December 2023 arXiv papers — page 92

Bingbin Liu, Sebastien Bubeck, Ronen Eldan, Janardhan Kulkarni

Small-scale models offer various computational advantages, and yet to which extent size is critical for problem-solving abilities remains an open question. Specifically for solving grade school math, the smallest model size so far required to break the 80\% barrier on the GSM8K benchmark remains to be 34B. Our work studies how high-quality datasets may be th

Olga Arroyo-Gascón, Sergio Bravo, Leonor Chico, Mónica Pacheco

We perform a systematic study of the signatures of fragile topology in over 50 nonmagnetic two-dimensional materials with formula AB$_2$, belonging to space group $P$-$3m1$. Using group theory analysis in the framework of topological quantum chemistry, we find fragile bands near the Fermi level for all the materials studied. Since stable topological bands ar

Karthik Chinni, Nicolás Quesada

We study the dynamics of the pump mode in the down-conversion Hamiltonian using the cumulant expansion method, perturbation theory, and the full numerical simulation of systems with a pump mean photon number of up to one hundred thousand. We particularly focus on the properties of the pump-mode such as depletion, entanglement, and squeezing for an experiment

Hao Li, Xue Yang, Zhaokai Wang, Xizhou Zhu

Many reinforcement learning environments (e.g., Minecraft) provide only sparse rewards that indicate task completion or failure with binary values. The challenge in exploration efficiency in such environments makes it difficult for reinforcement-learning-based agents to learn complex tasks. To address this, this paper introduces an advanced learning system,

Jiarui Xu, Xingyi Zhou, Shen Yan, Xiuye Gu

Large language models have achieved great success in recent years, so as their variants in vision. Existing vision-language models can describe images in natural languages, answer visual-related questions, or perform complex reasoning about the image. However, it is yet unclear how localization tasks, such as word grounding or referring localization, can be

Kieran Didi, Francisco Vargas, Simon V Mathis, Vincent Dutordoir

Many protein design applications, such as binder or enzyme design, require scaffolding a structural motif with high precision. Generative modelling paradigms based on denoising diffusion processes emerged as a leading candidate to address this motif scaffolding problem and have shown early experimental success in some cases. In the diffusion paradigm, motif

Collin C. D. Frink, Talise Oh, E. S. Joseph, Merritt P. Losert

Nanofabricated metal gate electrodes are commonly used to confine and control electrons in electrostatically defined quantum dots. However, these same gates impart strain-induced potential fluctuations that can potentially impair device functionality. Here we investigate strain fluctuations in Si/SiGe heterostructures, caused by (i) lattice mismatch, (ii) ma

Noa Moriel, Matthew Ricci, Mor Nitzan

Dynamical systems across the sciences, from electrical circuits to ecological networks, undergo qualitative and often catastrophic changes in behavior, called bifurcations, when their underlying parameters cross a threshold. Existing methods predict oncoming catastrophes in individual systems but are primarily time-series-based and struggle both to categoriz

Jacob Nibauer, Ana Bonaca, Mariangela Lisanti, Denis Erkal

Stellar streams are sensitive tracers of the gravitational potential, which is typically assumed to be static in the inner Galaxy. However, massive mergers like Gaia-Sausage-Enceladus can impart torques on the stellar disk of the Milky Way that result in the disk tilting at rates of up to 10-20 deg/Gyr. Here, we demonstrate the effects of disk tilting on the

Audrey Chung, Francis Li, Jeremy Ward, Andrew Hryniowski

As electronics manufacturers continue to face pressure to increase production efficiency amid difficulties with supply chains and labour shortages, many printed circuit board assembly (PCBA) manufacturers have begun to invest in automation and technological innovations to remain competitive. One such method is to leverage artificial intelligence (AI) to grea

Christopher Fechisin, Nathanan Tantivasadakarn, Victor V. Albert

Despite growing interest in beyond-group symmetries in quantum condensed matter systems, there are relatively few microscopic lattice models explicitly realizing these symmetries, and many phenomena have yet to be studied at the microscopic level. We introduce a one-dimensional stabilizer Hamiltonian composed of group-based Pauli operators whose ground state

Thibaut Loiseau, Tuan-Hung Vu, Mickael Chen, Patrick Pérez

Assessing the robustness of perception models to covariate shifts and their ability to detect out-of-distribution (OOD) inputs is crucial for safety-critical applications such as autonomous vehicles. By nature of such applications, however, the relevant data is difficult to collect and annotate. In this paper, we show for the first time how synthetic data ca

Rhys Gould, Euan Ong, George Ogden, Arthur Conmy

In this work we present successor heads: attention heads that increment tokens with a natural ordering, such as numbers, months, and days. For example, successor heads increment 'Monday' into 'Tuesday'. We explain the successor head behavior with an approach rooted in mechanistic interpretability, the field that aims to explain how models complete tasks in h

A. R. Mirotin

Let $\psi$ be a Bernstein function in one variable. A.~Carasso and T.~Kato obtained necessary and sufficient conditions for $\psi$ to have a property that $\psi(A)$ generates a quasibounded holomorphic semigroup for every generator $A$ of a bounded $C_0$-semigroup in a Banach space and deduced necessary conditions as well. We generalize their results to the

Zhiyin Qian, Shaofei Wang, Marko Mihajlovic, Andreas Geiger

We introduce an approach that creates animatable human avatars from monocular videos using 3D Gaussian Splatting (3DGS). Existing methods based on neural radiance fields (NeRFs) achieve high-quality novel-view/novel-pose image synthesis but often require days of training, and are extremely slow at inference time. Recently, the community has explored fast gri

Myles Workman

We consider a sequence of bubble converging minimal hypersurfaces, or H-CMC hypersurfaces, in compact Riemannian manifolds without boundary, of dimension 4, 5, 6 or 7, and prove upper semicontinuity of index plus nullity, for such a bubble converging sequence. This complements the previously known lower semicontinuity of index obtained by Buzano--Sharp, and

Mishary Alrashed, Zhong-Bo Kang, John Terry, Hongxi Xing

In this study, we extend our previous global analysis of nuclear-modified transverse momentum distribution functions (nTMDs) to also consider the nuclear-modified collinear fragmentation function. Our methodology incorporates the global set of experimental data from both Drell-Yan production and Semi-Inclusive Deep Inelastic Scattering. Through a comprehensi

Daniel Sanz-Alonso, Ruiyi Yang

Gaussian process regression is a classical kernel method for function estimation and data interpolation. In large data applications, computational costs can be reduced using low-rank or sparse approximations of the kernel. This paper investigates the effect of such kernel approximations on the interpolation error. We introduce a unified framework to analyze

Bence Csonka, Gábor Simonyi

We investigate the effect of the well-known Mycielski construction on the Shannon capacity of graphs and on one of its most prominent upper bounds, the (complementary) Lov\'asz theta number. We prove that if the Shannon capacity of a graph, the distinguishability graph of a noisy channel, is attained by some finite power, then its Mycielskian has strictly la

Anffany Chen

We adapt a machine-learning approach to study the many-body localization transition in interacting fermionic systems on disordered 1D and 2D lattices. We perform supervised training of convolutional neural networks (CNNs) using labelled many-body wavefunctions at weak and strong disorder. In these limits, the average validation accuracy of the trained CNNs e

Lior Yariv, Omri Puny, Natalia Neverova, Oran Gafni

Current diffusion or flow-based generative models for 3D shapes divide to two: distilling pre-trained 2D image diffusion models, and training directly on 3D shapes. When training a diffusion or flow models on 3D shapes a crucial design choice is the shape representation. An effective shape representation needs to adhere three design principles: it should all

Olivier Delouche, Joan Elias Miro, James Ingoldby

We develop the theory of Hamiltonian Truncation (HT) to systematically study RG flows that require the renormalization of coupling constants. This is a necessary step towards making HT a fully general method for QFT calculations. We apply this theory to a number of QFTs defined as relevant deformations of $d=1+1$ CFTs. We investigated three examples of incre

A. C. Krabbe, J. A. Hernandez-Jimenez, C. Mendes de Oliveira, Y. L. Jaffe

This paper presents a method for finding ram-pressure stripped (RPS) galaxy candidates by performing a morphological analysis of galaxy images obtained from the Legacy survey. We consider a sample of about 600 galaxies located in different environments such as groups and clusters, tidally interacting pairs and the field. The sample includes 160 RPS previousl

Bo Xiong, Mojtaba Nayyeri, Linhao Luo, Zihao Wang

Reasoning with knowledge graphs (KGs) has primarily focused on triple-shaped facts. Recent advancements have been explored to enhance the semantics of these facts by incorporating more potent representations, such as hyper-relational facts. However, these approaches are limited to \emph{atomic facts}, which describe a single piece of information. This paper

Bora Basyildiz, Casey Jameson, Zhexuan Gong

The speed of elementary quantum gates ultimately sets the limit on the speed at which quantum circuits can operate. For a fixed physical interaction strength between two qubits, the speed of any two-qubit gate is limited even with arbitrarily fast single-qubit gates. In this work, we explore the possibilities of speeding up two-qubit gates beyond such a limi

Ludvig Doeser, Drew Jamieson, Stephen Stopyra, Guilhem Lavaux

Analysing next-generation cosmological data requires balancing accurate modeling of non-linear gravitational structure formation and computational demands. We propose a solution by introducing a machine learning-based field-level emulator, within the Hamiltonian Monte Carlo-based Bayesian Origin Reconstruction from Galaxies (BORG) inference algorithm. Built

Martin Bojowald, Erick I. Duque, Dennis Hartmann

Emergent modified gravity presents a new class of gravitational theories in which the structure of space-time with Riemannian geometry of a certain signature is not presupposed. Relying on crucial features of a canonical formulation, the geometry of space-time is instead derived from the underlying dynamical equations for phase-space degrees of freedom toget

Vir B. Bulchandani, S. L. Sondhi, J. T. Chalker

We study the competition between Haar-random unitary dynamics and measurements for unstructured systems of qubits. For projective measurements, we derive various properties of the statistical ensemble of Kraus operators analytically, including the purification time and the distribution of Born probabilities. The latter generalizes the Porter-Thomas distribut

Abhishek Setty, Rasul Abdusalamov, Felix Motzoi

Chebyshev polynomials have shown significant promise as an efficient tool for both classical and quantum neural networks to solve linear and nonlinear differential equations. In this work, we adapt and generalize this framework in a quantum machine learning setting for a variety of problems, including the 2D Poisson's equation, second-order linear differenti

Maxence Mayrand

We introduce a notion of coisotropics on 1-shifted symplectic Lie groupoids (i.e. quasi-symplectic groupoids) using twisted Dirac structures and show that it satisfies properties analogous to the corresponding derived-algebraic notion in shifted Poisson geometry. In particular, intersections of 1-coisotropics are 0-shifted Poisson. We also show that 1-shifte

Shengqi Yang, Adam Lidz, Andrew Benson, Swathya Singh Chauhan

The \textit{JWST} is allowing new measurements of gas-phase metallicities in galaxies between cosmic noon and cosmic dawn. The most robust approach uses luminosity ratios between the excited auroral transition, [\oiii] 4364\,\AA, and the lower [\oiii] 5008\,\AA/4960\,\AA\ lines to determine the gas temperature. The ratio of the luminosities in the latter tra

Claudio Bonanno, Jorge Luis Dasilva Golán, Massimo D'Elia, Margarita García Pérez

We present a proposal for calculating the running of the coupling constant of the $\mathrm{SU}(3)$ pure-gauge theory, which combines the Twisted Gradient Flow (TGF) renormalization scheme with Parallel Tempering on Boundary Conditions (PTBC). The TGF is a gradient flow-based renormalization scheme formulated in an asymmetric lattice with twisted boundary con

Alireza Ghaffari, Justin Yu, Mahsa Ghazvini Nejad, Masoud Asgharian

Low-precision fine-tuning of language models has gained prominence as a cost-effective and energy-efficient approach to deploying large-scale models in various applications. However, this approach is susceptible to the existence of outlier values in activation. The outlier values in the activation can negatively affect the performance of fine-tuning language

Jameson Dong, Guglielmo Mastroserio, Javier A. Garcıa, Adam Ingram

Accretion around black holes is very often characterized by distinctive X-ray reflection features (mostly, iron inner-shell transitions), which arise due to the primary radiation being reprocessed by a dense and relatively colder medium, such as an accretion disk. Most reflection modeling assume that emission stops at the inner-most stable circular orbit (IS

Libor Caha, Xavier Coiteux-Roy, Robert Koenig

We present a computational problem with the following properties: (i) Every instance can be solved with near-certainty by a constant-depth quantum circuit using only nearest-neighbor gates in 3D even when its implementation is corrupted by noise. (ii) Any constant-depth classical circuit composed of unbounded fan-in AND, OR, as well as NOT gates, i.e., an AC

Omar Tout

It is known that for any graph $G,$ $\gamma (G\square P_2)\geq \gamma (G)$ where $\gamma$ stands for the domination number, $\square$ for the cartesian product and $P_2$ is the path graph on two vertices. In an attempt to prove Vizing's conjecture, Clark and Suen proved in $2000$ that $\gamma (X\square Y)\geq \frac{1}{2}\gamma (X)\gamma (Y)$ for any pair of

PHENIX Collaboration, N. J. Abdulameer, U. Acharya, A. Adare

The PHENIX experiment has performed a systematic study of identified charged-hadron ($\pi^\pm$, $K^\pm$, $p$, $\bar{p}$) production at midrapidity in $p$$+$Al, $^3$He$+$Au, Cu$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV and U$+$U collisions at $\sqrt{s_{_{NN}}}=193$ GeV. Identified charged-hadron invariant transverse-momentum ($p_T$) and transverse-mass ($

Benno Weck, Holger Kirchhoff, Peter Grosche, Xavier Serra

Multi-modal deep learning techniques for matching free-form text with music have shown promising results in the field of Music Information Retrieval (MIR). Prior work is often based on large proprietary data while publicly available datasets are few and small in size. In this study, we present WikiMuTe, a new and open dataset containing rich semantic descrip

Tudor Giurgica-Tiron, Adam Bouland

We show it is possible to obtain quantum pseudorandomness and pseudoentanglement from random subset states -- i.e. quantum states which are equal superpositions over (pseudo)random subsets of strings. This answers an open question of Aaronson et al. [arXiv:2211.00747], who devised a similar construction augmented by pseudorandom phases. Our result follows fr

George Barnes, Adrian Padellaro, Sanjaye Ramgoolam

Matrix models with continuous symmetry are powerful tools for studying quantum gravity and holography. Tensor models have also found applications in holographic quantum gravity. Matrix models with discrete permutation symmetry have been shown to satisfy large $N$ factorisation properties relevant to holography, while also having applications to the statistic

Rubén Seoane Souto, Martin Leijnse, Constantin Schrade, Marco Valentini

Josephson diodes are superconducting elements that show an asymmetry in the critical current depending on the direction of the current. Here, we theoretically explore how an alternating current bias can tune the response of such a diode. We show that for slow driving there is always a regime where the system can only carry zero-voltage dc current in one dire

Sean O'Hagan, Aaron Schein

Much of social science is centered around terms like ``ideology'' or ``power'', which generally elude precise definition, and whose contextual meanings are trapped in surrounding language. This paper explores the use of large language models (LLMs) to flexibly navigate the conceptual clutter inherent to social scientific measurement tasks. We rely on LLMs' r

Matthew Jenssen, Will Perkins, Aditya Potukuchi

We study the typical structure and the number of triangle-free graphs with $n$ vertices and $m$ edges where $m$ is large enough so that a typical triangle-free graph has a cut containing nearly all of its edges, but may not be bipartite. Erd\H{o}s, Kleitman, and Rothschild showed that almost every triangle-free graph is bipartite. Osthus, Pr\"omel, and Taraz

Alexander M. G. Cox, Annemarie M. Grass

The robust option pricing problem is to find upper and lower bounds on fair prices of financial claims using only the most minimal assumptions. It contrasts with the classical, model-based approach and gained prominence in the wake of the 2008 financial crisis, and can be used to understand the extent to which a model-based price is sensitive to the underlyi

V. G. Valle, L. L. Brugger, B. F. Rizzuti, Cristhiano Duarte

This work seeks to make explicit the operational connection between the preparation of two-level quantum systems with their corresponding description (as states) in a Hilbert space. This may sound outdated, but we show there is more to this connection than common sense may lead us to believe. To bridge these two separated realms -- the actual laboratory and

Shun Liu, Jianan Zhang, Ruocheng Song, Teik Toe Teoh

Object detection and localization are crucial tasks for biomedical image analysis, particularly in the field of hematology where the detection and recognition of blood cells are essential for diagnosis and treatment decisions. While attention-based methods have shown significant progress in object detection in various domains, their application in medical ob

Haozhe Wang, Long Chen, Aya Rutherford, Haidong Zhou

The recent discovery of superconductivity in Ruddlesden-Popper bilayer nickelate, specifically La$_3$Ni$_2$O$_7$, has generated significant interest in the exploration of high-temperature superconductivity within this material family. In this study, we present the crystallographic and electrical resistivity properties of two distinct Ruddlesden-Popper nickel

Aaron Fenyes, Arnaud Maret

We present a way to build hyperbolic spheres with conical singularities by gluing together simple building blocks. Our construction provides good control over the holonomy of the resulting hyperbolic cone sphere. In particular, it can be used to realize any Deroin-Tholozan (DT) representation as the holonomy of a hyperbolic cone sphere. Our construction is i

Quinten Steenhuis, David Colarusso, Bryce Willey

Can generative AI help us speed up the authoring of tools to help self-represented litigants? In this paper, we describe 3 approaches to automating the completion of court forms: a generative AI approach that uses GPT-3 to iteratively prompt the user to answer questions, a constrained template-driven approach that uses GPT-4-turbo to generate a draft of ques

Hao Chen, Abhishek Gupta, Yin Sun, Ness Shroff

This paper considers the change point detection problem under dependent samples. In particular, we provide performance guarantees for the MMD-CUSUM test under exponentially $\alpha$, $\beta$, and fast $\phi$-mixing processes, which significantly expands its utility beyond the i.i.d. and Markovian cases used in previous studies. We obtain lower bounds for ave

Shyam Nuggehalli, Jifan Zhang, Lalit Jain, Robert Nowak

Class imbalance severely impacts machine learning performance on minority classes in real-world applications. While various solutions exist, active learning offers a fundamental fix by strategically collecting balanced, informative labeled examples from abundant unlabeled data. We introduce DIRECT, an algorithm that identifies class separation boundaries and

Peter Dillery

We show that, over a nonarchimedean local field, the rigid refined local Langlands correspondence and associated endoscopic character identities for connected reductive $G$ follow if one only has them for all such $G$ with connected center. The strategy is to construct a projective system of central extensions and then take limits of the Langlands correspond

Hugo Latourelle-Vigeant, Elliot Paquette

This paper develops some theory of the Dyson equation for correlated linearizations and uses it to solve a problem on asymptotic deterministic equivalent for the test error in random features regression. The theory developed for the correlated Dyson equation includes existence-uniqueness, spectral support bounds, and stability properties. This theory is new

Yuri Alexeev, Maximilian Amsler, Paul Baity, Marco Antonio Barroca

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their simulation, analysis, and data resources. Quantum computing, on the other hand, is an emerging technology w

Zixiang Chen, Huizhuo Yuan, Yongqian Li, Yiwen Kou

Discrete diffusion models have emerged as powerful tools for high-quality data generation. Despite their success in discrete spaces, such as text generation tasks, the acceleration of discrete diffusion models remains under-explored. In this paper, we propose discrete non-Markov diffusion models (DNDM), which naturally induce the predetermined transition tim

Javier de Lucas, Julia Lange, Xavier Rivas

By using the theory of analytic vectors and manifolds modelled on normed spaces, we provide a rigorous symplectic differential geometric approach to $t$-dependent Schr\"odinger equations on separable (possibly infinite-dimensional) Hilbert spaces determined by unbounded $t$-dependent self-adjoint Hamiltonians satisfying a technical condition. As an applicati

Aravind Bharathi Valluvan, Ashwin Goyal, Devansh Jain, Abhinna Sundar Samantaray

We present a catalog of 6266 solar flares detected by the X-Ray Solar Monitor onboard the Chandrayaan-2 lunar orbiter between 1.55 and 12.4 keV (1 and 8 \AA) from 2019 September 12 to 2022 November 4, including 1469 type A flares. The catalog represents the first large sample, including both type A, hot thermal flares, and type B, impulsive flares, with a su

Kevin Tracy, Zachary Manchester, Ajinkya Jain, Keegan Go

Contact-rich manipulation tasks with stiff frictional elements like connector insertion are difficult to model with rigid-body simulators. In this work, we propose a new approach for modeling these environments by learning a quasi-static contact force model instead of a full simulator. Using a feature vector that contains information about the configuration

Innes Bigaran, Bogdan A. Dobrescu, Alessandro Russo

We study the properties of vectorlike fermions that have the same gauge charges as the Standard Model lepton doublets, but opposite lepton number. These antileptons undergo decays mediated by heavier scalar leptoquarks, while the symmetries of this renormalizable model protect the vectorlike fermions and the leptoquarks from standard decays probed so far at

Kate Baumli, Satinder Baveja, Feryal Behbahani, Harris Chan

Building generalist agents that can accomplish many goals in rich open-ended environments is one of the research frontiers for reinforcement learning. A key limiting factor for building generalist agents with RL has been the need for a large number of reward functions for achieving different goals. We investigate the feasibility of using off-the-shelf vision

Mateus Corradini Lopes, Alex Antonelli

The knowledge of Half-Heusler compounds have attracted much attention as materials for thermoelectric applications. In this work, we investigate, using first-principles calculations, the electronic, vibrational, and defect properties of TiXSn (X=Ni, Pd, Pt) half-Heusler compounds. The knowledge of such properties is vital for the understanding and improvemen

Anastasios Fragkos, A. Walton Green, Brett D. Wick

We prove a wavelet $T(1)$ theorem for compactness of multilinear Calder\'{o}n-Zygmund (CZ) operators. Our approach characterizes compactness in terms of testing conditions and yields a representation theorem for compact CZ forms in terms of wavelet and paraproduct forms that reflect the compact nature of the operator.

Oscar Garcia-Montero, Aleksas Mazeliauskas, Philip Plaschke, Sören Schlichting

We use QCD kinetic theory to compute photon production in the chemically equilibrating Quark-Gluon Plasma created in the early stages of high-energy heavy-ion collisions. We show that the photon spectrum radiated from an attractor evolution satisfies a simple scaling form in terms of the specific shear viscosity $\eta/s$ and entropy density $dS/d\zeta \sim {

A. Rodríguez-Ardila, D. May, S. Panda, M. A. Fonseca-Faria

We study in detail the inner 600 pc of the Seyfert 2 galaxy ESO138-G001 by means of the SOAR Integral Field Spectrograph (SIFS) attached to the SOAR telescope. This source is known for displaying a very rich coronal line spectrum and a blob of high-excitation emission ~3" SE of the active galactic nucleus (AGN). The nature of this emission has not been fully

Nianyi Chen, Diptajyoti Mukherjee, Tiziana Di Matteo, Yueying Ni

The elusive massive black hole (MBH) seeds stand to be revealed by the Laser Space Antenna Interferometer through mergers. As an aftermath of galaxy mergers, MBH coalescence is a vastly multi-scale process connected to galaxy formation. We introduce the "Massive black hole Assembly in Galaxies Informed by Cosmological Simulations" (MAGICS) suite, with galaxy

Drew Priebe, Burooj Ghani, Dan Stowell

The ongoing biodiversity crisis, driven by factors such as land-use change and global warming, emphasizes the need for effective ecological monitoring methods. Acoustic monitoring of biodiversity has emerged as an important monitoring tool. Detecting human voices in soundscape monitoring projects is useful both for analysing human disturbance and for privacy

P. K. Maslennikov, A. V. Volotka, S. S. Baturin

We investigate the twisted state of an atom and the possible effect of such a state on the properties of the photons emitted as a result of an electron transition in that atom. We first propose a framework for describing the twisted atomic state, and then explore possible differences in the nuclear recoil effects in the twisted atom compared to those in the

Huijie Zhang, Yifu Lu, Ismail Alkhouri, Saiprasad Ravishankar

Diffusion models, emerging as powerful deep generative tools, excel in various applications. They operate through a two-steps process: introducing noise into training samples and then employing a model to convert random noise into new samples (e.g., images). However, their remarkable generative performance is hindered by slow training and sampling. This is d

Yinhao Wu, Min-Kai Lin, Can Cui, Leonardo Krapp

Recent studies have shown that the large-scale gas dynamics of protoplanetary disks (PPDs) are controlled by non-ideal magneto-hydrodynamics (MHD), but how this influences dust dynamics is not fully understood. To this end, we investigate the stability of dusty, magnetized disks subject to the Hall effect, which applies to planet-forming regions of PPDs. We

Admir Greljo, Ajdin Palavrić, Aleks Smolkovič

The stability of the electroweak scale, challenged by the absence of deviations in flavor physics, prompts the consideration of SMEFT scenarios governed by approximate SM flavor symmetries. This study examines microscopic theories that match onto a set of $U(3)^5$-symmetric dimension-6 operators. Renormalization group mixing from the ultraviolet to the elect

Prathmesh Vinze, Sebastien Michelin

Janus phoretic particles exploit chemical energy stored in their environment to self-propel. These active particles modify and respond to their hydrodynamic and chemical environments, thus giving them a sensibility to external flows and other particles. Furthermore, experimental observations and analysis on biological or synthetic active suspensions indicate

Isidor Konrad Maier, Matthias Wolff

This paper presents a novel numeral decomposer based on arithmetic criteria. The criteria are not dependent on a base-10 assumption but only on Hurford's Packing Strategy. Hurford's Packing Strategy constitutes numerals by packing factors and summands to multiplicators. We found out that a numeral of value n has a multiplicator larger than sqrt(n), a summand

Shreyas S. Joglekar, Korbinian Baumgaertl, Andrea Mucchietto, Francis Berger

Spin waves (magnons) can enable wave-based neuromorphic computing by which one aims at overcoming limitations inherent to conventional electronics and the von Neumann architecture. In this study, we explore the storage of magnon signals and the magnetization switching of periodic and aperiodic arrays of Ni81Fe19 (Py) nanostripes with widths (w) between 50 nm

Agnese Marcato, Daniel O'Malley, Hari Viswanathan, Eric Guiltinan

Recreating complex, high-dimensional global fields from limited data points is a grand challenge across various scientific and industrial domains. Given the prohibitive costs of specialized sensors and the frequent inaccessibility of certain regions of the domain, achieving full field coverage is typically not feasible. Therefore, the development of algorith

Jason Kountouridis

We study the ramification on the cohomology of a smooth proper surface $X$ in mixed characteristic, in the particular case where $X$ degenerates to a surface over $\overline{\mathbb{F}}_p$ with simple singularities, also known as rational double points. We find that the associated monodromy action of inertia depends on a formal affine neighborhood of the sin

Michael Kölle, Afrae Ahouzi, Pascal Debus, Robert Müller

Quantum computing, with its potential to enhance various machine learning tasks, allows significant advancements in kernel calculation and model precision. Utilizing the one-class Support Vector Machine alongside a quantum kernel, known for its classically challenging representational capacity, notable improvements in average precision compared to classical

Sriram S. K. S Narayanan, Andrew Zheng, Umesh Vaidya

This paper presents a motion planning algorithm for quadruped locomotion based on density functions. We decompose the locomotion problem into a high-level density planner and a model predictive controller (MPC). Due to density functions having a physical interpretation through the notion of occupancy, it is intuitive to represent the environment with safety

P. Romano, E. Bozzo, N. Islam, R. H. D. Corbet

We present the first Swift/XRT long-term monitoring of 2S 0114+650, a wind-fed supergiant X-ray binary for which both orbital and superorbital periods are known (P_orb~11.6d and P_sup~0.8d). Our campaign, summing up to ~ 79ks, is the most intense and complete sampling of the X-ray light curve of this source with a sensitive pointed X-ray instrument, and cove

Paulo M. Sá

We investigate a coupled quintessence cosmological model in which a dark-energy scalar field with an exponential potential interacts directly with a dark-matter fluid through a dissipative term inspired by warm inflation. The evolution equations of this model give rise to a three-dimensional dynamical system for which a thorough qualitative analysis is perfo

Alexandr Marunchenko, Jitendra Kumar, Alexander Kiligaridis, Shraddha M. Rao

Neuromorphic computing promises to transform the current paradigm of traditional computing towards Non-Von Neumann dynamic energy-efficient problem solving. Thus, dynamic memory devices capable of simultaneously performing nonlinear operations (volatile) similar to neurons and also storing information (non-volatile) alike brain synapses are in the great dema

Guido Menichetti, Lorenzo Cavicchi, Leonardo Lucchesi, Fabio Taddei

Chirality-induced spin selectivity (CISS) is an effect that has recently attracted a great deal of attention in chiral chemistry and that remains to be understood. In the CISS effect, electrons passing through chiral molecules acquire a large degree of spin polarization. In this work we study the case of atomically-thin chiral crystals created by van der Waa

Pakkapon Phongthawee, Worameth Chinchuthakun, Nontaphat Sinsunthithet, Amit Raj

We present a simple yet effective technique to estimate lighting in a single input image. Current techniques rely heavily on HDR panorama datasets to train neural networks to regress an input with limited field-of-view to a full environment map. However, these approaches often struggle with real-world, uncontrolled settings due to the limited diversity and s

Pallavi Jain, Rohit Vaish

The maximum Nash social welfare (NSW) -- which maximizes the geometric mean of agents' utilities -- is a fundamental solution concept with remarkable fairness and efficiency guarantees. The computational aspects of NSW have been extensively studied for one-sided preferences where a set of agents have preferences over a set of resources. Our work deviates fro

Safinaz Salem

We introduce a new scenario to solve the hierarchy problem based on $\mathcal{N}=2$, five-dimensional supergravity compactified on Calabi-Yau threefold down from $\mathcal{D}=11$ supergravity. When modeling the universe as a 3-brane embedded in a five-dimensional bulk, the background metric is proportional to one of the hypermultiplets fields, namely the dil

Sumithra R. Yerasi, Jason R. Picardo, Anupam Gupta, Dario Vincenzi

Simulations of elastic turbulence, the chaotic flow of highly elastic and inertialess polymer solutions, are plagued by numerical difficulties: The chaotically advected polymer conformation tensor develops extremely large gradients and can loose its positive definiteness, which triggers numerical instabilities. While efforts to tackle these issues have produ

Colin Holm-Hansen, M. E. Putman, D. A. Kim

We present findings of 3D filamentary structures in the Smith Cloud, a high-velocity cloud (HVC) located at $l=38^{\circ}$, $b=-13^{\circ}$. We use data from the Galactic Arecibo L-Band Feed Array \ion{H}{i} (GALFA-\ion{H}{i}) along with our new filament detection algorithm, \texttt{fil3d}, to characterize these structures. In this paper, we also discuss how

Zuzanna M. Jedlinska, Robert A. Riggleman

Associative polymer networks have shown a major promise in fabrication of self-healing and responsive materials. The can also serve as simple models to study more complex biological systems where transient interactions play an important role. In this work we investigate the properties of charged polymer blends whose constituents are capable of creating dynam

Abu Mohammmad Hammad Ali, Boting Yang, Sandra Zilles

This paper studies the design and analysis of approximation algorithms for aggregating preferences over combinatorial domains, represented using Conditional Preference Networks (CP-nets). Its focus is on aggregating preferences over so-called \emph{swaps}, for which optimal solutions in general are already known to be of exponential size. We first analyze a

Johann Bouali

We show that a Hodge class of a complex smooth projective hypersurface is an analytic logarithmic De Rham class. On the other hand we show that for a complex smooth projective variety an analytic logarithmic De Rham class of of type $(d,d)$ is the class of codimension $d$ algebraic cycle. We deduce the Hodge conjecture for smooth projective hypersurfaces.

Yu Qiao, Zhaoru Shang

It has long been known that, fundamentally different from a large body of rarefied gas, when a Knudsen gas is immersed in a thermal bath, it may never reach thermal equilibrium. The root cause is nonchaoticity: as the particle-particle collisions are sparse, the particle trajectories tend to be independent of each other. Usually, this counterintuitive phenom

Aditya Kapilavai, Georg Nawratil

The kinematic/robotic community is not only interested in measuring the closeness of a given robot configuration to its next singular one but also in a geometric meaningful index evaluating how far the robot design is away from being architecturally singular. Such an architecture singularity distance, which can be used by engineers as a criterion within the

Andrew Jong, Mukai Yu, Devansh Dhrafani, Siva Kailas

We present the Wildland-fire Infrared Thermal (WIT-UAS) dataset for long-wave infrared sensing of crew and vehicle assets amidst prescribed wildland fire environments. While such a dataset is crucial for safety monitoring in wildland fire applications, to the authors' awareness, no such dataset focusing on assets near fire is publicly available. Presumably,

Junfeng Wu, Yi Jiang, Qihao Liu, Zehuan Yuan

We present GLEE in this work, an object-level foundation model for locating and identifying objects in images and videos. Through a unified framework, GLEE accomplishes detection, segmentation, tracking, grounding, and identification of arbitrary objects in the open world scenario for various object perception tasks. Adopting a cohesive learning strategy, GL

Emmanuel Letellier, GyeongHyeon Nam

We know from Letellier that if for some triple of partitions the corresponding Kronecker coefficient is non-zero then the corresponding multiplicities for unipotent characters of GL(n,q) is also non-zero. A conjecture of Saxl says that the tensor square of an irreducible character of the symmetric group corresponding to a staircase partition contains all the

Yasmin T. Davis, Amaury H. M. J. Triaud, Alix V. Freckelton, Annelies Mortier

In the hunt for Earth-like exoplanets it is crucial to have reliable host star parameters, as they have a direct impact on the accuracy and precision of the inferred parameters for any discovered exoplanet. For stars with masses between 0.35 and 0.5 ${\rm M_{\odot}}$ an unexplained radius inflation is observed relative to typical stellar models. However, for

Cheng Zheng

In this paper, we study a shrinking target problem with target at infinity in a homogeneous space of a semisimple algebraic group from the representation-theoretic point of view. Let $\rho:\mathbf G\to\mathbf{GL}(V)$ be an irreducible $\mathbb Q$-rational representation of a connected semisimple $\mathbb Q$-algebraic group $\mathbf G$ on a complex vector spa

Yingrui Wu, Mingyang Zhao, Keqiang Li, Weize Quan

This work presents an accurate and robust method for estimating normals from point clouds. In contrast to predecessor approaches that minimize the deviations between the annotated and the predicted normals directly, leading to direction inconsistency, we first propose a new metric termed Chamfer Normal Distance to address this issue. This not only mitigates

Joe H. Winter, Reyhan Ay, Bernd Braunecker, A. M. Cook

We introduce methods of characterizing entanglement, in which entanglement measures are enriched by the matrix representations of operators for observables. These observable operator matrix representations can enrich the partial trace over subsets of a system's degrees of freedom, yielding reduced density matrices useful in computing various measures of enta

Manuel Rebol, Krzysztof Pietroszek, Neal Sikka, Claudia Ranniger

Augmented reality (AR) has great potential for use in healthcare applications, especially remote medical training and supervision. In this paper, we analyze the usage of an AR communication system to teach a medical procedure, the placement of a central venous catheter (CVC) under ultrasound guidance. We examine various AR communication and collaboration com

Thibault Merle, Dimitri Pourbaix, Alain Jorissen, Christos Siopis

The Gaia mission is delivering a large number of astrometric orbits for binary stars. By combining these with spectroscopic orbits for systems with two observable spectra (SB2), it is possible to derive the masses of both components. However, to get masses with a good accuracy requires accurate spectroscopic orbits, which is the major aim of the present pape