May 2024 arXiv papers — page 12

D. O. Kashinski, D. Talbi, A. P. Hickman, O. E. Di Nallo

A quantitative theoretical study of the dissociative recombination of SH$^+$ with electrons has been carried out. Multireference, configuration interaction calculations were used to determine accurate potential energy curves for SH$^+$ and SH. The block diagonalization method was used to disentangle strongly interacting SH valence and Rydberg states and to c

P. Padovani, E. Resconi, M. Ajello, C. Bellenghi

We present a comprehensive multi-messenger study of NGC 1068, the prototype Seyfert II galaxy recently associated with high-energy IceCube neutrinos. Various aspects of the source, including its nuclear activity, jet, outflow, and starburst region, are analyzed in detail using a multi-wavelength approach and relevant luminosities are derived. We then explore

Frederick Riemenschneider, Kevin Krahn

Historical languages present unique challenges to the NLP community, with one prominent hurdle being the limited resources available in their closed corpora. This work describes our submission to the constrained subtask of the SIGTYP 2024 shared task, focusing on PoS tagging, morphological tagging, and lemmatization for 13 historical languages. For PoS and m

Moritz Sallermann, Amrita Goswami, Alejandro Peña-Torres, Rohit Goswami

Lava emplacement is a complex physical phenomenon, affected by several factors. These include, but are not limited to features of the terrain, the lava settling process, the effusion rate or total erupted volume, and the probability of effusion from different locations. One method, which has been successfully employed to predict lava flow emplacement and for

Ghislain Fourny

We show that a category of causal contextuality scenarios with no cycles, unique causal bridges, and causally secured covers is equivalent to a category containing a subclass of the formerly published spacetime games, which generalize game theory to decisions arbitrarily located in Minkowski spacetime. This insight leads to certain constructs and proofs bein

Chao Zhang, Weirong Cui, Jingjing Guo

Monitoring sleep states is essential for evaluating sleep quality and diagnosing sleep disorders. Traditional manual staging is time-consuming and prone to subjective bias, often resulting in inconsistent outcomes. Here, we developed an automated model for sleep staging and disorder classification to enhance diagnostic accuracy and efficiency. Considering th

Gonca Yilmaz, Songyou Peng, Marc Pollefeys, Francis Engelmann

Recently, Vision-Language Models (VLMs) have advanced segmentation techniques by shifting from the traditional segmentation of a closed-set of predefined object classes to open-vocabulary segmentation (OVS), allowing users to segment novel classes and concepts unseen during training of the segmentation model. However, this flexibility comes with a trade-off:

Mark Booth, Pamela Klaassen, Claudia Cicone, Tony Mroczkowski

Sub-mm and mm wavelengths provide a unique view of the Universe, from the gas and dust that fills and surrounds galaxies to the chromosphere of our own Sun. Current single-dish facilities have presented a tantalising view of the brightest (sub-)mm sources, and interferometers have provided the exquisite resolution necessary to analyse the details in small fi

Costas Mavromatis, George Karypis

Knowledge Graphs (KGs) represent human-crafted factual knowledge in the form of triplets (head, relation, tail), which collectively form a graph. Question Answering over KGs (KGQA) is the task of answering natural questions grounding the reasoning to the information provided by the KG. Large Language Models (LLMs) are the state-of-the-art models for QA tasks

Fuki Ito, Toshio Suzuki

Herein, we investigate the zero-error randomized complexity, which is the least cost against the worst input, of AND-OR tree computation by imposing various restrictions on the algorithm to find the Boolean value of the root of that tree and no restrictions on the tree shape. When a tree satisfies a certain condition regarding its symmetry, directional algor

Shifeng Xiong

Principal component analysis and factor analysis are fundamental multivariate analysis methods. In this paper a unified framework to connect them is introduced. Under a general latent variable model, we present matrix optimization problems from the viewpoint of loss function minimization, and show that the two methods can be viewed as solutions to the optimi

Liyun Deng, Lei Jin, Guangcheng Wang, Quan Shi

In response to the social issue of the increasing number of elderly vulnerable groups going missing due to the aggravating aging population in China, our team has developed a wearable anti-loss device and intelligent early warning system for elderly individuals with intermittent dementia using artificial intelligence and IoT technology. This system comprises

Kiri Choi, Will Rosenbluth, Isabella R. Graf, Nirag Kadakia

Living systems continually respond to signals from the surrounding environment. Survival requires that their responses adapt quickly and robustly to the changes in the environment. One particularly challenging example is olfactory navigation in turbulent plumes, where animals experience highly intermittent odor signals while odor concentration varies over ma

Serhii Bardyla, Luna Elliott, James Mitchell, Yann Péresse

We classify all Polish semigroup topologies on the symmetric inverse monoid on the natural numbers. This result answers a question of Elliott et al. There are countably infinitely many such topologies. Under containment, these Polish semigroup topologies form a join-semilattice with infinite descending chains, no infinite ascending chains, and arbitrarily la

Srijan Bharati Das, Lukas Einramhof, Lisa Bugnet

Recent measurements of magnetic field strength inside the radiative interior of red giant stars open the way towards the characterization of the geometry of stable large-scale magnetic fields. However, current measurements do not properly constrain the topology of magnetic fields due to degeneracies on the observed magnetic field signature on such $\ell=1$ m

Niki van Stein, Thomas Bäck

Large Language Models (LLMs) such as GPT-4 have demonstrated their ability to understand natural language and generate complex code snippets. This paper introduces a novel Large Language Model Evolutionary Algorithm (LLaMEA) framework, leveraging GPT models for the automated generation and refinement of algorithms. Given a set of criteria and a task definiti

Yingshan Chang, Yonatan Bisk

Counting is a fundamental example of generalization, whether viewed through the mathematical lens of Peano's axioms defining the natural numbers or the cognitive science literature for children learning to count. The argument holds for both cases that learning to count means learning to count infinitely. While few papers have tried to distill transformer "re

Bakar Chargeishvili, Levente Fekésházy, Gábor Somogyi, Sam Van Thurenhout

We present LinApart, a routine designed for efficiently performing the univariate partial fraction decomposition of large symbolic expressions. Our method is based on an explicit closed formula for the decomposition of rational functions with fully factorized denominators. We provide implementations in both the Wolfram Mathematica and C languages, made avail

Tsz-Kiu Aaron Chow, Jingze Zhu

This paper investigates quantitative metric inequalities for manifolds with positive isotropic curvature (PIC). Our results include upper bounds on the bandwidth and focal radius of hypersurfaces in PIC manifolds, contingent on boundary convexities and Betti numbers. The proof is based on exploiting the spectral properties of a twisted de Rham-Hodge operator

Pascal Weckesser, Kritsana Srakaew, Tizian Blatz, David Wei

The competition of different length scales in quantum many-body systems leads to various novel phenomena, including the emergence of correlated dynamics or non-local order. To access and investigate such effects in an itinerant lattice-based quantum simulator, it has been proposed to introduce tunable extended-range interactions using off-resonant optical co

Avetik Karagulyan, Egor Shulgin, Abdurakhmon Sadiev, Peter Richtárik

Cross-device training is a crucial subfield of federated learning, where the number of clients can reach into the billions. Standard approaches and local methods are prone to issues such as client drift and insensitivity to data similarities. We propose a novel algorithm (SPAM) for cross-device federated learning with non-convex losses, which solves both iss

Amine Bechar, Youssef Elmir, Yassine Himeur, Rafik Medjoudj

This review article discusses the roles of federated learning (FL) and transfer learning (TL) in cancer detection based on image analysis. These two strategies powered by machine learning have drawn a lot of attention due to their potential to increase the precision and effectiveness of cancer diagnosis in light of the growing importance of machine learning

Philippa S. Cole

It is possible that a multi-component dark matter model is required if primordial black holes only contribute to a fraction of the energy density in dark matter. This is increasingly more likely with respect to the case of $f_{\rm PBH} = 1$, since there is only one remaining window, on asteroid-mass scales, where primordial black holes can make up all of the

Man-Chung Yue, Yves Rychener, Daniel Kuhn, Viet Anh Nguyen

The state-of-the-art methods for estimating high-dimensional covariance matrices all shrink the eigenvalues of the sample covariance matrix towards a data-insensitive shrinkage target. The underlying shrinkage transformation is either chosen heuristically - without compelling theoretical justification - or optimally in view of restrictive distributional assu

Mauro Lucci, Daniel Severin, Paula Zabala

This work focuses on exact methods for a Simultaneous Vehicle Routing and Crew Scheduling Problem in long-haul transport. Pickup-and-delivery requests with time windows must be fullfiled over a multi-day planning horizon. Unlike some classic approaches, the correspondence between trucks and drivers is not fixed and they can be exchanged in some locations and

Jale Sadreddini, Omer Haliloglu, Andres Reial

Distributed Multiple-Input and Multiple-Output (D-MIMO) is envisioned to play a significant role in future wireless communication systems as an effective means to improve coverage and capacity. In this paper, we have studied the impact of a practical two-level data routing scheme on radio performance in a downlink D-MIMO scenario with segmented fronthaul. At

Sun Zhanbo, Dong Caiyin, Ji Ang, Zhao Ruibin

Accurate prediction of future trajectories for surrounding vehicles is vital for the safe operation of autonomous vehicles. This study proposes a Lane Graph Transformer (LGT) model with structure-aware capabilities. Its key contribution lies in encoding the map topology structure into the attention mechanism. To address variations in lane information from di

Ethan Baker, Dennis Bollweg, Peter Boyle, Ian Cloët

We present a direct lattice QCD calculation of the $x$-dependence of the pion distribution amplitude (DA), which is performed using the quasi-DA in large momentum effective theory on a domain-wall fermion ensemble at physical quark masses and spacing $a\approx 0.084$ fm. The bare quais-DA matrix elements are renormalized in the hybrid scheme and matched to $

Jiří Kalmár, František Karlický

Zr-based MXenes recently attracted attention because of its experimental preparation showing temperature stability, mechanical strength, and promising energy, sensoric, and electrochemistry applications. However, necessary theoretical predictions at a precise/predictive level are complicated due to essential excitonic features and strong electron correlation

Dong Hae Mangalindan, Vaibhav Srivastava

Using a dual-task paradigm, we explore how robot actions, performance, and the introduction of a secondary task influence human trust and engagement. In our study, a human supervisor simultaneously engages in a target-tracking task while supervising a mobile manipulator performing an object collection task. The robot can either autonomously collect the objec

Prashanth Chandran, Gaspard Zoss, Paulo Gotardo, Derek Bradley

In this paper, we examine 3 important issues in the practical use of state-of-the-art facial landmark detectors and show how a combination of specific architectural modifications can directly improve their accuracy and temporal stability. First, many facial landmark detectors require face normalization as a preprocessing step, which is accomplished by a sepa

Yunsoo Ha, Sara Shashaani, Raghu Pasupathy

Model update (MU) and candidate evaluation (CE) are classical steps incorporated inside many stochastic trust-region (TR) algorithms. The sampling effort exerted within these steps, often decided with the aim of controlling model error, largely determines a stochastic TR algorithm's sample complexity. Given that MU and CE are amenable to variance reduction,

Lucas Pollyceno, Anubhav Chaturvedi, Chithra Raj, Pedro R. Dieguez

Beyond the foundational significance, the problem of bounding nonlocal correlations by reasonable physical principles has meaningful practical consequences, particularly for device-independent (DI) cryptographic security. In this work, we advance in this direction, demonstrating that the IC is enough to ensure DI security on quantum key distribution (QKD) pr

Rustem Islamov, Yuan Gao, Sebastian U. Stich

Communication efficiency has garnered significant attention as it is considered the main bottleneck for large-scale decentralized Machine Learning applications in distributed and federated settings. In this regime, clients are restricted to transmitting small amounts of quantized information to their neighbors over a communication graph. Numerous endeavors h

Peter Græns Larsen, Anne E. B. Nielsen

We propose a type of phase transition in quantum many-body systems, which occurs in highly excited quantum many-body scar states, while most of the spectrum is largely unaffected. Such scar state phase transitions can be realized by embedding a matrix product state, known to undergo a phase transition, as a scar state into the thermal spectrum of a parent Ha

Zhiyuan He, Pin-Yu Chen, Tsung-Yi Ho

The rapid advances in generative AI models have empowered the creation of highly realistic images with arbitrary content, raising concerns about potential misuse and harm, such as Deepfakes. Current research focuses on training detectors using large datasets of generated images. However, these training-based solutions are often computationally expensive and

Juan Escudero Pedrosa, Ivan Agudo, Daniel Morcuende, Jorge Otero-Santos

IOP4 is a pipeline to perform photometry and polarimetry analysis of optical data from Calar Alto (CAHA) and Sierra Nevada (OSN) observatories. IOP4 implements Object Relational Mapping (ORM) to seamlessly integrate all information about the reduction and results in a database which can be used to query and plot results, flag data and inspect the reduction p

Thomas Maeke, John McCaskill, Dominic Funke, Pierre Mayr

Lablets are autonomous microscopic particles with programmable CMOS electronics that can control electrokinetic phenomena and electrochemical reactions in solution via actuator and sensor microelectrodes. In this paper, we describe the design and fabrication of optimized singulated lablets (CMOS3) with dimensions 140x140x50 micrometers carrying an integrated

Edoardo Arnaudo, Jacopo Lungo Vaschetti, Lorenzo Innocenti, Luca Barco

Very-High Resolution (VHR) remote sensing imagery is increasingly accessible, but often lacks annotations for effective machine learning applications. Recent foundation models like GroundingDINO and Segment Anything (SAM) provide opportunities to automatically generate annotations. This study introduces FMARS (Foundation Model Annotations in Remote Sensing),

Yury Grabovsky, Graeme W. Milton, Aaron Welters

We provide a complete characterization of a subclass of weakly associative means of positive operators in the class of symmetric Kubo-Ando means. This class, which includes the geometric mean, was first introduced and studied in L. Moln\'ar, ``Characterizations of certain means of positive operators," Linear Algebra Appl. 567 (2019) 143-166, where he gives a

Karl Strecker, Sabit Ekin, John F. O'Hara

This article discusses the challenges and opportunities of managing group delay dispersion (GDD) and its relation to the performance standards of future sixth-generation (6G) wireless communication systems utilizing terahertz frequency waves. The unique susceptibilities of 6G systems to GDD are described, along with a quantitative description of the sources

G. Bougas, G. C. Katsimiga, P. G. Kevrekidis, S. I. Mistakidis

We unravel stationary states in the form of dark soliton stripes, bubbles, and kinks embedded in a two-dimensional droplet-bearing setting emulated by an extended Gross-Pitaevskii approach. The existence of these configurations is corroborated through an effectively reduced potential picture demonstrating their concrete parametric regions of existence. The e

A. Thabuis, M. Koratzinos, G. Kirby, M. Liebsch

A single aperture Canted-Cosine-Theta (CCT) quadrupole magnet, made of NbTi superconductors, has been developed for the final focus region of the FCC-ee study. The conductor layout is optimised to mitigate edge effects on one of the two sides of the magnet that typically lead to undesired higher-order multipoles. Experimental results of a prototype, includin

Kuldeep R Barad, Antoine Richard, Jan Dentler, Miguel Olivares-Mendez

Generalizable perception is one of the pillars of high-level autonomy in space robotics. Estimating the structure and motion of unknown objects in dynamic environments is fundamental for such autonomous systems. Traditionally, the solutions have relied on prior knowledge of target objects, multiple disparate representations, or low-fidelity outputs unsuitabl

CJ Bradly, EJ Janse van Rensburg

Polymers in confined spaces are compressed and have reduced conformational entropy, and will partially or fully escape from confinement if conditions are suitable. This is in particular the case for a polymer grafted in a pore. The escape of the polymer from the pore may be considered a partial translocation from the pore into bulk solution, and the resultin

Akihiro Higashitani, Norihiro Nakashima

Characteristic quasi-polynomials are the enumerative functions counting the number of elements in the complement of hyperplane arrangements modulo positive integers. A notable phenomenon in this context is period collapse, where the quasi-polynomial reduces to a polynomial or has a smaller period than the lcm period. In this paper, we compute the characteris

Ihab Asaad, Maxime Jacquelin, Olivier Perrotin, Laurent Girin

Speech inpainting consists in reconstructing corrupted or missing speech segments using surrounding context, a process that closely resembles the pretext tasks in Self-Supervised Learning (SSL) for speech encoders. This study investigates using SSL-trained speech encoders for inpainting without any additional training beyond the initial pretext task, and sim

Federico Milano

This letter proposes a general dynamic formulation of slack bus. With this aim, the angle constraint imposed by the slack bus is redefined as a set of differential equations and an energy source. The existence and role of the transient component of this source is also discussed in the letter. Based on this framework, the letter shows that the swing equations

Chen Xiong, Xiangyu Qi, Pin-Yu Chen, Tsung-Yi Ho

Safety, security, and compliance are essential requirements when aligning large language models (LLMs). However, many seemingly aligned LLMs are soon shown to be susceptible to jailbreak attacks. These attacks aim to circumvent the models' safety guardrails and security mechanisms by introducing jailbreak prompts into malicious queries. In response to these

Yiu Man Wong, Angelina Zheng

We compute the rational cohomology of the moduli space $\mathcal{M}_{4,1}$ of non-singular genus $4$ curves with $1$ marked point, using Gorinov-Vassiliev's method.

Constantin P. Niculescu

A quadrilateral inequality established by C. Sch\"otz in the context of Hilbert spaces is extended to the framework of Banach spaces. Our approach is based on the majorization theory and a substitute for the parallelogram law associated with Clarkson's notion of von Neumann-Jordan constant. As a by-product, several functional inequalities that extend classic

Indubala I Satija

The hierarchical structure of the butterfly fractal -- the Hofstader butterfly, is found to be described by an octonary tree. In this framework of building the butterfly graph, every iteration generates sextuplets of butterflies, each with a tail that is made up of an infinity of butterflies. Identifying {\it butterfly with a tale} as the building block, the

A. Yu. Levochkina, H. G. Ahmad, P. Mastrovito, I. Chatterjee

Traveling Wave Parametric Amplifiers (TWPAs) are extensively employed in experiments involving weak microwave signals for their highly desirable quantum-limited and broadband characteristics. However, TWPAs' broadband nature comes with the disadvantage of admitting the activation of spurious nonlinear processes, such as harmonics generation, that can potenti

Quentin W. Richter, Jan M. Kaspari, Thomas K. Bracht, Leonid Yatsenko

With the realization of controlled quantum systems, exploring excitations beyond the resonant case opens new possibilities. We investigate an extended Jaynes-Cummings model where two photon modes are coupled off-resonantly to a quantum emitter. This allows us to identify few-photon scattering mechanisms that lead to a full inversion of the emitter while tran

Reza Arabpour, John Armstrong, Luca Galimberti, Anastasis Kratsios

Predicting the conditional evolution of Volterra processes with stochastic volatility is a crucial challenge in mathematical finance. While deep neural network models offer promise in approximating the conditional law of such processes, their effectiveness is hindered by the curse of dimensionality caused by the infinite dimensionality and non-smooth nature

Luca Barco, Angelica Urbanelli, Claudio Rossi

Rapid detection and well-timed intervention are essential to mitigate the impacts of wildfires. Leveraging remote sensed data from satellite networks and advanced AI models to automatically detect hotspots (i.e., thermal anomalies caused by active fires) is an effective way to build wildfire monitoring systems. In this work, we propose a novel dataset contai

Jingchang Chen, Hongxuan Tang, Zheng Chu, Qianglong Chen

Despite recent progress made by large language models in code generation, they still struggle with programs that meet complex requirements. Recent work utilizes plan-and-solve decomposition to decrease the complexity and leverage self-tests to refine the generated program. Yet, planning deep-inside requirements in advance can be challenging, and the tests ne

Miriam Navarro, Álvaro Becerra, Roberto Daza, Ruth Cobos

In this paper, we present an approach in the Multimodal Learning Analytics field. Within this approach, we have developed a tool to visualize and analyze eye movement data collected during learning sessions in online courses. The tool is named VAAD, an acronym for Visual Attention Analysis Dashboard. These eye movement data have been gathered using an eye-tr

Hao Cheng, Erjia Xiao, Jiayan Yang, Jinhao Duan

Multimodal Large Language Models (MLLMs) demonstrate exceptional performance in cross-modality interaction, yet they also suffer adversarial vulnerabilities. In particular, the transferability of adversarial examples remains an ongoing challenge. In this paper, we specifically analyze the manifestation of adversarial transferability among MLLMs and identify

David Stap, Eva Hasler, Bill Byrne, Christof Monz

Fine-tuning large language models (LLMs) for machine translation has shown improvements in overall translation quality. However, it is unclear what is the impact of fine-tuning on desirable LLM behaviors that are not present in neural machine translation models, such as steerability, inherent document-level translation abilities, and the ability to produce l

Dennis Shen, Anish Agarwal, Vishal Misra, Bjoern Schelter

The purpose of this article is to infer patient level outcomes from population level randomized control trials (RCTs). In this pursuit, we utilize the recently proposed synthetic nearest neighbors (SNN) estimator. At its core, SNN leverages information across patients to impute missing data associated with each patient of interest. We focus on two types of m

Gennadiy Feldman

The well-known Heyde theorem characterizes the Gaussian distributions on the real line by the symmetry of the conditional distribution of one linear form of independent random variables given another. We generalize this theorem to groups of the form $\mathbb{R}\times F$, where $F$ is a finite Abelian group such that its 2-component is isomorphic to the addit

Chunyu Pan, Quan Zhang, Yue Zhu, Shengzhou Kong

The network approach to characterizing psychopathology departs from traditional latent categorical and dimensional approaches. Causal interplay among symptoms contributed to dynamic psychopathology system. Therefore, analyzing the symptom clusters is critical for understanding mental disorders. Furthermore, despite extensive research studying the topological

Stephanie Seltmann, Alexander Hasse

The synthesis of compliant mechanisms (CMs) is frequently achieved through topology optimization. Many synthesis approaches simplify implementation by assuming small distortions, but this limits their practical application since CMs typically undergo large deformations that include geometric and material nonlinearities. CMs designed to generate a desired def

Benoit Oriol

Multi-target linear shrinkage is an extension of the standard single-target linear shrinkage for covariance estimation. We combine several constant matrices - the targets - with the sample covariance matrix. We derive the oracle and a \textit{bona fide} multi-target linear shrinkage estimator with exact and empirical mean. In both settings, we proved its con

Tomás Hüttebräucker, Mohamed Sana, Emilio Calvanese Strinati

Semantic channel equalization has emerged as a solution to address language mismatch in multi-user semantic communications. This approach aims to align the latent spaces of an encoder and a decoder which were not jointly trained and it relies on a partition of the semantic (latent) space into atoms based on the the semantic meaning. In this work we explore t

Muhammad Saif Ullah Khan, Dhavalkumar Limbachiya, Didier Stricker, Muhammad Zeshan Afzal

Human pose estimation is a key task in computer vision with various applications such as activity recognition and interactive systems. However, the lack of consistency in the annotated skeletons across different datasets poses challenges in developing universally applicable models. To address this challenge, we propose a novel approach integrating multi-teac

Philipp G. Haselwarter, Kwing Hei Li, Markus de Medeiros, Simon Oddershede Gregersen

We present Tachis, a higher-order separation logic to reason about the expected cost of probabilistic programs. Inspired by the uses of time credits for reasoning about the running time of deterministic programs, we introduce a novel notion of probabilistic cost credit. Probabilistic cost credits are a separation logic resource that can be used to pay for th

Shivam Grover, Amin Jalali, Ali Etemad

Existing approaches for learning representations of time-series keep the temporal arrangement of the time-steps intact with the presumption that the original order is the most optimal for learning. However, non-adjacent sections of real-world time-series may have strong dependencies. Accordingly, we raise the question: Is there an alternative arrangement for

Kai Wu, Boyuan Jiang, Zhengkai Jiang, Qingdong He

Multimodal large language models (MLLMs) contribute a powerful mechanism to understanding visual information building on large language models. However, MLLMs are notorious for suffering from hallucinations, especially when generating lengthy, detailed descriptions for images. Our analysis reveals that hallucinations stem from the inherent summarization mech

Nidhin Sudarsanan Ragini, Sk Sazim

Higher-order quantum theory deals with causal quantum processes, described by quantum combs, and test procedures, described by quantum testers, "measuring" these processes. In this work, we show that "jointly non-implementable" or incompatible quantum testers perform better in distinguishability tasks than their compatible counterparts. To demonstrate our fi

Elena Grazia Gado, Tommaso Martorella, Luca Zunino, Paola Mejia-Domenzain

Intelligent Tutoring Systems (ITS) enhance personalized learning by predicting student answers to provide immediate and customized instruction. However, recent research has primarily focused on the correctness of the answer rather than the student's performance on specific answer choices, limiting insights into students' thought processes and potential misco

Pranav Gupta, Anastasia Bizyaeva, Ravi Banavar

Lyapunov-Schmidt reduction is a dimensionality reduction technique in nonlinear systems analysis that is commonly utilised in the study of bifurcation problems in high-dimensional systems. The method is a systematic procedure for reducing the dimensionality of systems of algebraic equations that have singular points, preserving essential features of their so

Pedro Martin, Antonio Rodrigues, Joao Ascenso, Maria Paula Queluz

Neural radiance fields (NeRF) are a groundbreaking computer vision technology that enables the generation of high-quality, immersive visual content from multiple viewpoints. This capability has significant advantages for applications such as virtual/augmented reality, 3D modelling, and content creation for the film and entertainment industry. However, the ev

S. Sulis, L. Borsato, S. Grouffal, H. P. Osborn

HIP 41378 d is a long-period planet that has only been observed to transit twice, three years apart, with K2. According to stability considerations and a partial detection of the Rossiter-McLaughlin effect, $P_\mathrm{d} = 278.36$ d has been determined to be the most likely orbital period. We targeted HIP 41378 d with CHEOPS at the predicted transit timing b

S. Hubrig, M. Schöller, S. P. Jarvinen, A. Cikota

Studies of the magnetic characteristics of massive stars have recently received significant attention because they are progenitors of highly magnetised compact objects. Stars initially more massive than about 8M_sun leave behind neutron stars and black holes by the end of their evolution. The merging of binary compact remnant systems produces astrophysical t

Gert Vercleyen

This thesis explains the methods and algorithms we used to obtain explicit F symbols, R symbols, and pivotal coefficients of all multiplicity-free pivotal fusion categories up to rank 7. The thesis starts by introducing the concept of a unitary modular fusion system via two applications: modeling anyons for topological quantum computation and calculating bra

Nicolai Simon, Winnifried Wollner

In this work, we consider optimality conditions of an optimal control problem governed by an obstacle problem. Here, we focus on introducing a, matrix valued, control variable as the coefficients of the obstacle problem. As it is well known, obstacle problems can be formulated as a complementarity system and consequently the associated solution operator is n

Yifei Fan, Shaochuan Wu, Xixi Bi, Guoyu Li

Applying integrated sensing and communication (ISAC) to a cell-free massive multiple-input multiple-output (CF mMIMO) architecture has attracted increasing attention. This approach equips CF mMIMO networks with sensing capabilities and resolves the problem of unreliable service at cell edges in conventional cellular networks. However, existing studies on CF-

Xiao Xu, Xizhe Zhang, Yan Zhang

Mood disorders such as depression and anxiety often manifest through facial expressions, but existing machine learning algorithms designed to assess these disorders have been hindered by small datasets and limited real-world applicability. To address this gap, we analyzed facial videos of 11,427 participants - a dataset two orders of magnitude larger than th

Anjum Shaik, Kristoffer Larsen, Nancy E. Lane, Chen Zhao

Despite advancements in medical care, hip fractures impose a significant burden on individuals and healthcare systems. This paper focuses on the prediction of hip fracture risk in older and middle-aged adults, where falls and compromised bone quality are predominant factors. We propose a novel staged model that combines advanced imaging and clinical data to

Vilas Patil, Sanat Ghosh, Amit Basu, Kuldeep

Assembling atomic layers of van der Waals materials (vdW) combines the physics of two materials, offering opportunities for novel functional devices. Realization of this has been possible because of advancements in nanofabrication processes which often involve chemical processing of the materials under study; this can be detrimental to device performance. To

Daniel Bennett, Gabriel Martínez-Carracedo, Xu He, Jaime Ferrer

Two-dimensional (2D) materials that exhibit spontaneous magnetization, polarization or strain (referred to as ferroics) have the potential to revolutionize nanotechnology by enhancing the multifunctionality of nanoscale devices. However, multiferroic order is difficult to achieve, requiring complicated coupling between electron and spin degrees of freedom. W

Xinran Sun, Zhengming Zhang, Luxi Yang

The efficacy of massive multiple-input multiple-output (MIMO) techniques heavily relies on the accuracy of channel state information (CSI) in frequency division duplexing (FDD) systems. Many works focus on CSI compression and quantization methods to enhance CSI reconstruction accuracy with lower feedback overhead. In this letter, we propose CsiConformer, a n

Stavros Diolatzis, Tobias Zirr, Alexandr Kuznetsov, Georgios Kopanas

In the wake of many new ML-inspired approaches for reconstructing and representing high-quality 3D content, recent hybrid and explicitly learned representations exhibit promising performance and quality characteristics. However, their scaling to higher dimensions is challenging, e.g. when accounting for dynamic content with respect to additional parameters s

Eddie Aamari, Clément Berenfeld

Given i.i.d. sample from a stratified mixture of immersed manifolds of different dimensions, we study the minimax estimation of the underlying stratified structure. We provide a constructive algorithm allowing to estimate each mixture component at its optimal dimension-specific rate adaptively. The method is based on an ascending hierarchical co-detection of

Markus Bachmayr, Wolfgang Dahmen, Mathias Oster

This paper is about learning the parameter-to-solution map for systems of partial differential equations (PDEs) that depend on a potentially large number of parameters covering all PDE types for which a stable variational formulation (SVF) can be found. A central constituent is the notion of variationally correct residual loss function meaning that its value

Mingjie Chen, Hezhao Zhang, Yuanchao Li, Jiachen Luo

Speech emotion recognition is a challenging classification task with natural emotional speech, especially when the distribution of emotion types is imbalanced in the training and test data. In this case, it is more difficult for a model to learn to separate minority classes, resulting in those sometimes being ignored or frequently misclassified. Previous wor

Apostolos A. Christou, Maria Gritsevich

Meteor and bolide phenomena caused by the atmospheric ablation of incoming meteoroids are predicted to occur at the planet Venus. Their systematic observation would allow to measure and compare the sub-mm to m meteoroid flux at different locations in the solar system. Using a physical model of atmospheric ablation, we demonstrate that Venus meteors would be

Kagan Ozturk, Haiyu Wu, Kevin W. Bowyer

Appearance of a face can be greatly altered by growing a beard and mustache. The facial hairstyles in a pair of images can cause marked changes to the impostor distribution and the genuine distribution. Also, different distributions of facial hairstyle across demographics could cause a false impression of relative accuracy across demographics. We first show

ATLAS Collaboration

A search for dark mesons originating from strongly-coupled, $SU(2)$ dark flavor symmetry conserving models and decaying gaugephobically to pure Standard Model final states containing top and bottom quarks is presented. The search targets fully hadronic final states and final states with exactly one electron or muon and multiple jets. The analyzed data sample

Raghda Abdel Khaleq, Jayden L. Newstead, Cedric Simenel, Andrew E. Stuchbery

Any discovery of `new physics' in the neutrino sector first requires a precise prediction of the expected Standard Model cross section. Currently, Coherent Elastic neutrino-Nucleus Scattering (CEvNS) experiments are statistics limited. However, as new and future experiments scale up, it will be necessary to improve the theoretical predictions. Here we review

Adam Sorrenti

Separating vocal elements from musical tracks is a longstanding challenge in audio signal processing. This study tackles the distinct separation of vocal components from musical spectrograms. We employ the Short Time Fourier Transform (STFT) to extract audio waves into detailed frequency-time spectrograms, utilizing the benchmark MUSDB18 dataset for music se

Abdelmalik Ouamane, Ammar Chouchane, Yassine Himeur, Abderrazak Debilou

Machine learning has revolutionized the field of agricultural science, particularly in the early detection and management of plant diseases, which are crucial for maintaining crop health and productivity. Leveraging advanced algorithms and imaging technologies, researchers are now able to identify and classify plant diseases with unprecedented accuracy and s

Luca Geatti, Alessandro Gianola, Nicola Gigante

First-order linear temporal logic (FOLTL) is a flexible and expressive formalism capable of naturally describing complex behaviors and properties. Although the logic is in general highly undecidable, the idea of using it as a specification language for the verification of complex infinite-state systems is appealing. However, a missing piece, which has proved

Yu Wang, Dan Li, Huiqiu Lin

For two integers $r\geq 2$ and $h\geq 0$, the \emph{$h$-extra $r$-component connectivity} $\kappa^h_r(G)$ of a graph $G$ is defined to be the minimum size of a subset of vertices whose removal disconnects $G$, and there are at least $r$ connected components in $G\!-\!S$ and each component has at least $h+1$ vertices. Denote by $\mathcal{G}_{n,\delta}^{\kappa

Botao Zhu, Xianbin Wang

With the growing scale and intrinsic heterogeneity of Internet of Things (IoT) systems, distributed device collaboration becomes essential for effective task completion by dynamically utilizing limited communication and computing resources. However, the separated design and situation-agnostic operation of computing, communication and application layers creat

Urban Larsson, Indrajit Saha

In this survey we revisit {\sc finite subtraction}, one-heap subtraction games on finite rulesets. The main purpose is to give a general overview of the development, and specifically to draw attention to Flammenkamp's thesis (1997), where he, contrary to other studies, experimentally observes exponential eventual period length of the outcomes, for a carefull

Avelina Asada Hadji-Kyriacou, Ognjen Arandjelovic

Pre-trained Language Models (LMs) exhibit strong zero-shot and in-context learning capabilities; however, their behaviors are often difficult to control. By utilizing Reinforcement Learning from Human Feedback (RLHF), it is possible to fine-tune unsupervised LMs to follow instructions and produce outputs that reflect human preferences. Despite its benefits,

Mohammad Kalbasi, MohammadAli Shaeri, Vincent Alexandre Mendez, Solaiman Shokur

Advancements in neural engineering have enabled the development of Robotic Prosthetic Hands (RPHs) aimed at restoring hand functionality. Current commercial RPHs offer limited control through basic on/off commands. Recent progresses in machine learning enable finger movement decoding with higher degrees of freedom, yet the high computational complexity of su