February 2024 arXiv papers — page 145

Dezhi Dai, Haomin Yuan, Albert Y. Tong, Adrian Tentner

A novel numerical technique designed for interface flow simulations using the Volume of Fluid (VOF) method on arbitrary unstructured meshes has been introduced. The method is called SimPLIC, which seamlessly integrates Piecewise Linear Interface Calculation (PLIC) and Simpson's rule. The main focus of the proposed method is to compute the volume of the prima

Clayton Strawn, Santi Roca-Fàbrega, Joel R. Primack, Ji-hoon Kim

We analyze the circumgalactic medium (CGM) for eight commonly-used cosmological codes in the AGORA collaboration. The codes are calibrated to use identical initial conditions, cosmology, heating and cooling, and star formation thresholds, but each evolves with its own unique code architecture and stellar feedback implementation. Here, we analyze the results

Brian Hu Zhang, Tuomas Sandholm

We investigate two notions of correlated equilibrium for extensive-form games: extensive-form correlated equilibrium (EFCE) and behavioral correlated equilibrium (BCE). We show that the two are outcome-equivalent, in the sense that every outcome distribution achievable under one notion is achievable under the other. Our result implies, to our knowledge, the

Fabio Andrijauskas, Igor Sfiligoi, Diego Davila, Aashay Arora

Creating new materials, discovering new drugs, and simulating systems are essential processes for research and innovation and require substantial computational power. While many applications can be split into many smaller independent tasks, some cannot and may take hours or weeks to run to completion. To better manage those longer-running jobs, it would be d

Maria Sol Vidal-Saez, Oscar Vilarroya, Jordi Garcia-Ojalvo

One of the defining features of living systems is their adaptability to changing environmental conditions. This requires organisms to extract temporal and spatial features of their environment, and use that information to compute the appropriate response. In the last two decades, a growing body or work, mainly coming from the machine learning and computation

Sanaullah, Shamini Koravuna, Ulrich Rückert, Thorsten Jungeblut

With the motivation and the difficulties that currently exist in comprehending and utilizing the promising features of SNNs, we proposed a novel run-time multi-core architecture-based simulator called "RAVSim" (Runtime Analysis and Visualization Simulator), a cutting-edge SNN simulator, developed using LabVIEW and it is publicly available on their website as

Carmelo Cisto

Let $S$ and $\mathcal{C}$ be affine semigroups in $\mathbb{N}^d$ such that $S\subseteq \mathcal{C}$. We provide a characterization for the set $\mathcal{C}\setminus S$ to be finite, together with a procedure and computational tools to check whether such a set is finite and, if so, compute its elements. As a consequence of this result, we provide a characteri

Marcoen J. T. F. Cabbolet

Set Matrix Theory (SMT) has been introduced in Log. Anal. 225: 59-82 (2014) as a generalization of ZF, in which matrices constructed from sets are treated as urelements, that is, as objects that are not sets but that can be elements of sets. Here we prove that SMT is relatively consistent with ZF.

Tarik P. Cysne, W. J. M. Kort-Kamp, Tatiana G. Rappoport

We study the orbital Hall effect (OHE) in the AC regime using bilayer graphene (BLG) as a prototypical material platform. While the unbiased BLG has gapless electronic spectra, applying a perpendicular electric field creates an energy band gap that can be continuously tuned from zero to high values. By exploiting this flexibility, we demonstrate the ability

Robert V. Caddy, Evan E. Schneider

We present an extension of the massively parallel, GPU native, astrophysical hydrodynamics code Cholla to magnetohydrodynamics (MHD). Cholla solves the ideal MHD equations in their Eulerian form on a static Cartesian mesh utilizing the Van Leer + Constrained Transport integrator, the HLLD Riemann solver, and reconstruction methods at second and third order.

Jeongwan Haah, Yunchao Liu, Xinyu Tan

We construct random walks on simple Lie groups that quickly converge to the Haar measure for all moments up to order $t$. Specifically, a step of the walk on the unitary or orthognoal group of dimension $2^{\mathsf n}$ is a random Pauli rotation $e^{\mathrm i \theta P /2}$. The spectral gap of this random walk is shown to be $\Omega(1/t)$, which coincides wi

Jixin Hou, Xianyan Chen, Taotao Wu, Ellen Kuhl

We introduce a data-driven framework to automatically identify interpretable and physically meaningful hyperelastic constitutive models from sparse data. Leveraging symbolic regression, an algorithm based on genetic programming, our approach generates elegant hyperelastic models that achieve accurate data fitting through parsimonious mathematic formulae, whi

Karan Singh, Orest Pavlosiuk, Shovan Dan, Dariusz Kaczorowski

Unconventional anomalous Hall effect was observed in antiferromagnetic state of EuZn$_2$Sb$_2$. Scaling of unconventional Hall conductivity with the longitudinal conductivity, and the magnitude of Hall angle indicate spin chirality despite collinear magnetic structure. Anomalies in magnetoresistance culminate in the same fields, in which the unconventional a

Erik Warberg, Adam Miksits, Fernando S. Barbosa

Continuous maps representations, as opposed to traditional discrete ones such as grid maps, have been gaining traction in the research community. However, current approaches still suffer from high computation costs, making them unable to be used in large environments without sacrificing precision. In this paper, a scalable method building upon Gaussian Proce

Yash Kant, Ziyi Wu, Michael Vasilkovsky, Guocheng Qian

We present SPAD, a novel approach for creating consistent multi-view images from text prompts or single images. To enable multi-view generation, we repurpose a pretrained 2D diffusion model by extending its self-attention layers with cross-view interactions, and fine-tune it on a high quality subset of Objaverse. We find that a naive extension of the self-at

Elaine Lau, Stephen Zhewen Lu, Ling Pan, Doina Precup

Generative Flow Networks (GFlowNets; GFNs) are a family of energy-based generative methods for combinatorial objects, capable of generating diverse and high-utility samples. However, consistently biasing GFNs towards producing high-utility samples is non-trivial. In this work, we leverage connections between GFNs and reinforcement learning (RL) and propose t

Jyotika Roychowdhury, Kevin Derby, Daewook Kim

We explore the impact of different telescope apertures on the image simulation and deconvolution processes within the context of a synthetic star field. Using HCIPy and Python programming, we modelled six telescope apertures namely Circular, Hexagonal, Elliptical (with horizontal and vertical major axes), segmented hexagonal (JWST), and obstructed circular (

Razzi Masroor

We extend the Schur algebra and the polynomial web category of the symmetric group to the hyperoctahedral group. In particular, we define the hyperoctahedral web category diagrammatically by generators and relations, and prove that it is equivalent to the hyperoctahedral Schur category.

Allan Zhou, Chelsea Finn, James Harrison

A challenging problem in many modern machine learning tasks is to process weight-space features, i.e., to transform or extract information from the weights and gradients of a neural network. Recent works have developed promising weight-space models that are equivariant to the permutation symmetries of simple feedforward networks. However, they are not applic

Pei-Hung Chung, Shuhan He, Norawit Kijpaisalratana, Abdel-badih el Ariss

A Clinical Decision Support System (CDSS) is designed to enhance clinician decision-making by combining system-generated recommendations with medical expertise. Given the high costs, intensive labor, and time-sensitive nature of medical treatments, there is a pressing need for efficient decision support, especially in complex emergency scenarios. In these sc

David S Clausen, Sarah Teichman, Amy D Willis

We consider the problem of estimating fold-changes in the expected value of a multivariate outcome observed with unknown sample-specific and category-specific perturbations. This challenge arises in high-throughput sequencing studies of the abundance of microbial taxa because microbes are systematically over- and under-detected relative to their true abundan

Ahmed A. Abdelhakim

Let $\alpha \in (0,2)$ and let $\beta>0$. Fix $-\pi<\varphi\leq \pi$ such that $|\varphi|>\alpha \pi/2$. We obtain asymptotic upper bounds on the Fourier transform of the radially symmetric tempered distribution \begin{equation*} \mathbb{R}^n\ni x\mapsto E_{\alpha,\beta}(e^{\dot{\imath} \varphi} |x|^{\sigma}), \end{equation*} for $\sigma>(n-1)/2$, where $E_{

Xiaochen Yang, Yaozhong Hu

This paper studies the long time stability of both stochastic heat equations on a bounded domain driven by a correlated noise and their approximations. It is popular for researchers to prove the intermittency of the solution which means that the moments of solution to stochastic heat equation usually grow exponentially to infinite and this hints that the sol

Eric Sabo, Lane G. Gunderman, Benjamin Ide, Michael Vasmer

Stabilizer codes are the most widely studied class of quantum error-correcting codes and form the basis of most proposals for a fault-tolerant quantum computer. A stabilizer code is defined by a set of parity-check operators, which are measured in order to infer information about errors that may have occurred. In typical settings, measuring these operators i

Jakab Nádori, Gregory Morse, Barna Fülöp Villám, Zita Majnay-Takács

Variational quantum algorithms are viewed as promising candidates for demonstrating quantum advantage on near-term devices. These approaches typically involve the training of parameterized quantum circuits through a classical optimization loop. However, they often encounter challenges attributed to the exponentially diminishing gradient components, known as

Armed Tusha, Seda Dogan-Tusha, Hossein Nasiri, Muhammad I. Rochman

The Federal Communications Commission (FCC) in the U.S. has made the Citizens Broadband Radio Service (CBRS) band (3.55 - 3.7 GHz) available for commercial wireless usage under a shared approach using a three-tier hierarchical architecture, where the federal incumbent is the highest priority Tier 1 user, Priority Access License (PAL) holders, who have paid f

H. Frerichs

The FLARE code is a magnetic mesh generator that is integrated within a suite of tools for the analysis of the magnetic geometry in toroidal fusion devices. A magnetic mesh is constructed from field line segments and permits fast reconstruction of field lines in 3D plasma boundary codes such as EMC3-EIRENE. Both intrinsically non-axisymmetric configurations

Berk Atil, Mahsa Sheikhi Karizaki, Rebecca J. Passonneau

With an increasing focus in STEM education on critical thinking skills, science writing plays an ever more important role in curricula that stress inquiry skills. A recently published dataset of two sets of college level lab reports from an inquiry-based physics curriculum relies on analytic assessment rubrics that utilize multiple dimensions, specifying sub

Alexander Berndt, Sebastian Baltes, Thomas Bach

Regression testing aims to prevent code changes from breaking existing features. Flaky tests negatively affect regression testing because they result in test failures that are not necessarily caused by code changes, thus providing an ambiguous signal. Test timeouts are one contributing factor to such flaky test failures. With the goal of reducing test flakin

Kimia Ghaffari, Salil Bavdekar, Douglas E. Spearot, Ghatu Subhash

The number of published Machine Learning Interatomic Potentials (MLIPs) has increased significantly in recent years. These new data-driven potential energy approximations often lack the physics-based foundations that inform many traditionally-developed interatomic potentials and hence require robust validation methods for their applicability, accuracy, compu

Maria Gillespie

We introduce higher Specht polynomials - analogs of Specht polynomials in higher degrees - in two sets of variables $x_1,\ldots,x_n$ and $y_1,\ldots,y_n$ under the diagonal action of the symmetric group $S_n$. This generalizes the classical Specht polynomial construction in one set of variables, as well as the higher Specht basis for the coinvariant ring $R_

Adrian-Gabriel Chifu, Sébastien Déjean, Moncef Garouani, Josiane Mothe

Query performance prediction (QPP) aims to forecast the effectiveness of a search engine across a range of queries and documents. While state-of-the-art predictors offer a certain level of precision, their accuracy is not flawless. Prior research has recognized the challenges inherent in QPP but often lacks a thorough qualitative analysis. In this paper, we

Julia S. Schmid, Sean Simmons, Mark A. Lewis, Mark S. Poesch

Prediction of angler behaviors, such as catch rates and angler pressure, is essential to maintaining fish populations and ensuring angler satisfaction. Angler behavior can partly be tracked by online platforms and mobile phone applications that provide fishing activities reported by recreational anglers. Moreover, angler behavior is known to be driven by loc

Huy Nguyen, Khai Nguyen, Nhat Ho

We consider the parameter estimation problem in the deviated Gaussian mixture of experts in which the data are generated from $(1 - \lambda^{\ast}) g_0(Y| X)+ \lambda^{\ast} \sum_{i = 1}^{k_{\ast}} p_{i}^{\ast} f(Y|(a_{i}^{\ast})^{\top}X+b_i^{\ast},\sigma_{i}^{\ast})$, where $X, Y$ are respectively a covariate vector and a response variable, $g_{0}(Y|X)$ is

Pierre Michel, Livia Lancia, Albertine Oudin, Eugene Kur

Acousto-optics consists of launching acoustic waves in a medium (usually a crystal) in order to modulate its refractive index and create a tunable optical grating. In this article, we present the theoretical basis of a new scheme to generate acousto-optics in a gas, where the acoustic waves are initiated by the localized absorption (and thus gas heating) of

Felipe C. R. Salvagnini, Gerson O. Barbosa, Alexandre X. Falcao, Cid A. N. Santos

Accurate brain tumor segmentation in the early stages of the disease is crucial for the treatment's effectiveness, avoiding exhaustive visual inspection of a qualified specialist on 3D MR brain images of multiple protocols (e.g., T1, T2, T2-FLAIR, T1-Gd). Several networks exist for Glioma segmentation, being nnU-Net one of the best. In this work, we evaluate

Gil Kalai, Noam Lifshitz, Dor Minzer, Tamar Ziegler

The (low soundness) linearity testing problem for the middle slice of the Boolean cube is as follows. Let $\varepsilon>0$ and $f$ be a function on the middle slice on the Boolean cube, such that when choosing a uniformly random quadruple $(x,y,z ,x\oplus y\oplus z)$ of vectors of $2n$ bits with exactly $n$ ones, the probability that $f(x\oplus y \oplus z) =

Giacomo Bertazzoli, Carlo Miniussi, Petro Julkunen, Marta Bortoletto

Concurrent transcranial magnetic stimulation (TMS) and electroencephalography (EEG), or TMS-EEG, holds the potential to broaden the clinical applications of TMS beyond its traditional role in evaluating the cortico-spinal tract and motor cortices. TMS-evoked potentials (TEPs) have emerged as valuable tools in clinical research, enabling the assessment of cor

Tran T. A. Nghia

In this paper, we mainly study tilt stability and Lipschitz stability of convex optimization problems. Our characterizations are geometric and fully computable in many important cases. As a result, we apply our theory to the group Lasso problem and the nuclear norm minimization problem and reveal that the Lipschitz stability of the solution mapping in these

Erik Schroedter, Michael Bonitz

Correlated classical and quantum many-particle systems out of equilibrium are of high interest in many fields, including dense plasmas, correlated solids, and ultracold atoms. Accurate theoretical description of these systems is challenging both, conceptionally and with respect to computational resources. While for classical systems, in principle, exact simu

Hans J. H. Tuenter

We consider the minimum distance projection in the $L_2$-norm from an arbitrary point in an $n$-dimensional, Euclidian space onto the canonical simplex. It is shown that this problem reduces to a univariate problem that can be solved by a simple algorithm. This optimization problem occurs in the setting of credit risk, where one has stochastic matrices that

Prachi Shah, Santanu S. Dey, Marco Molinaro

Modern mixed-integer programming solvers use the branch-and-cut framework, where cutting planes are added to improve the tightness of the linear programming (LP) relaxation, with the expectation that the tighter formulation would produce smaller branch-and-bound trees. In this work, we consider the question of whether adding cuts will always lead to smaller

Xingyu Li, Zheng Zhang, Zhiyun Qian, Trent Jaeger

Open-source software is increasingly reused, complicating the process of patching to repair bugs. In the case of Linux, a distinct ecosystem has formed, with Linux mainline serving as the upstream, stable or long-term-support (LTS) systems forked from mainline, and Linux distributions, such as Ubuntu and Android, as downstreams forked from stable or LTS syst

Mark S. Fox, Bart Gajderowicz, Dishu Lyu

In the current environment of data generation and publication, there is an ever-growing number of datasets available for download. This growth precipitates an existing challenge: sourcing and integrating relevant datasets for analysis is becoming more complex. Despite efforts by open data platforms, obstacles remain, predominantly rooted in inadequate metada

Nicholas Konz, Yuwen Chen, Haoyu Dong, Maciej A. Mazurowski

Diffusion models have enabled remarkably high-quality medical image generation, yet it is challenging to enforce anatomical constraints in generated images. To this end, we propose a diffusion model-based method that supports anatomically-controllable medical image generation, by following a multi-class anatomical segmentation mask at each sampling step. We

Michael Bonitz, Anatoly Zagorodny

Yuri L'vovich Klimontovich (28.09.1924--26.10.2002) was an outstanding theoretical physicist who made major contributions to kinetic theory. On the occasion of his 100th birthday we recall his main scientific achievements.

Christian D. Rask, Daniel E. Lucani

We introduce RAGE, an image compression framework that achieves four generally conflicting objectives: 1) good compression for a wide variety of color images, 2) computationally efficient, fast decompression, 3) fast random access of images with pixel-level granularity without the need to decompress the entire image, 4) support for both lossless and lossy co

M Carmen Aguilera-Morillo, Ana M Aguilera, Francisco Jiménez-Molinos, Juan B Roldán

Resistive Random Access Memories (RRAMs) are being studied by the industry and academia because it is widely accepted that they are promising candidates for the next generation of high density nonvolatile memories. Taking into account the stochastic nature of mechanisms behind resistive switching, a new technique based on the use of functional data analysis

M. Fernandez, D. Guerra, Unai Gil, I. Pena

Radiofrequency fields are usually measured in order to be compared with electromagnetic exposure limits defined by international standardization organizations with the aim of preserving the human health. However, in the case of WiFi technology, accurate measurement of the radiation coming from user terminals and access points is a great challenge due to the

Saumya Gupta, Jessy Jose, Swagat Ranjan Das, Zhen Guo

Young stellar clusters are predominantly the hub of star formation and hence, ideal to perform comprehensive studies over the least explored sub-stellar regime. Various unanswered questions like the mass distribution in brown dwarf regime and the effect of diverse cluster environment on brown dwarf formation efficiency still plague the scientific community.

Pol van Rijn, Silvan Mertes, Kathrin Janowski, Katharina Weitz

Speech is a natural interface for humans to interact with robots. Yet, aligning a robot's voice to its appearance is challenging due to the rich vocabulary of both modalities. Previous research has explored a few labels to describe robots and tested them on a limited number of robots and existing voices. Here, we develop a robot-voice creation tool followed

Juliusz Banecki

We prove several positive results regarding representation of homotopy classes of spheres and algebraic groups by regular mappings. Most importantly we show that every mapping from a sphere to an orthogonal or a unitary group is homotopic to a regular one. Furthermore we prove that algebraic homotopy classes of spheres form a subgroup of the homotopy group,

Victor Smaluk, Timur Shaftan

For synchrotron light sources, the brightness of user X-ray beams is primarily determined by the electron beam emittance and energy spread at operational intensity. A common feature of fourth-generation synchrotrons is the short length of electron bunches combined with a very small transverse beam size. Consequently, the particle density is much higher than

Zitong Yang, Emmanuel Candès, Lihua Lei

We introduce Bellman Conformal Inference (BCI), a framework that wraps around any time series forecasting models and provides approximately calibrated prediction intervals. Unlike existing methods, BCI is able to leverage multi-step ahead forecasts and explicitly optimize the average interval lengths by solving a one-dimensional stochastic control problem (S

Yue Jiang, Luis A. Leiva, Paul R. B. Houssel, Hamed R. Tavakoli

Different types of user interfaces differ significantly in the number of elements and how they are displayed. To examine how such differences affect the way users look at UIs, we collected and analyzed a large eye-tracking-based dataset, UEyes (62 participants, 1,980 UI screenshots, near 20K eye movement sequences), covering four major UI types: webpage, des

Matthew Renze, Erhan Guven

In this research study, we empirically investigate the effect of sampling temperature on the performance of Large Language Models (LLMs) on various problem-solving tasks. We created a multiple-choice question-and-answer (MCQA) exam by randomly sampling problems from standard LLM benchmarks. Then, we used nine popular LLMs with five prompt-engineering techniq

Santiago Miret, N M Anoop Krishnan

Large Language Models (LLMs) create exciting possibilities for powerful language processing tools to accelerate research in materials science. While LLMs have great potential to accelerate materials understanding and discovery, they currently fall short in being practical materials science tools. In this position paper, we show relevant failure cases of LLMs

Julius Lehmann

In this paper, we utilize operational methods to obtain closed-form solutions for certain classes of integrals in the spirit of Ramanujan's Master Theorem and provide several analogs to it. Although the use of operational calculus makes the proofs formal in nature, they can still yield interesting and correct results and may stimulate further rigorous invest

Alberto Giuseppe Catalano

Matrix product states (MPSs) and matrix product operators (MPOs) are fundamental tools in the study of quantum many-body systems, particularly in the context of tensor network methods such as Time-Evolving Block Decimation (TEBD). However, constructing compact MPO representations for Hamiltonians with interactions beyond nearest-neighbors, such as those aris

Jon Arrizabalaga, Lukas Pries, Riddhiman Laha, Runkang Li

This work focuses on the agile transportation of liquids with robotic manipulators. In contrast to existing methods that are either computationally heavy, system/container specific or dependant on a singularity-prone pendulum model, we present a real-time slosh-free tracking technique. This method solely requires the reference trajectory and the robot's kine

Shoshannah Byrne-Mamahit, David R. Patton, Sara L. Ellison, Robert Bickley

We investigate the connection between the most luminous active galactic nuclei (AGN), galaxy pairs, and post-mergers in the IllustrisTNG simulation. We select galaxy pairs and post-mergers with a mass ratio between 1:10 $< \mu <$ 1:1 and a redshift between $0<z<1$. We compare the incidence of luminous AGN in pairs with matched non-pair controls, finding that

Maitreya Patel, Sangmin Jung, Chitta Baral, Yezhou Yang

Despite the recent advances in personalized text-to-image (P-T2I) generative models, it remains challenging to perform finetuning-free multi-subject-driven T2I in a resource-efficient manner. Predominantly, contemporary approaches, involving the training of Hypernetworks and Multimodal Large Language Models (MLLMs), require heavy computing resources that ran

M Carmen Aguilera-Morillo, Ana M Aguilera

A functional linear discriminant analysis approach to classify a set of kinematic data (human movement curves of individuals performing different physical activities) is performed. Kinematic data, usually collected in linear acceleration or angular rotation format, can be identified with functions in a continuous domain (time, percentage of gait cycle, etc.)

Deniz A. Bezgin, Aaron B. Buhendwa, Nikolaus A. Adams

In our effort to facilitate machine learning-assisted computational fluid dynamics (CFD), we introduce the second iteration of JAX-Fluids. JAX-Fluids is a Python-based fully-differentiable CFD solver designed for compressible single- and two-phase flows. In this work, the first version is extended to incorporate high-performance computing (HPC) capabilities.

Joao Prazeres, Rafael Rodrigues, Manuela Pereira, Antonio M. G. Pinheiro

The quality evaluation of three deep learning-based coding solutions for point cloud geometry, notably ADLPCC, PCC GEO CNNv2, and PCGCv2, is presented. The MPEG G-PCC was used as an anchor. Furthermore, LUT SR, which uses multi-resolution Look-Up tables, was also considered. A set of six point clouds representing landscapes and objects were used. As point cl

Jiaming Wang, Rohit Chhiber, Sohom Roy, Manuel E. Cuesta

A well-known property of solar wind plasma turbulence is the observed anisotropy of the autocorrelations, or equivalently the spectra, of velocity and magnetic field fluctuations. Here we explore the related but apparently not well-studied issue of the anisotropy of plasma density fluctuations in the energy-containing and inertial ranges of solar wind turbul

Emil Have, Kevin Nguyen, Stefan Prohazka, Jakob Salzer

Motivated by flat space holography, we demonstrate that massive spin-$s$ fields in Minkowski space near timelike infinity are massive carrollian fields on the carrollian counterpart of anti-de Sitter space called $\mathsf{Ti}$. Its isometries form the Poincar\'e group, and we construct the carrollian spin-$s$ fields using the method of induced representation

Giorgio Ottaviani, Ettore Teixeira Turatti

Let $f$ be a homogeneous polynomial of even degree $d$. We study the decompositions $f=\sum_{i=1}^r f_i^2$ where $\mathrm{deg} f_i=d/2$. The minimal number of summands $r$ is called the $2$-rank of $f$, so that the polynomials having $2$-rank equal to $1$ are exactly the squares. Such decompositions are never unique and they are divided into $\mathrm{O}(r)$-

Jiaqiang Ye Zhu, Carla Gomez Cano, David Vazquez Bermudez, Michal Drozdzal

One of the challenges in robotics is to enable robotic units with the reasoning capability that would be robust enough to execute complex tasks in dynamic environments. Recent advances in LLMs have positioned them as go-to tools for simple reasoning tasks, motivating the pioneering work of Liang et al. [35] that uses an LLM to translate natural language comm

Carlo Alfano, Sebastian Towers, Silvia Sapora, Chris Lu

Policy Mirror Descent (PMD) is a popular framework in reinforcement learning, serving as a unifying perspective that encompasses numerous algorithms. These algorithms are derived through the selection of a mirror map and enjoy finite-time convergence guarantees. Despite its popularity, the exploration of PMD's full potential is limited, with the majority of

Jiancheng Feng, Rowan J. Smith, Alvaro Hacar, Susan E. Clark

The interstellar medium is threaded by a hierarchy of filaments from large scales (~ 100 pc) to small scales (~ 0.1pc). The masses and lengths of these nested structures may reveal important constraints for cloud formation and evolution, but it is difficult to investigate from an evolutionary perspective using single observations. In this work, we extract si

Andrew D. Hanlon

Despite quantum chromodynamics (QCD) being established as the theory of the strong interaction and its many successes since then, significant challenges in our understanding of hadron physics remain. The lack of a full understanding for how the observed hadrons arise from the quark and gluon degrees of freedom which define QCD represents a real challenge in

Logan Sizemore, Diego Llanes, Marina Kounkel, Brian Hutchinson

Data-driven models, which apply machine learning to infer physical properties from large quantities of data, have become increasingly important for extracting stellar properties from spectra. In general, these methods have been applied to data in one wavelength regime or another. For example, APOGEE Net has been applied to near-IR spectra from the SDSS-V APO

Ka Ho Wong, Man Hoi Lee

An increasing number of compact planetary systems with multiple planets in a resonant chain have been detected. The resonant chain must be maintained by convergent migration of the planets due to planet-disk interactions if it is formed before the dispersal of the protoplanetary gas disk. For type I migration in an adiabatic disk, we show that an analytic cr

B. A. Ward, S. A. Eales, R. J. Ivison, V. Arumugam

Variations in the dust emissivity index, $\beta$, within and between galaxies, are evidence that the chemistry and physics of dust must vary on large scales, although the nature of the physical and/or chemical variations is still unknown. In this paper we estimate values of $\beta$ and dust temperature for a sample of 109 dusty star-forming galaxies (DSFGs)

Stephen Eales, Bradley Ward

We estimate how the mean density of dust in the universe varies with redshift, using submillimetre continuum observations and a method designed to minimise the effect of dust temperature. We have used the Herschel-ATLAS to show that the median temperature of dust in galaxies is ~22 K and does not vary significantly with redshift out to z=1. With this as our

Yue Yu, Han-Gyeol Suh, Mercè Roig, Daniel F. Agterberg

Realizing two-dimensional (2D) altermagnets is important for spintronics applications. Here we propose a microscopic template for stabilizing 2D altermagnetism through Van Hove singularities that are coincident in both energy and momentum. These coincident Van Hove singularities are a generic consequence of non-symmorphic symmetries in nine 2D space groups.

Gilles Parez, William Witczak-Krempa

Quantum entanglement manifests itself in non-local correlations between the constituents of a system. In its simplest realization, a measurement on one subsystem is affected by a prior measurement on its partner, irrespective of their separation. For multiple parties, purely collective types of entanglement exist but their detection, even theoretically, rema

Shyam Balaji, Malcolm Fairbairn, Maria Olalla Olea-Romacho

Strongly supercooled first order phase transitions (FOPTs) can produce primordial black hole (PBH) dark matter (DM) along with observable gravitational waves (GWs) from bubble collisions. Such FOPTs may also produce coherent magnetic fields generated by bubble collisions and by turbulence in the primordial plasma. Here we find that the requirement for PBH DM

David Pereñiguez, Marina de Amicis, Richard Brito, Rodrigo Panosso Macedo

Black hole superradiance has proven being very valuable in several realms of gravitational physics, and holds a promising discovery potential. In this paper, we consider the superradiant instability of magnetically-charged, rotating black holes and find a number of important differences with respect to neutral ones. Considering massive charged bosonic fields

Michael D. Albrow

We have determined the mass functions, mass-ratio distribution functions and fractions of binary stars with mass ratios above particular thresholds for radially-separated populations of stars in the nearby open clusters Hyades and Praesepe. Radial mass segregation is detected, with the populations of stars within the tidal radii having much flatter mass func

M. Badenas-Agusti, J. Viaña, A. Vanderburg, S. Blouin

Over the past several decades, conventional spectral analysis techniques of polluted white dwarfs have become powerful tools to learn about the geology and chemistry of extrasolar bodies. Despite their proven capabilities and extensive legacy of scientific discoveries, these techniques are however still limited by their manual, time-intensive, and iterative

Fabiola Cocchiararo, Alessia Franchini, Alessandro Lupi, Alberto Sesana

We study spectral and time variability of accreting massive black hole binaries (MBHBs) at milli-pc separations surrounded by a geometrically thin circumbinary disc. We present the first computed spectral energy distribution (SED) and light curves (LCs) from 3D hyper-Lagrangian resolution hydrodynamic simulations of these systems. We model binaries with mass

Tong-Zhi Yang, Xiaoyuan Zhang

We present the analytic calculation of the leading order three-point energy correlator (EEEC) in hadronic Higgs decays, including both gluon-initiated channel $H\rightarrow g g+X$ and quark-initiated channel $H\rightarrow q\bar q+X$. The phase space integration is evaluated directly using Mandelstam variables $s_{ij}=(p_i+p_j)^2$, and the appearing square ro

Itay Lavie, Guy Gur-Ari, Zohar Ringel

We study inductive bias in Transformers in the infinitely over-parameterized Gaussian process limit and argue transformers tend to be biased towards more permutation symmetric functions in sequence space. We show that the representation theory of the symmetric group can be used to give quantitative analytical predictions when the dataset is symmetric to perm

Evgeny Akhmedov, Andreas Trautner

Finding out if neutrinos are Dirac or Majorana particles is known to be extremely difficult due to the smallness of neutrino mass and the fact that in the limit $m_\nu=0$ both Dirac and Majorana neutrinos become Weyl particles, i.e. are indistinguishable. There have been suggestions in the literature that in the case of processes with production of a neutrin

Joey S. G. Mombarg, Conny Aerts, Geert Molenberghs

The theory the rotational and chemical evolution is incomplete, thereby limiting the accuracy of model-dependent stellar mass and age determinations. The $\gamma$ Doradus pulsators are excellent points of calibration for the current state-of-the-art stellar evolution models, as their gravity modes probe the physical conditions in the deep stellar interior. Y

Melissa van Beekveld, Mrinal Dasgupta, Basem Kamal El-Menoufi, Jack Helliwell

We study the collinear fragmentation of highly energetic jets defined with a small jet radius. In particular, we investigate how the corresponding fragmentation functions differ from their hadronic counterpart defined in the common $\overline{\rm MS}$ scheme. We find that the anomalous dimensions governing the perturbative evolution of the two fragmentation

Raphael E. Hviding, Kevin N. Hainline, Andy D. Goulding, Jenny E. Greene

We present the result of a spectroscopic campaign targeting Active Galactic Nucleus (AGN) candidates selected using a novel unsupervised machine-learning (ML) algorithm trained on optical and mid-infrared (mid-IR) photometry. AGN candidates are chosen without incorporating prior AGN selection criteria and are fainter, redder, and more numerous, $\sim$340 AGN

D. J. Fritzewski, C. Aerts, J. S. G. Mombarg, S. Gossage

Galactic archaeology largely relies on precise ages of distant evolved stars in the Milky Way. Nowadays, asteroseismology can deliver ages for many red giants observed with high-cadence, high-precision photometric space missions. Our aim is to quantify age uncertainties of slowly-rotating red giants due to the cumulative effect of their fast rotation during

Nicholas Agia, Daniel L. Jafferis

We explicitly construct the states of the 2d free boson on the infinite line with specified asymptotic charges. The Minkowski CFT states derive from (the analytic continuation of) the shrinking limit of Euclidean angular quantization, wherein the superselection sector for fixed asymptotic charges corresponds to specific endpoint operators for the angular qua

José P. S. Lemos, Oleg B. Zaslavskii

In a space with positive cosmological constant $\Lambda$, we consider a black hole surrounded by a heat reservoir at radius $R$ and temperature $T$, i.e., we analyze the Schwarzschild-de Sitter black hole in a cavity. We use the Euclidean path integral approach to quantum gravity to study its canonical ensemble and thermodynamics. We give the action, energy,

Melanie Kaasinen, Bram Venemans, Kevin C. Harrington, Leindert A. Boogaard

Probing the molecular gas reservoirs of z>~6 quasar (QSO) host galaxies is fundamental to understanding the coevolution of star formation and black hole growth in these extreme systems. Yet, there is still an inhomogeneous coverage of molecular gas tracers. To measure the average excitation and mass of the molecular gas reservoirs in the brightest z>6.5 QSO

Ya-Hui Zhang

Integer and fractional quantum anomalous Hall (QAH) effects have been widely seen in moir\'e systems. Recently there is even observation of a time reversal invariant fractional quantum spin hall (FQSH) state at filling $n=3$ in twisted MoTe$_2$ bilayer. We consider a pair of half-filled $C=\pm 1$ Chern band in the two valleys, similar to the well-studied qua

Rose E. Wang, Dorottya Demszky

We introduce Edu-ConvoKit, an open-source library designed to handle pre-processing, annotation and analysis of conversation data in education. Resources for analyzing education conversation data are scarce, making the research challenging to perform and therefore hard to access. We address these challenges with Edu-ConvoKit. Edu-ConvoKit is open-source (htt

Eric J. Michaud, Isaac Liao, Vedang Lad, Ziming Liu

We present MIPS, a novel method for program synthesis based on automated mechanistic interpretability of neural networks trained to perform the desired task, auto-distilling the learned algorithm into Python code. We test MIPS on a benchmark of 62 algorithmic tasks that can be learned by an RNN and find it highly complementary to GPT-4: MIPS solves 32 of the

Zachary Ankner, Rishab Parthasarathy, Aniruddha Nrusimha, Christopher Rinard

To combat the memory bandwidth-bound nature of autoregressive LLM inference, previous research has proposed the speculative decoding frame-work. To perform speculative decoding, a small draft model proposes candidate continuations of the input sequence that are then verified in parallel by the base model. One way to specify the draft model, as used in the re

Jean Douçot, Andreas Hohl

We give a topological description of the behaviour of Stokes matrices under the Fourier transform from infinity to infinity in a large number of cases of one level. This explicit, algorithmic statement is obtained by building on a recent result of T. Mochizuki about the Fourier transform of Stokes data of irregular connections on the Riemann sphere and by us

Jinyeop Song, Ziming Liu, Max Tegmark, Jeff Gore

Neural scaling laws characterize how model performance improves as the model size scales up. Inspired by empirical observations, we introduce a resource model of neural scaling. A task is usually composite hence can be decomposed into many subtasks, which compete for resources (measured by the number of neurons allocated to subtasks). On toy problems, we emp

Anastasiya D. Yarovova, Alexei V. Moiseev, Ivan S. Gerasimov, Milica M. Vučetić

We present a study of the nearby low-metallicity dwarf galaxy IC 1613, focusing on the search for massive stars and related feedback processes, as well as for faint supernova remnants (SNR) in late stages of evolution. We obtained the deepest images of IC 1613 in the narrow-band H{\alpha}, He II and [S II] emission lines and new long-slit spectroscopy observ