April 2023 arXiv papers — page 57

Cassidy Laidlaw, Stuart Russell, Anca Dragan

Deep reinforcement learning (RL) works impressively in some environments and fails catastrophically in others. Ideally, RL theory should be able to provide an understanding of why this is, i.e. bounds predictive of practical performance. Unfortunately, current theory does not quite have this ability. We compare standard deep RL algorithms to prior sample com

Akash Jain, Kristan Jensen, Ruochuan Liu, Eric Mefford

We construct a theory of hydrodynamic transport for systems with conserved dipole moment, U(1) charge, energy, and momentum. These models have been considered in the context of fractons, since their elementary and isolated charges are immobile by symmetry, and have two known translation-invariant gapless phases: a "p-wave dipole superfluid" phase where the d

Mohammad Yousuf Jamal, Jai Prakash, Indrani Nilima, Aritra Bandyopadhyay

We study the heavy quark energy loss in the presence of a background magnetic field. The analysis considers the high magnetic field generated by spectators from initial hard collisions that were incorporated using the medium-modified Debye mass, determined from quark condensates at finite temperature and magnetic field via recent lattice quantum chromodynami

Sander Tonkens, Alex Toofanian, Zhizhen Qin, Sicun Gao

Safe value functions, such as control barrier functions, characterize a safe set and synthesize a safety filter, overriding unsafe actions, for a dynamic system. While function approximators like neural networks can synthesize approximately safe value functions, they typically lack formal guarantees. In this paper, we propose a local dynamic programming-base

Mayara Benício de Barros Souza, Fabio Q. B. da Silva, Carolyn Seaman

Context: Task interdependence is a work design factor that expresses the mutual dependency between tasks that compose a whole work. In software development, task interdependencies are created by the technical dependencies between the components of the software system and by how the development tasks are allocated to individuals in a teamwork context. Despite

Nelson F. Liu, Tianyi Zhang, Percy Liang

Generative search engines directly generate responses to user queries, along with in-line citations. A prerequisite trait of a trustworthy generative search engine is verifiability, i.e., systems should cite comprehensively (high citation recall; all statements are fully supported by citations) and accurately (high citation precision; every cite supports its

Andy Li, Milan Markovic, Peter Edwards, Georgios Leontidis

Federated Learning (FL) presents a decentralized approach to model training in the agri-food sector and offers the potential for improved machine learning performance, while ensuring the safety and privacy of individual farms or data silos. However, the conventional FL approach has two major limitations. First, the heterogeneous data on individual silos can

James Bartusek, Dakshita Khurana, Giulio Malavolta, Alexander Poremba

We develop a simple compiler that generically adds publicly-verifiable deletion to a variety of cryptosystems. Our compiler only makes use of one-way functions (or one-way state generators, if we allow the public verification key to be quantum). Previously, similar compilers either relied on the use of indistinguishability obfuscation (Bartusek et. al., ePri

Yotam Wolf, Noam Wies, Oshri Avnery, Yoav Levine

An important aspect in developing language models that interact with humans is aligning their behavior to be useful and unharmful for their human users. This is usually achieved by tuning the model in a way that enhances desired behaviors and inhibits undesired ones, a process referred to as alignment. In this paper, we propose a theoretical approach called

Neil Epstein

The notion of an \emph{Egyptian} integral domain $D$ (where every fraction can be written as a sum of unit fractions with denominators from $D$) is extended here to the notion that a ring $R$ is \emph{$W$-Egyptian}, with $W$ a multiplicative set in $R$. The new notion allows denominators just from $W$. It is shown that several results about Egyptian domains

Maxime Flin, Mohsen Ghaffari, Magnús M. Halldórsson, Fabian Kuhn

We present an $O(\log^3\log n)$-round distributed algorithm for the $(\Delta+1)$-coloring problem, where each node broadcasts only one $O(\log n)$-bit message per round to its neighbors. Previously, the best such broadcast-based algorithm required $O(\log n)$ rounds. If $\Delta \in \Omega(\log^{3} n)$, our algorithm runs in $O(\log^* n)$ rounds. Our algorith

Pan Lu, Baolin Peng, Hao Cheng, Michel Galley

Large language models (LLMs) have achieved remarkable progress in solving various natural language processing tasks due to emergent reasoning abilities. However, LLMs have inherent limitations as they are incapable of accessing up-to-date information (stored on the Web or in task-specific knowledge bases), using external tools, and performing precise mathema

Weiren Zhao

We prove the nonlinear inviscid damping for a class of monotone shear flows with non-constant background density for the two-dimensional ideal inhomogeneous fluids in $\mathbb{T}\times [0,1]$ when the initial perturbation is in Gevrey-$\frac{1}{s}$ ($\frac{1}{2}<s<1$) class with compact support.

Serge Kas Hanna

Consider two or more strings $\mathbf{x}^1,\mathbf{x}^2,\ldots,$ that are concatenated to form $\mathbf{x}=\langle \mathbf{x}^1,\mathbf{x}^2,\ldots \rangle$. Suppose that up to $\delta$ deletions occur in each of the concatenated strings. Since deletions alter the lengths of the strings, a fundamental question to ask is: how much redundancy do we need to int

Jeremie Bec, Christophe Brouzet, Christophe Henry

Long, flexible fibers transported by a turbulent channel flow sample non-linear variations of the fluid velocity along their length. As the fibers tumble and collide with the boundaries, they bounce off with an impulse that propels them toward the center of the flow, similar to pole vaulting. As a result, the fibers migrate away from the walls, leading to de

David Holmes

We consider functions from the real numbers to the real numbers, output by a neural network with 1 hidden activation layer, arbitrary width, and ReLU activation function. We assume that the parameters of the neural network are chosen uniformly at random with respect to various probability distributions, and compute the expected distribution of the points of

Étienne Marcotte, Valentina Zantedeschi, Alexandre Drouin, Nicolas Chapados

Multivariate probabilistic time series forecasts are commonly evaluated via proper scoring rules, i.e., functions that are minimal in expectation for the ground-truth distribution. However, this property is not sufficient to guarantee good discrimination in the non-asymptotic regime. In this paper, we provide the first systematic finite-sample study of prope

Simon Letzgus, Klaus-Robert Müller

Wind turbine power curve models translate ambient conditions into turbine power output. They are essential for energy yield prediction and turbine performance monitoring. In recent years, increasingly complex machine learning methods have become state-of-the-art for this task. Nevertheless, they frequently encounter criticism due to their apparent lack of tr

Laura Smith, J. Chase Kew, Tianyu Li, Linda Luu

Legged robots have enormous potential in their range of capabilities, from navigating unstructured terrains to high-speed running. However, designing robust controllers for highly agile dynamic motions remains a substantial challenge for roboticists. Reinforcement learning (RL) offers a promising data-driven approach for automatically training such controlle

Sridevi Kuriyattil, Tomohiro Hashizume, Gregory Bentsen, Andrew J. Daley

In a fast scrambling many-body quantum system, information is spread and entanglement is built up on a timescale that grows logarithmically with the system size. This is of fundamental interest in understanding the dynamics of many-body systems, as well as in efficiently producing entangled resource states and error-correcting codes. In this work, we identif

Delaney A. Dunne, Kieran A. Cleary, Patrick C. Breysse, Dongwoo T. Chung

We present a new upper limit on the cosmic molecular gas density at $z=2.4-3.4$ obtained using the first year of observations from the CO Mapping Array Project (COMAP). COMAP data cubes are stacked on the 3D positions of 243 quasars selected from the Extended Baryon Oscillation Spectroscopic Survey (eBOSS) catalog, yielding a 95% upper limit for flux from CO

Kyle Stachowicz, Dhruv Shah, Arjun Bhorkar, Ilya Kostrikov

We present a system that enables an autonomous small-scale RC car to drive aggressively from visual observations using reinforcement learning (RL). Our system, FastRLAP (faster lap), trains autonomously in the real world, without human interventions, and without requiring any simulation or expert demonstrations. Our system integrates a number of important co

Aleksander J. Cianciara, Zachary Coleman, S. James Gates, Youngik Tom Lee

We study algorithms for recursively creating arbitrary N-extended `supermultiplets' given minimal matrix representations of off-shell, N = 1 supermultiplet matrices. We observe connections between the color vertex problems in graph theory and the different supermultiplet sets locations in the permutahedron by using the concepts of truncation and chromatic nu

Roberto Nesci, Antonio Vagnozzi, Stefano Valentini

The light curve of the cataclismic variable ASASSN-18aan is studied using recent observations of the MC589 Observatory, giving an orbital Period and Epoch fully consistent with the data obtained after the discovery flare in 2018. Archival data from ASASSN, ZTF and Gaia were used to check if its flares have a quasi-periodic behaviour. A recurrency time scale

Ogulcan E. Orsel, Jiho Noh, Gaurav Bahl

Undesirable light scattering is an important fundamental cause for photon loss in nanophotonics. Rayleigh backscattering can be particularly difficult to avoid in wave-guiding systems and arises from both material defects and geometric defects at the subwavelength scale. It has been previously shown that systems with broken time-reversal symmetry (TRS) can n

Guoming Wang, Daniel Stilck França, Gumaro Rendon, Peter D. Johnson

A major thrust in quantum algorithm development over the past decade has been the search for the quantum algorithms that will deliver practical quantum advantage first. Today's quantum computers - and even early fault-tolerant quantum computers - are limited in the number of operations they can implement per circuit. We introduce quantum algorithms for groun

V. A. Vassiliev

We study the topology of complements of caustics of function singularities of low codimensions, in particular 1) complete the enumeration of connected components of the complements of caustics of {\em simple} (in the sense of V.Arnold) singularities, in particular find the numbers of these components for the last two classes, $E_7$ and $E_8$, remaining unkno

Ramesh Sreekantan

Collino \cite{colo} discovered indecomposable motivic cycles in the group $H^{2g-1}_{\mathcal M}(J(C),{\mathds Z}(g))$. In an earlier paper we described the construction of some new motivic cycles which can be viewed as a generalization of Collino's cycle when $g=2$. In this paper we show that our new cycles are in fact related to Collino's cycles of higher

Tobias Mistele

It was recently claimed that black holes can explain the accelerated expansion of the universe. Here I point out that this claim is based on a confusion about the principle of least action, undermining the link between black holes and dark energy.

Jae-Young Kim, Tuomas Savolainen, Petr Voitsik, Evgeniya V. Kravchenko

We present results from the first 22 GHz space very-long-baseline interferometric (VLBI) imaging observations of M87 by RadioAstron. As a part of the Nearby AGN Key Science Program, the source was observed in Feb 2014 at 22 GHz with 21 ground stations, reaching projected $(u,v)$-spacings up to $\sim11\,$G$\lambda$. The imaging experiment was complemented by

Per Åhag, Rafał Czyż, Per-Håkan Lundow

With only a complete solution in dimension one and partially solved in dimension two, the Lenz-Ising model of magnetism is one of the most studied models in theoretical physics. An approach to solving this model in the high-dimensional case ($d>4$) is by modelling the magnetisation distribution with $p,q$-binomial coefficients. The connection between the par

Wei Kou, Chao Shi, Xurong Chen, Wenbao Jia

We calculate the leading twist pion unpolarized transverse momentum distribution $f_1(x,k_T^2)$ and the Boer-Mulders function $h_1^\perp(x,k_T^2)$, using leading Fock-state light front wave functions (LF-LFWFs) based on Dyson-Schwinger and Bethe-Salpeter equations. These DS-BSEs based LF-LFWFs provide dynamically generated s- and p-wave components, which are

Salvatore Certo, Anh Pham, Nicolas Robles, Andrew Vlasic

A framework to learn a multi-modal distribution is proposed, denoted as the Conditional Quantum Generative Adversarial Network (C-qGAN). The neural network structure is strictly within a quantum circuit and, as a consequence, is shown to represent a more efficient state preparation procedure than current methods. This methodology has the potential to speed-u

Bastian Pietras

Pulse-coupled spiking neural networks are a powerful tool to gain mechanistic insights into how neurons self-organize to produce coherent collective behavior. These networks use simple spiking neuron models, such as the $\theta$-neuron or the quadratic integrate-and-fire (QIF) neuron, that replicate the essential features of real neural dynamics. Interaction

Zhiyu Dong, Patrick A. Lee, Leonid S. Levitov

Different superconducting pairing mechanisms are markedly distinct in the underlying Cooper pair kinematics. Pairing interactions mediated by quantum-critical soft modes are dominated by highly collinear processes, falling into two classes: forward scattering and backscattering. In contrast, phonon mechanisms have a generic non-collinear character. We show t

Liangzhi Wang, Jiliang Zhang, Zitian Zhang, Jie Zhang

This paper proposes an interpretable user-behavior-based (UBB) network traffic prediction (NTP) method. Based on user behavior, a weekly traffic demand profile can be naturally sorted into three categories, i.e., weekday, Saturday, and Sunday. For each category, the traffic pattern is divided into three components which are mainly generated in three time per

Atrideb Chatterjee, Tirthankar Roy Choudhury

With the help of our previously built MCMC-based parameter estimation package \texttt{CosmoReionMC}, we investigate in detail the potential of 21 cm global signal, when combined with CMB and observations related to the QSO absorption spectra, to constraint the mass of Warm Dark Matter (WDM) particle. For the first time, we simultaneously vary all the free pa

Giacomo Ceoldo, Ernst C. Wit

The package hset for the R language contains an implementation of a S4 class for sets and multisets of numbers. The implementation, based on the hash table data structure from the package hash (Brown, 2019), allows for quick operations when the set is a dynamic object. An important example is when a set or a multiset is part of the state of a Markov chain in

Benjamin A. Foutty, Carlos R. Kometter, Trithep Devakul, Aidan P. Reddy

Semiconductor moir\'e superlattices have been shown to host a wide array of interaction-driven ground states. However, twisted homobilayers have been difficult to study in the limit of large moir\'e wavelength, where interactions are most dominant. Here, we conduct local electronic compressibility measurements of twisted bilayer WSe$_2$ (tWSe$_2$) at small t

Shaoyu Chen, Yunchi Zhang, Bencheng Liao, Jiafeng Xie

High-definition (HD) map serves as the essential infrastructure of autonomous driving. In this work, we build up a systematic vectorized map annotation framework (termed VMA) for efficiently generating HD map of large-scale driving scene. We design a divide-and-conquer annotation scheme to solve the spatial extensibility problem of HD map generation, and abs

Yangyu Fan, Xin Wan

Let $p$ be a prime and $\mathcal{K}$ be an imaginary quadratic field. In this paper we generalize a recent construction of a new type of $p$-adic $L$-function and $p$-adic Waldspurger formula by Andreatta-Iovita for $p$ non-split in $\mathcal{K}$, as long as the $\mathrm{GL}_2$ automorphic representation is principal series at $p$. Then we develop a new kind

Verena Blaschke, Hinrich Schütze, Barbara Plank

Despite much progress in recent years, the vast majority of work in natural language processing (NLP) is on standard languages with many speakers. In this work, we instead focus on low-resource languages and in particular non-standardized low-resource languages. Even within branches of major language families, often considered well-researched, little is know

Raziel Gartsman, Nati Linial

The collection of all $n$-point metric spaces of diameter $\le 1$ constitutes a polytope $\mathcal{M}_n \subset \mathbb{R}^{\binom{n}{2}}$, called the \emph{Metric Polytope}. In this paper, we consider the best approximations of $\mathcal{M}_n$ by ellipsoids. We give an exact explicit description of the largest volume ellipsoid contained in $\mathcal{M}_n$.

Anu V. S. Nath, Anubhab Roy, S. Ravichandran, Rama Govindarajan

Small heavy particles in a fluid flow respond to the flow on a time-scale proportional to their inertia, or Stokes number St. Their behaviour is thought to be gradually modified as St increases. We show, in the steady spatially-periodic laminar Taylor-Green flow, that particle dynamics, and their effective diffusivity, actually change in an irregular, non-mo

Philipp Spitzer, Joshua Holstein, Michael Vössing, Niklas Kühl

With the increased adoption of artificial intelligence (AI) in industry and society, effective human-AI interaction systems are becoming increasingly important. A central challenge in the interaction of humans with AI is the estimation of difficulty for human and AI agents for single task instances.These estimations are crucial to evaluate each agent's capab

Qiuhong Shen, Xingyi Yang, Xinchao Wang

3D reconstruction from a single-RGB image in unconstrained real-world scenarios presents numerous challenges due to the inherent diversity and complexity of objects and environments. In this paper, we introduce Anything-3D, a methodical framework that ingeniously combines a series of visual-language models and the Segment-Anything object segmentation model t

Avner Shultzman, Eyar Azar, Miguel R. D. Rodrigues, Yonina C. Eldar

Model-based neural networks provide unparalleled performance for various tasks, such as sparse coding and compressed sensing problems. Due to the strong connection with the sensing model, these networks are interpretable and inherit prior structure of the problem. In practice, model-based neural networks exhibit higher generalization capability compared to R

Chongjian Ge, Junsong Chen, Enze Xie, Zhongdao Wang

Perception systems in modern autonomous driving vehicles typically take inputs from complementary multi-modal sensors, e.g., LiDAR and cameras. However, in real-world applications, sensor corruptions and failures lead to inferior performances, thus compromising autonomous safety. In this paper, we propose a robust framework, called MetaBEV, to address extrem

Yuzhe Zhu

We explore quantitative propagation of smallness for solutions of two-dimensional elliptic equations from sets of positive $\delta$-dimensional Hausdorff content for any $\delta>0$. In particular, the gradients of solutions to divergence form equations with H\"older continuous coefficients, as well as those of nondivergence form equations with measurable coe

Diogo L. Pires, Igor Erovenko, Mark Broom

Evolutionary models are used to study the self-organisation of collective action, often incorporating population structure due to its ubiquitous presence and long-known impact on emerging phenomena. We investigate the evolution of multiplayer cooperation in mobile structured populations, where individuals move strategically on networks and interact with thos

Antonio M. Scielzo

We study the theory of Banach $L^p$ lattices with a distinguished automorphism, in the framework of continuous logic. Using a functional version of the Rokhlin lemma, we prove that it admits a model companion, which is stable and has quantifier elimination. We show that the types of this theory that are not trivial cannot be isolated. We then use this result

Chuanyang Zheng, Zhengying Liu, Enze Xie, Zhenguo Li

The performance of Large Language Models (LLMs) in reasoning tasks depends heavily on prompt design, with Chain-of-Thought (CoT) and self-consistency being critical methods that enhance this ability. However, these methods do not fully exploit the answers generated by the LLM to guide subsequent responses. This paper proposes a new prompting method, named Pr

Mrinal Kanti Giri, Biswajit Paul, Tapan Mishra

We study the QW of two interacting bosons on a two-leg ladder lattice in the presence of an artificial magnetic field. By considering an uniform flux piercing through the ladder, we show that in the limit of strong onsite repulsion and dominant rung-hopping, an initially slow dynamics becomes fast, then slow and fast again with increase in the flux strength

Xuchen Lin, Jing Wang, Virginia Kilborn, Eric W. Peng

Combining new HI data from a synergetic survey of ASKAP WALLABY and FAST with the ALFALFA data, we study the effect of ram pressure and tidal interactions in the NGC 4636 group. We develop two parameters to quantify and disentangle these two effects on gas stripping in HI-bearing galaxies: the strength of external forces at the optical-disk edge, and the out

The SunPy Community, Will Barnes, Steven Christe, Nabil Freij

The SunPy Project is a community of scientists and software developers creating an ecosystem of Python packages for solar physics. The project includes the sunpy core package as well as a set of affiliated packages. The sunpy core package provides general purpose tools to access data from different providers, read image and time series data, and transform be

Kunping Huang, Sen Zhang, Jing Zhang, Dacheng Tao

In recent decades, visual simultaneous localization and mapping (vSLAM) has gained significant interest in both academia and industry. It estimates camera motion and reconstructs the environment concurrently using visual sensors on a moving robot. However, conventional cameras are limited by hardware, including motion blur and low dynamic range, which can ne

Miguel N. Walsh

We develop tools to study the averaged Fourier uniformity conjecture and extend its known range of validity to intervals of length at least $\exp(C (\log X)^{1/2} (\log \log X)^{1/2})$.

Mehdi Naima, Matthieu Latapy, Clémence Magnien

Betweenness centrality measure assesses the importance of nodes in a graph and has been used in a variety of contexts. Betweenness centrality has also been extended to temporal graphs. Temporal graphs have edges that bear labels according to the time of the interactions between the nodes. Betweenness centrality has been extended to the temporal graph setting

Aydar Bulatov, Yuri Kuratov, Yermek Kapushev, Mikhail S. Burtsev

A major limitation for the broader scope of problems solvable by transformers is the quadratic scaling of computational complexity with input size. In this study, we investigate the recurrent memory augmentation of pre-trained transformer models to extend input context length while linearly scaling compute. Our approach demonstrates the capability to store i

Zhen Li, Zuo-Liang Zhu, Ling-Hao Han, Qibin Hou

We present All-Pairs Multi-Field Transforms (AMT), a new network architecture for video frame interpolation. It is based on two essential designs. First, we build bidirectional correlation volumes for all pairs of pixels, and use the predicted bilateral flows to retrieve correlations for updating both flows and the interpolated content feature. Second, we de

Elena Merlo, Marta Lagomarsino, Edoardo Lamon, Arash Ajoudani

This paper presents a new method to describe spatio-temporal relations between objects and hands, to recognize both interactions and activities within video demonstrations of manual tasks. The approach exploits Scene Graphs to extract key interaction features from image sequences while simultaneously encoding motion patterns and context. Additionally, the me

Jean Paul Barddal, Heitor Murilo Gomes, Fabrício Enembreck

Mining data streams is one of the main studies in machine learning area due to its application in many knowledge areas. One of the major challenges on mining data streams is concept drift, which requires the learner to discard the current concept and adapt to a new one. Ensemble-based drift detection algorithms have been used successfully to the classificati

Seung Wook Kim, Bradley Brown, Kangxue Yin, Karsten Kreis

Automatically generating high-quality real world 3D scenes is of enormous interest for applications such as virtual reality and robotics simulation. Towards this goal, we introduce NeuralField-LDM, a generative model capable of synthesizing complex 3D environments. We leverage Latent Diffusion Models that have been successfully utilized for efficient high-qu

Mats Esseldeurs, Lionel Siess, Frederik De Ceuster, Ward Homan

Stars with an initial mass below ~ 8 Msun evolve through the asymptotic giant branch (AGB) phase, during which they develop strong stellar winds. Recent observations have revealed significant morphological complexities in their outflows, most likely caused by a companion. We study the impact of the radiation force on such companion-perturbed AGB outflows. We

Lin Niu, Jiawei Liu, Zhihang Yuan, Dawei Yang

Efficient inference for object detection networks is a major challenge on edge devices. Post-Training Quantization (PTQ), which transforms a full-precision model into low bit-width directly, is an effective and convenient approach to reduce model inference complexity. But it suffers severe accuracy drop when applied to complex tasks such as object detection.

Claude Crépeau, John Stuart

A Zero-Knowledge Protocol (ZKP) allows one party to convince another party of a fact without disclosing any extra knowledge except the validity of the fact. For example, it could be used to allow a customer to prove their identity to a potentially malicious bank machine without giving away private information such as a personal identification number. This wa

Zihao Huang, Yue Wang, Weixing Xin, Xingtong Lin

Medical image recognition often faces the problem of insufficient data in practical applications. Image recognition and processing under few-shot conditions will produce overfitting, low recognition accuracy, low reliability and insufficient robustness. It is often the case that the difference of characteristics is subtle, and the recognition is affected by

Aslı Tuncer, Serhat C. Kadıoğlu

Spin-glass (SG) is a fascinating system that has garnered significant attention due to its intriguing properties and implications for various research fields. One of the key characteristics of spin glasses is that they contain random disorder, which leads to many possible states of the system occurring with very close probabilities. We explore the concept of

Baolin Li, Siddharth Samsi, Vijay Gadepally, Devesh Tiwari

This paper presents a solution to the challenge of mitigating carbon emissions from hosting large-scale machine learning (ML) inference services. ML inference is critical to modern technology products, but it is also a significant contributor to carbon footprint. We introduce Clover, a carbon-friendly ML inference service runtime system that balances perform

Mert Sinan Recep Kiraz, Roel Snieder, Kees Wapenaar

Marchenko algorithms retrieve the wavefields excited by virtual sources in the subsurface, these are the Green's functions consisting of the primary and multiple reflected waves. The requirements for these algorithms are the same as for conventional imaging algorithms; they need an estimate of the velocity model and the recorded reflected waves. We investiga

Edward A. Small, Kacper Sokol, Daniel Manning, Flora D. Salim

Group fairness is achieved by equalising prediction distributions between protected sub-populations; individual fairness requires treating similar individuals alike. These two objectives, however, are incompatible when a scoring model is calibrated through discontinuous probability functions, where individuals can be randomly assigned an outcome determined b

Jaeyun Moon, Lucas Lindsay, Takeshi Egami

Developing microscopic understanding of the thermal properties of liquids is challenging due to their strong dynamic disorder, which prevents characterization of the atomic degrees of freedom. There have been significant research interests in the past few decades to extend the normal mode analysis for solids to instantaneous structures of liquids. However, t

L. Balart, S. Belmar-Herrera, G. Panotopoulos, Á. Rincón

We investigate two novel models of charged black holes in the framework of non-linear electrodynamics of Born-Infeld type. In particular, starting from two concrete Lagrangian densities, the corresponding metric potentials, the electric field, the Smarr formula and finally, the (scalar) quasinormal modes are computed for each model. Our findings show that al

Wontae Kim, Juha Kinnunen, Lauri Särkiö

We discuss a Lipschitz truncation technique for parabolic double-phase problems of $p$-Laplace type in order to prove energy estimates and uniqueness results for the Dirichlet problem. Moreover, we show existence for a non-homogeneous double-phase problem. The Lipschitz truncation method is based on a Whitney-type covering result and a related partition of u

Li Han

Based on the statistical yearbook data and related patent data of 287 cities in China from 2000 to 2020, this study regards the policy of establishing the national high-tech zones as a quasi-natural experiment. Using this experiment, this study firstly estimated the treatment effect of the policy and checked the robustness of the estimation. Then the study e

Mohsen Ghaffari, Christoph Grunau, Jiahao Qu

We present the first parallel depth-first search algorithm for undirected graphs that has near-linear work and sublinear depth. Concretely, in any $n$-node $m$-edge undirected graph, our algorithm computes a DFS in $\tilde{O}(\sqrt{n})$ depth and using $\tilde{O}(m+n)$ work. All prior work either required $\Omega(n)$ depth, and thus were essentially sequenti

Edgardo Solano-Carrillo, Jannis Stoppe

Domain-adaptive trajectory imitation is a skill that some predators learn for survival, by mapping dynamic information from one domain (their speed and steering direction) to a different domain (current position of the moving prey). An intelligent agent with this skill could be exploited for a diversity of tasks, including the recognition of abnormal motion

Jernej Frank, Alexander Duplinskiy, Kaden Bearne, A. I. Lvovsky

We investigate Hermite Gaussian Imaging (HGI) -- a novel passive super-resolution technique -- for complex 2D incoherent objects in the sub-Rayleigh regime. The method consists of measuring the field's spatial mode components in the image plane in the overcomplete basis of Hermite-Gaussian modes and their superpositions and subsequently using a deep neural n

Elisa Balzani, Ramona Gröber, Marco Vitti

Yukawa couplings of the first quark generation are notoriously difficult to constrain due to their small values within the Standard Model. Here we propose Higgs off-shell production, with the Higgs boson decaying to four leptons, as a probe of the up- and down-quark Yukawa couplings. Using kinematic discriminants similar to the ones employed in the Higgs wid

Zhou Li, Yizhou Zhao, Hua Sun

In secure summation, $K$ users, each holds an input, wish to compute the sum of the inputs at a server without revealing any information about {\em all the inputs} even if the server may collude with {\em an arbitrary subset of users}. In this work, we relax the security and colluding constraints, where the set of inputs whose information is prohibited from

Tommaso Bevilacqua, Franco Dassi, Stefano Zampini, Simone Scacchi

The Virtual Element Method (VEM) is a novel family of numerical methods for approximating partial differential equations on very general polygonal or polyhedral computational grids. This work aims to propose a Balancing Domain Decomposition by Constraints (BDDC) preconditioner that allows using the conjugate gradient method to compute the solution of the sad

Soichiro Nishimori, Sotetsu Koyamada, Shin Ishii

Real-world decision-making problems are often partially observable, and many can be formulated as a Partially Observable Markov Decision Process (POMDP). When we apply reinforcement learning (RL) algorithms to the POMDP, reasonable estimation of the hidden states can help solve the problems. Furthermore, explainable decision-making is preferable, considering

Lei Li, Jin Cao, Chaoxi Cui, Zhi-Ming Yu

Using symmetry analysis and semiclassical Boltzmann equation, we theoretically explore the planar Hall effect (PHE) in three-dimensional materials. We demonstrate that PHE is a general phenomenon that can occur in various systems regardless of band topology. Both the Lorentz force and Berry curvature effects can induce significant PHE, and the leading contri

K. L. Romans, A. H. N. C. De Silva, B. P. Acharya, K. Foster

Rydberg atoms are in the focus of intense research due to the peculiar properties which make them interesting candidates for quantum optics and quantum information applications. In this work we study the ionization of Rydberg atoms due to their interaction with a trapping laser field, and a reaction microscope is used to measure photoelectron angular and ene

Ewa Czuchry, Nils A. Nilsson

Ho\v{r}ava-Lifshitz gravity has been proposed as a ghost-free quantum gravity model candidate with an anisotropic UV-scaling between space and time. We present here a cosmological background analysis of two different formulations of the theory, with particular focus on the running of the parameter $\lambda$. Using a large dataset consisting of Cosmic Microwa

U. K. Anandavardhanan, Basudev Pattanayak

Let $G$ be a compact group with two given subgroups $H$ and $K$. Let $\pi$ be an irreducible representation of $G$ such that its space of $H$-invariant vectors as well as the space of $K$-invariant vectors are both one dimensional. Let $v_H$ (resp. $v_K$) denote an $H$-invariant (resp. $K$-invariant) vector of unit norm in a given $G$-invariant inner product

Fuad Hasan, Hailong Huang

Anticipating the motion of neighboring vehicles is crucial for autonomous driving, especially on congested highways where even slight motion variations can result in catastrophic collisions. An accurate prediction of a future trajectory does not just rely on the previous trajectory, but also, more importantly, a simulation of the complex interactions between

Zhuojun Tang, Yuxin Zhu, Kexin Zhang, Zheyu Wang

Alzheimer's Disease (AD) research has shifted to focus on biomarker trajectories and their potential use in understanding the underlying AD-related pathological process. A conceptual framework was proposed in modern AD research that hypothesized biomarker cascades as a result of underlying AD pathology. In this paper, we leveraged this idea to jointly model

I. A. Cohn, S. V. Zaitsev-Zotov

We demonstrate that magnetoresistance of creeping charge-density waves in the quasi-one dimensional conductor {\it o}-TaS$_3$ changes its character from a negative parabolic at $T\gtrsim 10$ K where it obeys $1/T^2$ law to a weakly temperature dependent negative nearly linear one at lower temperatures. The dominant contribution into the negative parabolic ma

Anupam Gupta, Madhusudhan Reddy Pittu, Ola Svensson, Rachel Yuan

Given a set of points in $d$-dimensional space, an explainable clustering is one where the clusters are specified by a tree of axis-aligned threshold cuts. Dasgupta et al. (ICML 2020) posed the question of the price of explainability: the worst-case ratio between the cost of the best explainable clusterings to that of the best clusterings. We show that the p

Anwesh Ray, Tom Weston

Let $K$ be a number field and $\ell \geq 5$ a prime number. Mazur and Rubin introduced the notion of diophantine stability for a variety $X_{/K}$ at a prime $\ell$. We show that there is a positive density set of elliptic curves $E_{/\mathbb{Q}}$ of rank $1$ such that $E_{/K}$ is diophantine stable at $\ell$. This has implications for Hilbert's Tenth Problem

Yufan Huang, Man Li, Tao Zhao

The research on multi-robot coverage path planning (CPP) has been attracting more and more attention. In order to achieve efficient coverage, this paper proposes an improved DARP coverage algorithm. The improved DARP algorithm based on A* algorithm is used to assign tasks to robots and then combined with STC algorithm based on Up-First algorithm to achieve f

F. Robicheaux, Deepak A. Suresh

We theoretically and computationally investigate the behavior of infinite atom arrays when illuminated by nearly resonant light. We use higher order mean field equations to investigate the coherent reflection and transmission and incoherent scattering of photons from a single array and from a pair of arrays as a function of detuning for different values of t

Laurent Berger

Let $K$ be a $p$-adic field, and let $K_\infty/K$ be a Galois extension that is almost totally ramified, and whose Galois group is a $p$-adic Lie group of dimension $1$. We prove that $K_\infty$ is not dense in $(\mathbf{B}_{\mathrm{dR}}^+ / \operatorname{Fil}^2 \mathbf{B}_{\mathrm{dR}}^+ )^{\operatorname{Gal}(\overline{K}/K_\infty)}$. Moreover, the restrict

Muhammad Awais Aslam, Igor Stankovic, Gennadiy Murastov, Amy Carl

The interaction mechanisms of water with nanoscale geometries remain poorly understood. This study focuses on behaviour of water clusters under varying external electric fields with a particular focus on molecular ferroelectric devices. We employ a two-fold approach, combining experiments with large-scale molecular dynamics simulations on graphene nanoribbon

Jonathan Root, Mark Kon

We consider the sets of negatively associated (NA) and negatively correlated (NC) distributions as subsets of the space $\mathcal{M}$ of all probability distributions on $\mathbb{R}^n$, in terms of their relative topological structures within the topological space of all measures on a given measurable space. We prove that the class of NA distributions has a

JPAC Collaboration, M. Albaladejo, S. Gonzàlez-Solís, Ł. Bibrzycki

We study the decay $J/\psi\to\pi^{+}\pi^{-}\pi^{0}$ within the framework of the Khuri-Treiman equations. We find that the BESIII experimental di-pion mass distribution in the $\rho(770)$-region is well reproduced with a once-subtracted $P$-wave amplitude. Furthermore, we show that $F$-wave contributions to the amplitude improve the description of the data in

Ali Abedi, Paritosh Bisht, Riddhi Chatterjee, Rachit Agrawal

Physical exercise is an essential component of rehabilitation programs that improve quality of life and reduce mortality and re-hospitalization rates. In AI-driven virtual rehabilitation programs, patients complete their exercises independently at home, while AI algorithms analyze the exercise data to provide feedback to patients and report their progress to

Jimmy Envall, Roi Poranne, Stelian Coros

Task and motion planning is one of the key problems in robotics today. It is often formulated as a discrete task allocation problem combined with continuous motion planning. Many existing approaches to TAMP involve explicit descriptions of task primitives that cause discrete changes in the kinematic relationship between the actor and the objects. In this wor

Michael Schröder, Marc Goritschnig, Jürgen Cito

Background: Ad hoc parsers are pieces of code that use common string functions like split, trim, or slice to effectively perform parsing. Whether it is handling command-line arguments, reading configuration files, parsing custom file formats, or any number of other minor string processing tasks, ad hoc parsing is ubiquitous -- yet poorly understood. Objectiv