February 2024 arXiv papers — page 95

Melika Babakan, Arman Kashef, Laleh Memarzadeh

We study entanglement degradation when noise on one share of an entangled pair is described by the dissipative Landau-Zener model. We show that spin-coupling direction to the environment significantly affects entanglement dynamics. In particular, for zero bath temperature in the slow-driving regime with transversal coupling, entanglement remains intact and i

Om Kalthoum Wanassi, Delfim F. M. Torres

We propose a novel dynamical model for blood alcohol concentration that incorporates $\psi$-Caputo fractional derivatives. Using the generalized Laplace transform technique, we successfully derive an analytic solution for both the alcohol concentration in the stomach and the alcohol concentration in the blood of an individual. These analytical formulas provi

Ralf R. Müller

We propose a new continuous phase frequency shift keying that is particularly suited for multi-antenna communications when the link budget is critical and beam alignment is problematic. It combines the constant envelope of frequency modulation with low-rate repetition coding in order to compensate for the absence of transmit beamforming. Although it is a fre

Wojciech Kryński, Artur Sergyeyev

We introduce an integrable two-component extension of the general heavenly equation and prove that the solutions of this extension are in one-to-one correspondence with 4-dimensional hyper-para-Hermitian metrics. Furthermore, we demonstrate that if the metrics in question are hyper-para-K\"ahler, then our system reduces to the general heavenly equation. We a

Moulay Tahar Benameur, James L. Heitsch

In this paper we solve the general case of the cohomological relative index problem for foliations of non-compact manifolds. In particular, we significantly generalize the groundbreaking results of Gromov and Lawson, [GL83], to Dirac operators defined along the leaves of foliations of non-compact complete Riemannian manifolds, by involving all the terms of t

Zuobin Zhang, Cosimo Bambi, Honghui Liu, Jiachen Jiang

We present the results from the NICER observation data of MAXI J1803-298 across the entire 2021 outburst. In the intermediate and soft state, we detect significant absorption lines at $\sim 7.0$ keV and $\sim 6.7$ keV, arising from the X-ray disk wind outflowing with a velocity of hundreds of km per second along our line of sight. The fitting results from ph

Matthieu Fradelizi, Dylan Langharst, Mokshay Madiman, Artem Zvavitch

In "Weighted Brunn-Minkowski Theory I", the prequel to this work, we discussed how recent developments on concavity of measures have laid the foundations of a nascent weighted Brunn-Minkowski theory. In particular, we defined the mixed measures of three convex bodies and obtained its integral representation. In this work, we obtain inequalities for mixed mea

Aleksandr Chatrchyan, Cem Eröncel, Matthias Koschnitzke, Géraldine Servant

The misalignment mechanism for axion-like particles (ALPs) is a leading explanation for dark matter. In this work we investigate ALPs with non-periodic potentials, which allow for large misalignment of the field from the minimum and make it possible for ALPs to match the relic density of dark matter in a large part of the parameter space. Such potentials giv

Runlong Yu, Robert Ladwig, Xiang Xu, Peijun Zhu

Accurate prediction of dissolved oxygen (DO) concentrations in lakes requires a comprehensive study of phenological patterns across ecosystems, highlighting the need for precise selection of interactions amongst external factors and internal physical-chemical-biological variables. This paper presents the Multi-population Cognitive Evolutionary Search (MCES),

Yebowen Hu, Kaiqiang Song, Sangwoo Cho, Xiaoyang Wang

Large language models hold significant potential for integrating various data types, such as text documents and database records, for advanced analytics. However, blending text and numerical data presents substantial challenges. LLMs need to process and cross-reference entities and numbers, handle data inconsistencies and redundancies, and develop planning c

Bernhard Paus Graesdal, Shao Yuan Chew Chia, Tobia Marcucci, Savva Morozov

We present a novel method for global motion planning of robotic systems that interact with the environment through contacts. Our method directly handles the hybrid nature of such tasks using tools from convex optimization. We formulate the motion-planning problem as a shortest-path problem in a graph of convex sets, where a path in the graph corresponds to a

Ramon Ferrer-i-Cancho

The word order of a sentence is shaped by multiple principles. The principle of syntactic dependency distance minimization is in conflict with the principle of surprisal minimization (or predictability maximization) in single head syntactic dependency structures: while the former predicts that the head should be placed at the center of the linear arrangement

Wenliang Liu, Danyang Li, Erfan Aasi, Daniela Rus

Imitation learning methods have demonstrated considerable success in teaching autonomous systems complex tasks through expert demonstrations. However, a limitation of these methods is their lack of interpretability, particularly in understanding the specific task the learning agent aims to accomplish. In this paper, we propose a novel imitation learning meth

Tristan Deleu, Padideh Nouri, Nikolay Malkin, Doina Precup

We consider the problem of sampling from a discrete and structured distribution as a sequential decision problem, where the objective is to find a stochastic policy such that objects are sampled at the end of this sequential process proportionally to some predefined reward. While we could use maximum entropy Reinforcement Learning (MaxEnt RL) to solve this p

Timothy J Tyree, Patrick Murphy, Wouter-Jan Rappel

Pair-annihilation events are ubiquitous in a variety of spatially extended systems and are often studied using computationally expensive simulations. Here we develop an approach in which we simulate the pair-annihilation of spiral wave tips in cardiac models using a computationally efficient particle model. Spiral wave tips are represented as particles with

Ralf R. Müller

We propose Multi-Antenna (MA) Towards Inband Shift Keying (TISK): a new multi-carrier radio concept to cope with critical link budgets. In contrast to common proposals that rely on analog beamforming at both transmitter and receiver, MA-TISK does not require beam alignment. The transmitted signals have all constant envelope in continuous time, which allows f

Diego C. P. Blanco, Ardeshir Hanifi, Dan S. Henningson, André V. G. Cavalieri

Large-eddy simulations of a flat-plate boundary layer, without a leading edge, subject to multiple levels of incoming free stream turbulence are considered in the present work. Within an input-output model where non-linear terms of the incompressible Navier-Stokes equations are treated as an external forcing, we manage to separate inputs related to perturbat

Nihar Gargava, Vlad Serban, Maryna Viazovska, Ilaria Viglino

We prove tight probabilistic bounds for the shortest vectors in module lattices over number fields using the results of arXiv:2308.15275. Moreover, establishing asymptotic formulae for counts of fixed rank matrices with algebraic integer entries and bounded Euclidean length, we prove an approximate Rogers integral formula for discrete sets of module lattices

John W. Brooks, Matthew C. Paliwoda

A plasma impedance probe (PIP) is a type of in-situ, radio-frequency (RF) probe that is traditionally used to measure plasma properties (e.g. density) in low-density environments such as the Earth's ionosphere. We believe that PIPs are underrepresented in laboratory settings, in part because PIP operation and analysis has not been optimized for signal-to-noi

Kanu Sinha, Jennifer Parra-Contreras, Annyun Das, Pablo Solano

Arrays of atoms coupled to waveguides can behave as mirrors. We consider an array of $\Lambda$-type three-level atoms wherein preparing the atoms in one ground state or another leads to reflection or transmission of the guided electromagnetic field; a superposition of the two ground states thus corresponds to a coherent superposition of mirror-like and trans

Oleg Vasilyev, John Bohannon

We found that a simple property of clusters in a clustered dataset of news correlate strongly with importance and urgency of news (IUN) as assessed by LLM. We verified our finding across different news datasets, dataset sizes, clustering algorithms and embeddings. The found correlation should allow using clustering (as an alternative to LLM) for identifying

Aslak B. Buan, Eric J. Hanson, Bethany R. Marsh

We introduce a notion of mutation for $\tau$-exceptional sequences of modules over arbitrary finite dimensional algebras. For hereditary algebras, we show that this coincides with the classical mutation of exceptional sequences. For rank two algebras, we show that mutation of $\tau$-exceptional sequences is transitive if and only if mutation of support $\tau

Daniel Dylewsky, Madhur Anand, Chris T. Bauch

Recent work has highlighted the utility of methods for early warning signal detection in dynamic systems approaching critical tipping thresholds. Often these tipping points resemble local bifurcations, whose low dimensional dynamics can play out on a manifold embedded in a much higher dimensional state space. In many cases of practical relevance, the form of

Conghan Dong

We show that the Schwarzschild 3-manifold is stable for the 3-dimensional Riemannian Penrose inequality in the pointed measured Gromov-Hausdorff topology, modulo negligible domains and boundary area perturbations.

Jingjing Tan

Team formation problem is a very important problem in the labor market, and it is proved to be NP-hard. In this paper, we design an efficient bicriteria streaming algorithms to construct a balance between gain and cost in a team formation problem with cardinality constraint on the integer lattice. To solve this problem, we establish a model for maximizing th

Jurgen Julio-Batalla

We consider on a closed Riemannian spin manifold $(M^n,g,\sigma)$ the spinorial Yamabe type equation $D_g\varphi=\lambda|\varphi|^{\frac{2}{n-1}}\varphi$, where $\varphi$ is a spinor field and $\lambda$ is a positive constant. For a normalized solution $\varphi$ of this equation we find a positive lower bound for $\lambda^2$. As an application we obtain an e

T. Dahn

We discuss the possibility of very regular subgroups of a Lie group, in presence of an index figure. Further, representations that reduce action to a very regular boundary.

Kun Zhao, Matteo Baggioli, Wen-Sheng Xu, Jack F. Douglas

Quasicrystals have been observed in a variety of materials ranging from metal alloys to block copolymers. However, their structural and dynamical properties cannot be readily described in terms of conventional solid-state models of liquids and solids. We may expect the dynamics of this specific class of quasicrystalline materials to be more like glass-formin

Bryan Wang, Yuliang Li, Zhaoyang Lv, Haijun Xia

Video creation has become increasingly popular, yet the expertise and effort required for editing often pose barriers to beginners. In this paper, we explore the integration of large language models (LLMs) into the video editing workflow to reduce these barriers. Our design vision is embodied in LAVE, a novel system that provides LLM-powered agent assistance

Marco Carmosino, Ronald Fagin, Neil Immerman, Phokion Kolaitis

The number of quantifiers needed to express first-order properties is captured by two-player combinatorial games called multi-structural (MS) games. We play these games on linear orders and strings, and introduce a technique we call "parallel play", that dramatically reduces the number of quantifiers needed in many cases. Linear orders and strings are the mo

Diego Caso, Ana García-Prieto, Eugenia Sebastiani-Tofano, Akashdeep Kamra

Ferromagnetic/Antiferromagnetic (FM/AFM) bilayers dynamics have been a recent topic of interest due to the interaction occurring at the interface, where the magnetic moments of the AFM can be imprinted into the FM, and the exchange bias field can affect these dynamics. Here, we investigate Permalloy (Py) and NiO (Py/NiO) hybrids and for comparison single Py

Vijayalakshmi Saravanan, Perry Siehien, Shinjae Yoo, Hubertus Van Dam

Detecting abrupt changes in real-time data streams from scientific simulations presents a challenging task, demanding the deployment of accurate and efficient algorithms. Identifying change points in live data stream involves continuous scrutiny of incoming observations for deviations in their statistical characteristics, particularly in high-volume data sce

Sujay Nagesh Koujalgi, Jonathan Dodge

The project's aim is to create an AI agent capable of selecting good actions in a game-playing domain called Battlespace. Sequential domains like Battlespace are important testbeds for planning problems, as such, the Department of Defense uses such domains for wargaming exercises. The agents we developed combine Monte Carlo Tree Search (MCTS) and Deep Q-Netw

Mohasin Tarek, Ferdous Yasmeen, M. A. Basith

Aqueous supercapacitors (SCs) encounter limitations in operational voltage and energy density due to the low decomposition voltage of water. Here, we fabricate aqueous symmetric supercapacitors (ASSCs) employing DyFeO3 as an electrode material. This hybrid SC in a 0.5 M Na2SO4 aqueous electrolyte exhibits a significantly high working voltage of 2.5 V, with a

Hongju Park, Mohamad Kazem Shirani Faradonbeh

Contextual bandits constitute a classical framework for decision-making under uncertainty. In this setting, the goal is to learn the arms of highest reward subject to contextual information, while the unknown reward parameters of each arm need to be learned by experimenting that specific arm. Accordingly, a fundamental problem is that of balancing exploratio

Lin-Qing Chen, Flaminia Giacomini

Recent progress in table-top experiments offers the opportunity to show for the first time that gravity is not compatible with a classical description. In all current experimental proposals, such as the generation of gravitationally induced entanglement between two quantum sources of gravity, gravitational effects can be explained with the Newton potential,

Antoine Song

Let $S$ be a punctured Riemann surface with Euler characteristic $\chi(S)<0$. For any unitary representation $\rho: \pi_1(S) \to U(N)$, we introduce its renormalized energy and its harmonic representatives, which are equivariant harmonic maps from the universal cover of $S$ to the unit sphere in $\mathbb{C}^N$. Our main result is that if a sequence of unitar

John Mohanraj, Bipasa Samanta, Osbel Almora, Renán Escalante

Non-stoichiometric nickel oxide (NiOx) is the only metal oxide successfully used as hole transport material in p-i-n type perovskite solar cells (PSCs). Its favorable opto-electronic properties and facile large-scale preparation methods are potentially relevant for future commercialization of PSCs, though currently low operational stability of PSCs containin

Sabah Hussein Ali, Amina Basil Mohammed

Numerous countries have built urban stations for monitoring the amount of PM2.5 in the atmosphere. In Iraq, there aren't enough stations to monitor PM2.5 pollution levels across all governorates. As a result, satellite remote sensing data is used in the majority of studies aimed at monitoring PM2.5 and the impact of other factors on it. The current study aim

A. Choplin, L. Siess, S. Goriely, S. Martinet

The intermediate neutron capture process (i-process) can develop during proton ingestion events (PIE), potentially during the early stages of low-mass low-metallicity asymptotic giant branch (AGB) stars. We examine the impact of overshoot mixing on the triggering and development of i-process nucleosynthesis in AGB stars of various initial masses and metallic

He Cheng, Shuhan Yuan

Deep anomaly detection on sequential data has garnered significant attention due to the wide application scenarios. However, deep learning-based models face a critical security threat - their vulnerability to backdoor attacks. In this paper, we explore compromising deep sequential anomaly detection models by proposing a novel backdoor attack strategy. The at

Khaled Eldowa, Nicolò Cesa-Bianchi, Alberto Maria Metelli, Marcello Restelli

This work addresses the mediator feedback problem, a bandit game where the decision set consists of a number of policies, each associated with a probability distribution over a common space of outcomes. Upon choosing a policy, the learner observes an outcome sampled from its distribution and incurs the loss assigned to this outcome in the present round. We i

Constantin Bacuta, Cristina Bacuta, Daniel Hayes

For a model convection-diffusion problem, we obtain new error estimates for a general upwinding finite element discretization based on bubble modification of the test space. The key analysis tool is based on finding representations of the optimal norms on the trial spaces at the continuous and discrete levels. We analyze and compare the standard linear discr

Dian Chen, Paul Yang, Ing-Ray Chen, Dong Sam Ha

We propose a novel energy-aware federated learning (FL)-based system, namely SusFL, for sustainable smart farming to address the challenge of inconsistent health monitoring due to fluctuating energy levels of solar sensors. This system equips animals, such as cattle, with solar sensors with computational capabilities, including Raspberry Pis, to train a loca

Marianna Chatzakou

We develop a unified strategy to obtain the geometric logarithmic Hardy inequality on any open set M of a stratified group, provided the validity of the Hardy inequality in this setting, where the so-called "weight" is regarded to be any measurable non-negative function on M . Provided the legitimacy of the latter for some open set and for some weight, we al

André L. B. Ribeiro, Paulo A. A. Lopes, Dailer F. Morell, Christine C. Dantas

We study a sample of 936 early-type galaxies located in 48 low-z regular galaxy clusters with $M_{200}\geq 10^{14}~ M_\odot$ at $z< 0.1$. We examine variations in the Kormendy relation (KR) according to their location in the projected phase space (PPS) of the clusters. We have used a combination of Bayesian statistical methods to identify possible difference

Joe Gibbs, Zoë Holmes, Paul Stevenson

The Lipkin and Agassi models are simplified nuclear models that provide natural test beds for quantum simulation methods. Prior work has investigated the suitability of the Variational Quantum Eigensolver (VQE) to find the ground state of these models. There is a growing awareness that if VQE is to prove viable, we will need problem inspired ans\"{a}tze that

Kevin Uhl, Daniel Hackenbeck, Dieter Koelle, Reinhold Kleiner

Superconducting circuits with nonlinear elements such as Josephson tunnel junctions or kinetic inductance nanowires are the workhorse for microwave quantum and superconducting sensing technologies. For devices, which can be operated at high temperatures and large magnetic fields, nano-constrictions as nonlinear elements are recently under intense investigati

L. Leonforte, X. Sun, D. Valenti, B. Spagnolo

We present a general framework to tackle quantum optics problems with giant atoms, i.e. quantum emitters each coupled {\it non-locally} to a structured photonic bath (typically a lattice) of any dimension. The theory encompasses the calculation and general properties of Green's functions, atom-photon bound states (BSs), collective master equations and decohe

Marc Christ, Conrad Zimmermann, Sascha Neinert, Bastian Leykauf

Quantum technologies are advancing from fundamental research in specialized laboratories to practical applications in the field, driving the demand for robust, scalable, and reproducible system integration techniques. Ceramic components can be pivotal thanks to high stiffness, low thermal expansion, and excellent dimensional stability under thermal stress. W

Alexandre Alves, Eduardo da Silva Almeida, Igor Neiva Mesquita

The search for double Higgs production in $b\bar{b}W^+W^-$, where both $W$ bosons decay to leptons, has been rehabilitated as a good option to look for that key process to the Standard Model scalar sector study in the LHC. The missing neutrinos, however, hinder the reconstruction of useful information like the Higgs pair mass, which is very sensitive to the

Julius Lehmann

We employ the framework of operational calculus to derive the operators associated with the spherical mean and a class of related averaging means of a function in $n$-dimensional space. Beginning with the classical definition of the spherical mean, we obtain a compact operator representation in terms of confluent hypergeometric functions of the Laplacian. Th

Anton Kutlin, Carlo Vanoni

Resonance counting is an intuitive and widely used tool in Random Matrix Theory and Anderson Localization. Its undoubted advantage is its simplicity: in principle, it is easily applicable to any random matrix ensemble. On the downside, the notion of resonance is ill-defined, and the `number of resonances' does not have a direct mapping to any commonly used p

Mattia Sirressi, Angela Adamo, Matthew Hayes, Thøger Emil Rivera-Thorsen

We analyze the far-ultraviolet spectroscopy of 20 young and massive star clusters (YSCs) in 11 nearby star-forming galaxies. We probe the interstellar gas intervening along the line of sight, detecting several metal absorption lines of a wide range of ionization potentials, from 6.0 eV to 77.5 eV. Multiple-component Voigt fits to the absorption lines are use

David Osten

A universal structure of world-volume theories of half-BPS branes in string and M-theory in terms of exceptional generalised geometry is observed. Previous constructions are extended in two ways: from internal $d$-dimensional space to full 11- or 10-dimensional spacetimes, by coupling to the tensor hierarchy gauge fields, and from $E_{d(d)}$ with $d\leq 6$ t

A. Konstantinou, E. Ntormousi, K. Tassis, A. Pallottini

One of the most frequently used indicators to characterize the magnetic field's influence on star formation is the relation between magnetic field strength and gas density ($B-\rho$ relation), usually expressed as $B \propto \rho^{\kappa}$. The value of $\kappa$ is an indication of the dynamical importance of the magnetic field during gas compression. Invest

Viktoria Kabel, Anne-Catherine de la Hamette, Luca Apadula, Carlo Cepollaro

The study of quantum reference frames (QRFs) is motivated by the idea of taking into account the quantum properties of the reference frames used, explicitly or implicitly, in our description of physical systems. Like classical reference frames, QRFs can be used to define physical quantities relationally. Unlike their classical analogue, they relativise the n

Erik Wegner Hodt, Carla Cirillo, Angelo Di Bernardo, Carmine Attanasio

Identifying superconducting materials with spin-polarized Cooper pairs is an important objective both for exploration of new fundamental physics and for cryogenic applications in spintronics and quantum sensing. We here compute the critical temperature $T_c$ of the superconducting transition in a bilayer comprised of a superconductor with an intrinsic spin-t

Anthony N. Ciavarella, Christian W. Bauer

Quantum simulations of the dynamics of QCD have been limited by the complexities of mapping the continuous gauge fields onto quantum computers. By parametrizing the gauge invariant Hilbert space in terms of plaquette degrees of freedom, we show how the Hilbert space and interactions can be expanded in inverse powers of N_c. At leading order in this expansion

Cara Giovanetti, Martin Schmaltz, Neal Weiner

After neutrinos decouple from the photon bath, they can populate a thermal dark sector. If this occurs at a temperature above ~100 keV, this can have measurable impacts on light element abundances. We calculate light element abundances in this scenario, studying the impact from rapid cooling of the Standard Model neutrinos, and from an increase in the number

Patrick Barnes, Raymond T. Co, Keisuke Harigaya, Aaron Pierce

We study lepto-axiogenesis in theories where the right-handed neutrino is light enough that its dynamics affect the determination of the baryon asymmetry. When compared with theories of high-scale lepto-axiogenesis where the Majorana neutrino mass may be treated as an effective dimension-five operator, we find that the predicted saxion mass is lower. Two dis

Elliott Gesteau, Leonardo Santilli

We construct a large family of quantum mechanical systems that give rise to an emergent type III$_1$ von Neumann algebra in the large $N$ limit. Their partition functions are matrix integrals that appear in the study of various gauge theories. We calculate the real-time, finite temperature correlation functions in these systems and show that they are describ

Christopher Ekman, Emil J. Bergholtz

Strongly interacting non-equilibrium systems are of great fundamental interest, yet their inherent complexity make then notoriously hard to analyze. We demonstrate that the dynamics of the Bose-Hubbard model, which by itself evades solvability, can be solved exactly at any interaction strength in the presence of loss tuned to a rate matching the hopping ampl

Raunaq Bhirangi, Chenyu Wang, Venkatesh Pattabiraman, Carmel Majidi

Reasoning from sequences of raw sensory data is a ubiquitous problem across fields ranging from medical devices to robotics. These problems often involve using long sequences of raw sensor data (e.g. magnetometers, piezoresistors) to predict sequences of desirable physical quantities (e.g. force, inertial measurements). While classical approaches are powerfu

Huizhuo Yuan, Zixiang Chen, Kaixuan Ji, Quanquan Gu

Fine-tuning Diffusion Models remains an underexplored frontier in generative artificial intelligence (GenAI), especially when compared with the remarkable progress made in fine-tuning Large Language Models (LLMs). While cutting-edge diffusion models such as Stable Diffusion (SD) and SDXL rely on supervised fine-tuning, their performance inevitably plateaus a

Adrien Bardes, Quentin Garrido, Jean Ponce, Xinlei Chen

This paper explores feature prediction as a stand-alone objective for unsupervised learning from video and introduces V-JEPA, a collection of vision models trained solely using a feature prediction objective, without the use of pretrained image encoders, text, negative examples, reconstruction, or other sources of supervision. The models are trained on 2 mil

Calla Tschanz

In this paper, we explore different possible choices of expanded degenerations and define appropriate stability conditions in order to construct good degenerations of Hilbert schemes of points over semistable degenerations of surfaces, given as proper Deligne-Mumford stacks. These stacks provide explicit examples of constructions arising from the work of Mau

Eliahu Horwitz, Jonathan Kahana, Yedid Hoshen

The dominant paradigm in generative modeling consists of two steps: i) pre-training on a large-scale but unsafe dataset, ii) aligning the pre-trained model with human values via fine-tuning. This practice is considered safe, as no current method can recover the unsafe, pre-fine-tuning model weights. In this paper, we demonstrate that this assumption is often

Rui Yang, Xiaoman Pan, Feng Luo, Shuang Qiu

We consider the problem of multi-objective alignment of foundation models with human preferences, which is a critical step towards helpful and harmless AI systems. However, it is generally costly and unstable to fine-tune large foundation models using reinforcement learning (RL), and the multi-dimensionality, heterogeneity, and conflicting nature of human pr

Maria Bånkestad, Jennifer R. Andersson, Sebastian Mair, Jens Sjölund

Reducing a graph while preserving its overall properties is an important problem with many applications. Typically, reduction approaches either remove edges (sparsification) or merge nodes (coarsening) in an unsupervised way with no specific downstream task in mind. In this paper, we present an approach for subsampling graph structures using an Ising model d

Alexandra Souly, Qingyuan Lu, Dillon Bowen, Tu Trinh

Most jailbreak papers claim the jailbreaks they propose are highly effective, often boasting near-100% attack success rates. However, it is perhaps more common than not for jailbreak developers to substantially exaggerate the effectiveness of their jailbreaks. We suggest this problem arises because jailbreak researchers lack a standard, high-quality benchmar

Kirsten R. Hall, Huiqun Wang, Amir H. Souri, Xiong Liu

As a result of their important role in weather and the global hydrological cycle, understanding atmospheric rivers' (ARs) connection to synoptic-scale climate patterns and atmospheric dynamics has become increasingly important. In addition to case studies of two extreme AR events, we produce a December climatology of the three-dimensional structure of water

Mariia Drozdova, Vitaliy Kinakh, Omkar Bait, Olga Taran

Reconstructing sky models from dirty radio images for accurate source localization and flux estimation is crucial for studying galaxy evolution at high redshift, especially in deep fields using instruments like the Atacama Large Millimetre Array (ALMA). With new projects like the Square Kilometre Array (SKA), there's a growing need for better source extracti

Rahul Ghosh, Ipsita Mandal

We compute the magnetoelectric conductivity tensors in planar Hall set-ups, which are built with tilted Weyl semimetals (WSMs) and multi-Weyl semimetals (mWSMs), considering all possible relative orientations of the electromagnetic fields ($\mathbf E $ and $\mathbf B $) and the direction of the tilt. The non-Drude part of the response arises from a nonzero B

Rylan Schaeffer, Nika Zahedi, Mikail Khona, Dhruv Pai

Associative memory and probabilistic modeling are two fundamental topics in artificial intelligence. The first studies recurrent neural networks designed to denoise, complete and retrieve data, whereas the second studies learning and sampling from probability distributions. Based on the observation that associative memory's energy functions can be seen as pr

Daniil S. Antonenko, Rafael M. Fernandes, Jorn W. F. Venderbos

The electronic spectra of altermagnets are a fertile ground for nontrivial topology due to the unique interplay between time-reversal and crystalline symmetries. This is reflected in the unconventional Zeeman splitting between bands of opposite spins, which emerges in the absence of spin-orbit coupling (SOC) and displays nodes along high-symmetry directions.

Xuezhi Wang, Denny Zhou

In enhancing the reasoning capabilities of large language models (LLMs), prior research primarily focuses on specific prompting techniques such as few-shot or zero-shot chain-of-thought (CoT) prompting. These methods, while effective, often involve manually intensive prompt engineering. Our study takes a novel approach by asking: Can LLMs reason effectively

Yuki Yayama

Given a weakly almost additive sequence of continuous functions with bounded variation $\mathcal{F}=\{\log f_n\}_{n=1}^{\infty}$ on a subshift $X$ over finitely many symbols, we study properties of a function $f$ on $X$ such that $\lim_{n\to\infty}\frac{1}{n}\int \log f_n d\mu=\int f d\mu$ for every invariant measure $\mu$ on $X$. Under some conditions we co

Romain Ilbert, Ambroise Odonnat, Vasilii Feofanov, Aladin Virmaux

Transformer-based architectures achieved breakthrough performance in natural language processing and computer vision, yet they remain inferior to simpler linear baselines in multivariate long-term forecasting. To better understand this phenomenon, we start by studying a toy linear forecasting problem for which we show that transformers are incapable of conve

Sofiia Dubova, Kevin Yang

We consider an ensemble of non-Hermitian matrices with independent identically distributed real entries that have finite moments. We show that its $k$-point correlation function in the bulk away from the real line converges to a universal limit.

Lingbo Mo, Zeyi Liao, Boyuan Zheng, Yu Su

Language agents powered by large language models (LLMs) have seen exploding development. Their capability of using language as a vehicle for thought and communication lends an incredible level of flexibility and versatility. People have quickly capitalized on this capability to connect LLMs to a wide range of external components and environments: databases,

Lewis Hutton, Andres Moreno Carrascosa, Andrew W. Prentice, Mats Simmermacher

Using the recently developed multistate mapping approach to surface hopping (multistate MASH) method combined with SA(3)-CASSCF(12,12)/aug-cc-pVDZ electronic structure calculations, the gas-phase isotropic ultrafast electron diffraction (UED) of cyclobutanone is predicted and analyzed. After excitation into the n-3s Rydberg state (S$_2$), cyclobutanone can r

Kyle Hansen

We define simple tilings in the general context of a $G$-tiling on a Riemannian homogeneous space $M$ to be tilings by Riemannian simplices. As evidence that this definition is natural, we prove that a large class of tilings of $M$ are MLD to simple ones. We demonstrate the utility of this definition by generalizing previously known results about simple tili

James Liu, Guangxuan Xiao, Kai Li, Jason D. Lee

Large Language Models (LLMs) are typically trained in two phases: pre-training on large internet-scale datasets, and fine-tuning for downstream tasks. Given the higher computational demand of pre-training, it's intuitive to assume that fine-tuning adds less new information to the model, and is thus more compressible. We explore this assumption by decomposing

Jimi Sanchez

In the realm of amateur radio, the effective classification of signals and the mitigation of noise play crucial roles in ensuring reliable communication. Traditional methods for signal classification and noise reduction often rely on manual intervention and predefined thresholds, which can be labor-intensive and less adaptable to dynamic radio environments.

Philip A. LeMaitre, Marius Krumm, Hans J. Briegel

With the impressive progress of deep learning, applications relying on machine learning are increasingly being integrated into daily life. However, most deep learning models have an opaque, oracle-like nature making it difficult to interpret and understand their decisions. This problem led to the development of the field known as eXplainable Artificial Intel

Xinchi Qiu, Yan Gao, Lorenzo Sani, Heng Pan

Federated learning (FL) is a distributed learning paradigm that facilitates collaborative training of a shared global model across devices while keeping data localized. The deployment of FL in numerous real-world applications faces delays, primarily due to the prevalent reliance on supervised tasks. Generating detailed labels at edge devices, if feasible, is

Christoph Kehle, Ryan Unger

In this paper, we prove that extremal black holes arise on the threshold of gravitational collapse. More precisely, we construct smooth one-parameter families of smooth, spherically symmetric solutions to the Einstein-Maxwell-Vlasov system which interpolate between dispersion and collapse and for which the critical solution is an extremal black hole. Physica

Chen Ling, Xujiang Zhao, Xuchao Zhang, Wei Cheng

In-context learning has emerged as a groundbreaking ability of Large Language Models (LLMs) and revolutionized various fields by providing a few task-relevant demonstrations in the prompt. However, trustworthy issues with LLM's response, such as hallucination, have also been actively discussed. Existing works have been devoted to quantifying the uncertainty

Joel Rajakumar, John Golden, Andreas Bärtschi, Stephan Eidenbenz

The Quantum Alternating Operator Ansatz (QAOA) is a prominent variational quantum algorithm for solving combinatorial optimization problems. Its effectiveness depends on identifying input parameters that yield high-quality solutions. However, understanding the complexity of training QAOA remains an under-explored area. Previous results have given analytical

Euclid Collaboration, B. Aussel, S. Kruk, M. Walmsley

The Euclid mission is expected to image millions of galaxies with high resolution, providing an extensive dataset to study galaxy evolution. We investigate the application of deep learning to predict the detailed morphologies of galaxies in Euclid using Zoobot a convolutional neural network pretrained with 450000 galaxies from the Galaxy Zoo project. We adap

Chen Yang, Sikuang Li, Jiemin Fang, Ruofan Liang

Reconstructing and rendering 3D objects from highly sparse views is of critical importance for promoting applications of 3D vision techniques and improving user experience. However, images from sparse views only contain very limited 3D information, leading to two significant challenges: 1) Difficulty in building multi-view consistency as images for matching

Gengyuan Hu, Gengchen Wei, Zekun Lou, Philip H. S. Torr

Machine learning has emerged as a significant approach to efficiently tackle electronic structure problems. Despite its potential, there is less guarantee for the model to generalize to unseen data that hinders its application in real-world scenarios. To address this issue, a technique has been proposed to estimate the accuracy of the predictions. This metho

Mathilde Tavares, Christophe Josserand, Alexandre Limare, José-Maria Lopez-Herrera

We present an hybrid VOF/embedded boundary method allowing to model two-phase flows in presence of solids with arbitrary shapes. The method relies on the coupling of existing methods: a geometric Volume of fluid (VOF) method to tackle the two-phase flow and an embedded boundary method to sharply resolve arbitrary solid geometries. Coupling these approaches c

Tianyi Qiu, Fanzhi Zeng, Jiaming Ji, Dong Yan

Existing alignment methods share a common topology of information flow, where reward information is collected from humans, modeled with preference learning, and used to tune language models. However, this shared topology has not been systematically characterized, nor have its alternatives been thoroughly explored, leaving the problems of low data efficiency

Lipei Du, Agnieszka Sorensen, Mikhail Stephanov

We review recent theoretical developments relevant to heavy-ion experiments carried out within the Beam Energy Scan program at the Relativistic Heavy Ion Collider. Our main focus is on the description of the dynamics of systems created in heavy-ion collisions and establishing the necessary connection between the experimental observables and the QCD phase dia

Jingqi Li, Anand Siththaranjan, Somayeh Sojoudi, Claire Tomlin

Autonomous agents should coordinate effectively without prior knowledge of others' intents. While prior work has focused on intent inference, we address the inverse problem: how agents can strategically demonstrate their intents within general-sum dynamic games. We model this problem and propose an algorithm that balances intent demonstration with task perfo

Christopher Vairogs, Bin Yan

Magic quantum states (non-stabilizer states) play a pivotal role in fault-tolerant quantum computation. Simultaneously, random resources have emerged as a key element in various randomized techniques within contemporary quantum science. In this study, we establish a direct connection between these two notions. More specifically, our research demonstrates tha

Sara A. Elrafei, T. V. Raziman, Sandra de Vega, F. Javier García de Abajo

Two-dimensional materials give access to the ultimate physical limits of Photonics with appealing properties for ultracompact optical components such as waveguides and modulators. Specifically, in monolayer semiconductors, a strong excitonic resonance leads to a sharp oscillation in permittivity; at energies close to an exciton, the real part of the permitti

Christopher Mohri, Tatsunori Hashimoto

Guaranteeing the correctness and factuality of language model (LM) outputs is a major open problem. In this work, we propose conformal factuality, a framework that can ensure high probability correctness guarantees for LMs by connecting language modeling and conformal prediction. We observe that the correctness of an LM output is equivalent to an uncertainty