February 2024 arXiv papers — page 49

Qixiang Luo, John D. Shimanek, Timothy W. Simpson, Allison M. Beese

The mitigation of material defects from additive manufacturing (AM) processes is critical to reliability in their fabricated parts and is enabled by modeling the complex relations between available build monitoring signals and final mechanical performance. To this end, the present study investigates a machine learning approach for predicting mechanical prope

Ryan C. Spieler

In recent years, the concept of global symmetry has generalized considerably. Two dramatic examples of this generalization are the exotic symmetries that govern theories with fractons and non-invertible symmetries, which do not fuse according to a group law. Only recently has the interplay between these two been examined. In this paper, we provide further ex

M. A. Gómez-Muñoz, D. A. García-Hernández, R. Barzaga, A. Manchado

Fullerenes have been observed in several astronomical objects since the discovery of C$_{60}$ in the mid-infrared (mid-IR) spectrum of the planetary nebula (PN) Tc 1. It has been suggested that the carriers of the broad unidentified infrared (UIR) plateau features, such as the 9$-$13$\mu$m emission feature (12$\mu$m hereafter), may be related to the formatio

Jeffrey Zhang, Eric Tchetgen Tchetgen

We consider identification of optimal dynamic treatment regimes in a setting where time-varying treatments are confounded by hidden time-varying confounders, but proxy variables of the unmeasured confounders are available. We show that, with two independent proxy variables at each time point that are sufficiently relevant for the hidden confounders, identifi

Eugene Stephane Mananga, Aissata Diop, Paulin Dongomale, Fambougouri Diane

Electronic structure theory provides a foundation for understanding chemical transformations and processes in complex chemical environments. Our work is focused on the NWChemEx project that has selected two interrelated science challenges that address the production of advanced biomass-derived fuels and other value-added chemical compounds. One of which is t

Youssef Er-Rays, Meriem M'dioud

Background: The public hospital network in Morocco plays a crucial role in providing healthcare services. However, this network faces challenges in terms of technical efficiency in healthcare management. Objectives: This study aimed to assess the technical efficiency of the public hospital network in Morocco. Methods: This article compares the efficiency of

Youssef Er-Rays, Meriem M'dioud

The study investigated the impact of healthcare system efficiency on the delivery of maternal, newborn, and child services in Africa. Data Envelopment Analysis and Tobit regression were employed to assess the efficiency of 46 healthcare systems across the continent, utilizing the Variable Returns to Scale model with Input orientation to evaluate technical ef

Guilherme Ferraz de Arruda, Alberto Aleta, Yamir Moreno

Understanding the dissemination of diseases, information, and behavior stands as a paramount research challenge in contemporary network and complex systems science. The COVID-19 pandemic and the proliferation of misinformation are relevant examples of the importance of these dynamic processes, which have recently gained more attention due to the potential of

Lucas Fenaux, Florian Kerschbaum

Adversarial examples are malicious inputs to machine learning models that trigger a misclassification. This type of attack has been studied for close to a decade, and we find that there is a lack of study and formalization of adversary knowledge when mounting attacks. This has yielded a complex space of attack research with hard-to-compare threat models and

Damyn Chipman, Donna Calhoun, Carsten Burstedde

We describe a fast, direct solver for elliptic partial differential equations on a two-dimensional hierarchy of adaptively refined, Cartesian meshes. Our solver, inspired by the Hierarchical Poincar\'e-Steklov (HPS) method introduced by Gillman and Martinsson (SIAM J. Sci. Comput., 2014) uses fast solvers on locally uniform Cartesian patches stored in the le

Salvatore Federico, Fausto Gozzi, Daria Ghilli

This paper represents the first attempt to develop a theory for linear-quadratic mean field games in possibly infinite dimensional Hilbert spaces. As a starting point, we study the case, considered in most finite dimensional contributions on the topic, where the dependence on the distribution enters just in the objective functional through the mean. This fea

Yin Chen

Let $k$ be a field of any characteristic, $V$ a finite-dimensional vector space over $k$, and $S^d(V^*)$ be the $d$-th symmetric power of the dual space $V^*$. Given a linear map $\varphi$ on $V$ and an eigenvector $w$ of $\varphi$, we prove that the pair $(\varphi, w)$ can be used to construct a new Lie algebra structure on $S^d(V^*)$. We prove that this Li

Yanliang Huang, Liguo Zhou, Chang Liu, Alois Knoll

The implementation of Autonomous Driving (AD) technologies within urban environments presents significant challenges. These challenges necessitate the development of advanced perception systems and motion planning algorithms capable of managing situations of considerable complexity. Although the end-to-end AD method utilizing LiDAR sensors has achieved signi

Alexander Chunikhin

Three types of the Parikh mapping are introduced, namely, alphabetic, alphabetic-basis and basis. Explicit expressions for attractors of the k-th order in bases n >= 8, including countable ones, are found. Properties for the alphabetic, alphabetic-basis and basis Parikh vectors are given at each step of the Parikh mapping. The maximum number of iterations le

M. R. Emamy-K., Gustavo A. Meléndez Ríos

This paper presents two shortcuts to a classical proof of the $\mathbf{M}_3$-$\mathbf{N}_5$ Theorem, which can be found in B. Davey and H. Priestley [2] and S. Burris and H. Sankappanavar [1]. To be precise, the shortcuts pertain a particular step of the proof that requires showing an algebraic equality. In addition, we briefly discuss how to compare the len

K. M. Fonseca-Romero

Most textbooks introduce the concept of spin by presenting the Stern-Gerlach experiment with the aid of Newtonian atomic trajectories. However, to understand how both spatial and spin degrees of freedom evolve over time and how the latter influence experimental outcomes, it is essential to employ a quantum approach. In this paper, we offer two simple methods

Afroditi Papadaki, Natalia Martinez, Martin Bertran, Guillermo Sapiro

Current approaches to group fairness in federated learning assume the existence of predefined and labeled sensitive groups during training. However, due to factors ranging from emerging regulations to dynamics and location-dependency of protected groups, this assumption may be unsuitable in many real-world scenarios. In this work, we propose a new approach t

M. Suvarna, O. Tehrani

In this work, we explore a data-driven learning-based approach to learning the kinodynamic model of a small autonomous vehicle, and observe the effect it has on motion planning, specifically autonomous drifting. When executing a motion plan in the real world, there are numerous causes for error, and what is planned is often not what is executed on the actual

Jacob Focke, Fabian Frei, Shaohua Li, Dániel Marx

We study a general family of problems that form a common generalization of classic hitting (also referred to as covering or transversal) and packing problems. An instance of X-HitPack asks: Can removing k (deletable) vertices of a graph G prevent us from packing $\ell$ vertex-disjoint objects of type X? This problem captures a spectrum of problems with stand

H. Khabaoui, K. El Fahri, J. H'michane

This note looks at two mistakes in the paper "Some characterizations of the ideals Ea which are discrete" by Khabaoui et al., published in "Rend. Circ. Mat. Palermo, II. Ser 72, 1325-1336 (2023)".

Mathieu Guillame-Bert, Richard Nock

More often than not in benchmark supervised ML, tabular data is flat, i.e. consists of a single $m \times d$ (rows, columns) file, but cases abound in the real world where observations are described by a set of tables with structural relationships. Neural nets-based deep models are a classical fit to incorporate general topological dependence among descripti

Leighton Barnes, Stephen Cameron, Timothy Chow, Emma Cohen

An unbiased $m$-sparsification of a vector $p\in \mathbb{R}^n$ is a random vector $Q\in \mathbb{R}^n$ with mean $p$ that has at most $m<n$ nonzero coordinates. Unbiased sparsification compresses the original vector without introducing bias; it arises in various contexts, such as in federated learning and sampling sparse probability distributions. Ideally, un

Hwancheol Jeong, Carleton DeTar, Aida El-Khadra, Elvira Gámiz

We compute the vector, scalar, and tensor form factors for the $B\to \pi$, $B\to K$, and $B_s\to K$ amplitudes, which are needed to describe semileptonic $B$-meson decay rates for both the charged and neutral current cases. We use the highly improved staggered quark (HISQ) action for the sea and light valence quarks. The bottom quark is described by the clov

A. J. Barclay, A. R. W. McKellar, N. Moazzen-Ahmadi

The O-D stretch fundamental region of the deuterated water dimer, (D2O)2, is further studied using a pulsed supersonic slit jet and a tunable optical parametric oscillator infrared source. The previously unobserved acceptor symmetric O-D stretch fundamental vibration is detected, with Ka = 0 <-- 0 and 1 <-- 0 sub-bands at about 2669 and 2674 cm-1, respective

Norah Alballa, Ahmed M. Abdelmoniem, Marco Canini

This research investigates the enhancement of knowledge distillation (KD) processes in pre-trained models, an emerging field in knowledge transfer with significant implications for distributed training and federated learning environments. These environments benefit from reduced communication demands and accommodate various model architectures. Despite the ad

Rupak Majumder, Rohitashwa Chattopadhyay, Shamik Gupta

We introduce and investigate the effects of a new class of stochastic resetting protocol called subsystem resetting, whereby a subset of the system constituents in a many-body interacting system undergoes bare evolution interspersed with simultaneous resets at random times, while the remaining constituents evolve solely under the bare dynamics. We pursue our

Andrew Daw, Galit B. Yom-Tov

On many dimensions, services can be seen to exist along spectra measuring the degree of interaction between customer and agent. For instance, every interaction features some number of contributions by each of those two sides, creating a spectrum of interdependence. Additionally, each interaction is further characterized by the relative pacing of these contri

Matteo Messa, Miroslava Dessauges-Zavadsky, Angela Adamo, Johan Richard

We study the populations of stellar clumps in three high-redshift galaxies, at z=4.92, 4.88 and 4.03, gravitationally lensed by the foreground galaxy clusters MS1358, RCS0224 and MACS0940, respectively. The lensed galaxies consist of multiple counter-images with large magnifications, mostly above $\rm \mu>5$ and in some cases reaching $\rm \mu>20$. We use re

Ivo Sengo, Pedro V. P. Cunha, Carlos A. R. Herdeiro, Eugen Radu

We analyse the lensing images by dynamically robust rotating (mini-)Proca stars surrounded by thin accretion disks. Due to their peculiar geodesic structure we show that these images exhibit striking similarities with the ones of BHs, for appropriately chosen disk intensity profile, when imposing a GRMHD-motivated emission cut off. Additionally, and unlike t

Ying Xiong, Mark S. Rudner, Justin C. W. Song

Screening is a ubiquitous phenomenon through which the polarization of bound or mobile charges tends to reduce the strengths of electric fields inside materials. Here we show how photoexcitation can be used as a knob to transform conventional out-of-plane screening into anti-screening - the amplification of electric fields - in multilayer graphene stacks. We

Will Barker, Sebastian Zell

It is well-known that the Einstein-Hilbert action exhibits a projective invariance in metric-affine gravity, generated by a single vector (just like diffeomorphisms). However, this symmetry offers no protection against formulating inconsistent models, e.g., with ghost and strong coupling problems. In this letter, we observe that non-minimal kinetic terms of

Ting-Tung Wang, Menghan Song, Liuke Lyu, William Witczak-Krempa

We employ a protocol, dubbed entanglement microscopy, to reveal the multipartite entanglement encoded in the full reduced density matrix of microscopic subregion both in spin and fermionic many-body systems. We exemplify our method by studying the phase diagram near quantum critical points (QCP) in 2 spatial dimensions: the transverse field Ising model and a

Gui-Jun Ding, Ferruccio Feruglio, Xiang-Gan Liu

We analyze a general class of locally supersymmetric, CP and modular invariant models of lepton masses depending on two complex moduli taking values in the vicinity of a fixed point, where the theory enjoys a residual symmetry under a finite group. Like in models that depend on a single modulus, we find that all physical quantities exhibit a universal scalin

Yuliya Bilinskaya, Michael Hughes, Paolo Molignini

The manipulation of dipolar interactions within ultracold molecular ensembles represents a pivotal advancement in experimental physics, aiming at the emulation of quantum phenomena unattainable through mere contact interactions. Our study uncovers regimes of multiband occupation which allow to probe more realistic, complex long-range interacting lattice mode

Raúl Carballo-Rubio, Francesco Di Filippo, Stefano Liberati, Matt Visser

Mass inflation is a well established instability, conventionally associated to Cauchy horizons (which are also inner trapping horizons) of stationary geometries, leading to a divergent exponential buildup of energy. We show here that finite (but often large) exponential buildups of energy are generically present for dynamical geometries endowed with slowly-e

Pablo Bueno, Pablo A. Cano, Robie A. Hennigar

As shown in the classic work of Belinski, Khalatnikov and Lifshitz, the approach to a generic space-like singularity in general relativity consists of a sequence of eras and epochs in which the metric is locally Kasner, connected by brief transitions. When quantum gravity effects are included, we argue that a new type of transition arises, giving rise to Kas

Dionysios Gakis, Konstantinos N. Gourgouliatos

Context. Large thermal variations have been observed in neutron stars that typically are not aligned with density gradients. Such terms may activate the Biermann battery effect, leading to thermoelectric interactions and to the generation of electromotive force. Aims. We aim to identify the possible impact of a temperature anisotropy on the crust of a neutro

Silvia Pla, Ben A. Stefanek

It has been suggested that the effects of renormalization significantly reduce the amplitude of the inflationary spectra at scales measurable in the cosmic microwave background. Via a gauge-invariant analysis, we compute the renormalized scalar and tensor power spectra and follow their evolution in an inflating universe that undergoes a transition to an FRW

Michael G. Jones, Steven Janowiecki, Swapnaneel Dey, David J. Sand

We have discovered the stellar counterpart to the ALFALFA Virgo 7 cloud complex, which has been thought to be optically dark and nearly star-free since its discovery in 2007. This ~190 kpc long chain of enormous atomic gas clouds ($M_\mathrm{HI} \sim 10^9 \; \mathrm{M_\odot}$) is embedded in the hot intracluster medium of the Virgo galaxy cluster but is isol

María R. Preciado-Rivas, Manar Naeem, Robert B. Mann, Jorma Louko

An Unruh-DeWitt (UDW) detector falling radially into a four-dimensional Schwarzschild black hole, coupled linearly to a massless scalar field that has been prepared in the Hartle-Hawking or Unruh state, has recently been shown to exhibit a local extremum in its transition probability near the black hole horizon [K.K. Ng et al., New J. Phys. 24 (2022) 103018]

Adam M. Dillamore, Vasily Belokurov, N. Wyn Evans

Overdensities in the radial phase space $(r,v_r)$ of the Milky Way's halo have previously been associated with the phase-mixed debris of a highly radial merger event, such as Gaia Sausage-Enceladus. We present and test an alternative theory in which the overdense 'chevrons' are instead composed of stars trapped in resonances with the Galactic bar. We develop

Ali Lavasani, Sagar Vijay

The codespace of a quantum error-correcting code can often be identified with the degenerate ground-space within a gapped phase of quantum matter. We argue that the stability of such a phase is directly related to a set of coherent error processes against which this quantum error-correcting code (QECC) is robust: such a quantum code can recover from adiabati

Muhammad Maaz, Hanoona Rasheed, Abdelrahman Shaker, Salman Khan

In pursuit of more inclusive Vision-Language Models (VLMs), this study introduces a Large Multilingual Multimodal Model called PALO. PALO offers visual reasoning capabilities in 10 major languages, including English, Chinese, Hindi, Spanish, French, Arabic, Bengali, Russian, Urdu, and Japanese, that span a total of ~5B people (65% of the world population). O

Jason Y. Zhang, Amy Lin, Moneish Kumar, Tzu-Hsuan Yang

Estimating camera poses is a fundamental task for 3D reconstruction and remains challenging given sparsely sampled views (<10). In contrast to existing approaches that pursue top-down prediction of global parametrizations of camera extrinsics, we propose a distributed representation of camera pose that treats a camera as a bundle of rays. This representation

Jiří Etrych, Gevorg Martirosyan, Alec Cao, Christopher J. Ho

Predicting the emergent properties of impurities immersed in a quantum bath is a fundamental challenge that can defy quasiparticle treatments. Here, we measure the spectral properties and real-time dynamics of mobile impurities injected into a weakly interacting homogeneous Bose-Einstein condensate, using two broad Feshbach resonances to tune both the impuri

Yuzhe Yang, Yujia Liu, Xin Liu, Avanti Gulhane

Advances in artificial intelligence (AI) have achieved expert-level performance in medical imaging applications. Notably, self-supervised vision-language foundation models can detect a broad spectrum of pathologies without relying on explicit training annotations. However, it is crucial to ensure that these AI models do not mirror or amplify human biases, th

Philipp Lunt, Paul Hill, Johannes Reiter, Philipp M. Preiss

We realize a Laughlin state of two rapidly rotating fermionic atoms in an optical tweezer. By utilizing a single atom and spin resolved imaging technique, we sample the Laughlin wavefunction, thereby revealing its distinctive features, including a vortex distribution in the relative motion, correlations in the particles' relative angle, and suppression of th

Fabio Apruzzi, Francesco Bedogna, Nicola Dondi

Symmetry topological field theory (SymTFT) is a convenient tool for studying finite generalized symmetries of a given quantum field theory (QFT). In particular, SymTFTs encode all the symmetry structures and properties, including anomalies. Recently, this tool has been applied for non-finite symmetries as well. In this paper, we take a different route, which

Lianghui Zhu, Junwei Zhou, Yan Liu, Xin Hao

Weakly supervised visual recognition using inexact supervision is a critical yet challenging learning problem. It significantly reduces human labeling costs and traditionally relies on multi-instance learning and pseudo-labeling. This paper introduces WeakSAM and solves the weakly-supervised object detection (WSOD) and segmentation by utilizing the pre-learn

Nikhil Prakash, Tamar Rott Shaham, Tal Haklay, Yonatan Belinkov

Fine-tuning on generalized tasks such as instruction following, code generation, and mathematics has been shown to enhance language models' performance on a range of tasks. Nevertheless, explanations of how such fine-tuning influences the internal computations in these models remain elusive. We study how fine-tuning affects the internal mechanisms implemente

Xueyi Liu, Li Yi

In this work, we tackle the challenging problem of denoising hand-object interactions (HOI). Given an erroneous interaction sequence, the objective is to refine the incorrect hand trajectory to remove interaction artifacts for a perceptually realistic sequence. This challenge involves intricate interaction noise, including unnatural hand poses and incorrect

Zicheng Lin, Zhibin Gou, Tian Liang, Ruilin Luo

The ability of Large Language Models (LLMs) to critique and refine their reasoning is crucial for their application in evaluation, feedback provision, and self-improvement. This paper introduces CriticBench, a comprehensive benchmark designed to assess LLMs' abilities to critique and rectify their reasoning across a variety of tasks. CriticBench encompasses

Zechun Liu, Changsheng Zhao, Forrest Iandola, Chen Lai

This paper addresses the growing need for efficient large language models (LLMs) on mobile devices, driven by increasing cloud costs and latency concerns. We focus on designing top-quality LLMs with fewer than a billion parameters, a practical choice for mobile deployment. Contrary to prevailing belief emphasizing the pivotal role of data and parameter quant

Lei Zhu, Xinjiang Wang, Wayne Zhang, Rynson W. H. Lau

A practical large language model (LLM) service may involve a long system prompt, which specifies the instructions, examples, and knowledge documents of the task and is reused across requests. However, the long system prompt causes throughput/latency bottlenecks as the cost of generating the next token grows w.r.t. the sequence length. This paper aims to impr

Nikhil Behari, Edwin Zhang, Yunfan Zhao, Aparna Taneja

Restless multi-armed bandits (RMAB) have demonstrated success in optimizing resource allocation for large beneficiary populations in public health settings. Unfortunately, RMAB models lack flexibility to adapt to evolving public health policy priorities. Concurrently, Large Language Models (LLMs) have emerged as adept automated planners across domains of rob

Reed River Chen, Christopher Ribaudo, Jennifer Sleeman, Chace Ashcraft

Human health is negatively impacted by poor air quality including increased risk for respiratory and cardiovascular disease. Due to a recent increase in extreme air quality events, both globally and locally in the United States, finer resolution air quality forecasting guidance is needed to effectively adapt to these events. The National Oceanic and Atmosphe

Xiaoyang Song, Yuta Adachi, Jessie Feng, Mouwei Lin

As Large Language Models (LLMs) are integrated with human daily applications rapidly, many societal and ethical concerns are raised regarding the behavior of LLMs. One of the ways to comprehend LLMs' behavior is to analyze their personalities. Many recent studies quantify LLMs' personalities using self-assessment tests that are created for humans. Yet many c

Ke Wang, Junting Pan, Weikang Shi, Zimu Lu

Recent advancements in Large Multimodal Models (LMMs) have shown promising results in mathematical reasoning within visual contexts, with models approaching human-level performance on existing benchmarks such as MathVista. However, we observe significant limitations in the diversity of questions and breadth of subjects covered by these benchmarks. To address

Tony Metger, Alexander Poremba, Makrand Sinha, Henry Yuen

Pseudorandom unitaries (PRUs) are ensembles of efficiently implementable unitary operators that cannot be distinguished from Haar random unitaries by any quantum polynomial-time algorithm with query access to the unitary. We present a simple PRU construction that is a concatenation of a random Clifford unitary, a pseudorandom binary phase operator, and a pse

Andrea Giuseppe Di Francesco, Francesco Caso, Maria Sofia Bucarelli, Fabrizio Silvestri

The message-passing mechanism underlying Graph Neural Networks (GNNs) is not naturally suited for heterophilic datasets, where adjacent nodes often have different labels. Most solutions to this problem remain confined to the task of node classification. In this article, we focus on the valuable task of link prediction under heterophily, an interesting proble

Durga Keerthi Mandarapu, Nicholas James, Milind Kulkarni

Collision Detection (CD) has several applications across the domains such as robotics, visual graphics, and fluid mechanics. Finding exact collisions between the objects in the scene is quite computationally intensive. To quickly filter the object pairs that do not result in a collision, bounding boxes are built on the objects, indexed using a Bounding Volum

Xudong Lu, Qi Liu, Yuhui Xu, Aojun Zhou

A pivotal advancement in the progress of large language models (LLMs) is the emergence of the Mixture-of-Experts (MoE) LLMs. Compared to traditional LLMs, MoE LLMs can achieve higher performance with fewer parameters, but it is still hard to deploy them due to their immense parameter sizes. Different from previous weight pruning methods that rely on specific

Artem Lopatin, Carlos Arturo Rodriguez Palma, Liming Tang

In this paper we investigate weak polynomial identities for the Weyl algebra $\mathsf{A}_1$ over an infinite field of arbitrary characteristic. Namely, we describe weak polynomial identities of the minimal degree, which is three, and of degrees 4 and 5. We also describe weak polynomial identities is two variables.

Tom Sander, Pierre Fernandez, Alain Durmus, Matthijs Douze

We investigate the radioactivity of text generated by large language models (LLM), i.e. whether it is possible to detect that such synthetic input was used to train a subsequent LLM. Current methods like membership inference or active IP protection either work only in settings where the suspected text is known or do not provide reliable statistical guarantee

Nathaniel Weir, Kate Sanders, Orion Weller, Shreya Sharma

Recent language models enable new opportunities for structured reasoning with text, such as the construction of intuitive, proof-like textual entailment trees without relying on brittle formal logic. However, progress in this direction has been hampered by a long-standing lack of a clear protocol for determining what valid compositional entailment is. This a

Willi Menapace, Aliaksandr Siarohin, Ivan Skorokhodov, Ekaterina Deyneka

Contemporary models for generating images show remarkable quality and versatility. Swayed by these advantages, the research community repurposes them to generate videos. Since video content is highly redundant, we argue that naively bringing advances of image models to the video generation domain reduces motion fidelity, visual quality and impairs scalabilit

Xiao-Jie Zhu

We give explicit formulae for a class of complex linear unitary characters of the congruence subgroups $\Gamma_0(N)$ which involve a variant of Rademacher's $\Psi$ function. We then prove that these characters cover all characters of $\Gamma_0(N)$ precisely when $N=1,2,3,4,5,6,7,8,10,12,13$.

Jun Wang, Yuzhe Qin, Kaiming Kuang, Yigit Korkmaz

We introduce CyberDemo, a novel approach to robotic imitation learning that leverages simulated human demonstrations for real-world tasks. By incorporating extensive data augmentation in a simulated environment, CyberDemo outperforms traditional in-domain real-world demonstrations when transferred to the real world, handling diverse physical and visual condi

Albert Georg Passegger, Rainer Verch

For any local, translation-covariant quantum field theory on Minkowski spacetime, we prove that two distinct states that are invariant under the inertial time evolutions in different inertial reference frames are disjoint, i.e. neither state is a perturbation of the other, if the states are primary, have separating Gelfand-Naimark-Segal (GNS) vectors, and sa

Danish Khan, Alastair James Arthur Price, Maximilian L. Ach, O. Anatole von Lilienfeld

Exact exchange contributions are known to crucially affect electronic states, which in turn govern covalent bond formation and breaking in chemical species. Empirically averaging the exact exchange admixture over compositional degrees of freedom, hybrid density functional approximations have been widely successful, yet have fallen short to reach high level q

Or Patashnik, Rinon Gal, Daniel Cohen-Or, Jun-Yan Zhu

Large-scale text-to-image models enable a wide range of image editing techniques, using text prompts or even spatial controls. However, applying these editing methods to multi-view images depicting a single scene leads to 3D-inconsistent results. In this work, we focus on spatial control-based geometric manipulations and introduce a method to consolidate the

Sophia Simon, Matthias Degroote, Nikolaj Moll, Raffaele Santagati

We provide a method for estimating the expectation value of an operator that can utilize prior knowledge to accelerate the learning process on a quantum computer. Specifically, suppose we have an operator that can be expressed as a concise sum of projectors whose expectation values we know a priori to be $O(\epsilon)$. In that case, we can estimate the expec

stefan Krömer, Martin Kružík, Marco Morandotti, Elvira Zappale

Measure structured deformations are introduced to present a unified theory of deformations of continua. The energy associated with a measure structured deformation is defined via relaxation departing either from energies associated with classical deformations or from energies associated with structured deformations. A concise integral representation of the e

Johnathan Xie, Yoonho Lee, Annie S. Chen, Chelsea Finn

Self-supervised learning excels in learning representations from large amounts of unlabeled data, demonstrating success across multiple data modalities. Yet, extending self-supervised learning to new modalities is non-trivial because the specifics of existing methods are tailored to each domain, such as domain-specific augmentations which reflect the invaria

Michele Mazzoni, Riccardo Travaglino, Olalla A. Castro-Alvaredo

In this work we present a computation of the averages of conserved charge densities and currents of (1+1)-dimensional Integrable Quantum Field Theories in Generalised Gibbs Ensembles. Our approach is based on the quasi-particle description provided by the Thermodynamic Bethe Ansatz combined with the principles of Generalised Hydrodynamics, and we focus on No

Zoltán Péli, Zoltán Trócsányi

We study how the recent experimental results constrain the gauge sectors of U(1) extensions of the standard model using a novel representation of the parameter space. We determine the bounds on the mixing angle between the massive gauge bosons, or equivalently, the new gauge coupling as a function of the mass $M_{Z'}$ of the new neutral gauge boson $Z'$ in t

Gianluca Calcagni, Leonardo Modesto

We introduce the Lorentzian path integral of nonlocal quantum gravity. After introducing the functional measure, the Faddeev-Popov sector and the field correlators, we move to perturbation theory and describe Efimov analytic continuation of scattering amplitudes to Euclidean momenta and back to Lorentzian. We show that the conformal instability problem in th

Jeehyun Yang, Renyu Hu

With the advent of JWST and spectroscopic characterization of exoplanet atmospheres with unprecedented detail, there is a demand for a more complete picture of chemical and photochemical reactions and their impact on atmospheric composition. Traditionally, building reaction networks for (exo)planetary atmospheres involves manually tracking relevant species a

Jesus Silva-Rodriguez, Xingpeng Li

As NASA prepares to carry out its Artemis lunar missions, the design and planning of robust power systems tailored to the lunar environment become necessary and urgent. Solar photovoltaic (PV) systems are among the most suitable power generators for lunar applications given the abundant solar irradiance the lunar surface receives as a result of the lack of a

Eleonora Vanzan, Alvise Raccanelli, Nicola Bartolo

Many different techniques to analyze galaxy clustering data and obtain cosmological constraints have been proposed, tested and used. Given the large amount of data that will be available soon, it is worth investigating new observables and ways to extract information from such datasets. In this paper, we focus on antisymmetric correlations, that arise in the

Christian Toth, Christian Knoll, Franz Pernkopf, Robert Peharz

The traditional two-stage approach to causal inference first identifies a single causal model (or equivalence class of models), which is then used to answer causal queries. However, this neglects any epistemic model uncertainty. In contrast, Bayesian causal inference does incorporate epistemic uncertainty into query estimates via Bayesian marginalisation (po

Yixuan Ren, Yang Zhou, Jimei Yang, Jing Shi

Image customization has been extensively studied in text-to-image (T2I) diffusion models, leading to impressive outcomes and applications. With the emergence of text-to-video (T2V) diffusion models, its temporal counterpart, motion customization, has not yet been well investigated. To address the challenge of one-shot video motion customization, we propose C

Samuël Borza, Mattia Magnabosco, Tommaso Rossi, Kenshiro Tashiro

In this paper, we investigate the validity of synthetic curvature-dimension bounds in the sub-Finsler Heisenberg group, equipped with a positive smooth measure. Firstly, we study the measure contraction property, in short $\mathsf{MCP}$, proving that its validity depends on the norm generating the sub-Finsler structure. Indeed, we show that, if it is neither

Nadezhda Chirkova, Vassilina Nikoulina

Instruction tuning (IT) is widely used to teach pretrained large language models (LLMs) to follow arbitrary instructions, but is under-studied in multilingual settings. In this work, we conduct a systematic study of zero-shot cross-lingual transfer in IT, when an LLM is instruction-tuned on English-only data and then tested on user prompts in other languages

Alvaro Ribot, Chandler Squires, Caroline Uhler

We consider the task of causal imputation, where we aim to predict the outcomes of some set of actions across a wide range of possible contexts. As a running example, we consider predicting how different drugs affect cells from different cell types. We study the index-only setting, where the actions and contexts are categorical variables with a finite number

Xianming Li, Zongxi Li, Jing Li, Haoran Xie

Common approaches rely on fixed-length embedding vectors from language models as sentence embeddings for downstream tasks such as semantic textual similarity (STS). Such methods are limited in their flexibility due to unknown computational constraints and budgets across various applications. Matryoshka Representation Learning (MRL) \cite{aditya2022matryoshka

Kai Z Teh, Kayvan Sadeghi, Terry Soo

By relaxing conditions for natural structure learning algorithms, a family of constraint-based algorithms containing all exact structure learning algorithms under the faithfulness assumption, we define localised natural structure learning algorithms (LoNS). We also provide a set of necessary and sufficient assumptions for consistency of LoNS, which can be th

Haidong Tian, Emilio Codecido, Dan Mao, Kevin Zhang

Geometric frustration is a phenomenon in a lattice system where not all interactions can be satisfied, the simplest example being antiferromagnetically coupled spins on a triangular lattice. Frustrated systems are characterized by their many nearly degenerate ground states, leading to non-trivial phases such as spin ice and spin liquids. To date most studies

Pierre Germain, Hui Zhu

On compact Riemannian manifolds with chaotic geometries, specifically those exhibiting the random wave model conjectured by Berry, we derive heuristically a homogeneous kinetic wave equation that is universal for all such manifolds.

Kamil Orzechowski

We show some results related to the classical Banach-Tarski paradox in the setting of finite-dimensional normed spaces over a non-Archimedean valued field $K$. For instance, all balls and spheres in $K^n$, and the whole space $K^n$ (for $n\ge 2$) are paradoxical with respect to certain groups of isometries of $K^n$. If $K$ is locally compact (e.g., $K$ is th

Joseph H. Silverman

Let $\mathbb{F}$ be the function field of a curve over an algebraically closed field with $\operatorname{char}(\mathbb{F})\ne2,3$, and let $E/\mathbb{F}$ be an elliptic curve. Then for all finite extensions $\mathbb{K}/\mathbb{F}$ and all non-torsion points $P\in{E(\mathbb{K})}$, the $\mathbb{F}$-normalized canonical height of $P$ is bounded below by \[ \hat

O. F. Bandtlow, W. Just, J. Slipantschuk

Chaotic hyperbolic dynamical systems enjoy a surprising degree of rigidity, a fact which is well known in the mathematics community but perhaps less so in theoretical physics circles. Low-dimensional hyperbolic systems are either conjugate to linear automorphisms, that is, dynamically equivalent to the Arnold cat map and its variants, or their hyperbolic str

Ryan Plestid

We discuss QED radiative corrections to contact operators coupling two heavy fields and one light field. New eikonal identities are derived in the static limit that demonstrate the equivalence of a class of ladder graphs to an equivalent theory with a single heavy-light vertex and a background Coulomb field which communicates exclusively with the light field

Eric Enos, Daniel Herber

This paper is based on a case study of an IT organization in a large, US-based healthcare provider, and develops simluation models to identify areas for performance improvement. These organizations are often grouped into departments by technical skill and support both operational work (tickets) and project work (tasks) of various priorities. From a practical

Yuhang Cao, Pan Zhang, Xiaoyi Dong, Dahua Lin

We present DualFocus, a novel framework for integrating macro and micro perspectives within multi-modal large language models (MLLMs) to enhance vision-language task performance. Current MLLMs typically singularly focus on inputs at a predefined resolution, resulting in deficiencies in detailed questions involving local regions. We introduced a DualFocus mec

Shoaib Imran, Gouranga Charan, Ahmed Alkhateeb

Millimeter-wave (mmWave) and terahertz (THz) communication systems require large antenna arrays and use narrow directive beams to ensure sufficient receive signal power. However, selecting the optimal beams for these large antenna arrays incurs a significant beam training overhead, making it challenging to support applications involving high mobility. In rec

David W. Kanaar, H. Ekmel Ercan, Mark F. Gyure, J. P. Kestner

We theoretically propose a method to perform in situ measurements of charge noise during logical operations in silicon quantum dot spin qubits. Our method does not require ancillary spectator qubits but makes use of the valley degree of freedom in silicon. Sharp interface steps or alloy disorder in the well provide a valley transition dipole element that cou

Purevdorj Tuvaandorj

This paper establishes a combinatorial central limit theorem for stratified randomization, which holds under a Lindeberg-type condition. The theorem allows for an arbitrary number or sizes of strata, with the sole requirement being that each stratum contains at least two units. This flexibility accommodates both a growing number of large and small strata sim

Tadao Hoshino

This study introduces a novel spatial autoregressive model in which the dependent variable is a function that may exhibit functional autocorrelation with the outcome functions of nearby units. This model can be characterized as a simultaneous integral equation system, which, in general, does not necessarily have a unique solution. For this issue, we provide

Ge Bai, Jie Liu, Xingyuan Bu, Yancheng He

The advent of Large Language Models (LLMs) has drastically enhanced dialogue systems. However, comprehensively evaluating the dialogue abilities of LLMs remains a challenge. Previous benchmarks have primarily focused on single-turn dialogues or provided coarse-grained and incomplete assessments of multi-turn dialogues, overlooking the complexity and fine-gra