May 2024 arXiv papers — page 85

Leonardo Oliveira, Mao-Hua Zhang, Jurij Koruza, Raquel Rodiquez-Lamas

NaNbO3-based antiferroelectric materials offer a promising pathway towards greener and more cost-effective energy storage devices. However, their intrinsic structural instabilities often lead to reduced energy density that compromises their performance and longevity. In this brief communication, we demonstrate how Dark-Field X-ray Microscopy, when carried ou

Esmaeil Ebrahimi, Ahmad Sheykhi

According to the thermodynamics-gravity conjecture, any modification to the entropy expression leads to the modified cosmological field equations. Based on this, we investigate the cosmological consequences of the modified Friedmann equations when the entropy associated with the horizon is in the form of Tsallis/Barrow entropy and the dark energy (DE) compon

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Using a sample of $448.1 \times 10^6$ $\psi(2S)$ events collected with the BESIII detector, we perform a study of the decay $J/\psi\rightarrow K^+K^-$ via $\psi(2S)\rightarrow \pi^+\pi^-J/\psi$. The branching fraction of $J/\psi\rightarrow K^+K^-$ is determined to be $\mathcal{B}_{K^+K^-}=(3.072\pm 0.023({\rm stat.})\pm 0.050({\rm syst.}))\times 10^{-4}$, wh

Sen Guo, Yu-Xiang Huang, Kuan Liu, En-Wei Liang

Using a model of an accretion disk around a Schwarzschild black hole, the analytic estimates for image polarization were derived by Narayan $et~al.$. [Astrophys. J, 102, 912 (2021)]. Recently, the EHT team also obtained polarization images of the Sgr A$^{*}$ and measured both linear and circular polarization [Astrophys. J. Lett, 964, L25 (2024)]. We find tha

Dongseong Hwang

This paper establishes a mathematical foundation for the Adam optimizer, elucidating its connection to natural gradient descent through Riemannian and information geometry. We provide an accessible and detailed analysis of the diagonal empirical Fisher information matrix (FIM) in Adam, clarifying all detailed approximations and advocating for the use of log

Hongsheng Wang, Xiang Cai, Xi Sun, Jinhong Yue

Single-view clothed human reconstruction holds a central position in virtual reality applications, especially in contexts involving intricate human motions. It presents notable challenges in achieving realistic clothing deformation. Current methodologies often overlook the influence of motion on surface deformation, resulting in surfaces lacking the constrai

Haruko M. Wainwright, Chloe Christiaen, Milos Atz, John Sebastian Tchakerian

This study presents a cross-disciplinary reactor-to-repository framework to compare different advanced reactors with respect to their spent nuclear fuel (SNF). The framework consists of (1) OpenMC for simulating neutronics, fuel depletion, and radioactive decays; (2) NWPY for computing the repository footprint for SNF disposal given the thermal constraints;

Qiufu Chen, Yuanmei Li, Xiaopeng Yin, Luosai Zhang

The impossibility theorem in Roth (1982) states that no stable mechanism satisfies strategy-proofness. This paper explores the Machiavellian frontier of stable mechanisms by weakening strategy-proofness. For a fixed mechanism $\varphi$ and a true preference profile $\succ$, a $(\varphi,\succ)$-boost mispresentation of agent i is a preference of i that is obt

Joshua Nielsen, Didier Sornette, Maziar Raissi

The Log-Periodic Power Law Singularity (LPPLS) model offers a general framework for capturing dynamics and predicting transition points in diverse natural and social systems. In this work, we present two calibration techniques for the LPPLS model using deep learning. First, we introduce the Mono-LPPLS-NN (M-LNN) model; for any given empirical time series, a

Igor Kavrakov, Gledson Rodrigo Tondo, Guido Morgenthal

Advancements in machine learning and an abundance of structural monitoring data have inspired the integration of mechanical models with probabilistic models to identify a structure's state and quantify the uncertainty of its physical parameters and response. In this paper, we propose an inference methodology for classical Kirchhoff-Love plates via physics-in

Himanshu Maheshwari, Sambaran Bandyopadhyay, Aparna Garimella, Anandhavelu Natarajan

Automatically generating a presentation from the text of a long document is a challenging and useful problem. In contrast to a flat summary, a presentation needs to have a better and non-linear narrative, i.e., the content of a slide can come from different and non-contiguous parts of the given document. However, it is difficult to incorporate such non-linea

Jonghyun Song, Cheyon Jin, Wenlong Zhao, Andrew McCallum

A common retrieve-and-rerank paradigm involves retrieving relevant candidates from a broad set using a fast bi-encoder (BE), followed by applying expensive but accurate cross-encoders (CE) to a limited candidate set. However, relying on this small subset is often susceptible to error propagation from the bi-encoders, which limits the overall performance. To

Jan-Hendrik Ewers, David Anderson, Douglas Thomson

Traditional search and rescue methods in wilderness areas can be time-consuming and have limited coverage. Drones offer a faster and more flexible solution, but optimizing their search paths is crucial. This paper explores the use of deep reinforcement learning to create efficient search missions for drones in wilderness environments. Our approach leverages

Doğa Gürsoy, Kaan Alp Yay, Elliot Kisiel, Michael Wojcik

We introduce a structured illumination technique for dark-field x-ray microscopy optimized for three-dimensional imaging of ordered materials at sub-micrometer length scales. Our method utilizes a coded aperture to spatially modulate the incident x-ray beam on the sample, enabling the reconstruction of the sample's 3D structure from images captured at variou

Sören Striewski, Olga Zagovora, Isabella Peters

YouTube is a valuable source of user-generated content on a wide range of topics, and it encourages user participation through the use of a comment system. Video content is increasingly addressing scientific topics, and there is evidence that both academics and consumers use video descriptions and video comments to refer to academic research and scientific p

Cencheng Shen, Jonathan Larson, Ha Trinh, Carey E. Priebe

This paper introduces a refined graph encoder embedding method, enhancing the original graph encoder embedding through linear transformation, self-training, and hidden community recovery within observed communities. We provide the theoretical rationale for the refinement procedure, demonstrating how and why our proposed method can effectively identify useful

Hong Chen, Xin Wang, Yipeng Zhang, Yuwei Zhou

Generating customized content in videos has received increasing attention recently. However, existing works primarily focus on customized text-to-video generation for single subject, suffering from subject-missing and attribute-binding problems when the video is expected to contain multiple subjects. Furthermore, existing models struggle to assign the desire

Lukas Wenzl, Rui An, Nick Battaglia, Rachel Bean

We derive new constraints on the $E_G$ statistic as a test of gravity, combining the CMB lensing map estimated from Data Release 6 (DR6) of the Atacama Cosmology Telescope with SDSS BOSS CMASS and LOWZ galaxy data. We develop an analysis pipeline to measure the cross-correlation between CMB lensing maps and galaxy data, following a blinding policy and testin

Fatemeh Mostafavi, Mingyuan Hong, Riley B. Dawkins, Jannatul Ferdous

It is thought that schemes for quantum imaging are fragile against realistic environments in which the background noise is often stronger than the nonclassical signal of the imaging photons. Unfortunately, it is unfeasible to produce brighter quantum light sources to alleviate this problem. Here, we overcome this paradigmatic limitation by developing a quant

Jairo. K. Mengue, Elismar R. Oliveira

We consider two compact metric spaces $J$ and $X$ and a uniform contractible iterated function system $\{\phi_j: X \to X \, | \, j \in J \}$. For a Lipschitz continuous function $A$ on $J \times X$ and for each $\beta>0$ we consider the Gibbs probability $\rho_{_{\beta A}}$. Our goal is to study a large deviation principle for such family of probabilities as

Karl D. Gordon, E. L. Fitzpatrick, Derck Massa, Ralph Bohlin

The Small Magellanic Cloud (SMC) shows a large variation in ultraviolet (UV) dust extinction curves, ranging from Milky Way-like (MW) to significantly steeper curves with no detectable 2175 A bump. This result is based on a sample of only nine sightlines. From HST/STIS and IUE spectra of OB stars, we have measured UV extinction curves along 32 SMC sightlines

Eloïse Inacio, Luc Lafitte, Laurent Facq, Clair Poignard

Objective: In medical imaging, it is often crucial to accurately assess and correct movement during image-guided therapy. Deformable image registration (DIR) consists in estimating the required spatial transformation to align a moving image with a fixed one. However, it is acknowledged that, boundary conditions applied to the solution are critical in prevent

Sarah Swinton, Jan-Hendrik Ewers, Euan McGookin, David Anderson

One of the fundamental limiting factors in planetary exploration is the autonomous capabilities of planetary exploration rovers. This study proposes a novel methodology for trustworthy autonomous multi-robot teams which incorporates data from multiple sources (HiRISE orbiter imaging, probability distribution maps, and on-board rover sensors) to find efficien

Victoria Manousaki, Konstantinos Bacharidis, Filippos Gouidis, Konstantinos Papoutsakis

In this work, we introduce (a) the new problem of anticipating object state changes in images and videos during procedural activities, (b) new curated annotation data for object state change classification based on the Ego4D dataset, and (c) the first method for addressing this challenging problem. Solutions to this new task have important implications in vi

Yafu Li, Huajian Zhang, Jianhao Yan, Yongjing Yin

Recent advances have made non-autoregressive (NAT) translation comparable to autoregressive methods (AT). However, their evaluation using BLEU has been shown to weakly correlate with human annotations. Limited research compares non-autoregressive translation and autoregressive translation comprehensively, leaving uncertainty about the true proximity of NAT t

David Villringer

In this work we investigate the phenomenon of enhanced dissipation using techniques from the Malliavin Calculus. In particular, we construct a concise, elementary argument, that allows us to recover the well-known enhanced dissipation timescale for shear flows, first obtained by Bedrossian and Coti Zelati in 2017, as well as the precise hypoelliptic regulari

Jiahao Chen, Zhiqiang Shen, Yuwen Pu, Chunyi Zhou

Face Recognition Systems (FRS) have increasingly integrated into critical applications, including surveillance and user authentication, highlighting their pivotal role in modern security systems. Recent studies have revealed vulnerabilities in FRS to adversarial (e.g., adversarial patch attacks) and backdoor attacks (e.g., training data poisoning), raising s

Jakub Jakubowski, Natalia Wojak-Strzelecka, Rita P. Ribeiro, Sepideh Pashami

Predictive Maintenance (PdM) emerged as one of the pillars of Industry 4.0, and became crucial for enhancing operational efficiency, allowing to minimize downtime, extend lifespan of equipment, and prevent failures. A wide range of PdM tasks can be performed using Artificial Intelligence (AI) methods, which often use data generated from industrial sensors. T

Jiajian Ma, Fangqi Lu, Silin Huang, Song Wu

Inpainting lesions within different normal backgrounds is a potential method of addressing the generalization problem, which is crucial for polyp segmentation models. However, seamlessly introducing polyps into complex endoscopic environments while simultaneously generating accurate pseudo-masks remains a challenge for current inpainting methods. To address

Tian Qin, Wei-Min Huang

In this paper, we bridge Variational Autoencoders (VAEs) and kernel density estimations (KDEs) by approximating the posterior by KDEs and deriving an upper bound of the Kullback-Leibler (KL) divergence in the evidence lower bound (ELBO). The flexibility of KDEs makes the optimization of posteriors in VAEs possible, which not only addresses the limitations of

Enhui Shi, Hui Xu

Inspired by the Erd\H{o}s' problem in Ramsey theory, we propose a dynamical version of the problem and answer it positively for circle maps.

Abhiroop Talasila, Maitreya Maity, U. Deva Priyakumar

Unsupervised pre-training has emerged as a transformative paradigm, displaying remarkable advancements in various domains. However, the susceptibility to domain shift, where pre-training data distribution differs from fine-tuning, poses a significant obstacle. To address this, we augment the Swin Transformer to learn from different medical imaging modalities

Dominik Lentrodt, Christoph H. Keitel, Jörg Evers

A method to compute the excitation of narrow transitions at hard x-ray energies by short focused x-ray pulses is developed. In particular, the effect of thin-film cavities on the pulse propagation is incorporated via a semi-analytical algorithm requiring the numerical evaluation of only one two-dimensional Fourier transform. We investigate various limiting c

Jing Gao, Ning Cheng, Bin Fang, Wenjuan Han

The Transformer model, initially achieving significant success in the field of natural language processing, has recently shown great potential in the application of tactile perception. This review aims to comprehensively outline the application and development of Transformers in tactile technology. We first introduce the two fundamental concepts behind the s

O. Kochukhov, A. M. Amarsi, A. Lavail, H. L. Ruh

The A-type metallic-line (Am) stars are typically considered to be non-magnetic or possessing very weak sub-G magnetic fields. This view has been repeatedly challenged in the literature, most commonly for the bright hot Am star o Peg. Several studies claimed to detect 1-2 kG field of unknown topology in this object, possibly indicating a new process of magne

Jake Iles-Smith, Mark Kamper Svendsen, Angel Rubio, Martijn Wubs

We propose a novel mechanism for generating single photons in the mid-Infrared (MIR) using a solid-state or molecular quantum emitter. The scheme utilises cavity QED effects to selectively enhance a Frank-Condon transition, deterministically preparing a single Fock state of a polar phonon mode. By coupling the phonon mode to an antenna, the resulting excitat

Daniela Spreng, Korbinian Urban, Marco Carminati, Frank Edzards

Sterile neutrinos are hypothetical particles in the minimal extension of the Standard Model of Particle Physics. They could be viable dark matter candidates if they have a mass in the keV range. The Karlsruhe tritium neutrino (KATRIN) experiment, extended with a silicon drift detector focal plane array (TRISTAN), has the potential to search for keV-scale ste

Igor Makienko, Michael Grebshtein, Eli Gildish

Vibrations of rotating machinery primarily originate from two sources, both of which are distorted by the machine's transfer function on their way to the sensor: the dominant gear-related vibrations and a low-energy signal linked to bearing faults. The proposed method facilitates the blind separation of vibration sources, eliminating the need for any informa

Dominik Lentrodt, Christoph H. Keitel, Jörg Evers

Strong excitation of nuclear resonances, particularly of M\"ossbauer nuclei, has been a longstanding goal and the advance of novel x-ray sources is promising new options in this regard. Here we map out the necessary experimental conditions for the more general goal of realizing nonlinear optics with nuclei and compare with available technology. In particular

Jane C. Nelson, Marissa L. Weichman

Gas-phase molecules are a promising platform through which to elucidate the mechanisms of action and scope of polaritons for optical control of chemistry. Polaritons arise from the strong coupling of a dipole-allowed molecular transition with the photonic mode of an optical cavity. There is mounting evidence of modified reactivity under polaritonic condition

Sylvy Anscombe, Arno Fehm

We study various universal-existential fragments of first-order theories of fields, in particular of function fields and of equicharacteristic henselian valued fields. For example we discuss to what extent the theory of a field k determines the universal-existential theories of the rational function field over k and of the field of Laurent series over k, and

Matteo Marchesini, Michelangelo Dondi, Leonardo Rossi, Gabriele Bolognini

One of the prominent platforms for quantum technologies, cold atoms require reliable laser systems. We present the design, implementation, and characterization of a simple, compact, and economical laser system at 780 nm, entirely based on fiber components. Two semiconductor lasers at 1560 nm are amplified in a single Erbium-doped fiber amplifier and frequenc

Ben Eckardt

In string theory, black holes can be made from brane systems that are partially delocalized in string theory's extra dimensions. It is expected that they correspond to an ensemble of pure, horizonless microstates sourced by localized versions of this brane system. This thesis aims at studying the relationship between supersymmetric black holes and their micr

Philippe van der Beck, Jean-Philippe Bouchaud, Dario Villamaina

Many active funds hold concentrated portfolios. Flow-driven trading in these securities causes price pressure, which pushes up the funds' existing positions resulting in realized returns. We decompose fund returns into a price pressure (self-inflated) and a fundamental component and show that when allocating capital across funds, investors are unable to iden

Yazhi Niu, Jialin Li, Lupei Qin, Xin-Qi Li

We propose to embed the atomic magnetometer (AM) into an optical Mach-Zehnder interferometer (MZI). We analyze the effect of amplification of the Faraday rotation (FR) angle of the probe laser light, by properly postselecting the path-information state of the laser photons when passing through the MZI. In the presence of saturation of photo-detectors and exi

Facundo Molina, Alessandra Gorla

The effectiveness of a test suite in detecting faults highly depends on the correctness and completeness of its test oracles. Large Language Models (LLMs) have already demonstrated remarkable proficiency in tackling diverse software testing tasks, such as automated test generation and program repair. This paper aims to enable discussions on the potential of

Ulrich Brenner, Anna Silvanus

We consider the fundamental problem of constructing fast and small circuits for binary addition. We propose a new algorithm with running time $\mathcal O(n \log_2 n)$ for constructing linear-size $n$-bit adder circuits with a significantly better depth guarantee compared to previous approaches: Our circuits have a depth of at most $\log_2 n + \log_2 \log_2 n

Vedran Sekara, Ivan Dotu, Manuel Cebrian, Esteban Moro

Social connections are conduits through which individuals communicate, information propagates, and diseases spread. Identifying individuals who are more likely to adopt ideas and spread them is essential in order to develop effective information campaigns, maximize the reach of resources, and fight epidemics. Influence maximization algorithms are used to ide

Petter Andreas Bergh, David A. Jorgensen, Peder Thompson

Given a graded-commutative ring acting centrally on a triangulated category, our main result shows that if cohomology of a pair of objects of the triangulated category is finitely generated over the ring acting centrally, then the asymptotic vanishing of the cohomology is well-behaved. In particular, enough consecutive asymptotic vanishing of cohomology impl

Paul J. Goulart, Yuwen Chen

We present a general-purpose interior-point solver for convex optimization problems with conic constraints. Our method is based on a homogeneous embedding method originally developed for general monotone complementarity problems and more recently applied to operator splitting methods, and here specialized to an interior-point method for problems with quadrat

Yusong Zhang, Kun Xie, Xingyi Zhang, Xiangyu Dong

The spread of rumors on social media, particularly during significant events like the US elections and the COVID-19 pandemic, poses a serious threat to social stability and public health. Current rumor detection methods primarily rely on propagation graphs to improve the model performance. However, the effectiveness of these methods is often compromised by n

Chengbo Wang, Xiaoran Zhang

In this paper, we determine the sharp criteria for the nonlinearities in the John problem $\partial_{t}^2 u-Δ_{\R^3}u= F(u)$ so that the problem admits global solutions for small initial data with compact support. The criteria is of Dini type. For the situation in which the criteria is not satisfied, we show that the solution will blow up in finite time for

Jessica Whitney, Tobías Liaudat, Matt Price, Matthijs Mars

Understanding the large-scale structure of the Universe and unravelling the mysteries of dark matter are fundamental challenges in contemporary cosmology. Reconstruction of the cosmological matter distribution from lensing observables, referred to as 'mass-mapping' is an important aspect of this quest. Mass-mapping is an ill-posed problem, meaning there is i

Jordy Butter

These proceedings discuss recent measurements by the LHCb experiment on mixing and $C\!P$ violation with beauty and charm mesons, as presented at the Moriond QCD 2024 conference. All discussed measurements show agreement with the Standard Model.

Samuel Brucker, Stefanie Walz, Mario Bijelic, Felix Heide

Gated cameras flood-illuminate a scene and capture the time-gated impulse response of a scene. By employing nanosecond-scale gates, existing sensors are capable of capturing mega-pixel gated images, delivering dense depth improving on today's LiDAR sensors in spatial resolution and depth precision. Although gated depth estimation methods deliver a million of

Aaron Keehn, Eran Nevo

We characterize the possible exterior shiftings of $K$, where $K$ runs over all triangulation of the torus, or the projective plane, or the Klein bottle. Further, we give a deterministic polynomial-time algorithm for computing the exterior shifting of a given triangulation $K$ as above.

Zhifan Wan, Jie Zhang, Changzhen Li, Shiguang Shan

The visual pathway of human brain includes two sub-pathways, ie, the ventral pathway and the dorsal pathway, which focus on object identification and dynamic information modeling, respectively. Both pathways comprise multi-layer structures, with each layer responsible for processing different aspects of visual information. Inspired by visual information proc

Ryoya Yamasaki, Toshiyuki Tanaka

Ordinal regression (OR) is classification of ordinal data in which the underlying categorical target variable has a natural ordinal relation for the underlying explanatory variable. For $K$-class OR tasks, threshold methods learn a one-dimensional transformation (1DT) of the explanatory variable so that 1DT values for observations of the explanatory variable

Satvik Golechha

Grokking, a phenomenon where machine learning models generalize long after overfitting, has been primarily observed and studied in algorithmic tasks. This paper explores grokking in real-world datasets using deep neural networks for classification under the cross-entropy loss. We challenge the prevalent hypothesis that the $L_2$ norm of weights is the primar

Yutao Du, Qin Li, Raghav Gnanasambandam, Mengnan Du

Exploring the outer atmosphere of the sun has remained a significant bottleneck in astrophysics, given the intricate magnetic formations that significantly influence diverse solar events. Magnetohydrodynamics (MHD) simulations allow us to model the complex interactions between the sun's plasma, magnetic fields, and the surrounding environment. However, MHD s

Agniva Chatterjee

The space of Laplace transforms of holomorphic Hardy-space functions have been characterized as weighted Bergman spaces of entire functions in two cases: that of planar convex domains (Lutsenko--Yumulmukhametov, 1991), and that of strongly convex domains in higher dimensions (Lindholm, 2002). In this paper, we establish such a Paley--Weiner result for a clas

Ji Ma, Wei Suo, Peng Wang, Yanning Zhang

Vision-Language Instruction Tuning (VLIT) is a critical training phase for Large Vision-Language Models (LVLMs). With the improving capabilities of open-source LVLMs, researchers have increasingly turned to generate VLIT data by using open-source LVLMs and achieved significant progress. However, such data generation approaches are bottlenecked by the followi

Yuwen Pu, Zhuoyuan Ding, Jiahao Chen, Chunyi Zhou

As a novel privacy-preserving paradigm aimed at reducing client computational costs and achieving data utility, split learning has garnered extensive attention and proliferated widespread applications across various fields, including smart health and smart transportation, among others. While recent studies have primarily concentrated on addressing privacy le

Mohamed Amine Ferrag, Fatima Alwahedi, Ammar Battah, Bilel Cherif

This paper provides a comprehensive review of the future of cybersecurity through Generative AI and Large Language Models (LLMs). We explore LLM applications across various domains, including hardware design security, intrusion detection, software engineering, design verification, cyber threat intelligence, malware detection, and phishing detection. We prese

Chanaprom Cholsuk, Ashkan Zand, Asli Cakan, Tobias Vogl

Color centers in hexagonal boron nitride (hBN) have become an intensively researched system due to their potential applications in quantum technologies. There has been a large variety of defects being fabricated, yet, for many of them, the atomic origin remains unclear. The direct imaging of the defect is technically very challenging, in particular since, in

Jonathan Holland, George Sparling

This article describes the symmetries of plane wave spacetimes in dimension four and greater. It begins with a description of the isometric automorphisms, and in particular the homogeneous plane waves. Then the article turns to describing isometries from one plane wave to another. The structure of the isometries is relevant for the problem of classifying vac

Rashid Ummer N. T., B. Sundar Rajan

Coded caching scheme originally proposed by Maddah-Ali and Niesen (MN) considered a broadcast network consisting of a single server connected to a set of users each having a cache memory. Motivated by practical scenarios, Karamchandani \textit{et al.} in [16] proposed a coded caching scheme for a two-layer hierarchical network consisting of a single server c

Alicia Tierz, Iciar Alfaro, David González, Francisco Chinesta

Thermodynamics-informed neural networks employ inductive biases for the enforcement of the first and second principles of thermodynamics. To construct these biases, a metriplectic evolution of the system is assumed. This provides excellent results, when compared to uninformed, black box networks. While the degree of accuracy can be increased in one or two or

Martin Bruderer

Queueing theory is used for modeling biological processes, traffic flows and many more real-life situations. Beyond that, queues describe systems out of equilibrium and can thus be considered as minimal models of non-equilibrium statistical mechanics. We demonstrate that non-equilibrium phase transitions of queues in the steady state are accompanied by a non

Imre Hagymási, Nils Niggemann, Johannes Reuther

We study the phase diagram of the antiferromagnetic $J_1$-$J_2$ Heisenberg model on the pyrochlore lattice with $S=1$ spins at zero and finite temperatures. We use a combination of complementary state-of-the-art quantum many-body approaches such as density matrix renormalization group (DMRG), density-matrix purification and pseudo-Majorana functional renorma

Rochelle Choenni, Anne Lauscher, Ekaterina Shutova

Texts written in different languages reflect different culturally-dependent beliefs of their writers. Thus, we expect multilingual LMs (MLMs), that are jointly trained on a concatenation of text in multiple languages, to encode different cultural values for each language. Yet, as the 'multilinguality' of these LMs is driven by cross-lingual sharing, we also

Zhengqing Miao, Meirong Zhao

Motor imagery (MI) based EEG represents a frontier in enabling direct neural control of external devices and advancing neural rehabilitation. This study introduces a novel time embedding technique, termed traveling-wave based time embedding, utilized as a pseudo channel to enhance the decoding accuracy of MI-EEG signals across various neural network architec

Shawn Berry

The persistence of lying by some consumers in their online posts of experiences with businesses is problematic, and taints the global pool of information that is used for decision making by people that assume they are true accounts of experiences. This study is based on data from my dissertation about fake online Google reviews of restaurants (Berry, 2024),

Arushi Jain, Shubham Paliwal, Monika Sharma, Vikram Jamwal

Creative story illustration requires a consistent interplay of multiple characters or objects. However, conventional text-to-image models face significant challenges while producing images featuring multiple personalized subjects. For example, they distort the subject rendering, or the text descriptions fail to render coherent subject interactions. We presen

George A. Blaylock-Squibbs, Richard J. Parker, Emma C. Daffern-Powell

The architectures of extrasolar planetary systems often deviate considerably from the ``standard" model for planet formation, which is largely based on our own Solar System. In particular, gas giants on close orbits are not predicted by planet formation theory and so some process(es) are thought to move the planets closer to their host stars. Recent research

Anna Lisa Amadori

We investigate the (linearized) Morse index of solutions to Hamiltonan systems, with a focus on convex Hamiltonians functions and sign-changing radial solutions. For strongly coupled systems, we describe the profile of the radial solutions and give an estimate of their Morse index.

Xingzhou Lou, Junge Zhang, Jian Xie, Lifeng Liu

Human preference alignment is critical in building powerful and reliable large language models (LLMs). However, current methods either ignore the multi-dimensionality of human preferences (e.g. helpfulness and harmlessness) or struggle with the complexity of managing multiple reward models. To address these issues, we propose Sequential Preference Optimizati

Lixiang An, Qian Li, Minmin Zhang

Suppose ${\bf b}=\{b_n\}_{n=1}^{\infty}$ is a sequence of integers bigger than 1 and ${\bf D}=\{{\mathcal D}_{n}\}_{n=1}^{\infty}$ is a sequence of consecutive digit sets. Let $\mu_{{\bf b},{\bf D}}$ be the Cantor-Moran measure defined by \begin{eqnarray*} \mu_{{\bf b},{\bf D}}&=& \delta_{\frac{1}{b_1}{\mathcal D}_{1}}\ast\delta_{\frac{1}{b_1b_2}{\mathcal D}

Michael Björklund, Mattias Byléhn

We investigate lower asymptotic bounds of number variances for invariant locally square-integrable random measures on Euclidean and real hyperbolic spaces. In the Euclidean case we show that there are subsequences of radii for which the number variance grows at least as fast as the volume of the boundary of Euclidean balls, generalizing a classical result of

Daniel Weihmayr, Fatih Sezgin, Leon Tolksdorf, Christian Birkner

Pedestrians are among the most endangered traffic participants in road traffic. While pedestrian detection in nominal conditions is well established, the sensor and, therefore, the pedestrian detection performance degrades under adverse weather conditions. Understanding the influences of rain and fog on a specific radar and lidar sensor requires extensive te

Layal Chahine, Cecilia Ceccarelli, Marta De Simone, Claire J. Chandler

The exploration of outflows in protobinary systems presents a challenging yet crucial endeavour, offering valuable insights into the dynamic interplay between protostars and their evolution. In this study, we examine the morphology and dynamics of jets and outflows within the IRAS\,4A protobinary system. This analysis is based on ALMA observations of SiO(5--

Olga Zagovora, Talisa Schwal, Katrin Weller

This study explores the interplay between YouTube engagement metrics and the academic impact of cited publications within video descriptions, amid declining trust in traditional journalism and increased reliance on social media for information. By analyzing data from Altmetric.com and YouTube's API, it assesses how YouTube video features relate to citation i

Amitayu Banerjee, Zalán Molnár, Alexa Gopaulsingh

We prove analogs of Brooks' Theorem for the list-distinguishing chromatic number of different classes of simple finite connected graphs. Moreover, we determine two upper bounds for the list-distinguishing chromatic number of a graph G in terms of the coloring number of G and the list-chromatic number of G. We also determine the list-distinguishing chromatic

Yongsheng Yang, Huan Liu, Ali Mostafavi, Hirokazu Tatano

Infrastructure systems play a critical role in providing essential products and services for the functioning of modern society; however, they are vulnerable to disasters and their service disruptions can cause severe societal impacts. To protect infrastructure from disasters and reduce potential impacts, great achievements have been made in modeling interdep

Ketai Qiu, Niccolò Puccinelli, Matteo Ciniselli, Luca Di Grazia

In the rapidly evolving landscape of software engineering, the integration of Artificial Intelligence (AI) into the Software Development Life-Cycle (SDLC) heralds a transformative era for developers. Recently, we have assisted to a pivotal shift towards AI-assisted programming, exemplified by tools like GitHub Copilot and OpenAI's ChatGPT, which have become

Kohtaroh Sakaue, Hiroshi Shinaoka, Rihito Sakurai

Tensor cross interpolation (TCI) is a powerful technique for learning a tensor train (TT) by adaptively sampling a target tensor based on an interpolation formula. However, when the tensor evaluations contain random noise, optimizing the TT is more advantageous than interpolating the noise. Here, we propose a new method that starts with an initial guess of T

Erich Novak, Friedrich Pillichshammer

We study tractability properties of the weighted $L_p$-discrepancy. The concept of {\it weighted} discrepancy was introduced by Sloan and Wo\'{z}\-nia\-kowski in 1998 in order to prove a weighted version of the Koksma-Hlawka inequality for the error of quasi-Monte Carlo integration rules. The weights have the aim to model the influence of different coordinat

Antoine Legrand, Renaud Detry, Christophe De Vleeschouwer

We address the estimation of the 6D pose of an unknown target spacecraft relative to a monocular camera, a key step towards the autonomous rendezvous and proximity operations required by future Active Debris Removal missions. We present a novel method that enables an "off-the-shelf" spacecraft pose estimator, which is supposed to known the target CAD model,

S. Winning, M. Lietzow-Sinjen, S. Wolf

Context. As a new growing field, exocartography aims to map the surface features of exoplanets that are beyond the resolution of traditional observing techniques. While photometric approaches have been discussed extensively, polarimetry has received less attention despite its promising prospects. Aims. We demonstrate that the limb polarization of an exoplane

K. Chakrabarti, J. Zs Mezei, I. F. Schneider, J. Tennyson

Calculations are performed for electron collision with the methylene molecular ion CH$_2^+$ in its bent equilibrium geometry, with the goal to obtain cross sections for electron impact excitation and dissociation. The polyatomic version of the UK molecular R-matrix codes was used to perform an initial configuration-interaction calculation on the doublet and

Boheng Li, Yishuo Cai, Haowei Li, Feng Xue

Model quantization is widely used to compress and accelerate deep neural networks. However, recent studies have revealed the feasibility of weaponizing model quantization via implanting quantization-conditioned backdoors (QCBs). These special backdoors stay dormant on released full-precision models but will come into effect after standard quantization. Due t

Hongsheng Wang, Lizao Zhang, Zhangnan Zhong, Shuolin Xu

Reconstructing 3D human bodies from realistic motion sequences remains a challenge due to pervasive and complex occlusions. Current methods struggle to capture the dynamics of occluded body parts, leading to model penetration and distorted motion. RemoCap leverages Spatial Disentanglement (SD) and Motion Disentanglement (MD) to overcome these limitations. SD

Ildefonso Castro-Infantes, Jorge Hidalgo

We prove that on every compact Riemann surface $M$ there is a Cantor set $C \subset M$ such that $M \setminus C$ admits a proper conformal constant mean curvature one ($\mathrm{CMC\text{-}1}$) immersion into hyperbolic $3$-space $\mathbb{H}^3$. Moreover, we obtain that every bordered Riemann surface admits an almost proper $\mathrm{CMC\text{-}1}$ face into d

Vicente A. Arévalo, Sebastián Valladares, Clara Rojas

In this article, we solve the Duffin--Kemmer--Petiau (DKP) equation in the presence of the cusp potential for spin--one particles. We derived the scattering solutions and calculated the bound states in terms of the Whittaker functions. We show that transmission resonances are present, as well as the particle--anti-particle bound states.

Xin Jin, Hongyu Zhu, Mounîm A. El Yacoubi, Haiyang Li

As a representative of a new generation of biometrics, vein identification technology offers a high level of security and convenience.Convolutional neural networks (CNNs), a prominent class of deep learning architectures, have been extensively utilized for vein identification. Since their performance and robustness are limited by small \emph{Effective Recept

Ivory Fronteau

We introduce a fragment of continuous first-order logic, analogue of Palyutin formulas (or h-formulas) in classical model theory, which is preserved under reduced products in both directions. We use it to extend classical results on complete theories which are preserved under reduced product and their stability. We also characterize the set of Palyutin sente

Yuwen Pu, Jiahao Chen, Chunyi Zhou, Zhou Feng

The efficacy of deep learning models is profoundly influenced by the quality of their training data. Given the considerations of data diversity, data scale, and annotation expenses, model trainers frequently resort to sourcing and acquiring datasets from online repositories. Although economically pragmatic, this strategy exposes the models to substantial sec

Alessandra De Luca, Veronica Felli, Stefano Vita

We provide fine asymptotics of solutions of fractional elliptic equations at boundary points where the domain is locally conical; that is, corner type singularities appear. Our method relies on a suitable smoothing of the corner singularity and an approximation scheme, which allow us to provide a Pohozaev type inequality. Then, the asymptotics of solutions a

Eiolf Kaspersen, Gereon Quick

We give a complete description of which non-torsion generators are not in the image of the Thom morphism from complex cobordism to integral cohomology for the classifying space of exceptional Lie groups except for E_8. We then show that the Thom morphism is not surjective for the classifying space of the gauge group of a principal E_7-bundle over the four-di

Mian Ibad Ali Shah, Enda Barrett, Karl Mason

Farm businesses are increasingly adopting renewables to enhance energy efficiency and reduce reliance on fossil fuels and the grid. This shift aims to decrease dairy farms' dependence on traditional electricity grids by enabling the sale of surplus renewable energy in Peer-to-Peer markets. However, the dynamic nature of farm communities poses challenges, req