May 2024 arXiv papers — page 76

K. Andrzejewski

We analyse the motion of the spinning body (in the pole-dipole approximation) in the gravitational and electromagnetic fields described by the Mathisson-Papapetrou-Dixon-Souriau equations. First, we define a novel spin condition for the body with the magnetic dipole moment proportional to spin, which generalizes the one proposed by Ohashi-Kyrian-Semer\'ak fo

Soudeep Deb, Claudia Neves, Subhrajyoty Roy

In this paper, we develop a new and effective approach to nonparametric quantile regression that accommodates ultrahigh-dimensional data arising from spatio-temporal processes. This approach proves advantageous in staving off computational challenges that constitute known hindrances to existing nonparametric quantile regression methods when the number of pre

Christina Karafyllia, Dimitrios Ntalampekos

We prove that a domain in the Riemann sphere is Gromov hyperbolic if and only if it is conformally equivalent to a uniform circle domain. This resolves a conjecture of Bonk--Heinonen--Koskela and also verifies Koebe's conjecture (Kreisnormierungsproblem) for the class of Gromov hyperbolic domains. Moreover, the uniformizing conformal map from a Gromov hyperb

Yingxue Yu, Vidit Vidit, Andrey Davydov, Martin Engilberge

Animal Re-ID is crucial for wildlife conservation, yet it faces unique challenges compared to person Re-ID. First, the scarcity and lack of diversity in datasets lead to background-biased models. Second, animal Re-ID depends on subtle, species-specific cues, further complicated by variations in pose, background, and lighting. This study addresses background

Oleg Butkovsky, Leonid Mytnik

We put forward a new method for proving weak uniqueness of stochastic equations with singular drifts driven by a non-Markov or infinite-dimensional noise. We apply our method to study stochastic heat equation (SHE) driven by Gaussian space-time white noise $$ \frac{\partial}{\partial t} u_t(x)=\frac12 \frac{\partial^2}{\partial x^2}u_t(x)+b(u_t(x))+\dot{W}_{

Tarun Kalluri, Jihyeon Lee, Kihyuk Sohn, Sahil Singla

We present a simple and efficient method to leverage emerging text-to-image generative models in creating large-scale synthetic supervision for the task of damage assessment from aerial images. While significant recent advances have resulted in improved techniques for damage assessment using aerial or satellite imagery, they still suffer from poor robustness

CMS Collaboration

Astrophysical observations provide compelling evidence for gravitationally interacting dark matter in the universe that cannot be explained by the standard model of particle physics. The extraordinary amount of data from the CERN LHC presents a unique opportunity to shed light on the nature of dark matter at unprecedented collision energies. This Report comp

Angéline Pouget, Lucas Beyer, Emanuele Bugliarello, Xiao Wang

We study cultural and socioeconomic diversity in contrastive vision-language models (VLMs). Using a broad range of benchmark datasets and evaluation metrics, we bring to attention several important findings. First, the common filtering of training data to English image-text pairs disadvantages communities of lower socioeconomic status and negatively impacts

Sithara U. Nawagamuwage, Elliot S. Williams, Md Muhaiminul Islam, Igor V. Parshin

In an effort to increase the speed and efficiency of ballistic energy transport via oligomeric chains, we performed measurements of the transport in compounds featuring long alkyl chains of up to 37 methylene units. Compounds of the N3-(CH2)n-COOMe type (denoted as aznME) were synthesized with n = 5, 10, 15, 19, 28, 37 and studied using relaxation-assisted t

Marcin Michalski, Robert Rałowski, Szymon Żeberski

We work in the Cantor space $2^\omega$. The results of the paper adhere the following pattern. Let $\mathcal{I}\in \{\mathcal{M}, \mathcal{N}, \mathcal{M}\cap \mathcal{N}, \mathcal{E}\}$ and $T$ be a perfect, uniformly perfect or Silver tree. Then for every $A\in \mathcal{I}$ there exists $T'\subseteq T$ of the same kind as $T$ such that $A+\underbrace{[T']+

Yibo Gao, Reuven Hodges

The goal of this paper is twofold. Firstly, we provide a type-uniform formula for the torus complexity of the usual torus action on a Richardson variety, by developing the notion of algebraic dimensions of Bruhat intervals, strengthening a type $A$ result by Donten-Bury, Escobar and Portakal. In the process, we give an explicit description of the torus actio

Ambrogio Maria Bernardelli, Eleonora Vercesi, Stefano Gualandi, Monaldo Mastrolilli

In this work, we study the metric Steiner Tree problem on graphs focusing on computing lower bounds for the integrality gap of the bi-directed cut (BCR) formulation and introducing a novel formulation, the Complete Metric (CM) model, specifically designed to address the weakness of the BCR formulation on metric instances. A key contribution of our work is ex

Jorge Neves, Gonçalo Varejão

We study the minimal homogeneous generating sets of the Eulerian ideal associated with a simple graph and its maximal generating degree. We show that the Eulerian ideal is a lattice ideal and use this to give a characterization of binomials belonging to a minimal homogeneous generating set. In this way, we obtain an explicit minimal homogeneous generating se

Filippo Ruffini, Lorenzo Tronchin, Zhuoru Wu, Wenting Chen

In the fight against the COVID-19 pandemic, leveraging artificial intelligence to predict disease outcomes from chest radiographic images represents a significant scientific aim. The challenge, however, lies in the scarcity of large, labeled datasets with compatible tasks for training deep learning models without leading to overfitting. Addressing this issue

Zhuoyun Zhong, Zhi Li, Constantinos Chamzas

Global redundancy resolution (GRR) roadmaps is a novel concept in robotics that facilitates the mapping from task space paths to configuration space paths in a legible, predictable, and repeatable way. Such roadmaps could find widespread utility in applications such as safe teleoperation, consistent path planning, and motion primitives generation. However, p

Cyril Chhun, Fabian M. Suchanek, Chloé Clavel

Storytelling is an integral part of human experience and plays a crucial role in social interactions. Thus, Automatic Story Evaluation (ASE) and Generation (ASG) could benefit society in multiple ways, but they are challenging tasks which require high-level human abilities such as creativity, reasoning and deep understanding. Meanwhile, Large Language Models

Shoshana Abramovich

In this paper we improve results related to Normalized Jensen Functional for convex functions and Uniformly Convex Functions.

Dag-Björn Hering, Matthias R. Walther, Kai P. Schmidt, Götz S. Uhrig

We apply continuous similarity transformations (CSTs) to study the zero-temperature breakdown of long-range ordered quantum antiferromagnets. The CST flow equations are truncated in momentum space by the scaling dimension so that all operators with scaling dimension up to two are taken into account. We determine the quantum phase transition out of the N\'eel

Zijian Zhou, Xiaoqiang Lin, Xinyi Xu, Alok Prakash

In-context learning (ICL) allows transformer-based language models that are pre-trained on general text to quickly learn a specific task with a few "task demonstrations" without updating their parameters, significantly boosting their flexibility and generality. ICL possesses many distinct characteristics from conventional machine learning, thereby requiring

Yohai Trabelsi, Pan Xu, Sarit Kraus

Assigning tasks to service providers is a frequent procedure across various applications. Often the tasks arrive dynamically while the service providers remain static. Preventing task rejection caused by service provider overload is of utmost significance. To ensure a positive experience in relevant applications for both service providers and tasks, fairness

Andrea Nizzardo, Luca Genetti, Marco Pergher

This work introduces the Burdened Bayesian Logistic Regression Model (BBLRM), an enhancement of the Bayesian Logistic Regression Model (BLRM) for dose-finding in phase I oncology trials. The BLRM determines the maximum tolerated dose (MTD) based on dose limiting toxicities (DLTs). However, clinicians often perceive model-based designs like BLRM as complex an

Hanmin Li, Kirill Acharya, Peter Richtárik

We propose and study several server-extrapolation strategies for enhancing the theoretical and empirical convergence properties of the popular federated learning optimizer FedProx [Li et al., 2020]. While it has long been known that some form of extrapolation can help in the practice of FL, only a handful of works provide any theoretical guarantees. The phen

Travis E. Gibson, Sawal Acharya, Anjali Parashar, Joseph E. Gaudio

Gradient based optimization algorithms deployed in Machine Learning (ML) applications are often analyzed and compared by their convergence rates or regret bounds. While these rates and bounds convey valuable information they don't always directly translate to stability guarantees. Stability and similar concepts, like robustness, will become ever more importa

S. E. Pyatovsky

Irregularity in spectrum of the primary cosmic rays (PCR) mass composition at an energy of ~ 10 PeV is considered. To assess changes of the PCR mass composition, the X-ray emulsion chamber (XREC) method and the halo-method based on the XEC were used. The study of changes in the PCR mass composition was carried out based on the experimentally obtained charact

Nikita P. Kalinin, Christoph Lampert

Current state-of-the-art methods for differentially private model training are based on matrix factorization techniques. However, these methods suffer from high computational overhead because they require numerically solving a demanding optimization problem to determine an approximately optimal factorization prior to the actual model training. In this work,

Gwanghyun Kim, Alonso Martinez, Yu-Chuan Su, Brendan Jou

Training diffusion models for audiovisual sequences allows for a range of generation tasks by learning conditional distributions of various input-output combinations of the two modalities. Nevertheless, this strategy often requires training a separate model for each task which is expensive. Here, we propose a novel training approach to effectively learn arbi

Justin Rudie, Sylvester Amoah, Xiaoxin Wang, Rajesh Kumar

We demonstrate monolithically grown germanium-tin (GeSn) on silicon avalanche photodiodes (APDs) for infrared light detection. A relatively thinner Ge buffer design was adopted to allow effective photo carriers to transport from the GeSn absorber to the Si multiplication layer such that clear punch-through behavior and a saturated primary responsivity of 0.3

Daniel Naredo-Tuero

We present an updated and improved global fit analysis of precision and flavor observables so as to derive constraints on deviations from unitarity in the leptonic mixing matrix. We focus on the case in which these deviations are sourced by heavy neutrinos, such that our results can also be interpreted as bounds on their mixing with the active flavors. Our a

Hamidreza Eivazi, Mahyar Alikhani, Jendrik-Alexander Tröger, Stefan Wittek

Multiscale problems are widely observed across diverse domains in physics and engineering. Translating these problems into numerical simulations and solving them using numerical schemes, e.g. the finite element method, is costly due to the demand of solving initial boundary-value problems at multiple scales. On the other hand, multiscale finite element compu

Mohit Prabhushankar, Ghassan AlRegib

The widespread adoption of deep neural networks in machine learning calls for an objective quantification of esoteric trust. In this paper we propose GradTrust, a classification trust measure for large-scale neural networks at inference. The proposed method utilizes variance of counterfactual gradients, i.e. the required changes in the network parameters if

Etienne Chollet, Yael Balbastre, Caroline Magnain, Bruce Fischl

Serial sectioning Optical Coherence Tomography (sOCT) is a high-throughput, label free microscopic imaging technique that is becoming increasingly popular to study post-mortem neurovasculature. Quantitative analysis of the vasculature requires highly accurate segmentation; however, sOCT has low signal-to-noise-ratio and displays a wide range of contrasts and

Torin Greenwood, Samuel Simon

Lattice walks are used to model various physical phenomena. In particular, walks within Weyl chambers connect directly to representation theory via the Littelmann path model. We derive asymptotics for centrally weighted lattice walks within the Weyl chamber corresponding to $A_2$ by using tools from analytic combinatorics in several variables (ACSV). We find

Gergely Neu, Nneka Okolo

We study offline Reinforcement Learning in large infinite-horizon discounted Markov Decision Processes (MDPs) when the reward and transition models are linearly realizable under a known feature map. Starting from the classic linear-program formulation of the optimal control problem in MDPs, we develop a new algorithm that performs a form of gradient ascent i

Wondimagegnhue Tsegaye Tufa, Ilia Markov, Piek Vossen

Social media conversations frequently suffer from toxicity, creating significant issues for users, moderators, and entire communities. Events in the real world, like elections or conflicts, can initiate and escalate toxic behavior online. Our study investigates how real-world events influence the origin and spread of toxicity in online discussions across var

Tom Sühr, Samira Samadi, Chiara Farronato

Machine learning (ML) models are increasingly used in various applications, from recommendation systems in e-commerce to diagnosis prediction in healthcare. In this paper, we present a novel dynamic framework for thinking about the deployment of ML models in a performative, human-ML collaborative system. In our framework, the introduction of ML recommendatio

Edgar A. Duéñez-Guzmán, Suzanne Sadedin, Jane X. Wang, Kevin R. McKee

Traditionally, cognitive and computer scientists have viewed intelligence solipsistically, as a property of unitary agents devoid of social context. Given the success of contemporary learning algorithms, we argue that the bottleneck in artificial intelligence (AI) progress is shifting from data assimilation to novel data generation. We bring together evidenc

Bin Li, Yulan Cheng, Cong Zhu, Jie Cheng

Inspired by the first boron-carbon (B-C) clathrate SrB$_3$C$_3$ and the ternary borohydride KB$_2$H$_8$ [Miao et al., Phys. Rev. B 104 L100504 (2021)], we have performed first-principles density functional theory calculations of the electronic and phonon band structures for B-C compounds MB$_2$C$_8$ (M = Na, K, Rb, Cs). Our calculations reveal that these mat

Aoran Mei, Jianhua Wang, Guo-Niu Zhu, Zhongxue Gan

With their prominent scene understanding and reasoning capabilities, pre-trained visual-language models (VLMs) such as GPT-4V have attracted increasing attention in robotic task planning. Compared with traditional task planning strategies, VLMs are strong in multimodal information parsing and code generation and show remarkable efficiency. Although VLMs demo

Péter Antal, Tamás Péni, Roland Tóth

For complex nonlinear systems, it is challenging to design algorithms that are fast, scalable, and give an accurate approximation of the stability region. This paper proposes a sampling-based approach to address these challenges. By extending the parametrization of quadratic Lyapunov functions with the system dynamics and formulating an $\ell_1$ optimization

Andrew Estornell, Stylianos Loukas Vasileiou, William Yeoh, Daniel Borrajo

In today's competitive financial landscape, understanding and anticipating customer goals is crucial for institutions to deliver a personalized and optimized user experience. This has given rise to the problem of accurately predicting customer goals and actions. Focusing on that problem, we use historical customer traces generated by a realistic simulator an

Igor Strakovsky

This paper describes my personal appreciation for some of the great research achievements of Mitya Diakonov, Vitya Petrov, and Maxim Polyakov and how my own research career has followed the paths they opened. Among the topics where they have been the most influential have been the pursuit and study of the exotic pentaquark. The search for exotics may require

Tian Lan, Qinwei Lin, Haoqian Wang

Recently,3DGaussianSplattinghasshowngreatpotentialin visual Simultaneous Localization And Mapping (SLAM). Existing methods have achieved encouraging results on RGB-D SLAM, but studies of the monocular case are still scarce. Moreover, they also fail to correct drift errors due to the lack of loop closure and global optimization. In this paper, we present MG-S

Yanbin Chen, Innocenzo Fulginiti, Christian B. Mendl

Mid-circuit measurements and measurement-controlled gates are supported by an increasing number of quantum hardware platforms and will become more relevant as an essential building block for quantum error correction. However, mid-circuit measurements impose significant demands on the quantum hardware due to the required signal analysis and classical feedback

Huiwen Wu, Xiaogang Xu, Deyi Zhang, Xiaohan Li

The success of current Large-Language Models (LLMs) hinges on extensive training data that is collected and stored centrally, called Centralized Learning (CL). However, such a collection manner poses a privacy threat, and one potential solution is Federated Learning (FL), which transfers gradients, not raw data, among clients. Unlike traditional networks, FL

Qiujie Dong, Huibiao Wen, Rui Xu, Shuangmin Chen

Quadrilateral mesh generation plays a crucial role in numerical simulations within Computer-Aided Design and Engineering (CAD/E). Producing high-quality quadrangulation typically requires satisfying four key criteria. First, the quadrilateral mesh should closely align with principal curvature directions. Second, singular points should be strategically placed

Stephan Wiefling, Marian Hönscheid, Luigi Lo Iacono

HTTP client hints are a set of standardized HTTP request headers designed to modernize and potentially replace the traditional user agent string. While the user agent string exposes a wide range of information about the client's browser and device, client hints provide a controlled and structured approach for clients to selectively disclose their capabilitie

James Rawson

The distribution of degree $d$ points on curves is well understood, especially for low degrees. We refine this study to include information on the Galois group in the simplest interesting case: $d = 3$. For curves of genus at least 5, we show cubic points with Galois group $C_3$ arise from well-structured morphisms, along with providing computable tests for

Jisu Ryu, Sarang Yeola, David M. Jonas

We present Einstein coefficient spectra and a detailed-balance derivation of generalized Einstein relations between them that is based on the connection between spontaneous and stimulated emission. If two broadened levels or bands overlap in energy, transitions between them need not be purely absorptive or emissive. Consequently, spontaneous emission can occ

Byunghan Kim, Joonhee Kim, Hyoyoon Lee

We show that Kim-forking satisfies existence in all NSOP$_1$ theories.

Khouloud Oueslati, Gabriel Laberge, Maxime Lamothe, Foutse Khomh

Defect reduction planning plays a vital role in enhancing software quality and minimizing software maintenance costs. By training a black box machine learning model and "explaining" its predictions, explainable AI for software engineering aims to identify the code characteristics that impact maintenance risks. However, post-hoc explanations do not always fai

Dayeeta Pal, Yifan Wang, Ramya Gurunathan, Leora Dresselhaus-Marais

The behavior of dislocations is essential to understand material properties, but their subsurface dynamics that are representative of bulk phenomena cannot be resolved by conventional transmission electron microscopy (TEM). Dark field X-ray microscope (DFXM) was recently demonstrated to image hierarchical structures of bulk dislocations by imaging lattice di

Liam Madden

The minimal number of neurons required for a feedforward neural network to interpolate $n$ generic input-output pairs from $\mathbb{R}^d\times \mathbb{R}^{d'}$ is $\Theta(\sqrt{nd'})$. While previous results have shown that $\Theta(\sqrt{nd'})$ neurons are sufficient, they have been limited to sigmoid, Heaviside, and rectified linear unit (ReLU) as the activ

Iris van Gemeren, Tanja Hinderer, Stefan Vandoren

We study static black holes in scalar-Gauss-Bonnet (sGB) gravity with a massive scalar field as an example of higher curvature gravity. The scalar mass introduces an additional scale and leads to a strong suppression of the scalar field beyond its Compton wavelength. We numerically compute sGB black hole spacetimes and scalar configurations and also compare

Davide Bizzaro, Luciano Serafini, Sagar Malhotra

We initiate the study of counting Markov Equivalence Classes (MEC) under logical constraints. MECs are equivalence classes of Directed Acyclic Graphs (DAGs) that encode the same conditional independence structure among the random variables of a DAG model. Observational data can only allow to infer a DAG model up to Markov Equivalence. However, Markov equival

Alireza Nadali, Ashutosh Trivedi, Majid Zamani

Control barrier certificates have proven effective in formally guaranteeing the safety of the control systems. However, designing a control barrier certificate is a time-consuming and computationally expensive endeavor that requires expert input in the form of domain knowledge and mathematical maturity. Additionally, when a system undergoes slight changes, t

Fabian Gundlach

We explain how to construct a uniformly random cubic integral domain $S$ of given signature with $|\text{disc}(S)| \leq T$ in expected time $\widetilde O(\log T)$.

Carlos P. Herrero, Rafael Ramirez, Gabriela Herrero-Saboya

Silicon carbide, a semiconducting material, has gained importance in the fields of ceramics, electronics, and renewable energy due to its remarkable hardness and resistance. In this study, we delve into the impact of nuclear quantum motion, or vibrational mode quantization, on the structural and elastic properties of 3C-SiC. This aspect, elusive in conventio

A. Leleu, J. -B. Delisle, L. Delrez, E. M. Bryant

The TOI-178 system consists of a nearby late K-dwarf transited by six planets in the super-Earth to mini-Neptune regime, with radii ranging from 1.2 to 2.9 earth radius and orbital periods between 1.9 and 20.7 days. All planets but the innermost one form a chain of Laplace resonances. The fine-tuning and fragility of such orbital configurations ensure that n

Qijia Jiang, David Nabergoj

Leveraging connections between diffusion-based sampling, optimal transport, and stochastic optimal control through their shared links to the Schr\"odinger bridge problem, we propose novel objective functions that can be used to transport $\nu$ to $\mu$, consequently sample from the target $\mu$, via optimally controlled dynamics. We highlight the importance

Ty Trusty, Otman Benchekroun, Eitan Grinspun, Danny M. Kaufman

We propose a reduced space mixed finite element method (MFEM) built on a Skinning Eigenmode subspace and material-aware cubature scheme. Our solver is well-suited for simulating scenes with large material and geometric heterogeneities in real-time. This mammoth geometry is composed of 98,175 vertices and 531,565 tetrahedral elements and with a heterogenous c

Rui Xu, Jiepeng Wang, Hao Pan, Yang Liu

In this paper, we study an under-explored but important factor of diffusion generative models, i.e., the combinatorial complexity. Data samples are generally high-dimensional, and for various structured generation tasks, additional attributes are combined to associate with data samples. We show that the space spanned by the combination of dimensions and attr

Avik P. Chatterjee, Yuri Yu. Tarasevich

A lattice-based model for continuum percolation is applied to the case of randomly located, partially aligned sticks with unequal lengths in 2D which are allowed to cross each other. Results are obtained for the critical number of sticks per unit area at the percolation threshold in terms of the distributions over length and orientational angle and are compa

Shubham Sharma, Satyajit Puhan, Narinder Kumar, Harleen Dahiya

In this work, we have established the relations between the T-even proton transverse momentum-dependent parton distributions (TMDs) at all twist levels up to twist-4 using the light-front quark diquark model (LFQDM). From the parameterization equations of TMDs, we have found that there are multiple ways by which a particular TMD can be expressed in terms of

Ziqing Wen, Xiaoge Deng, Ping Luo, Tao Sun

Score-based generative models have demonstrated significant practical success in data-generating tasks. The models establish a diffusion process that perturbs the ground truth data to Gaussian noise and then learn the reverse process to transform noise into data. However, existing denoising methods such as Langevin dynamic and numerical stochastic differenti

Weiru Liu

In this paper, we show that if $L_p$ Gaussian surface area measure is proportional to the spherical Lebesgue measure, then the corresponding convex body has to be a centered disk when $p\in[0,1)$. Moreover, we investigate $C^0$ estimate of the corresponding convex bodies when the density function of their Gaussian surface area measures have the uniform upper

Alexander Jackson

Denote by $\mathfrak{o}$ the valuation ring of a non-Archimedean local field with prime ideal $\mathfrak{p}$ and finite residue field, and let $r\geq 1$ be an integer. We prove that for every smooth affine group scheme $G$ over $\mathbb{Z}$, the dimension of each irreducible representation of $G(\mathfrak{o}/\mathfrak{p}^r)$ is given by one of finitely many

Michael de Oliveira, Antonio Ambrosio

The exploration of light has traditionally focused on its spatial properties, particularly its orbital angular momentum (OAM), while its temporal dynamics have remained an underexplored frontier due to the slow response times of existing modulation techniques. In this context, we introduce a method to modulate the OAM of light on a femtosecond scale by engin

Yujun Shi, Jun Hao Liew, Hanshu Yan, Vincent Y. F. Tan

Accuracy and speed are critical in image editing tasks. Pan et al. introduced a drag-based image editing framework that achieves pixel-level control using Generative Adversarial Networks (GANs). A flurry of subsequent studies enhanced this framework's generality by leveraging large-scale diffusion models. However, these methods often suffer from inordinately

Zhiwei Bai, Jiajie Zhao, Yaoyu Zhang

Matrix factorization models have been extensively studied as a valuable test-bed for understanding the implicit biases of overparameterized models. Although both low nuclear norm and low rank regularization have been studied for these models, a unified understanding of when, how, and why they achieve different implicit regularization effects remains elusive.

Yuan Wei, Yi Tseng, Hebatalla Elnaggar, Wenliang Zhang

In the rich phases of van der Waals (vdW) materials featuring intertwined electronic order and collective phenomena, characterizing elementary dynamics that entail the low-energy Hamiltonian and electronic degrees of freedom is of paramount importance. Here we performed resonant inelastic X-ray scattering (RIXS) to elaborate the spin-orbital ground and excit

Sebastian Baader, Lukas Lewark, Filip Misev, Paula Truöl

We determine the locally flat cobordism distance between torus knots with small and large braid index, up to high precision. Here small means 2, 3, 4, or 6. As an application, we derive a surprising fact about torus knots that appear as cross-sections of almost minimal cobordisms between two-stranded torus knots and the trivial knot.

Liam Madden, Curtis Fox, Christos Thrampoulidis

Given a sequence of tokens, such as words, the task of next-token prediction is to predict the next-token conditional probability distribution. Decoder-only transformers have become effective models for this task, but their properties are still not fully understood. In particular, the largest number of distinct context sequences that a decoder-only transform

Llorenç Escolà-Farràs, Florian Speelman

Extended non-local games are a generalization of monogamy-of-entanglement games, played by two quantum parties and a quantum referee that performs a measurement on their local quantum system. Along the lines of the NPA hierarchy, the optimal winning probability of those games can be upper bounded by a hierarchy of semidefinite programs (SDPs) converging to t

Jiaxuan Zhang, Jintao Fan, Chao Mei, Günter Steinmeyer

Recent years have seen a resurgence of interest in multimode fibers due to their intriguing physics and applications, with spatial beam self-cleaning (BSC) having received special attention. In BSC light condenses into the fundamental fiber mode at elevated intensities. Despite extensive efforts utilizing optical thermodynamics to explain such counterintuiti

Ruolin Wang, Yuejiao Xu, Jianmin Ji

Formal representations of traffic scenarios can be used to generate test cases for the safety verification of autonomous driving. However, most existing methods are limited to highway or highly simplified intersection scenarios due to the intricacy and diversity of traffic scenarios. In response, we propose Traffic Scenario Logic (TSL), which is a spatial-te

Andrés Gúrpide, Noel Castro Segura, Roberto Soria, Matthew Middleton

While much has been learned in recent decades about the X-ray emission of the extragalactic Ultraluminous X-ray sources (ULXs), their radiative output in the UV band remains poorly constrained. Understanding of the full ULX spectral energy distribution (SED) is imperative to constrain the accretion flow geometry powering them, as well as their radiative powe

David Helm, Robert Kurinczuk, Daniel Skodlerack, Shaun Stevens

For an inner form $\mathrm{G}$ of a general linear group or classical group over a non-archimedean local field of odd residue characteristic, we decompose the category of smooth representations on $\mathbb{Z}[\mu_{p^{\infty}},1/p]$-modules by endo-parameter. We prove that parabolic induction preserves these decompositions, and hence that it preserves endo-pa

François Rozet, Gérôme Andry, François Lanusse, Gilles Louppe

Diffusion models recently proved to be remarkable priors for Bayesian inverse problems. However, training these models typically requires access to large amounts of clean data, which could prove difficult in some settings. In this work, we present DiEM, a novel method based on the expectation-maximization algorithm for training diffusion models from incomple

Yi Xiao, Qilong Jia, Wei Xue, Lei Bai

Data assimilation refers to a set of algorithms designed to compute the optimal estimate of a system's state by refining the prior prediction (known as background states) using observed data. Variational assimilation methods rely on the maximum likelihood approach to formulate a variational cost, with the optimal state estimate derived by minimizing this cos

Amine Marzouki, Zhuxian Guo, Qinghe Zeng, Camille Kurtz

Efficient and precise quantification of lymphocytes in histopathology slides is imperative for the characterization of the tumor microenvironment and immunotherapy response insights. We developed a data-centric optimization pipeline that attain great lymphocyte detection performance using an off-the-shelf YOLOv5 model, without any architectural modifications

Mark Whitmeyer, Cole Williams

For three natural classes of dynamic decision problems; 1. additively separable problems, 2. discounted problems, and 3. discounted problems for a fixed discount factor; we provide necessary and sufficient conditions for one sequential experiment to dominate another in the sense that the dominant experiment is preferred to the other for any decision problem

Diana Robinson, Christian Cabrera, Andrew D. Gordon, Neil D. Lawrence

What if end users could own the software development lifecycle from conception to deployment using only requirements expressed in language, images, video or audio? We explore this idea, building on the capabilities that generative Artificial Intelligence brings to software generation and maintenance techniques. How could designing software in this way better

Xinyi Gao, Guanhua Ye, Tong Chen, Wentao Zhang

The increasing prevalence of large-scale graphs poses a significant challenge for graph neural network training, attributed to their substantial computational requirements. In response, graph condensation (GC) emerges as a promising data-centric solution aiming to substitute the large graph with a small yet informative condensed graph to facilitate data-effi

Fahime Khoramnejad, Ekram Hossain

Next-generation (xG) wireless networks, with their complex and dynamic nature, present significant challenges to using traditional optimization techniques. Generative AI (GAI) emerges as a powerful tool due to its unique strengths. Unlike traditional optimization techniques and other machine learning methods, GAI excels at learning from real-world network da

William Delplanque, Evgeny Skvortsov

Massive higher spin fields are notoriously difficult to introduce interactions when they are described by symmetric (spin)-tensors. An alternative approach is to use chiral description that does not have unphysical longitudinal modes. For low spin fields we show that chiral and symmetric approaches can be related via a family of invertible change of variable

Jiri Vlasak, Jaroslav Klapálek, Adam Kollarčík, Michal Sojka

Automated driving systems are an integral part of the automotive industry. Tools such as Robot Operating System and simulators support their development. However, in the end, the developers must test their algorithms on a real vehicle. To better observe the difference between reality and simulation--the reality gap--digital twin technology offers real-time c

Yuejiao Xu, Ruolin Wang, Chengpeng Xu, Jianmin Ji

Autonomous vehicles necessitate a delicate balance between safety, efficiency, and user preferences in trajectory planning. Existing traditional or learning-based methods face challenges in adequately addressing all these aspects. In response, this paper proposes a novel component termed the Logical Guidance Layer (LGL), designed for seamless integration int

David Ohlin, Fethi Bencherki, Emma Tegling

We study opinion evolution in networks of stubborn agents discussing a sequence of issues, modeled through the so called concatenated Friedkin-Johnsen (FJ) model. It is concatenated in the sense that agents' opinions evolve for each issue, and the final opinion is then taken as a starting point for the next issue. We consider the scenario where agents {also

Manuel Lage Cañellas, Le Nguyen, Anirban Mukherjee, Constantino Álvarez Casado

In the domain of non-contact biometrics and human activity recognition, the lack of a versatile, multimodal dataset poses a significant bottleneck. To address this, we introduce the Oulu Multi Sensing (OMuSense-23) dataset that includes biosignals obtained from a mmWave radar, and an RGB-D camera. The dataset features data from 50 individuals in three distin

Yuyang Zhang, Lisheng Wang, Weijie Wu, Zhaoyang Wang

Broadband spectrum detectors exhibit great promise in fields such as multispectral imaging and optical communications. Despite significant progress, challenges like materials instability, complex manufacturing process and high costs still hinder further application. Here we present a method that achieves broadband spectral detect by impurity-level in SrSnO3.

Yibo Wang, Yuanyuan Mao, Lik-Hang Lee, Shi-ting Ni

The AR 3D book has shown significant potential in enhancing students' learning outcomes. However, the creation process of 3D books requires a significant investment of time, effort, and specialized skills. Thus, in this paper, we first conduct a three-day workshop investigating how AI can support the automated creation of 3D books. Informed by the design ins

Horst Lenske, Jessica Bellone, Maria Colonna, Danilo Gambacurta

The theory of heavy ion double charge exchange (DCE) reactions proceeding by effective rank-2 isotensor interactions is presented. Virtual pion--nucleon charge exchange interactions are investigated as the source for induced isotensor interactions, giving rise to the Majorana DCE (MDCE) reaction mechanism. MDCE is of a generic character, proceeding through p

Lorenzo Perini, Maja Rudolph, Sabrina Schmedding, Chen Qiu

Anomaly detection is the task of identifying examples that do not behave as expected. Because anomalies are rare and unexpected events, collecting real anomalous examples is often challenging in several applications. In addition, learning an anomaly detector with limited (or no) anomalies often yields poor prediction performance. One option is to employ auxi

Xi Wang, Laurence Aitchison

The scaling of the optimal AdamW weight decay hyperparameter with model and dataset size is critical as we seek to build larger models, but is poorly understood. We show that weights learned by AdamW can be understood as an exponential moving average (EMA) of recent updates. This gives critical insights for how to set the weight decay in AdamW, and how the w

Luca Aceto, Antonis Achilleos, Aggeliki Chalki, Anna Ingolfsdottir

Characteristic formulae give a complete logical description of the behaviour of processes modulo some chosen notion of behavioural semantics. They allow one to reduce equivalence or preorder checking to model checking, and are exactly the formulae in the modal logics characterizing classic behavioural equivalences and preorders for which model checking can b

Canberk Güvendik, Thomas Schaefer, Mithat Ünsal

There are two distinct regimes of Yang-Mills theory where we can demonstrate confinement, the existence of a mass gap, and fractional theta angle dependence using a reliable semi-classical calculation. The two regimes are Yang-Mills theory on $S^1 \times {\mathbb R}^3$ with a small circle and a double-trace deformation, and Yang-Mills theory on $T^2 \times {

The ATLAS Collaboration

The ATLAS Google Project was established as part of an ongoing evaluation of the use of commercial clouds by the ATLAS Collaboration, in anticipation of the potential future adoption of such resources by WLCG grid sites to fulfil or complement their computing pledges. Seamless integration of Google cloud resources into the worldwide ATLAS distributed computi

Licheng Shen, Ho Ngai Chow, Lingyun Wang, Tong Zhang

Recent advancements in neural rendering techniques have significantly enhanced the fidelity of 3D reconstruction. Notably, the emergence of 3D Gaussian Splatting (3DGS) has marked a significant milestone by adopting a discrete scene representation, facilitating efficient training and real-time rendering. Several studies have successfully extended the real-ti

Ahmed Shaaban, Zeineb Chaabouni, Maximilian Strobel, Wolfgang Furtner

Hand gesture recognition using radar often relies on computationally expensive fast Fourier transforms. This paper proposes an alternative approach that bypasses fast Fourier transforms using resonate-and-fire neurons. These neurons directly detect the hand in the time-domain signal, eliminating the need for fast Fourier transforms to retrieve range informat

Sergei Krutikov, Bulat Khaertdinov, Rodion Kiriukhin, Shubham Agrawal

Transformer-based neural networks, empowered by Self-Supervised Learning (SSL), have demonstrated unprecedented performance across various domains. However, related literature suggests that tabular Transformers may struggle to outperform classical Machine Learning algorithms, such as Gradient Boosted Decision Trees (GBDT). In this paper, we aim to challenge