May 2024 arXiv papers — page 103

Denis Kokosinskii, Nikolay Arefyev

Word Sense Induction (WSI) is the task of discovering senses of an ambiguous word by grouping usages of this word into clusters corresponding to these senses. Many approaches were proposed to solve WSI in English and a few other languages, but these approaches are not easily adaptable to new languages. We present multilingual substitution-based WSI methods t

A. R. Balasubramanian

Vector addition system with states (VASS) is a popular model for the verification of concurrent systems. VASS consists of finitely many control states and a set of counters which can be incremented and decremented, but not tested for zero. VASS is a relatively well-studied model of computation and many results regarding the decidability of decision problems

Sion An, Myeongkyun Kang, Soopil Kim, Philip Chikontwe

Electroencephalography (EEG) motor imagery (MI) classification is a fundamental, yet challenging task due to the variation of signals between individuals i.e., inter-subject variability. Previous approaches try to mitigate this using task-specific (TS) EEG signals from the target subject in training. However, recording TS EEG signals requires time and limits

Adrien Basdevant, Camille François, Victor Storchan, Kevin Bankston

Over the past year, there has been a robust debate about the benefits and risks of open sourcing foundation models. However, this discussion has often taken place at a high level of generality or with a narrow focus on specific technical attributes. In part, this is because defining open source for foundation models has proven tricky, given its significant d

William D. Banks

Let $y\ne 0$ and $C>0$. Under the Riemann Hypothesis, there is a number $T_*>0$ $($depending on $y$ and $C)$ such that for every $T\ge T_*$, both \[ \zeta(\tfrac12+i\gamma)=0 \quad\text{and}\quad\zeta(\tfrac12+i(\gamma+y))\ne 0 \] hold for at least one $\gamma$ in the interval $[T,T(1+\epsilon)]$, where $\epsilon:=T^{-C/\log\log T}$.

Michael Feffer, Ronald Xu, Yuekai Sun, Mikhail Yurochkin

In the advent of democratized usage of large language models (LLMs), there is a growing desire to systematize LLM prompt creation and selection processes beyond iterative trial-and-error. Prior works majorly focus on searching the space of prompts without accounting for relations between prompt variations. Here we propose a framework, Prompt Exploration with

A. O. Nelson, L. Schmitz, T. Cote, J. F. Parisi

Tokamak plasmas with strong negative triangularity (NT) shaping typically exhibit fundamentally different edge behavior than conventional L-mode or H-mode plasmas. Over the entire DIII-D database, plasmas with sufficiently negative triangularity are found to be inherently free of edge localized modes (ELMs), even at injected powers well above the predicted L

Dalton Durant, Andrey A. Popov, Renato Zanetti

This work focuses on the critical aspect of accurate weight computation during the measurement incorporation phase of Gaussian mixture filters. The proposed novel approach computes weights by linearizing the measurement model about each component's posterior estimate rather than the the prior, as traditionally done. This work proves equivalence with traditio

Tristram Bogart, Seyed Amin Seyed Fakhari

We present two families of numerical semigroups and show that for each family, the number of required components in an irreducible decomposition cannot be bounded by any given integer. This gives a negative answer to a question raised by Delgado, Garc\'ia-S\'anchez and Rosales.

Yaya Etiabi, Wafa Njima, El Mehdi Amhoud

The rapid growth of the Internet of Things fosters collaboration among connected devices for tasks like indoor localization. However, existing indoor localization solutions struggle with dynamic and harsh conditions, requiring extensive data collection and environment-specific calibration. These factors impede cooperation, scalability, and the utilization of

Vimal Manohar, Szu-Jui Chen, Zhiqi Wang, Yusuke Fujita

This paper summarizes our acoustic modeling efforts in the Johns Hopkins University speech recognition system for the CHiME-5 challenge to recognize highly-overlapped dinner party speech recorded by multiple microphone arrays. We explore data augmentation approaches, neural network architectures, front-end speech dereverberation, beamforming and robust i-vec

Maria Lopes, Armando Bernui, Camila Franco, Felipe Avila

The {\em bulk flow} in the Local Universe is a collective phenomenon due to the peculiar motions of matter structures, which, instead of moving in random directions, appears to follow an approximate dipole velocity flow. We apply a directional analysis to investigate, through the Hubble-Lema\^{\i}tre diagram, the angular dependence of the Hubble constant $H_

Jeremy Hansen, Kazuki Ikeda, Dmitri E. Kharzeev, Qiang Li

We propose to use the electromagnetic radiation induced by a few MeV electron beam in magnetic Weyl semimetals as a source of the circularly polarized photons in the THz frequency range.

Mitja Mastnak, Heydar Radjavi

We consider the following question: Let $\mathcal{A}$ be an abelian self-adjoint algebra of bounded operators on a Hilbert space $\mathcal{H}$. Assume that $\mathcal{A}$ is invariant under conjugation by a unitary operator $U$, i.e., $U^* AU$ is in $\mathcal{A}$ for every member $A$ of $\mathcal{A}$. Is there a maximal abelian self-adjoint algebra containing

Larry McLerran, Gerald A. Miller

The phase space density, $\rho^Q$, of quarks in nuclei is studied using realistic models of unintegrated quark distributions, known as transverse momentum densities (TMDs). If this density exceeds unity for matter at normal nuclear densities, the effects of the quark Pauli principle must play a role in nuclei, and models in which the nucleon density at low m

Mohamed Akrout, Faouzi Bellili, Amine Mezghani, Robert W. Heath

The ongoing fifth-generation (5G) standardization is exploring the use of deep learning (DL) methods to enhance the new radio (NR) interface. Both in academia and industry, researchers are investigating the performance and complexity of multiple DL architecture candidates for specific one-sided and two-sided use cases such as channel state estimation (CSI) f

Van Chien Le, Kristof Cools

This paper provides a rigorous analysis of boundary element methods for the magnetic field integral equation on Lipschitz polyhedra. The magnetic field integral equation is widely used in practical applications to model electromagnetic scattering by a perfectly conducting body. The governing operator is shown to be coercive by means of the electric field int

Karan Taneja, Pratyusha Maiti, Sandeep Kakar, Pranav Guruprasad

Conversational AI agents often require extensive datasets for training that are not publicly released, are limited to social chit-chat or handling a specific domain, and may not be easily extended to accommodate the latest advances in AI technologies. This paper introduces Jill Watson, a conversational Virtual Teaching Assistant (VTA) leveraging the capabili

Shantanu Basu, Xiyuan Li, Gianfranco Bino

An hourglass-shaped magnetic field pattern arises naturally from the gravitational collapse of a star-forming gas cloud. Most studies have focused on the prestellar collapse phase, when the structure has a smooth and monotonic radial profile. However, most observations target dense clouds that already contain a central protostar, and possibly a circumstellar

Mohit Tawarmalani

This paper introduces novel relaxation hierarchies for concavo-convex programs (CXP), a class of problems that includes disjoint bilinear programming (DBP) and concave minimization (CM) as special cases. At the core of these hierarchies is an algorithm based on double-description (DD) that computes the barycentric coordinates of a polyhedral cone as rational

Nisha L. Raichur, Lucas Heublein, Tobias Feigl, Alexander Rügamer

The primary objective of methods in continual learning is to learn tasks in a sequential manner over time (sometimes from a stream of data), while mitigating the detrimental phenomenon of catastrophic forgetting. This paper proposes a method to learn an effective representation between previous and newly encountered class prototypes. We propose a prototypica

Yanxiang Chen, Pablo de Oliveira Castro, Paolo Bientinesi, Roman Iakymchuk

Mixed-precision computing has the potential to significantly reduce the cost of exascale computations, but determining when and how to implement it in programs can be challenging. In this article, we consider Nekbone, a mini-application for the CFD solver Nek5000, as a case study, and propose a methodology for enabling mixed-precision with the help of comput

Zihao Zou, Shirin Shoushtari, Jiaming Liu, Jialiang Zhang

Nuclear Magnetic Resonance (NMR) spectroscopy is a widely-used technique in the fields of bio-medicine, chemistry, and biology for the analysis of chemicals and proteins. The signals from NMR spectroscopy often have low signal-to-noise ratio (SNR) due to acquisition noise, which poses significant challenges for subsequent analysis. Recent work has explored t

D. A. Velasco-Romero, R. Teyssier

Stellar convection poses two main gargantuan challenges for astrophysical fluid solvers: low-Mach number flows and minuscule perturbations over steeply stratified hydrostatic equilibria. Most methods exhibit excessive numerical diffusion and are unable to capture the correct solution due to large truncation errors. In this paper, we analyze the performance o

Evgeny Kozinov, Evgeny Vasiliev, Andrey Gorshkov, Valentina Kustikova

The emergence and rapid development of the open RISC-V instruction set architecture opens up new horizons on the way to efficient devices, ranging from existing low-power IoT boards to future high-performance servers. The effective use of RISC-V CPUs requires software optimization for the target platform. In this paper, we focus on the RISC-V-specific optimi

William Grimble, Joel Kastner, Christophe Pinte, Beth Sargent

Our understanding of how exoplanets form and evolve relies on analyses of both the mineralogy of protoplanetary disks and their detailed structures; however, these key complementary aspects of disks are usually studied separately. We present initial results from a hybrid model that combines the empirical characterization of the mineralogy of a disk, as deter

Antonio C. Gutiérrez-Piñeres, Hernando Quevedo

The $C^3$ approach is an invariant formalism that utilizes the eigenvalues of the Riemann curvature tensor to match spacetimes across a specific matching surface. We apply this approach to match an anisotropic fluid with an exterior vacuum solution, including the case in which discontinuities appear on the matching surface. As a particular example, a class o

Erdem Kuş, Özgür Akgün, Nguyen Dang, Ian Miguel

When solving decision and optimisation problems, many competing algorithms (model and solver choices) have complementary strengths. Typically, there is no single algorithm that works well for all instances of a problem. Automated algorithm selection has been shown to work very well for choosing a suitable algorithm for a given instance. However, the cost of

Anthony Badea

The exceptionally accurate Standard Model (SM) theory of fundamental interactions is known to be incomplete. Many new theories extend the SM, trying to solve some of the most compelling puzzles of nature. Since the start of LHC experiments, a wide range of the accessible phase space has been explored, setting robust limits on new physics. Yet, many alternati

Vedant Puri, Aviral Prakash, Levent Burak Kara, Yongjie Jessica Zhang

Reduced order modeling lowers the computational cost of solving PDEs by learning a low-order spatial representation from data and dynamically evolving these representations using manifold projections of the governing equations. While commonly used, linear subspace reduced-order models (ROMs) are often suboptimal for problems with a slow decay of Kolmogorov $

Hans J. Haubold, Arak M. Mathai

The paper utilizes data from the SuperKamiokande solar neutrino detection experiment and analyzes them by diffusion entropy analysis and standard deviation analysis to evaluate the scaling exponent of the probability density function. The result indicates that solar neutrinos are subject to Levy flights. Subsequently, the paper derives the probability densit

Prerana Achar, Mayank Patel, Anushka Mulik, Neha Katre

We present a comparison review that evaluates popular techniques for garment draping for 3D fashion design, virtual try-ons, and animations. A comparative study is performed between various methods for garment draping of clothing over the human body. These include numerous models, such as physics and machine learning based techniques, collision handling, and

Virgile Rennard, Guokan Shang, Michalis Vazirgiannis, Julie Hunter

We introduce an extractive summarization system for meetings that leverages discourse structure to better identify salient information from complex multi-party discussions. Using discourse graphs to represent semantic relations between the contents of utterances in a meeting, we train a GNN-based node classification model to select the most important utteran

L. D. Anderson, Matteo Luisi, B. Liu, Dylan J. Linville

The Galactic center lobe (GCL) is an object ~1{\deg} across that is located north of the Galactic center. In the mid-infrared (MIR) the GCL appears as two 8.0${\mu}$m filaments between which is strong 24${\mu}$m and radio continuum emission. Due to its morphology and location in the sky, previous authors have argued that the GCL is located in the Galactic ce

Guy Aridor, Duarte Goncalves, Ruoyan Kong, Daniel Kluver

An increasingly important aspect of designing recommender systems involves considering how recommendations will influence consumer choices. This paper addresses this issue by introducing a method for collecting user beliefs about un-experienced items - a critical predictor of choice behavior. We implemented this method on the MovieLens platform, resulting in

Bernard Parent, Felipe Martin Rodriguez Fuentes

A novel formulation of the electron energy relaxation terms is presented here, which is applicable to plasma flows and discharges wherein the electron temperature could be higher or lower than the gas temperature. It is demonstrated that the electron energy losses due to inelastic collisions can be expressed as a function of only two species-dependent parame

Alexey Kuznetsov, Minjian Yuan

Darboux transformation of a second-order linear differential operator is a well-known technique with many applications in mathematics and physics. We study Darboux transformation from the point of view of Markov semigroups of diffusion processes. We construct the Darboux transform of a diffusion process through a combination of Doob's $h$-transform and a ver

Arak M. Mathai, Hans J. Haubold

The pathway model for the real scalar variable case is re-explored and its connections to fractional integrals, solutions of fractional differential equations, Tsallis statistics and superstatistics in statistical mechanics, reaction-rate probability integral, Kraetzel transform, and pathway transform are explored. It is shown that the common thread in these

Tristan C. Collins, Adam Jacob, Yu-Shen Lin

We establish a convergence result for the mean curvature flow starting from a totally real submanifold which is "almost minimal" in a precise, quantitative sense. This extends, and makes effective, a result of H. Li for the Lagrangian mean curvature flow.

Paula Akemi Aoyagui, Sharon Ferguson, Anastasia Kuzminykh

An essential aspect of evaluating Large Language Models (LLMs) is identifying potential biases. This is especially relevant considering the substantial evidence that LLMs can replicate human social biases in their text outputs and further influence stakeholders, potentially amplifying harm to already marginalized individuals and communities. Therefore, recen

Jun Ueda, Jacob Blevins

This paper demonstrates the viability of perfectly undetectable affine transformation attacks against robotic manipulators where intelligent attackers can inject multiplicative and additive false data while remaining completely hidden from system users. The attacker can implement these communication line attacks by satisfying three Conditions presented in th

Maggie Bailey, Doug Nychka, Manajit Sengupta, Jaemo Yang

Global and regional climate model projections are useful for gauging future patterns of climate variables, including solar radiation, but data from these models is often too coarse to assess local impacts. Within the context of solar radiation, the changing climate may have an effect on photovoltaic (PV) production, especially as the PV industry moves to ext

Hongkun Zhang, Zherong Pan, Congyi Zhang, Lifeng Zhu

The recent success of pre-trained diffusion models unlocks the possibility of the automatic generation of textures for arbitrary 3D meshes in the wild. However, these models are trained in the screen space, while converting them to a multi-view consistent texture image poses a major obstacle to the output quality. In this paper, we propose a novel method to

Amelia Gibbs, Brian K. Miceli

The main goal of this paper is to assign two combinatorial interpretations to the elements of the Rascal Triangle defined by Angorro et al. The first interpretation involves counting ascents in binary words, while the second interpretation involves pattern avoidance in the ascent sequences studied by Duncan and Steingr\'imsson, and related directly to a more

Alexander Chen, Meng Zhang, Daniel Crowley, Nicholas Gangi

In this research, we developed a low-power silicon photonics foundry-fabricated slow-light thermal phase shifter (SLTPS) where the slow-light (SL) effect is achieved using an integrated Bragg grating (BG) waveguide. Heating the grating induces a red shift in the transmission spectrum, leading to an increased group index $n_g$ during operation, which facilita

S. Nagy, J. Polonyi

The renormalization group method, more specifically the Wegner-Houghton equation, is used to find first order phase transitions in a simple scalar field theory with a polynomial potential. An improved definition of the running parameters allows us to explore the renormalization group flow down to the IR end point and to locate phase transitions. Beyond the e

C. N. Pope, D. O. Rohrer, B. F. Whiting

The study of perturbations around black hole backgrounds in general relativity and Einstein-Maxwell theory has a long history, going back to the work of Regge and Wheeler in the 1950s. As part of a broader investigation of perturbations around black holes in supergravity, we describe here our results for the perturbations around the Gibbons-Maeda static char

A. Telveenus

The cryptosystem RSA is a very popular cryptosystem in the study of Cryptography. In this article, we explore how the idea of a primitive mth root of unity in a ring can be integrated into the Discrete Fourier Transform, leading to the development of new cryptosystems known as RSA-DFT and RSA-HGR.

Jace Grandinetti, Rafe McBeth

Large Language Models (LLMs) have achieved remarkable progress, yet their application in specialized fields, such as medical physics, remains challenging due to the need for domain-specific knowledge. This study introduces ARCoT (Adaptable Retrieval-based Chain of Thought), a framework designed to enhance the domain-specific accuracy of LLMs without requirin

Ilya Ilyankou, Meihui Wang, Stefano Cavazzi, James Haworth

The Common Crawl (CC) corpus is the largest open web crawl dataset containing 9.5+ petabytes of data captured since 2008. The dataset is instrumental in training large language models, and as such it has been studied for (un)desirable content, and distilled for smaller, domain-specific datasets. However, to our knowledge, no research has been dedicated to us

Andrea Cappelletti, Andrea Montoli

We show that non-pointed versions of the classical homological lemmas hold in regular protomodular categories equipped with a suitable posetal monocoreflective subcategory. Examples of such categories are all protomodular varieties of universal algebras having more than one constant, like the ones of unitary rings, Boolean algebras, Heyting algebras and MV-a

Hansung B. Gim, Amy E. Reines, Emmanuel Momjian, Jeremy Darling

Henize 2-10 is a dwarf galaxy experiencing positive black hole (BH) feedback from a radio-detected low-luminosity active galactic nucleus. Previous Green Bank Telescope (GBT) observations detected a H2O "kilomaser" in Henize 2-10, but the low angular resolution (33") left the location and origin of the maser ambiguous. We present new Karl G. Jansky Very Larg

U. Özdem

A systematic study of the electromagnetic properties of exotic states is conducted to elucidate their nature, which continues to be the subject of controversy and incomplete understanding in the field. In this study, the magnetic dipole and quadrupole moments of the tetraquarks $[sc] [\bar q \bar b]$ and $[sc] [\bar s \bar b]$ with spin-parity quantum number

Wenkai Li, Xiaoqi Li, Yuqing Zhang, Zongwei Li

Decentralized finance (DeFi) protocols are crypto projects developed on the blockchain to manage digital assets. Attacks on DeFi have been frequent and have resulted in losses exceeding \$77 billion. However, detection methods for malicious DeFi events are still lacking. In this paper, we propose DeFiTail, the first framework that utilizes deep learning to d

Song Wang, Yushun Dong, Binchi Zhang, Zihan Chen

Graph Machine Learning (Graph ML) has witnessed substantial advancements in recent years. With their remarkable ability to process graph-structured data, Graph ML techniques have been extensively utilized across diverse applications, including critical domains like finance, healthcare, and transportation. Despite their societal benefits, recent research high

Ritik Soni, Ashok Kumar Pathak

In this paper, we define a compound generalized fractional counting process (CGFCP) which is a generalization of the compound versions of several well-known fractional counting processes. We obtain its mean, variance, and the fractional differential equation governing the probability law. Motivated by Kumar et al. (2020), we introduce a fractional risk proce

Seyed Mohsen Moosavi Khansari, Fazlollah Kazemi Hasanvand

In this article, we investigate the entanglement evolution of three-qubit states in the presence of a spin chain environment. Utilizing negativity as a metric for entanglement assessment, we focus on the GHZ, W, and W_zeta quantum states as the initial system states. We explore and analyze the entanglement dynamics of these states based on various parameters

Xinyu Wang, Sai Koneru, Pranav Narayanan Venkit, Brett Frischmann

As social media has become a predominant mode of communication globally, the rise of abusive content threatens to undermine civil discourse. Recognizing the critical nature of this issue, a significant body of research has been dedicated to developing language models that can detect various types of online abuse, e.g., hate speech, cyberbullying. However, th

Sandeep Singh Sengar, Affan Bin Hasan, Sanjay Kumar, Fiona Carroll

In recent years, the study of artificial intelligence (AI) has undergone a paradigm shift. This has been propelled by the groundbreaking capabilities of generative models both in supervised and unsupervised learning scenarios. Generative AI has shown state-of-the-art performance in solving perplexing real-world conundrums in fields such as image translation,

Angel Hernandez, Roseanne M. Cheng, Nicole M. Lloyd-Ronning, Carl E. Fields

Although the association of gamma-ray bursts with massive stellar death is on firm footing, the nature of the progenitor system and the key ingredients required for a massive star to produce a gamma-ray burst remain open questions. Here, we investigate the evolution of a $15-25M_\odot$ massive star with a $10-15 M_\odot$ black hole using the MESA stellar evo

David K. Sing, Zafar Rustamkulov, Daniel P. Thorngren, Joanna K. Barstow

Observations of transiting gas giant exoplanets have revealed a pervasive depletion of methane, which has only recently been identified atmospherically. The depletion is thought to be maintained by disequilibrium processes such as photochemistry or mixing from a hotter interior. However, the interiors are largely unconstrained along with the vertical mixing

Anavi Uppal, Charlotte Ward, Suvi Gezari, Priyamvada Natarajan

Supermassive black holes (SMBHs) can be ejected from their galactic centers due to gravitational wave recoil or the slingshot mechanism following a galaxy merger. If an ejected SMBH retains its inner accretion disk, it may be visible as an off-nuclear active galactic nucleus (AGN). At present, only a handful of offset AGNs that are recoil or slingshot candid

E. Spitoni, F. Matteucci, R. Gratton, B. Ratcliffe

The analysis of several spectroscopic surveys indicates the presence of a bimodality between the disc stars in the abundance ratio space of [${\alpha}$/Fe] versus [Fe/H]. The two stellar groups are commonly referred to as the high-${\alpha}$ and low-${\alpha}$ sequences. Some models capable of reproducing such a bimodality, invoke the presence of a hiatus in

Zhanguang Zhang, Didier Chetelat, Joseph Cotnareanu, Amur Ghose

Boolean satisfiability (SAT) problems are routinely solved by SAT solvers in real-life applications, yet solving time can vary drastically between solvers for the same instance. This has motivated research into machine learning models that can predict, for a given SAT instance, which solver to select among several options. Existing SAT solver selection metho

Jay Armas, Akash Jain, Ruben Lier

Active matter concerns many-body systems comprised of living or self-driven agents that collectively exhibit macroscopic phenomena distinct from conventional passive matter. Using Schwinger-Keldysh effective field theory, we develop a novel hydrodynamic framework for thermal active matter that accounts for energy balance, local temperature variations, and th

G. Mountrichas, A. Ruiz, I. Georgantopoulos, E. Pouliasis

In this study, we utilize a sample of 338 galaxies within the redshift range of $\rm 0.02<z<0.1$, drawn from the Sloan Digital Sky Survey (SDSS), for which there are available classifications, based on their emission line ratios. We, further, identify and select Compton-thick (CT) AGN through the use of X-ray and infrared luminosities at $12\,\mu m$. We cons

Kyle Gao, Dening Lu, Hongjie He, Linlin Xu

3D urban scene reconstruction and modelling is a crucial research area in remote sensing with numerous applications in academia, commerce, industry, and administration. Recent advancements in view synthesis models have facilitated photorealistic 3D reconstruction solely from 2D images. Leveraging Google Earth imagery, we construct a 3D Gaussian Splatting mod

Misha Yutushui, Jinhong Park, Alexander D. Mirlin

The fractional quantum Hall (FQH) effect gives rise to abundant topological phases, presenting an ultimate platform for studying the transport of edge states. Generic FQH edge contains multiple edge modes, commonly including the counter-propagating ones. A question of the influence of Anderson localization on transport through such edges arises. Recent exper

G. Munoz-Sanchez, S. de Wit, A. Z. Bonanos, K. Antoniadis

This study delves into [W60] B90, one of the most luminous and extreme Red Supergiants (RSGs) in the Large Magellanic Cloud (LMC), aiming to search for evidence of episodic mass loss. Our discovery of a bar-like nebular structure at 1 pc, reminiscent of the bar around Betelgeuse, raised the question of whether [W60] B90 also has a bow shock. We collected and

Luis Welbanks, Taylor J. Bell, Thomas G. Beatty, Michael R. Line

Interactions between exoplanetary atmospheres and internal properties have long been hypothesized to be drivers of the inflation mechanisms of gaseous planets and apparent atmospheric chemical disequilibrium conditions. However, transmission spectra of exoplanets has been limited in its ability to observational confirm these theories due to the limited wavel

Yayaati Chachan, Paul A. Dalba, Daniel P. Thorngren, Stephen R. Kane

Systems hosting multiple giant planets are important laboratories for understanding planetary formation and migration processes. We present a nearly decade-long Doppler spectroscopy campaign from the HIRES instrument on the Keck-I telescope to characterize the two transiting giant planets orbiting Kepler-511 on orbits of 27 days and 297 days. The radial velo

Jerome Quintin, Xingang Chen, Reza Ebadi

We construct explicit models of classical primordial standard clocks in an alternative to inflation, namely the slowly contracting ekpyrotic scenario. We study the phenomenology of massive spectator fields added to a state-of-the-art ekpyrotic model, with coupling functions that allow for these heavy fields to be classically excited while the background is s

Stepan Fomichev, Kasra Hejazi, Ignacio Loaiza, Modjtaba Shokrian Zini

X-ray absorption spectroscopy is a crucial experimental technique for elucidating the mechanisms of structural degradation in battery materials. However, extracting information from the measured spectrum is challenging without high-quality simulations. In this work, we propose simulating near-edge X-ray absorption spectra as a promising application for quant

James M. M. Lane, Jo Bovy

We study accretion remnants around Milky Way analogs in the IllustrisTNG simulations to determine how well commonly used distribution functions (DFs) describe their phase-space distributions. We identify 30 Milky Way analogs and 116 remnants from mergers with stellar mass ratios greater than 1:20. Two-power density profiles, as well as rotating constant-anis

Bruno S. Soriano, Ki Sung Jung, Tarek Echekki, Jacqueline H. Chen

Reduced order models based on the transport of a lower dimensional manifold representation of the thermochemical state, such as Principal Component (PC) transport and Machine Learning (ML) techniques, have been developed to reduce the computational cost associated with the Direct Numerical Simulations (DNS) of reactive flows. Both PC transport and ML normall

Xiaodong Shi, Sakthi Sanjeev Mohanraj, Veerendra Dhyani, Angela Anna Baiju

Integrated photon-pair sources are crucial for scalable photonic quantum systems. Thin-film lithium niobate is a promising platform for on-chip photon-pair generation through spontaneous parametric down-conversion (SPDC). However, the device implementation faces practical challenges. Periodically poled lithium niobate (PPLN), despite enabling flexible quasi-

Shao-Hua Hu, George Biswas, Jun-Yi Wu

We employ quantum-volume random-circuit sampling to benchmark the two-QPU entanglement-assisted distributed quantum computing (DQC) and compare it with single-QPU quantum computing. We first specify a single-qubit depolarizing noise model in the random circuit. Based on this error model, we show the one-to-one correspondence of three figures of merits, namel

Tianyi Hao, Zichang He, Ruslan Shaydulin, Marco Pistoia

Variational quantum algorithms (VQAs) are a broad class of algorithms with many applications in science and industry. Applying a VQA to a problem involves optimizing a parameterized quantum circuit by maximizing or minimizing a cost function. A particular challenge associated with VQAs is understanding the properties of associated cost functions. Having the

Alessandra Venditti, Volker Bromm, Steven L. Finkelstein, Antonello Calabrò

The gas surrounding first-generation (Pop III) stars is expected to emit a distinct signature in the form of the HeII recombination line at 1640 \r{A} (HeII$\lambda$1640). Here we explore the challenges and opportunities in identifying this elusive stellar population via the HeII$\lambda$1640 in $M_\star > 10^{7.5} ~ \mathrm{M_\odot}$ galaxies during the Epo

Hariprasath Govindarajan, Per Sidén, Jacob Roll, Fredrik Lindsten

Self-distillation methods using Siamese networks are popular for self-supervised pre-training. DINO is one such method based on a cross-entropy loss between $K$-dimensional probability vectors, obtained by applying a softmax function to the dot product between representations and learnt prototypes. Given the fact that the learned representations are $L^2$-no

Yangjun Ruan, Chris J. Maddison, Tatsunori Hashimoto

Understanding how language model performance varies with scale is critical to benchmark and algorithm development. Scaling laws are one approach to building this understanding, but the requirement of training models across many different scales has limited their use. We propose an alternative, observational approach that bypasses model training and instead b

Kai Schmitz, Tobias Schröder

Cosmic strings are a common prediction in many grand unified theories and a promising source of stochastic gravitational waves (GWs) from the early Universe. In this paper, we point out that the GW signal from cosmic strings produced at a comparatively low energy scale, $v \lesssim 10^9 \textrm{GeV}$, exhibits several novel features that are not present in t

Kaiyu Huang, Fengran Mo, Xinyu Zhang, Hongliang Li

The rapid development of Large Language Models (LLMs) demonstrates remarkable multilingual capabilities in natural language processing, attracting global attention in both academia and industry. To mitigate potential discrimination and enhance the overall usability and accessibility for diverse language user groups, it is important for the development of lan

Keisuke Konosu, Yuji Okawa

The formula for correlation functions based on quantum $A_\infty$ algebras in arXiv:2203.05366, arXiv:2305.11634, and arXiv:2305.13103 requires us to divide the action into the free part and the interaction part. We present a new form of the formula which does not involve such division. The new formula requires us to choose a solution to the equations of mot

Ren Li, Corentin Dumery, Zhantao Deng, Pascal Fua

Modeling the shape of garments has received much attention, but most existing approaches assume the garments to be worn by someone, which constrains the range of shapes they can assume. In this work, we address shape recovery when garments are being manipulated instead of worn, which gives rise to an even larger range of possible shapes. To this end, we leve

Srinivasan Arunachalam, Arkopal Dutt, Francisco Escudero Gutiérrez, Carlos Palazuelos

We consider the problem of learning low-degree quantum objects up to $\varepsilon$-error in $\ell_2$-distance. We show the following results: $(i)$ unknown $n$-qubit degree-$d$ (in the Pauli basis) quantum channels and unitaries can be learned using $O(1/\varepsilon^d)$ queries (independent of $n$), $(ii)$ polynomials $p:\{-1,1\}^n\rightarrow [-1,1]$ arising

Edgar A. Bering, Bennett Haffner, Estephanie Ortiz, Olivia Sanchez

The sphere graph of $M_r$, a connect sum of $r$ copies of $S^1\times S^2$ was introduced by Hatcher as an analog of the curve graph of a surface to study the outer automorphism group of a free group $F_r$. Bestvina, Bromberg, and Fujiwara proved that the chromatic number of the curve graph is finite; bounds were subsequently improved by Gaster, Greene, and V

Abdelhadi Soudi, Violetta Cavalli-Sforza, Abderrahim Jamari

In this paper, we show that the multiple-stem approach to nouns with a broken plural pattern allows for greater generalizations to be stated in the morphological system. Such an approach dispenses with truncating/deleting rules and other complex rules that are required to account for the highly allomorphic broken plural system. The generation of inflected so

Alexander Dietmüller, Albert Gran Alcoz, Laurent Vanbever

Learning precise distributions of traffic features (e.g., burst sizes, packet inter-arrival time) is still a largely unsolved problem despite being critical for management tasks such as capacity planning or anomaly detection. A key limitation nowadays is the lack of feedback between the control plane and the data plane. Programmable data planes offer the opp

Jayanth Bhargav, Mahsa Ghasemi, Shreyas Sundaram

We consider the problem of selecting an optimal subset of information sources for a hypothesis testing/classification task where the goal is to identify the true state of the world from a finite set of hypotheses, based on finite observation samples from the sources. In order to characterize the learning performance, we propose a misclassification penalty fr

R. Chapman, P. Ashwin, J. Baker, R. A. Wood

The Atlantic Meridional Overturning Circulation (AMOC) exerts a major influence on global climate. There is much debate about whether the current strong AMOC may collapse as a result of anthropogenic forcing and/or internal variability. Increasing the noise in simple salt-advection models can change the apparent AMOC tipping threshold. However, it's not clea

Lucius Bushnaq, Stefan Heimersheim, Nicholas Goldowsky-Dill, Dan Braun

Mechanistic interpretability aims to understand the behavior of neural networks by reverse-engineering their internal computations. However, current methods struggle to find clear interpretations of neural network activations because a decomposition of activations into computational features is missing. Individual neurons or model components do not cleanly c

Lucius Bushnaq, Jake Mendel, Stefan Heimersheim, Dan Braun

Mechanistic Interpretability aims to reverse engineer the algorithms implemented by neural networks by studying their weights and activations. An obstacle to reverse engineering neural networks is that many of the parameters inside a network are not involved in the computation being implemented by the network. These degenerate parameters may obfuscate intern

Rylan Gajek-Leonard, Uri Tomer

The $p$-adic Newton polygon is a visual tool that encodes information about the roots and factorization of a polynomial relative to a prime $p$. In this article, we investigate how the Newton polygon changes under polynomial composition. If $f$ and $g$ are polynomials with rational (or $p$-adic) coefficients and the Newton polygon of $g$ is pure (has only on

Janick Weberpals, Pamela A. Shaw, Kueiyu Joshua Lin, Richard Wyss

Multiple imputation (MI) models can be improved by including auxiliary covariates (AC), but their performance in high-dimensional data is not well understood. We aimed to develop and compare high-dimensional MI (HDMI) approaches using structured and natural language processing (NLP)-derived AC in studies with partially observed confounders. We conducted a pl

Yuval Shapira, Naor Wiesel, Shahar Shabelman, Dana Drachsler-Cohen

Proving local robustness is crucial to increase the reliability of neural networks. While many verifiers prove robustness in $L_\infty$ $\epsilon$-balls, very little work deals with robustness verification in $L_0$ $\epsilon$-balls, capturing robustness to few pixel attacks. This verification introduces a combinatorial challenge, because the space of pixels

Laura Grigori, Edouard Timsit

This paper introduces a randomized Householder QR factorization (RHQR). This factorization can be used to obtain a well conditioned basis of a vector space and thus can be employed in a variety of applications. The RHQR factorization of the input matrix $W$ is equivalent to the standard Householder QR factorization of matrix $\Psi W$, where $\Psi$ is a sketc

Alexander Vidal, Samy Wu Fung, Stanley Osher, Luis Tenorio

Mean-field control (MFC) problems aim to find the optimal policy to control massive populations of interacting agents. These problems are crucial in areas such as economics, physics, and biology. We consider the non-local setting, where the interactions between agents are governed by a suitable kernel. For $N$ agents, the interaction cost has $\mathcal{O}(N^

Karma Dajani, Cornelis Kraaikamp, Hitoshi Nakada, Rie Natsui

We define two types of the $\alpha$-Farey maps $F_{\alpha}$ and $F_{\alpha, \flat}$ for $0 < \alpha < \tfrac{1}{2}$, which were previously defined only for $\tfrac{1}{2} \le \alpha \le 1$ by R.~Natsui (2004). Then, for each $0 < \alpha < \tfrac{1}{2}$, we construct the natural extension maps on the plane and show that the natural extension of $F_{\alpha, \fl

Shuxin Guo, Qiang Liu

We study the data-generating processes for factors expressed in return differences, which the literature on time-series asset pricing seems to have overlooked. For the factors' data-generating processes or long-short zero-cost portfolios, a meaningful definition of returns is impossible; further, the compounded market factor (MF) significantly underestimates