May 2024 arXiv papers — page 32

J. Harry Moore, Daniel J. Cole, Gabor Csanyi

Free energies play a central role in characterising the behaviour of chemical systems and are among the most important quantities that can be calculated by molecular dynamics simulations. Solvation free energies in various organic solvents, in particular, are well-studied physicochemical properties of drug-like molecules and are commonly used to assess and o

Renchi Zhang, Joost de Winter, Dimitra Dodou, Harleigh Seyffert

Recent advancements in AI have accelerated the evolution of versatile robot designs. Chess provides a standardized environment for evaluating the impact of robot behavior on human behavior. This article presents an open-source chess robot for human-robot interaction (HRI) research, specifically focusing on verbal and non-verbal interactions. The OpenChessRob

Doron Puder

What is the probability that a random walk in the free group ends in a proper power? Or in a primitive element? We present a formula that computes the exponential decay rate of the probability that a random walk on a regular tree ends in a given subset, in terms of the exponential decay rate of the analogous probability of the non-backtracking random walk. T

Philippos Papaphilippou, Wayne Luk

Aggregation queries are a series of computationally-demanding analytics operations on counted, grouped or time series data. They include tasks such as summation or finding the median among the items of the same group, and within a specified number of the last observed tuples for sliding window aggregation (SWAG). They have a wide range of applications includ

Ke Zou, Tian Lin, Zongbo Han, Meng Wang

Multi-modal ophthalmic image classification plays a key role in diagnosing eye diseases, as it integrates information from different sources to complement their respective performances. However, recent improvements have mainly focused on accuracy, often neglecting the importance of confidence and robustness in predictions for diverse modalities. In this stud

Wei Zhao, Zhe Li, Yige Li, Ye Zhang

Large language models (LLMs) are increasingly being adopted in a wide range of real-world applications. Despite their impressive performance, recent studies have shown that LLMs are vulnerable to deliberately crafted adversarial prompts even when aligned via Reinforcement Learning from Human Feedback or supervised fine-tuning. While existing defense methods

Robert Leppich, Vanessa Borst, Veronika Lesch, Samuel Kounev

Time series analysis remains a major challenge due to its sparse characteristics, high dimensionality, and inconsistent data quality. Recent advancements in transformer-based techniques have enhanced capabilities in forecasting and imputation; however, these methods are still resource-heavy, lack adaptability, and face difficulties in integrating both local

Jizhe Cui, Haozhi Sha, Liangze Mao, Kang Sun

Interstitial atoms are ubiquitous in solids and they are widely incorporated into materials to tune their lattice structure, electronic transportation, and mechanical properties. Because the distribution of interstitial atoms in matrix materials is usually disordered and most of them are light atoms with weak scattering ability, it remains a challenge to dir

Artur Kasymov, Bartosz Czekaj, Marcin Mazur, Jacek Tabor

One of the key advantages of 3D rendering is its ability to simulate intricate scenes accurately. One of the most widely used methods for this purpose is Gaussian Splatting, a novel approach that is known for its rapid training and inference capabilities. In essence, Gaussian Splatting involves incorporating data about the 3D objects of interest into a serie

Nicolas Bousquet, Quentin Chuet, Victor Falgas-Ravry, Amaury Jacques

In this short note, we prove that every twin-free graph on $n$ vertices contains a locating-dominating set of size at most $\lceil\frac{5}{8}n\rceil$. This improves the earlier bound of $\lfloor\frac{2}{3}n\rfloor$ due to Foucaud, Henning, L\"owenstein and Sasse from 2016, and makes some progress towards the well-studied locating-dominating conjecture of Gar

Angéline Pouget, Nikola Jovanović, Mark Vero, Robin Staab

The goal of Fair Representation Learning (FRL) is to mitigate biases in machine learning models by learning data representations that enable high accuracy on downstream tasks while minimizing discrimination based on sensitive attributes. The evaluation of FRL methods in many recent works primarily focuses on the tradeoff between downstream fairness and accur

Christoph Schönle, Marylou Gabrié, Tony Lelièvre, Gabriel Stoltz

We consider the problem of sampling a high dimensional multimodal target probability measure. We assume that a good proposal kernel to move only a subset of the degrees of freedoms (also known as collective variables) is known a priori. This proposal kernel can for example be built using normalizing flows. We show how to extend the move from the collective v

Yongjun Hou

We study positive properties of the quasilinear elliptic equation $$-\mathrm{div}\mathcal{A}(x,\nabla u)+V|u|^{p-2}u=0\quad (1<p<\infty)\qquad \mbox{ in } \Omega,$$ where the function $\mathcal{A}(x,\xi)$ is induced by a family of norms on $\mathbb{R}^{n}$ ($n\geq 2$) parameterized by points in the domain $\Omega\subseteq\mathbb{R}^{n}$, and $V$ belongs to a

Nina Kunert, Jonathan Gair, Peter T. H. Pang, Tim Dietrich

Matching gravitational-wave observations of binary neutron stars with theoretical model predictions reveals important information about the sources, such as the masses and the distance to the stars. The latter can be used to determine the Hubble constant, the rate at which the Universe expands. One general problem of all astrophysical measurements is that th

Qilin Wang, Zhengkai Jiang, Chengming Xu, Jiangning Zhang

Human image animation involves generating a video from a static image by following a specified pose sequence. Current approaches typically adopt a multi-stage pipeline that separately learns appearance and motion, which often leads to appearance degradation and temporal inconsistencies. To address these issues, we propose VividPose, an innovative end-to-end

Bo Zhang, Nikolaus Adams, Xiangyu Hu

Smoothed particle hydrodynamics (SPH) offers distinct advantages for modeling many engineering problems, yet achieving high-order consistency in its conservative formulation remains to be addressed. While zero- and higher-order consistencies can be obtained using particle-pair differences and the kernel gradient correction (KGC) approaches, respectively, for

Simon Marshall, Casper Gyurik, Vedran Dunjko

Advice classes in computational complexity have frequently been used to model real-world scenarios encountered in cryptography, quantum computing and machine learning, where some computational task may be broken down into a preprocessing and deployment phase, each associated with a different complexity. However, in these scenarios, the advice given by the pr

Ayman Kachmar, Vladimir Lotoreichik, Mikael Sundqvist

We study the magnetic Laplacian in a two-dimensional exterior domain with Neumann boundary condition and uniform magnetic field. For the exterior of the disk we establish accurate asymptotics of the low-lying eigenvalues in the weak magnetic field limit. For the exterior of a star-shaped domain, we obtain an asymptotic upper bound on the lowest eigenvalue in

Javier Naranjo-Alcazar, Jordi Grau-Haro, Ruben Ribes-Serrano, Pedro Zuccarello

Machine Listening focuses on developing technologies to extract relevant information from audio signals. A critical aspect of these projects is the acquisition and labeling of contextualized data, which is inherently complex and requires specific resources and strategies. Despite the availability of some audio datasets, many are unsuitable for commercial app

Matthew Hase-Liu

Sawin recently gave an axiomatic characterization of multiple Dirichlet series over the function field $\mathbb{F}_{q}(T)$ and proved their existence by exhibiting the coefficients as trace functions of specific perverse sheaves. However, he did not prove that these series actually converge anywhere, instead treating them as formal power series. In this pape

Fabian Frei, Matthias Gehnen, Dennis Komm, Rastislav Královič

Coloring is a notoriously hard problem, and even more so in the online setting, where each arriving vertex has to be colored immediately and irrevocably. Already on trees, which are trivially two-colorable, it is impossible to achieve anything better than a logarithmic competitive ratio. We show how to undercut this bound by a double-logarithmic factor in th

Jiachang Bi, Yu Lin, Qinghua Zhang, Zhanfeng Liu

Although transition-metal nitrides have been widely applied for several decades, experimental investigations of their high-resolution electronic band structures are rare due to the lack of high-quality single-crystalline samples. Here, we report on the first momentum-resolved electronic band structures of titanium nitride (TiN) films, a remarkable nitride su

Tong Chen, Danny Wang, Xurong Liang, Marten Risius

To counter the side effect brought by the proliferation of social media platforms, hate speech detection (HSD) plays a vital role in halting the dissemination of toxic online posts at an early stage. However, given the ubiquitous topical communities on social media, a trained HSD classifier easily becomes biased towards specific targeted groups (e.g., female

CMS Collaboration

The results of a search for a standard model-like Higgs boson decaying into two photons in the mass range between 70 and 110 GeV are presented. The analysis uses the data set collected by the CMS experiment in proton-proton collisions at $\sqrt{s}$ = 13 TeV corresponding to integrated luminosities of 36.3 fb$^{-1}$, 41.5 fb$^{-1}$ and 54.4 fb$^{-1}$ during t

JuneHyoung Kwon, Eunju Lee, Yunsung Cho, YoungBin Kim

Weakly supervised semantic segmentation (WSSS) employing weak forms of labels has been actively studied to alleviate the annotation cost of acquiring pixel-level labels. However, classifiers trained on biased datasets tend to exploit shortcut features and make predictions based on spurious correlations between certain backgrounds and objects, leading to a po

Ivan Zorin, Paul Gattinger, Giovanna Ricchiuti, Bernhard Lendl

We report on the design of an all-mirror wavefront-division interferometer capable of spectroscopic studies across multiple spectral ranges$\unicode{x2013}$from the plasma frequencies of metals to terahertz wavelengths and beyond. The proposed method leverages the properties of laser sources with high spatial coherence. A theoretical framework for the interf

Hao Yu, Minghao Fu, Jiandong Ding, Yusheng Zhou

Deep Click-Through Rate (CTR) prediction models play an important role in modern industrial recommendation scenarios. However, high memory overhead and computational costs limit their deployment in resource-constrained environments. Low-rank approximation is an effective method for computer vision and natural language processing models, but its application i

Keiichi Ando, Kohki Kawabata, Tatsuma Nishioka

We construct Narain conformal field theories (CFTs) from quantum subsystem codes, a more comprehensive class of quantum error-correcting codes than quantum stabilizer codes, for qudit systems of prime dimensions. The resulting code CFTs exhibit a global $\mathbb{Z}_2$ symmetry, enabling us to perform the $\mathbb{Z}_2$-gauging to derive their orbifolded and

Sike Wang, Pan Zhou, Jia Li, Hua Huang

Second-order optimizers, maintaining a matrix termed a preconditioner, are superior to first-order optimizers in both theory and practice. The states forming the preconditioner and its inverse root restrict the maximum size of models trained by second-order optimizers. To address this, compressing 32-bit optimizer states to lower bitwidths has shown promise

Maximilian Schwetz, Reinhard M. Noack

Simon's hidden subgroup algorithm was the first quantum algorithm to prove the superiority of quantum computing over classical computing in terms of complexity. Measurement-based quantum computing (MBQC) is a formulation of quantum computing that, while equivalent in terms of computational power, can be advantageous in experiments and in displaying the core

Francesco Micheli, Anastasios Tsiamis, John Lygeros

We present a novel approach for the control of uncertain, linear time-invariant systems, which are perturbed by potentially unbounded, additive disturbances. We propose a \emph{doubly robust} data-driven state-feedback controller to ensure reliable performance against both model mismatch and disturbance distribution uncertainty. Our controller, which leverag

Cyril Closset, Elias Furrer, Osama Khlaif

We study three-dimensional $\mathcal{N}=2$ supersymmetric Chern-Simons-matter gauge theories with a one-form symmetry in the $A$-model formalism on $\Sigma_g\times S^1$. We explicitly compute expectation values of topological line operators that implement the one-form symmetry. This allows us to compute the topologically twisted index on the closed Riemann s

Barnabás Deme

The long-term evolution of astrophysical systems is driven by a Hamiltonian that is independent of the fast angle. As this Hamiltonian may contain explicitly time-dependent parameters, the conservation of mechanical energy is not guaranteed in such systems. We derive how the semi-major axis evolves in these cases. We analyze two astrophysically interesting e

Sakir Hossain Faruque, Sharun Akter Khushbu, Sharmin Akter

A career is a crucial aspect for any person to fulfill their desires through hard work. During their studies, students cannot find the best career suggestions unless they receive meaningful guidance tailored to their skills. Therefore, we developed an AI-assisted model for early prediction to provide better career suggestions. Although the task is difficult,

Chiara Gambicchia, Aldo Pratelli

We prove the sharp quantitative isoperimetric inequality in the case of the barycentric asymmetry, for bounded sets. This generalizes the $2$-D case recently proved in~\cite{BCH}.

Kazuki Egashira, Mark Vero, Robin Staab, Jingxuan He

Quantization leverages lower-precision weights to reduce the memory usage of large language models (LLMs) and is a key technique for enabling their deployment on commodity hardware. While LLM quantization's impact on utility has been extensively explored, this work for the first time studies its adverse effects from a security perspective. We reveal that wid

Florian Lorkowski

A new measurement of inclusive jet production in deep inelastic scattering was recently published by the ZEUS Collaboration. This contribution presents a detailed discussion of the treatment of higher-order QED effects in this measurement. A comprehensive treatment of these effects is crucial for a more direct comparison between ever more precise measurement

Lukas Seidel, Julian Beier

The development of safety-critical aerospace systems is traditionally dominated by the C language. Its language characteristics make it trivial to accidentally introduce memory safety issues resulting in undefined behavior or security vulnerabilities. The Rust language aims to drastically reduce the chance of introducing bugs and consequently produces overal

Ahmad Biniaz, Jean-Lou De Carufel, Anil Maheshwari, Michiel Smid

Let $H$ be an edge-weighted graph, and let $G$ be a subgraph of $H$. We say that $G$ is an $f$-fault-tolerant $t$-spanner for $H$, if the following is true for any subset $F$ of at most $f$ edges of $G$: For any two vertices $p$ and $q$, the shortest-path distance between $p$ and $q$ in the graph $G \setminus F$ is at most $t$ times the shortest-path distanc

Jingrui Xing, Bin Wang, Mengyu Chu, Baoquan Chen

We present a grid-free fluid solver featuring a novel Gaussian representation. Drawing inspiration from the expressive capabilities of 3D Gaussian Splatting in multi-view image reconstruction, we model the continuous flow velocity as a weighted sum of multiple Gaussian functions. This representation is continuously differentiable, which enables us to derive

Areeg Fahad Rasheed, M. Zarkoosh

Within few-shot learning, in-context learning (ICL) has become a potential method for leveraging contextual information to improve model performance on small amounts of data or in resource-constrained environments where training models on large datasets is prohibitive. However, the quality of the selected sample in a few shots severely limits the usefulness

Qi Sun, Zhiyang Guo, Ziyu Wan, Jing Nathan Yan

In recent years, the increasing demand for dynamic 3D assets in design and gaming applications has given rise to powerful generative pipelines capable of synthesizing high-quality 4D objects. Previous methods generally rely on score distillation sampling (SDS) algorithm to infer the unseen views and motion of 4D objects, thus leading to unsatisfactory result

Ramana Sundararaman, Roman Klokov, Maks Ovsjanikov

Learning-based isosurface extraction methods have recently emerged as a robust and efficient alternative to axiomatic techniques. However, the vast majority of such approaches rely on supervised training with axiomatically computed ground truths, thus potentially inheriting biases and data artifacts of the corresponding axiomatic methods. Steering away from

Charalambia Varnava, Andreas Efstathiou

In this paper we present the publicly available open-source spectral energy distribution (SED) fitting code SMART (Spectral energy distributions Markov chain Analysis with Radiative Transfer models). Implementing a Bayesian Markov chain Monte Carlo (MCMC) method, SMART fits the ultraviolet to millimetre SEDs of galaxies exclusively with radiative transfer mo

Hugo Holland

Primordial black holes could constitute part or all of dark matter but they require large inhomogeneities to form in the early universe. These inhomogeneities can strongly backreact on the large scale dynamics of the universe. Stochastic inflation provides a way of studying this backreaction and getting an estimation of the abundance of primordial black hole

A. J. Macfarlane

This paper defines the set fib of fibbinary numbers and displays its structure in the form of a table of a specialised type, and in array form. It uses the Zeckendorf representation $n \in \mathbf{N}$ to define a bijection $\mathcal{Z}$ between $\mathbf{N}$ and fib. It is proved that the fibbinary array is the image under $\mathcal{Z}$ of the famous Wythoff

Antonin Joly, Nicolas Keriven

Graph coarsening aims to reduce the size of a large graph while preserving some of its key properties, which has been used in many applications to reduce computational load and memory footprint. For instance, in graph machine learning, training Graph Neural Networks (GNNs) on coarsened graphs leads to drastic savings in time and memory. However, GNNs rely on

Euclid Collaboration, M. Selwood, S. Fotopoulou, M. N. Bremer

We forecast the expected population of active galactic nuclei (AGN) observable in the Euclid Wide Survey (EWS) and Euclid Deep Survey (EDS). Starting from an X-ray luminosity function (XLF) we generate volume-limited samples of the AGN expected in the survey footprints. Each AGN is assigned an SED appropriate for its X-ray luminosity and redshift, with pertu

Michael Gjertsen, Franz Luef

We introduce an equivalence relation on the set of lattices in $\mathbb{R}^{2d}$ such that equivalent lattices support identical structures of Gabor systems, up to unitary equivalence, a notion we define. These equivalence classes are parameterized by symplectic forms on $\mathbb{R}^{2d}$ and they consist of lattices related by symplectic transformations. Th

Huiling Zhou, Xianhao Wu, Hongming Chen

Despite the superiority of convolutional neural networks (CNNs) and Transformers in single-image rain removal, current multi-scale models still face significant challenges due to their reliance on single-scale feature pyramid patterns. In this paper, we propose an effective rain removal method, the dual-path multi-scale Transformer (DPMformer) for high-quali

Martin Balla, George E. M. Long, James Goodman, Raluca D. Gaina

Modern Tabletop Games present various interesting challenges for Multi-agent Reinforcement Learning. In this paper, we introduce PyTAG, a new framework that supports interacting with a large collection of games implemented in the Tabletop Games framework. In this work we highlight the challenges tabletop games provide, from a game-playing agent perspective,

Andrea Causin, Gian Pietro Pirola

Every compact Riemann surface $X$ admits a natural projective structure $p_u$ as a consequence of the uniformization theorem. In this work we describe the construction of another natural projective structure on $X$, namely the Hodge projective structure $p_h$, related to the second fundamental form of the period map. We then describe how projective structure

Ashish Gupta, Sugata Mandal

In \cite{GQ2008} R. Gow and R. Quinlan have cast a new look on the endomorphism algebra of a $K$-vector space $V$ of dimension $n$ assuming that $K$ has a Galois extension $L$ of degree $n$. In this approach the $K$-space $L$ may serve as a model for $V$ and Galois-theoretic ideas and results may be applied to elucidate the structure of endomorphisms and oth

Zoe Hackshaw, Keith Hawkins, Carrie Filion, Danny Horta

Chemical cartography of the Galactic disk provides insights to its structure and assembly history over cosmic time. In this work, we use chemical cartography to explore chemical gradients and azimuthal substructure in the Milky Way disk with giant stars from APOGEE DR17. We confirm the existence of a radial metallicity gradient in the disk of $\Delta$[Fe/H]/

Wei Cheng, Hongrui Ye, Xiao Wen, Jiachen Zhang

Deep learning has significantly improved the accuracy of crop classification using multispectral temporal data. However, these models have complex structures with numerous parameters, requiring large amounts of data and costly training. In low-resource situations with fewer labeled samples, deep learning models perform poorly due to insufficient data. Conver

Pavel Osinenko, Grigory Yaremenko, Georgiy Malaniya, Anton Bolychev

Reinforcement learning is commonly concerned with problems of maximizing accumulated rewards in Markov decision processes. Oftentimes, a certain goal state or a subset of the state space attain maximal reward. In such a case, the environment may be considered solved when the goal is reached. Whereas numerous techniques, learning or non-learning based, exist

Gali Eytan, Vincent Desjacques, Robin Buehler

We consider the motion of a circularly-moving perturber in a self-gravitating, collisional system with spherically symmetric density profile. We concentrate on the singular isothermal sphere which, despite its pathological features, admits a simple polarization function in linear response theory. This allows us to solve for the acoustic wake trailing the per

Nirbhay Patil, Jean-Philippe Bouchaud

Rising inequalities around the globe bring into question our economic systems and the origin of such inequalities. Here we propose a toy agent-based model where each entity is simultaneously producing and consuming indivisible goods. We find that the system exhibits a non-trivial phase transition beyond which a market clearing equilibrium exists but becomes

Gili Goldin, Nick Howell, Noam Ordan, Ella Rabinovich

We present the Knesset Corpus, a corpus of Hebrew parliamentary proceedings containing over 30 million sentences (over 384 million tokens) from all the (plenary and committee) protocols held in the Israeli parliament between 1998 and 2022. Sentences are annotated with morpho-syntactic information and are associated with detailed meta-information reflecting d

Nikol Figalová, Hans Joachim Bieg, Michael Schulz, Jürgen Pichen

Drivers' role changes with increasing automation from the primary driver to a system supervisor. This study investigates how supervising an SAE L2 and L3 automated vehicle (AV) affects drivers' mental workload and sleepiness compared to manual driving. Using an AV prototype on a test track, the oscillatory brain activity of 23 adult participants was recorded

Hongda Sun, Hongzhan Lin, Haiyu Yan, Yang Song

Online recruitment platforms have reshaped job-seeking and recruiting processes, driving increased demand for applications that enhance person-job matching. Traditional methods generally rely on analyzing textual data from resumes and job descriptions, limiting the dynamic, interactive aspects crucial to effective recruitment. Recent advances in Large Langua

Sen Zhao, Keqi Wang, Kaibo Xie, Chenxi Fu

The 4H-SiC material exhibits good detection performance, but there are still many problems like signal distortion and poor signal quality. The 4H-SiC low gain avalanche detector (LGAD) has been fabricated for the first time to solve these problems, which named SICAR (SIlicon CARbide). The results of electrical characteristics and charge collection performanc

Junda Zhu, Lingyong Yan, Haibo Shi, Dawei Yin

Large language models (LLMs) are proven to benefit a lot from retrieval-augmented generation (RAG) in alleviating hallucinations confronted with knowledge-intensive questions. RAG adopts information retrieval techniques to inject external knowledge from semantic-relevant documents as input contexts. However, since today's Internet is flooded with numerous no

Xinran Li, Zifan Liu, Shibo Chen, Jun Zhang

In multi-agent reinforcement learning (MARL), effective exploration is critical, especially in sparse reward environments. Although introducing global intrinsic rewards can foster exploration in such settings, it often complicates credit assignment among agents. To address this difficulty, we propose Individual Contributions as intrinsic Exploration Scaffold

Romaric Journet, Félix Faisant, Sanghyeop Lee, Marc Cheneau

We measure the scalar, vector and tensor components of the differential dynamic polarizability of the strontium intercombination transition at 1064 nm. We compare the experimental values with the theoretical prediction based on the most recently published spectroscopic data, and find a very good agreement. We also identify a close-to-circular `magic' polariz

Haifu Huang, Jorge Perez, Nicolas Alpy, Marc Medale

In order to enhance safety, nuclear reactors in the design phase consider natural circulation as a mean to remove residual power. The simulation of this passive mechanism must be qualified between the validation range and the scope of utilization (reactor case), introducing potential physical and numerical distortion effects. In this study, we simulate the f

Davide Tomasella, Santiago Tarrago Velez, Sissel Bay Nielsen, Joost Van der Heijden

Brillouin-based optomechanical systems with high-frequency acoustic modes provide a promising platform for implementing quantum-information processing and wavelength conversion applications, and for probing macroscopic quantum effects. Achieving strong coupling through electrostrictive Brillouin interaction is essential for coupling the massive mechanical mo

Kai Chen, Ye Wang, Yitong Li, Aiping Li

Temporal knowledge graph (TKG) reasoning has two settings: interpolation reasoning and extrapolation reasoning. Both of them draw plenty of research interest and have great significance. Methods of the former de-emphasize the temporal correlations among facts sequences, while methods of the latter require strict chronological order of knowledge and ignore in

Zsolt I. Tabi, Bence Bakó, Dániel T. R. Nagy, Péter Vaderna

This paper presents a comprehensive study on the possible hybrid quantum-classical autoencoder architectures for end-to-end radio communication against noisy channel conditions using standard encoded radio signals. The hybrid scenarios include single-sided, i.e., quantum encoder (transmitter) or quantum decoder (receiver), as well as fully quantum channel au

Tingting He, Lin Si

The goal of this paper is to study convex lattice sets by the discrete Legendre transform. The definition of the polar of convex lattice sets in $\mathbb{Z}^n$ is provided. It is worth mentioning that the polar of convex lattice sets have the self-dual property similar to that of convex bodies. Some properties of convex lattice sets are established, for inst

Timo Velten

Let $G$ be a finite, non-abelian group of the form $G = A N$, where $A \leq G$ is abelian, and $N \trianglelefteq G$ is cyclic. We prove that the commuting graph $\Gamma(G)$ of $G$ is either a connected graph of diameter at most four, or the disjoint union of $|G'| + 1$ complete graphs. These results apply to all finite metacyclic groups, and to groups of sq

Julian Chingoma, Ulle Endriss, Ronald de Haan, Adrian Haret

Apportionment is the task of assigning resources to entities with different entitlements in a fair manner, and specifically a manner that is as proportional as possible. The best-known application is the assignment of parliamentary seats to political parties based on their share in the popular vote. Here we enrich the standard model of apportionment by assoc

S. Van Thurenhout

We give an overview of recent developments in the computation of the anomalous dimension matrix of composite operators in non-forward kinematics. The elements of this matrix determine the scale dependence of non-perturbative parton distributions, such as GPDs, and hence constitute important input for phenomenological studies of exclusive processes like deepl

Onno Eberhard, Claire Vernade, Michael Muehlebach

Reinforcement learning has traditionally focused on learning state-dependent policies to solve optimal control problems in a closed-loop fashion. In this work, we introduce the paradigm of open-loop reinforcement learning where a fixed action sequence is learned instead. We present three new algorithms: one robust model-based method and two sample-efficient

Nikol Figalová, Jürgen Pichen, Vanchha Chandrayan, Olga Pollatos

Manipulating MW in driving simulator studies without the need to introduce a non-driving-related task remains challenging. This study aims to empirically evaluate the modified speed regulation n-back task, a tool to manipulate drivers' MW. Our experiment involved 23 participants who experienced a 0-back and 2-back driving condition, with task-irrelevant nove

Martin Burger, Matthias J. Ehrhardt, Lorenz Kuger, Lukas Weigand

We analyze a recently proposed class of algorithms for the problem of sampling from probability distributions $\mu^\ast$ in $\mathbb{R}^d$ with a Lebesgue density of the form $\mu^\ast(x) \propto \exp(-f(Kx)-g(x))$, where $K$ is a linear operator and $f,g$ convex and non-smooth. The method is a generalization of the primal-dual hybrid gradient optimization a

Mohammad Javad Vasli, Komeil Babaei Velni, M. Reza Mohammadi Mozaffar, Ali Mollabashi

We study the static entanglement structure in (1+1)-dimensional free Dirac-fermion theory with Lifshitz symmetry and arbitrary integer dynamical critical exponent. This model is different from the one introduced in [Hartmann et al., SciPost Phys. 11, no.2, 031 (2021)] due to a proper treatment of the square Laplace operator. Dirac fermion Lifshitz theory is

Tom Fournier, Kelvin Cruz, Marc Monthioux, Benjamin Lassagne

Creating defects in graphene by hydrogenation, either to achieve hydrogen chemisorption or partial etching, is a way to open an electronic band gap in graphene. Understanding the range of stability conditions of partially etched or hydrogenated graphene is crucial for application, as processing conditions (e.g. temperature) and quality control (characterizat

David W. Hogg, Soledad Villar

Machine learning (ML) methods are having a huge impact across all of the sciences. However, ML has a strong ontology - in which only the data exist - and a strong epistemology - in which a model is considered good if it performs well on held-out training data. These philosophies are in strong conflict with both standard practices and key philosophies in the

Mi-Song Dupuy, Virginie Ehrlacher, Clément Guillot

We prove in this paper that the solution of the time-dependent Schr{\"o}dinger equation can be expressed as the solution of a global space-time quadratic minimization problem that is amenable to Galerkin time-space discretization schemes, using an appropriate least-square formulation. The present analysis can be applied to the electronic many-body time-depen

Jinkyu Yim, Jaeyong Song, Yerim Choi, Jaebeen Lee

Training large language models (LLMs) is known to be challenging because of the huge computational and memory capacity requirements. To address these issues, it is common to use a cluster of GPUs with 3D parallelism, which splits a model along the data batch, pipeline stage, and intra-layer tensor dimensions. However, the use of 3D parallelism produces the a

Yuchen Xia, Daniel Dittler, Nasser Jazdi, Haonan Chen

This paper presents a novel design of a multi-agent system framework that applies large language models (LLMs) to automate the parametrization of simulation models in digital twins. This framework features specialized LLM agents tasked with observing, reasoning, decision-making, and summarizing, enabling them to dynamically interact with digital twin simulat

Henry W J Reeve

We consider a semi-supervised classification problem with non-stationary label-shift in which we observe a labelled data set followed by a sequence of unlabelled covariate vectors in which the marginal probabilities of the class labels may change over time. Our objective is to predict the corresponding class-label for each covariate vector, without ever obse

Emil Génetay Johansen

A planar superfluid is considered and interpreted in terms of electromagnetism and gravity. It has previously been suggested that the superfluid flow can be regarded as analogous to an electromagnetic field and that a non-vanishing density gradient give rise to a gravity-like force. The present work seeks to reconcile these hitherto distinct pictures into a

Felix Nickel, André Kubetzka, Mara Gutzeit, Roland Wiesendanger

Topological orbital moments can arise in non-coplanar spin structures even in the absence of spin-orbit coupling and a net topological orbital magnetization occurs for the triple-Q state and for isolated skyrmions. For atomic-scale skyrmion lattices, a significant effect can also be expected, however, no studies have been reported yet. Here, we observe via s

Lea Bogensperger, Dominik Narnhofer, Alexander Falk, Konrad Schindler

Medical image segmentation plays an important role in accurately identifying and isolating regions of interest within medical images. Generative approaches are particularly effective in modeling the statistical properties of segmentation masks that are closely related to the respective structures. In this work we introduce FlowSDF, an image-guided conditiona

Benjamin Magnelli, Sylvia Adscheid, Tsan-Ming Wang, Laure Ciesla

[Abridged] In recent years, conflicting results have provided an uncertain view of the dust-attenuated properties of $z>4$ star-forming galaxies (SFGs). To solve this, we used the deepest data publicly available in COSMOS to build a mass-complete ($>10^{9.5}\,M_{\odot}$) sample of SFGs at $4<z<5$ and measured their dust-attenuated properties by stacking all

Michał Czuba, Mateusz Nurek, Damian Serwata, Yu-Xuan Qiu

With the advancement of computational network science, its research scope has significantly expanded beyond static graphs to encompass more complex structures. The introduction of streaming, temporal, multilayer, and hypernetwork approaches has brought new possibilities and imposed additional requirements. For instance, by utilising these advancements, one c

Sebastien Origer, Dario Izzo

Inspired by the versatility of sinusoidal representation networks (SIRENs), we present a modified Guidance & Control Networks (G&CNETs) variant using periodic activation functions in the hidden layers. We demonstrate that the resulting G&CNETs train faster and achieve a lower overall training error on three different control scenarios on which G&CNETs have b

Bing Gao, Rui Gao

In this article, we show that for a typical non-uniformly expanding unimodal map, the unique maximizing measure of a generic Lipschitz function is supported on a periodic orbit.

Ngoc Do, Markus Haltmeier, Richard Kowar, Linh V. Nguyen

Standard photoacoustic tomography (PAT) provides data that consist of time-dependent signals governed by the wave equation, which are measured on an observation surface. In contrast, the measured data from the recently invented full-field PAT is the Radon transform of the solution of the wave equation on a spatial domain at a single instant in time. While re

Rishabh Dudeja, Songbin Liu, Junjie Ma

We study the problem of estimating a rank one signal matrix from an observed matrix generated by corrupting the signal with additive rotationally invariant noise. We develop a new class of approximate message-passing algorithms for this problem and provide a simple and concise characterization of their dynamics in the high-dimensional limit. At each iteratio

Shengchao Hu, Ziqing Fan, Li Shen, Ya Zhang

The purpose of offline multi-task reinforcement learning (MTRL) is to develop a unified policy applicable to diverse tasks without the need for online environmental interaction. Recent advancements approach this through sequence modeling, leveraging the Transformer architecture's scalability and the benefits of parameter sharing to exploit task similarities.

Pedro Freitas, Miguel Gama

We provide an answer to a question raised by Levine and Weinberger in their $1986$ paper concerning the difference between Dirichlet and Neumann eigenvalues of the Laplacian on bounded domains in $\mathbb{R}^{n}$. More precisely, we show that for a certain class of domains there exists a sequence $p(k)$ such that $\lambda_{k}\geq \mu_{k+ p(k)}$ for sufficien

Lianlei Shan, Weiqiang Wang, Ke Lv, Bin Luo

Semantic segmentation requires pixel-level annotation, which is time-consuming. Active Learning (AL) is a promising method for reducing data annotation costs. Due to the gap between aerial and natural images, the previous AL methods are not ideal, mainly caused by unreasonable labeling units and the neglect of class imbalance. Previous labeling units are bas

Daniel Vranješ, Oliver Niggemann

Empirical research plays a fundamental role in the machine learning domain. At the heart of impactful empirical research lies the development of clear research hypotheses, which then shape the design of experiments. The execution of experiments must be carried out with precision to ensure reliable results, followed by statistical analysis to interpret these

Oscar Trull, Angel Peiro-Signes, J. Carlos Garcia-Diaz, Marival Segarra-Ona

The increase in travelers and stays in tourist destinations is leading hotels to be aware of their ecological management and the need for efficient energy consumption. To achieve this, hotels are increasingly using digitalized systems and more frequent measurements are made of the variables that affect their management. Electricity can play a significant rol

Nataša Tagasovska, Vladimir Gligorijević, Kyunghyun Cho, Andreas Loukas

Across scientific domains, generating new models or optimizing existing ones while meeting specific criteria is crucial. Traditional machine learning frameworks for guided design use a generative model and a surrogate model (discriminator), requiring large datasets. However, real-world scientific applications often have limited data and complex landscapes, m

Vighnesh V. Alavani, P. Danumjaya, M. Thamban Nair

Recently, Nair and Danumjaya (2023) introduced a new regularization method for the homogeneous time-fractional backward heat conduction problem (TFBHCP) in a one-dimensional space variable, for determining the initial value function. In this paper, the authors extend the analysis done in the above referred paper to a more general setting of an inhomogeneous

Elfia Bezou-Vrakatseli, Oana Cocarascu, Sanjay Modgil

We argue that enabling human-AI dialogue, purposed to support joint reasoning (i.e., 'inquiry'), is important for ensuring that AI decision making is aligned with human values and preferences. In particular, we point to logic-based models of argumentation and dialogue, and suggest that the traditional focus on persuasion dialogues be replaced by a focus on i