May 2023 arXiv papers — page 180

Minati De, Saksham Jain, Sarat Varma Kallepalli, Satyam Singh

We consider the online version of the piercing set problem, where geometric objects arrive one by one, and the online algorithm must maintain a valid piercing set for the already arrived objects by making irrevocable decisions. It is easy to observe that any deterministic algorithm solving this problem for intervals in $\mathbb{R}$ has a competitive ratio of

Shixun Wu, Yujia Zhai, Jiajun Huang, Zizhe Jian

General matrix/matrix multiplication (GEMM) is crucial for scientific computing and machine learning. However, the increased scale of the computing platforms raises concerns about hardware and software reliability. In this poster, we present FT-GEMM, a high-performance GEMM being capable of tolerating soft errors on-the-fly. We incorporate the fault tolerant

Jessica E. Thorne, Aaron S. G. Robotham, Sabine Bellstedt, Luke J. M. Davies

Traditionally, the far ultraviolet (FUV) to far-infrared (FIR) and radio spectral energy distributions (SEDs) of galaxies have been considered separately despite the common physical process shaping them. In this work, we explore the utility of simultaneously fitting FUV-radio SEDs using an extended version of the ProSpect SED fitting code considering contrib

Sara Riva, Jean-Marie Lagniez, Gustavo Magaña López, Loïc Paulevé

Minimal trap spaces (MTSs) capture subspaces in which the Boolean dynamics is trapped, whatever the update mode. They correspond to the attractors of the most permissive mode. Due to their versatility, the computation of MTSs has recently gained traction, essentially by focusing on their enumeration. In this paper, we address the logical reasoning on univers

Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang

Most of the existing federated multi-armed bandits (FMAB) designs are based on the presumption that clients will implement the specified design to collaborate with the server. In reality, however, it may not be possible to modify the clients' existing protocols. To address this challenge, this work focuses on clients who always maximize their individual

Hossein Vaziri, Mohammad Reza Shojaei

This paper calculates the elastic form factors for nucleons based on generalized parton distributions using an extended new ansatz introduced in http://dx.doi.org/10.1103/PhysRevC.105.025202 (Phys. Rev. C 105, no.2, 025202 (2022)). Different parton distribution functions (PDFs) are considered, and modifications are made to the free parameters of the new ansa

Xin Cheng, Di Luo, Xiuying Chen, Lemao Liu

With direct access to human-written reference as memory, retrieval-augmented generation has achieved much progress in a wide range of text generation tasks. Since better memory would typically prompt better generation~(we define this as primal problem). The traditional approach for memory retrieval involves selecting memory that exhibits the highest similari

Debargha Banerjee, Pranjal Vishwakarma

We explicitly write down the {\it Eisenstein cycles} in the first homology groups of quotients of the hyperbolic three spaces as linear combinations of Cremona symbols (a generalization of Manin symbols) for imaginary quadratic fields. They generate the Eisenstein part of the homology groups. We also study the Eisenstein part of the cohomology groups. As an

Yuanlu Bai, Henry Lam

We consider the estimation of rare-event probabilities using sample proportions output by naive Monte Carlo or collected data. Unlike using variance reduction techniques, this naive estimator does not have a priori relative efficiency guarantee. On the other hand, due to the recent surge of sophisticated rare-event problems arising in safety evaluations of i

Lukas Haller, Wen-Tao Xu, Yu-Jie Liu, Frank Pollmann

Quantum phase transitions between different topologically ordered phases exhibit rich structures and are generically challenging to study in microscopic lattice models. In this work, we propose a tensor-network solvable model that allows us to tune between different symmetry enriched topological (SET) phases. Concretely, we consider a decorated two-dimension

Flavio Del Santo, Nicolas Gisin

We propose an interpretation of physics named potentiality realism. This view, which can be applied to classical as well as to quantum physics, regards potentialities (i.e. intrinsic, objective propensities for individual events to obtain) as elements of reality, thereby complementing the actual properties taken by physical variables. This allows one to natu

Vitor Hugo Ribeiro, David Dobrigkeit Chinellato, Michael Annan Lisa, Willian Matioli Serenone

We performed a systematic study on the formation of vorticity rings as the process for jet thermalization in the medium created in high-energy nuclear collisions. In this work, we expanded our previous analysis to a more realistic framework by considering non-central events and fluctuations in the initial condition. We simulate the formation and evolution of

Andrej Zlatos

We show that the generalized SQG equation with $α\in(0,\frac 14]$ is locally well-posed on the half-plane in spaces of bounded integrable solutions that are natural for its dynamic on domains with boundaries, and allow for some power growth of the solution derivative in the normal direction at the boundary. We also show existence of solutions exhibiting fini

Shuhui Liu, Yaozhong Hu, Xiong Wang

We determine the range of Hurst parameters that provide the necessary and sufficient conditions for the solvability, in $L^2(\Omega)$, of the stochastic wave equation: $ \frac{\partial^2 }{\partial t^2}u(t,x) =\Delta u(t,x)+\dot{W}(t,x)$, where $\{ W(t,x),\ t\geq 0, x\in \mathbb{R}^d\} $ is a fractional Brownian field with temporal Hurst parameter $H_0\in[\t

R. Albuquerque, S. Narison, D. Rabetiarivony

We scrutinize, improve some determinations of the masses and couplings of light scalar quarkonia ($\bar qq$ and four-quark states) and present new results for the $π^+π^-,K^+K^-,\dots$ molecules using QCD Laplace Sum Rule (LSR) truncated at the $D=6$ dimension vacuum condensates. We pay a special attention on the higher order perturbative (PT) corrections up

Nicholas Rabb, Desen Ozkan

Ethics have become an urgent concern for data science research, practice, and instruction in the wake of growing critique of algorithms and systems showing that they reinforce structural oppression. There has been increasing desire on the part of data science educators to craft curricula that speak to these critiques, yet much ethics education remains indivi

Elisabetta Perotti, Ana M. Ospina, Gianluca Bianchin, Andrea Simonetto

Governments, regulatory bodies, and manufacturers are proposing plans to accelerate the adoption of electric vehicles (EVs), with the goal of reducing the impact of greenhouse gases and pollutants from internal combustion engines on human health and climate change. In this context, the paper considers a scenario where ride-sharing enterprises utilize a 100%-

Lukas Nulens, Nicolas Lejeune, Joost Caeyers, Stefan Marinković

Macroscopic superconducting components are an important building block of various quantum circuits. Since several of the envisioned applications require exposure to magnetic fields, it is of utmost importance to explore the impact of magnetic fields on their performance. Here we explore the complex pattern of magnetic field penetration and identify its impac

Meagan Sundstrom, L. N. Simpfendoerfer, Annie Tan, Ashley B. Heim

Previous work has identified that recognition from others is an important predictor of students' participation, persistence, and career intentions in physics. However, research has also found a gender bias in peer recognition in which student nominations of strong peers in their physics course disproportionately favor men over women. In this study, we draw o

Tom Kelly, Sid Kolichala, Caleb McFarland, Jatong Su

We prove that every $n$-vertex planar graph $G$ with no triangle sharing an edge with a 4-cycle has independence ratio $n/\alpha(G) \leq 4 - \varepsilon$ for $\varepsilon = 1/30$. This result implies that the same bound holds for 4-cycle-free planar graphs and planar graphs with no adjacent triangles and no triangle sharing an edge with a 5-cycle. For the la

Yue Wu, So Yeon Min, Yonatan Bisk, Ruslan Salakhutdinov

Pre-trained large language models (LLMs) capture procedural knowledge about the world. Recent work has leveraged LLM's ability to generate abstract plans to simplify challenging control tasks, either by action scoring, or action modeling (fine-tuning). However, the transformer architecture inherits several constraints that make it difficult for the LLM t

N. Biniskos, F. J. dos Santos, M. dos Santos Dias, S. Raymond

The metallic compound Mn$_5$Si$_3$ hosts a series of antiferromagnetic phases which can be controlled by external stimuli such as temperature and magnetic field. In this work, we investigate the spin-excitation spectrum of bulk Mn$_5$Si$_3$ by combining inelastic neutron scattering measurements and density functional theory calculations. We study the evoluti

Nuno R. C. Gomes, Fabio Del Sordo, Luís Torgo

This work aims to develop a computationally inexpensive approach, based on machine learning techniques, to accurately predict thousands of stellar rotation periods. The innovation in our approach is the use of the XGBoost algorithm to predict the rotation periods of Kepler targets by means of regression analysis. Therefore, we focused on building a robust su

Yucheng Shi, Hehuan Ma, Wenliang Zhong, Qiaoyu Tan

ChatGPT, as a recently launched large language model (LLM), has shown superior performance in various natural language processing (NLP) tasks. However, two major limitations hinder its potential applications: (1) the inflexibility of finetuning on downstream tasks and (2) the lack of interpretability in the decision-making process. To tackle these limitation

Ran Liu, Maksim Eren, Charles Nicholas

With the increasing number and sophistication of malware attacks, malware detection systems based on machine learning (ML) grow in importance. At the same time, many popular ML models used in malware classification are supervised solutions. These supervised classifiers often do not generalize well to novel malware. Therefore, they need to be re-trained frequ

Jiazhao Li, Zhuofeng Wu, Wei Ping, Chaowei Xiao

Textual backdoor attack, as a novel attack model, has been shown to be effective in adding a backdoor to the model during training. Defending against such backdoor attacks has become urgent and important. In this paper, we propose AttDef, an efficient attribution-based pipeline to defend against two insertion-based poisoning attacks, BadNL and InSent. Specif

Shirin Mozaffari, William R. Meier, Richa P. Madhogaria, Nikolai Peshcherenko

The recent discovery of a charge density (CDW) state in ScV$_6$Sn$_6$ at $T_{\textrm{CDW}}$ = 91 K offers new opportunities to understand the origins of electronic instabilities in topological kagome systems. By comparing to the isostructural non-CDW compound LuV$_6$Sn$_6$, we unravel interesting electrical transport properties in ScV$_6$Sn$_6$, above and be

Lawrence Clegg, John Cartlidge

We present a replication and correction of a recent article (Ramirez, P., Reade, J.J., Singleton, C., Betting on a buzz: Mispricing and inefficiency in online sportsbooks, International Journal of Forecasting, 39:3, 2023, pp. 1413-1423, doi: 10.1016/j.ijforecast.2022.07.011). RRS measure profile page views on Wikipedia to generate a "buzz factor" metric for

Mark Kamper Svendsen, Kristian Sommer Thygesen, Angel Rubio, Johannes Flick

The emerging field of strongly coupled light-matter systems has drawn significant attention in recent years due to the prospect of altering physical and chemical properties of molecules and materials. Because this emerging field draws on ideas from both condensed-matter physics and quantum optics, it has attracted attention from theoreticians from both field

A. Chacoma, O. V. Billoni

This study applies complexity sciences to analyze the game of Padel. Data from 18 professional matches were collected, and the probability distributions of the total number of shots and the probability distribution of rallies' duration were analyzed. Based on these empirical observations and previous reports, a probabilistic model with two parameters was

Andrew Leroux, Ciprian Crainiceanu, Julia Wrobel

We propose a new fast generalized functional principal components analysis (fast-GFPCA) algorithm for dimension reduction of non-Gaussian functional data. The method consists of: (1) binning the data within the functional domain; (2) fitting local random intercept generalized linear mixed models in every bin to obtain the initial estimates of the person-spec

Yupeng Tang, Seung-seob Lee, Abhishek Bhattacharjee, Anurag Khandelwal

Caches at CPU nodes in disaggregated memory architectures amortize the high data access latency over the network. However, such caches are fundamentally unable to improve performance for workloads requiring pointer traversals across linked data structures. We argue for accelerating these pointer traversals closer to disaggregated memory in a manner that pres

Ghazal Khalighinejad, Ollie Liu, Sam Wiseman

We investigate the ability of transformer models to approximate the CKY algorithm, using them to directly predict a sentence's parse and thus avoid the CKY algorithm's cubic dependence on sentence length. We find that on standard constituency parsing benchmarks this approach achieves competitive or better performance than comparable parsers that make

Michael Benedict Davies, Alexander Rosu-Finsen, Christoph G. Salzmann, Angelos Michaelides

Low-density amorphous ice (LDA) is one of the most common solid materials in the Universe and a key material for understanding the many famous anomalies of liquid water. Yet, despite its significance and its discovery dating nearly 90 years, the structure of LDA is debated. It is unclear if LDA is a glassy state representing a liquid or a heavily disordered

Zhaohan Xi, Tianyu Du, Changjiang Li, Ren Pang

Knowledge graph reasoning (KGR) -- answering complex logical queries over large knowledge graphs -- represents an important artificial intelligence task, entailing a range of applications (e.g., cyber threat hunting). However, despite its surging popularity, the potential security risks of KGR are largely unexplored, which is concerning, given the increasing

Cencheng Shen, Jonathan Larson, Ha Trinh, Xihan Qin

Analyzing large-scale time-series network data, such as social media and email communications, poses a significant challenge in understanding social dynamics, detecting anomalies, and predicting trends. In particular, the scalability of graph analysis is a critical hurdle impeding progress in large-scale downstream inference. To address this challenge, we in

Fabian R. N. Schneider, Philipp Podsiadlowski, Eva Laplace

In binary black-hole mergers from isolated binary-star evolution, both black holes are from progenitor stars that have lost their hydrogen-rich envelopes by binary mass transfer. Envelope stripping is known to affect the pre-supernova core structures of such binary-stripped stars and thereby their final fates and compact remnant masses. In this paper, we sho

Romina M. Arroyo, Gabriela P. Ovando, Raquel Perales, Mariel Sáez

This work is a survey of the most relevant background material to motivate and understand the construction and classification of translating solutions to mean curvature flow on a family of solvmanifolds. We introduce the mean curvature flow and some known results in the field. In particular we explore the notion of translating solution in the Euclidean space

Ankit Kumar, Kush Kinra, Manil T. Mohan

In this article, we establish the \textsl{Wong-Zakai approximation} result for a class of stochastic partial differential equations (SPDEs) with fully local monotone coefficients perturbed by a multiplicative Wiener noise. This class of SPDEs encompasses various fluid dynamic models and also includes quasi-linear SPDEs, the convection-diffusion equation, the

Dong He, Jieyu Zhang, Maureen Daum, Alexander Ratner

Machine learning tasks over image databases often generate masks that annotate image content (e.g., saliency maps, segmentation maps, depth maps) and enable a variety of applications (e.g., determine if a model is learning spurious correlations or if an image was maliciously modified to mislead a model). While queries that retrieve examples based on mask pro

Davide Coluzzi, Valentina Bordin, Massimo Walter Rivolta, Igor Fortel

Alzheimer's Disease (AD) is the world leading cause of dementia, a progressively impairing condition leading to high hospitalization rates and mortality. To optimize the diagnostic process, numerous efforts have been directed towards the development of deep learning approaches (DL) for the automatic AD classification. However, their typical black box outline

Nizar Riane

This paper introduces the concepts of spectral influence and spectral cyclicality, both derived from the largest eigenvalue of a graph's adjacency matrix. These two novel centrality measures capture both diffusion and interdependence from a local and global perspective respectively. We propose a new clustering algorithm that identifies communities with high

Vladimir Igoshin, Mariia Tsimokha, Anastasia Nikitina, Mihail Petrov

Exceptional points (EPs) have been widely studied in quantum mechanics, condensed matter physics, optics and photonics. However, their potential in acoustics has only recently been recognized due to the rapid development of acoustic metamaterials. This paper proposes a method for achieving EP conditions in acoustic resonators by lowering their symmetry and e

Davide Coluzzi, Giuseppe Baselli

Schizophrenia (SZ) is a brain disorder leading to detached mind's normally integrated processes. Hence, the exploration of the symptoms in relation to functional connectivity (FC) had great relevance in the field. FC can be investigated on different levels, going from global features to single edges between regions, revealing diffuse and localized dysconnect

Hiroki Matsui, Alexandros Papageorgiou, Fuminobu Takahashi, Takahiro Terada

Inflationary models, especially those with plateau-type potentials, are consistent with the cosmological data, but inflation itself does not resolve the initial singularity. This singularity is resolved, for example, by the idea of the quantum creation of the Universe from nothing such as the tunneling and no-boundary proposals. The simplest one predicts a c

Hiroki Matsui, Alexandros Papageorgiou, Fuminobu Takahashi, Takahiro Terada

We study an inflation model with a flat scalar potential supported by observations and find that slow-roll inflation can emerge after a quasi-cyclic phase of the Universe, where it undergoes repeated expansions and contractions for a finite time period. The initial conditions and the positive spatial curvature required for such nontrivial dynamics align with

Silin Gao, Beatriz Borges, Soyoung Oh, Deniz Bayazit

Sustaining coherent and engaging narratives requires dialogue or storytelling agents to understand how the personas of speakers or listeners ground the narrative. Specifically, these agents must infer personas of their listeners to produce statements that cater to their interests. They must also learn to maintain consistent speaker personas for themselves th

Najoung Kim, Sebastian Schuster

Keeping track of how states of entities change as a text or dialog unfolds is a key prerequisite to discourse understanding. Yet, there have been few systematic investigations into the ability of large language models (LLMs) to track discourse entities. In this work, we present a task probing to what extent a language model can infer the final state of an en

Clement Charles, Erik J. Gustafson, Elizabeth Hardt, Florian Herren

The utility of quantum computers for simulating lattice gauge theories is currently limited by the noisiness of the physical hardware. Various quantum error mitigation strategies exist to reduce the statistical and systematic uncertainties in quantum simulations via improved algorithms and analysis strategies. We perform quantum simulations of $1+1d$ $\mathb

Tian-Gang Zhou, Wei Zheng, Pengfei Zhang

Universality is a crucial concept in modern physics, allowing us to capture the essential features of a system's behavior using a small set of parameters. In this work, we unveil universal spin relaxation dynamics in anisotropic random Heisenberg models with infinite-range interactions at high temperatures. Starting from a polarized state, the total magnetiz

Peter Athron, Csaba Balázs, Andrew Fowlie, Lachlan Morris

Gravitational waves (GWs) were recently detected for the first time. This revolutionary discovery opens a new way of learning about particle physics through GWs from first-order phase transitions (FOPTs) in the early Universe. FOPTs could occur when new fundamental symmetries are spontaneously broken down to the Standard Model and are a vital ingredient in s

Ren Zhang, Hui Zhai

In a quantum many-body system, autocorrelation functions can determine linear responses nearby equilibrium and quantum dynamics far from equilibrium. In this letter, we bring out the connection between the operator complexity and the autocorrelation function. In particular, we focus on a particular kind of operator complexity called the Krylov complexity. We

J. A. Sturm, M. K. McClure, J. B. Bergner, D. Harsono

The abundance and distribution of ice in protoplanetary disks (PPD) is critical to understand the linkage between the composition of circumstellar matter and the composition of exoplanets. Edge-on PPDs are a useful tool to constrain such ice composition and its location in the disk, as ice spectral signatures can be observed in absorption against the continu

Nina M. Coyle, Carlos E. M. Wagner

The QED hadronic vacuum polarization function plays an important role in the determination of precision electroweak observables and of the anomalous magnetic moment of the muon. These contributions have been computed from data, by means of dispersion relations affecting the electron positron hadronic cross sections, or by first principle lattice-QCD computat

Fabian Ziegner, Janos Borst, Andreas Niekler, Martin Potthast

This paper evaluates the viability of using fixed language models for training text classification networks on low-end hardware. We combine language models with a CNN architecture and put together a comprehensive benchmark with 8 datasets covering single-label and multi-label classification of topic, sentiment, and genre. Our observations are distilled into

Nemanja Kaloper

General covariant unimodular gravity frameworks, based on the Henneaux-Teitelboim formulation, are, in disguise, precisely $4$-form field theories corrected with higher dimension operators. In the presence of charged tensional membranes, any de Sitter space in all such theories is unstable and decays. If the fluxes sourced by membranes are mutually incommens

Alexander Chernoglazov, Hayk Hakobyan, Alexander A. Philippov

We present the results of 3D particle-in-cell (PIC) simulations that explore relativistic magnetic reconnection in pair plasma with strong synchrotron cooling and a small mass fraction of non-radiating ions. Our results demonstrate that the structure of the current sheet is highly sensitive to the dynamic efficiency of radiative cooling. Specifically, strong

L. Zappacosta, E. Piconcelli, F. Fiore, I. Saccheo

The existence of luminous quasars (QSO) at the Epoch of Reionization (EoR; i.e. z>6) powered by supermassive black holes (SMBH) with masses $\gtrsim10^9~M_\odot$ challenges models of early SMBH formation. To shed light on the nature of these sources we started a multiwavelength programme based on a sample of 18 HYPerluminous quasars at the Epoch of Reionizat

Hugo Perrin, Thibault Scoquart, Alexander Shnirman, Jörg Schmalian

We develop and apply an extension of the randomized compiling (RC) protocol that includes a special treatment of neighboring qubits and dramatically reduces crosstalk effects caused by the application of faulty gates on superconducting qubits in IBMQ quantum computers (\texttt{ibm\_lagos} and \texttt{ibmq\_ehningen}). Crosstalk errors, stemming from CNOT two

Giuseppe Di Giulio, Johanna Erdmenger

As a new ingredient for analyzing the fine structure of entanglement, we study the symmetry resolution of the modular flow of $U(1)$-invariant operators in theories endowed with a global $U(1)$ symmetry. We provide a consistent definition of symmetry-resolved modular flow that is defined for a local algebra of operators associated to a sector with fixed char

Henning Schlömer, Ulrich Schollwöck, Annabelle Bohrdt, Fabian Grusdt

Microscopically understanding competing orders in strongly correlated systems is a key challenge in modern quantum many-body physics. For example, the study of magnetic polarons and their relation to pairing in the Fermi-Hubbard model in different geometries remains one of the central questions, and may help to understand the mechanism underlying unconventio

Jiabin Yu, Christopher J. Ciccarino, Raffaello Bianco, Ion Errea

The coupling of electrons to phonons (electron-phonon coupling) is crucial for the existence of various phases of matter, in particular superconductivity and density waves. Here, we devise a theory that incorporates the quantum geometry of the electron bands into the electron-phonon coupling, demonstrating the crucial contributions of the Fubini-Study metric

Alexandra Dupuy, Hélène M. Courtois

This article delivers the dynamical cosmography of the Local Universe within z=0.1 (1 giga light-years). We exploit the gravitational velocity field computed using the CosmicFlows-4 catalog of galaxy distances to delineate superclusters as watersheds, publishing for the first time their size, shape, main streams of matter and the location of their central at

J. A. Sturm, M. K. McClure, C. J. Law, D. Harsono

Context. Observations of edge-on disks are an important tool for constraining general protoplanetary disk properties that cannot be determined in any other way. However, most radiative transfer models cannot simultaneously reproduce the spectral energy distributions (SEDs) and resolved scattered light and submillimeter observations of these systems, due to t

Aaron Sander, Lukas Burgholzer, Robert Wille

This paper proposes a novel approach to Hamiltonian simulation using Decision Diagrams (DDs), which are an exact representation based on exploiting redundancies in representations of quantum states and operations. While the simulation of Hamiltonians has been studied extensively, scaling these simulations to larger or more complex systems is often challengin

Alexander Nikolaenko, Subir Sachdev, Aavishkar A. Patel

We extend the theory of shot noise in coherent metals to shot noise in strange metals without quasiparticle excitations. This requires a generalization of the Boltzmann equation with a noise source to distribution functions which depend independently on the excitation momentum and energy. We apply this theory to a model of a strange metal with linear in temp

Jorge F. M. Delgado, Carlos A. R. Herdeiro, Eugen Radu

We study extreme mass ratio inspirals (EMRIs) due to an infalling Light Compact Object (LCO) onto a generic class of stationary and axi-symmetric massive compact objects (MCO - with or without a horizon). Using the quadrupole hybrid formalism we obtain a master formula for the evolution of the radius of the LCO and find qualitatively different behaviours dep

Lukas Homeier, Timothy J. Harris, Tizian Blatz, Sebastian Geier

The combination of optical tweezer arrays with strong interactions -- via dipole-exchange of molecules and van-der-Waals interactions of Rydberg atoms -- has opened the door for the exploration of a wide variety of quantum spin models. A next significant step will be the combination of such settings with mobile dopants: This will enable to simulate the physi

Karen Zhou, Chenhao Tan

Growing literature has shown that NLP systems may encode social biases; however, the political bias of summarization models remains relatively unknown. In this work, we use an entity replacement method to investigate the portrayal of politicians in automatically generated summaries of news articles. We develop an entity-based computational framework to asses

Weichen Winston Yin, Liang Dai, Simone Ferraro

We demonstrate that the Peccei-Quinn-electromagnetic anomaly coefficient $\mathcal A$ can be directly measured from axion string-induced cosmic birefringence by applying scattering transform to the anisotropic polarization rotation of the cosmic microwave background. This breaks the degeneracy between $\mathcal A$ and the effective number of string loops in

Daniel Rose, Vaishnavi Himakunthala, Andy Ouyang, Ryan He

Recent advances in large language models elicit reasoning in a chain-of-thought that allows models to decompose problems in a human-like fashion. Though this paradigm improves multi-step reasoning ability in language models, it is limited by being unimodal and applied mainly to question-answering tasks. We claim that incorporating visual augmentation into re

Ayan Sahoo, Utkarsh Mishra, Debraj Rakshit

We show that the delocalization-localization transition in a quantum-many body (QMB) systems is a compelling quantum resource for achieving quantum-enhanced sensitivity in parameter estimation. We exploit the vulnerability of a near-transition QMB state against the parameter shift for devising efficient sensing tools. In this realm the main focus of this wor

Jim Lundin, Roman Mauch, Lorenzo Ruggeri

We compute the Coulomb branch partition function of the 4d $\mathcal{N}=2$ vector multiplet on closed simply-connected quasi-toric manifolds $B$. This includes a large class of theories, localising to either instantons or anti-instantons at the torus fixed points (including Donaldson-Witten and Pestun-like theories as examples). The main difficulty is to obt

Erik Nijkamp, Hiroaki Hayashi, Caiming Xiong, Silvio Savarese

Large language models (LLMs) have demonstrated remarkable abilities in representation learning for program synthesis and understanding tasks. The quality of the learned representations appears to be dictated by the neural scaling laws as a function of the number of model parameters and observations, while imposing upper bounds on the model performance by the

Minjae Park, Joshua Pfeffer, Scott Sheffield, Pu Yu

Although lattice Yang-Mills theory on finite subgraphs of $\mathbb Z^d$ is easy to rigorously define, the construction of a satisfactory continuum theory on $\mathbb R^d$ is a major open problem when $d \geq 3$. Such a theory should in some sense assign a Wilson loop expectation to each suitable finite collection $\mathcal L$ of loops in $\mathbb R^d$. One c

Helena Lofstrom, Tuwe Lofstrom, Ulf Johansson, Cecilia Sonstrod

While local explanations for AI models can offer insights into individual predictions, such as feature importance, they are plagued by issues like instability. The unreliability of feature weights, often skewed due to poorly calibrated ML models, deepens these challenges. Moreover, the critical aspect of feature importance uncertainty remains mostly unaddres

Chiraag Kaushik, Andrew D. McRae, Mark A. Davenport, Vidya Muthukumar

The support vector machine (SVM) is a supervised learning algorithm that finds a maximum-margin linear classifier, often after mapping the data to a high-dimensional feature space via the kernel trick. Recent work has demonstrated that in certain sufficiently overparameterized settings, the SVM decision function coincides exactly with the minimum-norm label

Cheng-Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh, Hootan Nakhost

Deploying large language models (LLMs) is challenging because they are memory inefficient and compute-intensive for practical applications. In reaction, researchers train smaller task-specific models by either finetuning with human labels or distilling using LLM-generated labels. However, finetuning and distillation require large amounts of training data to

Mike Lasby, Anna Golubeva, Utku Evci, Mihai Nica

Dynamic Sparse Training (DST) methods achieve state-of-the-art results in sparse neural network training, matching the generalization of dense models while enabling sparse training and inference. Although the resulting models are highly sparse and theoretically less computationally expensive, achieving speedups with unstructured sparsity on real-world hardwa

José Santana Costa, Fernando Micena

This work is addressed to study Anosov endomorphisms of $\mathbb{T}^d,$ $d\geq 3.$ We are interested to obtain metric and topological information on such Anosov endomorphism by comparison between their Lyapunov exponents and the ones of its linearization. We can characterize when a weak unstable foliation of a special Anosov endomorphism near to linear is an

Zhora Nikoghosyan

A number of new sufficient conditions for generalized cycles (large cycles including Hamilton and dominating cycles as special cases) in an arbitrary $k$-connected graph $(k=1,2,...)$ and new lower bounds for the circumference (the length of a longest cycle) are derived, inspiring a number of modifications of famous conjectures of Bondy (1980) and Jung (2001

Eli Gafni, Giuliano Losa

In the 1980s, three related impossibility results emerged in the field of distributed computing. First, Fischer, Lynch, and Paterson demonstrated that deterministic consensus is unattainable in an asynchronous message-passing system when a single process may crash-stop. Subsequently, Loui and Abu-Amara showed the infeasibility of achieving consensus in async

George T. Cantwell, Alec Kirkley, Filippo Radicchi

Message passing (MP) is a computational technique used to find approximate solutions to a variety of problems defined on networks. MP approximations are generally accurate in locally tree-like networks but require corrections to maintain their accuracy level in networks rich with short cycles. However, MP may already be computationally challenging on very la

Ettore Aldrovandi, Cynthia Lester

We extend Deligne's notion of determinant functor to tensor triangulated categories. Specifically, to account for the multiexact structure of the tensor, we define a determinant functor on the 2-multicategory of triangulated categories and we provide a multicategorical version of the universal determinant functor for triangulated categories, whose multie

Valentin Blomer, Philippe Michel

We show that shifted pairs of discrete or continuous low-lying horocycles equidistribute in the product space of two modular curves.

Fritjof Bengtsson, Caterina Doglioni, Per Alexander Ekman, Axel Gallén

Storing and sharing increasingly large datasets is a challenge across scientific research and industry. In this paper, we document the development and applications of Baler - a Machine Learning based data compression tool for use across scientific disciplines and industry. Here, we present Baler's performance for the compression of High Energy Physics (HEP)

Błażej M. Szablikowski, Maciej Błaszak, Krzysztof Marciniak

In this article we investigate stationary cKdV systems and prove that every $N$-field stationary cKdV system can be written, after a careful reparametrization of jet variables, as a classical separable St\"ackel system on $N+1$ different ways. For each of these $N+1$ parametrizations we present an explicit map between the jet variables and the separation var

Lucía Santamaría-Sanz

The spectrum of bound and scattering states of the one dimensional Dirac Hamiltonian describing fermions distorted by a static background built from two Dirac delta potentials is studied. A distinction will be made between mass-spike and electrostatic Dirac delta-potentials. The second quantisation is then performed to promote the relativistic quantum mechan

Marius Garbea, Vasilis Gkatzelis, Xizhi Tan

We study the problem of allocating indivisible items to budget-constrained agents, aiming to provide fairness and efficiency guarantees. Specifically, our goal is to ensure that the resulting allocation is envy-free up to any item (EFx) while minimizing the amount of inefficiency that this needs to introduce. We first show that there exist two-agent problem

Qiufeng Wang, Xu Yang, Shuxia Lin, Jing Wang

During the continuous evolution of one organism's ancestry, its genes accumulate extensive experiences and knowledge, enabling newborn descendants to rapidly adapt to their specific environments. Motivated by this observation, we propose a novel machine learning paradigm Learngene to enable learning models to incorporate three key characteristics of gene

R. V. Ovcharov, B. A. Ivanov, J. Åkerman, R. S. Khymyn

Antiferromagnets (AFMs) are promising materials for future high-frequency field-free spintronic applications. Self-localized spin structures can enhance their capabilities and introduce new functionalities to AFM-based devices. Here we consider a domain wall (DW), a topological soliton that bridges a connection between two ground states, similar to a Josephs

Ioaquin Moulanier, Lewis Dickson, Charles Ballage, Ovidiu Vasilovici

The quality of electron bunches accelerated by laser wakefields is highly dependant on the temporal and spatial features of the laser driver. Analysis of experiments performed at APOLLON PW-class laser facility shows that spatial instabilities of the focal spot, such as shot-to-shot pointing fluctuations or asymmetry of the transverse fluence, lead to charge

A. E. Cárcamo Hernández, Vishnudath K. N., José W. F. Valle

We propose a minimal model where a dark sector seeds neutrino mass generation radiatively within the linear seesaw mechanism. Neutrino masses are calculable, since tree-level contributions are forbidden by symmetry. They arise from spontaneous lepton number violation by a small Higgs triplet vacuum expectation value. Lepton flavour violating processes e.g. $

Giovanni Antonio Chirilli

I examine the high-energy behavior of the Ioffe-time distribution for the quark bi-local space-like separated operator using the high-energy operator product expansion. These findings have significant implications for lattice calculations, which require extrapolation for large Ioffe-time values. I perform an explicit Fourier transform for both the pseudo-PDF

Yuma Mizuno

Nahm sums are specific $q$-hypergeometric series associated with symmetric positive definite matrices. In this paper we study Nahm sums associated with symmetrizable matrices. We show that one direction of Nahm's conjecture, which was proven by Calegari, Garoufalidis, and Zagier for the symmetric case, also holds for the symmetrizable case. This asserts that

Jack Borthwick, Niky Kamran

We investigate the property of boundary rigidity for the projective structures associated to torsion-free affine connections on connected analytic manifolds with boundary. We show that these structures are generically boundary rigid, meaning that any automorphism of a generic projective structure that restricts to the identity on the boundary must itself be

Yunxin Li, Baotian Hu, Yuxin Ding, Lin Ma

Pretrained Vision-Language Models (VLMs) have achieved remarkable performance in image retrieval from text. However, their performance drops drastically when confronted with linguistically complex texts that they struggle to comprehend. Inspired by the Divide-and-Conquer algorithm and dual-process theory, in this paper, we regard linguistically complex texts

Talya Eden, Quanquan C. Liu, Sofya Raskhodnikova, Adam Smith

Many deployments of differential privacy in industry are in the local model, where each party releases its private information via a differentially private randomizer. We study triangle counting in the local model with edge differential privacy (that, intuitively, requires that the outputs of the algorithm on graphs that differ in one edge be indistinguishab

Kelvin Wandia, Michael A. Garrett, Jack F. Radcliffe, Simon T. Garrington

The application of Very Long Baseline Interferometry (VLBI) to the Search for Extraterrestrial Intelligence (SETI) has been limited to date, despite the technique offering many advantages over traditional single-dish SETI observations. In order to further develop interferometry for SETI, we used the European VLBI Network (EVN) at $21$~cm to observe potential

Leander Fischer, Richard Naab, Alexandra Trettin

Estimating the impact of systematic uncertainties in particle physics experiments is challenging, especially since the detector response is unknown analytically in most situations and needs to be estimated through Monte Carlo (MC) simulations. Typically, detector property varia-tions are parameterized in ways that implicitly assume a specific physics model,