May 2023 arXiv papers — page 15

Hiroya Sato, Takuya Ikeda, Koichi Nishiwaki

In recent years, a deep learning framework has been widely used for object pose estimation. While quaternion is a common choice for rotation representation, it cannot represent the ambiguity of the observation. In order to handle the ambiguity, the Bingham distribution is one promising solution. However, it requires complicated calculation when yielding the

Youssef Jeyar, Kevin Austry, Minggang Luo, Brahim Guizal

We study the non equilibrium Casimir-Lifshitz force between graphene-based parallel structures held at different temperatures and in presence of an external thermal bath at a third temperature. The graphene conductivity, which is itself a function of temperature, as well as of chemical potential, allows us to tune in situ the Casimir-Lifshitz force. We explo

Dan Ghica, Fabio Zanasi

This tutorial gives an advanced introduction to string diagrams and graph languages for higher-order computation. The subject matter develops in a principled way, starting from the two dimensional syntax of key categorical concepts such as functors, adjunctions, and strictification, and leading up to Cartesian Closed Categories, the core mathematical model o

Kirill A. Bronnikov, Manuel E. Rodrigues, Marcos V. de S. Silva

We apply the Simpson-Visser phenomenological regularization method to a cylindrically symmetric solution of the Einstein-Maxwell equations known as an inverted black hole. In addition to analyzing some properties of thus regularized space-time, including the Carter-Penrose diagrams, we show that this solution can be obtained from the Einstein equations with

Yoeri Poels, Gijs Derks, Egbert Westerhof, Koen Minartz

Managing divertor plasmas is crucial for operating reactor scale tokamak devices due to heat and particle flux constraints on the divertor target. Simulation is an important tool to understand and control these plasmas, however, for real-time applications or exhaustive parameter scans only simple approximations are currently fast enough. We address this lack

Vagelis Plevris, George Papazafeiropoulos, Alejandro Jiménez Rios

A comparison between three chatbots which are based on large language models, namely ChatGPT-3.5, ChatGPT-4 and Google Bard is presented, focusing on their ability to give correct answers to mathematics and logic problems. In particular, we check their ability to Understand the problem at hand; Apply appropriate algorithms or methods for its solution; and Ge

R. A. W. Bradford

It is well known that there is an integral theorem for quaternion-valued functions analogous to Cauchys Theorem for complex-valued functions, namely Fueters Theorem. The class of quaternionic functions for which this applies are generally referred to as regular functions, and these provide the most productive means of generalising the class of holomorphic co

Martin Stoll, Markus Mazzola, Maxim Dolgov, Jürgen Mathes

We challenge the perceived consensus that the application of deep learning to solve the automated driving planning task necessarily requires huge amounts of real-world data or highly realistic simulation. Focusing on a roundabout scenario, we show that this requirement can be relaxed in favour of targeted, simplistic simulated data. A benefit is that such da

Louis Abraham

In this study, we present models where participants strategically select their risk levels and earn corresponding rewards, mirroring real-world competition across various sectors. Our analysis starts with a normal form game involving two players in a continuous action space, confirming the existence and uniqueness of a Nash equilibrium and providing an analy

M. Gangi, B. Nisini, C. F. Manara, K. France

Observing the spatial distribution and excitation processes of atomic and molecular gas in the inner regions (< 20 au) of young (< 10 Myr) protoplanetary disks helps us to understand the conditions for the formation and evolution of planetary systems. In the framework of the PENELLOPE and ULLYSES projects, we aim to characterize the atomic and molecular comp

Regina Stodden, Omar Momen, Laura Kallmeyer

Text simplification is an intralingual translation task in which documents, or sentences of a complex source text are simplified for a target audience. The success of automatic text simplification systems is highly dependent on the quality of parallel data used for training and evaluation. To advance sentence simplification and document simplification in Ger

D. D. Huff, L. Campestrini, G. R. Gonçalves da Silva, A. S. Bazanella

This paper extends the formulation of a data-driven control method - the Optimal Controller Identification (OCI) - to cope with non-minimum phase (NMP) systems, without a priori knowledge of the NMP transmission zero, i.e. without obtaining a prior model of the plant - as in any data-driven approach. The method is based on the Model Reference paradigm, in wh

Mohammed Alharthi, Sanaa H. Mohamed, Taisir E. H. El-Gorashi, Jaafar M. H. Elmirghani

Data centers based on Passive Optical Networks (PONs) can provide high capacity, low cost, scalability, elasticity and high energy-efficiency. This paper introduces the use of WDM-TDM multiple access in a PON-based data center that offers multipath routing via two-tier cascaded Arrayed Waveguide Grating Routers (AWGRs) to improve the utilization of resources

Bireswar Das, Jinia Ghosh, Anant Kumar

The isomorphism problem for graphs (GI) and the isomorphism problem for groups (GrISO) have been studied extensively by researchers. The current best algorithms for both these problems run in quasipolynomial time. In this paper, we study the isomorphism problem of graphs that are defined in terms of groups, namely power graphs, directed power graphs, and enh

Killian Long, Asaf Pe'er

We solve the full quasilinear kinetic equation governing nonresonant interactions of Alfv\'en waves with relativistic plasmas. This work was motivated by the need to determine the energy available for the synchrotron maser in the context of Fast Radio Bursts (FRBs). This interaction can result in plasma heating and the formation of population inversions nece

Tomoya Hayata, Katsumasa Nakayama, Arata Yamamoto

We discuss the chiral fermion in the Hamiltonian formalism of lattice gauge theory. Although the naive chiral charge operator does not commute with the Hamiltonian, the commutable one can be defined for the overlap fermion. The eigenvalues of the energy and the chiral charge can be defined simultaneously. We study how the eigenvalue spectrum reflects chiral

Akshay Srinivasan, Sowmya Vajjala

Adversarial evaluations of language models typically focus on English alone. In this paper, we performed a multilingual evaluation of Named Entity Recognition (NER) in terms of its robustness to small perturbations in the input. Our results showed the NER models we explored across three languages (English, German and Hindi) are not very robust to such change

R. Garcia, D. Tejada

The description of principal lines of the ellipsoid on the 3-dimensional Minkowski space is established. A global principal parametrization of a triple orthogonal system of quadrics is also achieved, and the focal set of the ellipsoid is sketched.

Yuwen Li, Miao Xiong, Bryan Hooi

Label errors have been found to be prevalent in popular text, vision, and audio datasets, which heavily influence the safe development and evaluation of machine learning algorithms. Despite increasing efforts towards improving the quality of generic data types, such as images and texts, the problem of mislabel detection in graph data remains underexplored. T

Maik Fröbe, Jan Heinrich Reimer, Sean MacAvaney, Niklas Deckers

We integrate ir_datasets, ir_measures, and PyTerrier with TIRA in the Information Retrieval Experiment Platform (TIREx) to promote more standardized, reproducible, scalable, and even blinded retrieval experiments. Standardization is achieved when a retrieval approach implements PyTerrier's interfaces and the input and output of an experiment are compatible w

Nicolas Lanchon, Daniel Odens Mora, Eduardo Monsalve, Pierre-Philippe Cortet

We present laboratory experiments on turbulence in a linearly stratified fluid driven by an ensemble of internal gravity waves which approaches statistical homogeneity and axi-symmetry. In a way similar to several recent experimental works, non-linearities develop through the establishment of a set of internal wave modes at discrete frequencies, when the for

Sarit Khirirat, Eduard Gorbunov, Samuel Horváth, Rustem Islamov

Motivated by the increasing popularity and importance of large-scale training under differential privacy (DP) constraints, we study distributed gradient methods with gradient clipping, i.e., clipping applied to the gradients computed from local information at the nodes. While gradient clipping is an essential tool for injecting formal DP guarantees into grad

Mateusz Zych, Vasileios Mavroeidis, Konstantinos Fysarakis, Manos Athanatos

As cyber systems become increasingly complex and cybersecurity threats become more prominent, defenders must prepare, coordinate, automate, document, and share their response methodologies to the extent possible. The CACAO standard was developed to satisfy the above requirements, providing a common machine-readable framework and schema for documenting cybers

Muhammad Danyal Malik, Danish Humair

In this paper, we explore the feasibility of using generative models, specifically Progressive Growing GANs (PG-GANs) and Stable Diffusion fine-tuning, to generate synthetic chest X-ray images for medical diagnosis purposes. Due to ethical concerns, obtaining sufficient medical data for machine learning is a challenge, which our approach aims to address by s

Xinyu Wang, Lin Gui, Yulan He

Document-level multi-event extraction aims to extract the structural information from a given document automatically. Most recent approaches usually involve two steps: (1) modeling entity interactions; (2) decoding entity interactions into events. However, such approaches ignore a global view of inter-dependency of multiple events. Moreover, an event is deco

Suzy J. Styles, Victoria Y. H. Chua, Fei Ting Woon, Hexin Liu

Language development experts need tools that can automatically identify languages from fluent, conversational speech, and provide reliable estimates of usage rates at the level of an individual recording. However, language identification systems are typically evaluated on metrics such as equal error rate and balanced accuracy, applied at the level of an enti

Luis García-Terriza, José L. Risco-Martín, Gemma Reig Roselló, José L. Ayala

This work presents a novel and promising approach to the clinical management of acute stroke. Using machine learning techniques, our research has succeeded in developing accurate diagnosis and prediction real-time models from hemodynamic data. These models are able to diagnose stroke subtype with 30 minutes of monitoring, to predict the exitus during the fir

Peter Baumgartner, Elena Tartaglia

This paper introduces the Fusemate probabilistic logic programming system. Fusemate's inference engine comprises a grounding component and a variable elimination method for probabilistic inference. Fusemate differs from most other systems by grounding the program in a bottom-up way instead of the common top-down way. While bottom-up grounding is attractive f

Ryan A. Parker, Jesús Arjona Martínez, Kevin C. Chen, Alexander M. Stramma

A contemporary challenge for the scalability of quantum networks is developing quantum nodes with simultaneous high photonic efficiency and long-lived qubits. Here, we present a fibre-packaged nanophotonic diamond waveguide hosting a tin-vacancy centre with a spin-1/2 $^{117}$Sn nucleus. The interaction between the electronic and nuclear spins results in a s

Anne Rodriguez, Konstantinos Papatryfonos, Edson Rafael Cardozo de Oliveira, Norberto Daniel Lanzillotti-Kimura

Topological interface states in periodic lattices have emerged as valuable assets in the fields of electronics, photonics, and phononics, owing to their inherent robustness against disorder. Unlike electronics and photonics, the linear dispersion relation of hypersound offers an ideal framework for investigating higher-order bandgaps. In this work, we propos

Guopeng Li, Yiru Jiao, Victor L. Knoop, Simeon C. Calvert

Car-Following (CF), as a fundamental driving behaviour, has significant influences on the safety and efficiency of traffic flow. Investigating how human drivers react differently when following autonomous vs. human-driven vehicles (HV) is thus critical for mixed traffic flow. Research in this field can be expedited with trajectory datasets collected by Auton

Gitae Kim, Ji-seong Chae, WooCheol Shin, Jae-Hyuk Oh

We explore the mathematical relationship between holographic Wilsonian renormalization group(HWRG) and stochastic quantization(SQ) of scalar field theory with its generic mass, self-interaction and $n$-multiple-trace deformation on the $d$-dimensional conformal boundary defined in AdS$_{d+1}$ spacetime. We understand that once we define our Euclidean action,

Francesca Pelusi, Fabio Guglietta, Marcello Sega, Othmane Aouane

The wetting dynamics of liquid particles, from coated droplets to soft capsules, holds significant technological interest. Motivated by the need to simulate liquid metal droplet with an oxidize surface layer, in this work we introduce a computational scheme that allows to simulate droplet dynamics with general surface properties and model different levels of

Tim Kokkeler, Alexander Golubov, Sebastián Bergeret, Yukio Tanaka

In non-centrosymmetric superconductors the pair potential has both even-parity singlet and odd-parity triplet components. If time-reversal symmetry is broken, the superconducting phase of these components is not the same, for example in anapole superconductors. In this paper it is shown that breaking time-reversal symmetry by a phase difference between the t

Yuval Reif, Roy Schwartz

NLP models often rely on superficial cues known as dataset biases to achieve impressive performance, and can fail on examples where these biases do not hold. Recent work sought to develop robust, unbiased models by filtering biased examples from training sets. In this work, we argue that such filtering can obscure the true capabilities of models to overcome

Ran Spiegler

When inferring the causal effect of one variable on another from correlational data, a common practice by professional researchers as well as lay decision makers is to control for some set of exogenous confounding variables. Choosing an inappropriate set of control variables can lead to erroneous causal inferences. This paper presents a model of lay decision

Jakob Prange, Emmanuele Chersoni

In this work we build upon negative results from an attempt at language modeling with predicted semantic structure, in order to establish empirical lower bounds on what could have made the attempt successful. More specifically, we design a concise binary vector representation of semantic structure at the lexical level and evaluate in-depth how good an increm

Tomohiro Tanogami, Tan Van Vu, Keiji Saito

We investigate fundamental limits on the performance of information processing systems from the perspective of information thermodynamics. We first extend the thermodynamic uncertainty relation (TUR) to a subsystem. Specifically, for a bipartite composite system consisting of a system of interest X and an auxiliary system Y, we show that the relative fluctua

Étienne Fournier, Dorilys Kilgus, Aurélie Landry, Belal Hmedan

Since cobots (collaborative robots) are increasingly being introduced in industrial environments, being aware of their potential positive and negative impacts on human collaborators is essential. This study guides occupational health workers by identifying the potential gains (reduced perceived time demand, number of gestures and number of errors) and concer

V. M. Litvyak, R. V. Cherbunin, V. K. Kalevich, K. V. Kavokin

At low lattice temperatures the nuclear spins in a solid form a closed thermodynamic system that is well isolated from the lattice. Thermodynamic properties of the nuclear spin system are characterized by the local field of spin-spin interactions, which determines its heat capacity and the minimal achievable nuclear spin temperature in demagnetization experi

Madhukrishna Chakraborty, Subenoy Chakraborty

The present work deals with the classical and quantum aspects of the Raychaudhuri equation in the framework of f(T)-gravity theory. In the background of homogeneous and isotropic Friedmann Lemaitre Robertson Walker space time, the Raychaudhuri equation has been formulated and used to examine the focusing theorem and convergence condition for different choice

Alexandre Verine, Benjamin Negrevergne, Muni Sreenivas Pydi, Yann Chevaleyre

Achieving a balance between image quality (precision) and diversity (recall) is a significant challenge in the domain of generative models. Current state-of-the-art models primarily rely on optimizing heuristics, such as the Fr\'echet Inception Distance. While recent developments have introduced principled methods for evaluating precision and recall, they ha

Assumpta Ezugwu, Elochukwu Ukwandu, Celestine Ugwu, Modesta Ezema

Passwords are used majorly for end-user authentication in information and communication technology (ICT) systems due to its perceived ease of use. The use for end-user authentication extends through mobile, computers and network-based products and services. But with the attendant issues relating to password hacks, leakages, and theft largely due to weak, reu

David Schmitt, Jan Philipp Bange, Wiebke Bennecke, Giuseppe Meneghini

The role and impact of spatial heterogeneity in two-dimensional quantum materials represents one of the major research quests regarding the future application of these materials in optoelectronics and quantum information science. In the case of transition-metal dichalcogenide heterostructures, in particular, direct access to heterogeneities in the dark-excit

Loukas Ilias, Dimitris Askounis

Stress and depression are prevalent nowadays across people of all ages due to the quick paces of life. People use social media to express their feelings. Thus, social media constitute a valuable form of information for the early detection of stress and depression. Although many research works have been introduced targeting the early recognition of stress and

Soumyakanti Bose, Jaskaran Singh, Adán Cabello, Hyunseok Jeong

We introduce a feasible scheme to produce high-rate long-distance entanglement which uses hybrid entanglement (HE) between continuous variables (CV) and discrete variables (DV). We show that HE can effectively remove the experimental limitations of existing CV and DV systems to produce long range entanglement. We benchmark the resulting DV entangled states u

Robin Peretzke, Klaus Maier-Hein, Jonas Bohn, Yannick Kirchhoff

Accurately identifying white matter tracts in medical images is essential for various applications, including surgery planning and tract-specific analysis. Supervised machine learning models have reached state-of-the-art solving this task automatically. However, these models are primarily trained on healthy subjects and struggle with strong anatomical aberra

Daniel Echtler, Holger Kammeyer

We construct pairs of residually finite groups with isomorphic profinite completions such that one has non-vanishing and the other has vanishing real second bounded cohomology. The examples are lattices in different higher rank simple Lie groups. Using Galois cohomology, we actually show that $\operatorname{SO}^0(n,2)$ for $n \ge 6$ and the exceptional group

Mart Kartasev, Petter Ögren

Behavior trees represent a modular way to create an overall controller from a set of sub-controllers solving different sub-problems. These sub-controllers can be created in different ways, such as classical model based control or reinforcement learning (RL). If each sub-controller satisfies the preconditions of the next sub-controller, the overall controller

Daniele Oriti, Yi-Li Wang

Candidate microstates of a spherically symmetric geometry are constructed in the group field theory formalism for quantum gravity, for models including both quantum geometric and scalar matter degrees of freedom. The latter are used as a material reference frame to define the spacetime localization of the various elements of quantum geometry. By computing qu

Hanyang Zhao, Wenpin Tang, David D. Yao

We study reinforcement learning (RL) in the setting of continuous time and space, for an infinite horizon with a discounted objective and the underlying dynamics driven by a stochastic differential equation. Built upon recent advances in the continuous approach to RL, we develop a notion of occupation time (specifically for a discounted objective), and show

Ba-Hien Tran, Giulio Franzese, Pietro Michiardi, Maurizio Filippone

Generative Models (GMs) have attracted considerable attention due to their tremendous success in various domains, such as computer vision where they are capable to generate impressive realistic-looking images. Likelihood-based GMs are attractive due to the possibility to generate new data by a single model evaluation. However, they typically achieve lower sa

Valeri Vardanyan, Deaglan J. Bartlett

The non-linear dynamics of scalar fields coupled to matter and gravity can lead to remarkable density-dependent screening effects. In this short review we present the main classes of screening mechanisms, and discuss their tests in laboratory and astrophysical systems. We particularly focus on reviewing numerical and technical aspects involved in modeling th

Theodoros Kouzelis, Georgios Paraskevopoulos, Athanasios Katsamanis, Vassilis Katsouros

The study of speech disorders can benefit greatly from time-aligned data. However, audio-text mismatches in disfluent speech cause rapid performance degradation for modern speech aligners, hindering the use of automatic approaches. In this work, we propose a simple and effective modification of alignment graph construction of CTC-based models using Weighted

Chuhao Jin, Wenhui Tan, Jiange Yang, Bei Liu

We propose a novel framework for learning high-level cognitive capabilities in robot manipulation tasks, such as making a smiley face using building blocks. These tasks often involve complex multi-step reasoning, presenting significant challenges due to the limited paired data connecting human instructions (e.g., making a smiley face) and robot actions (e.g.

Lucas Mourot, Ludovic Hoyet, François Le Clerc, Pierre Hellier

Motion retargeting is the long-standing problem in character animation that consists in transferring and adapting the motion of a source character to another target character. A typical application is the creation of motion sequences from off-the-shelf motions by transferring them onto new characters. Motion retargeting is also promising to increase interope

Junwon Seo, Sungdae Sim, Inwook Shim

Estimating the traversability of terrain should be reliable and accurate in diverse conditions for autonomous driving in off-road environments. However, learning-based approaches often yield unreliable results when confronted with unfamiliar contexts, and it is challenging to obtain manual annotations frequently for new circumstances. In this paper, we intro

Øystein Håvard Færder, Daniel Nóbrega-Siverio, Mats Carlsson

Magnetic reconnection is a fundamental mechanism in astrophysics. A common challenge in mimicking this process numerically in particular for the Sun is that the solar electrical resistivity is small compared to the diffusive effects caused by the discrete nature of codes. We aim to study different anomalous resistivity models and their respective effects on

J. Ortega, C. L. Folcia, J. Etxebarria

We have developed a numerical method for calculating the second harmonic generation (SHG) generated by an anisotropic material whose optical properties present an arbitrary modulation in one dimension. The method is based on the Berreman 4x4 matrix formalism, which is generalized to include nonlinear optical phenomena. It can be used under oblique incidences

Benjamin Minixhofer, Jonas Pfeiffer, Ivan Vulić

Many NLP pipelines split text into sentences as one of the crucial preprocessing steps. Prior sentence segmentation tools either rely on punctuation or require a considerable amount of sentence-segmented training data: both central assumptions might fail when porting sentence segmenters to diverse languages on a massive scale. In this work, we thus introduce

Emilien Bodiot

We study invariant boundary conditions for one dimensional discrete Gaussian Markov processes, basic toy models of spatial Markov processes in statistical mechanics. More precisely, we give a decomposition of boundary objects in a non trivial basis from the study of a meromorphic matrix-valued function $\Phi$ (inherent to the model) and its singularities. Th

Xingqun Qi, Chen Liu, Lincheng Li, Jie Hou

Generating vivid and diverse 3D co-speech gestures is crucial for various applications in animating virtual avatars. While most existing methods can generate gestures from audio directly, they usually overlook that emotion is one of the key factors of authentic co-speech gesture generation. In this work, we propose EmotionGesture, a novel framework for synth

Roland S. Zimmermann, Sjoerd van Steenkiste, Mehdi S. M. Sajjadi, Thomas Kipf

Self-supervised methods for learning object-centric representations have recently been applied successfully to various datasets. This progress is largely fueled by slot-based methods, whose ability to cluster visual scenes into meaningful objects holds great promise for compositional generalization and downstream learning. In these methods, the number of slo

Ayan Shymyrbay, Mohammed E. Fouda, Ahmed Eltawil

Deep neural networks have been proven to be highly effective tools in various domains, yet their computational and memory costs restrict them from being widely deployed on portable devices. The recent rapid increase of edge computing devices has led to an active search for techniques to address the above-mentioned limitations of machine learning frameworks.

Camila Kolling, Till Speicher, Vedant Nanda, Mariya Toneva

Machine learning (ML) algorithms can often exhibit discriminatory behavior, negatively affecting certain populations across protected groups. To address this, numerous debiasing methods, and consequently evaluation measures, have been proposed. Current evaluation measures for debiasing methods suffer from two main limitations: (1) they primarily provide a gl

Songge Zhang, Wen Wu, Penghui Hu, Shaofeng Li

In this paper, we propose a novel distributed learning scheme, named group-based split federated learning (GSFL), to speed up artificial intelligence (AI) model training. Specifically, the GSFL operates in a split-then-federated manner, which consists of three steps: 1) Model distribution, in which the access point (AP) splits the AI models and distributes t

Zhiyu Liang, Jianfeng Zhang, Chen Liang, Hongzhi Wang

Recent studies have shown great promise in unsupervised representation learning (URL) for multivariate time series, because URL has the capability in learning generalizable representation for many downstream tasks without using inaccessible labels. However, existing approaches usually adopt the models originally designed for other domains (e.g., computer vis

Kenji Kawaguchi, Zhun Deng, Xu Ji, Jiaoyang Huang

Numerous deep learning algorithms have been inspired by and understood via the notion of information bottleneck, where unnecessary information is (often implicitly) minimized while task-relevant information is maximized. However, a rigorous argument for justifying why it is desirable to control information bottlenecks has been elusive. In this paper, we prov

Herbert Egger, Jan Giesselmann

We study a doubly nonlinear parabolic problem arising in the modeling of gas transport in pipelines. Using convexity arguments and relative entropy estimates we show uniform bounds and exponential stability of discrete approximations obtained by a finite element method and implicit time stepping. Due to convergence of the approximations to weak solutions of

Jin-Duk Park, Siqing Li, Xin Cao, Won-Yong Shin

The multi-criteria (MC) recommender system, which leverages MC rating information in a wide range of e-commerce areas, is ubiquitous nowadays. Surprisingly, although graph neural networks (GNNs) have been widely applied to develop various recommender systems due to GNN's high expressive capability in learning graph representations, it has been still unexplor

Michal Hrbek, Giovanna Le Gros

We show that the small and large restricted injective dimensions coincide for Cohen-Macaulay rings of finite Krull dimension. Based on this, and inspired by the recent work of Sather-Wagstaff and Totushek, we suggest a new definition of Cohen-Macaulay Hom injective dimension. We show that the class of Cohen-Macaulay Hom injective modules is the right constit

Jovana Nikolic, Vuk Ovaskainen, Zoran Petric

A procedure of going from Heegaard diagrams to framed link diagrams is explained in this note.

Anatol Guglielmi, Alexey Zavyalov, Oleg Zotov, Boris Klain

After the main shock of an earthquake, a stream of aftershocks that does not subside for a long time is usually observed. Fusakichi Omori found that the frequency of aftershocks decreases hyperbolically with time. It has recently been observed that Omori's law can be viewed as a solution to a differential equation describing the evolution of aftershocks. An

Rui Yang, Yong Lin, Xiaoteng Ma, Hao Hu

Offline goal-conditioned RL (GCRL) offers a way to train general-purpose agents from fully offline datasets. In addition to being conservative within the dataset, the generalization ability to achieve unseen goals is another fundamental challenge for offline GCRL. However, to the best of our knowledge, this problem has not been well studied yet. In this pape

Victoria Y. H. Chua, Hexin Liu, Leibny Paola Garcia Perera, Fei Ting Woon

To enhance the reliability and robustness of language identification (LID) and language diarization (LD) systems for heterogeneous populations and scenarios, there is a need for speech processing models to be trained on datasets that feature diverse language registers and speech patterns. We present the MERLIon CCS challenge, featuring a first-of-its-kind Zo

Lin Wu, Rui Li, Wong-Hing Lam

Classifying the same event reported by different countries is of significant importance for public opinion control and intelligence gathering. Due to the diverse types of news, relying solely on transla-tors would be costly and inefficient, while depending solely on translation systems would incur considerable performance overheads in invoking translation in

Bogdan A. Pastrav

Following from our recent work, we present a detailed analysis of star-formation and interstellar medium (ISM) scaling relations, done on a representative sample of nearby galaxies. H$\alpha$ images are analysed in order to derive the integrated galaxy luminosity, known as a more instantenous and accurate star-formation rate (SFR) tracer, and the required ph

Zhisheng Wang, Haijun Yu, Yixing Huang, Shunli Wang

Micro-computed tomography (micro-CT) is a widely used state-of-the-art instrument employed to study the morphological structures of objects in various fields. However, its small field-of-view (FOV) cannot meet the pressing demand for imaging relatively large objects at high spatial resolutions. Recently, we devised a novel scanning mode called multiple sourc

Lin Wu, Rui Li, Jiaxuan Liu, Wong-Hing Lam

With the continuous maturation and expansion of neural network technology, deep neural networks have been widely utilized as the fundamental building blocks of deep learning in a variety of applications, including speech recognition, machine translation, image processing, and the creation of recommendation systems. Therefore, many real-world complex problems

Kim Myyryläinen

We introduce a weak Gurov-Reshetnyak class and discuss its connections to a weak Muckenhoupt $A_\infty$ condition and a weak reverse H\"older inequality in the setting of metric measure spaces with a doubling measure. A John-Nirenberg type lemma is shown for the weak Gurov-Reshetnyak class which gives a specific decay estimate for the oscillation of a functi

Wojciech Górny

In this paper, we study the parabolic and elliptic problems related to the anisotropic $p$-Laplacian operator in the case when it has linear growth on some of the coordinates. In order to define properly a notion of weak solutions and prove their existence, we first construct an anisotropic analogue of Anzellotti pairings and prove a weak Gauss-Green formula

Flora Charbonnier, Bei Peng, Thomas Morstyn, Malcolm McCulloch

This paper investigates how deep multi-agent reinforcement learning can enable the scalable and privacy-preserving coordination of residential energy flexibility. The coordination of distributed resources such as electric vehicles and heating will be critical to the successful integration of large shares of renewable energy in our electricity grid and, thus,

Filip Chudy, Paweł Woźny

New geometric methods for fast evaluation of derivatives of polynomial and rational B\'{e}zier curves are proposed. They apply an algorithm for evaluating polynomial or rational B\'{e}zier curves, which was recently given by the authors. Numerical tests show that the new approach is more efficient than the methods which use the famous de Casteljau algorithm.

Gen Ye, Jun-Qian Jiang, Yun-Song Piao

Recently, the cosmological tensions, $H_0$ and $S_8$ in particular, have inspired modification of both pre- and postrecombination physics simultaneously. Early dark energy is a promising pre-recombination solution of the $H_0$ tension, known to be compatible with the cosmic microwave background (CMB). However, the compatibility of early dark energy, as well

Kenji Nakahira

The identification of environmental changes is crucial in many fields. The present research is aimed at investigating the optimal performance for detecting change points in a quantum system when its Hamiltonian suddenly changes at a specific time. Assume that the Hamiltonians before and after the change are known and that the prior probability of each prospe

A. A. Araújo Filho, H. Hassanabadi, N. Heidari, J. Kríz

This work explores various manifestations of bumblebee gravity within the metric-affine formalism. We investigate the impact of the Lorentz violation parameter, denoted as $X$, on the modification of the Hawking temperature. Our calculations reveal that as $X$ increases, the values of the Hawking temperature attenuate. To examine the behavior of massless sca

Hirotaka Araki, Takashi Hikihara

Shift manipulation of intrinsic localized mode (ILM) is numerically discussed in an ac driven Klein Gordon lattice. Before the manipulation, we introduce the 2-degree of freedom nonlinear system, which is obtained by reducing the lattice. In the reduced system, two localized modes correspond to ILMs in the original system. These two localized modes are switc

Yingcong Li, Kartik Sreenivasan, Angeliki Giannou, Dimitris Papailiopoulos

Chain-of-thought (CoT) is a method that enables language models to handle complex reasoning tasks by decomposing them into simpler steps. Despite its success, the underlying mechanics of CoT are not yet fully understood. In an attempt to shed light on this, our study investigates the impact of CoT on the ability of transformers to in-context learn a simple t

Mehdi Belraouti, Mohamed Deffaf, Yazid Raffed, Abdelghani Zeghib

We study compact connected pseudo-Riemannian manifolds $(M,g)$ on which the conformal group $\operatorname{Conf}(M,g)$ acts essentially and transitively. We prove, in particular, that if the non-compact semi-simple part of $\operatorname{Conf}(M,g)$ is the M{\"o}bius group, then $(M,g)$ is conformally flat.

Espen Robstad Jakobsen, Artur Rutkowski

We prove existence and uniqueness of classical solutions of the master equation for mean field game (MFG) systems with fractional and nonlocal diffusions. We cover a large class of L\'evy diffusions of order greater than one, including purely nonlocal, local, and even mixed local-nonlocal operators. In the process we prove refined well-posedness results for

Mathias Weller

The Neighbor Joining Algorithm is among the most fundamental algorithmic results in computational biology. However, its definition and correctness proof are not straightforward. In particular, ''the question ''what does the NJ method seek to do?'' has until recently proved somewhat elusive'' [Gascuel \& Steel, 2006]. While a rigorous mathematical analysis is

Yinglin Zhang, Ruiling Xi, Huazhu Fu, Dave Towey

Robust and accurate segmentation for elongated physiological structures is challenging, especially in the ambiguous region, such as the corneal endothelium microscope image with uneven illumination or the fundus image with disease interference. In this paper, we present a spatial and scale uncertainty-aware network (SSU-Net) that fully uses both spatial and

JInwuk Seok, Changsik Cho

Stochastic learning dynamics based on Langevin or Levy stochastic differential equations (SDEs) in deep neural networks control the variance of noise by varying the size of the mini-batch or directly those of injecting noise. Since the noise variance affects the approximation performance, the design of the additive noise is significant in SDE-based learning

Nicolas Balacheff

It is nowadays common to consider that proof must be part of the learning of mathematics from Kindergarten to University1. As it is easy to observe, looking back to the history of mathematical curricula, this has not always been the case either because following an old pedagogical tradition of rote learning proof was reduced to the formalism of a text and de

Majdouline Borji, Christoph Kopper

In a previous work, we established perturbative renormalizability to all orders of the massive $\phi^4_4$-theory on a half-space also called the semi-infinite massive $\phi^4_4$-theory. Five counter-terms which are functions depending on the position in the space, were needed to make the theory finite. The aim of the present paper is to prove that these coun

Ilan Reuven Cohen, Debmalya Panigrahi

Online allocation is a broad class of problems where items arriving online have to be allocated to agents who have a fixed utility/cost for each assigned item so to maximize/minimize some objective. This framework captures a broad range of fundamental problems such as the Santa Claus problem (maximizing minimum utility), Nash welfare maximization (maximizing

Jianqing Chen, Qian Zhang

In this paper, we study the following coupled Choquard system in $\mathbb R^N$: $$\left\{\begin{align}&-\Delta u+A(x)u=\frac{2p}{p+q} \bigl(I_\alpha\ast |v|^q\bigr)|u|^{p-2}u,\\ &-\Delta v+B(x)v=\frac{2q}{p+q}\bigl(I_\alpha\ast|u|^p\bigr)|v|^{q-2}v,\\ &\ u(x)\to0\ \ \hbox{and}\ \ v(x)\to0\ \ \hbox{as}\ |x|\to\infty,\end{align}\right.$$ where $\alpha\in(0,N)$

David Fiedler, Jan Mrkos

Accurately predicting the real-life performance of algorithms solving the Dial-a-Ride Problem (DARP) in the context of Mobility on Demand (MoD) systems with ridesharing requires evaluating them on representative instances. However, the benchmarking of state-of-the-art DARP solution methods has been limited to small, artificial instances or outdated non-publi

Yérali Gandica, Guillaume Deffuant

This paper studies the bounded confidence model on growing fully-mixed populations. In this model, in addition to the usual opinion clusters, significant secondary clusters of smaller size appear systematically, while those secondary clusters appear erratically and include much fewer agents when the population is fixed. Through simulations, we derive the bif

Léo Girardin

This paper is concerned with asymptotic persistence, extinction and spreading properties for non-cooperative Fisher-KPP systems with space-time periodic coefficients. Results are formulated in terms of a family of generalized principal eigenvalues associated with the linearized problem. When the maximal generalized principal eigenvalue is negative, all solut