminosities, and (ii) we use a spectroscopically inferred parameter, SIP, to improve the precision of SNe Ia

Gabriel Maheson, Roberto Maiolino, Mirko Curti, Ryan Sanders

We explore the dependence of dust attenuation, as traced by the $\rm H_{\alpha}/\rm H_{\beta}$ Balmer decrement, on galactic properties by using a large sample of SDSS spectra. We use both Partial Correlation Coefficients (PCC) and Random Forest (RF) analysis to distinguish those galactic parameters that directly and primarily drive dust attenuation in galax

Eilat Glikman, Rachel Langgin, Makoto A. Johnstone, Ilsang Yoon

We report the discovery of a candidate dual QSO at z=1.889, a redshift that is in the era known as "cosmic noon" where most of the Universe's black hole and stellar mass growth occurred. The source was identified in Hubble Space Telescope WFC3/IR images of a dust-reddened QSO that showed two closely-separated point sources at a projected distance of 0.26", o

Nayan Das, Suruj Jyoti Das, Debasish Borah

We study the possibility of detecting dark radiation (DR) produced by a combination of interactions with the thermal bath and ultra-light primordial black hole (PBH) evaporation in the early universe. We show that the detection prospects via cosmic microwave background (CMB) measurements of the effective relativistic degrees of freedom ${\rm \Delta N_{eff}}$

Dylan J. Young, Anjun Chu, Eric Yilun Song, Diego Barberena

In conventional Bardeen-Cooper-Schrieffer (BCS) superconductors, electrons with opposite momenta bind into Cooper pairs due to an attractive interaction mediated by phonons in the material. While superconductivity naturally emerges at thermal equilibrium, it can also emerge out of equilibrium when the system's parameters are abruptly changed. The resulting o

Fei Huang, Yuan-Zhen Li, Jiang-Hao Yu

It is important to understand the implications of current observational constraints and potential signatures on the thermal history of dark matter. In this paper, we build the connection between the present-day velocities and the production mechanism of dark matter and find that the current observation on structure formation can be imposed to constrain the d

Olivia Liebman, Jonathan Curtis, Ioannis Petrides, Prineha Narang

The unusual magnetoelectric transport present in Weyl semimetals and 3D topological insula- tors can be compactly understood as manifestations of a background axion field, which itself is determined by the microscopic band structure. In the presence of correlations, an additional axion quasiparticle may emerge as the collective excitations on top of the mean

Li Fan, Yu Qiu

Let $\mathbf{D}_{3}$ be a bigraded 3-decorated disk with an arc system $\mathbf{A}$. We associate a bigraded simple closed arc $\widehat{\eta}_{\frac{r}{s}}$ on $\mathbf{D}_{3}$ to any rational number $\frac{r}{s}\in\overline{\mathbb{Q}}=\mathbb{Q}\cup\{\infty\}$. We show that the right (resp. left) $q$-deformed rational numbers associated to $\frac{r}{s}$,

Shuang-Qing Wu, Di Wu

In our previous work [D. Wu and S.-Q. Wu, Phys. Rev. D 108, 064034 (2023)], we have formulated consistent thermodynamic first law and Bekenstein-Smarr mass formulae for all higher even-dimensional uncharged Taub-NUT (AdS) spacetimes through the inclusion of a secondary hair $J_n = Mn$ as did in the four-dimensional NUT-charged cases [S.-Q. Wu and D. Wu, Phys

Sofiene Jerbi, Casper Gyurik, Simon C. Marshall, Riccardo Molteni

Quantum machine learning is often highlighted as one of the most promising practical applications for which quantum computers could provide a computational advantage. However, a major obstacle to the widespread use of quantum machine learning models in practice is that these models, even once trained, still require access to a quantum computer in order to be

Vasily Belokurov, Andrey Kravtsov

Anomalously high nitrogen-to-oxygen abundance ratios [N/O] are observed in globular clusters (GCs), among the field stars of the Milky Way (MW), and even in the gas in a $z\approx 11$ galaxy. Using data from the APOGEE Data Release 17 and the Gaia Data Release 3, we present several independent lines of evidence that most of the MW's high-[N/O] stars were bor