November 2024 arXiv papers — page 168

Eduardo Alves da Silva, Fernando Figueroa, Joaquín Moraga

Let $B\subset \mathbb{P}^3$ be an slc quartic surface. The existence of an embedding $\mathbb{G}_m^3\hookrightarrow \mathbb{P}^3\setminus B$ implies that $B$ has coregularity zero. In this article, we initiate the classification of coregularity zero slc quartic surfaces $B\subset \mathbb{P}^3$ for which $\mathbb{P}^3\setminus B$ contains an algebraic torus $

Andrew Heschl, Mauricio Murillo, Keyhan Najafian, Farhad Maleki

This paper introduces a methodology for generating synthetic annotated data to address data scarcity in semantic segmentation tasks within the precision agriculture domain. Utilizing Denoising Diffusion Probabilistic Models (DDPMs) and Generative Adversarial Networks (GANs), we propose a dual diffusion model architecture for synthesizing realistic annotated

Jiayi Zheng, Nicholas Rios

In the chemical, pharmaceutical, and food industries, sometimes the order of adding a set of components has an impact on the final product. These are instances of the order-of-addition (OofA) problem, which aims to find the optimal sequence of the components. Extensive research on this topic has been conducted, but almost all designs are found by optimizing

Clifton Paul Robinson, Andrea Lacava, Pedram Johari, Francesca Cuomo

The wireless spectrum's increasing complexity poses challenges and opportunities, highlighting the necessity for real-time solutions and robust data processing capabilities. Digital Twin (DT), virtual replicas of physical systems, integrate real-time data to mirror their real-world counterparts, enabling precise monitoring and optimization. Incorporating DTs

Sophia Baum, Moritz Laber, Martin Bruckner, Liuhuaying Yang

Global food production and trade networks are highly dynamic, especially in response to shortages when countries adjust their supply strategies. In this study, we examine adjustments across 123 agri-food products from 192 countries resulting in 23616 individual scenarios of food shortage, and calibrate a multi-layer network model to understand the propagatio

Lekan Molu

This paper describes open-source scientific contributions in python surrounding the numerical solutions to hyperbolic Hamilton-Jacobi (HJ) partial differential equations viz., their implicit representation on co-dimension one surfaces; dynamics evolution with levelsets; spatial derivatives; total variation diminishing Runge-Kutta integration schemes; and the

Victor Giannankouris, Immanuel Trummer

We introduce {\lambda}-Tune, a framework that leverages Large Language Models (LLMs) for automated database system tuning. The design of {\lambda}-Tune is motivated by the capabilities of the latest generation of LLMs. Different from prior work, leveraging LLMs to extract tuning hints for single parameters, {\lambda}-Tune generates entire configuration scrip

Xavier Blot, Danilo Lewański, Sergey Shadrin

In 2016, Buryak and Rossi introduced the quantum Double Ramification (DR) hierarchies which associate a quantum integrable hierarchy to any Cohomological Field Theory (CohFT). Shortly after, they introduced, in collaboration with Dubrovin and Gu\'er\'e, the quantum tau functions of these hierarchies. In this work, we study quantum tau functions associated to

S. David Stupski, Laura Casas Ferrer, Jacob S. Harrison, Justina Jackson

Fieldwork is an essential component not just for organismal biology but also for the expanding umbrella of disciplines that have turned their attention toward the physics of living systems. Observing organisms in nature is a critical component of discovery; however, conducting field research can be a barrier for scientists who do not have experience working

Zizhang Chen, Peizhao Li, Xiaomeng Dong, Pengyu Hong

To facilitate healthcare delivery, language models (LMs) have significant potential for clinical prediction tasks using electronic health records (EHRs). However, in these high-stakes applications, unreliable decisions can result in high costs due to compromised patient safety and ethical concerns, thus increasing the need for good uncertainty modeling of au

Timothy Zaklama, Di Luo, Liang Fu

The structure factor is a useful observable for probing charge density correlations in real materials, and its long-wavelength behavior encapsulated by ``quantum weight'' has recently gained prominence in the study of quantum geometry and topological phases of matter. Here we employ the static structure factor, S(q), to explore the phase diagram of twisted t

Junior Cedric Tonga, Benjamin Clement, Pierre-Yves Oudeyer

The automatic generation of hints by Large Language Models (LLMs) within Intelligent Tutoring Systems (ITSs) has shown potential to enhance student learning. However, generating pedagogically sound hints that address student misconceptions and adhere to specific educational objectives remains challenging. This work explores using LLMs (GPT-4o and Llama-3-8B-

Murad Mehrab Abrar, Souryadeep Mondal, Michelle Hickner

This paper presents a Sim2Real (Simulation to Reality) approach to bridge the gap between a trained agent in a simulated environment and its real-world implementation in navigating a robot in a similar setting. Specifically, we focus on navigating a quadruped robot in a real-world grid-like environment inspired by the Gymnasium Frozen Lake -- a highly user-f

Sai Surya Duvvuri, Inderjit S. Dhillon

Transformers have had tremendous impact for several sequence related tasks, largely due to their ability to retrieve from any part of the sequence via softmax based dot-product attention. This mechanism plays a crucial role in Transformer's performance. We analyze the gradients backpropagated through the softmax operation in the attention mechanism and obser

Alexis A. A. Delgado, Devin A. Matthews

Reliable computational methodologies and basis sets for modeling x-ray spectra are essential for extracting and interpreting electronic and structural information from experimental x-ray spectra. In particular, the trade-off between numerical accuracy and computational cost due to the size of the basis set is a major challenge, since molecular orbitals under

Anthony Palladino, Dana Gajewski, Abigail Aronica, Patryk Deptula

We present a novel Automatic Target Recognition (ATR) system using open-vocabulary object detection and classification models. A primary advantage of this approach is that target classes can be defined just before runtime by a non-technical end user, using either a few natural language text descriptions of the target, or a few image exemplars, or both. Nuanc

Marc Salinas, Jorge Piekarewicz

Driven by recent laboratory experiments and astronomical observations, significant advances have deepened our understanding of neutron-star physics. NICER's Pulse Profile Modeling has refined our knowledge of neutron star masses and radii, while gravitational-wave detections have revealed key insights into the structure of neutron stars. Particularly relevan

Yuki Ohnishi, Fan Li

Cluster randomized trials (CRTs) with multiple unstructured mediators present significant methodological challenges for causal inference due to within-cluster correlation, interference among units, and the complexity introduced by multiple mediators. Existing causal mediation methods often fall short in simultaneously addressing these complexities, particula

Saptarshi Pal, Christian Hilbe, Nikoleta E Glynatsi

People often engage in costly cooperation, especially in repeated interactions. When deciding whether to cooperate, individuals typically take into account how others have acted in the past. For instance, when one person is deciding whether to cooperate with another, they may consider how they were treated by the other party (direct reciprocity), how the oth

Yichen Wang, Qiming Liu, Zhe Liu, Hesheng Wang

In the context of visual navigation in unknown scenes, both "exploration" and "exploitation" are equally crucial. Robots must first establish environmental cognition through exploration and then utilize the cognitive information to accomplish target searches. However, most existing methods for image-goal navigation prioritize target search over the generatio

Caleb Bradshaw, Caelen Miller, Sean Warnick

This paper introduces distribution-based prediction, a novel approach to using Large Language Models (LLMs) as predictive tools by interpreting output token probabilities as distributions representing the models' learned representation of the world. This distribution-based nature offers an alternative perspective for analyzing algorithmic fidelity, complemen

M. S. Guimaraes, I. Roditi, S. P. Sorella

A numerical setup for the Bell-CHSH inequality for causal diamonds in $1+1$ Minkowski spacetime is presented. Upon choosing a suitable set of test functions supported in the diamonds, sensible violations are reported for the correlation function of Weyl operators of a real scalar massive field in the vacuum state.

Viviane Torres da Silva, Alexandre Rademaker, Krystelle Lionti, Ronaldo Giro

Automated knowledge extraction from scientific literature can potentially accelerate materials discovery. We have investigated an approach for extracting synthesis protocols for reticular materials from scientific literature using large language models (LLMs). To that end, we introduce a Knowledge Extraction Pipeline (KEP) that automatizes LLM-assisted parag

Caio Mucchiani, Dimitrios Chatziparaschis, Konstantinos Karydis

The integration of augmented reality (AR), extended reality (XR), and virtual reality (VR) technologies in agriculture has shown significant promise in enhancing various agricultural practices. Mobile robots have also been adopted as assessment tools in precision agriculture, improving economic efficiency and productivity, and minimizing undesired effects su

Martin Hairer, Wenhao Zhao

We consider the 2D stochastic Navier-Stokes equations driven by noise that has the regularity of space-time white noise but doesn't exactly coincide with it. We show that, provided that the intensity of the noise is sufficiently weak at high frequencies, this systems admits uniform bounds in time, so that it has an invariant measure, for which we obtain stre

Ananya Trivedi, Sarvesh Prajapati, Mark Zolotas, Michael Everett

Recent advances in quadrupedal locomotion have focused on improving stability and performance across diverse environments. However, existing methods often lack adequate safety analysis and struggle to adapt to varying payloads and complex terrains, typically requiring extensive tuning. To overcome these challenges, we propose a Chance-Constrained Model Predi

Aurélien Colin, Romain Husson

This paper introduces a data-driven approach to estimate precipitation rates from Synthetic Aperture Radar (SAR) at a spatial resolution of 200 meters per pixel. It addresses previous challenges related to the collocation of SAR and weather radar data, specifically the misalignment in collocations and the scarcity of rainfall examples under strong wind. To t

Wei-Kuo Li, Hsing-Ta Chen

The notion of strong light-matter coupling is typically associated with the observation of Rabi splitting, corresponding to the formation of the hybrid light-matter states known as polaritons. However, this relationship is derived based on the assumption that disorder can be ignored or acts as a perturbative effect. Contrary to conventional treatment of diso

M. Lupi S. Esposito, X. Llopart-Cudie, A. Pulli, S. Scarfí

Verification is a critical aspect of designing front-end (FE) readout ASICs for High-Energy Physics (HEP) experiments. These ASICs share several similar functional features, resulting in similar verification objectives, which can be addressed using comparable verification strategies. This contribution presents a set of re-usable verification components for a

Yimeng Liu, Misha Sra, Chang Xiao

Large Language Models (LLMs) have demonstrated remarkable potential across various design domains, including user interface (UI) generation. However, current LLMs for UI generation tend to offer generic solutions that lack a nuanced understanding of task context and user preferences. We present CrowdGenUI, a framework that enhances LLM-based UI generation wi

M. Lupi, G. Bergamin, D. Ceresa, D. Coko

The High-Granularity Calorimeter (HGCAL) will replace the current CMS Endcap Calorimeter during Long-Shutdown 3. The Endcap Concentrator (ECON) ASICs represent key elements in the readout chain, processing trigger (ECON-T) and data (ECON-D) streams from the HGCROC to the lpGBT. The ECONs will operate in a radiation environment with a High-Energy Hadron (${E\

Emmanuel Hartman, Nicolas Charon, Martin Bauer

This paper introduces self-supervised neural network models to tackle several fundamental problems in the field of 3D human body analysis and processing. First, we propose VariShaPE (Varifold Shape Parameter Estimator), a novel architecture for the retrieval of latent space representations of body shapes and poses. This network offers a fast and robust metho

Dhruv Gamdha, Ryan Fair, Adarsh Krishnamurthy, Enrique Gomez

Automated analysis of high-resolution transmission electron microscopy (HRTEM) images is increasingly essential for advancing research in organic electronics, where precise characterization of nanoscale crystal structures is crucial for optimizing material properties. This paper introduces an open-source computational framework called GRATEV2.0 (GRaph-based

Kenneth Bloom, Véronique Boisvert

Future accelerators and experiments for energy-frontier particle physics will be built and operated during a period in which society must also address the climate change emergency by significantly reducing emissions of carbon dioxide. The carbon intensity of many particle-physics activities is potentially significant, such that as a community particle physic

Mohsenialhosseini, Saheli

Let(X,d) be a metric space that has a directed graph G such that the sets V(G) and E(G) are respectively vertices and edges corresponding to X. We obtain sufficient conditions for the existence of an G-approximate best proximity pair of the mapping T in the metric space X endowed with a graph G such that the set V(G) of vertices of G coincides with X.

Manar Abdelatty, Jingxiao Ma, Sherief Reda

Large Language Models (LLMs) have been applied to various hardware design tasks, including Verilog code generation, EDA tool scripting, and RTL bug fixing. Despite this extensive exploration, LLMs are yet to be used for the task of post-synthesis metric reasoning and estimation of HDL designs. In this paper, we assess the ability of LLMs to reason about post

Karen Macías Cárdenas, Gopolang Mohlabeng, Aaron C. Vincent

We investigate a dark matter model that couples to the standard model through a one-loop interaction with neutrinos, where the mediator particles also generate neutrino masses. We perform a global fit that incorporates dark matter relic abundance, primordial nucleosynthesis, neutrino mass, collider and indirect detection constraints. Thanks to the loop suppr

Fabio Mastrogiacomo

Let $G$ be a finite permutation group acting on $\Omega$. A base for $G$ is a subset $B \subseteq \Omega$ such that the pointwise stabilizer $G_{(B)}$ is the identity. The base size of $G$, denoted by $b(G)$, is the cardinality of the smallest possible base. The minimal degree of $G$, denoted by $\mu(G)$, is the smallest cardinality of the support of a non t

Nikhil S Kumar

This problem was asked to K. Mahler by one of his Japanese colleagues, a Z-number is a positive real number $x$ such that the fractional parts of $x(\frac{3}{2})^n $ are less than $\frac{1}{2}$ for all integers $n$ such that $n \ge 0$. Kurt Mahler conjectured in 1968 that there are no Z-numbers. In this paper, we show that there are no Z-numbers in $\mathbb{

Karim Shariff, Orkan M. Umurhan

A high resolution fourth-order Pad\'e scheme is used to simulate locally isothermal 3D disk turbulence driven by the vertical shear instability (VSI) using 268.4 M points. In the early non-linear period of axisymmetric VSI, angular momentum transport by vertical jets creates correlated N-shaped radial profiles of perturbation vertical and azimuthal velocity.

Solal Gaudin

In his study of generalised permutahedra, Postnikov considered the mixed volumes of hypersimplices, giving rise to the family of mixed Eulerian numbers. It comprises usual Eulerian numbers, binomial coefficients, Catalan numbers, and the large family of hit numbers. Nadeau and Tewari further gave a polynomial refinement of these, the remixed Eulerian numbers

Daniel Menges, Adil Rasheed

Autonomous surface vessels (ASVs) are becoming increasingly significant in enhancing the safety and sustainability of maritime operations. To ensure the reliability of modern control algorithms utilized in these vessels, digital twins (DTs) provide a robust framework for conducting safe and effective simulations within a virtual environment. Digital twins ar

Fan Wang, Zhilin Zou, Nicole Sakla, Luke Partyka

Characterization of breast parenchyma in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a challenging task owing to the complexity of underlying tissue structures. Existing quantitative approaches, like radiomics and deep learning models, lack explicit quantification of intricate and subtle parenchymal structures, including fibroglandular

William Delplanque

Massive higher-spin states/fields appear in the effective description of various systems from hadrons and nuclei to black holes, whenever the point-particle approximation is justified, as well as in the bottom-up approaches to the quantum gravity problem. In four dimensions the actions for massive higher-spin fields utilize either the Singh-Hagen/Zinoviev se

Nicolás Forero-Correa, Nicolas Varas-Salinas, Tomás M. Castillo, Sebastian E. Reyes-Lillo

Ferroelectric materials have promising applications in solar-energy conversion and electro-optic devices. The internal gradient fields produced by the macroscopic polarization may improve electronic and transport semiconducting properties. However, ferroelectrics tend to display relatively large band gaps and hence low solar-energy conversion efficiencies. I

Gabriel Poesia, Chloe Loughridge, Nada Amin

Formal verification has the potential to drastically reduce software bugs, but its high additional cost has hindered large-scale adoption. While Dafny presents a promise to significantly reduce the effort to write verified programs, users are often required to provide logical annotations to aid the verifier. Here, we explore using a combination of Large Lang

Alif Bin Abdul Qayyum, Susan D. Mertins, Amanda K. Paulson, Nathan M. Urban

The data-driven drug design problem can be formulated as an optimization task of a potentially expensive black-box objective function over a huge high-dimensional and structured molecular space. The junction tree variational autoencoder (JTVAE) has been shown to be an efficient generative model that can be used for suggesting legitimate novel drug-like small

Catherine Petretti, Matteo Braglia, Xingang Chen, Dhiraj Kumar Hazra

Several missions following Planck are currently under development, which will provide high-precision measurements of the Cosmic Microwave Background (CMB) anisotropies. Specifically, measurements of the E modes will become nearly limited by cosmic variance, which, especially when considering the sharpness of the E-mode transfer functions, may allow for the a

I-Chi Chen, Bharath Hebbe Madhusudhana

Detecting mitigating and correcting errors in quantum control is among the most pertinent contemporary problems in quantum technologies. We consider three of the most common bosonic error correction codes -- the CLY, binomial and dual rail and compare their performance under typical errors in bosonic systems. We find that the dual rail code shows the best pe

Eugene Serabyn, Michael Bottom

Detecting exoplanets and other faint sources of emitted and reflected light near a bright star requires deeply suppressing the starlight while efficiently transmitting the dim light from its surroundings. This suppression can be carried out by coronagraphs, nulling interferometers, and starshades. This chapter provides a brief overview of these technologies,

Rina Bao, Yangming Ou

Hypoxic Ischemic Encephalopathy (HIE) affects approximately 1-5/1000 newborns globally and leads to adverse neurocognitive outcomes in 30% to 50% of cases by two years of age. Despite therapeutic advances with Therapeutic Hypothermia (TH), prognosis remains challenging, highlighting the need for improved biomarkers. This paper introduces the second release o

Benjamin Rombaut, Sogol Masoumzadeh, Kirill Vasilevski, Dayi Lin

Large language models (LLMs) are increasingly integrated into autonomous systems, giving rise to a new class of software known as Agentware, where LLM-powered agents perform complex, open-ended tasks in domains such as software engineering, customer service, and data analysis. However, their high autonomy and opaque reasoning processes pose significant chall

Claudia Aguilera-Gómez, Laura K. Rogers, Amy Bonsor, Paula Jofré

Planets form from the same cloud of molecular gas and dust as their host stars. Confirming if planetary bodies acquire the same refractory element composition as their natal disc during formation, and how efficiently volatile elements are incorporated into growing planets, is key to linking the poorly constrained interior composition of rocky exoplanets to t

Matthew S. Clement, Andre Izidoro, Sean N. Raymond, Rogerio Deienno

Our understanding of the process of terrestrial planet formation has grown markedly over the past 20 years, yet key questions remain. This review begins by first addressing the critical, earliest stage of dust coagulation and concentration. While classic studies revealed how objects that grow to $\sim$meter sizes are rapidly removed from protoplanetary disks

Juan Herrero-Garcia, Giacomo Landini, Tsutomu T. Yanagida

The seesaw mechanism with three heavy Majorana right-handed neutrinos provides an elegant explanation for neutrino masses and, combined with leptogenesis, can generate the baryon asymmetry of the universe (BAU). Naturally embedded in a Grand Unified Theory, this framework stands as one of the best-motivated extensions beyond the Standard Model, but it is ver

Mohammad Samin Nur Chowdhury, Diyu Yang, Shimin Tang, Singanallur V. Venkatakrishnan

Hyperspectral neutron computed tomography enables 3D non-destructive imaging of the spectral characteristics of materials. In traditional hyperspectral reconstruction, the data for each neutron wavelength bin is reconstructed separately. This per-bin reconstruction is extremely time-consuming due to the typically large number of wavelength bins. Furthermore,

George Tambouratzis, Marina Cortês, Andrew R. Liddle

Generative Artificial Intelligence (AI) technologies are in a phase of unprecedented rapid development following the landmark release of Chat-GPT, which brought the phenomenon to wide public attention. As the deployment of AI products rises geometrically, considerable attention is being given to the threats and opportunities that AI technologies offer, and t

Francesco Ziparo, Simona Gallerani, Andrea Ferrara

The presence of supermassive black holes (SMBHs, $M_{\bullet}\sim 10^{6-10}~M_{\odot}$) in the first cosmic Gyr ($z\gtrsim 6$) challenges current models of BH formation and evolution. We propose a novel mechanism for the formation of early SMBH seeds based on primordial black holes (PBHs). We assume a non-Gaussian primordial power spectrum as expected in inf

Adam R. Brown, Luca V. Iliesiu, Geoff Penington, Mykhaylo Usatyuk

Charged particle emission from black holes with sufficiently large charge is exponentially suppressed. As a result, such black holes are driven towards extremality by the emission of neutral Hawking radiation. Eventually, an isolated black hole gets close enough to extremality that the gravitational backreaction of a single Hawking photon becomes important,

Mikhail Lisakov, Svetlana Jorstad, Maciek Wielgus, Evgeniya V. Kravchenko

The advancement of the Event Horizon Telescope has enabled the study of relativistic jets in active galactic nuclei down to sub-parsec linear scales even at high redshift. Quasi-simultaneous multifrequency observations provide insights into the physical conditions in compact regions and allow testing accretion theories. Initially we aimed at measuring the ma

Todd Huster, Peter Lin, Razvan Stefanescu, Emmanuel Ekwedike

Neural networks can conceal malicious Trojan backdoors that allow a trigger to covertly change the model behavior. Detecting signs of these backdoors, particularly without access to any triggered data, is the subject of ongoing research and open challenges. In one common formulation of the problem, we are given a set of clean and poisoned models and need to

Maxim van den Berg, Pranjal Dutta, Fulvio Gesmundo, Christian Ikenmeyer

In the algebraic metacomplexity framework we prove that the decomposition of metapolynomials into their isotypic components can be implemented efficiently, namely with only a quasipolynomial blowup in the circuit size. We use this to resolve an open question posed by Grochow, Kumar, Saks & Saraf (2017). Our result means that many existing algebraic complexit

Zohar Schwartzman-Nowik, Benjamin J. Brown

Different choices of quantum error-correcting codes can reduce the demands on the physical hardware needed to build a quantum computer. To achieve the full potential of a code, we must develop practical decoding algorithms that can correct errors that have occurred with high likelihood. Matching decoders are very good at correcting local errors while also de

Gal Shkolnik, Sarang Gopalakrishnan, David A. Huse, Snir Gazit

We consider measurement-induced phase transitions in monitored quantum circuits with a measurement rate that fluctuates in time, remaining spatially uniform at each time. The spatially correlated fluctuations in the measurement rate disrupt the volume-law phase for low measurement rates; at a critical measurement rate, they give rise to an entanglement phase

Andrzej J. Buras

We summarize the recent strategy for an efficient hunting of new animalcula with the help of rare K and B decays that avoids the use of the $V_{cb}$ and $V_{ub}$ parameters that are subject to tensions between their determinations from inclusive and exclusive decays. In particular we update the values of the $V_{cb}$-independent ratios of various K and B dec

Jason Pollack, Dylan VanAllen

Some quantum algorithms have "quantum speedups": improved time complexity as compared with the best-known classical algorithms for solving the same tasks. Can we understand what fuels these speedups from an entropic perspective? Information theory gives us a multitude of metrics we might choose from to measure how fundamentally 'quantum' is the behavior of a

Csaba Csaki, Raffaele Tito D'Agnolo, Eric Kuflik, Pablo Sesma

We propose a model that provides a simultaneous solution to the doublet-triplet splitting problem of grand unified theories, the electroweak hierarchy problem and the strong CP problem. The mechanism is based on the dynamics of two axion-like particles that would crunch the universe at the time of the QCD phase transition if triplets were light or had a VEV

Arpit Arora, Jonathan B. Curtis, Prineha Narang

Photo-control of correlated phases is central to advancing and manipulating novel functional properties of quantum materials. Here, we explore microwave enhancement of superconductivity in flat bands through generation of nonequilibrium quasiparticles at subgap frequencies. In conventional superconductors, it is known to occur via radiation absorption determ

Francisco Duque, Shubham Kejriwal, Laura Sberna, Lorenzo Speri

We study the evolution of eccentric, equatorial extreme-mass-ratio inspirals (EMRIs) immersed in the accretion disks of active galactic nuclei. We find that single gravitational-wave observations from these systems could provide measurements with ~ 10 % relative precision of, simultaneously, the disk viscosity and mass accretion rate of the central supermass

Andreas Athenodorou, Sergei Dubovsky, Conghuan Luo, Michael Teper

We present a major update on the spectrum of closed flux tubes in $D=3+1$ $SU(N)$ gauge theories. We measure the excitation spectrum of confining strings wound around a spatial dimension of a size $R$. We do so for the $SU(N)$ Yang-Mills theory with $N=3,5,6$ and for two different values of the lattice spacing. We employ the generalized eigenvalue problem in

David Vartanyan, Benny T. H. Tsang, Daniel Kasen, Adam Burrows

In order to better connect core-collapse supernovae (CCSN) theory with its observational signatures, we have developed a simulation pipeline from the onset of core collapse to beyond shock breakout. Using this framework, we present a three-dimensional simulation study following the evolution from five seconds to over five days of a 17-M$_{\odot}$ progenitor

Connor Stratman, Tongyan Lin

At sufficiently low nuclear recoil energy, the scattering of dark matter (DM) in crystals gives rise to single phonon and multiphonon excitations. In anisotropic crystals, the scattering rate into phonons modulates over each sidereal day as the crystal rotates with respect to the DM wind. This gives a potential avenue for directional detection of DM. The dai

M. Smith, Kartiek Agarwal, Ivar Martin

We present a theory for nonlinear, resonant excitation of dynamical axions by counter-propagating electromagnetic waves in materials that break both $\mathcal{P}$ and $\mathcal{T}$ symmetries. We show that dynamical axions can mediate an exponential growth in the amplitude of the lower frequency (Stokes) beam. We also discuss spontaneous generation of a coun

Rui An, Ethan O. Nadler, Andrew Benson, Vera Gluscevic

We present $24$ cosmological dark matter (DM)-only zoom-in simulations of a Milky Way analog with initial conditions appropriate for scenarios where non-cold dark matter (NCDM) is a fraction of the total DM abundance (f-NCDM models) as the second installment of the COZMIC suite. We initialize our simulations using transfer functions, $T_{\mathrm{f-NCDM}}(k)\

A. Fraser-McKelvie, J. van de Sande, D. A. Gadotti, E. Emsellem

The vertical evolution of galactic discs is governed by the sub-structures within them. We examine the diversity of kinematic sub-structure present in the first 12 galaxies observed from the GECKOS survey, a VLT/MUSE large programme providing a systematic study of 36 edge-on, Milky Way-mass disc galaxies. Employing the nGIST analysis pipeline, we derive the

Kristian Tyn Kai Chung

We map the complete classical phase diagram of the spin Hamiltonian describing pyrochlore rare-earth magnets with all symmetry-allowed nearest-neighbor bond-dependent anisotropic two-spin interactions. We provide a simple derivation of the organization of spins into tensor degrees of freedom describing the multipole moments of a tetrahedron, whose components

Jeffery Yu, Sean R. Muleady, Yu-Xin Wang, Nathan Schine

We present an algorithm utilizing mid-circuit measurement and feedback that prepares Dicke states with polylogarithmically many ancillas and polylogarithmic depth. Our algorithm uses only global mid-circuit projective measurements and adaptively-chosen global rotations. This improves over prior work that was only efficient for Dicke states of low weight, or

Albert Sneppen, Oliver Just, Andreas Bauswein, Rasmus Damgaard

The time until black hole formation in a binary neutron-star (NS) merger contains invaluable information about the nuclear equation of state (EoS) but has thus far been difficult to measure. We propose a new way to constrain the merger remnant's NS lifetime, which is based on the tendency of the NS remnant neutrino-driven winds to enrich the ejected material

Takato Mori, Beni Yoshida

The Ryu-Takayanagi formula predicts that two boundary subsystems $A$ and $C$ can exhibit large mutual information $I(A:C)$ even when they are spatially disconnected on the boundary and separated by a buffer subsystem $B$, as long as $A$ and $C$ have connected entanglement wedge in the bulk. However, whether the reduced state $\rho_{AC}$ contains distillable

Simone Giacomelli, William Harding, Noppadol Mekareeya, Alessandro Mininno

A surprising relation between 4d $\mathcal{N}=2$ class $\mathcal{S}$ superconformal field theories of Type-$A$ and 6d $\mathcal{N}=(1,0)$ orbi-instanton theories is investigated. We find that all of the theories in the former class can be obtained by a series of deformations of the 4d theories arising from compactifying the latter on a torus. This is demonst

David J. Setton, Jenny E. Greene, Anna de Graaff, Yilun Ma

Among the most puzzling early discoveries of JWST are "Little Red Dots" -- compact red sources that host broad Balmer emission lines and, in many cases, exhibit a "V shaped" change in slope in the rest-optical. The physical properties of Little Red Dots currently have order-of-magnitude uncertainties, because models to explain the continuum of these sources

Vijay Balasubramanian, Monica Jinwoo Kang, Chitraang Murdia, Simon F. Ross

We study multiparty entanglement signals, which are functions of a quantum state that are non-zero only when the state has multiparty entanglement. We consider known signals of three- and four-party entanglement, and propose new signals for four- and higher-party entanglement. We make some remarks on their general properties, but mainly focus on using hologr

Ziliang Gan, Yu Lu, Dong Zhang, Haohan Li

In recent years, multimodal benchmarks for general domains have guided the rapid development of multimodal models on general tasks. However, the financial field has its peculiarities. It features unique graphical images (e.g., candlestick charts, technical indicator charts) and possesses a wealth of specialized financial knowledge (e.g., futures, turnover ra

Jin-Fu Chen, Kshiti Sneh Rai, Patrick Emonts, Donato Farina

Understanding and optimizing the relaxation dynamics of many-body systems is essential both for foundational studies in quantum thermodynamics and for applications such as quantum simulation and quantum computing. Efficient preparation of thermal states of a many-body Hamiltonian is governed by the spectral properties of the associated Lindbladian, in partic

Henning Schlömer, Annabelle Bohrdt, Fabian Grusdt

One of the key features of hole-doped cuprates is the presence of an extended pseudogap phase, whose microscopic origin has been the subject of intense investigation since its discovery and is believed to be crucial for understanding high-temperature superconductivity. Various explanations have been proposed for the pseudogap, including links to symmetry-bre

Henock M. Mboko, Mouhamadou A. M. T. Balde, Babacar M. Ndiaye

We study the analysis of all the movements of the population on the basis of their mobility from one node to another, to observe, measure, and predict the impact of traffic according to this mobility. The frequency of congestion on roads directly or indirectly impacts our economic or social welfare. Our work focuses on exploring some machine learning methods

Mikhail Razumovskiy, Boris Fomin, Denis Astanin

We present MARFA (Molecular atmospheric Absorption with Rapid and Flexible Analysis) -- an open-source line-by-line tool for calculating absorption coefficients and cross-sections in planetary atmospheres, particularly under conditions of uncertain spectroscopic data and missing continuum functions. With incorporated eleven-grid interpolation technique MARFA

Zilong Huang, Qinghao Ye, Bingyi Kang, Jiashi Feng

We introduce SuperClass, a super simple classification method for vision-language pre-training on image-text data. Unlike its contrastive counterpart CLIP who contrast with a text encoder, SuperClass directly utilizes tokenized raw text as supervised classification labels, without the need for additional text filtering or selection. Due to the absence of the

Alexander Goldberg, Ihsan Ullah, Thanh Gia Hieu Khuong, Benedictus Kent Rachmat

Large language models (LLMs) represent a promising, but controversial, tool in aiding scientific peer review. This study evaluates the usefulness of LLMs in a conference setting as a tool for vetting paper submissions against submission standards. We conduct an experiment at the 2024 Neural Information Processing Systems (NeurIPS) conference, where 234 paper

Zinuo Chang, Hongzhe Yu, Patricio Vela, Yongxin Chen

We cast motion planning under uncertainty as a stochastic optimal control problem, where the optimal posterior distribution has an explicit form. To approximate this posterior, this work frames an optimization problem in the space of Gaussian distributions by solving a Variational Inference (VI) in the path distribution space. For linear-Gaussian stochastic

Nicolò Scapin, Jiarong Wu, J. Thomas Farrar, Bertrand Chapron

We investigate the momentum fluxes between a turbulent air boundary layer and a growing-breaking wave field by solving the air-water two-phase Navier-Stokes equations through direct numerical simulations (DNS). A fully-developed turbulent airflow drives the growth of a narrowbanded wave field, whose amplitude increases until reaching breaking conditions. The

Thomas Coudert, Aurélien Delphin, Antoine Barrier, Loïc Legris

This study proposes a novel, contrast-free Magnetic Resonance Fingerprinting (MRF) method using balanced Steady-State Free Precession (bSSFP) sequences for the quantification of cerebral blood volume (CBV), vessel radius (R), and relaxometry parameters (T1, T2 T2*) in the brain. The technique leverages the sensitivity of bSSFP sequences to intra-voxel freque

Xiaoyu Chen, Zongchen Chen, Yitong Yin, Xinyuan Zhang

Over the past decades, a fascinating computational phase transition has been identified in sampling from Gibbs distributions. Though, the computational complexity at the critical point remains poorly understood, as previous algorithmic and hardness results all required a constant slack from this threshold. In this paper, we resolve this open question at the

Guy Moshkovitz, Daniel G. Zhu

Chen and Ye recently proved that the analytic rank of tensors is stable under field extensions, assuming a fixed base field. Using a more careful analysis, we show that this assumption is unnecessary.

Kevin Y. Li, Sachin Goyal, Joao D. Semedo, J. Zico Kolter

Vision Language Models (VLMs) have demonstrated strong capabilities across various visual understanding and reasoning tasks, driven by incorporating image representations into the token inputs of Large Language Models (LLMs). However, their real-world deployment is often constrained by high latency during inference due to the substantial compute required by

Wenhao Wang, Yi Yang

Video generation models are revolutionizing content creation, with image-to-video models drawing increasing attention due to their enhanced controllability, visual consistency, and practical applications. However, despite their popularity, these models rely on user-provided text and image prompts, and there is currently no dedicated dataset for studying thes

Samuel Kwan, Jesse Chan

We introduce a robust first order accurate meshfree method to numerically solve time-dependent nonlinear conservation laws. The main contribution of this work is the meshfree construction of first order consistent summation by parts differentiations. We describe how to efficiently construct such operators on a point cloud. We then study the performance of su

Yifan Wang

Crystalline topological insulators and superconductors have been a prominent topic in the field of condensed matter physics. These systems obey certain crystalline (spatial) symmetries that depend on the geometry of the lattice. The presence of spatial symmetries can lead to shift in the classification of ten fold Altland Zirnbauer class, given rise to new s

Laura Smith, Alex Irpan, Montserrat Gonzalez Arenas, Sean Kirmani

The complexity of the real world demands robotic systems that can intelligently adapt to unseen situations. We present STEER, a robot learning framework that bridges high-level, commonsense reasoning with precise, flexible low-level control. Our approach translates complex situational awareness into actionable low-level behavior through training language-gro

Adrian Röfer, Russell Buchanan, Max Argus, Sethu Vijayakumar

Efficient learning from demonstration for long-horizon tasks remains an open challenge in robotics. While significant effort has been directed toward learning trajectories, a recent resurgence of object-centric approaches has demonstrated improved sample efficiency, enabling transferable robotic skills. Such approaches model tasks as a sequence of object pos