July 2023 arXiv papers — page 97

Adriana Bariego-Quintana, Felipe J. Llanes-Estrada

Flat rotation curves v(r) are naturally explained by elongated (prolate) Dark Matter (DM) distributions, and we have provided competitive fits to the SPARC database. To further probe the geometry of the halo one needs out-of-plane observables. Stellar streams, poetically analogous to airplane contrails, but caused by tidal dispersion of massive substructures

Charlotte Dietze

We review recent results on the semiclassical behaviour of Schr\"{o}dinger operators with Neumann boundary conditions. In this setting, the validity of Weyl's law requires additional conditions on the potential. We will explain the techniques needed to control the number of bound states near the boundary, thus leading to universal estimates on the number of

Ugo Dal Lago, Maurizio Murgia

We introduce contextual behavioural metrics (CBMs) as a novel way of measuring the discrepancy in behaviour between processes, taking into account both quantitative aspects and contextual information. This way, process distances by construction take the environment into account: two (non-equivalent) processes may still exhibit very similar behaviour in some

Jemima Graham, Fei Teng

Electric vehicle (EV) charge points (CPs) can be used by aggregators to provide frequency response (FR) services. Aggregators must have day-ahead half-hourly forecasts of minimum aggregate vehicle-to-grid (V2G) plug-in to produce meaningful bids for the day-ahead ancillary services market. However, there is a lack of understanding on what features should be

Anagh Malik, Parsa Mirdehghan, Sotiris Nousias, Kiriakos N. Kutulakos

Neural radiance fields (NeRFs) have become a ubiquitous tool for modeling scene appearance and geometry from multiview imagery. Recent work has also begun to explore how to use additional supervision from lidar or depth sensor measurements in the NeRF framework. However, previous lidar-supervised NeRFs focus on rendering conventional camera imagery and use l

Michele Pizzardo, Margaret J. Geller, Scott J. Kenyon, Ivana Damjanov

We use simulated cluster member galaxies from Illustris TNG300-1 to develop a technique for measuring the galaxy cluster mass accretion rate (MAR) that can be applied directly to observations. We analyze 1318 IllustrisTNG clusters of galaxies with $M_{200c}>10^{14}$M$_\odot$ and $0.01\leq z \leq 1.04$. The MAR we derive is the ratio between the mass of a sph

Zhengbo Wang, Jian Liang, Ran He, Nan Xu

With the growing interest in pretrained vision-language models like CLIP, recent research has focused on adapting these models to downstream tasks. Despite achieving promising results, most existing methods require labeled data for all classes, which may not hold in real-world applications due to the long tail and Zipf's law. For example, some classes may la

Thibault Marette, Pauli Miettinen, Stefan Neumann

Finding (bi-)clusters in bipartite graphs is a popular data analysis approach. Analysts typically want to visualize the clusters, which is simple as long as the clusters are disjoint. However, many modern algorithms find overlapping clusters, making visualization more complicated. In this paper, we study the problem of visualizing \emph{a given clustering} o

Abdu Saif, Nor Shahida Mohd Shah, Soreen Ameen Fattah, Saeed Hamood Alsamhi

Unmanned Aerial Vehicles (UAVs) are being used for wireless communications in smart environments. However, the need for mobility, scalability of data transmission over wide areas, and the required coverage area make UAV beamforming essential for better coverage and user experience. To this end, we propose a flexible beamforming approach to improve tethered U

Muhammad Sohaib Ayub, Naimat Ullah, Sarwan Ali, Imdad Ullah Khan

Cricket is the second most popular sport after soccer in terms of viewership. However, the assessment of individual player performance, a fundamental task in team sports, is currently primarily based on aggregate performance statistics, including average runs and wickets taken. We propose Context-Aware Metric of player Performance, CAMP, to quantify individu

Matthias Christandl, Vladimir Lysikov, Vincent Steffan, Albert H. Werner

Tensor networks provide succinct representations of quantum many-body states and are an important computational tool for strongly correlated quantum systems. Their expressive and computational power is characterized by an underlying entanglement structure, on a lattice or more generally a (hyper)graph, with virtual entangled pairs or multipartite entangled s

Yasar Abbas Ur Rehman, Yan Gao, Pedro Porto Buarque de Gusmão, Mina Alibeigi

The ubiquity of camera-enabled devices has led to large amounts of unlabeled image data being produced at the edge. The integration of self-supervised learning (SSL) and federated learning (FL) into one coherent system can potentially offer data privacy guarantees while also advancing the quality and robustness of the learned visual representations without n

G. M. Shahariar, Tonmoy Talukder, Rafin Alam Khan Sotez, Md. Tanvir Rouf Shawon

With the increasing need for text summarization techniques that are both efficient and accurate, it becomes crucial to explore avenues that enhance the quality and precision of pre-trained models specifically tailored for summarizing Bengali texts. When it comes to text summarization tasks, there are numerous pre-trained transformer models at one's disposal.

Á. Tóth, T. Szücs, T. N. Szegedi, Gy. Gyürky

The $^3$He($\alpha$,$\gamma$)$^7$Be reaction plays a major role both in the BBN producing the majority of the primordial $^7$Li, and in the pp-chain, where it is the branching point. As a few-nucleon system, this reaction is often used to validate ab-initio theoretical calculations and/or test R-matrix theory and code implementations. For the latter, experim

Mehran H. Bazargani, Szymon Urbas, Karl Friston

Even though the brain operates in pure darkness, within the skull, it can infer the most likely causes of its sensory input. An approach to modelling this inference is to assume that the brain has a generative model of the world, which it can invert to infer the hidden causes behind its sensory stimuli, that is, perception. This assumption raises key questio

Giulia Marinelli

We review GENEVA Monte Carlo framework, that combines three theoretical tools used for QCD precise predictions into a single structure. In this talk we highlight its main features, discussing some new improvements involving both colour singlet productions, as well as for the production of final states with heavy coloured partons and jets.

Mingjian Ni, Guangyao Chen, Xiawu Zheng, Peixi Peng

The sparsity of Deep Neural Networks is well investigated to maximize the performance and reduce the size of overparameterized networks as possible. Existing methods focus on pruning parameters in the training process by using thresholds and metrics. Meanwhile, feature similarity between different layers has not been discussed sufficiently before, which coul

Christian El Emam

We introduce and study a novel uniformization metric model for the quasi-Fuchsian space QF(S) of a closed oriented surface S, defined through a class of C-valued bilinear forms on S, called Bers metrics, which coincide with hyperbolic Riemannian metrics along the Fuchsian locus. By employing this approach, we present a new model of the holomorphic tangent bu

Gang Chen, Lu Zhang, Shangyou Zhang

We present and analyze a new hybridizable discontinuous Galerkin method (HDG) for the Reissner-Mindlin plate bending system. Our method is based on the formulation utilizing Helmholtz Decomposition. Then the system is decomposed into three problems: two trivial Poisson problems and a perturbed saddle-point problem. We apply HDG scheme for these three problem

Volker Ziemann

We simulate a recursive least-squares estimator to determine the bandwidth $\omega_{12}$ and the detuning $\Delta\omega$ of a cavity that is controlled with a low-level RF system and we present a comprehensive analysis of the convergence and asymptotic behavior of the algorithm for static and time-varying systems.

So Chigusa, Takeo Moroi, Kazunori Nakayama, Thanaporn Sichanugrist

We consider the possibility to detect cosmic light dark matter (DM), i.e., axions and dark photons, of mass $\sim 10^{-6}$ eV and $\sim 10^{-4}$ eV, by magnetic excitation in a magnet with strong hyperfine interaction. In particular, we consider a canted anti-ferromagnet, MnCO$_3$, as a concrete candidate material. With spin transfer between nuclear and elec

Georgios Amanatidis, Sophie Klumper, Evangelos Markakis, Guido Schäfer

Budget-feasible procurement has been a major paradigm in mechanism design since its introduction by Singer (2010). An auctioneer (buyer) with a strict budget constraint is interested in buying goods or services from a group of strategic agents (sellers). In many scenarios it makes sense to allow the auctioneer to only partially buy what an agent offers, e.g.

Rong-Li Liu, Yan-Xia Ren, Yingrui Wang

Let $X^I_n$ be the coalescence time of two particles picked at random from the $n$th generation of a critical Galton-Watson process with immigration, and let $A^I_n$ be the coalescence time of the whole population in the $n$th generation. In this paper, we study the limiting behaviors of $X^I_n$ and $A^I_n$ as $n\to\infty$.

Massimiliano Incudini, Francesco Martini, Alessandra Di Pierro

Topological data analysis (TDA) has emerged as a powerful tool for extracting meaningful insights from complex data. TDA enhances the analysis of objects by embedding them into a simplicial complex and extracting useful global properties such as the Betti numbers, i.e. the number of multidimensional holes, which can be used to define kernel methods that are

Yunnuo Xu, Longfei Yin, Yijie Mao, Wonjae Shin

Future wireless networks, in particular, 5G and beyond, are anticipated to deploy dense Low Earth Orbit (LEO) satellites to provide global coverage and broadband connectivity. However, the limited frequency band and the coexistence of multiple constellations bring new challenges for interference management. In this paper, we propose a robust multilayer inter

Krishna Ronanki, Christian Berger, Jennifer Horkoff

Natural Language Processing (NLP) for Requirements Engineering (RE) (NLP4RE) seeks to apply NLP tools, techniques, and resources to the RE process to increase the quality of the requirements. There is little research involving the utilization of Generative AI-based NLP tools and techniques for requirements elicitation. In recent times, Large Language Models

Sachin J. Chanchani, Ruihong Huang

Vector representations of natural language are ubiquitous in search applications. Recently, various methods based on contrastive learning have been proposed to learn textual representations from unlabelled data; by maximizing alignment between minimally-perturbed embeddings of the same text, and encouraging a uniform distribution of embeddings across a broad

Einius Pultinevicius, Marian Rockenhäuser, Felix Kogel, Phillip Groß

Many experiments in atomic and molecular physics require simultaneous frequency stabilization of multiple lasers. We present a stabilization scheme based on a scanning transfer cavity lock that is simple, stable and easily scalable to many lasers at minimal cost. The scheme is based on the Red Pitaya STEMlab platform, with custom software developed and imple

Xin Bao, Ying Lv, Zeng-Qi Ou

In this paper, we study a class of fractional Schr\"{o}dinger equation \begin{equation} \label{eq0} \left\{ \begin{aligned} &(-\Delta)^{s}u=\lambda u+a(x)|u|^{p-2}u,\\ &\int_{\mathbb{R}^{N}}|u|^{2}dx=c^{2},\ u\in H^{s}(\mathbb{R}^{N}), \end{aligned} \right. \end{equation} where $N>2s$, $s\in(0,1)$ and $p\in(2,2+4s/N), c>0$. $a(x)\in C(\mathbb{R}^{N},\mathbb{

Xiao Liu, Alessandra Mileo, Alan F. Smeaton

The development of computer vision and in-situ monitoring using visual sensors allows the collection of large datasets from the additive manufacturing (AM) process. Such datasets could be used with machine learning techniques to improve the quality of AM. This paper examines two scenarios: first, using convolutional neural networks (CNNs) to accurately class

Jemima Graham, Evelyn Heylen, Yuankai Bian, Fei Teng

This paper investigates the performance of a day-ahead explanatory model for inertia forecasting based on field data in the Nordic system, which achieves a 43% reduction in mean absolute percentage error (MAPE) against a state-of-the-art time-series forecast model. The generalizability of the explanatory model is verified by its consistent performance on Nor

Félicien Comtat, Jolanta Marzec-Ballesteros, Abhishek Saha

Let $F$ be an $L^2$-normalized Siegel cusp form for $\mathrm{Sp}_4(\mathbb{Z})$ of weight $k$ that is a Hecke eigenform and not a Saito--Kurokawa lift. Assuming the Generalized Riemann Hypothesis, we prove that its Fourier coefficients satisfy the bound $|a(F,S)| \ll_\epsilon \frac{k^{1/4+\epsilon} (4\pi)^k}{\Gamma(k)} c(S)^{-\frac12} \det(S)^{\frac{k-1}2+\e

Kevin G. Hare, Tomáš Vávra

In this paper we investigate $p$-adic self-similar sets and $p$-adic self-similar measures. We show that $p$-adic self-similar sets are $p$-adic path set fractals, and that the converse is not necessarily true. For $p$-adic self-similar sets and $p$-adic self-similar measures, we show the existence of a unique essential class. We show that, under mild assump

Daniele Agostini, Mario Kummer

We establish a connection between the theory of Ulrich sheaves and $\mathbb{A}^1$-homotopy theory. For instance, we prove that the $\mathbb{A}^1$-degree of a morphism between projective varieties, that is relatively oriented by an Ulrich sheaf, is constant on the target even when it is not $\mathbb{A}^1$-chain connected or $\mathbb{A}^1$-connected. Further i

Yiding Feng, Brendan Lucier, Aleksandrs Slivkins

We study a game played between advertisers in an online ad platform. The platform sells ad impressions by first-price auction and provides autobidding algorithms that optimize bids on each advertiser's behalf, subject to advertiser constraints such as budgets. Crucially, these constraints are strategically chosen by the advertisers. The chosen constraints de

Jan Vanek

Polarization of $\Lambda$ hyperons has been observed in various collision systems over a wide range of collision energies over the last 50 years since its discovery in Fermilab in the 70's. The existing experimental and theoretical techniques were not able to provide a conclusive answer about the origin of the polarization. In these proceedings, we discuss t

Polona Durcik, Lenka Slavíková, Christoph Thiele

We prove an $r$-variation estimate, $r>4$, in the norm for ergodic averages with respect to three commuting transformations. It is not known whether such estimates hold for all $r\ge 2$ as in the analogous cases for one or two commuting transformations, or whether such estimates hold for any $r<\infty$ for more than three commuting transformations.

Randy Lafler, Mark L. Eickhoff, Scott C. Newey, Yamil Nieves Gonzalez

High-precision remote clock synchronization is crucial for many classical and quantum network applications. Evaluating options for space-Earth links, we find that traditional solutions may not produce the desired synchronization for low Earth orbits and unnecessarily complicate quantum-networking architectures. Demonstrating an alternative, we use commercial

Guoyun Tu, Ying Liu, Vladimir Vlassov

Image captioning is a significant field across computer vision and natural language processing. We propose and present AIC-AB NET, a novel Attribute-Information-Combined Attention-Based Network that combines spatial attention architecture and text attributes in an encoder-decoder. For caption generation, adaptive spatial attention determines which image regi

Todd Eisworth

We investigate a combinatorial game on $\omega_1$ and show that mild large cardinal assumptions imply that every normal ideal on $\omega_1$ satisfies a weak version of precipitousness. As an application, we show that that the Raghavan-Todor\v{c}evi\'{c} proof of a longstanding conjecture of Galvin (done assuming the existence of a Woodin cardinal) can be pus

Stefano Viaggiu

In this paper we study the perturbative regime in the static patch of de Sitter metric in the Regge-Wheeler formalism. After realizing that perturbative regime in a de Sitter spacetime depicted in terms of usual spherical coordinates cannot be extended up to the cosmological horizon, we study perturbative equations, in particular the axial ones, in terms of

Maria del Rio-Chanona, Nadzeya Laurentsyeva, Johannes Wachs

Large language models like ChatGPT efficiently provide users with information about various topics, presenting a potential substitute for searching the web and asking people for help online. But since users interact privately with the model, these models may drastically reduce the amount of publicly available human-generated data and knowledge resources. Thi

Mohamed Ragab, Emadeldeen Eldele, Min Wu, Chuan-Sheng Foo

Source-free domain adaptation (SFDA) aims to adapt a pretrained model from a labeled source domain to an unlabeled target domain without access to the source domain data, preserving source domain privacy. Despite its prevalence in visual applications, SFDA is largely unexplored in time series applications. The existing SFDA methods that are mainly designed f

Marc Demoustier, Yue Zhang, Venkatesh Narasimha Murthy, Florin C. Ghesu

Device tracking is an important prerequisite for guidance during endovascular procedures. Especially during cardiac interventions, detection and tracking of guiding the catheter tip in 2D fluoroscopic images is important for applications such as mapping vessels from angiography (high dose with contrast) to fluoroscopy (low dose without contrast). Tracking th

Martin Abendroth, Alexander Malik, Bjoern Kiefer

This paper describes a modification of Ehlers' model for the inelastic behavior of granular media. The modified model can be applied for describing the inelastic behavior of porous media. The key feature is a subtle change of the yield potential, which allows the correct orientation of the triangular-shaped yield surface cross sections depending on the hydro

Jinyu Guo, Feng Zhang, Hang Zhao, Baoxiang Pan

Satellite-collected nighttime light provides a unique perspective on human activities, including urbanization, population growth, and epidemics. Yet, long-term and fine-grained nighttime light observations are lacking, leaving the analysis and applications of decades of light changes in urban facilities undeveloped. To fill this gap, we developed an innovati

Christian Berthold, Johann Gross, Christian Frey, Malte Krack

For the first time, a fully-coupled Harmonic Balance method is developed for the forced response of turbomachinery blades. The method is applied to a state-of-the-art model of a turbine bladed disk with interlocked shrouds subjected to wake-induced loading. The recurrent opening and closing of the pre-loaded shroud contact causes a softening effect, leading

Wei Wang, Jie Hu, Jingjing Liu, Yang Tan

Non-destructive testing and medical diagnostic techniques using ultrasound has become indispensable in evaluating the state of materials or imaging the internal human body, respectively. To conduct spatially resolved high-quality observations, conventionally, sophisticated phased arrays are used both at the emitting and receiving ends of the setup. In compar

Alberto Torralba-Torregrosa, Siddhartha Gurung-López, Pablo Arnalte-Mur, Daniele Spinoso

We present the Lyman-$a$ (Lya) Luminosity Function (LF) at $2.05<z<3.75$, estimated from a sample of 67 Lya-emitter (LAE) candidates in the J-PAS Pathfinder surveys: miniJPAS and J-NEP. These two surveys cover a total effective area of $\sim 1.14$ deg$^2$ with 54 Narrow Band (NB) filters across the optical range, with typical limiting magnitudes of $\sim 23$

Kevin G. Hare

Let $c(x)$ be a monic integer polynomial with coefficients $0$ or $1$. Write $c(x) = a(x) b(x)$ where $a(x)$ and $b(x)$ are monic polynomials with non-negative real (not necessarily integer) coefficients. The unfair 0--1 polynomial conjecture states that $a(x)$ and $b(x)$ are necessarily integer polynomials with coefficients $0$ or $1$. Let $a(x)$ be a candi

Chengyu Fang, Xianfeng Han

In this paper, we investigate the problem of automatically controllable artistic character line drawing generation from photographs by proposing a Vector Flow Aware and Line Controllable Image-to-Image Translation architecture, which can be viewed as an appealing intersection between Artificial Intelligence and Arts. Specifically, we first present an Image-t

Zhaoyi Sun, Mingquan Lin, Qingqing Zhu, Qianqian Xie

Computer-assisted diagnostic and prognostic systems of the future should be capable of simultaneously processing multimodal data. Multimodal deep learning (MDL), which involves the integration of multiple sources of data, such as images and text, has the potential to revolutionize the analysis and interpretation of biomedical data. However, it only caught re

Aoxiong Yin, Tianyun Zhong, Li Tang, Weike Jin

Most sign language translation (SLT) methods to date require the use of gloss annotations to provide additional supervision information, however, the acquisition of gloss is not easy. To solve this problem, we first perform an analysis of existing models to confirm how gloss annotations make SLT easier. We find that it can provide two aspects of information

Che-Yu Chen, Petr Kotlařík

The ringdown phase of gravitational waves emitted by a perturbed black hole is described by a superposition of exponentially decaying sinusoidal modes, called quasinormal modes (QNMs), whose frequencies depend only on the property of the black hole geometry. The extraction of QNM frequencies of an isolated black hole would allow for testing how well the blac

Mohamed Akrout, Amine Mezghani, Ekram Hossain, Faouzi Bellili

Most research studies on deep learning (DL) applied to the physical layer of wireless communication do not put forward the critical role of the accuracy-generalization trade-off in developing and evaluating practical algorithms. To highlight the disadvantage of this common practice, we revisit a data decoding example from one of the first papers introducing

Guanqun Cao, Jiaqi Jiang, Danushka Bollegala, Shan Luo

The missing signal caused by the objects being occluded or an unstable sensor is a common challenge during data collection. Such missing signals will adversely affect the results obtained from the data, and this issue is observed more frequently in robotic tactile perception. In tactile perception, due to the limited working space and the dynamic environment

Pedro Zattoni Scroccaro, Piet van Beek, Peyman Mohajerin Esfahani, Bilge Atasoy

We propose a method for learning decision-makers' behavior in routing problems using Inverse Optimization (IO). The IO framework falls into the supervised learning category and builds on the premise that the target behavior is an optimizer of an unknown cost function. This cost function is to be learned through historical data, and in the context of routing

Jia-Hui Pan, Ka-Hei Hui, Xiaojie Gao, Shize Zhu

Robotic bin packing is very challenging, especially when considering practical needs such as object variety and packing compactness. This paper presents SDF-Pack, a new approach based on signed distance field (SDF) to model the geometric condition of objects in a container and compute the object placement locations and packing orders for achieving a more com

Eric Atkinson, Ellie Y. Cheng, Guillaume Baudart, Louis Mandel

In this extended abstract, we discuss the opportunity to formally verify that inference systems for probabilistic programming guarantee good performance. In particular, we focus on hybrid inference systems that combine exact and approximate inference to try to exploit the advantages of each. Their performance depends critically on a) the division between exa

Stefano Ceri, Anna Bernasconi, Alessia Gagliardi, Davide Martinenghi

Graph databases are emerging as the leading data management technology for storing large knowledge graphs; significant efforts are ongoing to produce new standards (such as the Graph Query Language, GQL), as well as enrich them with properties, types, schemas, and keys. In this article, we introduce PG-Triggers, a complete proposal for adding triggers to Pro

Bodo Rosenhahn, Tobias J. Osborne

The building blocks of quantum algorithms and software are quantum gates, with the appropriate combination of quantum gates leading to a desired quantum circuit. Deep expert knowledge is necessary to discover effective combinations of quantum gates to achieve a desired quantum algorithm for solving a specific task. This is especially challenging for quantum

Hui-hui Miao, Wanshun Li

We investigate the quantum correlation between light and matter in bipartite quantum systems, drawing on the Jaynes-Cummings model and the Tavis-Cummings model, which are well-established in cavity quantum electrodynamics. Through the resolution of the quantum master equation, we can derive the dissipative dynamics in open systems. To assess the extent of qu

Washington R. de Carvalho, Abha Khakurdikar

RDSim is a fast, accurate and flexible framework for the simulation of the radio emission of downgoing air showers and its detection by an arbitrary array, including showers initiated by neutrino interactions or tau-lepton decays. RDSim was build around speed and is based on simple and fast, yet still accurate, toymodel-like approaches. It models the radio e

Ioannis Caragiannis, Karl Fehrs

{\em Distortion} is a well-established notion for quantifying the loss of social welfare that may occur in voting. As voting rules take as input only ordinal information, they are essentially forced to neglect the exact values the agents have for the alternatives. Thus, in worst-case electorates, voting rules may return low social welfare alternatives and ha

Vinzenz Stummer, Tobias Flöry, Matthias Schneller, Markus Zeiler

Generation of high-fidelity amplified pulse bursts with a regular interpulse interval yields, in the spectral domain, an equidistant pattern of narrowband spectral modes, similar to frequency combs produced by cw mode-locked lasers, but with greatly increased pulse energy. Despite their great potential for nonlinear spectroscopy, material processing, etc., s

Samuele Giuli, Adriano Amaricci, Massimo Capone

We study the conditions to realize an excitonic condensed phase in an electron-hole bilayer system with local Hubbard-like interactions at half-filling, where we can address the interplay with Mott localization. Using Dynamical Mean-Field Theory, we find that an excitonic state is stable in a sizeable region of a phase diagram spanned by the intra-layer (U)

Justin Whitehouse, Zhiwei Steven Wu, Aaditya Ramdas

In the kernelized bandit problem, a learner aims to sequentially compute the optimum of a function lying in a reproducing kernel Hilbert space given only noisy evaluations at sequentially chosen points. In particular, the learner aims to minimize regret, which is a measure of the suboptimality of the choices made. Arguably the most popular algorithm is the G

Zhixiong Li, Xiansi Wang, Xuejuan Liu, Peng Yan

Twisted magnons (TMs) have great potential applications in communication and computing owing to the orbital angular momentum (OAM) degree of freedom. Realizing the unidirectional propagation of TMs is the key to design functional magnonics devices. Here we theoretically study the propagation of TMs in one-dimensional magnetic nanodisk arrays. By performing m

Lianyu Hu, Junjie Dong, Mudi Jiang, Yan Liu

The objective of clusterability evaluation is to check whether a clustering structure exists within the data set. As a crucial yet often-overlooked issue in cluster analysis, it is essential to conduct such a test before applying any clustering algorithm. If a data set is unclusterable, any subsequent clustering analysis would not yield valid results. Despit

Danilo Gregorin Afonso, Alessandro Iacopetti, Filomena Pacella

In this paper, we consider semilinear elliptic problems in a bounded domain $\Omega$ contained in a given unbounded Lipschitz domain $\mathcal C \subset \mathbb R^N$. Our aim is to study how the energy of a solution behaves with respect to volume-preserving variations of the domain $\Omega$ inside $\mathcal C$. Once a rigorous variational approach to this qu

Chuanyu Luo, Nuo Cheng, Sikun Ma, Han Li

Large-scale LiDAR-based point cloud semantic segmentation is a critical task in autonomous driving perception. Almost all of the previous state-of-the-art LiDAR semantic segmentation methods are variants of sparse 3D convolution. Although the Transformer architecture is becoming popular in the field of natural language processing and 2D computer vision, its

Connor Esterwood

As robots become increasingly prevalent in work-oriented collaborations, trust has emerged as a critical factor in their acceptance and effectiveness. However, trust is dynamic and can erode when mistakes are made. Despite emerging research on trust repair in human-robot interaction, significant questions remain about identifying reliable approaches to resto

Chaoyu Liu, Zhonghua Qiao, Chao Li, Carola-Bibiane Schönlieb

Traditional image processing methods employing partial differential equations (PDEs) offer a multitude of meaningful regularizers, along with valuable theoretical foundations for a wide range of image-related tasks. This makes their integration into neural networks a promising avenue. In this paper, we introduce a novel regularization approach inspired by th

Linkai Luo, Qiaoling Yang, Hong Peng, Yiding Wang

The selection of Gaussian kernel parameters plays an important role in the applications of support vector classification (SVC). A commonly used method is the k-fold cross validation with grid search (CV), which is extremely time-consuming because it needs to train a large number of SVC models. In this paper, a new approach is proposed to train SVC and optimi

Patrick Zietkiewicz, Ioannis Kosmidis

The widespread use of maximum Jeffreys'-prior penalized likelihood in binomial-response generalized linear models, and in logistic regression, in particular, are supported by the results of Kosmidis and Firth (2021, Biometrika), who show that the resulting estimates are always finite-valued, even in cases where the maximum likelihood estimates are not, which

Giuseppe Siracusano, Davide Sanvito, Roberto Gonzalez, Manikantan Srinivasan

Cyber Threat Intelligence (CTI) plays a crucial role in assessing risks and enhancing security for organizations. However, the process of extracting relevant information from unstructured text sources can be expensive and time-consuming. Our empirical experience shows that existing tools for automated structured CTI extraction have performance limitations. F

Zixin Guo, Tzu-Jui Julius Wang, Selen Pehlivan, Abduljalil Radman

Vision-language (VL) Pre-training (VLP) has shown to well generalize VL models over a wide range of VL downstream tasks, especially for cross-modal retrieval. However, it hinges on a huge amount of image-text pairs, which requires tedious and costly curation. On the contrary, weakly-supervised VLP (W-VLP) explores means with object tags generated by a pre-tr

Zhe Wang, Jiayi Zhang, Hongyang Du, Dusit Niyato

Extremely large-scale multiple-input-multiple-output (XL-MIMO), which offers vast spatial degrees of freedom, has emerged as a potentially pivotal enabling technology for the sixth generation (6G) of wireless mobile networks. With its growing significance, both opportunities and challenges are concurrently manifesting. This paper presents a comprehensive sur

Washington R. de Carvalho, Abha Khakurdikar

We present RDSim, a fast and comprehensive framework for the simulation of the radio emission and detection of downgoing air showers. It can handle any downgoing shower that can be simulated with ZHAireS including those induced by CC and NC neutrino interactions and $\tau$ decays. RDSim is based on a superposition toymodel that disentangles the Askaryan and

Vincent Caudrelier, Marta Dell'Atti, Anup Anand Singh

Lagrangian multiforms provide a variational framework to describe integrable hierarchies. The case of Lagrangian $1$-forms covers finite-dimensional integrable systems. We use the theory of Lie dialgebras introduced by Semenov-Tian-Shansky to construct a Lagrangian $1$-form. Given a Lie dialgebra associated with a Lie algebra $\mathfrak{g}$ and a collection

Taeho Ryu, Pau Amaro Seoane, Andrew M. Taylor, Sebastian T. Ohlmann

In stellar-dense environments, stars can collide with each other. For collisions close to a supermassive black hole (SMBH), the collisional kinetic energy can be so large that the colliding stars can be completely destroyed, potentially releasing an amount of energy comparable to that of a supernova. Such violent collisions, which we call BH-driven disruptiv

Lena Baumann, Lukas Einkemmer, Christian Klingenberg, Jonas Kusch

Computational methods for thermal radiative transfer problems exhibit high computational costs and a prohibitive memory footprint when the spatial and directional domains are finely resolved. A strategy to reduce such computational costs is dynamical low-rank approximation (DLRA), which represents and evolves the solution on a low-rank manifold, thereby sign

Andrzej Cegielski

We give properties of strict pseudocontractions and demicontractions defined on a Hilbert space, which constitute wide classes of operators that arise in iterative methods for solving fixed point problems. In particular, we give necessary and sufficient conditions under which a convex combination and composition of strict pseudocontractions as well as demico

Shaina Raza, Chen Ding, Deval Pandya

Discriminatory language and biases are often present in hate speech during conversations, which usually lead to negative impacts on targeted groups such as those based on race, gender, and religion. To tackle this issue, we propose an approach that involves a two-step process: first, detecting hate speech using a classifier, and then utilizing a debiasing co

Kaiwen Cai, Chris Xiaoxuan Lu, Xingyu Zhao, Xiaowei Huang

Most image retrieval research prioritizes improving predictive performance, often overlooking situations where the reliability of predictions is equally important. The gap between model performance and reliability requirements highlights the need for a systematic approach to analyze and address the risks associated with image retrieval. Uncertainty quantific

Vicente Pina Canelles, Manuel G. Algaba, Hermanni Heimonen, Miha Papič

Digital-Analog Quantum Computation (DAQC) has recently been proposed as an alternative to the standard paradigm of digital quantum computation. DAQC creates entanglement through a continuous or analog evolution of the whole device, rather than by applying two-qubit gates. This manuscript describes an in-depth analysis of DAQC by extending its implementation

Dang Thanh Vu, Vo Hoang Trong, Yu Gwang-Hyun, Kim Jin-Young

This study introduces "shortcut routing," a novel routing mechanism in capsule networks that addresses computational inefficiencies by directly activating global capsules from local capsules, eliminating intermediate layers. An attention-based approach with fuzzy coefficients is also explored for improved efficiency. Experimental results on Mnist, smallnorb,

Nicolas Mateos, Parijat Sil, Sankarshan Talluri, Carlo Manzo

Over recent years, super-resolution and single-molecule imaging methods have delivered unprecedented details on the nanoscale organization and dynamics of individual molecules in different contexts. Yet, visualizing single-molecule processes in living cells with the required spatial and temporal resolution remains highly challenging. Here, we report on an an

Zhili Ng, Haozhe Wang, Zhengshen Zhang, Francis Tay Eng Hock

In this work, we present SynTable, a unified and flexible Python-based dataset generator built using NVIDIA's Isaac Sim Replicator Composer for generating high-quality synthetic datasets for unseen object amodal instance segmentation of cluttered tabletop scenes. Our dataset generation tool can render complex 3D scenes containing object meshes, materials, te

Xiaomeng Ren, Hermes Bloomfield-Gadêlha

Sperm modulate their flagellar symmetry to navigate through complex physico-chemical environments and achieve reproductive function. Yet it remains elusive how sperm swim forwards despite the inherent asymmetry of several components that constitutes the flagellar engine. Despite the critical importance of symmetry, or the lack of it, on sperm navigation and

Akshat Gupta, Xiaomo Liu, Sameena Shah

Domain adaptation is an important and widely studied problem in natural language processing. A large body of literature tries to solve this problem by adapting models trained on the source domain to the target domain. In this paper, we instead solve this problem from a dataset perspective. We modify the source domain dataset with simple lexical transformatio

J. E. García-Ramos, A. Sáiz, J. M. Arias, L. Lamata

In this paper, the application of quantum simulations and quantum machine learning to solve low-energy nuclear physics problems is explored. The use of quantum computing to deal with nuclear physics problems is, in general, in its infancy and, in particular, the use of quantum machine learning in the realm of nuclear physics at low energy is almost nonexiste

Ibrahim Tolga Öztürk, Rostislav Nedelchev, Christian Heumann, Esteban Garces Arias

Recent studies have demonstrated how to assess the stereotypical bias in pre-trained English language models. In this work, we extend this branch of research in multiple different dimensions by systematically investigating (a) mono- and multilingual models of (b) different underlying architectures with respect to their bias in (c) multiple different language

Michele Azzone, Roberto Baviera

Monroe (1978) demonstrates that any local semimartingale can be represented as a time-changed Brownian Motion (BM). A natural question arises: does this representation theorem hold when the BM and the time-change are independent? We prove that a local semimartingale is not equivalent to a BM with a time-change that is independent from the BM. Our result is o

Zihao Zhu, Mingda Zhang, Shaokui Wei, Li Shen

Data-poisoning based backdoor attacks aim to insert backdoor into models by manipulating training datasets without controlling the training process of the target model. Existing attack methods mainly focus on designing triggers or fusion strategies between triggers and benign samples. However, they often randomly select samples to be poisoned, disregarding t

Hafsa Bousbiat, Yassine Himeur, Abbes Amira, Wathiq Mansoor

Non-intrusive Load Monitoring (NILM) algorithms, commonly referred to as load disaggregation algorithms, are fundamental tools for effective energy management. Despite the success of deep models in load disaggregation, they face various challenges, particularly those pertaining to privacy and security. This paper investigates the sensitivity of prominent dee

Jeppe Heini Mikkelsen, Matteo Fumagalli

This paper presents a novel planning method that achieves navigation of multi-robot formations in cluttered environments, while maintaining the formation throughout the robots motion. The method utilises a decentralised approach to find feasible formation parameters that guarantees formation constraints for rigid formations. The method proves to be computati

Miguel Angel Principe Martin

The Large Hadron Collider (LHC) produces a vast sample of top-quark pairs and single-top quarks. Measurements of the inclusive top-quark production rates at the LHC have reached a precision of several percent and test advanced next-to-next-to-leading-order predictions in QCD. Measurements of production cross sections test the Standard Model predictions and h

Varun Krishna, Tarun Sai, Sriram Ganapathy

The representation learning of speech, without textual resources, is an area of significant interest for many low resource speech applications. In this paper, we describe an approach to self-supervised representation learning from raw audio using a hidden unit clustering (HUC) framework. The input to the model consists of audio samples that are windowed and

Alina Joch, Götz S. Uhrig

Optically driven electronic spins coupled in quantum dots to nuclear spins show a pre-pulse signal (revival amplitude) after having been trained by long periodic sequences of pulses. The size of this revival amplitude depends on the external magnetic field in a specific way due to the varying commensurability of the nuclear Larmor precession period with the