October 2024 arXiv papers — page 114

Samuel L. Krushkal

Biunivalent holomorphic functions form an interesting class in geometric function theory and are connected with special functions and solutions of complex differential equations. The paper reveals a deep connection between biunivalence and geometry of Teichmuller balls and provides some sufficient conditions for biunivalence of holomorphic functions on the d

Satya Krishna Gorti, Ilan Gofman, Zhaoyan Liu, Jiapeng Wu

Text-to-SQL generation enables non-experts to interact with databases via natural language. Recent advances rely on large closed-source models like GPT-4 that present challenges in accessibility, privacy, and latency. To address these issues, we focus on developing small, efficient, and open-source text-to-SQL models. We demonstrate the benefits of sampling

Zhiyuan Yao, Nir Mandelker, S. Peng Oh, Han Aung

Theory and observations reveal that the circumgalactic medium (CGM) and the cosmic web at high redshifts are multiphase, with small clouds of cold gas embedded in a hot, diffuse medium. A proposed mechanism is `shattering' of large, thermally unstable clouds into tiny cloudlets of size lshatter~min(cs*tcool). We study these processes using idealized numerica

Ameet Gadekar, Aristides Gionis, Suhas Thejaswi

Data summarization tasks are often modeled as $k$-clustering problems, where the goal is to choose $k$ data points, called cluster centers, that best represent the dataset by minimizing a clustering objective. A popular objective is to minimize the maximum distance between any data point and its nearest center, which is formalized as the $k$-center problem.

Ron Donagi, Nadia Ott

We construct a measure on the moduli space of super Riemann surfaces with Ramond punctures using the super Mumford isomorphism and a super period map.

Omri Reved, Lazar Friedland, Nicholas C. Stone

Massive black hole (MBH) binaries in galactic nuclei are one of the leading sources of $\sim$ mHz gravitational waves (GWs) for future missions such as $\rm{\textit{LISA}}$. However, the poor sky localization of GW interferometers will make it challenging to identify the host galaxy of MBH mergers absent an electromagnetic counterpart. One such counterpart i

Heino Falcke, Michael F. Wondrak, Walter D. van Suijlekom

Black holes are assumed to decay via Hawking radiation. Recently we found evidence that spacetime curvature alone without the need for an event horizon leads to black hole evaporation. Here we investigate the evaporation rate and decay time of a non-rotating star of constant density due to spacetime curvature-induced pair production and apply this to compact

Jaime Hoefken Zink, Shihwen Hor, Maura E. Ramirez-Quezada

White dwarfs offer a compelling avenue for probing interactions of dark matter particles, particularly in the challenging sub-GeV mass regime. The constraints derived from these celestial objects strongly depend on the existence of high dark matter densities in the corresponding regions of the Universe, where white dwarfs are observed. This implies that excl

Xiangdong Ji, Yizhuang Liu, Yushan Su, Rui Zhang

Parton distribution functions (PDFs) at large $x$ are challenging to extract from experimental data, yet they are essential for understanding hadron structure and searching for new physics beyond the Standard Model. Within the framework of the large momentum $P^z$ expansion of lattice quasi-PDFs, we investigate large $x$ PDFs, where the matching coefficient

Jake S. Bennett, Matthew C. Smith, Drummond B. Fielding, Greg L. Bryan

We deploy the new Arkenstone galactic wind model in cosmological simulations for the first time, allowing us to robustly resolve the evolution and impact of high specific energy winds. In a (25 $h^{-1}$ Mpc)$^3$ box we perform a set of numerical experiments that systematically vary the mass and energy loadings of such winds, finding that their energy content

Martin Ritter, David M. Long, Qianao Yue, Anushya Chandran

Floquet engineering, in which the properties of a quantum system are modified through the application of strong periodic drives, is an indispensable tool in atomic and condensed matter systems. However, it is inevitably limited by intrinsic heating processes. We describe a simple autonomous scheme, which exploits a static coupling between the driven system a

Julius F. Grimminger, William Harding, Noppadol Mekareeya

't Hooft anomalies of discrete global symmetries and gaugings thereof have rich mathematical structures and far-reaching physical consequences. We examine each subgroup $G$, up to automorphisms, of the permutation group $S_4$ that acts on the four legs of the affine $D_4$ quiver diagram, which is mirror dual to the 3d $\mathcal{N}=4$ $\mathrm{SU}(2)$ gauge t

Joshua N. Winn

A transiting planet invites us to measure its size, mass, orbital parameters, atmospheric composition, and other characteristics. But the invitation can only be accepted if the host star is bright enough for precise measurements of its flux and spectrum. NASA's Transiting Exoplanet Survey Satellite (TESS) is dedicated to finding such favorable systems. Opera

Elliot Fox, Nigel Glover, Matteo Marcoli

In this paper we discuss the definition, the construction and the implementation of \textit{generalised antenna functions} for final-state radiation up to Next-to-Next-to-Leading Order (NNLO) in QCD. Generalised antenna functions encapsulate the singular behaviour of unresolved emissions when these occur within multiple hard radiators and not just two of the

Rebeca Gonzalez Suarez, Baibhab Pattnaik, José Zurita

We examine the possibility to detect new SM-neutral vector bosons ($Z'$) that couple exclusively to leptons in the electron-positron mode of the Future Circular Collider (FCC-ee). Focusing on the $Z'$ production with a radiated photon search channel, we show that the FCC-ee can significantly extend the unprobed parameter space by increasing the exclusion in

Tiago V. Fernandes, José P. S. Lemos

We establish the connection between the Gibbons-Hawking Euclidean path integral approach applied to the canonical ensemble of a Reissner-Nordstr\"om black hole and the thermodynamic theory of black holes of Davies. We build the ensemble, characterized by a reservoir at infinity at temperature $T$ and electric charge $Q$, in $d$ dimensions. The Euclidean path

Lucas M. Valenzuela, Duncan A. Forbes, Rhea-Silvia Remus

The formation details of globular clusters (GCs) are still poorly understood due to their old ages and the lack of detailed observations of their formation. A large variety of models for the formation and evolution of GCs have been created to improve our understanding of their origins, based on GC properties observed at z=0. We present the first side-by-side

Jeet Shah, Christopher Fechisin, Yu-Xin Wang, Joseph T. Iosue

Recent experimental progress in controlling open quantum systems enables the pursuit of mixed-state nonequilibrium quantum phases. We investigate whether open quantum systems hosting mixed-state symmetry-protected topological states as steady states retain this property under symmetric perturbations. Focusing on the decohered cluster state -- a mixed-state s

Roberto Tejada Arevalo, Ankan Sur, Yubo Su, Adam Burrows

We address the issue of which broad set of initial conditions for the planet Jupiter best matches the current presence of a ``fuzzy core" of heavy elements, while at the same time comporting with measured parameters such as its effective temperature, atmospheric helium abundance, radius, and atmospheric metallicity. Our focus is on the class of fuzzy cores t

David Trestini

The orbit-averaged fluxes of energy and angular momentum generated by a compact binary system of nonspinning particles are obtained in a popular class of massless scalar-tensor theories with first-and-a-half post-Newtonian (1.5PN) accuracy, i.e., 2.5PN accuracy beyond the leading -1PN dipolar radiation. The secular evolution of the orbital elements (frequenc

Ross Deans Kristensen-McLachlan, Rebecca M. M. Hicke, Márton Kardos, Mette Thunø

Does the People's Republic of China (PRC) interfere with European elections through ethnic Chinese diaspora media? This question forms the basis of an ongoing research project exploring how PRC narratives about European elections are represented in Chinese diaspora media, and thus the objectives of PRC news media manipulation. In order to study diaspora medi

Ce Zhang, Simon Stepputtis, Katia Sycara, Yaqi Xie

Test-time adaptation, which enables models to generalize to diverse data with unlabeled test samples, holds significant value in real-world scenarios. Recently, researchers have applied this setting to advanced pre-trained vision-language models (VLMs), developing approaches such as test-time prompt tuning to further extend their practical applicability. How

Tyna Eloundou, Alex Beutel, David G. Robinson, Keren Gu-Lemberg

Evaluating chatbot fairness is crucial given their rapid proliferation, yet typical chatbot tasks (e.g., resume writing, entertainment) diverge from the institutional decision-making tasks (e.g., resume screening) which have traditionally been central to discussion of algorithmic fairness. The open-ended nature and diverse use-cases of chatbots necessitate n

Jihao Zhao, Zhiyuan Ji, Yuchen Feng, Pengnian Qi

While Retrieval-Augmented Generation (RAG) has emerged as a promising paradigm for boosting large language models (LLMs) in knowledge-intensive tasks, it often overlooks the crucial aspect of text chunking within its workflow. This paper proposes the Meta-Chunking framework, which specifically enhances chunking quality through a dual strategy that identifies

Sicong Leng, Yun Xing, Zesen Cheng, Yang Zhou

Recent advancements in large multimodal models (LMMs) have significantly enhanced performance across diverse tasks, with ongoing efforts to further integrate additional modalities such as video and audio. However, most existing LMMs remain vulnerable to hallucinations, the discrepancy between the factual multimodal input and the generated textual output, whi

A. Tortosa, L. Zappacosta, E. Piconcelli, M. Bischetti

One of the main open questions in the field of luminous ($L_{\rm bol}>10^{47}\,\rm erg\,s^{-1}$) quasars (QSOs) at $z \gtrsim 6$ is the rapid formation ($< 1\,$Gyr) of their supermassive black holes (SMBHs). For this work we analysed the relation between the X-ray properties and other properties describing the physics and growth of both the accretion disc an

Nathaniel Lee, Noel Ngu, Harshdeep Singh Sahdev, Pramod Motaganahall

Predicting price spikes in critical metals such as Cobalt, Copper, Magnesium, and Nickel is crucial for mitigating economic risks associated with global trends like the energy transition and reshoring of manufacturing. While traditional models have focused on regression-based approaches, our work introduces a neurosymbolic ensemble framework that integrates

Abdoljalil Addeh, G. Bruce Pike, M. Ethan MacDonald

Acquiring accurate external respiratory data during functional Magnetic Resonance Imaging (fMRI) is challenging, prompting the exploration of machine learning methods to estimate respiratory variation (RV) from fMRI data. Respiration induces head motion, including real and pseudo motion, which likely provides useful information about respiratory events. Reco

Sijun Tan, Siyuan Zhuang, Kyle Montgomery, William Y. Tang

LLM-based judges have emerged as a scalable alternative to human evaluation and are increasingly used to assess, compare, and improve models. However, the reliability of LLM-based judges themselves is rarely scrutinized. As LLMs become more advanced, their responses grow more sophisticated, requiring stronger judges to evaluate them. Existing benchmarks prim

Amirhesam Abedsoltan, Adityanarayanan Radhakrishnan, Jingfeng Wu, Mikhail Belkin

Transformers exhibit In-Context Learning (ICL), where these models solve new tasks by using examples in the prompt without additional training. In our work, we identify and analyze two key components of ICL: (1) context-scaling, where model performance improves as the number of in-context examples increases and (2) task-scaling, where model performance impro

Yida Yin, Zekai Wang, Yuvan Sharma, Dantong Niu

Recently, Large Language Models (LLMs) have achieved remarkable success using in-context learning (ICL) in the language domain. However, leveraging the ICL capabilities within LLMs to directly predict robot actions remains largely unexplored. In this paper, we introduce RoboPrompt, a framework that enables off-the-shelf text-only LLMs to directly predict rob

Chen Ziwen, Hao Tan, Kai Zhang, Sai Bi

We propose Long-LRM, a feed-forward 3D Gaussian reconstruction model for instant, high-resolution, 360{\deg} wide-coverage, scene-level reconstruction. Specifically, it takes in 32 input images at a resolution of 960x540 and produces the Gaussian reconstruction in just 1 second on a single A100 GPU. To handle the long sequence of 250K tokens brought by the l

M. Matys, P. Hadjisolomou, R. Shaisultanov, P. Valenta

The interaction of a high-power laser with a solid target provides ways to produce beams of $\gamma$-photons. For normal incidence of the laser on the target the beams usually appear in a form of two lobes, which are symmetric with respect to the laser propagation axis. In this work we demonstrate via three-dimensional particle-in-cell simulations a regime w

Xingzhi Sun, Danqi Liao, Kincaid MacDonald, Yanlei Zhang

Rapid growth of high-dimensional datasets in fields such as single-cell RNA sequencing and spatial genomics has led to unprecedented opportunities for scientific discovery, but it also presents unique computational and statistical challenges. Traditional methods struggle with geometry-aware data generation, interpolation along meaningful trajectories, and tr

Antonio Cuéllar, Enrico Amico, Jacopo Serpieri, Gioacchino Cafiero

We present an experimental setup to perform time-resolved convective heat transfer measurements in a turbulent channel flow with air as the working fluid. We employ a heated thin foil coupled with high-speed infrared thermography. The measurement technique is challenged by the thermal inertia of the foil, the high frequency of turbulent fluctuations, and the

Hongcheng Gao, Tianyu Pang, Chao Du, Taihang Hu

With the rapid progress of diffusion-based content generation, significant efforts are being made to unlearn harmful or copyrighted concepts from pretrained diffusion models (DMs) to prevent potential model misuse. However, it is observed that even when DMs are properly unlearned before release, malicious finetuning can compromise this process, causing DMs t

Philip Massey, Kathryn F. Neugent, Nidia I. Morrell, Desmond John Hillier

LMCe055-1 was recently discovered in a survey for WRs in the Large Magellanic Cloud, and classified as a WN4/O4, a lower excitation version of the WN3/O3 class discovered as part of the same survey. Its absolute magnitude precluded it from being a WN4+O4 binary. OGLE photometry show shallow primary and secondary eclipses with a 2.2 day period. The spectral c

Fawzi Aly, Mahmoud A. Mansour, Dejan Stojkovic

We investigate the possibility of electromagnetic fields leaving imprints on gravitational wave (GW) signals from Neutron Star-Black hole (NSBH) mergers, specifically in the context of extreme mass ratio inspirals (EMRIs). Using black hole perturbation theory (BHPT) in the context of a minimally coupled Einstein-Maxwell system, we demonstrate that electromag

Yingya Li, Timothy Miller, Steven Bethard, Guergana Savova

The success of multi-task learning can depend heavily on which tasks are grouped together. Naively grouping all tasks or a random set of tasks can result in negative transfer, with the multi-task models performing worse than single-task models. Though many efforts have been made to identify task groupings and to measure the relatedness among different tasks,

Zhenyu Jiang, Yuqi Xie, Jinhan Li, Ye Yuan

Humanoid robots, with their human-like embodiment, have the potential to integrate seamlessly into human environments. Critical to their coexistence and cooperation with humans is the ability to understand natural language communications and exhibit human-like behaviors. This work focuses on generating diverse whole-body motions for humanoid robots from lang

Hunmin Lee, Hongju Seong, Wonbin Kim, Hyeokchan Kwon

Automatic modulation classification (AMC) serves a vital role in ensuring efficient and reliable communication services within distributed wireless networks. Recent developments have seen a surge in interest in deep neural network (DNN)-based AMC models, with Federated Learning (FL) emerging as a promising framework. Despite these advancements, the presence

Tatsuyuki Hikita

We propose several properties of elliptic lifts of the $K$-theoretic canonical bases for conical symplectic resolutions defined in our previous work. As an example, we construct elliptic lifts of canonical bases for the Hilbert scheme of 2-points in the affine plane.

Jiří Vyskočil, Lukas Picek

This paper describes the search for an alternative approach to the automatic categorization of camera trap images. First, we benchmark state-of-the-art classifiers using a single model for all images. Next, we evaluate methods combining MegaDetector with one or more classifiers and Segment Anything to assess their impact on reducing location-specific overfit

Giuseppe Pucacco

We analyse the stability of the de Sitter equilibria in multi-resonant planetary systems. The de Sitter equilibrium is the dynamical state of the Laplace resonance in which all resonant arguments are librating. The sequence of equilibria exists all along the possible states balancing resonance offsets and forced eccentricities. Possible additional new-de Sit

Yan Cheng, Kui Ren, Nathan Soedjak

This work studies phase retrieval for wave fields, aiming to recover the phase of an incoming wave from multi-plane intensity measurements behind different types of linear and nonlinear media. We show that unique phase retrieval can be achieved by utilizing intensity data produced by multiple media. This uniqueness does not require prescribed boundary condit

Ekansh Sharma, Daniel M. Roy, Gintare Karolina Dziugaite

Model merging aims to efficiently combine the weights of multiple expert models, each trained on a specific task, into a single multi-task model, with strong performance across all tasks. When applied to all but the last layer of weights, existing methods -- such as Task Arithmetic, TIES-merging, and TALL mask merging -- work well to combine expert models ob

Lukas Einkemmer, Trung-Hau Hoang, Alexander Ostermann

In this paper, we consider the application of exponential integrators to problems that are advection dominated, either on the entire or on a subset of the domain. In this context, we compare Leja and Krylov based methods to compute the action of exponential and related matrix functions. We set up a performance model by counting the different operations neede

Wim Cosyn, Brandon Roldan Tomei, Alan Sosa, Allison Zec

In the near future, the Jefferson Lab $b_1$ experiment will provide the second measurement of tensor polarized asymmetries in inclusive DIS on the deuteron. In this asymmetry, 4 independent tensor polarized structure functions contribute. This necessitates systematic approximations in the extraction of the leading twist structure function $b_1$ from a single

Linfei Pan, Marc Pollefeys, Dániel Baráth

Reconstructing a 3D scene from unordered images is pivotal in computer vision and robotics, with applications spanning crowd-sourced mapping and beyond. While global Structure-from-Motion (SfM) techniques are scalable and fast, they often compromise on accuracy. To address this, we introduce a principled approach that integrates gravity direction into the ro

Zong-Gang Mou, Paul M. Saffin, Anders Tranberg

We present a method to compute real-time path integrals numerically, by Monte-Carlo sampling on near-Lefschetz thimbles. We present a collection of tools based on the Lefschetz thimble methods, which together provide an alternative to existing methods such as the Generalised thimble. These involve a convenient coordinate parameterization of the thimble, dire

Jaehong Yoon, Shoubin Yu, Vaidehi Patil, Huaxiu Yao

Recent advances in diffusion models have significantly enhanced their ability to generate high-quality images and videos, but they have also increased the risk of producing unsafe content. Existing unlearning/editing-based methods for safe generation remove harmful concepts from models but face several challenges: (1) They cannot instantly remove harmful con

Felipe Martin Rodriguez Fuentes, Bernard Parent

This study provides the first comprehensive analysis of how ion mobility affects electron density and temperature in hypersonic flows. We compare two ion mobility models: one derived from Gupta-Yos cross-sections, and the other from swarm drift velocity experiments. The ion mobility model significantly alters the plasma density around a hypersonic waverider,

Hao-Yuan Chang, Kang L. Wang

Recent developments in adversarial attacks on deep learning leave many mission-critical natural language processing (NLP) systems at risk of exploitation. To address the lack of computationally efficient adversarial defense methods, this paper reports a novel, universal technique that drastically improves the robustness of Bidirectional Encoder Representatio

Tatsuyuki Hikita

We give a probabilistic interpretation of the coefficients of the elementary symmetric function expansion of the chromatic quasisymmetric function for any unit interval graph. As a corollary, we prove the Stanley--Stembridge conjecture.

Ajay Patel, Jiacheng Zhu, Justin Qiu, Zachary Horvitz

Style representations aim to embed texts with similar writing styles closely and texts with different styles far apart, regardless of content. However, the contrastive triplets often used for training these representations may vary in both style and content, leading to potential content leakage in the representations. We introduce StyleDistance, a novel appr

José Soto, Victor Verdugo

In the online 2-bounded auction problem, we have a collection of items represented as nodes in a graph and bundles of size two represented by edges. Agents are presented sequentially, each with a random weight function over the bundles. The goal of the decision-maker is to find an allocation of bundles to agents of maximum weight so that every item is assign

Jacquelyn Ho, Yue-Hui Lu, Tai Xiang, Cosimo C. Rusconi

Increasing the number of particles in a system often leads to qualitative changes in its properties, such as breaking of symmetries and the appearance of phase transitions. This renders a macroscopic system fundamentally different from its individual microscopic constituents. Lying between these extremes, mesoscopic systems exhibit microscopic fluctuations t

Patrick Graf, Aryaman Patel

Given a complex-projective klt pair $(X, \Delta)$ with standard coefficients and such that $K_X + \Delta$ is ample, we determine necessary and sufficient conditions for the pair $(X, \Delta)$ to be uniformized by a bounded symmetric domain. As an application, we obtain characterizations of orbifold quotients of the polydisc and of the four classical irreduci

Victor Chen, Joel Merker

With various jet orders $k$ and weights $n$, let $E_{k,n}^{\rm GG}$ be the Green-Griffiths bundles over the projective space $\mathbb{P}^N (\mathbb{C})$. Denote by $\mathcal{O} (d)$ the tautological line bundle over $\mathbb{P}^N (\mathbb{C})$. Although only negative twists are of interest for applications to complex hyperbolicity (above general type project

Pravakar Paul

In this paper, we introduce the notion of "$Lucas-Coloring$" associated with a planar graph $g$. When $g$ is a $4$-regular, the enumeration of $Lucas-Coloring$ has an interesting interpretation. Specifically, it yields a numerical invariant of the associated Khovanov-Lee complex of any link diagram $D$ whose projection is equal to $g$. This complex resides i

Grace Yang, Zhiyi Li, Yadong Liu, Jungyeul Park

Named entity recognition (NER) is a crucial task that aims to identify structured information, which is often replete with complex, technical terms and a high degree of variability. Accurate and reliable NER can facilitate the extraction and analysis of important information. However, NER for other than English is challenging due to limited data availability

Juechu Dong, Jonah Rosenblum, Satish Narayanasamy

Trusted hardware's freshness guarantee ensures that an adversary cannot replay an old value in response to a memory read request. They rely on maintaining a version number for each cache block and ensuring their integrity using a Merkle tree. However, these existing solutions protect only a small amount of main memory (few MBs), as the extraneous memory acce

Taha El Hajji, Antti Lehikoinen, Anouar Belahcen

Circulating currents in windings refer to unwanted electrical currents flowing between the parallel conductors of a winding. These currents arise due to several phenomena such as asymmetries, imperfections in the winding layout, and differences in electric potential between the parallel conductors. This effect is visible typically in windings of transformers

Yuya Seki, Hyakka Nakada, Shu Tanaka

This paper presents an initialization method that can approximate a given approximate Ising model with a high degree of accuracy using a factorization machine (FM), a machine learning model. The construction of an Ising models using an FM is applied to black-box combinatorial optimization problems using factorization machine with quantum annealing (FMQA). It

Sebastian Gallon, Fabio Feraco, Raffaele Marino, Alain Pumir

The transport of matter by turbulent flows plays an important role, in particular in a geophysical context. Here, we study the relative movement of groups of two (pairs) and four (tetrahedra) Lagrangian particles using direct numerical simulations of the stably-stratified Boussinsesq equations, with Brunt-V\"ais\"al\"a frequency $N$ and Coriolis parameter $f

Sungbok Shin, Inyoup Na, Niklas Elmqvist

We present drillboards, a technique for adaptive visualization dashboards consisting of a hierarchy of coordinated charts that the user can drill down to reach a desired level of detail depending on their expertise, interest, and desired effort. This functionality allows different users to personalize the same dashboard to their specific needs and expertise.

Jaideep P. Vaidya, Tyler N. Shendruk, Sumesh P. Thampi

Active nematic fluids exhibit complex dynamics in both bulk and in simple confining geometries. However, complex confining geometries could have substantial impact on active spontaneous flows. Using multiparticle collision dynamics simulations adapted for active nematic particles, we study the dynamic behaviour of an active nematic fluid confined in a corrug

Asish Bera, Debotosh Bhattacharjee, Ondrej Krejcar

Crop yield production could be enhanced for agricultural growth if various plant nutrition deficiencies, and diseases are identified and detected at early stages. The deep learning methods have proven its superior performances in the automated detection of plant diseases and nutrition deficiencies from visual symptoms in leaves. This article proposes a new d

Pier Giuseppe Catinari, Alfredo Urbano

In this work, we critically reanalyze the explicit breaking of the Peccei-Quinn global symmetry -- and the corresponding corrections to the QCD axion potential -- induced by gravity. Specifically, we examine the role of gravitational instantons, which are non-perturbative, finite-action solutions to the Euclidean Einstein equations. These instantons represen

Qianyi Chen, Bo Li

Recent research in causal inference under network interference has explored various experimental designs and estimation techniques to address this issue. However, existing methods, which typically rely on single experiments, often reach a performance bottleneck and face limitations in handling diverse interference structures. In contrast, we propose leveragi

Daqian Bao, Alex Saad-Falcon, Justin Romberg

Data acquisition in array signal processing (ASP) is costly because achieving high angular and range resolutions necessitates large antenna apertures and wide frequency bandwidths, respectively. The data requirements for ASP problems grow multiplicatively with the number of viewpoints and frequencies, significantly increasing the burden of data collection, e

Mohammad Bagher Heydari, Ali Abdollahi, Sina Asgari

In this paper, a new heterostructure based on the hybridization of graphene-LiF layers with a nonlinear material is introduced and studied. The numerical results are depicted and discussed in detail. A high value of FOM (FOM=24.5) at the frequency of 9.22 THz is reported for the chemical potential of 0.2 ev. Our results show that the propagation features of

Matthew Szydagis

Claims of the retrieval of crashed craft or vehicles from non-human intelligence(s) (NHI) abound in the popular culture and media. For this article, the number of unsubstantiated claims is utilized to estimate the time expected until a "catastrophic disclosure" occurs. The term was defined at the 2023 Sol Foundation's inaugural conference as an accidental di

Vikas Soni, Kinsuk Acharyya

Vertical mixing is a crucial disequilibrium process in exoplanet atmospheres, significantly impacting chemical abundance and observed spectra. While current state-of-the-art observations have detected its signatures, the effect of vertical mixing on atmospheric spectra varies widely based on planetary parameters. In this study, we explore the influence of di

Konstantinos Bourazas

In recent years, self-starting methods have garnered increasing attention in Statistical Process Control and Monitoring (SPC/M), as they offer real-time disorder detection without the need for a calibration phase (Phase I). This study focuses on evaluating parametric self-starting CUSUM-type control charts, specifically the Bayesian Predictive Ratio CUSUM (P

Zhaoyang Wang, Weilei He, Zhiyuan Liang, Xuchao Zhang

Recent self-rewarding large language models (LLM) have successfully applied LLM-as-a-Judge to iteratively improve the alignment performance without the need of human annotations for preference data. These methods commonly utilize the same LLM to act as both the policy model (which generates responses) and the reward model (which scores and ranks those respon

Marco Calautti, Damiano Duranti, Paolo Giorgini

Managing the growing data from renewable energy production plants for effective decision-making often involves leveraging Ontology-based Data Access (OBDA), a well-established approach that facilitates querying diverse data through a shared vocabulary, presented in the form of an ontology. Our work addresses one of the common problems in this context, derivi

Elena Chachkarova, Terence Tse, Yordan Yordanov, Yao Wei

The real world obeys quantum physics and quantum computing presents an alternative way to map physical problems to systems that follow the same laws. Such computation fundamentally constitutes a better way to understand the most challenging quantum problems. One such problem is the accurate simulation of highly correlated quantum systems. Due to the high dim

Bartosz Langowski, Adam Nowak

We prove sharp estimates of the heat kernel associated with Fourier-Dini expansions on $(0,1)$ equipped with Lebesgue measure and the Neumann condition imposed on the right endpoint. Then we give several applications of this result including sharp bounds for the corresponding Poisson and potential kernels, sharp mapping properties of the maximal heat semigro

Brendan Kline, Elie Tamer

We address counterfactual analysis in empirical models of games with partially identified parameters, and multiple equilibria and/or randomized strategies, by constructing and analyzing the counterfactual predictive distribution set (CPDS). This framework accommodates various outcomes of interest, including behavioral and welfare outcomes. It allows a variet

Yulun Wu, Louie McConnell, Claudia Iriondo

Estimating an individual's counterfactual outcomes under interventions is a challenging task for traditional causal inference and supervised learning approaches when the outcome is high-dimensional (e.g. gene expressions, facial images) and covariates are relatively limited. In this case, to predict one's outcomes under counterfactual treatments, it is cruci

Oleg Karpenkov, Fatemeh Mohammadi, Christian Müller, Bernd Schulze

In this paper, we introduce new classes of infinite and combinatorially periodic tensegrities, derived from algebraic multidimensional continued fractions in the sense of F. Klein. We describe the stress coefficients on edges through integer invariants of these continued fractions, as initiated by V.I. Arnold, thereby creating a novel connection between geom

Yaoqi Guo, Zhenpeng Chen, Jie M. Zhang, Yang Liu

Code generation, the automatic creation of source code from natural language descriptions, has garnered significant attention due to its potential to streamline software development. Inspired by research that links task-personality alignment with improved development outcomes, we conduct an empirical study on personality-guided code generation using large la

Kumar Srinivas Bobba, Kartheeban K, Vamsi Krishna Sai, Dinesh Bugga

This project proposes the development of a comprehensive real-time biodiversity monitoring system that harnesses sound data through a network of acoustic sensors and advanced artificial intelligence algorithms. The system analyzes sound recordings from various ecosystems to identify and classify different species, providing valuable insights into ecosystem h

Antonio Pérez, Mario Santa Cruz, Daniel San Martín, José Manuel Gutiérrez

Super-resolution (SR) is a promising cost-effective downscaling methodology for producing high-resolution climate information from coarser counterparts. A particular application is downscaling regional reanalysis outputs (predictand) from the driving global counterparts (predictor). This study conducts an intercomparison of various SR downscaling methods foc

Adrian Thananopavarn

We generalize Rock Paper Scissors to complete directed graphs, or tournaments, on $n$ vertices. Properties of the mixed-strategy Nash equilibria of these tournaments are discussed, particularly those with Nash equilibria where all of the strategies have a nonzero probability. We find graph-theoretic properties of such games and tabulate them for $n \leq 7$.

Aryabrat Mahapatra, Adarsh Pandey, Debojyoti Garain, Tapobrata Sarkar

We present the results of a suite of numerical simulations using smoothed particle hydrodynamics to study partial tidal disruption events (TDEs) of white dwarfs (WDs) in off-equatorial orbits in intermediate mass spinning (Kerr) black hole backgrounds. We carry out this analysis for both parabolic and eccentric WD orbits and also take into account possible i

Dominic M. Bowman, Pieterjan Van Daele, Mathias Michielsen, Timothy Van Reeth

Massive main-sequence stars have convective cores and radiative envelopes, but also sub-surface convection zones caused by partial ionisation. However, the convective properties depend on opacity and a star's metallicity. Non-rotating 1D evolution models of main-sequence stars with the metallicity of the SMC suggest tenuous sub-surface convection zones using

Shen Fan, Przemyslaw Musialski

Implicit neural representations have emerged as a powerful tool in learning 3D geometry, offering unparalleled advantages over conventional representations like mesh-based methods. A common type of INR implicitly encodes a shape's boundary as the zero-level set of the learned continuous function and learns a mapping from a low-dimensional latent space to the

Jiajun Meng, Jing Chen, Dongfang Zhao, Lin Liu

Federated learning (FL) is a collaborative technique for training large-scale models while protecting user data privacy. Despite its substantial benefits, the free-riding behavior raises a major challenge for the formation of FL, especially in competitive markets. Our paper explores this under-explored issue on how the free-riding behavior in a competitive m

João Matos, Shan Chen, Siena Placino, Yingya Li

Multimodal/vision language models (VLMs) are increasingly being deployed in healthcare settings worldwide, necessitating robust benchmarks to ensure their safety, efficacy, and fairness. Multiple-choice question and answer (QA) datasets derived from national medical examinations have long served as valuable evaluation tools, but existing datasets are largely

Deven Mithal, Lorenzo Orecchia

In this note, we realize the half-steps of a general class of Markov chains as alternating projections with respect to the reverse Kullback-Leibler divergence between convex sets of joint probability distributions. Using this characterization, we provide a geometric proof of an information-theoretic duality between the Markov chains defined by the even and o

Emmanuele Lacavalla, Shuyi Yang, Riccardo Crupi, Joseph E. Gonzalez

This project, named HEnRY, aims to introduce a Multi-Agent System (MAS) into Intesa Sanpaolo. The name HEnRY summarizes the project's core principles: the Hierarchical organization of agents in a layered structure for efficient resource management; Efficient optimization of resources and operations to enhance overall performance; Reactive ability of agents t

Mohamed Awadein, Abhishek Kumar, Yuqing Wang, Mingdong Dong

The accelerated demand for electrochemical energy storage urges the need for new, sustainable, stable and lightweight materials able to store high energy densities rapidly and efficiently. Development of these functional materials requires specialized techniques that can provide a close insight into the electrochemical properties at the nanoscale. For this r

Asish Bera, Ondrej Krejcar, Debotosh Bhattacharjee

Deep Convolutional Neural Networks (CNNs) have facilitated remarkable success in recognizing various food items and agricultural stress. A decent performance boost has been witnessed in solving the agro-food challenges by mining and analyzing of region-based partial feature descriptors. Also, computationally expensive ensemble learning schemes using multiple

Salvatore Romano, Harsharan Kaur, Moritz Zelenka, Pablo Montero De Hijes

Magnetite, a naturally abundant mineral, frequently interacts with water in both natural settings and various technical applications, making the study of its surface chemistry highly relevant. In this work, we investigate the hydrogen bonding dynamics and the presence of hydroxyl species at the magnetite-water interface using a combination of neural network

Kalin V. Staykov, Peter Y. Yordanov, Daniela D. Doneva, Stoytcho S. Yazadjiev

Neutron stars are natural laboratories for testing gravity in the strong field regime. That is why the full spectrum of neutron star solutions in different modified theories should be thoroughly studied. Among the most natural modifications of general relativity are the theories in which additional scalar degrees of freedom are present. That is why scalar-te

Phillip S. Harrington

Generalizing a result of Berndtsson and Charpentier, we provide sufficient conditions for $L^2$ Sobolev regularity of the Bergman projection acting on $L^2$ sections of a holomorphic line bundle restricted to a relatively compact domain with Lipschitz boundary in a Hermitian manifold. We provide examples to show that our methods work for domains in Hopf mani

Josef Rukavicka

The palindromic length of the finite word $v$ is equal to the minimal number of palindromes whose concatenation is equal to $v$. It was conjectured in 2013 that for every infinite aperiodic word $x$, the palindromic length of its factors is not bounded. We prove this conjecture to be true.

Zeyu Jia, Jian Qian, Alexander Rakhlin, Chen-Yu Wei

We consider realizable contextual bandits with general function approximation, investigating how small reward variance can lead to better-than-minimax regret bounds. Unlike in minimax bounds, we show that the eluder dimension $d_\text{elu}$$-$a complexity measure of the function class$-$plays a crucial role in variance-dependent bounds. We consider two types