arXiv papers from October 2023

Ben Doyle

We extend the notion of chip-firing to weighted graphs, and generalize the Greedy Algorithm and Dhar's Burning Algorithm to weighted graphs. For a vertex $q \in V(\Gamma)$, we give an upper bound for the number of linearly equivalent $q$-reduced divisors. Finally, we illustrate a method of finding all maximal unwinnable divisors on weighted graphs.

Bei Cao, Chongze Han, Xiang Hao, Chen Wang

We present a study on inelastic thermoelectric devices, wherein charge currents and electronic and phononic heat currents are intricately interconnected. The employment of double quantum dots in conjunction with a phonon bath positions them as promising candidates for quantum thermoelectric diodes and transistors. Within this study, we illustrate that quantu

Kyle Brown, Dylan M. Asmar, Mac Schwager, Mykel J. Kochenderfer

Mobile autonomous robots have the potential to revolutionize manufacturing processes. However, employing large robot fleets in manufacturing requires addressing challenges including collision-free movement in a shared workspace, effective multi-robot collaboration to manipulate and transport large payloads, complex task allocation due to coupled manufacturin

Nedeljko Stefanović

Here we present ZFC theorems yielding the Halpern-L\a"uchli theorem and avoiding metamathematical notions in their formulations.

Stefan Frauendorf, Gowhar Bhat, Nazira Nazir, Niyaz Rather

It is demonstrated that the Triaxial Projected Shell Model reproduces the energies and transition probabilities of the nucleus 104Ru and the rigid triaxial nucleus 112Ru. An interpretation in terms of band mixing is provided.

Ishmeet Kaur, Adwaita Janardhan Jadhav

Advancement in the field of machine learning is unavoidable, but something of major concern is preserving the privacy of the users whose data is being used for training these machine learning algorithms. Federated learning(FL) has emerged as a promising paradigm for training machine learning models in a distributed and privacy-preserving manner which enables

Daniel Hajialigol, Hanwen Liu, Xuan Wang

Text classification aims to effectively categorize documents into pre-defined categories. Traditional methods for text classification often rely on large amounts of manually annotated training data, making the process time-consuming and labor-intensive. To address this issue, recent studies have focused on weakly-supervised and extremely weakly-supervised se

Di Xu, Qihui Lyu, Dan Ruan, Ke Sheng

Dual-energy computed tomography (DECT) utilizes separate X-ray energy spectra to improve multi-material decomposition (MMD) for various diagnostic applications. However accurate decomposing more than two types of material remains challenging using conventional methods. Deep learning (DL) methods have shown promise to improve the MMD performance, but typical

Dehao Yuan, Furong Huang, Cornelia Fermüller, Yiannis Aloimonos

We propose Hyper-Dimensional Function Encoding (HDFE). Given samples of a continuous object (e.g. a function), HDFE produces an explicit vector representation of the given object, invariant to the sample distribution and density. Sample distribution and density invariance enables HDFE to consistently encode continuous objects regardless of their sampling, an

Peng Jia, Jiameng Lv, Runyu Ning, Yu Song

Large-scale astronomical surveys can capture numerous images of celestial objects, including galaxies and nebulae. Analysing and processing these images can reveal intricate internal structures of these objects, allowing researchers to conduct comprehensive studies on their morphology, evolution, and physical properties. However, varying noise levels and poi

Jaeff Hong, Duong Dung, Danielle Hutchinson, Zubair Akhtar

Relation Extraction from News Articles (RENA) is a browser-based tool designed to extract key entities and their semantic relationships in English language news articles related to infectious diseases. Constructed using the React framework, this system presents users with an elegant and user-friendly interface. It enables users to input a news article and se

Gérard Cornuéjols, Yatharth Dubey

In this paper, we consider a theoretical framework for comparing branch-and-bound with classical lift-and-project hierarchies. We simplify our analysis of streamlining the definition of branch-and-bound. We introduce "skewed $k$-trees" which give a hierarchy of relaxations that is incomparable to that of Sherali-Adams, and we show that it is much better for

Andrew B. Newman, Mahdi Qezlou, Nima Chartab, Gwen C. Rudie

The connection between galaxies and dark matter halos is often quantified using the stellar mass-halo mass (SMHM) relation. Optical and near-infrared imaging surveys have led to a broadly consistent picture of the evolving SMHM relation based on measurements of galaxy abundances and angular correlation functions. Spectroscopic surveys at $z \gtrsim 2$ can al

Petros-Andreas Pantazopoulos, Johannes Feist, Akashdeep Kamra, Francisco J. García-Vidal

The use of cavity quantum electrodynamical effects, i.e., of vacuum electromagnetic fields, to modify material properties in cavities has rapidly gained popularity and interest in the last few years. However, there is still a scarcity of general results that provide guidelines for intuitive understanding and limitations of what kind of effects can be achieve

Gregory Schwartzman

We bound the smoothed running time of the FLIP algorithm for local Max-Cut as a function of $\alpha$, the arboricity of the input graph. We show that, with high probability and in expectation, the following holds (where $n$ is the number of nodes and $\phi$ is the smoothing parameter): 1) When $\alpha = O(\log^{1-\delta} n)$ FLIP terminates in $\phi poly(n)$

Neelkamal Bhuyan, Debankur Mukherjee, Adam Wierman

We study the smoothed online quadratic optimization (SOQO) problem where, at each round $t$, a player plays an action $x_t$ in response to a quadratic hitting cost and an additional squared $\ell_2$-norm cost for switching actions. This problem class has strong connections to a wide range of application domains including smart grid management, adaptive contr

Ce Zhang, Changcheng Fu, Shijie Wang, Nakul Agarwal

This paper focuses on building object-centric representations for long-term action anticipation in videos. Our key motivation is that objects provide important cues to recognize and predict human-object interactions, especially when the predictions are longer term, as an observed "background" object could be used by the human actor in the future. We observe

Anuj Kumar, Wojciech Ożański

We consider the construction of linear instability of parallel shear flows, which was developed by Zhiwu Lin (SIAM J. Math. Anal. 35(2), 2003). We give an alternative simple proof in Sobolev setting of the problem, which exposes the mathematical role of the Plemelj-Sochocki formula in the emergence of the instability, as well as does not require the cone con

Riju Banerjee, Emily L. Wang, Eric W. Hudson

Central to the enigma of the cuprates is ubiquitous electronic inhomogeneity arising from a variety of electronic orders that coexist with superconductivity, the individual signatures of which have been impossible to disentangle despite four decades of intense research. This strong nanoscale inhomogeneity complicates interpretation of measurements both by pr

Wannita Takerngsaksiri, Cleshan Warusavitarne, Christian Yaacoub, Matthew Hee Keng Hou

AI Code Completion (e.g., GitHub's Copilot) has revolutionized how computer science students interact with programming languages. However, AI code completion has been studied from the developers' perspectives, not the students' perspectives who represent the future generation of our digital world. In this paper, we investigated the benefits, challenges, and

Mingjie Liu, Teodor-Dumitru Ene, Robert Kirby, Chris Cheng

ChipNeMo aims to explore the applications of large language models (LLMs) for industrial chip design. Instead of directly deploying off-the-shelf commercial or open-source LLMs, we instead adopt the following domain adaptation techniques: domain-adaptive tokenization, domain-adaptive continued pretraining, model alignment with domain-specific instructions, a

Thomas Y. Steiman-Cameron, Richard H. Durisen, Aaron C. Boley, Scott Michael

During early phases of a protoplanetary disks's life, gravitational instabilities can produce significant mass transport, can dramatically alter disk structure, can mix and shock-process gas and solids, and may be instrumental in planet formation. We present a 3D grid-based radiative hydrodynamics study with varied resolutions of a 0.07 M$_\odot$ disk orbiti

Ze-Feng Lei, Junlong Tian, Jie Peng

We study the two-qubit asymmetric quantum Rabi model (AQRM) and find its dark-state solution. Such solutions have at most one photon and constant eigenenergy in the whole coupling regime, causing level crossings in the spectrum, although there is no explicit conserved quantity except energy. We find an operator in the eigenenergy basis to label all the degen

Andreas Greven, Frank den Hollander, Anton Klimovsky, Anita Winter

This paper introduces graphemes for constructing and analyzing stochastic processes that describe the evolution of large dynamic graphs. Unlike graphons, which capture the static properties of dense graphs via exchangeability or subgraph densities, graphemes are capable of modeling the full space-time evolution of graphs. A grapheme is an equivalence class o

Jinhwa Kim, Ali Derakhshan, Ian G. Harris

Large Language Models' safety remains a critical concern due to their vulnerability to adversarial attacks, which can prompt these systems to produce harmful responses. In the heart of these systems lies a safety classifier, a computational model trained to discern and mitigate potentially harmful, offensive, or unethical outputs. However, contemporary safet

Simone Cavazzoni, Luca Razzoli, Giovanni Ragazzi, Paolo Bordone

We address metrological problems where the parameter of interest is encoded in the internal degree of freedom of a discrete-time quantum walker, and provide evidence that coin dimensionality is a potential resource to enhance precision. In particular, we consider estimation problems where the coin parameter governs rotations around a given axis and show that

Lauren E. Altman, Menachem Stern, Andrea J. Liu, Douglas J. Durian

Coupled learning is a contrastive scheme for tuning the properties of individual elements within a network in order to achieve desired functionality of the system. It takes advantage of physics both to learn using local rules and to "compute" the output response to input data, thus enabling the system to perform decentralized computation without the need for

George W. Patrick

There is an explicit resolution of the Poisson reduction of four planar point vortices, in the case that three of the vortex strengths are equal and the total vorticity is zero. The resolution, a Hamiltonian system on a unified symplectic phase space with a symmetry breaking parameter, is obtained by appending redundant states. Though single point vortices d

Nathan Lambert, Roberto Calandra

Reinforcement learning from human feedback (RLHF) has emerged as a powerful technique to make large language models (LLMs) more capable in complex settings. RLHF proceeds as collecting human preference data, training a reward model on said data, and optimizing a base ML model with respect to said reward for extrinsic evaluation metrics (e.g. MMLU, GSM8k). RL

Younghyun Koo, Maryam Rahnemoonfar

Forecasting sea ice concentration (SIC) and sea ice velocity (SIV) in the Arctic Ocean is of great significance as the Arctic environment has been changed by the recent warming climate. Given that physical sea ice models require high computational costs with complex parameterization, deep learning techniques can effectively replace the physical model and imp

Wenkui Du, Yang Yang

We prove a Bernstein theorem for $\Phi$-anisotropic minimal hypersurfaces in all dimensional Euclidean spaces that the only entire smooth solutions $u: \mathbb{R}^{n}\rightarrow \mathbb{R}$ of $\Phi$-anisotropic minimal hypersurfaces equation are linear functions provided the anisotropic area functional integrand $\Phi$ is sufficiently $C^{3}$-close to class

V. I. Yukalov, E. P. Yukalova, V. S. Bagnato

The review presents the methods of generation of nonlinear coherent excitations in strongly nonequilibrium Bose-condensed systems of trapped atoms and their properties. Non-ground-state Bose-Einstein condensates are represented by nonlinear coherent modes. The principal difference of nonlinear coherent modes from linear collective excitations is emphasized.

Abhinav Nippani, Dongyue Li, Haotian Ju, Haris N. Koutsopoulos

We consider the problem of traffic accident analysis on a road network based on road network connections and traffic volume. Previous works have designed various deep-learning methods using historical records to predict traffic accident occurrences. However, there is a lack of consensus on how accurate existing methods are, and a fundamental issue is the lac

Slavoljub Mijovic

Global climate change is one of main concern of modern society. To estimate this change usually one estimates the global mean temperature. Measuring and calculating the Earth's average temperature are multi-steps complex processes which combine data from various sources and use statistical techniques. Nowadays, the dataset containing the spatial-temporal dat

James Fullwood

The theory of quantum states over time provides an approach to the dynamics of quantum information which is in direct analogy with spacetime and its relation to classical dynamics. In this work, we further such an analogy by formulating a notion of general covariance for the theory of quantum states over time. We then associate a canonical state over time wi

Jimin Mun, Emily Allaway, Akhila Yerukola, Laura Vianna

Counterspeech, i.e., responses to counteract potential harms of hateful speech, has become an increasingly popular solution to address online hate speech without censorship. However, properly countering hateful language requires countering and dispelling the underlying inaccurate stereotypes implied by such language. In this work, we draw from psychology and

Noah Stevenson, Ian Tice

In this paper we study solitary traveling wave solutions to a damped shallow water system, which is in general quasilinear and of mixed type. We develop a small data well-posedness theory and prove that traveling wave solutions are a generic phenomenon that persist with and without viscosity or surface tension and for all nontrivial traveling wave speeds, ev

Xinting Huang, Jiajing Wan, Ioannis Kritikos, Nora Hollenstein

Humans read texts at a varying pace, while machine learning models treat each token in the same way in terms of a computational process. Therefore, we ask, does it help to make models act more like humans? In this paper, we convert this intuition into a set of novel models with fixation-guided parallel RNNs or layers and conduct various experiments on langua

Mauro Artigiani, Anja Randecker, Chandrika Sadanand, Ferrán Valdez

We provide a complete classification of groups that can be realized as isometry groups of a translation surface $M$ with non-finitely generated fundamental group and no planar ends. Furthermore, we demonstrate that if $S$ has no non-displaceable subsurfaces and its space of ends is self-similar, then every countable subgroup of $\operatorname{GL}^+(2,\mathbb

Diana McSpadden, Yasir Alanazi, Bryan Hess, Laura Hild

The dataset was collected for 332 compute nodes throughout May 19 - 23, 2023. May 19 - 22 characterizes normal compute cluster behavior, while May 23 includes an anomalous event. The dataset includes eight CPU, 11 disk, 47 memory, and 22 Slurm metrics. It represents five distinct hardware configurations and contains over one million records, totaling more th

Guoxuan Xia, Duolikun Danier, Ayan Das, Stathi Fotiadis

Recently, Zhang et al. have proposed the Diffusion Exponential Integrator Sampler (DEIS) for fast generation of samples from Diffusion Models. It leverages the semi-linear nature of the probability flow ordinary differential equation (ODE) in order to greatly reduce integration error and improve generation quality at low numbers of function evaluations (NFEs

Chunxu Tang, Yi Wang, Bin Fan, Beinan Wang

This paper explores a prevailing trend in the industry: migrating data-intensive analytics applications from on-premises to cloud-native environments. We find that the unique cost models associated with cloud-based storage necessitate a more nuanced understanding of optimizing performance. Specifically, based on traces collected from Uber's Presto fleet in p

J. L. Ross, P-I. Gavriloaea, F. Freimuth, T. Adamantopoulos

Efficient manipulation of the N\'eel vector in antiferromagnets can be induced by generation of spin orbit (SOT) or spin-transfer (STT) torques. Here we predict another possibility for antiferromagnetic domain switching by using a non-zero staggered field induced from optical laser excitation. We present results on the atomistic scale dynamic simulations fro

Pervaiz Iqbal Khan, Andreas Dengel, Sheraz Ahmed

There are not many large medical image datasets available. For these datasets, too small deep learning models can't learn useful features, so they don't work well due to underfitting, and too big models tend to overfit the limited data. As a result, there is a compromise between the two issues. This paper proposes a training strategy Medi-CAT to overcome the

Jake Brawer, Kayleigh Bishop, Bradley Hayes, Alessandro Roncone

Task assignment and scheduling algorithms are powerful tools for autonomously coordinating large teams of robotic or AI agents. However, the decisions these system make often rely on components designed by domain experts, which can be difficult for non-technical end-users to understand or modify to their own ends. In this paper we propose a preliminary desig

Jordan Schwartz, Madison Bohannan, Jacob Yim, Yuerou Tang

In this work, we present the development of an automated extension tool to assist educators and increase the success and well-being of students by implementing flexible extension policies. Flexible extension policies materialize in many ways, yet there are similarities in students' interactions with them; students tend to request multi-day long extensions re

Hugo Therssen, Laure Catala, Sandra Mazérat, Talal Mallah

We report a study of the electron transport (ET) properties at the nanoscale (conductive-AFM noted C-AFM thereafter) of individual Prussian Blue Analog (PBA) cubic nanocrystals (NCs) of CsCo(III)Fe(II), with size between 15 and 50 nm deposited on HOPG. We demonstrate that these PBA NCs feature an almost size independent electron injection barriers of 0.41 +/

Diego Manco

Yau defines the notion of pseudo symmetric $\mathbf{Cat}$-enriched multifunctor between $\mathbf{Cat}$-enriched multicategories and proves that Mandell's inverse $K$-theory multifunctor is pseudo symmetric. We prove a coherence theorem for pseudo symmetric $\mathbf{Cat}$-enriched multifunctors. As an application, we prove that pseudo symmetric $\mathbf{Cat}$

Florent Capelli, Alberto Del Pia, Silvia Di Gregorio

The Binary Polynomial Optimization (BPO) problem is defined as the problem of maximizing a given polynomial function over all binary points. The main contribution of this paper is to draw a novel connection between BPO and the field of Knowledge Compilation. This connection allows us to unify and significantly extend the state-of-the-art for BPO, both in ter

Murilo Corato-Zanarella

We give explicit models for spherical functions on $p$-adic symmetric spaces $X=H\backslash G$ for pairs of $p$-adic groups $(G,H)$ of the form $(\mathrm{U}(2r),\mathrm{U}(r)\times \mathrm{U}(r)),$ $(\mathrm{O}(2r),\mathrm{O}(r)\times \mathrm{O}(r)),$ $(\mathrm{Sp}(4r),\mathrm{Sp}(2r)\times\mathrm{Sp}(2r))),$ $(\mathrm{U}(2r+1),\mathrm{U}(r+1)\times \mathrm{

Murilo Corato-Zanarella

For all $r\ge1,$ we verify the following conjecture of Hironaka: for a $p$-adic field $F$ with $p$ odd, the space of spherical functions of $\mathrm{Sym}_{r\times r}(F)\cap\mathrm{GL}_r(F)$ is free of rank $4^r$ over the Hecke algebra.

Yiwen Shao, Shi-Xiong Zhang, Dong Yu

Automatic speech recognition (ASR) on multi-talker recordings is challenging. Current methods using 3D spatial data from multi-channel audio and visual cues focus mainly on direct waves from the target speaker, overlooking reflection wave impacts, which hinders performance in reverberant environments. Our research introduces RIR-SF, a novel spatial feature b

H. P. Saldaño, M. Rubio, A. D. Bolatto, K. Sandstrom

We present the CO(3-2) APEX survey at 6 pc resolution of the bar of the SMC. We aboard the CO analysis in the SMC-Bar comparing the CO(3-2) survey with that of the CO(2-1) of similar resolution. We study the CO(3-2)-to-CO(2-1) ratio (R32) that is very sensitive to the environment properties (e.g., star-forming regions). We analyzed the correlation of this ra

Xi Li, Songhe Wang, Chen Wu, Hao Zhou

Federated learning (FL) represents a novel paradigm to machine learning, addressing critical issues related to data privacy and security, yet suffering from data insufficiency and imbalance. The emergence of foundation models (FMs) provides a promising solution to the problems with FL. For instance, FMs could serve as teacher models or good starting points f

Fatemeh Rajabi, Ali Mohades

In elections around the world, the candidates may turn their campaigns toward negativity due to the prospect of failure and time pressure. In the digital age, social media platforms such as Twitter are rich sources of political discourse. Therefore, despite the large amount of data that is published on Twitter, the automatic system for campaign negativity de

Mark Hillery, Camilla Polvara, Vadim Oganesyan, Nada Ali

We examine two conditions that can be used to detect bipartite entanglement, and show that they can be used to provide lower bounds on the negativity of states. We begin with two-qubit states, and then show how what was done there can be extended to more general states. The resulting bounds are then studied by means of a number of examples. We also show that

Jacob Bedrossian, Siming He, Sameer Iyer, Fei Wang

In this work, we prove a threshold theorem for the 2D Navier-Stokes equations posed on the periodic channel, $\mathbb{T} \times [-1,1]$, supplemented with Navier boundary conditions $\omega|_{y = \pm 1} = 0$. Initial datum is taken to be a perturbation of Couette in the following sense: the shear component of the perturbation is assumed small (in an appropri

Zhaoyang Shi, Krishnakumar Balasubramanian, Wolfgang Polonik

We show both adaptive and non-adaptive minimax rates of convergence for a family of weighted Laplacian-Eigenmap based nonparametric regression methods, when the true regression function belongs to a Sobolev space and the sampling density is bounded from above and below. The adaptation methodology is based on extensions of Lepski's method and is over both the

Jiali He, Manuel Zahn, Ivan N. Ushakov, Leonie Richarz

Extraordinary physical properties arise at polar interfaces in oxide materials, including the emergence of two-dimensional electron gases, sheet-superconductivity, and multiferroicity. A special type of polar interface are ferroelectric domain walls, where electronic reconstruction phenomena can be driven by bound charges. Great progress has been achieved in

Niels N. Oehlmann, Johannes G. Rebelein

Protein engineering is an established method for tailoring enzymatic reactivity. A commonly used method is directed evolution, where the mutagenesis and natural selection process is mimicked and accelerated in the laboratory. Here, we describe a reliable method for generating saturation mutagenesis libraries by golden gate cloning in a broad host range plasm

Philip Ernst, Hongwei Mei

Consider the sample path of a one-dimensional diffusion for which the diffusion coefficient is given and where the drift may take on one of two values: $\mu_0$ or $\mu_1$. Suppose that the signal-to-noise ratio (defined as the difference between the two possible drifts divided by the diffusion coefficient) is non-constant. Given an initial state for the obse

Antonis Antoniades, Yiyi Yu, Joseph Canzano, William Wang

State-of-the-art systems neuroscience experiments yield large-scale multimodal data, and these data sets require new tools for analysis. Inspired by the success of large pretrained models in vision and language domains, we reframe the analysis of large-scale, cellular-resolution neuronal spiking data into an autoregressive spatiotemporal generation problem.

Keith R. Fratus, Juha Leppäkangas, Michael Marthaler, Jan-Michael Reiner

When modeling the effects of noise on quantum circuits, one often makes the assumption that these effects can be accounted for by individual decoherence events following an otherwise noise-free gate. In this work, we address the validity of this model. We find that under a fairly broad set of assumptions, this model of individual decoherence events provides

Mykhailo Shvets, Dongxu Zhao, Marc Niethammer, Roni Sengupta

Multi-task approaches to joint depth and segmentation prediction are well-studied for monocular images. Yet, predictions from a single-view are inherently limited, while multiple views are available in many robotics applications. On the other end of the spectrum, video-based and full 3D methods require numerous frames to perform reconstruction and segmentati

Kateřina Trlifajová

The paper introduces the notion of the size of countable sets that preserves the Part-Whole Principle and generalizes the notion of the cardinality of finite sets. The sizes of natural numbers, integers, rational numbers, and all their subsets, unions, and Cartesian products are algorithmically enumerable up to one element as sequences of natural numbers. Th

Eric Bonnetier, Matias Courdurier, Axel Osses, Faouzi Triki

We investigate the Helmholtz equation in a two dimensional open waveguide with a thin and high contrast core layer. We develop an asymptotic analysis of the Green function of the problem, and through it we identify and characterize the appearance of resonant frequencies. For waves originating outside of the core, the waveguide response at these resonant freq

Alexey Golovnev

Rastall gravity is the same as General Relativity, with a simple algebraic redefinition of what is called the energy-momentum tensor. Despite it having been very clearly explained by M. Visser several years go, there are still many papers claiming big differences between the two formulations of gravitational equations and trying to use them for problems of p

CMS Collaboration

A search for an exotic decay of the Higgs boson to a Z boson and a light pseudoscalar particle (a), decaying to a pair of leptons and a pair of photons, respectively, is presented. The search is based on proton-proton collision data at a center-of-mass energy of $\sqrt{s}$ = 13 TeV, collected with the CMS detector and corresponding to an integrated luminosit

Seonghoon Choi, Ignacio Loaiza, Robert A. Lang, Luis A. Martínez-Martínez

We consider the question of how correlated the system hardness is between classical algorithms of electronic structure theory in ground state estimation and quantum algorithms. To define the system hardness for classical algorithms we employ empirical criterion based on the deviation of electronic energies produced by coupled cluster and configuration intera

Mattia Opper, J. Morrison, N. Siddharth

This work explores the degree to which grammar acquisition is driven by language `simplicity' and the source modality (speech vs. text) of data. Using BabyBERTa as a probe, we find that grammar acquisition is largely driven by exposure to speech data, and in particular through exposure to two of the BabyLM training corpora: AO-Childes and Open Subtitles. We

Manuel Hoff

We develop tools to study spaces of $p$-divisible groups and Abelian varieties with additional structure. More precisely, we extend the definition of parahoric (Dieudonn\'e) $(\mathcal{G}, \mu)$-displays given by Pappas to not necessarily $p$-torsionfree base rings and also introduce the notion of an $(m, n)$-truncated $(\mathcal{G}, \mu)$-display. Then we s

Viet-Anh Le, Behdad Chalaki, Filippos N. Tzortzoglou, Andreas A. Malikopoulos

Addressing safe and efficient interaction between connected and automated vehicles (CAVs) and human-driven vehicles in a mixed-traffic environment has attracted considerable attention. In this paper, we develop a framework for stochastic time-optimal trajectory planning for coordinating multiple CAVs in mixed-traffic merging scenarios. We present a data-driv

Louis Rosenberg, Gregg Willcox, Hans Schumann

Conversational Swarm Intelligence (CSI) is a new technology that enables human groups of potentially any size to hold real-time deliberative conversations online. Modeled on the dynamics of biological swarms, CSI aims to optimize group insights and amplify group intelligence. It uses Large Language Models (LLMs) in a novel framework to structure large-scale

Luiz L. Lopes, Debora P. Menezes, Mateus R. Pelicer

We study how the nuclear symmetry energy slope ($L$) can affect the hadron-quark phase transition and neutron star properties. We show that the main physical quantities as the critical chemical potential and pressure are strongly influenced by the symmetry energy slope. In extreme cases, the total amount of deconfined quarks can reach up to 99$\%$ of the hyb

K. A. L. Lima, L. A. Ribeiro

Silicon-based two-dimensional (2D) materials, including well-known silicene, have garnered considerable attention due to their potential in advanced electronic and optoelectronic applications. Here, we introduce a novel 2D silicon variant, pentahexoctite silicon (PH-Si), inspired by the unique structural attributes of pentahexoctite carbon. By using state-of

Ali Devran Kara, Serdar Yuksel

As a primary contribution, we present a convergence theorem for stochastic iterations, and in particular, Q-learning iterates, under a general, possibly non-Markovian, stochastic environment. Our conditions for convergence involve an ergodicity and a positivity criterion. We provide a precise characterization on the limit of the iterates and conditions on th

Srijan Sengupta

This article provides a brief overview of statistical network analysis, a rapidly evolving field of statistics, which encompasses statistical models, algorithms, and inferential methods for analyzing data in the form of networks. Particular emphasis is given to connecting the historical developments in network science to today's statistical network analysis,

Shelley Tong, James Corcoran, Max Fieg, Michael Fenton

Searches for new physics in the top quark sector are of great theoretical interest, yet some powerful avenues for discovery remain unexplored. We characterize the expected statistical power of the LHC dataset to constrain the single production of heavy top partners $T$ decaying to a top quark and a photon or a top quark and a gluon. We describe an effective

Ricardo Peredo-Ortiz, Luis F. Elizondo-Aguilera, Pedro E. Ramírez-González, Edilio Lázaro-Lázaro

This paper proposes a simple mathematical model of non-stationary and non-linear stochastic dynamics, which approximates a (globally) non-stationary and non-linear stochastic process by its locally (or \emph{"piecewise"}) stationary version. Profiting from the elegance and simplicity of both, the exact mathematical model referred to as the Ornstein-Uhlenbeck

Filipe Moura, João Rodrigues

After a brief introduction to quasinormal modes in dissipative systems, we review the WKB formalism in the context of the analytical calculation of quasinormal frequencies. We apply these results to the calculation of quasinormal frequencies associated with gravitational perturbations of d-dimensional spherically symmetric black holes with string corrections

Gabriele D'Acunto, Francesco Bonchi, Gianmarco De Francisci Morales, Giovanni Petri

The bulk of the research effort on brain connectivity revolves around statistical associations among brain regions, which do not directly relate to the causal mechanisms governing brain dynamics. Here we propose the multiscale causal backbone (MCB) of brain dynamics, shared by a set of individuals across multiple temporal scales, and devise a principled meth

Pranav Gade, Simon Lermen, Charlie Rogers-Smith, Jeffrey Ladish

Llama 2-Chat is a collection of large language models that Meta developed and released to the public. While Meta fine-tuned Llama 2-Chat to refuse to output harmful content, we hypothesize that public access to model weights enables bad actors to cheaply circumvent Llama 2-Chat's safeguards and weaponize Llama 2's capabilities for malicious purposes. We demo

Aarohi Srivastava, David Chiang

Real-world NLP applications often deal with nonstandard text (e.g., dialectal, informal, or misspelled text). However, language models like BERT deteriorate in the face of dialect variation or noise. How do we push BERT's modeling capabilities to encompass nonstandard text? Fine-tuning helps, but it is designed for specializing a model to a task and does not

Zhijin Guo, Zhaozhen Xu, Martha Lewis, Nello Cristianini

Knowledge Graphs are a widely used method to represent relations between entities in various AI applications, and Graph Embedding has rapidly become a standard technique to represent Knowledge Graphs in such a way as to facilitate inferences and decisions. As this representation is obtained from behavioural data, and is not in a form readable by humans, ther

Lin Zhang

The complete characterization of a generic $d$-dimensional Floquet topological phase is usually hard for the requirement of information about the micromotion throughout the entire driving period. In a recent work [L. Zhang et al., Phys. Rev. Lett. 125, 183001 (2020)], an experimentally feasible dynamical detection scheme was proposed to characterize the inte

Maksym Deliyergiyev, Antonino Del Popolo, Morgan Le Delliou

This paper investigates a hypothesis proposed in previous research relating neutron star (NS) mass and its dark matter (DM) accumulation. As DM accumulates, NS mass decreases, predicting lower NS masses toward the Galactic center. Due to limited NSs data near the galactic center, we examine NSs located within DM clumps. Using the CLUMPY code simulations, we

Alberto Rigo, Muqun Hu, Satyandra K. Gupta, Quan Nguyen

In recent years, the field of legged robotics has seen growing interest in enhancing the capabilities of these robots through the integration of articulated robotic arms. However, achieving successful loco-manipulation, especially involving interaction with heavy objects, is far from straightforward, as object manipulation can introduce substantial disturban

FRB Collaboration, Mandana Amiri, Bridget C. Andersen, Shion Andrew

In 2021, a catalog of 536 fast radio bursts (FRBs) detected with the Canadian Hydrogen Intensity Mapping Experiment (CHIME) radio telescope was released by the CHIME/FRB Collaboration. This large collection of bursts, observed with a single instrument and uniform selection effects, has advanced our understanding of the FRB population. Here we update the resu

John Talbot, Jun Yan

A simple graph is triangular if every edge is contained in a triangle. A sequence of integers is graphical if it is the degree sequence of a simple graph. Egan and Nikolayevsky recently conjectured that every graphical sequence whose terms are all at least 4 is the degree sequence of a triangular simple graph, and proved this in some special cases. In this p

Zikai Xiong, Niccolò Dalmasso, Alan Mishler, Vamsi K. Potluru

Recent years have seen a surge of machine learning approaches aimed at reducing disparities in model outputs across different subgroups. In many settings, training data may be used in multiple downstream applications by different users, which means it may be most effective to intervene on the training data itself. In this work, we present FairWASP, a novel p

R. A. García, C. Gourvès, A. R. G. Santos, A. Strugarek

In 2013 a dearth of close-in planets around fast-rotating host stars was found using statistical tests on Kepler data. The addition of more Kepler and Transiting Exoplanet Survey Satellite (TESS) systems in 2022 filled this region of the diagram of stellar rotation period (Prot) versus the planet orbital period (Porb). We revisited the Prot extraction of Kep

Maayan Gelboim, Amir Adler, Yen Sun, Mauricio Araya-Polo

We consider the problem of 3D seismic inversion from pre-stack data using a very small number of seismic sources. The proposed solution is based on a combination of compressed-sensing and machine learning frameworks, known as compressed-learning. The solution jointly optimizes a dimensionality reduction operator and a 3D inversion encoder-decoder implemented

Amit Acharya, Ambar N. Sengupta

A method for designing variational principles for the dynamics of a possibly dissipative and non-conservatively forced chain of particles is demonstrated. Some qualitative features of the formulation are discussed.

G. A. P. Ribeiro, Gustavo Rigolin

In [Phys. Rev. A 107, 052420 (2023)] we showed that the quantum teleportation protocol can be used to detect quantum critical points (QCPs) associated with a couple of different classes of quantum phase transitions, even when the system is away from the absolute zero temperature (T=0). Here, working in the thermodynamic limit (infinite chains), we extend the

Yumeng Zhang, Sundar Aditya, Bruno Clerckx

The wireless domain is witnessing a flourishing of integrated systems, e.g. (a) integrated sensing and communications, and (b) simultaneous wireless information and power transfer, due to their potential to use resources (spectrum, power) judiciously. Inspired by this trend, we investigate integrated sensing, communications and powering (ISCAP), through the

Yuanjie Ren, Peter Shor

In this paper, we explore topological quantum computation augmented by subphases and phase transitions. We commence by investigating the anyon tunneling map, denoted as $\varphi$, between subphases of the quantum double model $\mathcal{D}(G)$ for any arbitrary finite group $G$. Subsequently, we delve into the relationship between $\varphi$ and the Floquet co

Aymen Al-Marjani, Andrea Tirinzoni, Emilie Kaufmann

Several recent works have proposed instance-dependent upper bounds on the number of episodes needed to identify, with probability $1-\delta$, an $\varepsilon$-optimal policy in finite-horizon tabular Markov Decision Processes (MDPs). These upper bounds feature various complexity measures for the MDP, which are defined based on different notions of sub-optima

Yolanda Cabrera Casado, Dolores Martín Barquero, Cándido Martín González, Alicia Tocino

We consider the intersection $\mathfrak{M}(A)$ of all maximal ideals of an evolution algebra $A$ and study the structure of the quotient $A/\M(A)$. In a previous work, maximal ideals have been related to hereditary subsets of a graph associated to the given algebra. We investigate the superfluous members both in the family of maximal ideals and also in the s

Hugo Casquero, Kyle Dakota Mathews

Quadratic NURBS-based discretizations of the Galerkin method suffer from membrane locking when applied to Kirchhoff-Love shell formulations. Membrane locking causes not only smaller displacements than expected, but also large-amplitude spurious oscillations of the membrane forces. Continuous-assumed-strain (CAS) elements have been recently introduced to remo

Carlo Alberto Antonini

We provide a novel approach to approximate bounded Lipschitz domains via a sequence of smooth, bounded domains. The flexibility of our method allows either inner or outer approximations of Lipschitz domains which also possess weakly defined curvatures, namely, domains whose boundary can be locally described as the graph of a function belonging to the Sobolev