October 2023 arXiv papers — page 61

Engin Bahar, Hakan V. Şenavcı, Emre Işık, Gaitee A. J. Hussain

Measuring coverage of dark spots on cool stars is important in understanding how stellar magnetic activity scales with the rotation rate and convection zone depth. In this respect, it is crucial to infer surface magnetic patterns on G and K stars, to reveal solar-like stellar dynamos in action. Molecular bands serve as invaluable indicators of cool spots on

Chenfan Weng, Zhongguo Li

In Reinforcement Learning (RL), agents aim at maximizing cumulative rewards in a given environment. During the learning process, RL agents face the dilemma of exploitation and exploration: leveraging existing knowledge to acquire rewards or seeking potentially higher ones. Using uncertainty as a guiding principle provides an active and effective approach to

Renato P. dos Santos

This research delves into the comparative advantages of Generative AI chatbots (GenAIbots) -- ChatGPT, Bing Chat, Bard, and Claude -- in the context of Chemistry education, framed within a constructivist perspective. Our primary objective was to identify which of these four AI tools is more effective for enhancing Chemistry learning. Employing a single-case

Jan Philip Wahle, Bela Gipp, Terry Ruas

Current approaches in paraphrase generation and detection heavily rely on a single general similarity score, ignoring the intricate linguistic properties of language. This paper introduces two new tasks to address this shortcoming by considering paraphrase types - specific linguistic perturbations at particular text positions. We name these tasks Paraphrase

Wanlu Lei, Caterina Fuster-Barceló, Gabriel Reder, Arrate Muñoz-Barrutia

We present the BioImage$.$IO Chatbot, an AI assistant powered by Large Language Models and supported by a community-driven knowledge base and toolset. This chatbot is designed to cater to a wide range of user needs through a flexible extension mechanism that spans from information retrieval to AI-enhanced analysis and microscopy control. Embracing open-sourc

David Juher, Francesc Mañosas, David Rojas

Consider, for any integer $n\ge3$, the set $\text{Pos}_n$ of all $n$-periodic tree patterns with positive topological entropy and the set $\text{Irr}_n\subset\text{Pos}_n$ of all $n$-periodic irreducible tree patterns. The aim of this paper is to determine the elements of minimum entropy in the families $\text{Pos}_n$, $\text{Irr}_n$ and $\text{Pos}_n\setmin

Saskia Bondza, Tobias Leopold, Roman Schwarz, Christian Lisdat

We present a novel achromatic, planar, periodic mirror structure for single-beam magneto-optical trapping and demonstrate its use in first- and second-stage cooling and trapping for different isotopes of strontium. We refer to it as Fresnel MOT as the structure is inspired by Fresnel lenses. By design, it avoids many of the problems that arise for multi-colo

Yu Wang, Zhouyi Zheng, Chen Chen, Zezheng Wang

Acquiring human skills offers an efficient approach to tackle complex task planning challenges. When performing a learned skill model for a continuous contact task, such as robot polishing in an uncertain environment, the robot needs to be able to adaptively modify the skill model to suit the environment and perform the desired task. The environmental pertur

Mehdi Fatan, Emanuele Mincato, Dimitra Pintzou, Mariella Dimiccoli

Predicting turn-taking in multiparty conversations has many practical applications in human-computer/robot interaction. However, the complexity of human communication makes it a challenging task. Recent advances have shown that synchronous multi-perspective egocentric data can significantly improve turn-taking prediction compared to asynchronous, single-pers

Patrick Holzer, Tania Jacob, Shubham Kavane

Federated clustering, an integral aspect of federated machine learning, enables multiple data sources to collaboratively cluster their data, maintaining decentralization and preserving privacy. In this paper, we introduce a novel federated clustering algorithm named Dynamically Weighted Federated k-means (DWF k-means) based on Lloyd's method for k-means clus

A. Indika Perera, K. B. Shashika Manosha, Nandana Rajatheva, Matti Latva-aho

The fifth-generation (5G) of mobile communication supported by millimetre-wave (mmWave) technology and higher base station (BS) densification facilitate to enhance user equipment (UE) positioning. Therefore, 5G cellular system is designed with many positioning measurements and special positioning reference signals with a multitude of configurations for a var

Jianju Tang, Songlei Wang, Hongyi Yu

We theoretically studied the exciton geometric structure in layered semiconducting transition metal dichalcogenides. Based on a three-orbital tight-binding model for Bloch electrons which incorporates their geometric structures, an effective exciton Hamiltonian is constructed and solved perturbatively to reveal the relation between the exciton and its electr

Sai Koneru, Miriam Exel, Matthias Huck, Jan Niehues

Large Language Models (LLM's) have demonstrated considerable success in various Natural Language Processing tasks, but they have yet to attain state-of-the-art performance in Neural Machine Translation (NMT). Nevertheless, their significant performance in tasks demanding a broad understanding and contextual processing shows their potential for translation. T

Benedikt Tissot, Hugo Ribeiro, Florian Marquardt

Reservoir engineering has become a prominent tool to control quantum systems. Recently, there have been first experiments applying it to many-body systems, especially with a view to engineer particle-conserving dissipation for quantum simulations using bosons. In this work, we explore the dissipative dynamics of these systems in the classical limit. We deriv

Libo Zhao, Kai Fan, Wei Luo, Jing Wu

Simultaneous machine translation (SiMT) requires a robust read/write policy in conjunction with a high-quality translation model. Traditional methods rely on either a fixed wait-$k$ policy coupled with a standalone wait-$k$ translation model, or an adaptive policy jointly trained with the translation model. In this study, we propose a more flexible approach

Pavan Vynatheya, Taeho Ryu, Ruediger Pakmor, Selma E. de Mink

In the centers of dense star clusters, close encounters between stars and compact objects are likely to occur. We study tidal disruption events of main-sequence (MS) stars by stellar-mass black holes (termed $\mu$TDEs), which can shed light on the processes occurring in these clusters, including being an avenue in the mass growth of stellar-mass BHs. Using t

Gilles Mordant

We investigate the link between regularised self-transport problems and maximum likelihood estimation in Gaussian mixture models (GMM). This link suggests that self-transport followed by a clustering technique leads to principled estimators at a reasonable computational cost. Also, robustness, sparsity and stability properties of the optimal transport plan a

Mingwu Li, Shobhit Jain, George Haller

For mechanical systems subject to periodic excitation, forced response curves (FRCs) depict the relationship between the amplitude of the periodic response and the forcing frequency. For nonlinear systems, this functional relationship is different for different forcing amplitudes. Forced response surfaces (FRSs), which relate the response amplitude to both f

Hyuhng Joon Kim, Hyunsoo Cho, Sang-Woo Lee, Junyeob Kim

When deploying machine learning systems to the wild, it is highly desirable for them to effectively leverage prior knowledge to the unfamiliar domain while also firing alarms to anomalous inputs. In order to address these requirements, Universal Domain Adaptation (UniDA) has emerged as a novel research area in computer vision, focusing on achieving both adap

Zhibo Xing, Zijian Zhang, Ziang Zhang, Zhen Li

Over recent decades, machine learning has significantly advanced network communication, enabling improved decision-making, user behavior analysis, and fault detection. Decentralized approaches, where participants exchange computation results instead of raw private data, mitigate these risks but introduce challenges related to trust and verifiability. A criti

Leif Albers, Malte Hagemann, Roman Schnabel

High-precision laser interferometric instruments require optical surfaces with a close to perfect contour, as well as low scattering and absorption. Especially point absorbers are problematic because they heat up at high optical intensities and locally deform the otherwise flat surface, resulting in correlations between absorption and contour. Here, we prese

José Ayala

A gordian unlink is a finite number of unknots that are not topologically linked, each with prescribed length and thickness, and that cannot be disentangled into the trivial link by an isotopy preserving length and thickness throughout. In this note, we provide the first examples of gordian unlinks. As a consequence, we identify the existence of isotopy clas

Mouxiang Chen, Zemin Liu, Chenghao Liu, Jundong Li

Recent research has demonstrated the efficacy of pre-training graph neural networks (GNNs) to capture the transferable graph semantics and enhance the performance of various downstream tasks. However, the semantic knowledge learned from pretext tasks might be unrelated to the downstream task, leading to a semantic gap that limits the application of graph pre

M. D. Afonina, A. V. Biryukov, S. B. Popov

We model long-term magneto-rotational evolution of isolated neutron stars with long initial spin periods. This analysis is motivated by the recent discovery of young long-period neutron stars observed as periodic radio sources: PSR J0901-4046, GLEAM-X J1627-52, and GPM J1839-10. Our calculations demonstrate that for realistically rapid spin-down during the p

Mauricio Monteiro, Bruno Castelo Branco, Samuel Silvestre, Guilherme Avelino

In this paper, we first report an exploratory study where three participants were instructed to use ChatGPT to implement a simple Web-based application. A key finding of this study revealed that ChatGPT does not offer a user-friendly interface for building applications, even small web systems. For example, one participant with limited experience in software

D. Lairez

Although information theory resolves inconsistencies (known under the form of famous enigmas) of the traditional approach of thermostatistics, its place in the corresponding literature is not what it deserves. This article supports the idea that this is mainly due to epistemological rather than scientific reasons: the subjectivity introduced into physics is

Moritz Erlacher, Martin Zach

Magnetic resonance imaging (MRI) is a potent diagnostic tool, but suffers from long examination times. To accelerate the process, modern MRI machines typically utilize multiple coils that acquire sub-sampled data in parallel. Data-driven reconstruction approaches, in particular diffusion models, recently achieved remarkable success in reconstructing these da

Ryosuke Kobashi, Shiu-Hang Lee, Takaaki Tanaka, Keiichi Maeda

Among Type Ia supernova remnants (SNRs), Tycho's SNR has been considered as a typical object from the viewpoints of its spectroscopic, morphological and environmental properties. A recent reanalysis of Chandra data shows that its forward shock is experiencing a substantial deceleration since around 2007, which suggests recent shock interactions with a dense

Jaap Jumelet, Willem Zuidema

We present a setup for training, evaluating and interpreting neural language models, that uses artificial, language-like data. The data is generated using a massive probabilistic grammar (based on state-split PCFGs), that is itself derived from a large natural language corpus, but also provides us complete control over the generative process. We describe and

Qiugang Zhan, Ran Tao, Xiurui Xie, Guisong Liu

In recent years, studies on image generation models of spiking neural networks (SNNs) have gained the attention of many researchers. Variational autoencoders (VAEs), as one of the most popular image generation models, have attracted a lot of work exploring their SNN implementation. Due to the constrained binary representation in SNNs, existing SNN VAE method

Mouxiang Chen, Lefei Shen, Han Fu, Zhuo Li

Recent years have witnessed the success of introducing deep learning models to time series forecasting. From a data generation perspective, we illustrate that existing models are susceptible to distribution shifts driven by temporal contexts, whether observed or unobserved. Such context-driven distribution shift (CDS) introduces biases in predictions within

Daniel Biermann, Fabrizio Palumbo, Morten Goodwin, Ole-Christoffer Granmo

Many machine learning models use the manipulation of dimensions as a driving force to enable models to identify and learn important features in data. In the case of sequential data this manipulation usually happens on the token dimension level. Despite the fact that many tasks require a change in sequence length itself, the step of sequence length reduction

Chaoqian Wang

The Matthew effect describes the phenomenon where the rich tend to get richer. Such a success-driven mechanism has been studied in spatial public goods games in an inter-group way, where each individual's social power is enhanced across all groups. For instance, factors like knowledge can exert an advantage across various social contexts. In contrast, certai

S. R. Sahu, S. Khan, A. Tripathy, K. Dey

Multiple exciton generation (MEG) is a widely studied phenomenon in semiconductor nanocrystals and quantum dots, aimed at improving the energy conversion efficiency of solar cells. MEG is the process wherein incident photon energy is significantly larger than the band gap, and the resulting photoexcited carriers relax by generating additional electron-hole p

Roman Bezrukavnikov, Tanmay Deshpande

Let $G$ be a connected reductive algebraic group over an algebraically closed field $k$ of characteristic $p>0$ and let $\ell$ be a prime number different from $p$. Let $U\subset G$ be a maximal unipotent subgroup, and let $T$ be a maximal torus normalizing $U$ with normalizer $N=N_G(T)$. Let $W=N/T$ be the Weyl group of $G$. Let $\mathcal{L}$ be a non-degen

Léo Dort, Christina Goldschmidt, Grégory Miermont

We establish a large deviation principle for the normalized excursion and bridge of an $\alpha$-stable L\'evy process without negative jumps, with $1<\alpha<2$. Based on this, we derive precise asymptotics for the tail distributions of functionals of the normalized excursion and bridge, in particular, the area and maximum functionals. We advocate the use of

Baokai Wang, Yi-Chun Hung, Xiaoting Zhou, Tzen Ong

Topology is a fundamental aspect of quantum physics, and it has led to key breakthroughs and results in various fields of quantum materials. In condensed matters, this has culminated in the recent discovery of symmetry-protected topological phases. However, symmetry-based topological characterizations rely heavily on symmetry analysis and are incapable of de

Stefano Ferrero, Cecilia Ceccarelli, Piero Ugliengo, Mariona Sodupe

The reactivity of interstellar carbon atoms (C) on the water-dominated ices is one of the possible ways to form interstellar complex organic molecules (iCOMs). In this work, we report a quantum chemical study of the coupling reaction of C ($^3$P) with an icy water molecule, alongside possible subsequent reactions with the most abundant closed shell frozen sp

Lukáš Nevosád, Božena Klodová, David Honys, Radka Svobodová

Motivation: The regulation of gene expression during tissue development is extremely complex. One of the key regulatory mechanisms of gene expression involves the recognition of regulatory motifs by various proteins in the promoter regions of many genes. Localisation of these motifs in proximity to the transcription start site (TSS) or translation start site

Jean-Philippe Anker, Bartosz Trojan

We establish sharp upper and lower estimates of the Dunkl kernel in the case of dihedral groups.

Samuel Ackerman, George Kour, Eitan Farchi

A corpus of vector-embedded text documents has some empirical distribution. Given two corpora, we want to calculate a single metric of distance (e.g., Mauve, Frechet Inception) between them. We describe an abstract quality, called `distributionality', of such metrics. A non-distributional metric tends to use very local measurements, or uses global measuremen

Giovanni Mistretta, Tomislav Prokopec

We formulate gauge theories on noncompact Lorentzian manifolds. For definiteness we choose an SO(1,4) gauge theory -- the isometry group of the five dimensional Minkowski space. We make use of the natural inner product to construct the Yang-Mills gauge action on four dimensional spacetime, on which the natural tetrad and metric are induced, thus breaking the

Anass Aghbalou, François Portier, Anne Sabourin

When dealing with imbalanced classification data, reweighting the loss function is a standard procedure allowing to equilibrate between the true positive and true negative rates within the risk measure. Despite significant theoretical work in this area, existing results do not adequately address a main challenge within the imbalanced classification framework

Ludmila Botelho, Özlem Salehi

The Operational Fixed Interval Scheduling Problem aims to find an assignment of jobs to machines that maximizes the total weight of the completed jobs. We introduce a new variant of the problem where we consider the additional goal of minimizing the idle time, the total duration during which the machines are idle. The problem is expressed using quadratic unc

H. Netzel

Pulsations in RR Lyrae stars and classical Cepheids were thought to be relatively simple since they typically pulsate only in one or two radial modes. This picture changes at a closer look when modulation or additional low-amplitude signals are detected. I will review different multi-periodic groups known among classical pulsators, including stars showing th

Yidi Jiang, Zhengyang Chen, Ruijie Tao, Liqun Deng

We introduce a novel task named `target speech diarization', which seeks to determine `when target event occurred' within an audio signal. We devise a neural architecture called Prompt-driven Target Speech Diarization (PTSD), that works with diverse prompts that specify the target speech events of interest. We train and evaluate PTSD using sim2spk, sim3spk a

Felipe Lopes Oliveira, Pierre Mothé Esteves

Covalent Organic Frameworks (COFs) have gained significant popularity in recent years due to their unique ability to provide a high surface area and customizable pore geometry and chemistry. These traits make COFs a highly promising choice for a range of applications. However, with their vast potential structures, exploring COFs experimentally can be challen

Kushal Babel, Andrey Chursin, George Danezis, Anastasios Kichidis

We introduce Mysticeti-C, the first DAG-based Byzantine consensus protocol to achieve the lower bounds of latency of 3 message rounds. Since Mysticeti-C is built over DAGs it also achieves high resource efficiency and censorship resistance. Mysticeti-C achieves this latency improvement by avoiding explicit certification of the DAG blocks and by proposing a n

Xunjian Yin, Baizhou Huang, Xiaojun Wan

With the rapid development of NLP, large-scale language models (LLMs) excel in various tasks across multiple domains now. However, existing benchmarks may not adequately measure these models' capabilities, especially when faced with new knowledge. In this paper, we address the lack of benchmarks to evaluate LLMs' ability to handle new knowledge, an important

Sabri Boughorbel, Majd Hawasly

While significant progress has been made in benchmarking Large Language Models (LLMs) across various tasks, there is a lack of comprehensive evaluation of their abilities in responding to multi-turn instructions in less-commonly tested languages like Arabic. Our paper offers a detailed examination of the proficiency of open LLMs in such scenarios in Arabic.

Mike R Jeffrey

A practical method was proposed recently for finding local bifurcation points in an n-dimensional vector field F by seeking their 'underlying catastrophes'. Here we apply the idea to the homogeneous steady states of a partial differential equation as an example of the role that catastrophes can play in reaction diffusion. What are these 'underlying' catastro

Wei Chen, Qiushi Wang, Zefei Long, Xianyin Zhang

We propose Multiple Experts Fine-tuning Framework to build a financial large language model (LLM), DISC-FinLLM. Our methodology improves general LLMs by endowing them with multi-turn question answering abilities, domain text processing capabilities, mathematical computation skills, and retrieval-enhanced generation capabilities. We build a financial instruct

Fengjun Wang, Moran Beladev, Ofri Kleinfeld, Elina Frayerman

Multi-label text classification is a critical task in the industry. It helps to extract structured information from large amount of textual data. We propose Text to Topic (Text2Topic), which achieves high multi-label classification performance by employing a Bi-Encoder Transformer architecture that utilizes concatenation, subtraction, and multiplication of e

Tao Lin, Chengfei Yue, Ziran Liu, Xibin Cao

Task and Motion Planning (TAMP) algorithms can generate plans that combine logic and motion aspects for robots. However, these plans are sensitive to interference and control errors. To make TAMP more applicable in real-world, we propose the modular multi-level replanning TAMP framework(MMRF), blending the probabilistic completeness of sampling-based TAMP al

Sara Sacchi, Bappaditya Dey, Iacopo Mochi, Sandip Halder

The technological advance of High Numerical Aperture Extreme Ultraviolet Lithography (High NA EUVL) has opened the gates to extensive researches on thinner photoresists (below 30nm), necessary for the industrial implementation of High NA EUVL. Consequently, images from Scanning Electron Microscopy (SEM) suffer from reduced imaging contrast and low Signal-to-

Ambroise Odonnat, Vasilii Feofanov, Ievgen Redko

Self-training is a well-known approach for semi-supervised learning. It consists of iteratively assigning pseudo-labels to unlabeled data for which the model is confident and treating them as labeled examples. For neural networks, softmax prediction probabilities are often used as a confidence measure, although they are known to be overconfident, even for wr

Mike R. Jeffrey

Practical conditions are given here for finding and classifying high codimension intersection points of $n$ hypersurfaces in $n$ dimensions. By interpreting those hypersurfaces as the nullclines of a vector field in $\mathbb R^n$, we broaden the concept of Thom's catastrophes to find bifurcation points of (non-gradient) vector fields of any dimension. We int

Vincent E. Debets, Lila Sarfati, Thomas Voigtmann, Liesbeth M. C. Janssen

One of the key hallmarks of dense active matter in the liquid, supercooled, and solid phases is so-called equal-time velocity correlations. Crucially, these correlations can emerge spontaneously, i.e., they require no explicit alignment interactions, and therefore represent a generic feature of dense active matter. This indicates that for a meaningful compar

Florian Holzinger, Andreas Beham

Industrial manufacturing is currently amidst it's fourth great revolution, pushing towards the digital transformation of production processes. One key element of this transformation is the formalization and digitization of processes, creating an increased potential to monitor, understand and optimize existing processes. However, one major obstacle in this pr

Giovanni Pezzulo, Leo D'Amato, Francesco Mannella, Matteo Priorelli

This paper considers neural representation through the lens of active inference, a normative framework for understanding brain function. It delves into how living organisms employ generative models to minimize the discrepancy between predictions and observations (as scored with variational free energy). The ensuing analysis suggests that the brain learns gen

Alex D. Richardson, Tibor Antal, Richard A. Blythe, Linus J. Schumacher

Neural Cellular Automata (NCA) are a powerful combination of machine learning and mechanistic modelling. We train NCA to learn complex dynamics from time series of images and PDE trajectories. Our method is designed to identify underlying local rules that govern large scale dynamic emergent behaviours. Previous work on NCA focuses on learning rules that give

Igor Barahona

A comprehensive examination of data science vocabulary usage over the past 13 years in this work is conducted. The investigation commences with a dataset comprising 16,018 abstracts that feature the term "data science" in either the title, abstract, or keywords. The study involves the identification of documents that introduce novel vocabulary and subsequent

Saeed Salehi

It would be a heavenly reward if there were a method of weighing theories and sentences in such a way that a theory could never prove a heavier sentence (Chaitin's Heuristic Principle). Alas, no satisfactory measure has been found so far, and this dream seemed too good ever to come true. In the first part of this paper, we attempt to revive Chaitin's lost pa

Sara Papi, Peidong Wang, Junkun Chen, Jian Xue

The growing need for instant spoken language transcription and translation is driven by increased global communication and cross-lingual interactions. This has made offering translations in multiple languages essential for user applications. Traditional approaches to automatic speech recognition (ASR) and speech translation (ST) have often relied on separate

Danis Alukaev, Semen Kiselev, Ilya Pershin, Bulat Ibragimov

Concept Bottleneck Models (CBMs) assume that training examples (e.g., x-ray images) are annotated with high-level concepts (e.g., types of abnormalities), and perform classification by first predicting the concepts, followed by predicting the label relying on these concepts. The main difficulty in using CBMs comes from having to choose concepts that are pred

Young-Jun Lee, Dokyong Lee, Joo Won Sung, Jonghwan Hyeon

This paper explores the image-sharing capability of Large Language Models (LLMs), such as GPT-4 and LLaMA 2, in a zero-shot setting. To facilitate a comprehensive evaluation of LLMs, we introduce the PhotoChat++ dataset, which includes enriched annotations (i.e., intent, triggering sentence, image description, and salient information). Furthermore, we presen

Mareen Hoppe, Marek Schönherr, Frank Siegert

Measurements of vector boson polarisation in vector boson production processes offer a powerful probe of the electroweak symmetry breaking mechanism, scrutinising the Standard Model and new physics scenarios alike. Since massive vector bosons can only be observed as intermediate particles, polarised cross section templates from simulation are necessary to ex

Hao Wang, Qingxuan Wang, Yue Li, Changqing Wang

The use of visually-rich documents (VRDs) in various fields has created a demand for Document AI models that can read and comprehend documents like humans, which requires the overcoming of technical, linguistic, and cognitive barriers. Unfortunately, the lack of appropriate datasets has significantly hindered advancements in the field. To address this issue,

Herbert Edelsbrunner, János Pach

The Upper Bound Theorem for convex polytopes implies that the $p$-th Betti number of the \v{C}ech complex of any set of $N$ points in $\mathbb R^d$ and any radius satisfies $\beta_{p} = O(N^{m})$, with $m = \min \{ p+1, \lceil d/2 \rceil \}$. We construct sets in even and odd dimensions that prove this upper bound is asymptotically tight. For example, we des

Clara J. Lapp, Julia M. Link, Carsten Timm

Noncentrosymmetric superconductors can support flat bands of zero-energy surface states in part of their surface Brillouin zone. This requires that they obey time-reversal symmetry and have a sufficiently strong triplet-to-singlet-pairing ratio to exhibit nodal lines in the bulk. These bands are protected by a winding number that relies on chiral symmetry, w

Libo Qin, Qiguang Chen, Fuxuan Wei, Shijue Huang

Chain-of-thought (CoT) is capable of eliciting models to explicitly generate reasoning paths, thus promoting reasoning accuracy and attracting increasing attention. Specifically, zero-shot CoT achieves remarkable improvements in a wide range of reasoning tasks by simply instructing the LLM with the prompt "Let's think step by step!". Despite the success of z

Masato Ida

Bale et al. [1] perform a numerical study of droplet/aerosol transport in the air to assess the probability of airborne transmission of COVID-19 from an infected person to a nearby healthy person. In their numerical study, the air flow field is solved by an implicit large eddy simulation model, and the airborne transport of the droplets/aerosols exhaled from

Matteo Romano, Timoteo Carletti, Jérôme Daquin

Near-Earth space continues to be the focus of critical services and capabilities provided to the society. With the steady increase of space traffic, the number of Resident Space Objects (RSOs) has recently boomed in the context of growing concern due to space debris. The need of a holistic and unified approach for addressing orbital collisions, assess the gl

Amit Gajbhiye, Zied Bouraoui, Na Li, Usashi Chatterjee

Concepts play a central role in many applications. This includes settings where concepts have to be modelled in the absence of sentence context. Previous work has therefore focused on distilling decontextualised concept embeddings from language models. But concepts can be modelled from different perspectives, whereas concept embeddings typically mostly captu

Brendan Harris, Leonardo L. Gollo, Ben D. Fulcher

Many real-world systems undergo abrupt changes in dynamics as they move across critical points, often with dramatic consequences. Much existing theory on identifying the time-series signatures of nearby critical points -- such as increased variance and slower timescales -- is derived for the case of fixed, low-amplitude noise. However, real-world systems are

Charles Duquet

Let $1<p\not=2<\infty$ and let $S^p_n$ be the associated Schatten von Neumann class over $n\times n$ matrices. We prove new characterizations of unital positive Schur multipliers $S^p_n\to S^p_n$ which can be dilated into an invertible complete isometry acting on a non-commutative $L^p$-space. Then we investigate the infinite dimensional case.

Kyung Soo Rim

We consider an equation of multiple variables in which a partial derivative does not vanish at a point. The implicit function theorem provides a local existence and uniqueness of the function for the equation. In this paper, we propose an algorithm to approximate the function by a polynomial without using higher-order differentiability, which depends essenti

Rajneesh Kashyap, R. Thundil Karuppa Raj

Objectives: The main objective of this review article is to evaluate the usability of Computational Fluid Dynamics (CFD) as a supporting clinical tool for respiratory system. Data Source: The English articles referred for this review paper were identified from various International peer reviewed journals indexed in Science citation index. Study Selection: 26

Hao Wang, Xiahua Chen, Rui Wang, Chenhui Chu

Extracting meaningful entities belonging to predefined categories from Visually-rich Form-like Documents (VFDs) is a challenging task. Visual and layout features such as font, background, color, and bounding box location and size provide important cues for identifying entities of the same type. However, existing models commonly train a visual encoder with we

Wenhao Yan, He Li, Kaoru Ota, Mianxiong Dong

Widely available healthcare services are now getting popular because of advancements in wearable sensing techniques and mobile edge computing. People's health information is collected by edge devices such as smartphones and wearable bands for further analysis on servers, then send back suggestions and alerts for abnormal conditions. The recent emergence of f

Benjamin De Bruyne

This thesis is devoted to the study of extreme value statistics in stochastic processes and their applications. In the first part, we obtain exact analytical results on the extreme value statistics of both discrete-time and continuous-time random walks. In particular, we focus on the gap statistics of random walks and exhibit their asymptotic universality wi

Jinzheng Zhao, Yong Xu, Xinyuan Qian, Davide Berghi

Audio-visual speaker tracking has drawn increasing attention over the past few years due to its academic values and wide applications. Audio and visual modalities can provide complementary information for localization and tracking. With audio and visual information, the Bayesian-based filter and deep learning-based methods can solve the problem of data assoc

Pola Schwöbel, Jacek Golebiowski, Michele Donini, Cédric Archambeau

Large language models (LLMs) encode vast amounts of world knowledge. However, since these models are trained on large swaths of internet data, they are at risk of inordinately capturing information about dominant groups. This imbalance can propagate into generated language. In this work, we study and operationalise a form of geographical erasure, wherein lan

Jens Jørgen Mortensen, Ask Hjorth Larsen, Mikael Kuisma, Aleksei V. Ivanov

We review the GPAW open-source Python package for electronic structure calculations. GPAW is based on the projector-augmented wave method and can solve the self-consistent density functional theory (DFT) equations using three different wave-function representations, namely real-space grids, plane waves, and numerical atomic orbitals. The three representation

Sudeshna Sen, Andrew K. Mitchell

Molecular junctions -- whether actual single molecules in nanowire break junctions or artificial molecules realized in coupled quantum dot devices -- offer unique functionality due to their orbital complexity, strong electron interactions, gate control, and many-body effects from hybridization with the external electronic circuit. Inverse design involves fin

Zhou Lan, Ben Liu, Yi Feng, Danhuang Dong

Daily electricity consumption forecasting is a classical problem. Existing forecasting algorithms tend to have decreased accuracy on special dates like holidays. This study decomposes the daily electricity consumption series into three components: trend, seasonal, and residual, and constructs a two-stage prediction method using piecewise linear regression as

Anqi Mao, Mehryar Mohri, Yutao Zhong

We present a study of surrogate losses and algorithms for the general problem of learning to defer with multiple experts. We first introduce a new family of surrogate losses specifically tailored for the multiple-expert setting, where the prediction and deferral functions are learned simultaneously. We then prove that these surrogate losses benefit from stro

Nicola Gaspari, Andrew J. Levan, Ashley A. Chrimes, Gijs Nelemans

The merger locations of binary neutron stars (BNSs) encode their galactic kinematics and provide insights into their connection to short gamma-ray bursts (SGRBs). In this work, we use the sample of Galactic BNSs with measured proper motions to investigate their kinematics and predict their merger locations. Using a synthetic image of the Milky Way and its Ga

Anqi Mao, Mehryar Mohri, Yutao Zhong

We study the key framework of learning with abstention in the multi-class classification setting. In this setting, the learner can choose to abstain from making a prediction with some pre-defined cost. We present a series of new theoretical and algorithmic results for this learning problem in the predictor-rejector framework. We introduce several new familie

Blerta Veseli, Simon Razniewski, Jan-Christoph Kalo, Gerhard Weikum

Structured knowledge bases (KBs) are an asset for search engines and other applications, but are inevitably incomplete. Language models (LMs) have been proposed for unsupervised knowledge base completion (KBC), yet, their ability to do this at scale and with high accuracy remains an open question. Prior experimental studies mostly fall short because they onl

Anqi Mao, Mehryar Mohri, Yutao Zhong

Learning with abstention is a key scenario where the learner can abstain from making a prediction at some cost. In this paper, we analyze the score-based formulation of learning with abstention in the multi-class classification setting. We introduce new families of surrogate losses for the abstention loss function, which include the state-of-the-art surrogat

Jiangkun Gong, Jun Yan, Deyong Kong, Deren Li

This paper presents a brief examination of Automatic Target Recognition (ATR) technology within ground-based radar systems. It offers a lucid comprehension of the ATR concept, delves into its historical milestones, and categorizes ATR methods according to different scattering regions. By incorporating ATR solutions into radar systems, this study demonstrates

Satoshi Hayakawa, Tetsuro Morimura

Reward evaluation of episodes becomes a bottleneck in a broad range of reinforcement learning tasks. Our aim in this paper is to select a small but representative subset of a large batch of episodes, only on which we actually compute rewards for more efficient policy gradient iterations. We build a Gaussian process modeling of discounted returns or rewards t

Christine Hedde-von Westernhagen, Javier Garcia-Bernardo, Ayoub Bagheri

Understanding the spread of SARS-CoV-2 has been one of the most pressing problems of the recent past. Network models present a potent approach to studying such spreading phenomena because of their ability to represent complex social interactions. While previous studies have shown that network centrality measures are generally able to identify influential spr

Jatan Shrestha, Simon Idoko, Basant Sharma, Arun Kumar Singh

Trajectory sampling in the Frenet(road-aligned) frame, is one of the most popular methods for motion planning of autonomous vehicles. It operates by sampling a set of behavioural inputs, such as lane offset and forward speed, before solving a trajectory optimization problem conditioned on the sampled inputs. The sampling is handcrafted based on simple heuris

O. Sgouros, M. Cutuli, F. Cappuzzello, M. Cavallaro

The present article reports new data on the $^{48}$Ti($^{18}$O,$^{17}$O)$^{49}$Ti reaction at 275 MeV incident energy as part of the systematic research pursued within the NUMEN project. Supplementary measurements of the same reaction on $^{16}$O and $^{27}$Al targets were also performed in order to estimate the background arising from the use of a composite

Ruth Veevers, Dan MacLean

Protein-protein interactions drive many biological processes, including the detection of phytopathogens by plants' R-Proteins and cell surface receptors. Many machine learning studies have attempted to predict protein-protein interactions but performance is highly dependent on training data; models have been shown to accurately predict interactions when the

Sofia Ek, Dave Zachariah

Randomized trials are widely considered as the gold standard for evaluating the effects of decision policies. Trial data is, however, drawn from a population which may differ from the intended target population and this raises a problem of external validity (aka. generalizability). In this paper we seek to use trial data to draw valid inferences about the ou

Yixuan Huang, Zongmin Wu, Shengxin Zhu

We prove that the native space of a Wu function is a dense subspace of a Sobolev space. An explicit characterization of the native spaces of Wu functions is given. Three definitions of Wu functions are introduced and proven to be equivalent. Based on these new equivalent definitions and the so called $f$-form tricks, we can generalize the Wu functions into t

Erman Cineli

In this note we give examples of Hamiltonian diffeomorphisms which are on one hand dynamically complicated, for instance with positive topological entropy, and on the other hand minimal from the perspective of Floer theory. The minimality is in the sense that the barcode of the Floer complex of all iterates of these maps consists of only infinite bars. In pa