October 2023 arXiv papers — page 56

Tianyu Liang, Riley Murray, Aydın Buluç, James Demmel

This work focuses on accelerating the multiplication of a dense random matrix with a (fixed) sparse matrix, which is frequently used in sketching algorithms. We develop a novel scheme that takes advantage of blocking and recomputation (on-the-fly random number generation) to accelerate this operation. The techniques we propose decrease memory movement, there

Tao Wang, Sylvia Herbert, Sicun Gao

Policy gradient lies at the core of deep reinforcement learning (RL) in continuous domains. Despite much success, it is often observed in practice that RL training with policy gradient can fail for many reasons, even on standard control problems with known solutions. We propose a framework for understanding one inherent limitation of the policy gradient appr

Ju Wu, Xiaochen Zheng, Marco Madlena, Dimitrios Kyritsis

The digital transformation of pharmaceutical industry is a challenging task due to the high complexity of involved elements and the strict regulatory compliance. Maintenance activities in the pharmaceutical industry play an essential role in ensuring product quality and integral functioning of equipment and premises. This paper first identifies the key chall

Chih-Yu Lai, Fan-Keng Sun, Zhengqi Gao, Jeffrey H. Lang

Time series anomaly detection is challenging due to the complexity and variety of patterns that can occur. One major difficulty arises from modeling time-dependent relationships to find contextual anomalies while maintaining detection accuracy for point anomalies. In this paper, we propose a framework for unsupervised time series anomaly detection that utili

Qiang Zhang, Jason Naradowsky, Yusuke Miyao

Knowing how to end and resume conversations over time is a natural part of communication, allowing for discussions to span weeks, months, or years. The duration of gaps between conversations dictates which topics are relevant and which questions to ask, and dialogue systems which do not explicitly model time may generate responses that are unnatural. In this

Bidipta Sarkar, Andy Shih, Dorsa Sadigh

Conventions are crucial for strong performance in cooperative multi-agent games, because they allow players to coordinate on a shared strategy without explicit communication. Unfortunately, standard multi-agent reinforcement learning techniques, such as self-play, converge to conventions that are arbitrary and non-diverse, leading to poor generalization when

Guanyu Tian, Qun Zhou Sun, Yiyuan Qiao

The power flexibility from smart buildings makes them suitable candidates for providing grid services. The building automation system (BAS) that employs model predictive control (MPC) for grid services relies heavily on sensor data gathered from IoT-based HVAC systems through communication networks. However, cyber-attacks that tamper sensor values can compro

Debsuvra Mukhopadhyay, Jung-Tsung Shen

The future of quantum information processing hinges on chip-scale nanophotonics, specifically cavity QED and waveguide QED. One of the foremost processes underpinning quantum photonic technologies is the phenomenon of Rabi oscillations, which manifests when a qubit is irradiated by an intense laser source. Departing from the conventional semiclassical framew

René Mboro

This note presents some properties of the variety of planes $F_2(X)\subset G(3,7)$ of a cubic $5$-fold $X\subset \mathbb P^6$. A cotangent bundle exact sequence is first derived from the remark made by Iliev and Manivel that $F_2(X)$ sits as a Lagrangian subvariety of the variety of lines of a cubic $4$-fold, which is a hyperplane section of $X$. Using the s

Yinan Li, Chicheng Zhang

We study the problem of computationally and label efficient PAC active learning $d$-dimensional halfspaces with Tsybakov Noise~\citep{tsybakov2004optimal} under structured unlabeled data distributions. Inspired by~\cite{diakonikolas2020learning}, we prove that any approximate first-order stationary point of a smooth nonconvex loss function yields a halfspace

Tim Cunningham, Pier-Emmanuel Tremblay, Mairi O'Brien

We present an initial-final mass relation derived from the spectroscopically-complete volume-limited 40 pc sample of white dwarfs. The relation is modelled using population synthesis methods to derive an initial stellar population which can be fit to the observed mass distribution of white dwarfs. The population synthesis accounts for binary evolution, where

José Cano Torres, Pedro Fortuny Ayuso, Javier Ribón

We relate the complexity of both differential and $q$-difference equations of order one and degree one and their solutions. Our point of view is to show that if the solutions are complicated, the initial equation is complicated too. In this spirit, we bound from below an invariant of the differential or $q$-difference equation, the height of its Newton polyg

Weixiang Sun, Yang Huang, Han Shen, Chun Wang

We present a comprehensive study of the kinematic properties of the different Galactic disk populations, as defined by the chemical abundance ratios and stellar ages, across a large disk volume (4.5 $\leq$ R $\leq$ 15.0 kpc and $|Z|$ $\leq$ 3.0 kpc), by using the LAMOST-Gaia red clump sample stars. We determine the median velocities for various spatial and p

Ju Wu, Tong Wang, Min Ma

This paper investigates the finite-time adaptive fuzzy tracking control problem for a class of pure-feedback system with full-state constraints. With the help of Mean-Value Theorem, the pure-feedback nonlinear system is transformed into strict-feedback case. By employing finite-time-stable like function and state transformation for output tracking error, the

Alexandre Filipowicz, Scott Carter, Nayeli Bravo, Rumen Iliev

Visualizations are common methods to convey information but also increasingly used to spread misinformation. It is therefore important to understand the factors people use to interpret visualizations. In this paper, we focus on factors that influence interpretations of scatter plots, investigating the extent to which common visual aspects of scatter plots (o

Ju Wu, Tong Wang

This brief constructs the adaptive backstepping control scheme for a class of pure-feedback systems with input delay and full state constraints. With the help of Mean Value Theorem, the pure-feedback system is transformed into strict-feedback one. Barrier Lyapunov functions are employed to guarantee all of the states remain constrained within predefined sets

Ting-Yao Hsu, Chieh-Yang Huang, Ryan Rossi, Sungchul Kim

There is growing interest in systems that generate captions for scientific figures. However, assessing these systems output poses a significant challenge. Human evaluation requires academic expertise and is costly, while automatic evaluation depends on often low-quality author-written captions. This paper investigates using large language models (LLMs) as a

Mairym Busnego-Barrientos

In this thesis, we investigate the method of conformal renormalization applied to theories with degrees of freedom beyond the metric ones. Specifically, we examine this method in the presence of a scalar field. To do this, as part of a review, we revisit the action principle of General Relativity and Einstein's equations, in addition to re-examining the cond

Rakesh Kanjilal, Reshmi Maity

This research explores the design and conducts a comparative analysis of a noninsulated Capacitive Micromachined Ultrasonic Transducer (CMUT) featuring an innovative asymmetric electrode configuration to improve the performance of the device. Specifically, this configuration involves the utilization of a top electrode with a smaller radius in comparison to t

Sandrine Bédard, Enamundram Naga Karthik, Charidimos Tsagkas, Emanuele Pravatà

Spinal cord segmentation is clinically relevant and is notably used to compute spinal cord cross-sectional area (CSA) for the diagnosis and monitoring of cord compression or neurodegenerative diseases such as multiple sclerosis. While several semi and automatic methods exist, one key limitation remains: the segmentation depends on the MRI contrast, resulting

Tony Zeng

Suppose a finite, unweighted, combinatorial graph $G = (V,E)$ is the union of several (degree-)regular graphs which are then additionally connected with a few additional edges. $G$ will then have only a small number of vertices $v \in V$ with the property that one of their neighbors $(v,w) \in E$ has a higher degree $\mbox{deg}(w) > \mbox{deg}(v)$. We prove

Dimitri Breda, Davide Liessi

We propose a method for computing the Lyapunov exponents of renewal equations (delay equations of Volterra type) and of coupled systems of renewal and delay differential equations. The method consists in the reformulation of the delay equation as an abstract differential equation, the reduction of the latter to a system of ordinary differential equations via

Ayako Yamamoto, Toshio Irino, Fuki Miyazaki, Honoka Tamaru

We propose an objective intelligibility measure (OIM), called the Gammachirp Envelope Similarity Index (GESI), which can predict the speech intelligibility (SI) of simulated hearing loss (HL) sounds for normal hearing (NH) listeners. GESI is an intrusive method that computes the SI metric using the gammachirp filterbank (GCFB), the modulation filterbank, and

Li Lucy, Su Lin Blodgett, Milad Shokouhi, Hanna Wallach

Fairness-related assumptions about what constitute appropriate NLG system behaviors range from invariance, where systems are expected to behave identically for social groups, to adaptation, where behaviors should instead vary across them. To illuminate tensions around invariance and adaptation, we conduct five case studies, in which we perturb different type

Leonardo A. M. Souza

This study explores a detailed examination of various classes of single- and two-mode Gaussian states as key elements for an estimation process, specifically targeting the evaluation of an unknown squeezing parameter encoded in one mode. To quantify the efficacy of each probe, we employ the concept of Average Quantum Fisher Information (AvQFI) as a robust me

Charlotte Gervillie-Mouravieff, Wurigumula Bao, Daniel A Steingart, Ying Shirley-Meng

As global energy demands escalate, and the use of non-renewable resources become untenable, renewable resources and electric vehicles require far better batteries to stabilize the new energy landscape. To maximize battery performance and lifetime, understanding and monitoring the fundamental mechanisms that govern their operation throughout their life cycle

Ryan Janish, Elena Pinetti

Dark matter particles with a mass around 1 eV can decay into near-infrared photons. Utilising available public blank sky observations from the NIRSpec IFU on the James Webb Space Telescope (JWST), we search for a narrow emission line due to decaying dark matter and derive leading constraints in the mass range 0.8-3 eV on the decay rate to photons, and more s

Dmitri Bykov

We summarize some (mostly geometric) facts underlying the relation between 2D integrable sigma models and generalized Gross-Neveu models, emphasizing connections to the theory of nilpotent orbits, Springer resolutions and quiver varieties. This is meant to shed light on the general setup when this correspondence holds.

Simin Fan, Matteo Pagliardini, Martin Jaggi

The coverage and composition of the pretraining data significantly impacts the generalization ability of Large Language Models (LLMs). Despite its importance, recent LLMs still rely on heuristics and trial and error to increase or reduce the influence of data-domains. We propose DOmain reweighting with Generalization Estimation (DoGE), which optimizes the pr

Neal P. Avis Kozar, Pat Scott, Aaron C. Vincent

We perform a global fit of dark matter interactions with nucleons using a non-relativistic effective operator description, considering both direct detection and neutrino data. We examine the impact of combining the direct detection experiments CDMSlite, CRESST-II, CRESST-III, DarkSide-50, LUX, LZ, PandaX-II, PandaX-4T, PICO-60, SIMPLE, SuperCDMS, XENON100, a

L. M. Rodrigues, L. Marques Fagundes, D. C. Salles, G. H. dos Santos

We experimentally demonstrate resonance of first-order vector vortex beams (VVB) with a triangular optical cavity. We also show that, due to their symmetry properties, the VVBs commonly known as radial and azimuthal beams do not resonate at the same cavity length, which could be explored to use the triangular resonator as a mode sorter. In addition, an intra

Kimberly Moreland, Maher Dayeh, Hazel M. Bain, Subhamoy Chatterjee

We introduce a new multivariate data set that utilizes multiple spacecraft collecting in-situ and remote sensing heliospheric measurements shown to be linked to physical processes responsible for generating solar energetic particles (SEPs). Using the Geostationary Operational Environmental Satellites (GOES) flare event list from Solar Cycle (SC) 23 and part

Simin Fan, Martin Jaggi

Automatic data selection and curriculum design for training large language models is challenging, with only a few existing methods showing improvements over standard training. Furthermore, current schemes focus on domain-level selection, overlooking the more fine-grained contributions of each individual training point. It is difficult to apply traditional da

Divij Gupta, Ali Etemad

Recent advances in deep learning have made it increasingly feasible to estimate heart rate remotely in smart environments by analyzing videos. However, a notable limitation of deep learning methods is their heavy reliance on extensive sets of labeled data for effective training. To address this issue, self-supervised learning has emerged as a promising avenu

Mahmud Hasan, Hailin Sang

The generative adversarial network (GAN) is an important model developed for high-dimensional distribution learning in recent years. However, there is a pressing need for a comprehensive method to understand its error convergence rate. In this research, we focus on studying the error convergence rate of the GAN model that is based on a class of functions enc

Mahan Fathi, Clement Gehring, Jonathan Pilault, David Kanaa

Koopman representations aim to learn features of nonlinear dynamical systems (NLDS) which lead to linear dynamics in the latent space. Theoretically, such features can be used to simplify many problems in modeling and control of NLDS. In this work we study autoencoder formulations of this problem, and different ways they can be used to model dynamics, specif

Dasharadhan Mahalingam, Aditya Patankar, Khiem Phi, Nilanjan Chakraborty

Containerized vertical farming is a type of vertical farming practice using hydroponics in which plants are grown in vertical layers within a mobile shipping container. Space limitations within shipping containers make the automation of different farming operations challenging. In this paper, we explore the use of cobots (i.e., collaborative robots) to autom

Tatiana Tatarenko, Angelia Nedich

This work proposes a novel distributed approach for computing a Nash equilibrium in convex games with restricted strongly monotone pseudo-gradients. By leveraging the idea of the centralized operator extrapolation method presented in [4] to solve variational inequalities, we develop the algorithm converging to Nash equilibria in games, where players have no

Ross Ure Anderson

This article gives some properties of intervals in $\mathbb{R}$ and discusses some problems involving intervals for which the concept of outer measure on $\mathbb{R}$ provides a more efficient solution than an elementary approach. The outer measure is then defined and some of its main properties in relation to intervals are developed, culminating in the coun

Mehar Bhatia, Vered Shwartz

With the increasing integration of AI into everyday life, it's becoming crucial to design AI systems that serve users from diverse backgrounds by making them culturally aware. In this paper, we present GD-COMET, a geo-diverse version of the COMET commonsense inference model. GD-COMET goes beyond Western commonsense knowledge and is capable of generating infe

Marco Galoppo

The van Stockum-Bonner class of spacetimes can be interpreted as fully general relativistic models for rigidly rotating disc galaxies. Frame-dragging effects in these geometries demand a recalibration of the dark matter content relative to models based on Newtonian gravity. We investigate the previously overlooked topological structure of these spacetimes, i

John M. Tranquada

The high-temperature superconductivity in layered cuprates discovered by Bednorz and M\"uller arrived as a shock. Gradually, the presence of competing orders, such as antiferromagnetism and charge order, were discovered; however, the relationship to the superconductivity has been confusing. It so happens that the original cuprate superconductor family La$_{2

Whitney T. Powers, Evan H. Anders, Benjamin P. Brown

In stars and planets natural processes heat convective flows in the bulk of a convective region rather than at hard boundaries. By characterizing how convective dynamics are determined by the strength of an internal heating source we can gain insight into the processes driving astrophysical convection. Internally heated convection has been studied extensivel

Yale Fan, Anil Thapa

We present a mathematical framework for constructing the most general neutrino mass matrices that yield the observed spectrum of light active neutrino masses in conjunction with arbitrarily many heavy sterile neutrinos, without the need to assume a hierarchy between Dirac and Majorana mass terms. The seesaw mechanism is a byproduct of the formalism, along wi

Ricardo A. Podestá, Denis E. Videla

We study the spectrum of generalized Paley graphs $\Gamma(k,q)=Cay(\mathbb{F}_q,R_k)$, undirected or not, with $R_k=\{x^k:x\in \mathbb{F}_q^*\}$ where $q=p^m$ with $p$ prime and $k\mid q-1$. We first show that the eigenvalues of $\Gamma(k,q)$ are given by the Gaussian periods $\eta_{i}^{(k,q)}$ with $0\le i\le k-1$. Then, we explicitly compute the spectrum o

Yohn Jairo Parra Bautista, Vinicious Lima, Carlos Theran, Richard Alo

Health disparities are differences in health outcomes and access to healthcare between different groups, including racial and ethnic minorities, low-income people, and rural residents. An artificial intelligence (AI) program called large language models (LLMs) can understand and generate human language, improving health communication and reducing health disp

Daniel Müller, Alexey Toporensky

We have found numerically initial conditions in the $(R, H)$ plane leading to a successful Starobinsky inflation in $R+R^2$ gravity for a isotropic metrics with positive spatial curvature. Trajectories can reach inflation regime either directly or going through a bounce, and even recollapse followed by a bounce. Our numerical plots indicate that ``good" init

N. S. Srivatsa, Curt von Keyserlingk

The operator growth hypothesis (OGH) is a technical conjecture about the behaviour of operators -- specifically, the asymptotic growth of their Lanczos coefficients -- under repeated action by a Liouvillian. It is expected to hold for a sufficiently generic closed many-body system. When it holds, it yields bounds on the high frequency behavior of local corre

Chris Salahub, Jeffrey Uhlmann

We propose a general method for optimally approximating an arbitrary matrix $\mathbf{M}$ by a structured matrix $\mathbf{T}$ (circulant, Toeplitz/Hankel, etc.) and examine its use for estimating the spectra of genomic linkage disequilibrium matrices. This application is prototypical of a variety of genomic and proteomic problems that demand robustness to inc

Alexander Rasskazov, Roman R. Rafikov

Origin of LIGO/Virgo gravitational wave events may involve production of binaries with relativistic components in dense stellar systems - globular or nuclear star clusters - and their subsequent evolution towards merger. Orbital parameters of these binaries (the inner orbit) and their motion inside the cluster (the outer orbit) evolve due to both external ag

Sayed Hoseini, Johannes Theissen-Lipp, Christoph Quix

In recent years, data lakes emerged as away to manage large amounts of heterogeneous data for modern data analytics. One way to prevent data lakes from turning into inoperable data swamps is semantic data management. Some approaches propose the linkage of metadata to knowledge graphs based on the Linked Data principles to provide more meaning and semantics t

Gabriele Prato, Jerry Huang, Prasannna Parthasarathi, Shagun Sodhani

In the age of artificial intelligence, the role of large language models (LLMs) is becoming increasingly central. Despite their growing prevalence, their capacity to consolidate knowledge from different training documents - a crucial ability in numerous applications - remains unexplored. This paper presents the first study examining the capability of LLMs to

Yongsong Huang, Wanqing Xie, Mingzhen Li, Mingmei Cheng

Federated learning (FL) enables multiple client medical institutes collaboratively train a deep learning (DL) model with privacy protection. However, the performance of FL can be constrained by the limited availability of labeled data in small institutes and the heterogeneous (i.e., non-i.i.d.) data distribution across institutes. Though data augmentation ha

Miroslav Grmela

Approach of mesoscopic state variables to time independent equilibrium sates (zero law of thermodynamics) gives birth to the classical equilibrium thermodynamics. Approach of fluxes and forces to fixed points (equilibrium fluxes and forces) that drive reduced mesoscopic dynamics gives birth to the rate thermodynamics that is applicable to driven systems. We

Abdellah Tahenni, Fatiha Merazka

Software-defined wide area network (SD-WAN) enhances network traffic management, while Multiprotocol Label Switching (MPLS) offers efficient data transmission. This paper analyzes SD-WAN over MPLS in the Housing Bank, a major Algerian financial institution. We deploy FortiGate for the SD-WAN solution, comparing it to traditional MPLS and direct internet acce

Wenbing Wu, Alain Kadar, Sang Hyun Lee, Bum Chul Park

Multifunctional coatings are central for information, biomedical, transportation and energy technologies. These coatings must possess hard-to-attain properties and be scalable, adaptable, and sustainable, which makes layer-by-layer assembly (LBL) of nanomaterials uniquely suitable for these technologies. What remains largely unexplored is that LBL enables co

Oren Barkan, Yehonatan Elisha, Jonathan Weill, Yuval Asher

This paper presents Deep Integrated Explanations (DIX) - a universal method for explaining vision models. DIX generates explanation maps by integrating information from the intermediate representations of the model, coupled with their corresponding gradients. Through an extensive array of both objective and subjective evaluations spanning diverse tasks, data

Omid Amini, Matthieu Piquerez

This paper is the first in a series devoted to the development of a Hodge theory for tropical varieties. We introduce a notion of T-stability for tropical fans and prove that various geometric properties of tropical fans are T-stable. As a consequence, we establish K\"ahler properties for the Chow ring in a large class of tropical fans, going beyond the case

Aadi Krishna

Long GRB 190114C, identified on January 14th, 2019, was the first Gamma-ray Burst that substantially violated the defined 10 GeV energy limit of the Synchrotron model, with an observed emission between 0.2 - 1 TeV and a low redshift of z = 0.425. This paper analyzes its immediate afterglow broadband spectrum from 10$^{17}$ to 10$^{26}$ Hz based on observatio

Nicolaos Petropoulos, Miles M. P. Couchman, Ali Mashayek, Stephen M. de Bruyn Kops

Relatively strongly stratified turbulent flows tend to self-organise into a 'layered anisotropic stratified turbulence' (LAST) regime, characterised by relatively deep and well-mixed density 'layers' separated by relatively thin 'interfaces' of enhanced density gradient. Understanding the associated mixing dynamics is a central problem in geophysical fluid d

Timothy M. Chan, Pingan Cheng, Da Wei Zheng

We present the first optimal randomized algorithm for constructing the order-$k$ Voronoi diagram of $n$ points in two dimensions. The expected running time is $O(n\log n + nk)$, which improves the previous, two-decades-old result of Ramos (SoCG'99) by a $2^{O(\log^*k)}$ factor. To obtain our result, we (i) use a recent decision-tree technique of Chan and Zhe

Kohei Noda

As is widely known, a non-Hermitian matrix exhibits distinct left and right eigenvectors, which form a bi-orthogonal system. Chalker and Mehling initiated the study of the joint statistics of the eigenvalues and the overlaps defined by the left and right eigenvectors of the Ginibre unitary ensemble. Later, Akemann et al. continued their investigation by stud

Haard Panchal, Ergun Akleman, Vinayak Krishnamurthy, Tolga Talha Yildiz

In this paper, we present a new approach to obtain symmetric tiles with curved edges. Our approach is based on using higher-order Voronoi sites that are closed under wallpaper symmetries. The resulting Voronoi tessellations provide us with symmetric tiles with curved edges. We have developed a web application that provides real-time tile design. Our applicat

Jakub Łopuszański

One of the most popular setups for a back-end of a high performance website consists of a relational database and a cache which stores results of performed queries. Several application frameworks support caching of queries made to the database, but few of them handle cache invalidation correctly, resorting to simpler solutions such as short TTL values, or fl

Mickael Bestard, Emmanuel Franck, Laurent Navoret, Yannick Privat

How to free a road from vehicle traffic as efficiently as possible and in a given time, in order to allow for example the passage of emergency vehicles? We are interested in this question which we reformulate as an optimal control problem. We consider a macroscopic road traffic model on networks, semi-discretized in space and decide to give ourselves the pos

Yuhong Luo, Austin Hoag, Philip S. Thomas

Representation learning is increasingly employed to generate representations that are predictive across multiple downstream tasks. The development of representation learning algorithms that provide strong fairness guarantees is thus important because it can prevent unfairness towards disadvantaged groups for all downstream prediction tasks. To prevent unfair

L. N. Simpfendoerfer, Meagan Sundstrom, Matthew Dew, N. G. Holmes

Research suggests that interacting with more peers about physics course material is correlated with higher student performance. Some studies, however, have demonstrated that different topics of peer interactions may correlate with their performance in different ways, or possibly not at all. In this study, we probe both the peers with whom students interact a

Khoa Tran, Melvin Leok

The problem of 3-dimensional, convex rigid-body collision over a plane is fully investigated; this includes bodies with sharp corners that is resolved without the need for nonsmooth convex analysis of tangent and normal cones. In particular, using nonsmooth Lagrangian mechanics, the equations of motion and jump equations are derived, which are largely depend

Adam Bouyamourn

We show that LLMs hallucinate because their output is not constrained to be synonymous with claims for which they have evidence: a condition that we call evidential closure. Information about the truth or falsity of sentences is not statistically identified in the standard neural probabilistic language model setup, and so cannot be conditioned on to generate

Alberto Padoan, Florian Dörfler, John Lygeros

The paper studies conical, convex, and affine models in the framework of behavioral systems theory. We investigate basic properties of such behaviors and address the problem of constructing models from measured data. We prove that closed, shift-invariant, conical, convex, and affine models have the intersection property, thereby enabling the definition of mo

Shayan Roofeh, Vahid Karimipour

The interest in the Werner-Holevo channel $\Lambda_{1} (\rho)=\frac{1}{2}(\text{tr}(\rho)I-\rho^T)$ has been mainly due to its abstract mathematical properties. We show that in three dimensions and with a slight modification, this channel can be realized as the rotation of qutrit states in random directions by random angles. Our modification takes the form $

Sudeep Salgia, Sattar Vakili, Qing Zhao

We consider Bayesian optimization using Gaussian Process models, also referred to as kernel-based bandit optimization. We study the methodology of exploring the domain using random samples drawn from a distribution. We show that this random exploration approach achieves the optimal error rates. Our analysis is based on novel concentration bounds in an infini

G. M. Wysin

The uniform states of a model for one-dimensional chains of thin magnetic islands on a nonmagnetic substrate coupled via dipolar interactions are described here. Magnetic islands oriented with their long axes perpendicular to the chain direction are assumed, whose shape anisotropy imposes a preference for the dipoles to point perpendicular to the chain. The

Ying Liu, Haozhu Wang, Huixue Zhou, Mingchen Li

Reinforcement learning (RL) has emerged as a powerful approach for tackling complex medical decision-making problems such as treatment planning, personalized medicine, and optimizing the scheduling of surgeries and appointments. It has gained significant attention in the field of Natural Language Processing (NLP) due to its ability to learn optimal strategie

Andreas Fyrillas, Olivier Faure, Nicolas Maring, Jean Senellart

Photonic integrated circuits offer a compact and stable platform for generating, manipulating, and detecting light. They are instrumental for classical and quantum applications. Imperfections stemming from fabrication constraints, tolerances and operation wavelength impose limitations on the accuracy and thus utility of current photonic integrated devices. M

Daniel G. Allman, Parth Sabharwal, Kevin C. Wright

We have observed the spontaneous appearance of currents in a ring of ultracold fermionic atoms (6Li) with attractive interactions, following a quench to a BCS-like pair superfluid. We have measured the winding number probability distribution for a range of quench rates, with a quench protocol using simultaneous forced evaporation and interaction ramps to ach

Alberto Padoan, Jeremy Coulson, Florian Dörfler

We study the controller implementability problem, which seeks to determine if a controller can make the closed-loop behavior of a given plant match that of a desired reference behavior. We establish necessary and sufficient conditions for controller implementability which only rely on raw data. Subsequently, we consider the problem of constructing controller

Sebastien Loranger, Foroogh Jafari, Joseba Zubia, David Novoa

We study the generation of narrowband terahertz (THz) pulses by stimulated Raman scattering and molecular modulation in hydrogen-filled hybrid hollow-core fibers. Using a judicious combination of materials and transverse structures, this waveguide design enables simultaneous confinement of optical and THz signals with reasonably low attenuation, as well as h

Anjana Mohandasan, Antonino P. Milone, Giacomo Cordoni, Emanuele Dondoglio

Binary stars play a major role in determining the dynamic evolution of star clusters. We used images collected with the Hubble Space Telescope to study fourteen Magellanic Clouds star clusters that span an age interval between $\sim 0.6$ and $2.1$ Gyr and masses of $10^{4}-10^{5}$ M$_{\odot}$. We estimated the fraction of binary systems composed of two main-

F. P. Mena, D. Valenzuela, C. Espinoza, F. Pizarro

Achieving amplification with high gain and quantum-limited noise is a difficult problem to solve. Parametric amplification using a superconducting transmission line with high kinetic inductance is a promising technology not only to solve this problem but also adding several benefits. When compared with other technologies, they have the potential of improving

Vitória Biesek, Pedro Henrique de Almeida Konzen

The Burgers equation is a well-established test case in the computational modeling of several phenomena such as fluid dynamics, gas dynamics, shock theory, cosmology, and others. In this work, we present the application of Physics-Informed Neural Networks (PINNs) with an implicit Euler transfer learning approach to solve the Burgers equation. The proposed ap

Fuyuan Lyu, Xing Tang, Dugang Liu, Chen Ma

Deep sparse networks are widely investigated as a neural network architecture for prediction tasks with high-dimensional sparse features, with which feature interaction selection is a critical component. While previous methods primarily focus on how to search feature interaction in a coarse-grained space, less attention has been given to a finer granularity.

Kenji Matsuki

We present an addendum/erratum to the paper "Weyl Groups and Birational Transformations among Minimal Models" written by the author and published in 1995, adding the analysis of the "88-th" deformation type of a smooth Fano 3-fold with $B_2 = 4$ denoted as $n^o\ 13$, which was missing from the original classification table by Mori-Mukai and added later to th

Logan J. Cross, Xiangxiong Zhang

The monotonicity of discrete Laplacian implies discrete maximum principle, which in general does not hold for high order schemes. The $Q^2$ spectral element method has been proven monotone on a uniform rectangular mesh. In this paper we prove the monotonicity of the $Q^2$ spectral element method on quasi-uniform rectangular meshes under certain mesh constrai

Patrick Cousot

We study transformational program logics for correctness and incorrectness that we extend to explicitly handle both termination and nontermination. We show that the logics are abstract interpretations of the right image transformer for a natural relational semantics covering both finite and infinite executions. This understanding of logics as abstractions of

Xingyang Yu

We propose a top-down approach to non-invertible symmetries in 2D QFTs and their associated symmetry topological field theories. We focus on the gauge theory engineered on D1-branes probing a particular Calabi-Yau 4-fold singularity. We show how to derive the symmetry topological field theory, a 3D Dijkgraaf-Witten theory, from the IIB supergravity under dim

Uriel Conod, Kate Jackson, Paolo Turri, Scott Chapman

The Gemini Infrared Multi-Object Spectrograph (GIRMOS) will be a near-infrared, multi-object, medium spectral resolution, integral field spectrograph (IFS) for Gemini North Telescope, designed to operate behind the future Gemini North Adaptive Optics system (GNAO). In addition to a first ground layer Adaptive Optics (AO) correction in closed loop carried out

Marwa Abdulhai, Gregory Serapio-Garcia, Clément Crepy, Daria Valter

Moral foundations theory (MFT) is a psychological assessment tool that decomposes human moral reasoning into five factors, including care/harm, liberty/oppression, and sanctity/degradation (Graham et al., 2009). People vary in the weight they place on these dimensions when making moral decisions, in part due to their cultural upbringing and political ideolog

Wenbin Lu, Tatsuya Matsumoto, Christopher D. Matzner

We analytically and numerically study the hydrodynamic propagation of a precessing jet in the context of tidal disruption events (TDEs) where the star's angular momentum is misaligned with the black hole spin. We assume that a geometrically thick accretion disk undergoes Lense-Thirring precession around the black hole spin axis and that the jet is aligned wi

Giovanna Liberato, Denise R. Gonçalves, Luis A. Gutiérrez-Soto, Stavros Akras

About 3,500 planetary nebulae (PNe) are currently known in the Milky Way, which shows a great discrepancy with the expected number for these objects, regardless of the reference used, $33-59 \times 10^{3}$. The same holds for symbiotic stars (SySts) as well, since the expected number in the Galaxy ($3-400 \times 10^{3}$) differs considerably from the amount

Jintao Xu, Yifei Li, Wenxun Xing

We propose both serial and parallel proximal (linearized) alternating direction method of multipliers (ADMM) algorithms for training residual neural networks. In contrast to backpropagation-based approaches, our methods inherently mitigate the exploding gradient issue and are well-suited for parallel and distributed training through regional updates. Theoret

Harsh Parikh, Quinn Lanners, Zade Akras, Sahar F. Zafar

Recent statistical and reinforcement learning methods have significantly advanced patient care strategies. However, these approaches face substantial challenges in high-stakes contexts, including missing data, inherent stochasticity, and the critical requirements for interpretability and patient safety. Our work operationalizes a safe and interpretable frame

André Magalhães de Sá Gomes, Christian S. Rodrigues

Let $G\curvearrowright M$ be a proper isometric action of a compact Lie group on a complete, connected and orientable Riemannian manifold of dimension $N$. We characterize the local $K$-displacement convexity of the internal-energy functional $H$ on the space $\mathcal P^{ac}_G(M)$ of absolutely continuous $G$-invariant probability measures. Via disintegrati

Anirudh Chandrasekaran, Luke C. Rhodes, Edgar Abarca Morales, Carolina A. Marques

The properties of correlated electron materials are often intricately linked to Van Hove singularities (VHS) in the vicinity of the Fermi energy. The class of these VHS is of great importance, with higher-order ones -- with power-law divergence in the density of states -- leaving frequently distinct signatures in physical properties. We use a new theoretical

Ye Tian, Haolei Weng, Yang Feng

While supervised federated learning approaches have enjoyed significant success, the domain of unsupervised federated learning remains relatively underexplored. Several federated EM algorithms have gained popularity in practice, however, their theoretical foundations are often lacking. In this paper, we first introduce a federated gradient EM algorithm (FedG

Sanjeev V. Namjoshi, Reese Green, Krishi Sharma, Zhangzhang Si

Federated learning is becoming increasingly relevant and popular as we witness a surge in data collection and storage of personally identifiable information. Alongside these developments there have been many proposals from governments around the world to provide more protections for individuals' data and a heightened interest in data privacy measures. As dee

Matheus del-Valle, Lariza Laura de Oliveira, Henrique Cursino Vieira, Henrique Min Ho Lee

Thoracic aortic aneurysm (TAA) is a fatal disease which potentially leads to dissection or rupture through progressive enlargement of the aorta. It is usually asymptomatic and screening recommendation are limited. The gold-standard evaluation is performed by computed tomography angiography (CTA) and radiologists time-consuming assessment. Scans for other ind

Adil Belhaj, Salah Eddine Ennadifi

Motivated by the growing attention devoted to the Quantum Chromodynamics sector of the Standard Model, and the recent observations of non-standard hadronic states, a possible substructure of quarks $q_{f}\equiv C_{ij}|k_{i}k_{j}\rangle $ in terms of four colorless bound particles $% k_{i(=0,\ldots,3)}$ "hyperparticles" is investigated in a $SO(10)$-inspired

Chufan Shi, Yixuan Su, Cheng Yang, Yujiu Yang

The potential of large language models (LLMs) to simultaneously perform a wide range of natural language processing (NLP) tasks has been the subject of extensive research. Although instruction tuning has proven to be a data-efficient method for transforming LLMs into such generalist models, their performance still lags behind specialist models trained exclus

Maryam Hashemi, Ghazaleh Mahmoudi, Sara Kodeiri, Hadi Sheikhi

Large-scale pretrained models such as LXMERT are becoming popular for learning cross-modal representations on text-image pairs for vision-language tasks. According to the lottery ticket hypothesis, NLP and computer vision models contain smaller subnetworks capable of being trained in isolation to full performance. In this paper, we combine these observations