April 2024 arXiv papers — page 157

M. B. Erdogan, N. Tzirakis

In this paper we study the effect of rotation on nonlinear wave phenomena in weakly dispersive media modeled by the Korteweg-de Vries equation on the real line. It is well known that smoothing in the case of the KdV equation with periodic boundary conditions is a result of the presence of high frequency waves that weaken the nonlinearity through time averagi

Yihao Hu, Amir Gilad, Kristin Stephens-Martinez, Sudeepa Roy

We describe a system called Qr-Hint that, given a (correct) target query Q* and a (wrong) working query Q, both expressed in SQL, provides actionable hints for the user to fix the working query so that it becomes semantically equivalent to the target. It is particularly useful in an educational setting, where novices can receive help from Qr-Hint without req

Truman Yuen, Graham A. Watt, Yuri Lawryshyn

Generative Large Language Models enable efficient analytics across knowledge domains, rivalling human experts in information comparisons. However, the applications of LLMs for information comparisons face scalability challenges due to the difficulties in maintaining information across large contexts and overcoming model token limitations. To address these ch

Peter Gladbach, Bernhard Kepka

We consider optimization problems for interacting particle systems. We show that critical points solve a Vlasov equation, and that in general no minimizers exist despite continuity of the action functional. We prove an explicit representation of the relaxation of the action functional. We show convergence of N-particle minimizers to minimizers of the relaxed

Adam Přenosil

The purpose of this note is to provide a transparent and unified retelling of both Skvortsov's proof of the structural completeness of Medvedev's logic of finite problems, which is a classical result originally due to Prucnal, and of Levin's proof that Medvedev's logic of finite problems is the largest extension of the (weak) Kreisel-Putnam logic with the di

Maria F. Gamal'

Let $T$ be a quasinilpotent operator on a Banach space. Under assumptions of a certain nonsymmetry in the growth of the resolvent of $T$, it is proved that every operator in the commutant of $T$ is not unicellular. In particular, $T$ has nontrivial hyperinvariant subspaces. The proof is based on a modification of the reasoning of [S].

Ali Madanshekaf, Adam Přenosil, Zeinab Khanjanzadeh Seresti, Constantine Tsinakis

The pioneering work of Blok and J\'onsson and its further development by Galatos and Tsinakis initiated an abstract study of consequence relations using the tools of module theory, where consequence relations over all types of syntactic objects are put on an equal footing. However, the assumption that in a consequence relation the premises form merely a set,

Reuben Tan, Ximeng Sun, Ping Hu, Jui-hsien Wang

Long video question answering is a challenging task that involves recognizing short-term activities and reasoning about their fine-grained relationships. State-of-the-art video Large Language Models (vLLMs) hold promise as a viable solution due to their demonstrated emergent capabilities on new tasks. However, despite being trained on millions of short secon

Yifei Sun, Suzanne D. Vernon, Shad Roundy

Objective: We present a simple parameter, calculated from a single wearable sensor, that can be used to objectively measure disease severity in people with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) or Long COVID. We call this parameter UpTime. Methods: Prior research has shown that the amount of time a person spends upright, defined as lowe

Tom Hanika, Robert Jäschke

Data is always at the center of the theoretical development and investigation of the applicability of formal concept analysis. It is therefore not surprising that a large number of data sets are repeatedly used in scholarly articles and software tools, acting as de facto standard data sets. However, the distribution of the data sets poses a problem for the s

Elisa Perrone, Roberto Fontana, Fabio Rapallo

We study the problem of transforming a multi-way contingency table into an equivalent table with uniform margins and same dependence structure. Such a problem relates to recent developments in copula modeling for discrete random vectors. Here, we focus on three-way binary tables and show that, even in such a simple case, the situation is quite different than

Dipayan Mukherjee, Atul Sandur, Kirill Mechitov, Pratik Lahiri

Managing the limited energy on mobile platforms executing long-running, resource intensive streaming applications requires adapting an application's operators in response to their power consumption. For example, the frame refresh rate may be reduced if the rendering operation is consuming too much power. Currently, predicting an application's power consumpti

Messoud Efendiev, Vitali Vougalter

In the article we establish the global well-posedness in W^{1, 2, 2}(R\times R^{+}) of the integro-differential equation in the case of the anomalous diffusion when the one dimensional negative Laplace operator is raised to a fractional power in the presence of the transport term. The model is relevant to the cell population dynamics in the Mathematical Biol

James H. Leftley, Nicole P. H. Nesvadba, Geoff Bicknell, Reinier M. J. Janssen

The physical link between AGN activity and the suppression of star formation in their host galaxies is one of the major open questions of AGN feedback. The Spitzer space mission revealed a subset of nearby radio galaxies with unusually bright line emission from warm ($T\ge 100$ K) H$_2$, while typical star-formation tracers were exceptionally faint or undete

Sergio Blanes, Fernando Casas, Alejandro Escorihuela-Tomàs

New families of composition methods with processing of order 4 and 6 are presented and analyzed. They are specifically designed to be used for the numerical integration of differential equations whose vector field is separated into three or more parts which are explicitly solvable. The new schemes are shown to be more efficient than previous state-of-the-art

Hardik Bohra, Sumit R. Das, Gautam Mandal, Kanhu Kishore Nanda

Realizations of the holographic correspondence in String/M theory typically involve spacetimes of the form $AdS \times Y$ where $Y$ is some internal space which geometrizes an internal symmetry of the dual field theory, hereafter referred to as an "$R$ symmetry". It has been speculated that areas of Ryu-Takayanagi surfaces anchored on the boundary of a subre

İlker Işık, Onur Yigit Arpali, Ebru Aydin Gol

Motivated by the post-disaster distribution system restoration problem, in this paper, we study the problem of synthesizing the optimal policy for a Markov Decision Process (MDP) from a sequence of goal sets. For each goal set, our aim is to both maximize the probability to reach and minimize the expected time to reach the goal set. The order of the goal set

Yong Zheng, Jian-Nan Ding, Dong-Hao Li, Lei-Yi Li

The similar densities of dark matter and baryons in the universe imply that they might arise from the same ultraviolet model. The B-Mesogenesis, which assumes dark matter is charged under the baryon number, attempts to simultaneously explain the origin of baryon asymmetry and dark matter in the universe. In particular, the B-Mesogenesis might induce bottom-b

Spyros Alexakis

We study the complete electrode model boundary condition for second order elliptic PDE. A specific case of this is the PDE describing the electrostatic potential for a conductive body into which current is injected through electrodes that touch the boundary. We obtain the optimal description of the gradient of the electrostatic potential upon approach to the

Boyao Wu, Difang Huang, Muzi Chen

This paper proposes a time-zone vector autoregression (VAR) model to investigate comovements in the global financial market. Analyzing daily data from 36 national equity markets, we explore the subprime and European debt crises using static analysis and the COVID-19 crisis through a rolling window method. Our study of comovements using VAR coefficients revea

Kang-Le Cai, Meng Cheng

Disorder operators are a type of non-local observables for quantum many-body systems, measuring the fluctuations of symmetry charges inside a region. It has been shown that disorder operators can reveal global aspects of many-body states that are otherwise difficult to access through local measurements. We study the disorder operator for U(1) (charge or spin

Jiang-Chuan Yu, Yan Cao, Yong Tang, Yue-Liang Wu

Ultralight dark matter (ULDM) is one of the leading well-motivated dark matter candidates, predicted in many theories beyond the standard model of particle physics and cosmology. There have been increasing interests in searching for ULDM in physical and astronomical experiments, mostly assuming there are additional interactions other than gravity between ULD

Gaurav Kamath, Sebastian Schuster, Sowmya Vajjala, Siva Reddy

Sentences containing multiple semantic operators with overlapping scope often create ambiguities in interpretation, known as scope ambiguities. These ambiguities offer rich insights into the interaction between semantic structure and world knowledge in language processing. Despite this, there has been little research into how modern large language models tre

Xiao-Chuan Wu

Fluctuations of conserved quantities within a subsystem are non-local observables that provide unique insights into quantum many-body systems. In this paper, we study bipartite charge (and spin) fluctuations across interaction-driven ``metal-insulator transitions'' out of Landau Fermi liquids. The ``charge insulators'' include a class of non-Fermi-liquid sta

Vrutant Mehta, Jack Sullivan, Robert Fisher, Yuken Ohshiro

Suzaku X-ray observations of the Type Ia supernova remnant (SNR) 3C 397 discovered exceptionally high mass ratios of Mn/Fe, Ni/Fe, and Cr/Fe, consistent with a near $M_{\rm Ch}$ progenitor white dwarf (WD). The Suzaku observations have established 3C 397 as our best candidate for a near-$M_{\rm Ch}$ SNR Ia, and opened the way to address additional outstandin

Hyeok-Jun Yang, Yi-Ting Hsu

Due to the diverging density of states (DOS), Van Hove singularities (VHS) near the Fermi level are known to boost the susceptibility to a wide variety of electronic instabilities, including superconductivity. We theoretically show that the number of VHS in the normal state can be qualitatively inferred from the optical absorption spectra Re $\sigma_{ii}(\om

Somsubhra Ghosh, Indranil Paul, K. Sengupta

We study the possible signatures of prethermal strong Hilbert space fragmentation (HSF) for one-dimensional (1D) fermions subjected to a periodic drive. We extend the results of Phys. Rev. Lett. 130, 120401 (2023) to show the possibility of such fragmentation for a large class of experimentally relevant drive protocols. Moreover, we demonstrate the persisten

Soumak Maitra, Stefano Cristiani, Matteo Viel, Roberto Trotta

We aim to present a robust parameter estimation with simulated Lya forest spectra from Sherwood-Relics simulations suite using Information Maximizing Neural Network(IMNN) to extract maximal information from Lya 1D-transmitted flux in Fourier space. We perform 1D estimations using IMNN for IGM thermal parameters $T_0$ & $\gamma$ at z=2-4 and cosmological para

Yangqiaoyu Zhou, Haokun Liu, Tejes Srivastava, Hongyuan Mei

Effective generation of novel hypotheses is instrumental to scientific progress. So far, researchers have been the main powerhouse behind hypothesis generation by painstaking data analysis and thinking (also known as the Eureka moment). In this paper, we examine the potential of large language models (LLMs) to generate hypotheses. We focus on hypothesis gene

Kevin N. Hainline, Francesco D'Eugenio, Peter Jakobsen, Jacopo Chevallard

We describe new ultra-deep James Webb Space Telescope (JWST) NIRSpec PRISM and grating spectra for the galaxies JADES-GS-z11-0 ($z_{\mathrm{spec}} = 11.122^{+0.005}_{-0.003}$) and JADES-GS-z13-0 ($z_{\mathrm{spec}} = 13.20^{+0.03}_{-0.04}$), the most distant spectroscopically-confirmed galaxy discovered in the first year of JWST observations. The extraordina

Akshat Pandey, Aditya Cowsik

A family of $\mathbb Z_n$-symmetric non-Hermitian models of Baxter was shown by Fendley to be exactly solvable via a parafermionic generalization of the Clifford algebra. We study these models with spatially random couplings, and obtain several exact results on thermodynamic singularities as the distributions of couplings are varied. We find that these singu

Michael P. Kinach, Matthew W. Choptuik

We investigate the collision dynamics of $U(1)$ gauged Q-balls by performing high-resolution numerical simulations in axisymmetry. Focusing on the case of relativistic head-on collisions, we consider the effects of the initial velocity, relative phase, relative charge, and electromagnetic coupling strength on the outcome of the collision. We find that the co

Marcos Crichigno, Anupam Prakash

We consider the question of what quantum spin chains naturally encode in their Hilbert space. It turns out that quantum spin chains are rather rich systems, naturally encoding solutions to various problems in combinatorics, group theory, and algebraic geometry. In the case of the XX Heisenberg spin chain these are given by skew Kostka numbers, skew character

Eunmo Ahn, M. James Jee, Wonki Lee, Hyungjin Joo

Abell 514 (A514) at $z=0.071$ is an intriguing merging system exhibiting highly elongated (~1 Mpc) X-ray features and three large-scale (300~500 kpc) bent radio jets. To dissect this system with its multi-wavelength data, it is critical to robustly identify and quantify its dark matter (DM) substructures. We present a weak-lensing analysis of A514 using deep

Matthieu Béthermin, Alberto D. Bolatto, François Boulanger, Charles M. Bradford

Because of their limited angular resolution, far-infrared telescopes are usually affected by confusion phenomenon. Since several galaxies can be located in the same instrumental beam, only the brightest objects emerge from the fluctuations caused by fainter sources. The probe far-infrared mission for astrophysics imager (PRIMAger) will observe the mid- and f

Olli Siltanen, Kimmo Luoma, Andrew J. Musser, Konstantinos S. Daskalakis

Organic light-emitting diodes (OLEDs) have redefined lighting with their environment-friendliness and flexibility. However, only 25 % of the electronic states of organic molecules can emit light upon electrical excitation, limiting the overall efficiency of OLEDs. Strong light-matter coupling, achieved by confining light within OLEDs using mirrors, creates h

Zifu Wan, Pingping Zhang, Yuhao Wang, Silong Yong

Multi-modal semantic segmentation significantly enhances AI agents' perception and scene understanding, especially under adverse conditions like low-light or overexposed environments. Leveraging additional modalities (X-modality) like thermal and depth alongside traditional RGB provides complementary information, enabling more robust and reliable prediction.

Yuxi Xiao, Qianqian Wang, Shangzhan Zhang, Nan Xue

Recovering dense and long-range pixel motion in videos is a challenging problem. Part of the difficulty arises from the 3D-to-2D projection process, leading to occlusions and discontinuities in the 2D motion domain. While 2D motion can be intricate, we posit that the underlying 3D motion can often be simple and low-dimensional. In this work, we propose to es

Umar Javed, Riccardo J. Valencia-Tortora, Jamir Marino, Vadim Oganesyan

In few-qubit systems, the quantum Zeno effect arises when measurement occurs sufficiently frequently that the spins are unable to relax between measurements. This can compete with Hamiltonian terms, resulting in interesting relaxation processes which depend non-monotonically on the ratio of measurement rate to coherent oscillations. While Zeno physics for a

Zhengyuan Jiang, Moyang Guo, Yuepeng Hu, Yupu Wang

Several companies have deployed watermark-based detection to identify AI-generated content. However, attribution--the ability to trace back to the user of a generative AI (GenAI) service who created a given AI-generated content--remains largely unexplored despite its growing importance. In this work, we aim to bridge this gap by conducting the first systemat

Tim Seyde, Peter Werner, Wilko Schwarting, Markus Wulfmeier

Recent reinforcement learning approaches have shown surprisingly strong capabilities of bang-bang policies for solving continuous control benchmarks. The underlying coarse action space discretizations often yield favourable exploration characteristics while final performance does not visibly suffer in the absence of action penalization in line with optimal c

Dylan Britt, Daniel Gruen, Oliver Friedrich, Sihan Yuan

The joint probability distribution of matter overdensity and galaxy counts in cells is a powerful probe of cosmology, and the extent to which variance in galaxy counts at fixed matter density deviates from Poisson shot noise is not fully understood. The lack of informed bounds on this stochasticity is currently the limiting factor in constraining cosmology w

Michael Saxon, Fatima Jahara, Mahsa Khoshnoodi, Yujie Lu

With advances in the quality of text-to-image (T2I) models has come interest in benchmarking their prompt faithfulness -- the semantic coherence of generated images to the prompts they were conditioned on. A variety of T2I faithfulness metrics have been proposed, leveraging advances in cross-modal embeddings and vision-language models (VLMs). However, these

Kei Ikemura, Yiming Huang, Felix Heide, Zhaoxiang Zhang

Existing depth sensors are imperfect and may provide inaccurate depth values in challenging scenarios, such as in the presence of transparent or reflective objects. In this work, we present a general framework that leverages polarization imaging to improve inaccurate depth measurements from various depth sensors. Previous polarization-based depth enhancement

Francisco Gancedo, Antonio Hidalgo-Torné, Francisco Mengual

In this paper, we prove the first existence result of weak solutions to the 3D Euler equation with initial vorticity concentrated in a circle and velocity field in $C([0,T],L^{2^-})$. The energy becomes finite and decreasing for positive times, with vorticity concentrated in a ring that thickens and moves in the direction of the symmetry axis. With our appro

Fabrice R. Noreils

Launched by Elon Musk and its Optimus, we are witnessing a new race in which many companies have already engaged. The objective it to put at work a new generation of humanoid robots in demanding industrial environments within 2 or 3 years. Is this objective realistic ? The aim of this document and its main contributions is to provide some hints by covering t

The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, A. G. Abac

We report the observation of a coalescing compact binary with component masses $2.5\text{-}4.5~M_\odot$ and $1.2\text{-}2.0~M_\odot$ (all measurements quoted at the 90% credible level). The gravitational-wave signal GW230529_181500 was observed during the fourth observing run of the LIGO-Virgo-KAGRA detector network on 2023 May 29 by the LIGO Livingston Obse

Kihyun Kim, Frank Merle

We consider the global dynamics of finite energy solutions to energy-critical equivariant harmonic map heat flow (HMHF) and radial nonlinear heat equation (NLH). It is known that any finite energy equivariant solutions to (HMHF) decompose into finitely many harmonic maps (bubbles) separated by scales and a body map, as approaching to the maximal time of exis

Grant T. Barkley, Christian Gaetz

We show that the Combinatorial Invariance Conjecture for Kazhdan-Lusztig polynomials due to Lusztig and to Dyer, its parabolic analog due to Marietti, and a refined parabolic version that we introduce, are equivalent. We use this to give a new proof of Marietti's conjecture in the case of lower Bruhat intervals and to prove several new cases of the parabolic

Trilokesh Ranjan Sarkar, Nilanjan Das, Pralay Sankar Maitra, Bijoy Some

This technical report delves into an in-depth exploration of adversarial attacks specifically targeted at Deep Neural Networks (DNNs) utilized for image classification. The study also investigates defense mechanisms aimed at bolstering the robustness of machine learning models. The research focuses on comprehending the ramifications of two prominent attack m

Wenxuan Zuo, Zifan Zhu, Yuxuan Du, Yi-Chun Yeh

High-dimensional longitudinal time series data is prevalent across various real-world applications. Many such applications can be modeled as regression problems with high-dimensional time series covariates. Deep learning has been a popular and powerful tool for fitting these regression models. Yet, the development of interpretable and reproducible deep-learn

Jiong Wu, Shuang Zhou, Li Lin, Xin Wang

Diffeomorphic image registration is a fundamental step in medical image analysis, owing to its capability to ensure the invertibility of transformations and preservation of topology. Currently, unsupervised learning-based registration techniques primarily extract features at the image level, potentially limiting their efficacy. This paper proposes a novel un

Sangwon Jang, Jaehyeong Jo, Kimin Lee, Sung Ju Hwang

Text-to-image diffusion models have shown remarkable success in generating personalized subjects based on a few reference images. However, current methods often fail when generating multiple subjects simultaneously, resulting in mixed identities with combined attributes from different subjects. In this work, we present MuDI, a novel framework that enables mu

Albert J. Zhai, Yuan Shen, Emily Y. Chen, Gloria X. Wang

Can computers perceive the physical properties of objects solely through vision? Research in cognitive science and vision science has shown that humans excel at identifying materials and estimating their physical properties based purely on visual appearance. In this paper, we present a novel approach for dense prediction of the physical properties of objects

Jordan Thompson, Brian Y. Cho, Daniel S. Brown, Alan Kuntz

Tendon-driven continuum robot kinematic models are frequently computationally expensive, inaccurate due to unmodeled effects, or both. In particular, unmodeled effects produce uncertainties that arise during the robot's operation that lead to variability in the resulting geometry. We propose a novel solution to these issues through the development of a Gauss

Gavin Kerrigan, Giosue Migliorini, Padhraic Smyth

We study the geometry of conditional optimal transport (COT) and prove a dynamical formulation which generalizes the Benamou-Brenier Theorem. Equipped with these tools, we propose a simulation-free flow-based method for conditional generative modeling. Our method couples an arbitrary source distribution to a specified target distribution through a triangular

Alexandru Pascadi

We prove new large sieve inequalities for the Fourier coefficients $\rho_{j\mathfrak{a}}(n)$ of exceptional Maass forms of a given level, weighted by sequences $(a_n)$ with sparse Fourier transforms - including two key types of sequences that arise in the dispersion method. These give the first savings in the exceptional spectrum for the critical case of seq

Eduardo V. Stock, Roberto da Silva, Sebastián Gonçalves

In this work, we investigate the statistical properties of drink serving in a nightclub bar, utilizing a stochastic model to characterize pedestrian dynamics within the venue. Our model comprises a system of n agents moving across an underlying square lattice of size l representing the nightclub venue. Each agent can exist in one of three states: thirsty, se

Harsh Kohli, Sachin Kumar, Huan Sun

The rapid progress of large language models (LLMs) has seen them excel and frequently surpass human performance on standard benchmarks. This has enabled many downstream applications, such as LLM agents, to rely on their reasoning to address complex task requirements. However, LLMs are known to unexpectedly falter in simple tasks and under seemingly straightf

Peijing Liu, Alper Atamtürk, Andrés Gómez, Simge Küçükyavuz

In this paper, we consider convex quadratic optimization problems with indicators on the continuous variables. In particular, we assume that the Hessian of the quadratic term is a Stieltjes matrix, which naturally appears in sparse graphical inference problems and others. We describe an explicit convex formulation for the problem by studying the Stieltjes po

Naveen Dukiya, Anjasha Gangopadhyay, Kuntal Misra, Griffin Hosseinzadeh

We present long-term photometric and spectroscopic studies of Circumstellar Material (CSM)-Ejecta interacting supernova (SN) ASASSN-14il in the galaxy PGC 3093694. The SN reaches a peak $r$-band magnitude of $\sim$ $-20.3 \pm 0.2$ mag rivaling SN 2006tf and SN 2010jl. The multiband and the pseudo-bolometric lightcurve show a plateau lasting $\sim 50$ days. S

Tianze Wang, Maryam Honari-Jahromi, Styliani Katsarou, Olga Mikheeva

Methods for learning latent user representations from historical behavior logs have gained traction for recommendation tasks in e-commerce, content streaming, and other settings. However, this area still remains relatively underexplored in video and mobile gaming contexts. In this work, we present a novel method for overcoming this limitation by extending a

Chanchal Kumar Salode, Prasanna Ramamoorthy

Metro train operations is becoming more challenging due to overcrowding and unpredictable irregular passenger demand. To avoid passenger dissatisfaction, metro operators employ various operational strategies to increase the number of train services using limited number of trains. This paper integrates metro timetabling with several operational strategies to

Tianqi Zhong, Zhaoyi Li, Quan Wang, Linqi Song

Compositional generalization, representing the model's ability to generate text with new attribute combinations obtained by recombining single attributes from the training data, is a crucial property for multi-aspect controllable text generation (MCTG) methods. Nonetheless, a comprehensive compositional generalization evaluation benchmark of MCTG is still la

Ji-Jia Wu, Andy Chia-Hao Chang, Chieh-Yu Chuang, Chun-Pei Chen

This paper addresses text-supervised semantic segmentation, aiming to learn a model capable of segmenting arbitrary visual concepts within images by using only image-text pairs without dense annotations. Existing methods have demonstrated that contrastive learning on image-text pairs effectively aligns visual segments with the meanings of texts. We notice th

Enze Chen, Tae Wook Heo, Brandon C. Wood, Mark Asta

Grain boundaries (GBs) profoundly influence the properties and performance of materials, emphasizing the importance of understanding the GB structure and phase behavior. As recent computational studies have demonstrated the existence of multiple GB phases associated with varying the atomic density at the interface, we introduce a validated, open-source GRand

Hao-Lin Li, Ling-Xiao Xu

We discuss heavy particles that can be used to pin down the faithful Standard Model (SM) gauge group and their patterns in the SM effective field theory (SMEFT). These heavy particles are not invariant under a specific $\mathbb{Z}_6$ subgroup of $SU(3)_c\times SU(2)_L \times U(1)_Y$, which however acts trivially on all the SM particles, hence the faithful SM

Elena Massara, ChangHoon Hahn, Michael Eickenberg, Shirley Ho

We present the first $\Lambda$CDM cosmological analysis performed on a galaxy survey using marked power spectra. The marked power spectrum is the two-point function of a marked field, where galaxies are weighted by a function that depends on their local density. The presence of the mark leads these statistics to contain higher-order information of the origin

Lauren Dreier, Trevor J. Jones, Abigail Plummer, Andrej Košmrlj

From the pragmatic to the symbolic, textiles play a prominent role in some of the most demanding yet ubiquitous scenarios, such as covering the complex and dynamic geometries of the human body. Textiles are made by repeated manipulations of slender fibers into structures with emergent properties. Today, these ancient metamaterials are being examined in a new

Md Sariful Sheikh, Lin Lin, Ryan Jacobs, Martin E. Kordesch

Perovskite SrVO$_3$ has recently been proposed as a novel electron emission cathode material. Density functional theory (DFT) calculations suggest multiple low work function surfaces and recent experimental efforts have consistently demonstrated effective work functions of ~2.7 eV for polycrystalline samples, both results suggesting, but not directly confirm

Marcus Schwarting, Nathan A. Seifert, Michael J. Davis, Ben Blaiszik

Rotational spectroscopy is the most accurate method for determining structures of molecules in the gas phase. It is often assumed that a rotational spectrum is a unique "fingerprint" of a molecule. The availability of large molecular databases and the development of artificial intelligence methods for spectroscopy makes the testing of this assumption timely.

Zachary R. Fox, Ayana Ghosh

Predicting and enhancing inherent properties based on molecular structures is paramount to design tasks in medicine, materials science, and environmental management. Most of the current machine learning and deep learning approaches have become standard for predictions, but they face challenges when applied across different datasets due to reliance on correla

Pablo López

It is usually assumed that the angular momentum (AM) of dark matter halos arises during the linear stages of structure formation, as a consequence of the coupling between the proto-haloes' shape and the tidal field produced by their surrounding density perturbations. This approach, known as linear tidal torque theory (TTT), has been shown to make fairly good

Patrick Geitner

New technology for energy storage is necessary for the large-scale adoption of renewable energy sources like wind and solar. The ability to discover suitable catalysts is crucial for making energy storage more cost-effective and scalable. The Open Catalyst Project aims to apply advances in graph neural networks (GNNs) to accelerate progress in catalyst disco

Alex Viguerie, Chaitra Gopalappa, Cynthia M. Lyles, Paul G. Farnham

The OraQuick In-Home HIV self-test represents a fast, inexpensive, and convenient method for users to assess their HIV status. If integrated thoughtfully into existing testing practices, accompanied by efficient pathways to formal diagnosis, self-testing could both enhance HIV awareness and reduce HIV incidence. However, currently available self-tests are le

Harsh Kohli, Helian Feng, Nicholas Dronen, Calvin McCarter

In contemporary machine learning approaches to bilingual lexicon induction (BLI), a model learns a mapping between the embedding spaces of a language pair. Recently, retrieve-and-rank approach to BLI has achieved state of the art results on the task. However, the problem remains challenging in low-resource settings, due to the paucity of data. The task is co

Tommaso Torda, Andrea Ciardiello, Simona Gargiulo, Greta Grillo

In recent years Artificial Intelligence has emerged as a fundamental tool in medical applications. Despite this rapid development, deep neural networks remain black boxes that are difficult to explain, and this represents a major limitation for their use in clinical practice. We focus on the segmentation of medical images task, where most explainability meth

Enrico Donato, Egidio Falotico, Thomas George Thuruthel

Perception is essential for the active interaction of physical agents with the external environment. The integration of multiple sensory modalities, such as touch and vision, enhances this perceptual process, creating a more comprehensive and robust understanding of the world. Such fusion is particularly useful for highly deformable bodies such as soft robot

Lanpei Li, Enrico Donato, Vincenzo Lomonaco, Egidio Falotico

Dexterous manipulation, often facilitated by multi-fingered robotic hands, holds solid impact for real-world applications. Soft robotic hands, due to their compliant nature, offer flexibility and adaptability during object grasping and manipulation. Yet, benefits come with challenges, particularly in the control development for finger coordination. Reinforce

Filip Koprivec, Matija Pretnar

Algebraic effect handlers are becoming an increasingly popular way of structuring effectful computations, and their performance is often a concern. One of the proposed approaches towards efficient compilation is tracking effect information through explicit subtyping coercions. However, in the presence of polymorphism, these coercions are compiled into additi

Giulia Piumini, Martin P. A. Assen, Detlef Lohse, Roberto Verzicco

We use three-dimensional direct numerical simulations of homogeneous isotropic turbulence in a cubic domain to investigate the dynamics of heavy, chiral, finite-size inertial particles and their effects on the flow. Using an immersed-boundary method and a complex collision model, four-way coupled simulations have been performed, and the effects of particle-t

Zhaoxiang Shen, Raúl I. Sosa, Stéphane P. A. Bordas, Alexandre Tkatchenko

The macroscopic behaviors of materials are determined by interactions that occur at multiple lengths and time scales. Depending on the application, describing, predicting, and understanding these behaviors require models that rely on insights from electronic and atomic scales. In such cases, classical simplified approximations at those scales are insufficien

Christian Küchler, Antonio Ibanez Landeta, Jan Molacek, Eberhard Bodenschatz

Particle tracking in turbulent flows is fundamental to the study of the transport of tracers, inertial particles or even active objects in space and time, i.e. the Lagrangian frame of reference. It provides experimental tests of theoretical predictions (e.g. for the statistics of fluid accelerations and particle dispersion) and helps to understand important

Moshik Hershcovitch, Leshem Choshen, Andrew Wood, Ilias Enmouri

With the growth of model sizes and scale of their deployment, their sheer size burdens the infrastructure requiring more network and more storage to accommodate these. While there is a vast literature about reducing model sizes, we investigate a more traditional type of compression -- one that compresses the model to a smaller form and is coupled with a deco

Denis Gessert, Henrik Christiansen, Wolfhard Janke

Aging in phase-ordering kinetics of the $d=3$ Ising model following a quench from infinite to zero temperature is studied by means of Monte Carlo simulations. In this model the two-time spin-spin autocorrelator $C_\text{ag}$ is expected to obey dynamical scaling and to follow asymptotically a power-law decay with the autocorrelation exponent $\lambda$. Previ

Dimitris Karlis, Rouven Michels, Marius Otting

Handball has received growing interest during the last years, including academic research for many different aspects of the sport. On the other hand modelling the outcome of the game has attracted less interest mainly because of the additional challenges that occur. Data analysis has revealed that the number of goals scored by each team are under-dispersed r

Tong Su, Xin Peng, Sarubi Thillainathan, David Guzmán

Parameter-efficient fine-tuning (PEFT) methods are increasingly vital in adapting large-scale pre-trained language models for diverse tasks, offering a balance between adaptability and computational efficiency. They are important in Low-Resource Language (LRL) Neural Machine Translation (NMT) to enhance translation accuracy with minimal resources. However, t

François Darmon, Lorenzo Porzi, Samuel Rota-Bulò, Peter Kontschieder

In this paper, we address common error sources for 3D Gaussian Splatting (3DGS) including blur, imperfect camera poses, and color inconsistencies, with the goal of improving its robustness for practical applications like reconstructions from handheld phone captures. Our main contribution involves modeling motion blur as a Gaussian distribution over camera po

Qian Xiang, Run Zhou, Sougato Bose, Anupam Mazumdar

Utilizing the Stern-Gerlach apparatus to create matter-wave superposition states is a long-sought-after goal, not only due to its potential applications in the quantum realm but also because of its fundamental implications for studying the quantum properties of gravity. The main challenge in creating a macroscopic quantum interferometer arises from the loss

Shohini Bhattacharya, Renaud Boussarie, Yoshitaka Hatta

In our previous work [Phys. Rev. Lett. 128, 182002 (2022)], we introduced a pioneering observable aimed at experimentally detecting the orbital angular momentum (OAM) of gluons. Our focus was on the longitudinal double spin asymmetry observed in exclusive dijet production during electron-proton scattering. We demonstrated the sensitivity of the $\cos \phi$ a

Shohini Bhattacharya, Renaud Boussarie, Yoshitaka Hatta

We compute the spin-orbit correlations of quarks and gluons at small-$x$ and show that the helicity and the orbital angular momentum of individual partons are strongly anti-aligned even in unpolarized or spinless hadrons and nuclei. Combined with the fact that gluons in the Color Glass Condensate are linearly polarized, our finding indicates that the helicit

Bin Gao, Félix Desrochers, David W. Tam, Paul Steffens

A quantum spin liquid (QSL) arises from a highly entangled superposition of many degenerate classical ground states in a frustrated magnet, and is characterized by emergent gauge fields and deconfined fractionalized excitations (spinons). Because such a novel phase of matter is relevant to high-transition-temperature superconductivity and quantum computation

Nika Gribova, Sebastyan Osmanov, Sergey Lyashko, Polina Shilina

Here we demonstrate nanometer thick iron garnet films suitable for the magneto-optical applica-tions. Bismuth-substituted iron garnet films of compositions Bi{_1}Y{_2}Fe{_5}O{_{12}} and Bi{_1}Tm{_2}Fe{_5}O{_{12}} deposited on gadolinium gallium garnet substrate are fabricated and characterized. Their thicknesses range from 2 to 10 nm, which corresponds to ju

Gaith Rjoub, Saidul Islam, Jamal Bentahar, Mohammed Amin Almaiah

The proliferation of the Internet of Things (IoT) has led to an explosion of data generated by interconnected devices, presenting both opportunities and challenges for intelligent decision-making in complex environments. Traditional Reinforcement Learning (RL) approaches often struggle to fully harness this data due to their limited ability to process and in

Diyi Yang, Caleb Ziems, William Held, Omar Shaikh

People rely on social skills like conflict resolution to communicate effectively and to thrive in both work and personal life. However, practice environments for social skills are typically out of reach for most people. How can we make social skill training more available, accessible, and inviting? Drawing upon interdisciplinary research from communication a

Nebojsa Elez, Ognjen Papaz

In this paper we will give two different natural generalizations of compact spaces and connected spaces simultaneously. We will show that these generalizations coincide for the subspaces of the real line and that they differ for subspaces of plane.

Jianxin Zhou, Kadishe Fejza, Massimiliano Salvatori, Daniele Della Latta

Our understanding of organs at risk is progressing to include physical small tissues such as coronary arteries and the radiosensitivities of many small organs and tissues are high. Therefore, the accurate segmentation of small volumes in external radiotherapy is crucial to protect them from over-irradiation. Moreover, with the development of the particle the

Xiaodong Jia, Gang Tan

Accurate description of program inputs remains a critical challenge in the field of programming languages. Active learning, as a well-established field, achieves exact learning for regular languages. We offer an innovative grammar inference tool, V-Star, based on the active learning of visibly pushdown automata. V-Star deduces nesting structures of program i

Sparsh Srivastava

This literature review investigates the transformative potential of mixed reality (MR) technology, where we explore the intersection of contemporary technological advancements, modern deep learning recommendation systems, and social psychology frameworks. This interdisciplinary study informs the understanding of MR's role in improving social presence, cataly

Yisheng Lei, Haechan An, Zongfeng Li, Mahdi Hosseini

Quantum memory devices with high storage efficiency and bandwidth are essential elements for future quantum networks. Solid-state quantum memories can provide broadband storage, but they primarily suffer from low storage efficiency. We use passive optimization and machine learning techniques to demonstrate nearly a 6-fold enhancement in quantum memory effici