March 2023 arXiv papers — page 82

Yingnan Cui, Anuradha M. Annaswamy

We propose a new discrete-time online parameter estimation algorithm that combines two different aspects, one that adds momentum, and another that includes a time-varying learning rate. It is well known that recursive least squares based approaches that include a time-varying gain can lead to exponential convergence of parameter errors under persistent excit

Boqi Chen, Marc Niethammer

Multiple imaging modalities are often used for disease diagnosis, prediction, or population-based analyses. However, not all modalities might be available due to cost, different study designs, or changes in imaging technology. If the differences between the types of imaging are small, data harmonization approaches can be used; for larger changes, direct imag

Somayeh Eskandari, János Koltai, István László, Mehran Vaezi

Carbon nanotubes can serve as one-dimensional nanoreactors for the in-tube synthesis of various nanostructures. Experimental observations have shown that chains, inner tubes, or nanoribbons can grow by the thermal decomposition of organic/organometallic molecules encapsulated in carbon nanotubes. The result of the process depends on the temperature, the diam

David Korcak, Jiri Matas

We present a method for estimating the shutter angle, a.k.a. exposure fraction - the ratio of the exposure time and the reciprocal of frame rate - of videoclips containing motion. The approach exploits the relation of the exposure fraction, optical flow, and linear motion blur. Robustness is achieved by selecting image patches where both the optical flow and

P. V. Dovbush

In this paper, as an application of Zalcman's lemma in $\mathbb{C}^n$, we give a sufficient condition for normality of holomorphic functions of several complex variables, which generalizes previous known one-dimensional criterion of A.J. Lohwater and Ch. Pommerenke \cite[Theorem 1]{MR0338381}.

Paolo Grazieschi, Konstantin Matetski, Hendrik Weber

We consider multiple stochastic integrals with respect to c\`adl\`ag martingales, which approximate a cylindrical Wiener process. We define a chaos expansion, analogous to the case of multiple Wiener stochastic integrals, for these integrals and use it to show moment bounds. Key tools include an iteration of the Burkholder-Davis-Gundy inequality and a multi-

Duyal Yolcu

The textbook adversary bound for function evaluation states that to evaluate a function $f\colon D\to C$ with success probability $\frac{1}{2}+\delta$ in the quantum query model, one needs at least $\left( 2\delta -\sqrt{1-4\delta^2} \right) Adv(f)$ queries, where $Adv(f)$ is the optimal value of a certain optimization problem. For $\delta \ll 1$, this only

Joseph Breeden, Dimitra Panagou

This paper presents extensions of control barrier function (CBF) and control Lyapunov function (CLF) theory to systems wherein all actuators cause impulsive changes to the state trajectory, and can only be used again after a minimum dwell time has elapsed. These rules define a hybrid system, wherein the controller must at each control cycle choose whether to

Paolo Grazieschi, Konstantin Matetski, Hendrik Weber

We consider the Glauber dynamics of a ferromagnetic Ising-Kac model on a three-dimensional periodic lattice of size $(2N + 1)3$, in which the flipping rate of each spin depends on an average field in a large neighborhood of radius $\frac1\gamma << N$. We study the random fluctuations of a suitably rescaled coarse-grained spin field as $N \to \infty$ and $\ga

Bin Gao, Tong Chen, Xiao-Chuan Wu, Michael Flynn

Magnetic order in most materials occurs when magnetic ions with finite moments in a crystalline lattice arrange in a particular pattern below the ordering temperature determined by exchange interactions between the ions. However, when the crystal electric field (CEF) effect results in a spin-singlet ground state on individual magnetic sites, the collective g

Vasilii Rozhdestvenskii

We consider the classical Steenrod problem on realization of integral homology classes by continuous images of smooth oriented manifolds. Let $k(n)$ be the smallest positive integer such that any integral $n$-dimensional homology class becomes realizable in the sense of Steenrod after multiplication by $k(n)$. The best known upper bound for $k(n)$ was obtain

Andrew Stasiuk, Paola Cappellaro

A time crystal is a state of periodically driven matter which breaks discrete time translation symmetry. Time crystals have been demonstrated experimentally in various programmable quantum simulators and exemplify how non-equilibrium, driven quantum systems can exhibit intriguing and robust properties absent in systems at equilibrium. These robust driven sta

Alexander Kirillovich, Olga Nevzorova, Evgeny Lipachev

This paper is devoted to the problems of ontology-based mathematical knowledge management and representation. The main attention is paid to the development of a formal model for the representation of mathematical statements in the Open Linked Data cloud. The proposed model is intended for applications that extract mathematical facts from natural language mat

Min-Seok Seo

We investigate the connection between the distance conjecture and the uplift potential. For this purpose, we consider the concrete model, the warped deformed conifold embedded into Type IIB flux compactifications, with the uplift potential produced by $\overline{\rm D3}$-branes at the tip of the throat. Whereas the various mass scales associated with towers

Nicolás Cardozo, Ivana Dusparic, Christian Cabrera

Reinforcement Learning (RL) is being increasingly used to learn and adapt application behavior in many domains, including large-scale and safety critical systems, as for example, autonomous driving. With the advent of plug-n-play RL libraries, its applicability has further increased, enabling integration of RL algorithms by users. We note, however, that the

Dmitry Dolgopyat, Kasun Fernando

We consider sums of independent identically distributed random variables whose distributions have $d+1$ atoms. Such distributions never admit an Edgeworth expansion of order $d$ but we show that for almost all parameters the Edgeworth expansion of order $d-1$ is valid and the error of the order $d-1$ Edgeworth expansion is typically of order $n^{-d/2}.$

Emily Hwang, Nathan Harper, Ryoto Sekine, Luis Ledezma

On-chip ultraviolet sources are of great interest for building compact and scalable atomic clocks, quantum computers, and spectrometers; however, few material platforms are suitable for integrated ultraviolet light generation. Of these materials, thin-film lithium niobate is the most competitive due to its ability to be quasi-phase-matched, optical confineme

Jennifer Pestana, David J. Silvester

This paper develops efficient preconditioned iterative solvers for incompressible flow problems discretised by an enriched Taylor-Hood mixed approximation, in which the usual pressure space is augmented by a piecewise constant pressure to ensure local mass conservation. This enrichment process causes over-specification of the pressure when the pressure space

Rui Li, Xiaowei Zhao

Super-resolution, which aims to reconstruct high-resolution images from low-resolution images, has drawn considerable attention and has been intensively studied in computer vision and remote sensing communities. The super-resolution technology is especially beneficial for Unmanned Aerial Vehicles (UAV), as the amount and resolution of images captured by UAV

Maegan Tucker, Aaron D. Ames

Uneven terrain necessarily transforms periodic walking into a non-periodic motion. As such, traditional stability analysis tools no longer adequately capture the ability of a bipedal robot to locomote in the presence of such disturbances. This motivates the need for analytical tools aimed at generalized notions of stability -- robustness. Towards this, we pr

Yong Zheng

With the development of recommender systems (RS), several promising systems have emerged, such as context-aware RS, multi-criteria RS, and group RS. However, the education domain may not benefit from these developments due to missing information, such as contexts and multiple criteria, in educational data sets. In this paper, we announce and release an open

Toby Aldape, Jingyi Liu, Gregory Pylypovych, Adam Sheffer

We study the minimum number of distinct distances between point sets on two curves in $R^3$. Assume that one curve contains $m$ points and the other $n$ points. Our main results: (a) When the curves are conic sections, we characterize all cases where the number of distances is $O(m+n)$. This includes new constructions for points on two parabolas, two ellipse

Rui Zhang, Jun Jiang, Alec Mishkin, James N. Fry

We advance the algorithm for ab initio calculations of Raman spectra for large systems via applying external electric field, and complement it by a code implementation we name RASCBEC. With the RASCBEC code, we have successfully benchmark crystalline materials and compute Raman spectra of large molecules, and amorphous oxides. Our results demonstrate a remar

Dirk Väth, Lindsey Vanderlyn, Ngoc Thang Vu

Conversational interfaces provide a flexible and easy way for users to seek information that may otherwise be difficult or inconvenient to obtain. However, existing interfaces generally fall into one of two categories: FAQs, where users must have a concrete question in order to retrieve a general answer, or dialogs, where users must follow a predefined path

Pavlina K. Jordanova, Mladen Savov, Assen Tchorbadjieff, Milan Stehlík

Stacy distribution defined for the first time in 1961 provides a flexible framework for modelling of a wide range of real-life behaviours. It appears under different names in the scientific literature and contains many useful particular cases. Homogeneous Poisson processes are appropriate apriori models for the number of renewals up to a given time $t>0$. Th

Ren Wang, Yuxuan Li, Can Chen, Dakuo Wang

Adversarial robustness is a critical measure of a neural network's ability to withstand adversarial attacks at inference time. While robust training techniques have improved defenses against individual $\ell_p$-norm attacks (e.g., $\ell_2$ or $\ell_\infty$), models remain vulnerable to diversified $\ell_p$ perturbations. To address this challenge, we propose

Fida K. Dankar

The debate on data access and privacy is an ongoing one. It is kept alive by the never-ending changes/upgrades in (i) the shape of the data collected (in terms of size, diversity, sensitivity and quality), (ii) the laws governing data sharing, (iii) the amount of free public data available on individuals (social media, blogs, population-based databases, etc.

S. Martynenko, F. Pietropaolo, B. Viren, X. Qian

Liquid Argon Time Projection Chamber (LArTPC) technology is commonly utilized in neutrino detector designs. It enables detailed reconstruction of neutrino events with high spatial precision and low energy threshold. Its field response (FR) model describes the time-dependent electric currents induced in the anode-plane electrodes when ionization electrons dri

Taras Goy, Mark Shattuck

In this paper, we find determinant formulas of several Hessenberg-Toeplitz matrices whose nonzero entries are derived from the small and large Schroder and Fine number sequences. Algebraic proofs of these results can be given which make use of Trudi's formula and the generating function of the associated sequence of determinants. We also provide direct argum

Bhaswati Singha Deo, Mayukha Pal, Prasanta K. Panigarhi, Asima Pradhan

Purpose: Cervical cancer is one of the primary causes of death in women. It should be diagnosed early and treated according to the best medical advice, as with other diseases, to ensure that its effects are as minimal as possible. Pap smear images are one of the most constructive ways for identifying this type of cancer. This study proposes a cross-attention

Weiqiong Huang, Emily C. Hector, Joshua Cape, Chris McKennan

The recent explosion of genetic and high dimensional biobank and 'omic' data has provided researchers with the opportunity to investigate the shared genetic origin (pleiotropy) of hundreds to thousands of related phenotypes. However, existing methods for multi-phenotype genome-wide association studies (GWAS) do not model pleiotropy, are only applicable to a

Nizar Malangadan, Gaurav Raina, Debayani Ghosh

The design of transport protocols, embedded in end-systems, and the choice of buffer sizing strategies, within network routers, play an important role in performance analysis of the Internet. In this paper, we take a dynamical systems perspective on the interplay between fluid models for transport protocols and some router buffer sizing regimes. Among the fl

Zachary T. Draper, Maxwell T. Hansen, Fernando Romero-López, Stephen R. Sharpe

We generalize the relativistic field-theoretic (RFT) three-particle finite-volume formalism to systems of three identical, massive, spin-$1/2$ fermions, such as three neutrons. This allows, in principle, for the determination of the three-neutron interaction from the finite-volume spectrum of three-neutron states, which can be obtained from lattice QCD calcu

Alekh Agarwal, H. Brendan McMahan, Zheng Xu

As the adoption of federated learning increases for learning from sensitive data local to user devices, it is natural to ask if the learning can be done using implicit signals generated as users interact with the applications of interest, rather than requiring access to explicit labels which can be difficult to acquire in many tasks. We approach such problem

Weiqi Zhang, Victor M. Zavala

The power grid is undergoing significant restructuring driven by the adoption of wind/solar power and the incorporation of new flexible technologies that can shift load in space and time (e.g., data centers, battery storage, and modular manufacturing). Load shifting is needed to mitigate space-time fluctuations associated with wind/solar power and other disr

Konstandinos Kotsiopoulos, Alexey Miroshnikov, Khashayar Filom, Arjun Ravi Kannan

In recent years, many Machine Learning (ML) explanation techniques have been designed using ideas from cooperative game theory. These game-theoretic explainers suffer from high complexity, hindering their exact computation in practical settings. In our work, we focus on a wide class of linear game values, as well as coalitional values, for the marginal game

Mark Abdelshiheed, Mehak Maniktala, Tiffany Barnes, Min Chi

One fundamental goal of learning is preparation for future learning (PFL) and being able to extend acquired skills and problem-solving strategies to different domains and environments. While substantial research has shown that PFL can be accelerated by obtaining metacognitive skills or influenced by the individual's motivation, no prior work investigated whe

Kimberly A. Hochstedler Webb, Martin T. Wells

In biomedical and public health association studies, binary outcome variables may be subject to misclassification, resulting in substantial bias in effect estimates. The feasibility of addressing binary outcome misclassification in regression models is often hindered by model identifiability issues. In this paper, we characterize the identifiability problems

Jun Wu, Xuesong Ye, Chengjie Mou

An essential topic in online social network security is how to accurately detect bot accounts and relieve their harmful impacts (e.g., misinformation, rumor, and spam) on genuine users. Based on a real-world data set, we construct behavioral sequences from raw event logs. After extracting critical characteristics from behavioral time series, we observe diffe

Mark Abdelshiheed, Guojing Zhou, Mehak Maniktala, Tiffany Barnes

In this work, we investigate how two factors, metacognitive skills and motivation, would impact student learning across domains. More specifically, our primary goal is to identify the critical, yet robust, interaction patterns of these two factors that would contribute to students' performance in learning logic first and then their performance on a subsequen

I. Vallejo-Fabila, E. Jonathan Torres-Herrera

We study the probability distribution function of the long-time values of observables being time-evolved by Hamiltonians modeling clean and disordered one-dimensional chains of many spin-1/2 particles. In particular, we analyze the return probability and its version for a completely extended initial state, the so-called spectral form factor. We complement ou

Luca Rüegg, Gaurav Chaudhary, Robert-Jan Slager

Motivated by recent experimental indications of preformed electron-hole pairs in $\nu_T=1$ quantum Hall bilayers at relatively large separation, we formulate a Chern-Simons (CS) theory of the coupled composite electron liquid (CEL) and composite hole liquid (CHL). We show that the effective action of the CS gauge field fluctuations around the saddle-point le

Joel Honkamaa, Pekka Marttinen

Deep learning has emerged as a strong alternative for classical iterative methods for deformable medical image registration, where the goal is to find a mapping between the coordinate systems of two images. Popular classical image registration methods enforce the useful inductive biases of symmetricity, inverse consistency, and topology preservation by const

Pietro Bergamini, Ana Acebron, Claudio Grillo, Piero Rosati

We present a new high-precision, JWST-based, strong lensing model for the galaxy cluster Abell 2744 at $z=0.3072$. By combining the deep, high-resolution JWST imaging from the GLASS-JWST and UNCOVER programs and a Director's Discretionary Time program, with newly obtained VLT/MUSE data, we identify 32 multiple images from 11 background sources lensed by two

Kaixin Xiong, Shi Gong, Xiaoqing Ye, Xiao Tan

In this paper, we address the problem of detecting 3D objects from multi-view images. Current query-based methods rely on global 3D position embeddings (PE) to learn the geometric correspondence between images and 3D space. We claim that directly interacting 2D image features with global 3D PE could increase the difficulty of learning view transformation due

Anarul Islam Mondal, Sangita Jha

In this article, we study the novel concept of non-stationary iterated function systems (IFSs) introduced by Massopust in 2019. At first, using a sequence of different contractive operators, we construct non-stationary $\alpha$-fractal functions on the space of all continuous functions. Next, we provide some elementary properties of the fractal operator asso

Vesko Valov

We investigate to what extend finite-dimensional homogeneous locally compact $ANR$-spaces have common properties with Euclidean manifolds. Specially, the local structure of homogeneous $ANR$-spaces is described. Using that description, we provide a positive solution of the problem whether every finite-dimensional homogeneous metric $ANR$-compactum $X$ is dim

Michael Howard, R. Bruce Irvin

The ESP32 is a popular microcontroller from Espressif that can be used in many embedded applications. Robotic joints, smart car chargers, beer vat agitators and automated bread mixers are a few examples where this system-on-a-chip excels. It is cheap to buy and has a number of vendors providing low-cost development board kits that come with the microcontroll

Rémy Larue, Jérémie Quevillon

We complete the so-called Universal One-Loop Effective Action (UOLEA) with effects of gravity and provide a systematic approach to incorporate higher dimensional operators in curved spacetime. The functional determinant stemming from the path integral is computed using the Covariant Derivative Expansion (CDE), in a momentum representation that does not rely

Lotte Nijskens, Cornelis, AT van den Berg, Joost JC Verhoeff

Background: Synthetic computed tomography (sCT) has been proposed and increasingly clinically adopted to enable magnetic resonance imaging (MRI)-based radiotherapy. Deep learning (DL) has recently demonstrated the ability to generate accurate sCT from fixed MRI acquisitions. However, MRI protocols may change over time or differ between centres resulting in l

Luca Banszerus, Samuel Möller, Katrin Hecker, Eike Icking

Particle-hole symmetry plays an important role for the characterization of topological phases in solid-state systems. It is found, for example, in free-fermion systems at half filling, and it is closely related to the notion of antiparticles in relativistic field theories. In the low energy limit, graphene is a prime example of a gapless particle-hole symmet

Logan A. Pearce, Jared R. Males, Sebastiaan Y. Haffert, Laird M. Close

We report the confirmation of HIP 67506 C, a new stellar companion to HIP 67506 A. We previously reported a candidate signal at 2$\lambda$/D (240~mas) in L$^{\prime}$ in MagAO/Clio imaging using the binary differential imaging technique. Several additional indirect signals showed that the candidate signal merited follow-up: significant astrometric accelerati

Olivier Giraud, Mark-Oliver Goerbig, Daniel Braun

We calculate the quantum Fisher information for a generic many-body fermionic system in a pure state depending on a parameter. We discuss the situations where the parameter is imprinted in the basis states, in the state coefficients, or both. In the case where the parameter dependence of coefficients results from a Hamiltonian evolution, we derive a particul

Jiafeng Lu, Mostafa Hassan, Francois Courvoisier, Enrique Garcia-Caurel

Polarization plays crucial role in light-matter interactions; hence its overall manipulation is an essential key to unlock the versatility of light manufacturing, especially in femtosecond laser direct writing. Existing polarization-shaping techniques, however, only focus on the manipulation in transverse plane of a light beam, namely a two-dimensional contr

L. Theerthagiri, R. Ganesh

The spread of entanglement is a problem of great interest. It is particularly relevant to quantum state synthesis, where an initial direct-product state is sought to be converted into a highly entangled target state. In devices based on pairwise exchange interactions, such a process can be carried out and optimized in various ways. As a benchmark problem, we

Lixiang Yan, Lele Sha, Linxuan Zhao, Yuheng Li

Educational technology innovations leveraging large language models (LLMs) have shown the potential to automate the laborious process of generating and analysing textual content. While various innovations have been developed to automate a range of educational tasks (e.g., question generation, feedback provision, and essay grading), there are concerns regardi

Ashutosh Kinikar, Ian J. Ford

We investigate stochastic entropy production in a two-level quantum system that performs Rabi oscillations while undergoing quantum measurement brought about by continuous random disturbance by an external measuring device or environment. The dynamics produce quantum Zeno and anti-Zeno effects for certain measurement regimes, and the stochastic entropy produ

Furkan Kaynar, Sudarshan Rajagopalan, Shaobo Zhou, Eckehard Steinbach

A robot operating in unstructured environments must be able to discriminate between different grasping styles depending on the prospective manipulation task. Having a system that allows learning from remote non-expert demonstrations can very feasibly extend the cognitive skills of a robot for task-oriented grasping. We propose a novel two-step framework towa

Manoranjan Singha, Sima Roy

In 2011, the theory of $\mathcal I^K$-convergence gets birth as an extension of the concept of $\mathcal{I}^*$-convergence of sequences of real numbers. $\mathcal I^K$-limit points and $\mathcal I^K$-cluster points of functions are introduced and studied to some extent, where $\mathcal{I}$ and $\mathcal{K}$ are ideals on a non-empty set $S$. In a first count

Debshikha Banerjee, Jinu Thomas, Alberto Nocera, Steven Johnston

We present a density matrix renormalization group (DMRG) study of the doped one-dimensional (1D) Hubbard-Su-Schrieffer-Hegger (Hubbard-SSH) model, where the atomic displacements linearly modulate the nearest-neighbor hopping integrals. Focusing on an optical variant of the model in the strongly correlated limit relevant for cuprate spin chains, we examine ho

Alfred Mallet, Seth Dorfman, Mel Abler, Trevor Bowen

We study the nonlinear evolution of very oblique small-scale Alfv\'en waves with $k_\perp d_i\gtrsim 1$. At these scales, the waves become significantly compressive, unlike in MHD, due to the Hall term in the equations. We demonstrate that when frequencies are small compared to the ion gyrofrequency and amplitudes small compared to unity, no new nonlinear in

Kris K. Dreher, Leonardo Ayala, Melanie Schellenberg, Marco Hübner

Synthetic medical image generation has evolved as a key technique for neural network training and validation. A core challenge, however, remains in the domain gap between simulations and real data. While deep learning-based domain transfer using Cycle Generative Adversarial Networks and similar architectures has led to substantial progress in the field, ther

Laura V. Sales, Julio F. Navarro

The arrangement of dwarf galaxies in a thin plane surrounding the Milky Way has been thought to contradict the prevailing cosmological model of cold dark matter in the Universe. New work suggests that this arrangement may just be a temporary alignment, bringing our galaxy back into agreement with theoretical expectations once the radial distribution of satel

Joshua W. Jones, Eugene Chiang, Gaspard Duchene, Paul Kalas

Certain debris disks have non-axisymmetric shapes in scattered light which are unexplained. The appearance of a disk depends on how its constituent Keplerian ellipses are arranged. The more the ellipses align apsidally, the more non-axisymmetric the disk. Apsidal alignment is automatic for fragments released from a catastrophic collision between solid bodies

Stefano Torniamenti, Mark Gieles, Zephyr Penoyre, Tereza Jerabkova

Astrophysical models of binary-black hole mergers in the Universe require a significant fraction of stellar-mass black holes (BHs) to receive negligible natal kicks to explain the gravitational wave detections. This implies that BHs should be retained even in open clusters with low escape velocities ($\lesssim1~\mathrm{km \, s^{-1}}$). We search for signatur

Poojan Agrawal, Jarrod Hurley, Simon Stevenson, Carl L. Rodriguez

Massive binaries are vital sources of various transient processes, including gravitational-wave mergers. However, large uncertainties in the evolution of massive stars, both physical and numerical, present a major challenge to the understanding of their binary evolution. In this paper, we upgrade our interpolation-based stellar evolution code METISSE to incl

Ibrahima Bah, Pierre Heidmann

We provide the first example of a geometric transition that resolves the Schwarzschild black hole into a smooth microstructure in eleven-dimensional supergravity on a seven-torus. The geometry is indistinguishable from a Schwarzschild black hole dressed with a scalar field in four dimensions, referred to as a Schwarzschild scalarwall. In eleven dimensions, t

Michael S. Calzadilla, Lindsey E. Bleem, Michael McDonald, Michael D. Gladders

We present the discovery of the most distant, dynamically relaxed cool core cluster, SPT-CL J2215-3537 (SPT2215) and its central brightest cluster galaxy (BCG) at z=1.16. Using new X-ray observations, we demonstrate that SPT2215 harbors a strong cool core, with a central cooling time of 200 Myr (at 10 kpc) and a maximal intracluster medium cooling rate of 19

Wen-Yuan Ai, Benoit Laurent, Jorinde van de Vis

Accurately determining bubble wall velocities in first-order phase transitions is of great importance for the prediction of gravitational wave signals and the matter-antimatter asymmetry. However, it is a challenging task which typically depends on the underlying particle physics model. Recently, it has been shown that assuming local thermal equilibrium can

Anton V. Sokolov, Andreas Ringwald

Using the path integral approach, we derive the low energy interactions between axions and electromagnetic field that arise in models with heavy dyons charged under a spontaneously broken global axial $U(1)$ symmetry. Hence, we obtain generic axion-Maxwell equations relevant for experimental searches. We find that the structure of the axion Maxwell equations

M. Buzzi, D. Nicoletti, E. Rowe, E. Wang

In the manuscript arXiv:2210.01114, Dodge and co-authors discuss the influence of pump-probe profile deformations on the reconstructed non-equilibrium optical conductivity of K$_3$C$_{60}$. They state that when pump-induced saturation of the probe response is taken into account, the reconstructed optical properties are not superconducting-like, as was claime

Diksha, Sumanta Kundu, Bikas K. Chakrabarti, Soumyajyoti Biswas

Prediction of an imminent catastrophic event in a driven disordered system is of paramount importance - from the laboratory scale controlled fracture experiment to the largest scale of mechanical failure i.e., earthquakes. It has been long conjectured that the statistical regularities in the energy emission time series mirrors the "health" of such driven sys

Kenneth S. Berenhaut, John D. Foley, Liangdongsheng Lyu

In this paper we provide a generalization of the concept of cohesion as introduced recently by Berenhaut, Moore and Melvin [Proceedings of the National Academy of Sciences, 119 (4) (2022)]. The formulation presented builds on the technique of partitioned local depth by distilling two key probabilistic concepts: local relevance and support division. Earlier r

Junkai Zhang, Weitong Zhang, Quanquan Gu

We study reward-free reinforcement learning (RL) with linear function approximation, where the agent works in two phases: (1) in the exploration phase, the agent interacts with the environment but cannot access the reward; and (2) in the planning phase, the agent is given a reward function and is expected to find a near-optimal policy based on samples collec

Vinu Sankar Sadasivan, Aounon Kumar, Sriram Balasubramanian, Wenxiao Wang

Large Language Models (LLMs) perform impressively well in various applications. However, the potential for misuse of these models in activities such as plagiarism, generating fake news, and spamming has raised concern about their responsible use. Consequently, the reliable detection of AI-generated text has become a critical area of research. AI text detecto

Xiaoxiong Liu, Ivo Souza, Stepan S. Tsirkin

Structural chirality gives rise to characteristic responses that change sign with the handedness of the crystal structure. One example is electrical magnetochiral anisotropy (eMChA), a change in resistivity that depends linearly on the applied current and on the magnetic field. Motivated by recent measurements of a strong eMChA in $p$-doped trigonal telluriu

Jing Wang, Ipsita Mandal

We investigate the parameter regimes favourable for the emergence of plasmons in isotropic, anisotropic, and band-mass symmetric and asymmetric Luttinger semimetals (LSMs). An LSM harbours a quadratic band-crossing point (QBCP) in its bandstructure, where the upper and lower branches of dispersion are doubly degenerate. While a nonzero temperature ($T$) can

Olga Movilla Miangolarra, Amirhossein Taghvaei, Tryphon T. Georgiou

Gradients in temperature and particle concentration fuel many processes in the physical and biological world. In the present work we study a thermodynamic engine powered by anisotropic thermal excitation (that may be due to e.g., a temperature gradient), and draw parallels with the well-known principle of impedance matching in circuit theory, where for maxim

Daochen Zha, Zaid Pervaiz Bhat, Kwei-Herng Lai, Fan Yang

Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler of its great success is the availability of abundant and high-quality data for building machine learning models. Recently, the role of data in AI has been significantly magnified, giving rise to the emerging concept of data-centric AI. The attention of researcher

Maximilian Weingart, Siyu Chen, Clara Donat, Vanessa Helmbrecht

Alkaline vents (AV) are hypothesized to have been a setting for the emergence of life, by creating strong gradients across inorganic membranes within chimney structures. In the past, 3-dimensional chimney structures were formed under laboratory conditions, however, no in situ visualisation or testing of the gradients was possible. We develop a quasi-2-dimens

Ritwik Sadhu, Ziv Goldfeld, Kengo Kato

We study statistical inference for the optimal transport (OT) map (also known as the Brenier map) from a known absolutely continuous reference distribution onto an unknown finitely discrete target distribution. We derive limit distributions for the $L^p$-error with arbitrary $p \in [1,\infty)$ and for linear functionals of the empirical OT map, together with

Tobias Haug, Lorenzo Piroli

We study different aspects of the stabilizer entropies (SEs) and compare them against known nonstabilizerness monotones such as the min-relative entropy and the robustness of magic. First, by means of explicit examples, we show that, for R\'enyi index $0\leq n<2$, the SEs are not monotones with respect to stabilizer protocols which include computational-basi

Galen O'Shea, Majid Komeili

Gaze tracking is a valuable tool with a broad range of applications in various fields, including medicine, psychology, virtual reality, marketing, and safety. Therefore, it is essential to have gaze tracking software that is cost-efficient and high-performing. Accurately predicting gaze remains a difficult task, particularly in real-world situations where im

Ryan Webster, Julien Rabin, Loic Simon, Frederic Jurie

Generative models, such as DALL-E, Midjourney, and Stable Diffusion, have societal implications that extend beyond the field of computer science. These models require large image databases like LAION-2B, which contain two billion images. At this scale, manual inspection is difficult and automated analysis is challenging. In addition, recent studies show that

Brian Foster, Richard D'Arcy, Carl Andreas Lindstrom

The construction of an electron--positron collider "Higgs factory" has been stalled for a decade, not because of feasibility but because of the cost of conventional radio-frequency (RF) acceleration. Plasma-wakefield acceleration promises to alleviate this problem via significant cost reduction based on its orders-of-magnitude higher accelerating gradients.

Niclas Vödisch, Daniele Cattaneo, Wolfram Burgard, Abhinav Valada

Visual odometry is a fundamental task for many applications on mobile devices and robotic platforms. Since such applications are oftentimes not limited to predefined target domains and learning-based vision systems are known to generalize poorly to unseen environments, methods for continual adaptation during inference time are of significant interest. In thi

Allison Xu, Shuo Han, Xiangyang Ju, Haichen Wang

In this paper, we explore the potential of generative machine learning models as an alternative to the computationally expensive Monte Carlo (MC) simulations commonly used by the Large Hadron Collider (LHC) experiments. Our objective is to develop a generative model capable of efficiently simulating detector responses for specific particle observables, focus

Niclas Vödisch, Kürsat Petek, Wolfram Burgard, Abhinav Valada

Operating a robot in the open world requires a high level of robustness with respect to previously unseen environments. Optimally, the robot is able to adapt by itself to new conditions without human supervision, e.g., automatically adjusting its perception system to changing lighting conditions. In this work, we address the task of continual learning for de

Kitty Varghese, Sudarshan Rajagopalan, Mohit Lamba, Kaushik Mitra

Saliency detection methods are central to several real-world applications such as robot navigation and satellite imagery. However, the performance of existing methods deteriorate under low-light conditions because training datasets mostly comprise of well-lit images. One possible solution is to collect a new dataset for low-light conditions. This involves pi

Nicolai Dorka, Tim Welschehold, Wolfram Burgard

Early stopping based on the validation set performance is a popular approach to find the right balance between under- and overfitting in the context of supervised learning. However, in reinforcement learning, even for supervised sub-problems such as world model learning, early stopping is not applicable as the dataset is continually evolving. As a solution,

Gabriella Pinzari

We review a recent generalization of Normal Form Theory to systems (Hamiltonian ones or general ODEs) where the perturbing term is not periodic in one coordinate variable. The main difference with the standard case relies on the non uniqueness of the Normal Form and the total absence of the small divisors problem. The exposition is quite general, so as to al

Vicente Moret-Bonillo, Eduardo Mosqueira-Rey, Samuel Magaz-Romero, Diego Alvarez-Estevez

Despite the great current relevance of Artificial Intelligence, and the extraordinary innovations that this discipline has brought to many fields -among which, without a doubt, medicine is found-, experts in medical applications of Artificial Intelligence are looking for new alternatives to solve problems for which current Artificial Intelligence programs do

T. Deb, M. A. W. Verheijen, J. M. van der Hulst

Context: To study the effects of environment on galaxies we use HI observations of galaxies in and around the cluster A2626. The cluster can effectively be divided in three different environments: the cluster itself, a group environment in the periphery of the cluster (we call it the Swarm) and substructure in the cluster itself. We use these to study the de

Michael Kilgour, Jutta Rogal, Mark Tuckerman

We develop and test new machine learning strategies for accelerating molecular crystal structure ranking and crystal property prediction using tools from geometric deep learning on molecular graphs. Leveraging developments in graph-based learning and the availability of large molecular crystal datasets, we train models for density prediction and stability ra

Tamara Pereira, Erik Nascimento, Lucas E. Resck, Diego Mesquita

Explaining node predictions in graph neural networks (GNNs) often boils down to finding graph substructures that preserve predictions. Finding these structures usually implies back-propagating through the GNN, bonding the complexity (e.g., number of layers) of the GNN to the cost of explaining it. This naturally begs the question: Can we break this bond by e

Carlos Gemmell, Jeffrey Dalton

Tabular question answering (TQA) presents a challenging setting for neural systems by requiring joint reasoning of natural language with large amounts of semi-structured data. Unlike humans who use programmatic tools like filters to transform data before processing, language models in TQA process tables directly, resulting in information loss as table size i

Tirtha Chanda, Katja Hauser, Sarah Hobelsberger, Tabea-Clara Bucher

Although artificial intelligence (AI) systems have been shown to improve the accuracy of initial melanoma diagnosis, the lack of transparency in how these systems identify melanoma poses severe obstacles to user acceptance. Explainable artificial intelligence (XAI) methods can help to increase transparency, but most XAI methods are unable to produce precisel

Yunqing Zhao, Tianyu Pang, Chao Du, Xiao Yang

Diffusion models (DMs) have demonstrated advantageous potential on generative tasks. Widespread interest exists in incorporating DMs into downstream applications, such as producing or editing photorealistic images. However, practical deployment and unprecedented power of DMs raise legal issues, including copyright protection and monitoring of generated conte

Ziyu Wu, Quan Wan, Mingjie Zhao, Yi Ke

Body weight, as an essential physiological trait, is of considerable significance in many applications like body management, rehabilitation, and drug dosing for patient-specific treatments. Previous works on the body weight estimation task are mainly vision-based, using 2D/3D, depth, or infrared images, facing problems in illumination, occlusions, and especi

Matan Atad, Jianxiang Feng, Ismael Rodríguez, Maximilian Durner

Automatic Robotic Assembly Sequence Planning (RASP) can significantly improve productivity and resilience in modern manufacturing along with the growing need for greater product customization. One of the main challenges in realizing such automation resides in efficiently finding solutions from a growing number of potential sequences for increasingly complex