May 2023 arXiv papers — page 11

Justino Sánchez

Let $\alpha,\beta$ be real parameters and let $a>0$. We study radially symmetric solutions of \begin{equation*} S_k(D^2v)+\alpha v+\beta \xi\cdot\nabla v=0,\, v>0\;\; \mbox{in}\;\; \mathbb{R}^n,\; v(0)=a, \end{equation*} where $S_k(D^2v)$ denotes the $k$-Hessian operator of $v$. For $\alpha\leq\frac{\beta(n-2k)}{k}\;\;\mbox{and}\;\;\beta>0$, we prove the exi

Ben Knudsen, Eric Ramos

We formulate a categorification of Robertson's conjecture analogous to the categorical graph minor conjecture of Miyata--Proudfood--Ramos. We show that these conjectures imply the existence of a finite list of atomic graphs generating the homology of configuration spaces of graphs -- in fixed degree, with a fixed number of particles, under topological embedd

Maximilian A. Weissflog, Romain Dezert, Vincent Vinel, Carlo Gigli

Entangled photon states are a fundamental resource for optical quantum technologies and investigating the fundamental predictions of quantum mechanics. Up to now such states are mainly generated in macroscopic nonlinear optical systems with elaborately tailored optical properties. In this theoretical work, we extend the understanding on the generation of ent

Liang Li, Jun Zhu, Yong-Tao Zhang

High order fast sweeping methods for efficiently solving steady state solutions of hyperbolic PDEs were not available yet on unstructured meshes. In this paper, we extend high order fast sweeping methods to unstructured triangular meshes by applying fixed-point iterative sweeping techniques to a fifth-order finite volume unstructured WENO scheme, for solving

John Paul Ward

We consider the problem of discretizing one-dimensional, real-valued functions as graphs. The goal is to find a small set of points, from which we can approximate the remaining function values. The method for approximating the unknown values is interpolation on a discrete graph structure. From the discrete graph structure, we build a refined approximation to

Jader P. Santos, Ivan Henao, Raam Uzdin

Quantum processors can already execute tasks beyond the reach of classical simulation, albeit for artificial problems. At this point, it is essential to design error metrics that test the experimental accuracy of quantum algorithms with potential for a practical quantum advantage. The distinction between coherent errors and incoherent errors is crucial, as t

William Rudman, Carsten Eickhoff

Given the success of Large Language Models (LLMs), there has been considerable interest in studying the properties of model activations. The literature overwhelmingly agrees that LLM representations are dominated by a few "outlier dimensions" with exceedingly high variance and magnitude. Several studies in Natural Language Processing (NLP) have sought to mit

Emily K Pass, Jennifer G Winters, David Charbonneau, Jonathan M Irwin

Cold Jovian planets play an important role in sculpting the dynamical environment in which inner terrestrial planets form. The core accretion model predicts that giant planets cannot form around low-mass M dwarfs, although this idea has been challenged by recent planet discoveries. Here, we investigate the occurrence rate of giant planets around low-mass (0.

Mario Fernández Navarro

We discuss a theory of flavour which can simultaneously explain and connect the origin of the Standard Model flavour hierarchies with the $U_{1}(\mathbf{3},\mathrm{\mathbf{1},2/3})$ leptoquark explanation of the $B$-decay flavour anomalies. First, we motivate the vector leptoquark $U_{1}$ as an excellent mediator to address the updated picture of $B$-anomali

Rico Landman, Ignas Snellen, Cristoph Keller, Mamadou N'Diaye

Context: Combining high-contrast imaging with medium- or high-resolution integral field spectroscopy has the potential to boost the detection rate of exoplanets, especially at small angular separations. Furthermore, it immediately provides a spectrum of the planet that can be used to characterise its atmosphere. The achievable spectral resolution, wavelength

Yuning Zhang, Aditya Deshmukh, K. W. Wang

The recent advances in autonomous systems have prompted a strong demand for the next generation of adaptive structures and materials to possess more built-in intelligence in their mechanical domain, the so-called mechano-intelligence (MI). Previous MI attempts mainly focused on specific designs and case studies to realize limited aspects of MI, and there is

Thanh Truong Nguyen, Dung Van Vu, Tuynh Van Pham, Minh Hoang Trinh

This paper studies the problem of stabilizing a leader-follower formation specified by a set of bearing constraints and being disturbed by some unknown uniformly bounded disturbance{s}. A set of leaders are positioned at their desired positions, while each follower is modeled by a single integrator with an additive time-varying disturbance. Adaptive variable

Artem Lykov, Dzmitry Tsetserukou

This paper presents a novel approach in autonomous robot control, named LLM-BRAIn, that makes possible robot behavior generation, based on operator's commands. LLM-BRAIn is a transformer-based Large Language Model (LLM) fine-tuned from Stanford Alpaca 7B model to generate robot behavior tree (BT) from the text description. We train the LLM-BRAIn on 8,5k inst

Jonathan Roberts, Timo Lüddecke, Sowmen Das, Kai Han

Large language models (LLMs) have shown remarkable capabilities across a broad range of tasks involving question answering and the generation of coherent text and code. Comprehensively understanding the strengths and weaknesses of LLMs is beneficial for safety, downstream applications and improving performance. In this work, we investigate the degree to whic

Harold Steinacker, Tung Tran

We explore the semi-classical relation between the fuzzy 4-hyperboloid $H_N^4$ and non-compact quantized twistor space $\mathbb{P}^{1,2}$ at large $N$. This provides two backgrounds $N$ of the IKKT matrix model via two natural stereographic projections, leading to higher-spin gauge theories with Euclidean and Minkowski signature denoted by HS-IKKT. The resul

Wei Deng

The rise of artificial intelligence (AI) hinges on the efficient training of modern deep neural networks (DNNs) for non-convex optimization and uncertainty quantification, which boils down to a non-convex Bayesian learning problem. A standard tool to handle the problem is Langevin Monte Carlo, which proposes to approximate the posterior distribution with the

Zihao Hu, Guanghui Wang, Jacob Abernethy

The projection operation is a critical component in a wide range of optimization algorithms, such as online gradient descent (OGD), for enforcing constraints and achieving optimal regret bounds. However, it suffers from computational complexity limitations in high-dimensional settings or when dealing with ill-conditioned constraint sets. Projection-free algo

Daniel Friedan

Euclidean quantum gravity (EQG) separates into a local theory and a global theory. The local theory operates in every compact $d$-manifold with boundary to produce a state on the boundary. The global theory then sums these boundary states over the diffeomorphism classes of $d$-manifolds with boundary to make the Hartle-Hawking state. Global EQG is formulated

Gabriela Fernandes Martins, Jordan M. Horowitz

Limits on a system's response to external perturbations inform our understanding of how physical properties can be shaped by microscopic characteristics. Here, we derive constraints on the steady-state nonequilibrium response of physical observables in terms of the topology of the microscopic state space and the strength of thermodynamic driving. Notably, ev

Zag ElSayed, Murat Ozer, Nelly Elsayed, Ahmed Abdelgawad

A seizure tracking system is crucial for monitoring and evaluating epilepsy treatments. Caretaker seizure diaries are used in epilepsy care today, but clinical seizure monitoring may miss seizures. Monitoring devices that can be worn may be better tolerated and more suitable for long-term ambulatory use. Many techniques and methods are proposed for seizure d

Tassos Bountis, Konstantinos Kaloudis, Helen Christodoulidi

The important phenomenon of "stickiness" of chaotic orbits in low dimensional dynamical systems has been investigated for several decades, in view of its applications to various areas of physics, such as classical and statistical mechanics, celestial mechanics and accelerator dynamics. Most of the work to date has focused on two-degree of freedom Hamiltonian

Alexios P. Polychronakos, Konstantinos Sfetsos

We compute the multiplicity of the irreducible representations in the decomposition of the tensor product of an arbitrary number $n$ of fundamental representations of $SU(N)$, and we identify a duality in the representation content of this decomposition. Our method utilizes the mapping of the representations of $SU(N)$ to the states of free fermions on the c

Jaehyung Kim, Yekyung Kim, Karin de Langis, Jinwoo Shin

The success of NLP systems often relies on the availability of large, high-quality datasets. However, not all samples in these datasets are equally valuable for learning, as some may be redundant or noisy. Several methods for characterizing datasets based on model-driven meta-information (e.g., model's confidence) have been developed, but the relationship an

Hichem Sahbi

Graph convolutional networks (GCNs) are nowadays becoming mainstream in solving many image processing tasks including skeleton-based recognition. Their general recipe consists in learning convolutional and attention layers that maximize classification performances. With multi-head attention, GCNs are highly accurate but oversized, and their deployment on edg

Yashar Kiarashi, Chaitra Hedge, Venkata Siva Krishna Madala, ArjunSinh Nakum

Spatial navigation of indoor space usage patterns reveals important cues about the cognitive health of individuals. In this work, we present a low-cost, scalable, open-source edge computing system using Bluetooth Low Energy (BLE) and Inertial Measurement Unit sensors (IMU) for tracking indoor movements for a large indoor facility (over 1600 m^2) that was des

Erickson Tjoa

We present a construction of the Wigner function for a bosonic quantum field theory that has well-defined ultraviolet (UV) and infrared (IR) properties. Our construction uses the local mode formalism in algebraic quantum field theory that is valid in any globally hyperbolic curved spacetimes, i.e., without invoking the path integral formalism. The idea is to

Clément de Seguins Pazzis

Let $(b,u)$ be a pair consisting of a symplectic form $b$ on a finite-dimensional vector space $V$ over a field $\mathbb{F}$, and of a $b$-alternating endomorphism $u$ of $V$ (i.e. $b(x,u(x))=0$ for all $x$ in $V$). Let $p$ and $q$ be arbitrary polynomials of degree $2$ with coefficients in $\mathbb{F}$. We characterize, in terms of the invariant factors of

Liyan Tang, Yifan Peng, Yanshan Wang, Ying Ding

A human decision-maker benefits the most from an AI assistant that corrects for their biases. For problems such as generating interpretation of a radiology report given findings, a system predicting only highly likely outcomes may be less useful, where such outcomes are already obvious to the user. To alleviate biases in human decision-making, it is worth co

Masoud Zargar

The union-closed sets conjecture, attributed to P\'eter Frankl from 1979, states that for any non-empty finite union-closed family of finite sets not consisting of only the empty set, there is an element that is in at least half of the sets in the family. We prove a version of Frankl's conjecture for families distributed according to any one of infinitely ma

Mahdi Karami

Most real-world graphs exhibit a hierarchical structure, which is often overlooked by existing graph generation methods. To address this limitation, we propose a novel graph generative network that captures the hierarchical nature of graphs and successively generates the graph sub-structures in a coarse-to-fine fashion. At each level of hierarchy, this model

Simone Albanesi, Sebastiano Bernuzzi, Thibault Damour, Alessandro Nagar

We present a new effective-one-body (EOB) waveform for eccentric, nonspinning, binaries in the extreme mass ratio limit, with initial eccentricities up to $0.95$. The EOB analytical waveform, that includes noncircular corrections up to second post-Newtonian order, is completed by a phenomenological ringdown model that is informed by Regge-Wheeler-Zerilli (RW

Mike Cummings, Sergio Da Silva, Megumi Harada, Jenna Rajchgot

A regular nilpotent Hessenberg Schubert cell is the intersection of a regular nilpotent Hessenberg variety with a Schubert cell. In this paper, we describe a set of minimal generators of the defining ideal of a regular nilpotent Hessenberg Schubert cell in the type $A$ setting. We show that these minimal generators are a Gr\"obner basis for an appropriate le

Gabriele Lo Monaco, Luca Innocenti, Dario Cilluffo, Diana A Chisholm

Quantum information scrambling (QIS), from the perspective of quantum information theory, is generally understood as local non-retrievability of information evolved through some dynamical process, and is often quantified via entropic quantities such as the tripartite information. We argue that this approach comes with a number of issues, in large part due to

Sungwon Ahn, Matthew Junge, Hanbaek Lyu, Lily Reeves

We study a family of interacting particle systems with annihilating and coalescing reactions. Two types of particles are interspersed throughout a transitive unimodular graph. Both types diffuse as simple random walks with possibly different jump rates. Upon colliding, like particles coalesce up to some cap and unlike particles annihilate. We describe a phas

Andrew Spiers, Adam Pound, Jordan Moxon

To fully exploit the capabilities of next-generation gravitational wave detectors, we need to significantly improve the accuracy of our models of gravitational-wave-emitting systems. This paper focuses on one way of doing so: by taking black hole perturbation theory to second perturbative order. Such calculations are critical for the development of nonlinear

Benjamin Magnelli, Carlos Gómez-Guijarro, David Elbaz, Emanuele Daddi

[Abridged] We combined HST images from the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey with JWST images from the Cosmic Evolution Early Release Science (CEERS) survey to measure the stellar and dust-obscured star formation distributions of a mass-complete ($>10^{10}M_\odot$) sample of 69 star-forming galaxies (SFGs) at $0.1<z<2.5$. Rest-mi

Josef Jon, Ondřej Bojar

We propose a genetic algorithm (GA) based method for modifying n-best lists produced by a machine translation (MT) system. Our method offers an innovative approach to improving MT quality and identifying weaknesses in evaluation metrics. Using common GA operations (mutation and crossover) on a list of hypotheses in combination with a fitness function (an arb

Henry W. Whitehead, James H. Matthews

We present two- and three-dimensional hydrodynamic simulations of $\sim$kpc-scale AGN jets with mean jet powers in the range $1-7\times10^{45}\,$erg~s$^{-1}$, in which the jet power varies (through variation of the Lorentz factor) according to a flicker or pink noise power spectrum. We find the morphology and dynamics of the jet-cocoon system depends on the

Zhiheng Liu, Yifei Zhang, Yujun Shen, Kecheng Zheng

Synthesizing images with user-specified subjects has received growing attention due to its practical applications. Despite the recent success in single subject customization, existing algorithms suffer from high training cost and low success rate along with increased number of subjects. Towards controllable image synthesis with multiple subjects as the const

Apollonas S. Matsoukas-Roubeas, Tomaž Prosen, Adolfo del Campo

The survival probability of an initial Coherent Gibbs State (CGS) is a natural extension of the Spectral Form Factor (SFF) to open quantum systems. To quantify the interplay between quantum chaos and decoherence away from the semi-classical limit, we investigate the relation of this generalized SFF with the corresponding $l_1$-norm of coherence. As a working

Raffaele Tito D'Agnolo, Florian Nortier, Gabriele Rigo, Pablo Sesma

Higgs coupling deviations from Standard Model predictions contain information about two scales of Nature: that of new physics responsible for the deviation, and the scale where new bosons must appear. The two can coincide, but they do not have to. The scale of new bosons can be calculated by going beyond an effective field theory description of the coupling

A. de Oliveira Junior, Martí Perarnau-Llobet, Nicolas Brunner, Patryk Lipka-Bartosik

Catalysis plays a key role in many scientific areas, most notably in chemistry and biology. Here we present a catalytic process in a paradigmatic quantum optics setup, namely the Jaynes-Cummings model, where an atom interacts with an optical cavity. The atom plays the role of the catalyst, and allows for the deterministic generation of non-classical light in

Shin Toriumi, Hideyuki Hotta, Kanya Kusano

Solar flares and coronal mass ejections, the primary space weather disturbances affecting the entire heliosphere and near-Earth environment, mainly emanate from sunspot regions harbouring high degrees of magnetic twist. However, it is not clear how magnetic helicity, the quantity for measuring the magnetic twist, is supplied to the upper solar atmosphere via

Khaldoon Ghanem, Alexander Schuckert, Henrik Dreyer

Simulating properties of quantum materials is one of the most promising applications of quantum computation, both near- and long-term. While real-time dynamics can be straightforwardly implemented, the finite temperature ensemble involves non-unitary operators that render an implementation on a near-term quantum computer extremely challenging. Recently, [Lu,

David Berenstein, Elliot Maderazo, Robinson Mancilla, Anayeli Ramirez

We study null geodesics of the ten-dimensional LLM geometries. In particular, we show that there are a subset of these null geodesics that are confined to the LLM plane. The effective dynamics of these in-plane geodesics is that of a Hamiltonian system with two degrees of freedom (a phase space of dimension 4). We show that these are chaotic. In the two-colo

Nicola Galesi, Joshua A. Grochow, Toniann Pitassi, Adrian She

The Tensor Isomorphism problem (TI) has recently emerged as having connections to multiple areas of research within complexity and beyond, but the current best upper bound is essentially the brute force algorithm. Being an algebraic problem, TI (or rather, proving that two tensors are non-isomorphic) lends itself very naturally to algebraic and semi-algebrai

Daniel R. Hey, Daniel Huber, Benjamin J. Shappee, Joss Bland-Hawthorn

The Galactic bulge and bar are critical to our understanding of the Milky Way. However, due to the lack of reliable stellar distances, the structure and kinematics of the bulge/bar beyond the Galactic center have remained largely unexplored. Here, we present a method to measure distances of luminous red giants using a period-amplitude-luminosity relation anc

Subhajit Dandapat, Michael Ebersold, Abhimanyu Susobhanan, Prerna Rana

Close hyperbolic encounters of black holes (BHs) generate certain Burst With Memory (BWM) events in the frequency windows of the operational, planned, and proposed gravitational wave (GW) observatories. We present detailed explorations of the detectable parameter space of such events that are relevant for the LIGO-Virgo-KAGRA and the International Pulsar Tim

Kyohei Nakamura, Akito Daido, Youichi Yanase

Much ink has recently been spilled on nonreciprocal phenomena in superconductors, especially the superconducting diode effect (SDE) characterized by the nonreciprocity of the critical current $\Delta J_c$. Contrary to the fundamental and practical significance of the SDE, the precise underlying mechanism remains unclear. In this paper, we investigate the imp

Haowen Zhang, Peter Behroozi, Marta Volonteri, Joseph Silk

We present the redshift evolution of quasar luminosity functions decomposed by halo mass, galaxy mass, supermassive black hole (SMBH) mass, and Eddington ratio, as well as SMBH kinetic/radiative energy output ratios from TRINITY, a flexible empirical model that self-consistently infers the halo--galaxy--SMBH connection that match observational data. Key find

Yoav Afik, P. S. Bhupal Dev, Anil Thapa

We show that a new leptophilic Higgs sector can resolve some intriguing anomalies in current experimental data across multiple energy ranges. Motivated by the recent CMS excess in the resonant $e\mu$ channel at 146 GeV, we focus on a leptophilic two-Higgs-doublet model, and propose a resonant production mechanism for the neutral components of the second Higg

Nicola Franchini

In this paper, we present a detailed analysis of first-order perturbations of the Kerr metric in the slow-rotation limit. We perform the calculation by perturbing the Schwarzschild metric plus up to second-order corrections in the spin in the Regge-Wheeler gauge. The apparent coupling between different angular momentum axial-led and polar-led modes can be re

Diogo Belloni, Matthias R. Schreiber

Context. AM CVn binaries are potential progenitors of thermonuclear supernovae and strong sources of persistent gravitational wave radiation. For a long time, it has been believed that these systems cannot descend from cataclysmic variables (CVs), at least not in large numbers, because the initial conditions need to be fine-tuned and, even worse, the resulti

Stella T. Schindler, Iain W. Stewart, Zhiquan Sun

The energy-energy correlator (EEC) is an observable of wide interest for collider physics and Standard Model measurements, due to both its simple theoretical description in terms of the energy-momentum tensor and its novel features for experimental studies. Significant progress has been made in both applications and higher-order perturbative predictions for

Yifei Luo, Alexie Leauthaud, Jenny Greene, Song Huang

The Merian survey is mapping $\sim$ 850 degrees$^2$ of the Hyper Suprime-Cam Strategic Survey Program (HSC-SSP) wide layer with two medium-band filters on the 4-meter Victor M. Blanco telescope at the Cerro Tololo Inter-American Observatory, with the goal of carrying the first high signal-to-noise (S/N) measurements of weak gravitational lensing around dwarf

David Curtin, Yonatan Kahn, Rachel Nguyen

The production and subsequent re-scattering of secondary pions produced in proton beam dumps provides additional opportunities for the production of light new particles like dark photons. This new mechanism has been overlooked in the past but can extend the mass reach of the SpinQuest experiment and its proposed DarkQuest upgrade. We use chiral perturbation

Olivia Beckwith, Martin Raum, Olav Richter

Given an odd prime $\ell$ and finite set of odd primes $S_+$, we prove the existence of an imaginary quadratic field whose class number is indivisible by $\ell$ and which splits at every prime in $S_+$. Notably, we do not require that $p \not\equiv -1 \pmod{\ell}$ for any of the split primes $p$ that we impose. Our theorem is in the spirit of a result by Wil

Abhilash Potluri, Fangyuan Xu, Eunsol Choi

Long-form question answering systems provide rich information by presenting paragraph-level answers, often containing optional background or auxiliary information. While such comprehensive answers are helpful, not all information is required to answer the question (e.g. users with domain knowledge do not need an explanation of background). Can we provide a c

Da-Wei Zhou, Yuanhan Zhang, Yan Wang, Jingyi Ning

Class-Incremental Learning (CIL) or continual learning is a desired capability in the real world, which requires a learning system to adapt to new tasks without forgetting former ones. While traditional CIL methods focus on visual information to grasp core features, recent advances in Vision-Language Models (VLM) have shown promising capabilities in learning

Hongxin Li, Jingran Su, Yuntao Chen, Qing Li

Computer end users have spent billions of hours completing daily tasks like tabular data processing and project timeline scheduling. Most of these tasks are repetitive and error-prone, yet most end users lack the skill to automate these burdensome works. With the advent of large language models (LLMs), directing software with natural language user requests b

Rongjie Huang, Chunlei Zhang, Yongqi Wang, Dongchao Yang

Various applications of voice synthesis have been developed independently despite the fact that they generate "voice" as output in common. In addition, the majority of voice synthesis models currently rely on annotated audio data, but it is crucial to scale them to self-supervised datasets in order to effectively capture the wide range of acoustic variations

Arash Ahmadian, Saurabh Dash, Hongyu Chen, Bharat Venkitesh

Emergent properties have been widely adopted as a term to describe behavior not present in smaller models but observed in larger models. Recent work suggests that the trade-off incurred by quantization is also an emergent property, with sharp drops in performance in models over 6B parameters. In this work, we ask "are quantization cliffs in performance solel

Jesús Torrado, Nils Schöneberg, Jonas El Gammal

Bayesian inference remains one of the most important tool-kits for any scientist, but increasingly expensive likelihood functions are required for ever-more complex experiments, raising the cost of generating a Monte Carlo sample of the posterior. Recent attention has been directed towards the use of emulators of the posterior based on Gaussian Process (GP)

Joanna W. Lis, Aruku Senoo, William F. McGrew, Felix Rönchen

We implement mid-circuit operations in a 48-site array of neutral atoms, enabled by new methods for control of the $\textit{omg}$ (optical-metastable-ground state qubit) architecture present in ${}^{171}$Yb. We demonstrate laser-based control of ground, metastable and optical qubits with average single-qubit fidelities of $F_{g} = 99.968(3)$, $F_{m} = 99.12(

Hengyuan Ma, Yang Qi, Li Zhang, Wenlian Lu

Building robust, interpretable, and secure AI system requires quantifying and representing uncertainty under a probabilistic perspective to mimic human cognitive abilities. However, probabilistic computation presents significant challenges for most conventional artificial neural network, as they are essentially implemented in a deterministic manner. In this

Umang Gupta, Aram Galstyan, Greg Ver Steeg

Efficient finetuning of pretrained language transformers is becoming increasingly prevalent for solving natural language processing tasks. While effective, it can still require a large number of tunable parameters. This can be a drawback for low-resource applications and training with differential-privacy constraints, where excessive noise may be introduced

Jacopo Fumagalli

We question the existence of one-loop corrections to the large-scale power spectrum from small-scale modes in non-slow-roll dynamics which are not volume suppressed by the ratio of the short to long distance scales. One-loop contributions proportional to the long wavelength tree-level power spectrum, and not sharing this suppression, have appeared in studies

Maico Hendrikus Wilhelmus Engelaar, Sofie Haesaert, Mircea Lazar

This work introduces a stochastic model predictive control scheme for dynamic chance constraints. We consider linear discrete-time systems affected by unbounded additive stochastic disturbance. To synthesize an optimal controller, we solve two subsequent stochastic optimization problems. The first problem concerns finding the maximal feasible probabilities o

Cory Arnold, Gabriela Pena Carmona, David A. Quiroz, Chung X. Thai

Significant progress has been made to increase access to droplet microfluidics for labs with limited microfluidics expertise or fabrication equipment. In particular, using off-the-shelf systems has been a valuable approach. However, the ability to modify a channel design and, thus, the functional characteristics of the system is of great value. In this work,

AbdAlGhaffar K. Amer, F. Robicheaux

Simulations and theory are presented for the power spectral density functions (PSDs) of particles in time dependent and anharmonic potentials including the effects of a thermal environment leading to damping and fluctuating forces. We investigate three one dimensional perturbations to the harmonic oscillator of which two are time dependent changes in the nat

Anastasia Koloskova, Nikita Doikov, Sebastian U. Stich, Martin Jaggi

In machine learning and neural network optimization, algorithms like incremental gradient, and shuffle SGD are popular due to minimizing the number of cache misses and good practical convergence behavior. However, their optimization properties in theory, especially for non-convex smooth functions, remain incompletely explored. This paper delves into the conv

Yuta Watanabe

The generalized wreath product of symmetric association schemes was introduced by R.A.Bailey in the European Journal of Combinatorics 27 (2006) 428-435. It is recognized as a unification of both the wreath product and the direct product of symmetric association schemes. While its potential applicability to any association scheme had been implied, this paper

Sina Hooshangi, Mohammad Hossein Namjoo, Mahdiyar Noorbala

The tail of the distribution of primordial fluctuations (corresponding to the likelihood of realization of large fluctuations) is of interest, from both theoretical and observational perspectives. In particular, it is relevant for the accurate evaluation of the primordial black hole (PBH) abundance. In this paper, we first analyze the non-perturbative $\delt

Giannis Daras, Kulin Shah, Yuval Dagan, Aravind Gollakota

We present the first diffusion-based framework that can learn an unknown distribution using only highly-corrupted samples. This problem arises in scientific applications where access to uncorrupted samples is impossible or expensive to acquire. Another benefit of our approach is the ability to train generative models that are less likely to memorize individu

Sebastian P. Bayerl, Dominik Wagner, Ilja Baumann, Florian Hönig

Most stuttering detection and classification research has viewed stuttering as a multi-class classification problem or a binary detection task for each dysfluency type; however, this does not match the nature of stuttering, in which one dysfluency seldom comes alone but rather co-occurs with others. This paper explores multi-language and cross-corpus end-to-

Pedro Sandoval-Segura, Vasu Singla, Jonas Geiping, Micah Goldblum

In an era of widespread web scraping, unlearnable dataset methods have the potential to protect data privacy by preventing deep neural networks from generalizing. But in addition to a number of practical limitations that make their use unlikely, we make a number of findings that call into question their ability to safeguard data. First, it is widely believed

P. K. Meena, S. Jangid, R. K. Kushwaha, R. P. Singh

High upper-critical field superconducting alloys are required for superconducting device applications. In this study, we extensively characterized the structure and superconducting properties of alloys Ta$_{x}$ Hf$_{1-x}$ (x = 0.2, 0.4, 0.5, 0.6 and 0.8). The substitution of Hf (T$_{C}$ = 0.12 K, type-I superconductor) with Ta (T$_{C}$ = 4.4 K, type-I superc

Xiangze Zeng

We prove the boundedness of $n$-complements for surface pairs in a generalized case without restrictions on multiplicities or the Fano type assumption.

Panagiotis Aivasiliotis, Aris Pagourtzis

Domination problems in general can capture situations in which some entities have an effect on other entities (and sometimes on themselves). The usual goal is to select a minimum number of entities that can influence a target group of entities or to influence a maximum number of target entities with a certain number of available influencers. In this work, we

A. Salch

It is well-known that the Steenrod algebra $A$ is self-injective as a graded ring. We make the observation that simply changing the grading on $A$ can make it cease to be self-injective. We see also that $A$ is not self-injective as an ungraded ring. These observations follow from the failure of certain coproducts of injective $A$-modules to be injective. He

Guande He, Jianfei Chen, Jun Zhu

Large pre-trained language models (PLMs) have demonstrated strong performance on natural language understanding (NLU) tasks through fine-tuning. However, fine-tuned models still suffer from overconfident predictions, especially in out-of-domain settings. In this paper, we tackle the problem of calibrating fine-tuned language models. We demonstrate that the P

Jihao Liu, V. V. Shokurov

We prove that the first gap of $\mathbb R$-complementary thresholds of surfaces is $\frac{1}{13}$. More precisely, the largest $\mathbb R$-complementary threshold for surfaces that is strictly less than $1$ is $\frac{12}{13}$. This result has many applications in explicit birational geometry of surfaces and threefolds and allows us to find several other opti

Lucas E. A. Porto, Rafael Rabelo, Marcelo Terra Cunha, Adán Cabello

A necessary condition for the probabilities of a set of events to exhibit Bell nonlocality or Kochen-Specker contextuality is that the graph of exclusivity of the events contains induced odd cycles with five or more vertices, called odd holes, or their complements, called odd antiholes. From this perspective, events whose graph of exclusivity are odd holes o

Nathanial Lowry

In this paper, we classify the possible group structures on the set of $R$-valued points of an abelian variety, where $R$ is any real closed field. We make use of a family of abelian varieties that, in effect, allows one to quantify over all abelian varieties of a fixed dimension and degree of polarization in a first-order fashion.

Thu Nguyen-Phuoc, Gabriel Schwartz, Yuting Ye, Stephen Lombardi

This paper presents a method that can quickly adapt dynamic 3D avatars to arbitrary text descriptions of novel styles. Among existing approaches for avatar stylization, direct optimization methods can produce excellent results for arbitrary styles but they are unpleasantly slow. Furthermore, they require redoing the optimization process from scratch for ever

Yunzhe Zhou, Chengchun Shi, Lexin Li, Qiwei Yao

The Markov property is widely imposed in analysis of time series data. Correspondingly, testing the Markov property, and relatedly, inferring the order of a Markov model, are of paramount importance. In this article, we propose a nonparametric test for the Markov property in high-dimensional time series via deep conditional generative learning. We also apply

Xitong Zhang, Avrajit Ghosh, Guangliang Liu, Rongrong Wang

Previous research on PAC-Bayes learning theory has focused extensively on establishing tight upper bounds for test errors. A recently proposed training procedure called PAC-Bayes training, updates the model toward minimizing these bounds. Although this approach is theoretically sound, in practice, it has not achieved a test error as low as those obtained by

Annie S. Booth, Andrew Cooper, Robert B. Gramacy

We provide a survey of nonstationary surrogate models which utilize Gaussian processes (GPs) or variations thereof, including nonstationary kernel adaptations, partition and local GPs, and spatial warpings through deep Gaussian processes. We also overview publicly available software implementations and conclude with a bake-off involving an 8-dimensional sate

Emma Dauterman, Danny Lin, Henry Corrigan-Gibbs, David Mazières

Credential compromise is hard to detect and hard to mitigate. To address this problem, we present larch, an accountable authentication framework with strong security and privacy properties. Larch protects user privacy while ensuring that the larch log server correctly records every authentication. Specifically, an attacker who compromises a user's device can

Ulyana Piterbarg, Lerrel Pinto, Rob Fergus

Neural policy learning methods have achieved remarkable results in various control problems, ranging from Atari games to simulated locomotion. However, these methods struggle in long-horizon tasks, especially in open-ended environments with multi-modal observations, such as the popular dungeon-crawler game, NetHack. Intriguingly, the NeurIPS 2021 NetHack Cha

Guillaume Saës

The theory of orthonormal wavelet bases is a useful tool in multifractal analysis, as it provides a characterization of the different exponents of pointwise regularities (H{\"o}lder, p-exponent, lacunarity, oscillation, etc.). However, for some homogeneous self-similar processes, such as sums of random pulses (sums of regular, well-localized functions whose

Peter Maták

We study the role of perturbative unitarity in the resonant annihilation of two dark matter particles into the standard model bath. Systematically including all kinematically allowed holomorphic cuts of the corresponding forward-scattering diagram, cancelation of the singularities occurs, resulting in a fixed-order correction to the narrow-width approximatio

Stein K. F. Stoter, Tom B. van Sluijs, Tristan H. B. Demont, E. Harald van Brummelen

Binary-fluid flows can be modeled using the Navier-Stokes-Cahn-Hilliard equations, which represent the boundary between the fluid constituents by a diffuse interface. The diffuse-interface model allows for complex geometries and topological changes of the binary-fluid interface. In this work, we propose an immersed isogeometric analysis framework to solve th

Pêdra D. S. Andrade, Julio C. Correa

In this article, we study a class of fully nonlinear double-divergence systems with free boundaries associated with a minimization problem. The variational structure of Hessian-dependent functional plays a fundamental role in proving the existence of the minimizers and then the existence of the solutions for the system. In addition, we establish gains of the

Antonio Marino, Claudio Pacchierotti, Paolo Robuffo Giordano

In this paper, we aim to find the conditions for input-state stability (ISS) and incremental input-state stability ($\delta$ISS) of Gated Graph Neural Networks (GGNNs). We show that this recurrent version of Graph Neural Networks (GNNs) can be expressed as a dynamical distributed system and, as a consequence, can be analysed using model-based techniques to a

Bailin Wang, Zi Wang, Xuezhi Wang, Yuan Cao

Large language models (LLMs) can learn to perform a wide range of natural language tasks from just a handful of in-context examples. However, for generating strings from highly structured languages (e.g., semantic parsing to complex domain-specific languages), it is challenging for the LLM to generalize from just a few exemplars. We propose \emph{grammar pro

Anna Chmiel, Julian Sienkiewicz

In this paper, we examine the interplay between the lobby size $q$ in the $q$-neighbor Ising model of opinion formation (Phys. Rev. E 92, 052105) and the level of overlap $v$ of two fully connected graphs. Results suggest that for each lobby size $q \ge 3$, a specific level of overlap $v^*$ exists, which destroys initially polarized clusters of opinions. By

Shuo Liu, Leda Sarı, Chunyang Wu, Gil Keren

This paper presents a method for selecting appropriate synthetic speech samples from a given large text-to-speech (TTS) dataset as supplementary training data for an automatic speech recognition (ASR) model. We trained a neural network, which can be optimised using cross-entropy loss or Arcface loss, to measure the similarity of a synthetic data to real spee

Adrien Le Franc, Victor Magron, Jean-Bernard Lasserre, Manuel Ruiz

AC-OPF (Alternative Current Optimal Power Flow)aims at minimizing the operating costs of a power gridunder physical constraints on voltages and power injections.Its mathematical formulation results in a nonconvex polynomial optimizationproblem which is hard to solve in general,but that can be tackled by a sequence of SDP(Semidefinite Programming) relaxations

Ngo Nghi Truyen Huynh, Pierre-André Garambois, François Colleoni, Pierre Javelle

Classical calibration methods in hydrology typically rely on a single cost function computed on long-term streamflow series. Even when hydrological models achieve acceptable scores in NSE and KGE, imbalances can still arise between overall model performance and its ability to simulate flood events, particularly flash floods. In this study, the potential of u