May 2023 arXiv papers — page 92

Bernat Bassols-Cornudella, Francesco Viganò

Binomial Cayley graphs are obtained by considering the binomial coefficient of the weight function of a given Cayley graph and a natural number. We introduce these objects and study two families: one associated with symmetric groups and the other with powers of cyclic groups. We determine various combinatorial properties of these graphs through the spectral

Ruotong Wang, Ruijia Cheng, Denae Ford, Thomas Zimmermann

As AI-powered code generation tools such as GitHub Copilot become popular, it is crucial to understand software developers' trust in AI tools -- a key factor for tool adoption and responsible usage. However, we know little about how developers build trust with AI, nor do we understand how to design the interface of generative AI systems to facilitate their a

Shahar Hadar, Sreehari Harikesh, Doron Chelouche

Rapid progress in electromagnetic black hole observation presents a theoretical challenge: how can the universal signatures of extreme gravitational lensing be distilled from stochastic astrophysical signals? With this motivation, the two-point correlation function of specific intensity fluctuations across image positions, times, and frequencies is here cons

Robert Brandenberger, Vahid Kamali, Rudnei O. Ramos

An oscillating inflaton field induces small amplitude oscillations of the Hubble parameter at the end of inflation. These Hubble parameter induced oscillations, in turn, trigger parametric particle production of all light fields, even if they are not directly coupled to the inflaton. We here study the induced particle production for a light scalar field (e.g

Sandra Costa, Joan Nieves, Augusto Gonzalez

A biological transition from a state N to a state T is characterized by a rearrangement of the gene expression profile in the system, quantitatively measured through the differential expression of genes. In contrast, changes in genetic pathways are usually evaluated by means of hypothesis testing schemes. We introduce a quantitative measure in order to evalu

Srijith Radhakrishnan, Chao-Han Huck Yang, Sumeer Ahmad Khan, Narsis A. Kiani

In this work, we explore Parameter-Efficient-Learning (PEL) techniques to repurpose a General-Purpose-Speech (GSM) model for Arabic dialect identification (ADI). Specifically, we investigate different setups to incorporate trainable features into a multi-layer encoder-decoder GSM formulation under frozen pre-trained settings. Our architecture includes residu

Eliza Kosoy, Emily Rose Reagan, Leslie Lai, Alison Gopnik

Developmental psychologists have spent decades devising experiments to test the intelligence and knowledge of infants and children, tracing the origin of crucial concepts and capacities. Moreover, experimental techniques in developmental psychology have been carefully designed to discriminate the cognitive capacities that underlie particular behaviors. We pr

Sharon Levy, Neha Anna John, Ling Liu, Yogarshi Vyas

Studies in bias and fairness in natural language processing have primarily examined social biases within a single language and/or across few attributes (e.g. gender, race). However, biases can manifest differently across various languages for individual attributes. As a result, it is critical to examine biases within each language and attribute. Of equal imp

Niall Jeffrey, Benjamin D. Wandelt

Evidence Networks can enable Bayesian model comparison when state-of-the-art methods (e.g. nested sampling) fail and even when likelihoods or priors are intractable or unknown. Bayesian model comparison, i.e. the computation of Bayes factors or evidence ratios, can be cast as an optimization problem. Though the Bayesian interpretation of optimal classificati

D N Gupta, Saurabh Kumar, S Kar

We demonstrate a scheme for enhanced proton acceleration from near-critical-density targets by splitting a laser pulse into a linearly and a circularly polarized laser pulse. The combination of two laser pulses generates a shock wave as well as hole boring effect at the front surface of the target. Protons at the front get combined acceleration from the two

Thomas E. Moore, James L. Burch, Deirdre E. Wendel

A recently published, new analysis of current sheets updated the classic Harris 1D static solution by considering multiple classes of charged particle trajectories in a generalized dynamic current sheet. It used a 1D PIC simulation to describe dynamic pinching and bifurcation of the sheet. These 1D results strongly suggest that plasma beta or other propertie

Ramin Safavinejad, Hao-Hsuan Chang, Lingjia Liu

In dynamic spectrum access (DSA) networks, secondary users (SUs) need to opportunistically access primary users' (PUs) radio spectrum without causing significant interference. Since the interaction between the SU and the PU systems are limited, deep reinforcement learning (DRL) has been introduced to help SUs to conduct spectrum access. Specifically, deep re

Xinchi Qiu, Heng Pan, Wanru Zhao, Chenyang Ma

The majority of work in privacy-preserving federated learning (FL) has been focusing on horizontally partitioned datasets where clients share the same sets of features and can train complete models independently. However, in many interesting problems, such as financial fraud detection and disease detection, individual data points are scattered across differe

Mendrit Ljatifi

In this note we give a BRST interpretation of inner product of group averaging on de Sitter group SO(2,1). Quantization approaches can be divided in two classes, without introducing additional degrees of freedom and with introducing ghosts, antighosts, Lagrange multipliers and canonically conjugated momenta. The first class of quantization methods includes D

Garima Pant, Sanjay Kumar Pant

We study about solutions of certain kind of non-linear differential difference equations $$f^{n}(z)+wf^{n-1}(z)f^{'}(z)+f^{(k)}(z+c)=p_{1}e^{\alpha_{1}z}+p_{2}e^{\alpha_{2}z}$$ and $$f^{n}(z)+wf^{n-1}(z)f^{'}(z)+q(z)e^{Q(z)}f(z+c)=p_{1}e^{\alpha_{1}z}+p_{2}e^{\alpha_{2} z},$$ where $n\geq 2$, $k\geq0$ are integers, $w, p_{1}, p_{2}, \alpha_{1}$ $\&$ $\alpha_

Pablo Candela, Diego González-Sánchez, Balázs Szegedy

Compact finite-rank nilspaces have become central in the nilspace approach to higher-order Fourier analysis, notably through their role in a general form of the inverse theorem for the Gowers norms. This paper studies these nilspaces per se, and in connection with further refinements of this inverse theorem that have been conjectured recently. Our first main

Stephanie M. Urbano Stawinski, Kate H. R. Rubin, J. Xavier Prochaska, Joseph F. Hennawi

We use medium- and high-resolution spectroscopy of close pairs of quasars to analyze the circumgalactic medium (CGM) surrounding 32 damped Ly$\alpha$ absorption systems (DLAs). The primary quasar sightline in each pair probes an intervening DLA in the redshift range $1.6<z_\text{abs}<3.5$, such that the secondary sightline probes absorption from Ly$\alpha$ a

Mohammad Khosravani, Amine Trabelsi

Unsupervised summarization is a powerful technique that enables training summarizing models without requiring labeled datasets. This survey covers different recent techniques and models used for unsupervised summarization. We cover extractive, abstractive, and hybrid models and strategies used to achieve unsupervised summarization. While the main focus of th

Amoolya Tirumalai, Christos N. Mavridis, John S. Baras

In this work, we study the inverse problem of identifying complex flocking dynamics in a domain cluttered with obstacles. We get inspiration from animal flocks moving in complex ways with capabilities far beyond what current robots can do. Owing to the difficulty of observing and recovering the trajectories of the agents, we focus on the dynamics of their pr

Tiantian Feng, Rajat Hebbar, Shrikanth Narayanan

Recent studies have explored the use of pre-trained embeddings for speech emotion recognition (SER), achieving comparable performance to conventional methods that rely on low-level knowledge-inspired acoustic features. These embeddings are often generated from models trained on large-scale speech datasets using self-supervised or weakly-supervised learning o

Mang Hei Gordon Lee, Ciaran McCulloch, Enrico Pajer

Cosmological correlators from inflation are often generated at tree level and hence loop contributions are bounded to be small corrections by perturbativity. Here we discuss a scenario where this is not the case. Recently, it has been shown that for any number of scalar fields of any mass, the parity-odd trispectrum of a massless scalar must vanish in the li

Zixi Fang, Chen Fang, Kai Zhang

In this paper, we systematically investigate the impurity-induced bound states in 1D non-Hermitian systems. By establishing an exact relationship between impurity potential and bound-state energy, we determine the minimum impurity potential required to generate bound states within each point energy gap. We demonstrate that the absence of Bloch saddle points

Ioanna Kourkoulou, Michael J. Landry, Alberto Nicolis, Klaas Parmentier

We consider the superfluid phase of a specific renormalizable relativistic quantum field theory. We prove that, within the regime of validity of perturbation theory and of the superfluid effective theory, there are consistent and regular vortex solutions where the superfluid's velocity field as traditionally defined smoothly interpolates between zero and arb

Yoshiki Matsuoka, Masafusa Onoue, Kazushi Iwasawa, Michael A. Strauss

We present the quasar luminosity function (LF) at $z = 7$, measured with 35 spectroscopically confirmed quasars at $6.55 < z < 7.15$. The sample of 22 quasars from the Subaru High-$z$ Exploration of Low-Luminosity Quasars (SHELLQs) project, combined with 13 brighter quasars in the literature, covers an unprecedentedly wide range of rest-frame ultraviolet mag

Mathew W. Bub, Yanbei Chen, Yufeng Du, Dongjun Li

We study the effects of geontropic vacuum fluctuations in quantum gravity on next-generation terrestrial gravitational wave detectors. If the VZ effect proposed in Ref. [1], as modeled in Refs. [2, 3], appears in the upcoming GQuEST experiment, we show that it will be a large background for astrophysical gravitational wave searches in observatories like Cosm

Jarred Gillette, Fred Hamann, Marie Wingyee Lau, Serena Perrotta

Extremely Red Quasars (ERQs) are thought to represent a brief episode of young quasar and galactic evolution characterized by rapid outflows and obscured growth due to dusty environments. We use new redshift measurements from CO and Ly$\alpha$ emission-lines to better constrain outflow velocities from previous line measurements. We present sample of 82 ERQs,

Jeremy J. Webb

$clustertools$ is a Python package for analyzing star cluster simulations. The package is built around the $StarCluster$ class, which stores all data read in from the snapshot of a given model star cluster. The package contains functions for loading data from commonly used $N$-body codes, generic snapshots, and software for generating initial conditions. All

Yue Liu, Nathanan Tantivasadakarn, Kevin Slagle, David F. Mross

The Kitaev honeycomb model, which is exactly solvable by virtue of an extensive number of conserved quantities, supports a gapless quantum spin liquid phase as well as gapped descendants relevant for fault-tolerant quantum computation. We show that the anomalous edge modes of 1D cluster-state-like symmetry protected topological (SPT) phases provide natural b

M. A. T. Butt, P. Roth, G. K. L. Wong, M. H. Frosz

Polarization-preserving fibers maintain the two polarization states of an orthogonal basis. Quantum communication, however, requires sending at least two nonorthogonal states and these cannot both be preserved. We present a new scheme that allows for using polarization encoding in a fiber not only in the discrete, but also in the continuous-variable regime.

Simon Weng, Céline Péroux, Arjun Karki, Ramona Augustin

The flow of gas into and out of galaxies leaves traces in the circumgalactic medium which can then be studied using absorption lines towards background quasars. We analyse 27 log(N_HI) > 18.0 HI absorbers at z = 0.2 to 1.4 from the MUSE-ALMA Halos survey with at least one galaxy counterpart within a line of sight velocity of +/-500 km s^{-1}. We perform 3D k

Avery Kiihne, Sabrina M. Appel, Blakesley Burkhart, Vadim A. Semenov

We use a suite of 3D simulations of star-forming molecular clouds, with and without stellar feedback and magnetic fields, to investigate the effectiveness of different fitting methods for volume and column density probability distribution functions (PDFs). The first method fits a piecewise lognormal and power-law (PL) function to recover PDF parameters such

Ben Kain

Einstein-Dirac-Maxwell wormholes are asymptotically flat static wormhole solutions in general relativity that do not make use of exotic matter. The asymmetric static solutions are smooth, are regular everywhere, and violate the null energy condition, which suggests that they are traversable. To determine if in fact they are traversable, we numerically evolve

Alex Davey, Oscar J. C. Dias, Jorge E. Santos

Recent studies of the gravito-electromagnetic frequency spectra of Kerr-Newman (KN) black holes have revealed two families of quasinormal modes (QNMs), namely photon sphere modes and near-horizon modes. However, they can only be unambiguously distinguished in the Reissner-Nordstr\"om (RN) limit, due to a phenomenon called eigenvalue repulsion (also known as

Dario Cilluffo, Nicola Lorenzoni, Martin B. Plenio

Although the Schr{\"o}dinger and Heisenberg pictures are equivalent formulations of quantum mechanics, simulations performed choosing one over the other can greatly impact the computational resources required to solve a problem. Here we demonstrate that in Gaussian boson sampling, a central problem in quantum computing, a good choice of representation can sh

Rahul Ramesh, Dylan Nelson, Volker Heesen, Marcus Brüggen

We use the TNG50 cosmological magnetohydrodynamical simulation of the IllustrisTNG project to show that magnetic fields in the circumgalactic medium (CGM) have significant angular structure. This azimuthal anisotropy at fixed distance is driven by galactic feedback processes that launch strong outflows into the halo, preferentially along the minor axes of ga

Matthew Ho, Xiaosheng Zhao, Benjamin Wandelt

We present the information-ordered bottleneck (IOB), a neural layer designed to adaptively compress data into latent variables ordered by likelihood maximization. Without retraining, IOB nodes can be truncated at any bottleneck width, capturing the most crucial information in the first latent variables. Unifying several previous approaches, we show that IOBs

Florian Meier, Hayata Yamasaki

Energy consumption in solving computational problems has been gaining growing attention as one of the key performance measures for computers. Quantum computation is known to offer advantages over classical computation in terms of various computational resources; however, proving its energy-consumption advantage has been challenging due to the lack of a theor

Rohit Patil, Lucas Hackl, George R. Fagan, Marcos Rigol

We study the effect that the SU(2) symmetry, and the rich Hilbert space structure that it generates in lattice spin systems, has on the average entanglement entropy of highly excited eigenstates of local Hamiltonians and of random pure states. Focusing on the zero total magnetization sector ($J_z=0$) for different fixed total spin $J$, we argue that the aver

Masahiro Naritsuka, Izidor Benedičič, Luke C. Rhodes, Carolina A. Marques

Electronic nematicity has been found in a wide range of strongly correlated electron materials, resulting in the electronic states having a symmetry that is lower than that of the crystal that hosts them. One of the most astonishing examples is Sr$_3$Ru$_2$O$_7$, in which a small in-plane component of a magnetic field induces significant resistivity anisotro

Marco Meineri, Joao Penedones, Taro Spirig

We study correlation functions of the bulk stress tensor and boundary operators in Quantum Field Theories (QFT) in Anti-de Sitter (AdS) space. In particular, we derive new sum rules from the two-point function of the stress tensor and its three-point function with two boundary operators. In AdS2, this leads to a bootstrap setup that involves the central char

Masaya Amo, Keisuke Izumi, Hirotaka Yoshino, Yoshimune Tomikawa

In asymptotically flat spacetimes, bearing the null geodesics reaching the future null infinity in mind, we propose new concepts, the "dark horizons" as generalizations of the photon sphere. They are defined in terms of the structure of escape/capture cones of photons with respect to a unit timelike vector field. More specifically, considering a two-sphere t

Jakob Ehring, Sajad Abbar, Hans-Thomas Janka, Georg Raffelt

We present the first simulations of core-collapse supernovae in axial symmetry with feedback from fast neutrino flavor conversion (FFC). Our schematic treatment of FFCs assumes instantaneous flavor equilibration under the constraint of lepton-number conservation individually for each flavor. Systematically varying the spatial domain where FFCs are assumed to

Elena Caceres, Sanjit Shashi, Hao-Yu Sun

Under the AdS/CFT correspondence, asymptotically AdS geometries with backreaction can be viewed as CFT states subject to a renormalization group (RG) flow from an ultraviolet (UV) description towards an infrared (IR) sector. For black holes however, the IR point is the horizon, so one way to interpret the interior is as an analytic continuation to a "trans-I

Siyuan Huang, Zhengkai Jiang, Hao Dong, Yu Qiao

Foundation models have made significant strides in various applications, including text-to-image generation, panoptic segmentation, and natural language processing. This paper presents Instruct2Act, a framework that utilizes Large Language Models to map multi-modal instructions to sequential actions for robotic manipulation tasks. Specifically, Instruct2Act

Wenhai Wang, Zhe Chen, Xiaokang Chen, Jiannan Wu

Large language models (LLMs) have notably accelerated progress towards artificial general intelligence (AGI), with their impressive zero-shot capacity for user-tailored tasks, endowing them with immense potential across a range of applications. However, in the field of computer vision, despite the availability of numerous powerful vision foundation models (V

Bitan Roy

Due to the linearly vanishing density of states, emergent massless Dirac quasiparticles resulting from the free fermion motion in a family of two-dimensional half-filled bipartite hyperbolic lattices feature dynamic mass generation through quantum phase transitions only for sufficiently strong finite-range Coulomb repulsion. As such, strong nearest-neighbor

Peize Sun, Shoufa Chen, Chenchen Zhu, Fanyi Xiao

Object detection has been expanded from a limited number of categories to open vocabulary. Moving forward, a complete intelligent vision system requires understanding more fine-grained object descriptions, object parts. In this paper, we propose a detector with the ability to predict both open-vocabulary objects and their part segmentation. This ability come

Peng Wang, Shijie Wang, Junyang Lin, Shuai Bai

In this work, we explore a scalable way for building a general representation model toward unlimited modalities. We release ONE-PEACE, a highly extensible model with 4B parameters that can seamlessly align and integrate representations across vision, audio, and language modalities. The architecture of ONE-PEACE comprises modality adapters, shared self-attent

Zorik Gekhman, Jonathan Herzig, Roee Aharoni, Chen Elkind

Factual consistency evaluation is often conducted using Natural Language Inference (NLI) models, yet these models exhibit limited success in evaluating summaries. Previous work improved such models with synthetic training data. However, the data is typically based on perturbed human-written summaries, which often differ in their characteristics from real mod

Wangchunshu Zhou, Yuchen Eleanor Jiang, Ryan Cotterell, Mrinmaya Sachan

The primary way of building AI applications is shifting from training specialist models to prompting generalist models. A common practice for prompting generalist models, often referred to as in-context learning, is to append a few examples (demonstrations) to the prompt to help the model better understand the task. While effective, in-context learning can b

Charles Jin, Martin Rinard

We present evidence that language models (LMs) of code can learn to represent the formal semantics of programs, despite being trained only to perform next-token prediction. Specifically, we train a Transformer model on a synthetic corpus of programs written in a domain-specific language for navigating 2D grid world environments. Each program in the corpus is

Daichi Hiramatsu, Tatsuya Matsumoto, Edo Berger, Conor Ransome

We present optical photometry and spectroscopy of the Type IIn supernova (SN) 2021qqp. Its unusual light curve is marked by a long precursor for $\approx300$ days, a rapid increase in brightness for $\approx60$ days, and then a sharp increase of $\approx1.6$ mag in only a few days to a first peak of $M_r \approx -19.5$ mag. The light curve then declines rapi

Dongxu Zhao, Daniel Lichy, Pierre-Nicolas Perrin, Jan-Michael Frahm

We propose a fast and generalizable solution to Multi-view Photometric Stereo (MVPS), called MVPSNet. The key to our approach is a feature extraction network that effectively combines images from the same view captured under multiple lighting conditions to extract geometric features from shading cues for stereo matching. We demonstrate these features, termed

Alexandru D. Ionescu, Benoit Pausader, Xuecheng Wang, Klaus Widmayer

The goal of this article is twofold. First, we investigate the linearized Vlasov-Poisson system around a family of spatially homogeneous equilibria in $\mathbb{R}^3$ (the unconfined setting). Our analysis follows classical strategies from physics and their subsequent mathematical extensions. The main novelties are a unified treatment of a broad class of anal

Ingvar Ziemann, Stephen Tu, George J. Pappas, Nikolai Matni

We derive upper bounds for random design linear regression with dependent ($\beta$-mixing) data absent any realizability assumptions. In contrast to the strictly realizable martingale noise regime, no sharp instance-optimal non-asymptotics are available in the literature. Up to constant factors, our analysis correctly recovers the variance term predicted by

Joel Castaño, Silverio Martínez-Fernández, Xavier Franch, Justus Bogner

The rise of machine learning (ML) systems has exacerbated their carbon footprint due to increased capabilities and model sizes. However, there is scarce knowledge on how the carbon footprint of ML models is actually measured, reported, and evaluated. In light of this, the paper aims to analyze the measurement of the carbon footprint of 1,417 ML models and as

Andrew Herren, P. Richard Hahn

The finite sample variance of an inverse propensity weighted estimator is derived in the case of discrete control variables with finite support. The obtained expressions generally corroborate widely-cited asymptotic theory showing that estimated propensity scores are superior to true propensity scores in the context of inverse propensity weighting. However,

Maciej Besta, Robert Gerstenberger, Marc Fischer, Michał Podstawski

Graph databases (GDBs) are crucial in academic and industry applications. The key challenges in developing GDBs are achieving high performance, scalability, programmability, and portability. To tackle these challenges, we harness established practices from the HPC landscape to build a system that outperforms all past GDBs presented in the literature by order

Ruiyang Ren, Wayne Xin Zhao, Jing Liu, Hua Wu

Recently, model-based retrieval has emerged as a new paradigm in text retrieval that discards the index in the traditional retrieval model and instead memorizes the candidate corpora using model parameters. This design employs a sequence-to-sequence paradigm to generate document identifiers, which enables the complete capture of the relevance between queries

Andrew Holliday, Gregory Dudek

In this work we explore the combination of metaheuristics and learned neural network solvers for combinatorial optimization. We do this in the context of the transit network design problem, a uniquely challenging combinatorial optimization problem with real-world importance. We train a neural network policy to perform single-shot planning of individual trans

A. Sergyeyev

We give a complete description of nontrivial local conservation laws of all orders for a natural generalization of the nonlinear progressive wave equation and, in particular, show that there is an infinite number of such conservation laws.

Kai Zhang, Bernal Jiménez Gutiérrez, Yu Su

Recent work has shown that fine-tuning large language models (LLMs) on large-scale instruction-following datasets substantially improves their performance on a wide range of NLP tasks, especially in the zero-shot setting. However, even advanced instruction-tuned LLMs still fail to outperform small LMs on relation extraction (RE), a fundamental information ex

Anh Tuong Nguyen

Let $H$ be a Hopf algebra in a braided rigid monoidal category $\mathcal{V}$ admitting a coend $C$. We define a ``coend element'' of $H$ to be a morphism from $C$ to $H$. We then study certain coend elements of $H$, which generalize important elements (e.g., pivotal and ribbon elements) of a finite dimensional Hopf algebra over a field. This builds on prior

Lorenzo Carosini, Virginia Oddi, Francesco Giorgino, Lena M. Hansen

The interference of non-classical states of light enables quantum-enhanced applications reaching from metrology to computation. Most commonly, the polarisation or spatial location of single photons are used as addressable degrees-of-freedom for turning these applications into praxis. However, the scale-up for the processing of a large number of photons of su

Rustem Khasanov, Aline Ramires, Vadim Grinenko, Ilya Shipulin

We report on measurements of the in-plane magnetic penetration depth ($\lambda_{\rm ab}$) in single crystals of Sr$_2$RuO$_4$ down to $\simeq 0.015$ K by means of muon-spin rotation/relaxation. The linear temperature dependence of $\lambda^{-2}_{\rm ab}$ for $T\lesssim 0.7$ K suggests the presence of nodes in the superconducting gap. This statement is furthe

Márk Poór, Assaf Rinot

In a paper from 1980, Shelah constructed an uncountable group all of whose proper subgroups are countable. Assuming the continuum hypothesis, he constructed an uncountable group $G$ that moreover admits an integer $n$ satisfying that for every uncountable $X\subseteq G$, every element of $G$ may be written as a group word of length $n$ in the elements of $X$

Chunting Zhou, Pengfei Liu, Puxin Xu, Srini Iyer

Large language models are trained in two stages: (1) unsupervised pretraining from raw text, to learn general-purpose representations, and (2) large scale instruction tuning and reinforcement learning, to better align to end tasks and user preferences. We measure the relative importance of these two stages by training LIMA, a 65B parameter LLaMa language mod

Jean-Bernard Bru, Nathan Metraud

Differential equations on spaces of operators are very little developed in Mathematics, being in general very challenging. Here, we study a novel system of such (non-linear) differential equations. We show it has a unique solution for all times, for instance in the operator or Hilbert-Schmidt norm topologies. This system presents remarkable ellipticity prope

Eric W. Fischer, Peter Saalfrank

The emerging field of vibro-polaritonic chemistry studies the impact of light-matter hybrid states known as vibrational polaritons on chemical reactivity and molecular properties. Here, we discuss vibro-polaritonic chemistry from a quantum chemical perspective beyond the cavity Born-Oppenheimer (CBO) approximation and examine the role of electron-photon corr

Chenwei Ruan

This paper is about the positive part $U_q^+$ of the $q$-deformed enveloping algebra $U_q(\widehat{\mathfrak{sl}}_2)$. The literature contains at least three PBW bases for $U_q^+$, called the Damiani, the Beck, and the alternating PBW bases. These PBW bases are related via exponential formulas. In this paper, we introduce an exponential generating function w

Cong Han, Kevin Wilson, Scott Wisdom, John R. Hershey

A key challenge in machine learning is to generalize from training data to an application domain of interest. This work generalizes the recently-proposed mixture invariant training (MixIT) algorithm to perform unsupervised learning in the multi-channel setting. We use MixIT to train a model on far-field microphone array recordings of overlapping reverberant

Theodore D. Drivas, Daniel Ginsberg

We prove that stable fluid equilibria with trivial homology on curved, reflection-symmetric periodic channels must posses "islands", or cat's eye vortices. In this way, arbitrarily small disturbances of a flat boundary cause a change of streamline topology of stable steady states.

Siddhant Midha, Koustav Jana, Bhaskaran Muralidharan

Harnessing topological phases with their dissipationless edge-channels coupled with the effective engineering of quantum phase transitions is a spinal aspect of topological electronics. The accompanying symmetry protection leads to different kinds of topological edge-channels which include, for instance, the quantum spin Hall phase, and the spin quantum anom

Federico Butori, Eliseo Luongo

Using a weak convergence approach, we establish a Large Deviation Principle (LDP) for the solutions of fluid dynamic systems in two-dimensional bounded domains subjected to no-slip boundary conditions and perturbed by additive noise. Our analysis considers the convergence of both viscosity and noise intensity to zero. Specifically, we focus on three importan

Can Qin, Shu Zhang, Ning Yu, Yihao Feng

Achieving machine autonomy and human control often represent divergent objectives in the design of interactive AI systems. Visual generative foundation models such as Stable Diffusion show promise in navigating these goals, especially when prompted with arbitrary languages. However, they often fall short in generating images with spatial, structural, or geom

Jesse Berwald, Nick Chancellor, Raouf Dridi

It has previously been established that adiabatic quantum computation, operating based on a continuous Zeno effect due to dynamical phases between eigenstates, is able to realise an optimal Grover-like quantum speedup. In other words, is able to solve an unstructured search problem with the same $\sqrt{N}$ scaling as Grover's original algorithm. A natural qu

Gautam Bharali, Diganta Borah, Sushil Gorai

We present a new application of the squeezing function $s_D$, using which one may detect when a given bounded pseudoconvex domain $D\varsubsetneq \mathbb{C}^n$, $n\geq 2$, is not biholomorphic to any product domain. One of the ingredients used in establishing this result is also used to give an exact computation of the squeezing function (which is a constant

Paul Dütting, Evangelia Gergatsouli, Rojin Rezvan, Yifeng Teng

We study the prophet secretary problem, a well-studied variant of the classic prophet inequality, where values are drawn from independent known distributions but arrive in uniformly random order. Upon seeing a value at each step, the decision-maker has to either select it and stop or irrevocably discard it. Traditionally, the chosen benchmark is the expected

Kwang Sik Jeong, Wan-il Park

We propose a minimal gauged $U(1)_{B-L}$ extension of the minimal supersymmetric Standard Model (MSSM) which resolves the cosmological moduli problem via thermal inflation, and realizes late-time Affleck-Dine leptogensis so as to generate the right amount of baryon asymmetry at the end of thermal inflation. The present relic density of dark matter can be exp

Yuchen Eleanor Jiang, Tianyu Liu, Shuming Ma, Dongdong Zhang

Several recent papers claim human parity at sentence-level Machine Translation (MT), especially in high-resource languages. Thus, in response, the MT community has, in part, shifted its focus to document-level translation. Translating documents requires a deeper understanding of the structure and meaning of text, which is often captured by various kinds of d

David Ruhe, Johannes Brandstetter, Patrick Forré

We introduce Clifford Group Equivariant Neural Networks: a novel approach for constructing $\mathrm{O}(n)$- and $\mathrm{E}(n)$-equivariant models. We identify and study the $\textit{Clifford group}$, a subgroup inside the Clifford algebra tailored to achieve several favorable properties. Primarily, the group's action forms an orthogonal automorphism that ex

Chantal Amrhein, Florian Schottmann, Rico Sennrich, Samuel Läubli

Natural language generation models reproduce and often amplify the biases present in their training data. Previous research explored using sequence-to-sequence rewriting models to transform biased model outputs (or original texts) into more gender-fair language by creating pseudo training data through linguistic rules. However, this approach is not practical

Rankeya Datta, Neil Epstein, Kevin Tucker

We systematically study the intersection flatness and Ohm-Rush properties for modules over a commutative ring, drawing inspiration from the work of Ohm and Rush and of Hochster and Jeffries. We establish new structural results for modules that are intersection flat/Ohm-Rush by exhibiting intimate connections between these notions and the seminal work of Rayn

Satyabrata Mahapatra, Partha Kumar Paul, Narendra Sahu, Prashant Shukla

We propose a simple model in the type-III seesaw framework to explain the neutrino mass, asymmetric dark matter (ADM), and baryon asymmetry of the Universe. We extend the standard model with a vector-like singlet lepton ($\chi$) and a hypercharge zero scalar triplet ($\Delta$) in addition to three hypercharge zero triplet fermions($\Sigma_i~,i=1,2,3$). A $Z_

Joshua McGraw, Donsuk Lee, Justin Wood

What are the computational foundations of social grouping? Traditional approaches to this question have focused on verbal reasoning or simple (low-dimensional) quantitative models. In the real world, however, social preferences emerge when high-dimensional learning systems (brains and bodies) interact with high-dimensional sensory inputs during an animal's e

Alexander Medvedev, Anton V. Proskurnikov, Zhanybai T. Zhusubaliyev

The impulsive Goodwin's oscillator (IGO) is a hybrid model composed of a third-order continuous linear part and a pulse-modulated feedback. This paper introduces a design problem of the IGO to admit a desired periodic solution. The dynamics of the continuous states represent the plant to be controlled, whereas the parameters of the impulsive feedback constit

Haifeng Wen, Hong Xing, Osvaldo Simeone

One of the key challenges towards the deployment of over-the-air federated learning (AirFL) is the design of mechanisms that can comply with the power and bandwidth constraints of the shared channel, while causing minimum deterioration to the learning performance as compared to baseline noiseless implementations. For additive white Gaussian noise (AWGN) chan

Lehel Banjai, Matteo Ferrari

We present a novel generalized convolution quadrature method that accurately approximates convolution integrals. During the late 1980s, Lubich introduced convolution quadrature techniques, which have now emerged as a prevalent methodology in this field. However, these techniques were limited to constant time stepping, and only in the last decade generalized

A. Parvez

I considered a four-component magnetized plasma medium consisting of opposite polarity ions and super-thermal distributed positrons and electrons to investigate the stable/unstable frequency regimes of modulated ion-acoustic waves (IAWs) in the D-F regions of Earth's ionosphere and laboratory plasmas. A $(3+1)$-dimensional nonlinear Schr\"{o}dinger equation

Riccardo Giuseppe Margiotta, Sebastian Goldt, Guido Sanguinetti

Machine learning models are famously vulnerable to adversarial attacks: small ad-hoc perturbations of the data that can catastrophically alter the model predictions. While a large literature has studied the case of test-time attacks on pre-trained models, the important case of attacks in an online learning setting has received little attention so far. In thi

George Osipov, Magnus Wahlström

We study the parameterized complexity of MinCSP for so-called equality languages, i.e., for finite languages over an infinite domain such as $\mathbb{N}$, where the relations are defined via first-order formulas whose only predicate is $=$. This is an important class of languages that forms the starting point of all study of infinite-domain CSPs under the co

J. Manley, R. Stump, R. Petery, and S. Singh

We consider optical fibers as detectors for scalar ultralight dark matter (UDM) and propose using a fiber-based interferometer to search for scalar UDM with particle mass in the range $10^{-17} - 10^{-13}$ eV/$c^2$ $\left(10^{-3}- 10 \text{ Hz}\right)$. Composed of a solid core and a hollow core fiber, the proposed detector would be sensitive to relative osc

Junkai Zhou, Liang Pang, Huawei Shen, Xueqi Cheng

Language models trained on large-scale corpora can generate remarkably fluent results in open-domain dialogue. However, for the persona-based dialogue generation task, consistency and coherence are also key factors, which are great challenges for language models. Existing works mainly focus on valuable data filtering, model structure modifying, or objective

David Uthus, Santiago Ontañón, Joshua Ainslie, Mandy Guo

We present our work on developing a multilingual, efficient text-to-text transformer that is suitable for handling long inputs. This model, called mLongT5, builds upon the architecture of LongT5, while leveraging the multilingual datasets used for pretraining mT5 and the pretraining tasks of UL2. We evaluate this model on a variety of multilingual summarizat

Tsung-Han Lee, Corey Melnick, Ran Adler, Nicola Lanatà

We assess the accuracy of the ghost-rotationally-invariant slave-boson (g-RISB) theory in multiorbital systems by applying it to both the three-orbital degenerate Hubbard model and a realistic Sr2RuO4 model extracted from first principle simulations, and comparing the results to those obtained using the dynamical mean-field theory (DMFT). Our findings indica

Miaomiao Jin, Jilang Miao, Yongfeng Zhang, Marat Khafizov

Unfaulting of Frank loops in irradiated fluoride oxides are of significance to microstructural evolution. However, the mechanisms have not been directly observed. To this end, we utilize molecular dynamics to reveal the atomistic details related to the unfaulting process of interstitial Frank loop in ThO$_2$, which involve nucleation of single or multiple Sh

Mithil Sai Jakka

The exploration and study of exoplanets remain at the frontier of astronomical research, challenging scientists to continuously innovate and refine methodologies to navigate the vast, complex data these celestial bodies produce. This literature the review aims to illuminate the emerging trends and advancements within this sphere, specifically focusing on the

Ziyu Xu, Aaditya Ramdas

We develop a technique to improve the power of any e-value by a simple randomization involving one independent uniform random variable. Using this framework, we show that two procedures for false discovery rate (FDR) control -- the Benjamini-Yekutieli procedure for dependent p-values, and the e-Benjamini-Hochberg procedure for dependent e-values -- can be im

Daniel Alonso Villanueva Nunez, Yongmin Li

Cancerous skin lesions are one of the most common malignancies detected in humans, and if not detected at an early stage, they can lead to death. Therefore, it is crucial to have access to accurate results early on to optimize the chances of survival. Unfortunately, accurate results are typically obtained by highly trained dermatologists, who may not be acce

Amanda Younes, Wesley C. Campbell

Cooling of systems to sub-kelvin temperatures is usually done using either a cold bath of particles or spontaneous photon scattering from a laser field; in either case, cooling is driven by interaction with a well-ordered, cold (i.e. low entropy) system. However, there have recently been several schemes proposed for ``cooling by heating,'' in which raising t