April 2023 arXiv papers — page 50

Mayur Sawant, Ilia Polushin, Abdelhamid Tayebi

We develop an autonomous navigation algorithm for a robot operating in two-dimensional environments containing obstacles, with arbitrary non-convex shapes, which can be in close proximity with each other, as long as there exists at least one safe path connecting the initial and the target location. An instrumental transformation that modifies (virtually) the

Jielu Zhang, Zhongliang Zhou, Gengchen Mai, Mengxuan Hu

Remote sensing imagery has attracted significant attention in recent years due to its instrumental role in global environmental monitoring, land usage monitoring, and more. As image databases grow each year, performing automatic segmentation with deep learning models has gradually become the standard approach for processing the data. Despite the improved per

Akhil Kuniyil, Avinash Kshitij, Kasturi Mandal

This paper presents a study of the interconnectivity and interdependence of various Artificial intelligence (AI) technologies through the use of centrality measures, clustering coefficients, and degree of fusion measures. By analyzing the technologies through different time windows and quantifying their importance, we have revealed important insights into th

Leonel Rozo, Andras G. Kupcsik, Philipp Schillinger, Meng Guo

Robotic manipulation is currently undergoing a profound paradigm shift due to the increasing needs for flexible manufacturing systems, and at the same time, because of the advances in enabling technologies such as sensing, learning, optimization, and hardware. This demands for robots that can observe and reason about their workspace, and that are skillfull e

Azimkhon Ostonov, Mikhail Moshkov

In this paper, we consider classes of decision tables with many-valued decisions closed under operations of removal of columns, changing of decisions, permutation of columns, and duplication of columns. We study relationships among three parameters of these tables: the complexity of a decision table (if we consider the depth of decision trees, then the compl

Nandan Kumar Jha, Brandon Reagen

Prior work on Private Inference (PI) -- inferences performed directly on encrypted input -- has focused on minimizing a network's ReLUs, which have been assumed to dominate PI latency rather than FLOPs. Recent work has shown that FLOPs for PI can no longer be ignored and incur high latency penalties. In this paper, we develop DeepReShape, a technique that op

Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li

The recent GPT-4 has demonstrated extraordinary multi-modal abilities, such as directly generating websites from handwritten text and identifying humorous elements within images. These features are rarely observed in previous vision-language models. However, the technical details behind GPT-4 continue to remain undisclosed. We believe that the enhanced multi

Ian Weng Chan, Spark C. Tseung, Andrei L. Badescu, X. Sheldon Lin

With the advancement in technology, telematics data which capture vehicle movements information are becoming available to more insurers. As these data capture the actual driving behaviour, they are expected to improve our understanding of driving risk and facilitate more accurate auto-insurance ratemaking. In this paper, we analyze an auto-insurance dataset

Carlos Núñez-Molina, Pablo Mesejo, Juan Fernández-Olivares

In the field of Sequential Decision Making (SDM), two paradigms have historically vied for supremacy: Automated Planning (AP) and Reinforcement Learning (RL). In the spirit of reconciliation, this article reviews AP, RL and hybrid methods (e.g., novel learn to plan techniques) for solving Sequential Decision Processes (SDPs), focusing on their knowledge repr

Kush Kinra, Manil T. Mohan

In this work, we consider the two- and three-dimensional convective Brinkman-Forchheimer (CBF) equations (or damped Navier--Stokes equations) on a torus $\mathbb{T}^d,$ $d\in\{2,3\}$: $$ \frac{\partial \boldsymbol{u}}{\partial t}-\mu \Delta\boldsymbol{u}+(\boldsymbol{u}\cdot\nabla)\boldsymbol{u}+\alpha\boldsymbol{u}+\beta|\boldsymbol{u}|^{r-1}\boldsymbol{u}+

Zhuangzhuang Dai, Jinha Park, Aleksandra Kaszowska, Chen Li

The advent of industrial robotics and autonomous systems endow human-robot collaboration in a massive scale. However, current industrial robots are restrained in co-working with human in close proximity due to inability of interpreting human agents' attention. Human attention study is non-trivial since it involves multiple aspects of the mind: perception, me

Alex Povitsky

The velocity and trajectory of particle moving along the corrugated (rough) surface under action of gravity is obtained by meshless Boundary Singularity Method (BSM). This physical situation is found often in biological systems and microfluidic devices. The Stokes equations with no-slip boundary conditions are solved using the Green function for Stokeslets.

Hyunjin Kim, Youngjoon Choi, Étienne Lantagne-Hurtubise, Cyprian Lewandowski

Magic-angle twisted trilayer graphene (MATTG) exhibits a range of strongly correlated electronic phases that spontaneously break its underlying symmetries. The microscopic nature of these phases and their residual symmetries stands as a key outstanding puzzle whose resolution promises to shed light on the origin of superconductivity in twisted materials. Her

Ratun Rahman, Md Rafid Islam

Virtual reality is the way of the future. The use of virtual reality is expanding over time across all sectors, from the entertainment industry to the military and space. VREd is a similar concept where a virtual reality-based classroom is used for online education where the user will have better interaction and more control. Unity3D and WebGL software have

Cole Comfort

There is a bijection between odd prime dimensional qudit pure stabilizer states modulo invertible scalars and affine Lagrangian subspaces of finite dimensional symplectic $\mathbb{F}_p$-vector spaces. In the language of the stabilizer formalism, full rank stabilizer tableaux are exactly the bases for affine Lagrangian subspaces. This correspondence extends t

Mark H. Fischer, Patrick A. Lee, Jonathan Ruhman

Recently, unusual $\pi$ phase shifts in Little-Parks experiments performed on two systems derived from the layered superconductor 2H-TaS$_2$ were reported. These systems share the common feature that additional layers have been inserted between the 1H-TaS$_2$ layers. In both cases, the $\pi$ phase shift has been interpreted as evidence for the emergence of e

Md Manjurul Ahsan, Shivakumar Raman, Zahed Siddique

Over the years, the Invariant Scattering Transform (IST) technique has become popular for medical image analysis, including using wavelet transform computation using Convolutional Neural Networks (CNN) to capture patterns' scale and orientation in the input signal. IST aims to be invariant to transformations that are common in medical images, such as transla

Florian Kotthoff, Christoph Muschner, Deniz Tepe, Esther Vogt

The energy system in Germany consists of a large number of distributed facilities, including millions of PV plants, wind turbines, and biomass plants. To understand and manage this system efficiently, accurate and reliable information about all facilities is essential. In Germany, the Marktstammdatenregister (MaStR) serves as a central registry for units of

NA62 Collaboration

Measurement of the ultra-rare $K^+\to\pi^+\nu\bar\nu$ decay at the NA62 experiment at CERN requires high-performance particle identification to distinguish muons from pions. Calorimetric identification currently in use, based on a boosted decision tree algorithm, achieves a muon misidentification probability of $1.2\times 10^{-5}$ for a pion identification e

Torsten Bringmann, Valerie Domcke, Elina Fuchs, Joachim Kopp

High-frequency gravitational waves can be detected by observing the frequency modulation they impart on photons. We discuss fundamental limitations to this method related to the fact that it is impossible to construct a perfectly rigid detector. We then propose several novel methods to search for O(MHz-GHz) gravitational waves based on the frequency modulati

Miruna Oprescu, Jacob Dorn, Marah Ghoummaid, Andrew Jesson

Estimating heterogeneous treatment effects from observational data is a crucial task across many fields, helping policy and decision-makers take better actions. There has been recent progress on robust and efficient methods for estimating the conditional average treatment effect (CATE) function, but these methods often do not take into account the risk of hi

Esteban Villa-Turek, Hernan David Insuasti-Ceballos, Jairo Andres Ruiz-Saenz, Jacobo Campo-Robledo

The public policy cycle requires increasingly the use of evidence by policy makers. Evidence Gap Maps (EGMs) are a relatively new methodology that helps identify, process, and visualize the vast amounts of studies representing a rich source of evidence for better policy making. This document performs a methodological review of EGMs and presents the developme

Fedor Bakharev, Sergey Matveenko

The structure of the spectrum of the three-dimensional Dirichlet Laplacian in the 3D polyhedral layer of fixed width is studied. It appears that the essential spectrum is defined by the smallest dihedral angle that forms the boundary of the layer while the discrete spectrum is always finite. An example of a layer with the empty discrete spectrum is construct

Enzo Canonero, Alessandra Rosalba Brazzale, Glen Cowan

We present improved methods for calculating confidence intervals and $p$-values in situations where standard asymptotic approaches fail due to small sample sizes. We apply these techniques to a specific class of statistical model that can incorporate uncertainties in parameters that themselves represent uncertainties (informally, "errors on errors") called t

Philippe Hansen-Estruch, Ilya Kostrikov, Michael Janner, Jakub Grudzien Kuba

Effective offline RL methods require properly handling out-of-distribution actions. Implicit Q-learning (IQL) addresses this by training a Q-function using only dataset actions through a modified Bellman backup. However, it is unclear which policy actually attains the values represented by this implicitly trained Q-function. In this paper, we reinterpret IQL

Pranay Mundra, Jianhao Zhang, Fatemeh Nargesian, Nikolaus Augsten

We study the top-k set similarity search problem using semantic overlap. While vanilla overlap requires exact matches between set elements, semantic overlap allows elements that are syntactically different but semantically related to increase the overlap. The semantic overlap is the maximum matching score of a bipartite graph, where an edge weight between tw

F. Mazzola, H. Hassani, D. Amoroso, S. K. Chaluvadi

WO$_3$ is a binary 5d compound which has attracted remarkable attention due to the vast array of structural transitions that it undergoes in its bulk form. In the bulk, a wide range of electronic properties has been demonstrated, including metal-insulator transitions and superconductivity upon doping. In this context, the synthesis of WO$_3$ thin films holds

Fulvio Gesmundo

This is a collection of open problems and research ideas following the presentations and the discussions of the AGATES Kickoff Workshop held at the Institute of Mathematics of the Polish Academy of Sciences (IMPAN) and at the Department of Mathematics of University of Warsaw (MIM UW), September 19-26, 2022.

Sanaea C. Rose, Smadar Naoz, Re'em Sari, Itai Linial

Like most galaxies, the Milky Way harbors a supermassive black hole (SMBH) at its center, surrounded by a nuclear star cluster. In this dense star cluster, direct collisions can occur between stars before they evolve off the main-sequence. Using a statistical approach, we characterize the outcomes of these stellar collisions within the inner parsec of the Ga

Xiaochen Sun, Xue-Ning Bai

We present a new magnetohydrodynamic-particle-in-cell (MHD-PIC) code integrated into the Athena++ framework. It treats energetic particles as in conventional PIC codes while the rest of thermal plasmas are treated as background fluid described by MHD, thus primarily targeting at multi-scale astrophysical problems involving the kinetic physics of the cosmic-r

J. C. Rodríguez-Ramírez, C. R. Bom, B. Fraga, R. Nemmen

Active galactic nuclei (AGNs) have been proposed as plausible sites for hosting a sizable fraction of the binary black hole (BBH) mergers measured through gravitational waves (GWs) by the LIGO-Virgo-Kagra (LVK) experiment. These GWs could be accompanied by radiation feedback due to the interaction of the BBH merger remnant with the AGN disc. We present a new

Hannah Klion, Revathi Jambunathan, Michael E. Rowan, Eloise Yang

Relativistic magnetic reconnection is a non-ideal plasma process that is a source of non-thermal particle acceleration in many high-energy astrophysical systems. Particle-in-cell (PIC) methods are commonly used for simulating reconnection from first principles. While much progress has been made in understanding the physics of reconnection, especially in 2D,

Sihan Yuan, Alvaro Zamora, Tom Abel

Beyond standard summary statistics are necessary to summarize the rich information on non-linear scales in the era of precision galaxy clustering measurements. For the first time, we introduce the 2D k-th nearest neighbor (kNN) statistics as a summary statistic for discrete galaxy fields. This is a direct generalization of the standard 1D kNN by disentanglin

Akshat Pandey, Aditya Mahadevan, Aditya Cowsik

We study the low-energy physics of the critical (2+1)-dimensional random transverse-field Ising model. The one-dimensional version of the model is a paradigmatic example of a system governed by an infinite-randomness fixed point, for which many results on the distributions of observables are known via an asymptotically exact renormalization group (RG) approa

Bert Vander Meulen, Peter Camps, Marko Stalevski, Maarten Baes

Models of active galactic nuclei (AGN) suggest that their circumnuclear media are complex with clumps and filaments, while recent observations hint towards polar extended structures of gas and dust, as opposed to the classical torus paradigm. The X-ray band forms an interesting window to study these circumnuclear media in great detail. In this work, we exten

Carlos Heredia

This thesis aims to study nonlocal Lagrangians with a finite and an infinite number of degrees of freedom. We obtain an extension of Noether's theorem and Noether's identities for such Lagrangians. We then set up a Hamiltonian formalism for them. Furthermore, we show that $r^{\rm th}$-order Lagrangians can be treated as a particular case, and the expected re

João M. Alves, G. C. Branco, A. L. Cherchiglia, C. C. Nishi

We review the theory and phenomenology of isosinglet vector-like quarks (VLQs). In recent years, interest in VLQs has been increasing, due to their contributions to new physics effects that can be tested in experiments at LHC and High-Luminosity LHC. The similarities of models with isosinglet VLQs and the seesaw framework in the leptonic sector are pointed o

Arturo de Giorgi, Fotis Koutroulis, Luca Merlo, Stefan Pokorski

With no evidence of any exotic particle detected so far beyond the Standard Model, the new physics may lie above the presently accessible energies at colliders and, at low-energies, can be accounted for via an effective description. The interplay of flavour and Higgs physics data allows setting stringent bounds on the parameters of the effective Lagrangian.

Keisuke Inomata, Hayden Lee, Wayne Hu

We study the $N$-point function of the density contrast to quadratic order in the squeezed limit during the matter-dominated (MD) and radiation-dominated (RD) eras in synchronous gauge. Since synchronous gauge follows the free-fall frame of observers, the equivalence principle dictates that in the gradient approximation for the long-wavelength mode there is

Yjan A. Gordon, Christopher P. O'Dea, Stefi A. Baum, Keith Bechtol

Compact Steep Spectrum (CSS) radio sources are active galactic nuclei that have radio jets propagating only on galactic scales, defined as having projected linear sizes (LS) of up to $20\,$kpc. CSS sources are generally hosted by massive early-type galaxies with little on-going star formation, however a small fraction are known to have enhanced star formatio

Ziyu Wan, Christian Richardt, Aljaž Božič, Chao Li

Neural radiance fields (NeRFs) enable novel view synthesis with unprecedented visual quality. However, to render photorealistic images, NeRFs require hundreds of deep multilayer perceptron (MLP) evaluations - for each pixel. This is prohibitively expensive and makes real-time rendering infeasible, even on powerful modern GPUs. In this paper, we propose a nov

Sung-Han Tsai, Ke-Jung Chen, Daniel Whalen, Po-Sheng Ou

Numerical simulations have now shown that Population III (Pop III) stars can form in binaries and small clusters and that these stars can be in close proximity to each other. If so, they could be subject to binary interactions such as mass exchange that could profoundly alter their evolution, ionizing UV and Lyman-Werner (LW) photon emission and explosion yi

Stamatios Lefkimmiatis, Iaroslav Koshelev

We introduce a novel optimization algorithm for image recovery under learned sparse and low-rank constraints, which we parameterize as weighted extensions of the $\ell_p^p$-vector and $\mathcal S_p^p$ Schatten-matrix quasi-norms for $0\!<p\!\le1$, respectively. Our proposed algorithm generalizes the Iteratively Reweighted Least Squares (IRLS) method, used fo

Tomas Jakab, Ruining Li, Shangzhe Wu, Christian Rupprecht

We present Farm3D, a method for learning category-specific 3D reconstructors for articulated objects, relying solely on "free" virtual supervision from a pre-trained 2D diffusion-based image generator. Recent approaches can learn a monocular network that predicts the 3D shape, albedo, illumination, and viewpoint of any object occurrence, given a collection o

Pranav Sanghavi, Calvin Leung, Kevin Bandura, Tomas Cassanelli

The sensitivity and field of view of the Canadian Hydrogen Intensity Mapping Experiment (CHIME) has enabled its fast radio burst (FRB) backend to detect thousands of FRBs. However, the low angular resolution of CHIME prevents it from localizing most FRBs to their host galaxies. Very long baseline interferometry (VLBI) can readily provide the subarcsecond res

Frederik Møller, Philipp Schüttelkopf, Jörg Schmiedmayer, Sebastian Erne

The formation of dispersive shock waves in the one-dimensional Bose gas represents a limitation of Generalized Hydrodynamics (GHD) due to the coarse-grained nature of the theory. Nevertheless, GHD accurately captures the long wavelength behavior indicating an implicit knowledge of the underlying microscopic physics. Such representation are already known thro

Frederik Warburg, Ethan Weber, Matthew Tancik, Aleksander Holynski

Casually captured Neural Radiance Fields (NeRFs) suffer from artifacts such as floaters or flawed geometry when rendered outside the camera trajectory. Existing evaluation protocols often do not capture these effects, since they usually only assess image quality at every 8th frame of the training capture. To push forward progress in novel-view synthesis, we

Stanley Alama, Lia Bronsard, Silas Vriend

Inspired by a planar partitioning problem involving multiple improper chambers, this article investigates using classical techniques what can be said of the existence, uniqueness, and regularity of minimizers in a certain free-endpoint isoperimetric problem. By restricting to curves which are expressible as graphs of functions, a full existence-uniqueness-re

Ziqi Huang, Kelvin C. K. Chan, Yuming Jiang, Ziwei Liu

Diffusion models arise as a powerful generative tool recently. Despite the great progress, existing diffusion models mainly focus on uni-modal control, i.e., the diffusion process is driven by only one modality of condition. To further unleash the users' creativity, it is desirable for the model to be controllable by multiple modalities simultaneously, e.g.,

Vardan Oganesyan

We consider two categories related to symplectic manifolds: 1. Objects are symplectic manifolds and morphisms are symplectic embeddings. 2. Objects are symplectic manifolds endowed with compatible almost complex structure and morphisms are pseudoholomorphic maps. We define new homotopy theories for these categories. In particular, we give definitions of homo

Haiwen Feng, Peter Kulits, Shichen Liu, Michael J. Black

We address the problem of fitting a parametric human body model (SMPL) to point cloud data. Optimization-based methods require careful initialization and are prone to becoming trapped in local optima. Learning-based methods address this but do not generalize well when the input pose is far from those seen during training. For rigid point clouds, remarkable g

Mehrab Hosain, Most. Yeasmin Arafat, Gazi Zahirul Islam, Jia Uddin

There are a variety of features of the human voice that can be classified as pitch, timbre, loudness, and vocal tone. It is observed in numerous incidents that human expresses their feelings using different vocal qualities when they are speaking. The primary objective of this research is to recognize different emotions of human beings such as anger, sadness,

Xujiang Zhao

Deep neural networks (DNNs) have received tremendous attention and achieved great success in various applications, such as image and video analysis, natural language processing, recommendation systems, and drug discovery. However, inherent uncertainties derived from different root causes have been realized as serious hurdles for DNNs to find robust and trust

Michael Andreas Klatt, Christian Bair, Hartmut Löwen, René Wittmann

When can we map a classical density profile to an external potential? In equilibrium, without time dependence, the one-body density is known to specify the external potential that is applied to the many-body system. This mapping from a density to the potential is the cornerstone of classical density functional theory (DFT). Here, we consider non-equilibrium,

Sarah H. Cen, Aleksander Madry, Devavrat Shah

People form judgments and make decisions based on the information that they observe. A growing portion of that information is not only provided, but carefully curated by social media platforms. Although lawmakers largely agree that platforms should not operate without any oversight, there is little consensus on how to regulate social media. There is consensu

Sitan Chen, Zehao Dou, Surbhi Goel, Adam R Klivans

We consider the well-studied problem of learning a linear combination of $k$ ReLU activations with respect to a Gaussian distribution on inputs in $d$ dimensions. We give the first polynomial-time algorithm that succeeds whenever $k$ is a constant. All prior polynomial-time learners require additional assumptions on the network, such as positive combining co

Haitao Yang, Xiangru Huang, Bo Sun, Chandrajit Bajaj

This paper introduces GenCorres, a novel unsupervised joint shape matching (JSM) approach. Our key idea is to learn a mesh generator to fit an unorganized deformable shape collection while constraining deformations between adjacent synthetic shapes to preserve geometric structures such as local rigidity and local conformality. GenCorres presents three appeal

Claude Marion, Pedro V. Silva, Gareth Tracey

We introduce the pseudovariety of finite groups $\mathbf{U} = \displaystyle\bigvee_{p \in \mathbb{P}} {\bf Ab}(p) \ast {\bf Ab}(p-1)$, where $\mathbb{P}$ is the set of all primes. We show that $\mathbf{U}$ consists of all finite supersolvable groups with elementary abelian derived subgroup and abelian Sylow subgroups, being therefore decidable. We prove that

Philippe G. LeFloch, Jean-Marc Mercier

We introduce a numerical methodology, referred to as the transport-based mesh-free method, which allows us to deal with continuous, discrete, or statistical models in the same unified framework, and leads us to a broad class of numerical algorithms recently implemented in a Python library (namely, CodPy). Specifically, we propose a mesh-free discretization t

Johannes Lehner, Benedikt Alkin, Andreas Fürst, Elisabeth Rumetshofer

Masked Image Modeling (MIM) methods, like Masked Autoencoders (MAE), efficiently learn a rich representation of the input. However, for adapting to downstream tasks, they require a sufficient amount of labeled data since their rich features code not only objects but also less relevant image background. In contrast, Instance Discrimination (ID) methods focus

Chengyang Shao

This paper provides a para-differential calculus toolbox on compact Lie groups and homogeneous spaces. It helps to understand non-local, nonlinear partial differential operators with low regularity on manifolds with high symmetry. In particular, the paper provides a para-linearization formula for the Dirichlet-Neumann operator of a distorted 2-sphere, a key

Ivan Ahumada, James P. Edwards

We introduce modifications to Monte Carlo simulations of the Feynman path integral that improve sampling of localised interactions. The new algorithms generate trajectories in simple background potentials designed to concentrate them about the interaction region, reminiscent of importance sampling. This improves statistical sampling of the system and overcom

Maciej A. Mazurowski, Haoyu Dong, Hanxue Gu, Jichen Yang

Training segmentation models for medical images continues to be challenging due to the limited availability of data annotations. Segment Anything Model (SAM) is a foundation model that is intended to segment user-defined objects of interest in an interactive manner. While the performance on natural images is impressive, medical image domains pose their own s

Renata Kallosh, Yusuke Yamada

We study d=4, $N\geq 5$ supergravities and their deformation via candidate counterterms, with the purpose to absorb UV divergences. We generalize the earlier studies of deformation and twisted self-duality constraint to the case with unbroken local H-symmetry in presence of fermions. We find that the deformed action breaks nonlinear local supersymmetry. We s

Shen Zheng, Jie Huang, Kevin Chen-Chuan Chang

Recent advancements in large language models, such as ChatGPT, have demonstrated significant potential to impact various aspects of human life. However, ChatGPT still faces challenges in providing reliable and accurate answers to user questions. To better understand the model's particular weaknesses in providing truthful answers, we embark an in-depth explor

Usha Lokala, Orchid Chetia Phukan, Triyasha Ghosh Dastidar, Francois Lamy

Opioid and substance misuse is rampant in the United States today, with the phenomenon known as the "opioid crisis". The relationship between substance use and mental health has been extensively studied, with one possible relationship being: substance misuse causes poor mental health. However, the lack of evidence on the relationship has resulted in opioids

Quintina L. Campbell, Jonathan Herington, Andrew D. White

Machine learning models have dual-use potential, potentially serving both beneficial and malicious purposes. The development of open-source models in chemistry has specifically surfaced dual-use concerns around toxicological data and chemical warfare agents. We discuss a chain risk framework identifying three misuse pathways and corresponding mitigation stra

Samuel Laliberte, Suddhasattwa Brahma

Matrix theory is a proposed non-perturbative definition of superstring theory in which space is emergent. Recently, it was shown that space-time can emerge with a scale-invariant spectrum of cosmological perturbations which is sourced by thermal fluctuations of the BFSS model at finite temperature. Inspired by these results, we begin a study of the IKKT mode

Soumendu Das, Nagendra Kumar, Dharmendra Dixit

In this paper, we consider a dual-hop mixed THz-RF system model for backhaul-fronthaul applications where the link between source and destination is established only through the relay node in which decode-and-forward relaying protocol is used. The THz link suffers from the joint impact of antenna misalignment and stochastic characteristics of wireless channe

Claude Marion, Pedro V. Silva, Gareth Tracey

Let $F$ be a free group of arbitrary rank and let $H$ be a finitely generated subgroup of $F$. Given a pseudovariety $\mathbf{V}$ of finite groups, i.e. a class of finite groups closed under taking subgroups, quotients and finitary direct products, we endow $F$ with its pro-$\mathbf{V}$ topology. Our main result states that it is decidable whether $H$ is $\m

Yingzhen Yang, Ping Li

Nonconvex and nonsmooth optimization problems are important and challenging for statistics and machine learning. In this paper, we propose Projected Proximal Gradient Descent (PPGD) which solves a class of nonconvex and nonsmooth optimization problems, where the nonconvexity and nonsmoothness come from a nonsmooth regularization term which is nonconvex but p

Xiaohang Tang, Le Cong Dinh, Stephen Marcus McAleer, Yaodong Yang

By incorporating regret minimization, double oracle methods have demonstrated rapid convergence to Nash Equilibrium (NE) in normal-form games and extensive-form games, through algorithms such as online double oracle (ODO) and extensive-form double oracle (XDO), respectively. In this study, we further examine the theoretical convergence rate and sample comple

Sushanta Barman, Sudeep Bhattacharjee

Self-imaging in near-field diffraction is a practical application of coherent manipulation of matter waves in Talbot interferometry. In this work, near-field diffraction of protons by a nanostructured metallic grating under the influence of (a) uniform, (b) spatially modulated, and (c) temporally modulated electric fields are investigated. Time-domain simula

Matthew Enlow, Adam Larios, Jiahong Wu

We propose an approximate model for the 2D Kuramoto-Sivashinsky equations (KSE) of flame fronts and crystal growth. We prove that this new ``calmed'' version of the KSE is globally well-posed, and moreover, its solutions converge to solutions of the KSE on the time interval of existence and uniqueness of the KSE at an algebraic rate. In addition, we provide

Gabriel Berzunza Ojeda, Anita Winter

Since the work of Aldous and Pitman (1998), several authors have studied the pruning processes of Galton-Watson trees and their continuous analogue L\'evy trees. L\"ohr, Voisin and Winter (2015) introduced the space of bi-measure $\mathbb{R}$-trees equipped with the so-called leaf sampling weak vague topology which allows them to unify the discrete and the c

Nikolai A. Sinitsyn, Bin Yan

The Number Partitioning Problem (NPP) is one of the NP-complete computational problems. Its definite exact solution generally requires a check of all $N$ solution candidates, which is exponentially large. Here we describe a path to the fast solution of this problem in $\sqrt{N}$ quasi-adiabatic quantum annealing steps. We argue that the errors due to the fin

Ritik Soni, Ashok Kumar Pathak

In this paper, we introduce a generalized fractional negative binomial process (GFNBP) by time changing the fractional Poisson process with an independent Mittag-Leffler (ML) Levy subordinator. We study its distributional properties and its connection to PDEs. We examine the long-range dependence (LRD) property of the GFNBP and show that it is not infinitely

L. Pasquini, A. F. Pala, M. Salaris, H. G. Ludwig

We use the ESPRESSO spectrograph at the Very Large Telescope to measure velocity shifts and gravitational redshifts of eight bona fide Hyades white dwarfs, with an accuracy better than 1.5 percent. By comparing the gravitational redshift measurements of the mass-to-radius ratio with the same ratios derived by fitting the \textit{Gaia} photometry with theoret

Valerii Chuiko, Paul W. Ayers

Direct approaches to the quantum many-body problem suffer from the so-called "curse of dimensionality": the number of parameters needed to fully specify the exact wavefunction grows exponentially with increasing system size. This motivates the develop of accurate, but approximate, ways to parametrize the wavefunction, including methods like couple cluster th

Azat F. Aminov, Alexey A. Sokolik, Yurii E. Lozovik

We consider non-dissipative drag between Bose-condensed exciton polaritons in optical microcavity and embedded superconductors. This effect consists in induction of a non-dissipative electric current in the superconductor by motion of polariton Bose condensate due to electron-polariton interaction, or vice versa. Using many-body theory, we calculate the drag

Maria-Paola Forte, Peter Kulits, Chun-Hao Huang, Vasileios Choutas

Sign language (SL) is the primary method of communication for the 70 million Deaf people around the world. Video dictionaries of isolated signs are a core SL learning tool. Replacing these with 3D avatars can aid learning and enable AR/VR applications, improving access to technology and online media. However, little work has attempted to estimate expressive

Pradeep Niroula, Christopher David White, Qingfeng Wang, Sonika Johri

Magic is a property of quantum states that enables universal fault-tolerant quantum computing using simple sets of gate operations. Understanding the mechanisms by which magic is created or destroyed is, therefore, a crucial step towards efficient and practical fault-tolerant computation. We observe that a random stabilizer code subject to coherent errors ex

James Bartusek, Dakshita Khurana, Akshayaram Srinivasan

Can a sender non-interactively transmit one of two strings to a receiver without knowing which string was received? Does there exist minimally-interactive secure multiparty computation that only makes (black-box) use of symmetric-key primitives? We provide affirmative answers to these questions in a model where parties have access to shared EPR pairs, thus d

Jitendra Pal

We investigate a new class of $\eta$-deformed $AdS_5 \times T^{1,1}$ backgrounds produced by $r$-matrices that satisfy the modified classical Yang-Baxter equation [Jour. High Ener. Phys. 03 (2022) 094]. We examine the classical phase space of these (semi)classical strings by numerically studying the dynamics of the string sigma models over this deformed back

Gabriel Araújo, Igor A. Ferra, Max R. Jahnke, Luis F. Ragognette

We introduce new techniques to study the differential complexes associated to tube structures on $M \times \mathbb{T}^m$ of corank $m$, in which $M$ is a compact manifold and $\mathbb{T}^m$ is the $m$-torus. By systematically employing partial Fourier series, for complex tube structures, we completely characterize global solvability, in a given degree, in te

Woorak Choi, Lijie Liu, Martin Bureau, Michele Cappellari

We present high spatial resolution ($\approx24$ pc) Atacama Large Millimeter/sub-millimeter Array $^{12}$CO(2-1) observations of the central region of the nearby barred spiral galaxy NGC 5806. NGC 5806 has a highly structured molecular gas distribution with a clear nucleus, a nuclear ring and offset dust lanes. We identify $170$ spatially- and spectrally-res

Huaqing Mao, Paul Woodward, Falk Herwig, Pavel A. Denissenkov

We present 3-D hydrodynamical simulations of core convection with a stably stratified envelope of a \unit{25}{\Msun} star in the early phase of the main-sequence. We use the explicit gas-dynamics code \code{PPMstar} which tracks two fluids and includes radiation pressure and radiative diffusion. Multiple series of simulations with different luminosities and

R. Babjak, L. Willingale, A. Arefiev, M. Vranic

The direct laser acceleration (DLA) of electrons in underdense plasmas can provide 100s of nC of electrons accelerated to near-GeV energies using currently available lasers. Here we demonstrate the key role of electron transverse displacement in the acceleration and use it to analytically predict the expected maximum electron energies. The energy scaling is

Ruixiao Yao, Sungjae Chi, Biswaroop Mukherjee, Airlia Shaffer

The frictionless, directional propagation of particles at the boundary of topological materials is one of the most striking phenomena in transport. These chiral edge modes lie at the heart of the integer and fractional quantum Hall effects, and their extraordinary robustness against noise and disorder reflects the quantization of Hall conductivity in these s

Erik Burman

We revisit the celebrated Kohn-Vogelius penalty method and discuss how to use it for the unique continuation problem where data is given in the bulk of the domain. We then show that the primal-dual mixed finite element methods for the elliptic Cauchy problem introduced in \cite{BLO18} (\emph{E. Burman, M. Larson, L. Oksanen, Primal-dual mixed finite element

Andrei Kucharavy, Matteo Monti, Rachid Guerraoui, Ljiljana Dolamic

Modern machine learning (ML) models are capable of impressive performances. However, their prowess is not due only to the improvements in their architecture and training algorithms but also to a drastic increase in computational power used to train them. Such a drastic increase led to a growing interest in distributed ML, which in turn made worker failures a

Qiyang Li, Aviral Kumar, Ilya Kostrikov, Sergey Levine

Deep reinforcement learning algorithms that learn policies by trial-and-error must learn from limited amounts of data collected by actively interacting with the environment. While many prior works have shown that proper regularization techniques are crucial for enabling data-efficient RL, a general understanding of the bottlenecks in data-efficient RL has re

Shuyuan Tu, Qi Dai, Zuxuan Wu, Zhi-Qi Cheng

Contrastive language-image pretraining (CLIP) has demonstrated remarkable success in various image tasks. However, how to extend CLIP with effective temporal modeling is still an open and crucial problem. Existing factorized or joint spatial-temporal modeling trades off between the efficiency and performance. While modeling temporal information within straig

Yiduo Guo, Yaobo Liang, Chenfei Wu, Wenshan Wu

Large Language Models (LLMs) have shown remarkable performance in various basic natural language tasks. For completing the complex task, we still need a plan for the task to guide LLMs to generate the specific solutions step by step. LLMs can directly generate task plans, but these plans may still contain factual errors or are incomplete. A high-quality task

Po-Hsin Shih, Thi-Nga Do, Godfrey Gumbs, Danhong Huang

Photocurrent acts as one of measurable responses of material to light, which has proved itself to be crucial for sensing and energy harvesting. Topological semimetals with gapless energy dispersion and abundant topological surface and bulk states exhibit exotic photocurrent responses, such as novel quantized circular photogalvanic effect observed in Weyl sem

Nicetu Tibau Vidal, Lucia Vilchez-Estevez

We define creation and annihilation operators for any 2D non-abelian anyon theory by studying the algebraic structure from the anyon diagrammatic formalism. We construct the creation operators for Fibonacci anyons explicitly. We obtain that a single creation operator per particle type is not enough; we need an extra creation operator for every alternative fu

Polina Alexeenko, Vasileios Charisopoulos

Meeting growing demand for automotive battery resources is predicted to be costly from both economic and environmental perspectives. To minimize these costs, battery resources should be deployed as efficiently as possible. A potential source of inefficiency in battery deployment is the fact that the batteries of personal vehicles are typically much larger th

Ugo Nzongani, Pablo Arnault

Discrete-time quantum walks with position-dependent coin operators have numerous applications. For a position dependence that is sufficiently smooth, it has been provided in Ref. [1] an approximate quantum-circuit implementation of the coin operator that is efficient. If we want the quantum-circuit implementation to be exact (e.g., either, in the case of a s

Vishal Varma, T S Mahesh

Long-lived singlet states (LLS) of nuclear spin pairs have been extensively studied and utilized in the isotropic phase via liquid state NMR. However, there are hardly any reports of LLS in the anisotropic phase that allows contribution from the dipolar coupling in addition to the scalar coupling, thereby opening many exciting possibilities. Here we report o

Matthew Markovych, Asli Tandogan

We present an analytical method to solve the leading order (LO) Dokshitzer-Gribov-Lipatov-Altarelli-Parisi (DGLAP) evolution equations, which describe how parton distribution functions (PDFs) vary through different energy scales. Our approach utilizes the analytical technique that was previously employed to address the evolution of singular distribution ampl