October 2023 arXiv papers — page 36

Mehdi Naderi, Markos Papageorgiou, Dimitrios Troullinos, Iasson Karafyllis

Controlling automated vehicles on large lane-free roundabouts is challenging because of the geometrical complexity and frequent conflicts among entering, rotating, and exiting vehicles. This paper proposes a comprehensive methodology to control the vehicles within the roundabout and the connected road branches. The developed real-time vehicle movement strate

Anas Al-lahham, Obaidah Theeb, Khaled Elalem, Tariq A. Alshawi

Ahead-of-time forecasting of the output power of power plants is essential for the stability of the electricity grid and ensuring uninterrupted service. However, forecasting renewable energy sources is difficult due to the chaotic behavior of natural energy sources. This paper presents a new approach to estimate short-term solar irradiance from sky images. T

Mikhail Kudriavtsev, Marija Bezbradica, Andrew McCarren

Association rule mining techniques can generate a large volume of sequential data when implemented on transactional databases. Extracting insights from a large set of association rules has been found to be a challenging process. When examining a ruleset, the fundamental question is how to summarise and represent meaningful mined knowledge efficiently. Many a

Peter Bonart

Enriched motivic $\mathcal A$-spaces are introduced and studied in this paper, where $\mathcal A$ is an additive category of correspondences. They are linear counterparts of motivic $\Gamma$-spaces. It is shown that rational special enriched motivic $\widetilde{\mathrm{Cor}}$-spaces recover connective motivic bispectra with rational coefficients, where $\wid

Yong Cao, Yova Kementchedjhieva, Ruixiang Cui, Antonia Karamolegkou

Building upon the considerable advances in Large Language Models (LLMs), we are now equipped to address more sophisticated tasks demanding a nuanced understanding of cross-cultural contexts. A key example is recipe adaptation, which goes beyond simple translation to include a grasp of ingredients, culinary techniques, and dietary preferences specific to a gi

Andreas Klingler, Bernd Wetzel, Jan-Kristian Krueger

Latest since the landmark studies of Kovacs and co-workers on the glass transition of polymers, it is clear that thermally induced volume changes are of central importance for the understanding of the nature of the glass transition. Due to the kinetic background of the canonical (thermal) glass transition, it does not seem possible to derive a well-defined g

A. V. Kosyak

We introduced previously the generalized characteristic polynomial defined by $P_C(\lambda)={\rm det}\,C(\lambda),$ where $C(\lambda)=C+{\rm diag}\big(\lambda_1,\dots,\lambda_n\big)$ for $C\in {\rm Mat}(n,\mathbb C)$ and $\lambda=(\lambda_k)_{k=1}^n\in \mathbb C^n$ and gave the explicit formula for $P_C(\lambda)$. In this article we define an analogue of the

Samir Karaa, Kassem Mustapha, Naveed Ahmed

We propose and analyze a mixed finite element method for the spatial approximation of a time-fractional Fokker--Planck equation in a convex polyhedral domain, where the given driving force is a function of space. Taking into account the limited smoothing properties of the model, and considering an appropriate splitting of the errors, we employed a sequence o

Peter Bonart

Based on homological algebra of Grothendieck categories of enriched functors, two models for Voevodsky's category of big motives with reasonable correspondences are given in this paper.

Xiang Li, Jing Zhang, Yali Yuan, Cangqi Zhou

A network intrusion usually involves a number of network locations. Data flow (including the data generated by intrusion behaviors) among these locations (usually represented by IP addresses) naturally forms a graph. Thus, graph neural networks (GNNs) have been used in the construction of intrusion detection models in recent years since they have an excellen

Seyedmorteza Sadat, Jakob Buhmann, Derek Bradley, Otmar Hilliges

While conditional diffusion models are known to have good coverage of the data distribution, they still face limitations in output diversity, particularly when sampled with a high classifier-free guidance scale for optimal image quality or when trained on small datasets. We attribute this problem to the role of the conditioning signal in inference and offer

Christian Herglotz, Matthias Kränzler, Xixue Chu, Edouard Francois

In this paper, we discuss one aspect of the latest MPEG standard edition on energy-efficient media consumption, also known as Green Metadata (ISO/IEC 232001-11), which is the interactive signaling for remote decoder-power reduction for peer-to-peer video conferencing. In this scenario, the receiver of a video, e.g., a battery-driven portable device, can send

Sven Zschocke

The total light deflection represents a concept, which allows one to decide which multipoles need to be implemented in the light trajectory for a given astrometric accuracy. The fundamental quantity of total light deflection is the tangent vector of the light trajectory at future infinity. It is found that this tangent vector is naturally given by Chebyshev

Panagiotis Kourtesis, Agapi Papadopoulou, Petros Roussos

Background: Given that VR is applied in multiple domains, understanding the effects of cyber-sickness on human cognition and motor skills and the factors contributing to cybersickness gains urgency. This study aimed to explore the predictors of cybersickness and its interplay with cognitive and motor skills. Methods: 30 participants, 20-45 years old, complet

P. V. Cheri, Deblina Dey, Akhil K, Nirmal Kotal

In this article, we characterize Cohen-Macaulay permutation graphs. In particular, we show that a permutation graph is Cohen-Macaulay if and only if it is well-covered and there exists a unique way of partitioning its vertex set into $r$ disjoint maximal cliques, where $r$ is the cardinality of a maximal independent set of the graph. We also provide some suf

Hanchong Zhang, Ruisheng Cao, Lu Chen, Hongshen Xu

Recently Large Language Models (LLMs) have been proven to have strong abilities in various domains and tasks. We study the problem of prompt designing in the text-to-SQL task and attempt to improve the LLMs' reasoning ability when generating SQL queries. Besides the trivial few-shot in-context learning setting, we design our chain-of-thought (CoT) prompt wit

Andrei Buin, Hung Yi Chiang, S. Andrew Gadsden, Faraz A. Alderson

We present here a combination of two networks, Recurrent Neural Networks (RNN) and Temporarily Convolutional Neural Networks (TCN) in de novo reaction generation using the novel Reaction Smiles-like representation of reactions (CGRSmiles) with atom mapping directly incorporated. Recurrent Neural Networks are known for their autoregressive properties and are

Romanshu Garg, G. P. Singh, Ashwini R Lalke, Saibal Ray

In this paper, we examine the universe's expansion in $ f(R, L_{m}) $ gravity for a particular form of $ f(R, L_{m})=\frac{R}{2}+L_{m}^{n}$. The field equations for flat FLRW metric with matter Lagrangian $ L_{m}=\rho$ are derive. Hubble parameter in terms of red-shift$(z)$ are derived using the linear form of Equation of State (EoS) parameter $ \omega=w_{0}

Débora Aguiar Gomes, Rebecca Briffa, Aleksander Kozak, Jackson Levi Said

In this study, we investigate a Palatini $f(R)$ gravity model featuring a quadratic term correction, aligning it with the most recent expansion rate data, with a particular focus on the latest SNIa and BAO data. Our analysis employs CC data as the fundamental dataset, complemented by contributions from the SN sample and a combination of non-overlapping trans

Lionel Tondji, Ion Necoara, Dirk A. Lorenz

Optimizing strongly convex functions subject to linear constraints is a fundamental problem with numerous applications. In this work, we propose a block (accelerated) randomized Bregman-Kaczmarz method that only uses a block of constraints in each iteration to tackle this problem. We consider a dual formulation of this problem in order to deal in an efficien

Xiaohua Wei, Gang Zhang, Biao Zhao

Let $C_k$ be the cycle of length $k$. For any graph $G$, a subset $D \subseteq V(G)$ is a $C_k$-isolating set of $G$ if the graph obtained from $G$ by deleting the closed neighbourhood of $D$ contains no $C_k$ as a subgraph. The $C_k$-isolation number of $G$, denoted by $\iota(G,C_k)$, is the cardinality of a smallest $C_k$-isolating set of $G$. Borg (2020)

L. G. Mardoyan, G. S. Pogosyan, A. N. Sissakian, V. M. Ter-Antonyan

This monograph is the English version of the book "Quantum systems with hidden symmetry. Interbasis expansions" published in 2006 by the publishing house FIZMATLIT (Moscow) in Russian. When compiling this version of the book, typos and inaccuracies noted since the release of the Russian edition have been corrected.

Matteo Gabardi, Aurora Saibene, Francesca Gasparini, Daniele Rizzo

Electroencephalographic (EEG) signals are fundamental to neuroscience research and clinical applications such as brain-computer interfaces and neurological disorder diagnosis. These signals are typically a combination of neurological activity and noise, originating from various sources, including physiological artifacts like ocular and muscular movements. Un

James Willard, Shirin Golchi, Erica E. M. Moodie, Bruno Boulanger

Identification of optimal dose combinations in early phase dose-finding trials is challenging, due to the trade-off between precisely estimating the many parameters required to flexibly model the possibly non-monotonic dose-response surface, and the small sample sizes in early phase trials. This difficulty is even more pertinent in the context of personalize

Haneen Liqreina, Mustafa Jarrar, Mohammed Khalilia, Ahmed Oumar El-Shangiti

Traditional NER systems are typically trained to recognize coarse-grained entities, and less attention is given to classifying entities into a hierarchy of fine-grained lower-level subtypes. This article aims to advance Arabic NER with fine-grained entities. We chose to extend Wojood (an open-source Nested Arabic Named Entity Corpus) with subtypes. In partic

Rakshitha Godahewa, Christoph Bergmeir, Zeynep Erkin Baz, Chengjun Zhu

Forecasts are typically not produced in a vacuum but in a business context, where forecasts are generated on a regular basis and interact with each other. For decisions, it may be important that forecasts do not change arbitrarily, and are stable in some sense. However, this area has received only limited attention in the forecasting literature. In this pape

Tianfu Luo, Yelin Feng, Qingfu Huang, Zongliang Zhang

A Physics-Informed Neural Network (PINN) provides a distinct advantage by synergizing neural networks' capabilities with the problem's governing physical laws. In this study, we introduce an innovative approach for solving seepage problems by utilizing the PINN, harnessing the capabilities of Deep Neural Networks (DNNs) to approximate hydraulic head distribu

Seungjae Lee, Daesol Cho, Jonghae Park, H. Jin Kim

Recent curriculum Reinforcement Learning (RL) has shown notable progress in solving complex tasks by proposing sequences of surrogate tasks. However, the previous approaches often face challenges when they generate curriculum goals in a high-dimensional space. Thus, they usually rely on manually specified goal spaces. To alleviate this limitation and improve

Michael G. Jabbour, Nilanjana Datta

Uniform continuity bounds on entropies are generally expressed in terms of a single distance measure between a pair of probability distributions or quantum states, typically, the total variation distance or trace distance. However, if an additional distance measure between the probability distributions or states is known, then the continuity bounds can be si

Hai-Chau Nguyen

Shadow tomography is a scalable technique to characterise the quantum state of a quantum computer or quantum simulator. The protocol is based on the transformation of the outcomes of random measurements into the so-called classical shadows, which can later be transformed into samples of expectation values of the observables of interest. By construction, clas

Ang Chen, Li Chen, Yunfei Chen, Nan Zhao

Positioning and sensing over wireless networks are imperative for many emerging applications. However, since traditional wireless channel models over-simplify the user equipment (UE) as a point target, they cannot be used for sensing the attitude of the UE, which is typically described by the spatial orientation. In this paper, a comprehensive electromagneti

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

The production of $\eta$ and $\eta'$ mesons is studied in proton-proton and proton-lead collisions collected with the LHCb detector. Proton-proton collisions are studied at center-of-mass energies of $5.02$ and $13~{\rm TeV}$, and proton-lead collisions are studied at a center-of-mass energy per nucleon of $8.16~{\rm TeV}$. The studies are performed in cente

Xiaoyu Liu, Jiaxin Yuan, Bang An, Yuancheng Xu

Representation learning assumes that real-world data is generated by a few semantically meaningful generative factors (i.e., sources of variation) and aims to discover them in the latent space. These factors are expected to be causally disentangled, meaning that distinct factors are encoded into separate latent variables, and changes in one factor will not a

Mikhael Sayat, Oliver Thearle, Biveen Shajilal, Sebastian P. Kish

Continuous variable quantum key distribution (CVQKD) is the sharing of secret keys between different parties using the continuous amplitude and phase quadratures of light. There are many protocols in which different modulation schemes are used to implement CVQKD. However, there has been no tool for comparison between different CVQKD protocols to determine th

Tim J. Schoonbeek, Tim Houben, Hans Onvlee, Peter H. N. de With

Although action recognition for procedural tasks has received notable attention, it has a fundamental flaw in that no measure of success for actions is provided. This limits the applicability of such systems especially within the industrial domain, since the outcome of procedural actions is often significantly more important than the mere execution. To addre

Conor Nixon, Zachary Yahn, Ethan Duncan, Ian Neidel

In this paper we present two examples of recent investigations that we have undertaken, applying Machine Learning (ML) neural networks (NN) to image datasets from outer planet missions to achieve feature recognition. Our first investigation was to recognize ice blocks (also known as rafts, plates, polygons) in the chaos regions of fractured ice on Europa. We

Sergey Karpov, Julien Peloton

We assess the impact of satellite glints -- rapid flashes produced by reflections of a sunlight from flat surfaces of rotating satellites -- on current and future deep sky surveys such as the ones conducted by the Zwicky Transient Facility (ZTF) and the Vera C. Rubin Observatory upcoming Legacy Survey of Space and Time (LSST). In addition to producing a larg

Perla Doubinsky, Nicolas Audebert, Michel Crucianu, Hervé Le Borgne

With the availability of powerful text-to-image diffusion models, recent works have explored the use of synthetic data to improve image classification performances. These works show that it can effectively augment or even replace real data. In this work, we investigate how synthetic data can benefit few-shot class-agnostic counting. This requires to generate

Yucheng Liu

We prove a sufficient condition for the two-point function of a statistical mechanical model on $\mathbb{Z}^d$, $d > 2$, to be bounded uniformly near a critical point by $|x|^{-(d-2)} \exp [ -c|x| / \xi ]$, where $\xi$ is the correlation length. The condition is given in terms of a convolution equation satisfied by the two-point function, and we verify the c

Lars A. L. Janssen, Rob H. B. Fey, Bart Besselink, Nathan van de Wouw

In this work, a modular approach is introduced to select the most important eigenmodes for each component of a composed structural dynamics system to obtain the required accuracy of the reduced-order assembly model. To enable the use of models of complex (structural) dynamical systems in engineering practice, e.g., in a design, optimization and/or control co

Chenghan Xie, Chenxi Li, Chuwen Zhang, Qi Deng

In many important machine learning applications, the standard assumption of having a globally Lipschitz continuous gradient may fail to hold. This paper delves into a more general $(L_0, L_1)$-smoothness setting, which gains particular significance within the realms of deep neural networks and distributionally robust optimization (DRO). We demonstrate the si

Stefan Karlsson, Robbert Jongeling, Adnan Causevic, Daniel Sundmark

A common way of exposing functionality in contemporary systems is by providing a Web-API based on the REST API architectural guidelines. To describe REST APIs, the industry standard is currently OpenAPI-specifications. Test generation and fuzzing methods targeting OpenAPI-described REST APIs have been a very active research area in recent years. An open rese

Mohamed Rihan, Tim Due, MohammadAmin Vakilifard, Dirk Wubben

Leveraging non-terrestrial platforms in 6G networks holds immense significance as it opens up opportunities to expand network coverage, enhance connectivity, and support a wide range of innovative applications, including global-scale Internet of Things and ultra-high-definition content delivery. To accomplish the seamless integration between terrestrial and

Shuai Yang, Zhifei Chen, Pengguang Chen, Xi Fang

Defect inspection is paramount within the closed-loop manufacturing system. However, existing datasets for defect inspection often lack precision and semantic granularity required for practical applications. In this paper, we introduce the Defect Spectrum, a comprehensive benchmark that offers precise, semantic-abundant, and large-scale annotations for a wid

Amal Nayouf, Tymaa Hammouda, Mustafa Jarrar, Fadi Zaraket

This paper presents Nabra, a corpora of Syrian Arabic dialects with morphological annotations. A team of Syrian natives collected more than 6K sentences containing about 60K words from several sources including social media posts, scripts of movies and series, lyrics of songs and local proverbs to build Nabra. Nabra covers several local Syrian dialects inclu

Lajos Diósi

The covariant form of the multivariable diffusion-drift process is described by the covariant Fokker--Planck equation using the standard toolbox of Riemann geometry. The covariant form of the equivalent Langevin stochastic differential equation is long sought after in both physics and mathematics. We show that the simplest covariant Stratonovich stochastic d

Weikang Gao, Dan Li, Wei Liu, Hua Yue

The AUGER Collaboration has recently published the precise energy spectrum of cosmic rays above 1 EeV, which exhibits rich and interesting features. These features provide an opportunity to investigate the galactic propagation and interaction of ultra-high-energy cosmic rays (UHECRs). While the classic dip model, which describes the UHECR propagation and int

Vijini Liyanage, Davide Buscaldi

Thanks to the state-of-the-art Large Language Models (LLMs), language generation has reached outstanding levels. These models are capable of generating high quality content, thus making it a challenging task to detect generated text from human-written content. Despite the advantages provided by Natural Language Generation, the inability to distinguish automa

Suddhasattwa Brahma, Abhinove Nagarajan Seenivasan

It is now widely believed that if the gravitational field is (perturbatively) quantum, it would entangle two massive objects (in spatial superpositions) which were otherwise unentangled to begin with. Recently, actual table-top experiments have been proposed to test this idea in what would be the first detection of perturbative quantum gravity. In this essay

Linus Behn, Lars Diening

We study global regularity of nonlinear systems of partial differential equations depending on the symmetric part of the gradient with Dirichlet boundary conditions. These systems arise from variational problems in plasticity with power growth. We cover the full range of exponents $p \in (1,\infty)$. As a novelty the degenerate case for $p>2$ is included. We

Venktesh V, Sourangshu Bhattacharya, Avishek Anand

Answering complex questions is a challenging task that requires question decomposition and multistep reasoning for arriving at the solution. While existing supervised and unsupervised approaches are specialized to a certain task and involve training, recently proposed prompt-based approaches offer generalizable solutions to tackle a wide variety of complex q

Jacek Gulgowski, Anna Kamont, Markus Passenbrunner

In [Y.~K.~Hu, K.~A.~Kopotun, X.~M.~Yu, Constr. Approx. 2000], the authors have obtained a characterization of best $n$-term piecewise polynomial approximation spaces as real interpolation spaces between $L^p$ and some spaces of bounded dyadic ring variation. We extend this characterization to the general setting of binary filtrations and finite-dimensional s

Marina T. Dietrich, Dennis Dobler, Mathisca C. M. de Gunst

The wild bootstrap is a popular resampling method in the context of time-to-event data analyses. Previous works established the large sample properties of it for applications to different estimators and test statistics. It can be used to justify the accuracy of inference procedures such as hypothesis tests or time-simultaneous confidence bands. This paper co

Jonathan Ansari, Marcus Rockel

Motivated by recently investigated results on dependence measures and robust risk models, this paper provides an overview of dependence properties of many well-known bivariate copula families, where the focus is on the Schur order for conditional distributions, which has the fundamental property that minimal elements characterize independence and maximal ele

Mukul Singh, José Cambronero, Sumit Gulwani, Vu Le

Imagine a developer who can only change their last line of code, how often would they have to start writing a function from scratch before it is correct? Auto-regressive models for code generation from natural language have a similar limitation: they do not easily allow reconsidering earlier tokens generated. We introduce CodeFusion, a pre-trained diffusion

Mukul Singh, José Cambronero, Sumit Gulwani, Vu Le

Formatting is an important property in tables for visualization, presentation, and analysis. Spreadsheet software allows users to automatically format their tables by writing data-dependent conditional formatting (CF) rules. Writing such rules is often challenging for users as it requires them to understand and implement the underlying logic. We present Form

G. Labeyrie, J. G. M. Walker, G. R. M. Robb, R. Kaiser

We report on the observation of spontaneously drifting coupled spin and quadrupolar density waves in the ground state of laser driven Rubidium atoms. These laser-cooled atomic ensembles exhibit spontaneous magnetism via light mediated interactions when submitted to optical feedback by a retro-reflecting mirror. Drift direction and chirality of the waves aris

Yifan Xia, Ping He, Xuhong Zhang, Peiyu Liu

The emergence of WebAssembly allows attackers to hide the malicious functionalities of JavaScript malware in cross-language interoperations, termed JavaScript-WebAssembly multilingual malware (JWMM). However, existing anti-virus solutions based on static program analysis are still limited to monolingual code. As a result, their detection effectiveness decrea

Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines

Incorporating expert demonstrations has empirically helped to improve the sample efficiency of reinforcement learning (RL). This paper quantifies theoretically to what extent this extra information reduces RL's sample complexity. In particular, we study the demonstration-regularized reinforcement learning that leverages the expert demonstrations by KL-regula

Mehraneh Tirandari

In this work, the electronic and optical properties of Si$_{1-x}$Ge$_x$O$_2$ at equilibrium and non-equilibrium condition were calculated using a full potential linear augmented plane wave plus local orbital method. The effect of Si and Ge on electronic and optical properties of Si$_{1-x}$Ge$_x$O$_2$ is investigated. It was found that Si$_{1-x}$Ge$_x$O$_2$ c

Michael Meixner, Henri Menke, Marcel Klett, Sarah Heinzelmann

The recently proposed center-focused post-processing procedure [Phys. Rev. Research 2, 033476 (2020)] of cellular dynamical mean-field theory suggests that central sites of large impurity clusters are closer to the exact solution of the Hubbard model than the edge sites. In this paper, we systematically investigate results in the spirit of this center-focuse

Shu-Chang Zhao, Lu Gao, Qiang Cheng, Qing-Feng Sun

We study the crossed Andreev reflection and the nonlocal transport in the proximitized graphene/supercondcutor/proximitized graphene junctions with the pseudospin staggered potential and the intrinsic spin-orbit coupling. The crossed Andreev reflection with the local Andreev reflection and the elastic cotunneling being completely eliminated can be realized f

Danai Korre

Embodied conversational agents (ECAs) are paradigms of conversational user interfaces in the form of embodied characters. While ECAs offer various manipulable features, this paper focuses on a study conducted to explore two distinct levels of presentation realism. The two agent versions are photorealistic and animated. The study aims to provide insights and

Dmitrii Krachun, Christoforos Panagiotis

We prove quantitative sub-ballisticity for the self-avoiding walk on the hexagonal lattice. Namely, we show that with high probability a self-avoiding walk of length $n$ does not exit a ball of radius $O(n/\log{n})$. Previously, only a non-quantitative $o(n)$ bound was known from the work of Duminil-Copin and Hammond \cite{DCH13}. As an important ingredient

Christian Herrmann

Based on an analogue for systems of partial isomorphisms between lower sections in a complemented modular lattice we prove that principal right ideals $aR \cong bR$ in a (von Neumann) regular ring $R$ are perspective if $aR \cap bR$ is of finite height in $L(R)$. This is applied to derive, for existence-varieties $\mathcal{V}$ of regular rings, equivalence o

Bryan Kumara, Mark Hooper, Carsten Maple, Timothy Hobson

Redactable signature schemes and sanitizable signature schemes are methods that permit modification of a given digital message and retain a valid signature. This can be applied to decentralized identity systems for delegating identity issuance and redacting sensitive information for privacy-preserving verification of identity. We propose implementing these p

Krzysztof Bardadyn

Let $p\in [1,\infty)$. We define an $L^p$-operator algebra crossed product by a transfer operator for the topological Bernoulli shift $\varphi$ on $X=\{1,...,n\}^{\mathbb{N}}$, and we prove it is isometrically isomorphic to the $L^p$-analog $\mathcal{O}_n^p$ of the Cuntz algebra introduced by Phillips. As an application, we prove that the spectrum of the ass

Mark Hopkins, Hans Leiß

Within the tensor product $K \mathop{\otimes_{\cal R}} C_2'$ of any ${}^*$-continuous Kleene algebra $K$ with the polycyclic ${}^*$-continuous Kleene algebra $C_2'$ over two bracket pairs there is a copy of the fixed-point closure of $K$: the centralizer of $C_2'$ in $K \mathop{\otimes_{\cal R}} C_2'$. Using an automata-theoretic representation of elements o

Zhewei Huang, Ailin Huang, Xiaotao Hu, Chen Hu

The Space-Time Video Super-Resolution (STVSR) task aims to enhance the visual quality of videos, by simultaneously performing video frame interpolation (VFI) and video super-resolution (VSR). However, facing the challenge of the additional temporal dimension and scale inconsistency, most existing STVSR methods are complex and inflexible in dynamically modeli

Guillaume Lecué, Lucie Neirac

Motivated by several examples, we consider a general framework of learning with linear loss functions. In this context, we provide excess risk and estimation bounds that hold with large probability for four estimators: ERM, minmax MOM and their regularized versions. These general bounds are applied for the problem of robustness in sparse PCA. In particular,

Andoni Rodriguez, Cesar Sanchez

In this paper, we address the problem of the (reactive) realizability of specifications of theories richer than Booleans, including arithmetic theories. Our approach transforms theory specifications into purely Boolean specifications by (1) substituting theory literals by Boolean variables, and (2) computing an additional Boolean requirement that captures th

Cédric Jockel, Laura Sagunski

Dark matter could accumulate around neutron stars in sufficient amounts to affect their global properties. In this work, we study the effect of a specific model for dark matter -- a massive and self-interacting vector (spin-1) field -- on neutron stars. We describe the combined systems of neutron stars and vector dark matter using Einstein-Proca theory coupl

Mengxue Qu, Yu Wu, Wu Liu, Xiaodan Liang

Intention-oriented object detection aims to detect desired objects based on specific intentions or requirements. For instance, when we desire to "lie down and rest", we instinctively seek out a suitable option such as a "bed" or a "sofa" that can fulfill our needs. Previous work in this area is limited either by the number of intention descriptions or by the

Taisuke Kageura, Yosuke Sasama, Keisuke Yamada, Kosuke Kimura

The surface conductivity of hydrogen-terminated diamond is a topic of great interest from both scientific and technological perspectives. This is primarily due to the fact that the conductivity is exceptionally high without the need for substitutional doping, thus enabling a wide range of electronic applications. Although the conductivity is commonly explain

Duván Cardona, André Pedroso Kowacs

We establish necessary and sufficient conditions for the global hypoellipticity of $G$-invariant operators on homogeneous vector bundles. These criteria are established in terms of the corresponding matrix-valued symbols as developed by Ruzhansky and Turunen and extended in [7] to homogeneous vector-bundles.

I. Tosta e Melo, J. -G. Ducoin, Z. Vidadi, C. Andrade

We present a campaign designed to train the GRANDMA network and its infrastructure to follow up on transient alerts and detect their early afterglows. In preparation for O4 II campaign, we focused on GRB alerts as they are expected to be an electromagnetic counterpart of gravitational-wave events. Our goal was to improve our response to the alerts and start

E. Abreu, E. Cuesta, A. Duran, W. Lambert

The paper is concerned with the mathematical theory and numerical approximation of systems of partial differential equations (pde) of hyperbolic, pseudo-parabolic type. Some mathematical properties of the initial-boundary-value problem (ibvp) with Dirichlet boundary conditions are first studied. They include the weak formulation, well-posedness and existence

Alberto De Marchi

Bringing together nonlinear optimization with polyhedral and integrality constraints enables versatile modeling, but poses significant computational challenges. We investigate a method to address these problems based on sequential mixed-integer linearization with trust region safeguard, computing feasible iterates via calls to a generic mixed-integer linear

Melika Behjati, Fabio Fehr, James Henderson

Learned representations at the level of characters, sub-words, words and sentences, have each contributed to advances in understanding different NLP tasks and linguistic phenomena. However, learning textual embeddings is costly as they are tokenization specific and require different models to be trained for each level of abstraction. We introduce a novel lan

Urvesh Patil, Nuala M. Caffrey

Liquid phase exfoliation (LPE) is the most promising method for the low-cost, scalable production of two-dimensional nanosheets from their bulk counterparts. Extensive exfoliation occurs in most solvents due to the huge amount of energy introduced by sonication or shear mixing. However, the subsequent dispersion is not always stable, with extensive reaggrega

Bryan Andrews, Joseph Ramsey, Ruben Sanchez-Romero, Jazmin Camchong

Learning graphical conditional independence structures is an important machine learning problem and a cornerstone of causal discovery. However, the accuracy and execution time of learning algorithms generally struggle to scale to problems with hundreds of highly connected variables -- for instance, recovering brain networks from fMRI data. We introduce the b

Aljoša Šubašić, Tanja Vojković

In this paper we are defining a special class of graphs called multilayered graphs and its subclass, multilayered cycles. For that subclass of graphs we are giving the values of all vertex spans (strong, direct, or Cartesian span). Surprisingly, our results reveal that, irrespective of the chosen movement rules, the span values only depend on the length of t

Corentin Sautier, Gilles Puy, Alexandre Boulch, Renaud Marlet

We present a surprisingly simple and efficient method for self-supervision of 3D backbone on automotive Lidar point clouds. We design a contrastive loss between features of Lidar scans captured in the same scene. Several such approaches have been proposed in the literature from PointConstrast, which uses a contrast at the level of points, to the state-of-the

Sonka Reimers, Lukas Odenbreit, Libor Smejkal, Vladimir N. Strocov

Altermagnetism represents an emergent collinear magnetic phase with compensated order and an unconventional alternating even-parity wave spin order in the non-relativistic band structure. We investigate directly this unconventional band splitting near the Fermi energy through spinintegrated soft X-ray angular resolved photoemission spectroscopy. The experime

Iñigo Alonso, Eneko Agirre

Table-to-text systems generate natural language statements from structured data like tables. While end-to-end techniques suffer from low factual correctness (fidelity), a previous study reported gains when using manual logical forms (LF) that represent the selected content and the semantics of the target text. Given the manual step, it was not clear whether

Matteo Barigozzi, Marc Hallin

Dynamic factor models have been developed out of the need of analyzing and forecasting time series in increasingly high dimensions. While mathematical statisticians faced with inference problems in high-dimensional observation spaces were focusing on the so-called spiked-model-asymptotics, econometricians adopted an entirely and considerably more effective a

Vivek Shripad Borkar

This article recalls the recent work on a linear programming formulation of infinite horizon risk-sensitive control via its equivalence with a single controller game, using a classic work of Vrieze. This is then applied to a constrained risk-sensitive control problem with a risk-sensitive cost and risk-sensitive constraint. This facilitates a Lagrange multip

Matthias R. Schreiber, Diogo Belloni, Jan van Roestel

The general prediction that more than half of all CVs have evolved past the period minimum is in strong disagreement with observational surveys, which show that the relative number of these objects is just a few per cent. Here, we investigate whether a large number of post-period minimum CVs could detach because of the appearance of a strong white dwarf magn

C. Farret Jentink, V. Bourrier, C. Lovis, R. Allart

Among highly irradiated exoplanets, some have been found to undergo significant hydrodynamic expansion traced by atmospheric escape. To better understand these processes in the context of planetary evolution, we propose NIGHT (the Near-Infrared Gatherer of Helium Transits). NIGHT is a high-resolution spectrograph dedicated to surveying and temporally monitor

Balakumar Sundaralingam, Siva Kumar Sastry Hari, Adam Fishman, Caelan Garrett

This paper explores the problem of collision-free motion generation for manipulators by formulating it as a global motion optimization problem. We develop a parallel optimization technique to solve this problem and demonstrate its effectiveness on massively parallel GPUs. We show that combining simple optimization techniques with many parallel seeds leads to

Masaki Adachi, Brady Planden, David A. Howey, Michael A. Osborne

Like many optimizers, Bayesian optimization often falls short of gaining user trust due to opacity. While attempts have been made to develop human-centric optimizers, they typically assume user knowledge is well-specified and error-free, employing users mainly as supervisors of the optimization process. We relax these assumptions and propose a more balanced

Ding-Ming Huang, Xu Wu, Kai Chang, Hao Hu

van der Waals (vdW) layered materials exhibit fruitful novel physical properties. The energy band of such materials depends strongly on their structures and a tremendous variation in their physical properties can be deduced from a tiny change in inter-layer spacing, twist angle, or in-plane strain. In this work, a kind of vdW layered material of spiral antim

Ahmed Alajrami, Katerina Margatina, Nikolaos Aletras

Understanding how and what pre-trained language models (PLMs) learn about language is an open challenge in natural language processing. Previous work has focused on identifying whether they capture semantic and syntactic information, and how the data or the pre-training objective affects their performance. However, to the best of our knowledge, no previous w

Patricia Rebello Teles, David d'Enterria, Victor P. Gonçalves, Daniel E. Martins

Opportunities for searches for axion-like particles (ALPs) coupling to photons in $\mathrm{e}^+\mathrm{e}^-$ collisions at the Future Circular Collider (FCC-ee) and International Linear Collider (ILC) are investigated. We perform a study of the photon-fusion production of ALPs decaying into two photons, $\mathrm{e}^+\mathrm{e}^- \overset{\gamma \gamma}{\long

Grigory Mikhalkin, Mikhail Shkolnikov

The paper studies intrinsic geometry in the tropical plane. Tropical structure in the real affine $n$-space is determined by the integer tangent vectors. Tropical isomorphisms are affine transformations preserving the integer lattice of the tangent space, they may be identified with the group $\operatorname{GL_n}(\mathbb{Z})$ extended by arbitrary real trans

Faith Hawthorn, Sam Gill, Daniel Bayliss, Hugh P. Osborn

Discovering transiting exoplanets with long orbital periods allows us to study warm and cool planetary systems with temperatures similar to the planets in our own Solar system. The TESS mission has photometrically surveyed the entire Southern Ecliptic Hemisphere in Cycle 1 (August 2018 - July 2019), Cycle 3 (July 2020 - June 2021) and Cycle 5 (September 2022

Alex Encinas-Bartos, George Haller

We derive asymptotic estimates for the projection of the vorticity onto principal directions of material stretching in 3D flows. In flows with pointwise bounded vorticity, these estimates predict vorticity alignment with Lyapunov vectors along trajectories with positive Lyapunov exponents. Specifically, we find that in inviscid flows with conservative body f

Justin C. Feng, Shinji Mukohyama, Sante Carloni

We consider a class of quasiregular singularities characterized by points possessing two future-directed light cones and two past-directed light cones. Such singularities appear in the $1+1$ trousers spacetime and the Deutsch-Politzer spacetime. We argue that these singularities are relevant for describing the end point of an evaporating black hole, and show

Qun Zhao, Xintao Wang, Menghui Yang

This study presents a novel heuristic algorithm called the "Minimal Positive Negative Product Strategy" to guide the CDCL algorithm in solving the Boolean satisfiability problem. It provides a mathematical explanation for the superiority of this algorithm over widely used heuristics such as the Dynamic Largest Individual Sum (DLIS) and the Variable State Ind

Fernando Roldán

In this article, we propose a splitting algorithm to find zeros of the sum of four maximally monotone operators in real Hilbert spaces. In particular, we consider a Lipschitzian operator, a cocoercive operator, and a linear composite term. In the case when the Lipschitzian operator is absent, our method reduces to the Condat-V\~u algorithm. On the other hand