December 2024 arXiv papers — page 167

Jonathan Niemann

The classification of maximal function fields over a finite field is a difficult open problem, and even determining isomorphism classes among known function fields is challenging in general. We study a particular family of maximal function fields defined over a finite field with $q^2$ elements, where $q$ is the power of an odd prime. When $d := (q+1)/2$ is a

Florian Schall, Felix A. Hahl, Lukas Lindner, Xavier Vidal

Magnetometry with nitrogen-vacancy (NV) centers has so far been measured via emission of light from NV centers or via absorption at the singlet transition at 1042 nm. Here, we demonstrate a phenomenon of broadband optical absorption by the NV centers starting in the emission wavelength and reaching up to 1000 nm. The measurements are enabled by a high-finess

Oleg S. Ugolnikov, Nikolay N. Pertsev, Vladimir I. Perminov, Ilya S. Yankovsky

The results of simultaneous measurements of noctilucent clouds (NLC) position in a number of ground-based locations are presented. Observational data of 14 bright NLC events over 5 years is used for building the altitude maps of cloud fields using triangulation technique updated for multi-location case. Statistical distribution of NLC altitude and its change

Thomas Bartz-Beielstein, Axel Wellendorf, Noah Pütz, Jens Brandt

The increasing shortage of nursing staff and the acute risk of falls in nursing homes pose significant challenges for the healthcare system. This study presents the development of an automated fall detection system integrated into care beds, aimed at enhancing patient safety without compromising privacy through wearables or video monitoring. Mechanical vibra

Satoshi Fukumori, Kayoko Miura, Ayako Takamori, Sadao Otsuka

Prospective memory (PM), defining the currently conceived intention of a future action, is crucial for daily functioning, particularly in aging populations. This study develops and validates a virtual reality prospective memory training (VR-PMT) system that integrates visual imagery training (VIT) and virtual reality training (VRT) to enhance the PM abilitie

Peng Yu, Cheng Deng, Beiya Dai, Xinbing Wang

Autoregressive large language models (LLMs) pre-trained by next token prediction are inherently proficient in generative tasks. However, their performance on knowledge-driven tasks such as factual knowledge querying remains unsatisfactory. Knowledge graphs (KGs), as high-quality structured knowledge bases, can provide reliable knowledge for LLMs, potentially

Yanyang Li, Tin Long Wong, Cheung To Hung, Jianqiao Zhao

Recent advances in large language models (LLMs) have shown significant promise, yet their evaluation raises concerns, particularly regarding data contamination due to the lack of access to proprietary training data. To address this issue, we present C$^2$LEVA, a comprehensive bilingual benchmark featuring systematic contamination prevention. C$^2$LEVA firstl

Lavínia Gabriela Teodoro dos Santos, Tuhin Malik, Constança Providência

The implications of including the scalar isovector $\delta$-meson in a relativistic mean-field description of nuclear matter are discussed. A Bayesian inference approach is used to determine the parameters that define the isovector properties of the model. The properties of nuclear matter and neutron stars are discussed. The inclusion of the $\delta$-meson h

Michael Schwimmbeck, Serouj Khajarian, Konstantin Holzapfel, Johannes Schmidt

In the context of medical Augmented Reality (AR) applications, object tracking is a key challenge and requires a significant amount of annotation masks. As segmentation foundation models like the Segment Anything Model (SAM) begin to emerge, zero-shot segmentation requires only minimal human participation obtaining high-quality object masks. We introduce a H

Matthijs Ebbens, Nicole Funk, Jan Höckendorff, Christian Sohler

We study the $k$-center problem in the context of individual fairness. Let $P$ be a set of $n$ points in a metric space and $r_x$ be the distance between $x \in P$ and its $\lceil n/k \rceil$-th nearest neighbor. The problem asks to optimize the $k$-center objective under the constraint that, for every point $x$, there is a center within distance $r_x$. We g

Haotian Ye, Axel Wisiorek, Antonis Maronikolakis, Özge Alaçam

Hate speech online remains an understudied issue for marginalized communities, particularly in the Global South, which includes developing societies with increasing internet penetration. In this paper, we aim to provide marginalized communities in societies where the dominant language is low-resource with a privacy-preserving tool to protect themselves from

Luca Schiavone

We prove a coisotropic embedding theorem \`a l\`a Gotay for pre-multisymplectic manifolds.

Robert Auffarth, Martí Lahoz, Juan Carlos Naranjo

We show that for every g greater or equal than 5, the locus of Prym varieties in the moduli space of principally polarized abelian varieties of dimension g-1 that possess a pseudoreflection of geometric origin is the union of three different non-empty explicit irreducible families. This is in stark contrast to the loci of Jacobian varieties that possess a ps

Zehao Wang, Xinpeng Liu, Yudonglin Zhang, Xiaoqian Wu

Multimodal Large Language Models (MLLMs) have garnered significant attention recently and demonstrate outstanding capabilities in various tasks such as OCR, VQA, captioning, $\textit{etc}$. However, hallucination remains a persistent issue. While numerous methods have been proposed to mitigate hallucinations, achieving notable improvements, these methods pri

Giorgio Tosti Balducci, Boyang Chen, Matthias Möller, Roeland De Breuker

Different hybrid quantum-classical algorithms have recently been developed as a near-term way to solve linear systems of equations on quantum devices. However, the focus has so far been mostly on the methods, rather than the problems that they need to tackle. In fact, these algorithms have been run on real hardware only for problems in quantum physics, such

Ryota Nonomura, Hiroki Mori

Multi-agent systems utilizing large language models (LLMs) have shown great promise in achieving natural dialogue. However, smooth dialogue control and autonomous decision making among agents still remain challenges. In this study, we focus on conversational norms such as adjacency pairs and turn-taking found in conversation analysis and propose a new framew

Zak Hussain, Rui Mata, Ben R. Newell, Dirk U. Wulff

Semantic representations are integral to natural language processing, psycholinguistics, and artificial intelligence. Although often derived from internet text, recent years have seen a rise in the popularity of behavior-based (e.g., free associations) and brain-based (e.g., fMRI) representations, which promise improvements in our ability to measure and mode

Mohammad Mohaiminul Islam, Coen de Vente, Bart Liefers, Caroline Klaver

In this paper, we present a new approach for uncertainty-aware retinal layer segmentation in Optical Coherence Tomography (OCT) scans using probabilistic signed distance functions (SDF). Traditional pixel-wise and regression-based methods primarily encounter difficulties in precise segmentation and lack of geometrical grounding respectively. To address these

Subhadeep Bandyopadhyay, Anoop Raj, Philippe Ghosez, Sumiran Pujari

We provide a theoretical demonstration of controllable non-relativistic spin splitting in both electronic and magnonic bands via targeted structural distortions tied to specific phonon modes. Using MnF$_2$ as a model system, we identify a $d$-wave magnon band splitting between magnon modes of specific handedness, directly correlated with the non-relativistic

Paolo Bellingeri, Eddy Godelle, Luis Paris

A new family of groups, called trickle groups, is presented. These groups generalize right-angled Artin and Coxeter groups, as well as cactus groups. A trickle group is defined by a presentation with relations of the form $xy = zx$ and $x^\mu = 1$, that are governed by a simplicial graph, called a trickle graph, endowed with a partial ordering on the vertice

Yishuo Chen, Boran Wang, Xinyu Guo, Wenbin Zhu

Object detection in poor-illumination environments is a challenging task as objects are usually not clearly visible in RGB images. As infrared images provide additional clear edge information that complements RGB images, fusing RGB and infrared images has potential to enhance the detection ability in poor-illumination environments. However, existing works in

Gaurav Shrivastava, Ser-Nam Lim, Abhinav Shrivastava

In the evolving landscape of video enhancement and editing methodologies, a majority of deep learning techniques often rely on extensive datasets of observed input and ground truth sequence pairs for optimal performance. Such reliance often falters when acquiring data becomes challenging, especially in tasks like video dehazing and relighting, where replicat

Gaurav Shrivastava, Abhinav Shrivastava

Diffusion models have made significant strides in image generation, mastering tasks such as unconditional image synthesis, text-image translation, and image-to-image conversions. However, their capability falls short in the realm of video prediction, mainly because they treat videos as a collection of independent images, relying on external constraints such

C. Faverjon, Julien Roques

Many articles have recently been devoted to Mahler equations, partly because of their links with other branches of mathematics such as automata theory. Hahn series (a generalization of the Puiseux series allowing arbitrary exponents of the indeterminate as long as the set that supports them is well-ordered) play a central role in the theory of Mahler equatio

Armando Pezo, Andrés Saul, Aurélien Manchon, Rémi Arras

We predict the giant ferroelectric control of interfacial properties of Ni/HfO2, namely, (i) the magnetocrystalline anisotropy and (ii) the inverse spin and orbital Rashba effects. The reversible control of magnetic properties using electric gating is a promising route to low-energy consumption magnetic devices, including memories and logic gates. Synthetic

X Lin, M T Hartman, P Goldner, B Fang

We explore the properties of ultra-narrow spectral holes in ensembles of solid-state emitters in crystals over a range of sub-kelvin temperatures, with a focus on their potential application in frequency stabilization schemes as an alternative to ultrastable cavities. We investigate how the parameters used to burn the spectral hole impact its shape, and how

Valeria Banica, Daniel Eceizabarrena, Andrea. R. Nahmod, Luis Vega

In this proceedings article we survey the results in [5] and their motivation, as presented at the 50th Journ\'ees EDP 2024. With the aim of quantifying turbulent behaviors of vortex filaments, we study the multifractality of a family of generalized Riemann's non-differentiable functions. These functions represent, in a certain limit, the trajectory of regul

Xiaojie Yin, Qilong Wang, Bing Cao, Qinghua Hu

Recently, many studies have been conducted to enhance the zero-shot generalization ability of vision-language models (e.g., CLIP) by addressing the semantic misalignment between image and text embeddings in downstream tasks. Although many efforts have been made, existing methods barely consider the fact that a class of images can be described by notably diff

Marien Renaud, Arthur Leclaire, Nicolas Papadakis

One key ingredient of image restoration is to define a realistic prior on clean images to complete the missing information in the observation. State-of-the-art restoration methods rely on a neural network to encode this prior. Moreover, typical image distributions are invariant to some set of transformations, such as rotations or flips. However, most deep ar

Antonello Ceravola, Frank Joublin, Ahmed R. Sadik, Bram Bolder

This paper presents HyperGraphOS, an innovative Operating System designed for the scientific and engineering domains. It combines model based engineering, graph modeling, data containers, and computational tools, offering users a dynamic workspace for creating and managing complex models represented as customizable graphs. Using a web based architecture, Hyp

Thevin Senath, Kumuthu Athukorala, Ransika Costa, Surangika Ranathunga

In this paper, we address the challenge of recipe personalization through ingredient substitution. We make use of Large Language Models (LLMs) to build an ingredient substitution system designed to predict plausible substitute ingredients within a given recipe context. Given that the use of LLMs for this task has been barely done, we carry out an extensive s

Benard Nsamba, Achim Weiss, Juma Kamulali

The inference of stellar parameters (such as radius and mass) through asteroseismic forward modelling depends on the number, accuracy, and precision of seismic and atmospheric constraints. ESA's Gaia space mission is providing precise parallaxes which yield an additional constraint to be included in the model grid search. Using a handful of main-sequence ben

Marian Arale Brännvall, Rickard Armiento, Björn Alling

When exploring new magnetic materials, the effect of alloying plays a crucial role for numerous properties. By altering the alloy composition, it is possible to tailor, e.g., the Curie temperature ($T_\text{C}$). In this work, $T_\text{C}$ of various alloys is investigated using a previously developed technique [Br\"{a}nnvall et al. Phys. Rev. Mat. (2024)] d

Mark Litterick, Aleksandar Ivankovic, Bojan Arsov, Aman Kumar

This paper presents pragmatic solutions for verifying complex mathematical algorithms implemented in hardware in an efficient and effective manner. Maximizing leverage of a known-answer-test strategy, based on predefined data scenarios combined with design-for-verification modes, we demonstrate how to find and isolate concept and design bugs early in the flo

David Heredia, Aldéric Joulin, Olivier Roustant

One-dimensional Poincare inequalities are used in Global Sensitivity Analysis (GSA) to provide derivative-based upper bounds and approximations of Sobol indices. We add new perspectives by investigating weighted Poincare inequalities. Our contributions are twofold. In a first part, we provide new theoretical results for weighted Poincare inequalities, guided

Ze Yuan, Yanqing Liu, Shujie Liu, Sheng Zhao

Recent advances in GPT-4o like multi-modality models have demonstrated remarkable progress for direct speech-to-speech conversation, with real-time speech interaction experience and strong speech understanding ability. However, current research focuses on discrete speech tokens to align with discrete text tokens for language modelling, which depends on an au

TEXONO Collaboration, H. B. Li, M. K. Pandey, C. H. Leung

After decades of experimental efforts, the DAMA/LIBRA(DL) annual modulation (AM) analysis on the $\chi N$ (WIMP Dark Matter interactions on nucleus) channel remains the only one which can be interpreted as positive signatures. This has been refuted by numerous time-integrated (TI) and AM analysis. It has been shown that $\chi e$ (WIMP interactions with elect

Khurram Azeem Hashmi, Talha Uddin Sheikh, Didier Stricker, Muhammad Zeshan Afzal

The primary challenge in Video Object Detection (VOD) is effectively exploiting temporal information to enhance object representations. Traditional strategies, such as aggregating region proposals, often suffer from feature variance due to the inclusion of background information. We introduce a novel instance mask-based feature aggregation approach, signific

Jari Peeperkorn, Simon De Vos

Predictive process monitoring focuses on forecasting future states of ongoing process executions, such as predicting the outcome of a particular case. In recent years, the application of machine learning models in this domain has garnered significant scientific attention. When using historical execution data, which may contain biases or exhibit unfair behavi

Hou-Wan Long, Nga-Man Wong, Wei Cai

Memecoins, driven by social media engagement and cultural narratives, have rapidly grown within the Web3 ecosystem. Unlike traditional cryptocurrencies, they are shaped by humor, memes, and community sentiment. This paper introduces the Coin-Meme dataset, an open-source collection of visual, textual, community, and financial data from the Pump.fun platform o

Yixin Gao, Xin Li, Xiaohan Pan, Runsen Feng

We present UniMIC, a universal multi-modality image compression framework, intending to unify the rate-distortion-perception (RDP) optimization for multiple image codecs simultaneously through excavating cross-modality generative priors. Unlike most existing works that need to design and optimize image codecs from scratch, our UniMIC introduces the visual co

Alexander D. Kurilov, Anastasia V. Gubareva, Sergei A. Zubkov, Yulia A. Alekhina

Magnetic fluids exhibit tunable structures and electrophysical properties, making them promising for adaptive optical systems, biomedical sensors, and microelectromechanical devices. However, the dynamic evolution of their microstructure under varying magnetic fields remains insufficiently explored. This study investigates the structural and dielectric prope

Emmanuel Abbe, Elisabetta Cornacchia, Jan Hązła, Donald Kougang-Yombi

Parities have become a standard benchmark for evaluating learning algorithms. Recent works show that regular neural networks trained by gradient descent can efficiently learn degree $k$ parities on uniform inputs for constant $k$, but fail to do so when $k$ and $d-k$ grow with $d$ (here $d$ is the ambient dimension). However, the case where $k=d-O_d(1)$ (alm

M. B. Amelchakov, A. Chiavassa, D. M. Gromushkin, S. S. Khokhlov

From 2012 to 2023, the PRISMA-32 array was in operation at the Experimental Complex NEVOD (MEPhI, Moscow). The purpose of the array was to study extensive air showers by detecting the air-shower neutron and electron-photon components using unshielded neutron detectors. To expand the capabilities of this facility, including for the study of cosmic and geophys

Mohammed Althubyani, Zhijin Meng, Shengyuan Xie, Cha Seung

The integration of conversational agents into our daily lives has become increasingly common, yet many of these agents cannot engage in deep interactions with humans. Despite this, there is a noticeable shortage of datasets that capture multimodal information from human-robot interaction dialogues. To address this gap, we have recorded a novel multimodal dat

Boris Kruglikov

In this note we give a criterion for the existence of a fractional-linear integral for a geodesic flow on a Riemannian surface and explain that modulo M\"obius transformations the moduli space of such local integrals (if nonempty) is either the two-dimensional projective plane or a finite number of points. We will also consider explicit examples and discuss

André Lieutier, Mathijs Wintraecken

This paper contains three main results. Firstly, we give an elementary proof of the following statement: Let $M$ be a (closed, in both the geometrical and topological sense of the word) topological manifold embedded in $\mathbb{R}^d$. If $M$ has positive reach, then M can locally be written as the graph of a $C^{1,1}$ from the tangent space to the normal spa

Minzheng Wang, Xinghua Zhang, Kun Chen, Nan Xu

Large language models (LLMs) enabled dialogue systems have become one of the central modes in human-machine interaction, which bring about vast amounts of conversation logs and increasing demand for dialogue generation. The dialogue's life-cycle spans from $\textit{Prelude}$ through $\textit{Interlocution}$ to $\textit{Epilogue}$, encompassing rich dialogue

Zhigang Song, Péter Udvarhelyi, Yidan Wang, Prineha Narang

Qubits are the fundamental units in quantum computing, but they are also pivotal for advancements in quantum communication and sensing. Currently, there are a variety of platforms for qubits, including cold atoms, superconducting circuits, point defects, and semiconductor quantum dots. In these systems, each qubit requires individual preparation, making iden

Yongxin Wang, Meng Cao, Haokun Lin, Mingfei Han

Multimodal large language models (MLLMs) have achieved remarkable progress on various visual question answering and reasoning tasks leveraging instruction fine-tuning specific datasets. They can also learn from preference data annotated by human to enhance their reasoning ability and mitigate hallucinations. Most of preference data is generated from the mode

Omer Sen, Yanico Aust, Simon Glomb, Andreas Ulbig

This study delves into the role of process awareness in enhancing intrusion detection within Smart Grids, considering the increasing fusion of ICT in power systems and the associated emerging threats. The research harnesses a co-simulation environment, encapsulating IT, OT, and ET layers, to model multi-stage cyberattacks and evaluate machine learning-based

Omer Sen, Mehdi Akbari Gurabi, Milan Deruelle, Andreas Ulbig

The shift to smart grids has made electrical power systems more vulnerable to sophisticated cyber threats. To protect these systems, holistic security measures that encompass preventive, detective, and reactive components are required, even with encrypted data. However, traditional intrusion detection methods struggle with encrypted traffic, our research foc

Omer Sen, Nathalie Bleser, Martin Henze, Andreas Ulbig

The integration of information and communication technology in distribution grids presents opportunities for active grid operation management, but also increases the need for security against power outages and cyberattacks. This paper examines the impact of cyberattacks on smart grids by replicating the power grid in a secure laboratory environment as a cybe

Hana Dal Poz Kouřimská, André Lieutier, Mathijs Wintraecken

Assumptions on the reach are crucial for ensuring the correctness of many geometric and topological algorithms, including triangulation, manifold reconstruction and learning, homotopy reconstruction, and methods for estimating curvature or reach. However, these assumptions are often coupled with the requirement that the manifold be smooth, typically at least

Gouranga Bala, Anuj Gupta, Subrat Kumar Behera, Amit Sethi

Deep learning models rely heavily on large volumes of labeled data to achieve high performance. However, real-world datasets often contain noisy labels due to human error, ambiguity, or resource constraints during the annotation process. Instance-dependent label noise (IDN), where the probability of a label being corrupted depends on the input features, pose

Shohely Tasnim Anindo, Daniela Täuber, Christin David

Powerful mid-infrared illumination combined with mechanical detection via force microscopy provides access to nanoscale spectroscopic imaging in Materials and Life Sciences. Photo-induced force microscopy (PiFM) employs pulsed illumination and noncontact force microscopy resulting in unprecedented spatial and high spectral resolution. The near-field-enhanced

Mahek Kantharia, Neeraj Badal, Zankhana Shah

Pansharpening is a crucial task in remote sensing, enabling the generation of high-resolution multispectral images by fusing low-resolution multispectral data with high-resolution panchromatic images. This paper provides a comprehensive analysis of traditional and deep learning-based pansharpening methods. While state-of-the-art deep learning methods have si

Drazen Adamovic, Shigenori Nakatsuka

In this article, we shall describe the center of the universal affine vertex superalgebra $V^{\kappa_c}(\mathfrak g)$ associated with $\mathfrak g=\mathfrak{sl}_{2|1}, \mathfrak {gl}_{2|1}$ at the critical level $\kappa_c$ and prove the conjecture of A. Molev and E. Ragoucy in this case. The center $\mathfrak{z}(V^{\kappa_c}(\mathfrak{sl}_{2|1}))$ turns out

Aurélien Djament, Antoine Touzé

We study several structure aspects of functor categories from a small additive category to a module category, in particular the category F(A,K) of functors from finitely generated free modules over a commutative ring A to vector spaces over a field K -- such functors are sometimes called \textit{generic representations} of linear groups over A with coefficie

Nina Královič-Kanjaková, Ali Asi Shirazi, Lukáš Hubčík, Mária Klacsová

The use of exogenous pulmonary surfactant (EPS) to deliver other relevant drugs to the lung is a promising strategy for combined therapy. We evaluated the interaction of polymyxin B (PxB) with clinically used EPS, the poractant alfa Curosurf (PSUR). The effect of PxB on the protein-free model system (MS) composed of four phospholipids (diC16:0PC/16:0-18:1PC/

Karyna Isaieva, Yves Laprie, Nicolas Turpault, Alexis Houssard

Tongue contour extraction from real-time magnetic resonance images is a nontrivial task due to the presence of artifacts manifesting in form of blurring or ghostly contours. In this work, we present results of automatic tongue delineation achieved by means of U-Net auto-encoder convolutional neural network. We present both intra- and inter-subject validation

Katherine Légaré, Guillaume Barrette, Laurent Giroux, Jean-Michel Parent

In the last few decades, ultrafast demagnetization elicited by ultrashort laser pulses has been the subject of a large body of work that aims to better understand and control this phenomenon. Although specific magnetic materials' properties play a key role in defining ultrafast demagnetization dynamics, features of the driving laser pulse such as its duratio

Martin Luttmann, Michel Luttmann

The notched stick, also known as the Gee-Haw-Whammy-Diddle, is a wooden toy able to convert linear vibration into rotational motion, whose behavior has been intriguing both children and physicists for decades. Here we derive an analytical model of the system, supported by experimental results. We predict the direction of rotation, and explain why the device

Boris Kruglikov, Vladimir S. Matveev, Wijnand Steneker

Conformal geodesics form an invariantly defined family of unparametrized curves in a conformal manifold generalizing unparametrized geodesics/paths of projective connections. The equation describing them is of third order, and it was an open problem whether they are given by an Euler--Lagrange equation. In dimension 3 (the simplest, but most important from t

Donghan Kim, Woojin Kim, Wonjun Lee

The Generalized Persistence Diagram (GPD) for multi-parameter persistence naturally extends the classical notion of persistence diagram for one-parameter persistence. However, unlike its classical counterpart, computing the GPD remains a significant challenge. The main hurdle is that, while the GPD is defined as the M\"obius inversion of the Generalized Rank

Xiaoyu Wang, Ningyuan Xi, Teng Chen, Qingqing Gu

Large Language Models (LLM) are usually fine-tuned to participate in dyadic or two-party dialogues, which can not adapt well to multi-party dialogues (MPD), which hinders their applications in such scenarios including multi-personal meetings, discussions and daily communication. Previous LLM-based researches mainly focus on the multi-agent framework, while t

Jie Wang, Mobing Cai, Zhongpan Zhu, Hongjun Ding

In the domain of autonomous vehicles, the human-vehicle co-pilot system has garnered significant research attention. To address the subjective uncertainties in driver state and interaction behaviors, which are pivotal to the safety of Human-in-the-loop co-driving systems, we introduce a novel visual-tactile perception method. Utilizing a driving simulation p

Jixuan Fan, Wanhua Li, Yifei Han, Tianru Dai

3D Gaussian Splatting has demonstrated notable success in large-scale scene reconstruction, but challenges persist due to high training memory consumption and storage overhead. Hybrid representations that integrate implicit and explicit features offer a way to mitigate these limitations. However, when applied in parallelized block-wise training, two critical

Italo Perrucci, Folkert Kuipers, Roberto Casadio

Using the effective field theory of quantum gravity at second order in curvature, we calculate quantum corrections to the metric of gravastars and the closely related dark energy stars. We find that the quantum corrections in the exterior region depend on the equation of state of the gravastar, thus providing an example of quantum gravitational hair. We cont

Mihailo M. Martinović, Kristopher G. Klein, Rossana De Marco, Daniel Verscharen

The stability of weakly collisional plasmas is well represented by linear theory, and the generated waves play an essential role in the thermodynamics of these systems. The velocity distribution functions (VDF) characterizing kinetic particle behavior are commonly represented as a sum of anisotropic bi-Maxwellians. For the majority of in situ observations of

Niloufar Delfan, Pardis Ketabi Moghadam, Mohammad Khoshnevisan, Mehdi Hosseini Chagahi

Non-alcoholic fatty liver disease (NAFLD) is one of the most widespread liver disorders on a global scale, posing a significant threat of progressing to more severe conditions like nonalcoholic steatohepatitis (NASH), liver fibrosis, cirrhosis, and hepatocellular carcinoma. Diagnosing and staging NAFLD presents challenges due to its non-specific symptoms and

Yue Liu, Chenlong Huang, Xingyu Zhang, Dahai He

In practice, qubit reset must be operated in an extremely short time, which incurs a thermodynamic cost within multiple orders of magnitude above the Landauer bound. We present a general framework to determine the minimal thermodynamic cost and the optimal protocol for arbitrary resetting speeds. Our study reveals the divergent behavior of minimal entropy pr

Filippo Airaldi, Bart De Schutter, Azita Dabiri

Global optimisation to optimise expensive-to-evaluate black-box functions without gradient information. Bayesian optimisation, one of the most well-known techniques, typically employs Gaussian processes as surrogate models, leveraging their probabilistic nature to balance exploration and exploitation. However, these processes become computationally prohibiti

Peter Athron, Csaba Balazs, Andrew Fowlie, Lachlan Morris

In recent years, the prospect of detecting gravitational waves sourced from a strongly first-order cosmological phase transition has emerged as one of the most exciting frontiers of gravitational wave astronomy. Cosmological phase transitions are an essential ingredient in the Standard Model of particle cosmology, and help explain the mechanism for creation

Pawel Tomasz Pieta, Peter Winkel Rasmussen, Anders Bjorholm Dahl, Jeppe Revall Frisvad

Influenced by the complexity of volumetric imaging, there is a shortage of established datasets useful for benchmarking volumetric deep-learning models. As a consequence, new and existing models are not easily comparable, limiting the development of architectures optimized specifically for volumetric data. To counteract this trend, we introduce MozzaVID -- a

Eric L. Wisotzky, Alexander Schill, Anna Hilsmann, Peter Eisert

In head and neck surgery, continuous intraoperative tissue differentiation is of great importance to avoid injury to sensitive structures such as nerves and vessels. Hyperspectral imaging (HSI) with neural network analysis could support the surgeon in tissue differentiation. A 3D Convolutional Neural Network with hyperspectral data in the range of $400-1000$

Ning Zhang, Chong Chen, Ping Wang

We propose a sequential measurement protocol for accurate low-temperature estimation. The resulting correlated outputs significantly enhance the low temperature precision compared to that of the independent measurement scheme. This enhancement manifests a Heisenberg scaling of the signal-to-noise ratio for small measurement numbers $N$. Detailed analysis rev

Xinghao Guo, Yin Xu, Dazhi He, Cixiao Zhang

The fluid antenna (FA) index modulation (IM)-enabled multiple-input multiple-output (MIMO) system, referred to as FA-IM, significantly enhances spectral efficiency (SE) compared to the conventional FA-assisted MIMO system. To improve robustness against the high spatial correlation among multiple activated ports of the fluid antenna, this paper proposes an in

Pramesh Gautam, Ravi Sharan Bhagavathula, Paolo Baracca, Carsten Bockelmann

The sixth generation (6G) industrial Sub-networks (SNs) face several challenges in meeting extreme latency and reliability requirements in the order of 0.1-1 ms and 99.999 -to-99.99999 percentile, respectively. Interference management (IM) plays an integral role in addressing these requirements, especially in ultra-dense SN environments with rapidly varying

A. S. Holevo

The aim of the present note is to show that the method of our paper ArXiv:2408.11400 with minor extra efforts can be extended to obtain upper bounds for the Bures distance between quantum Gaussian states. We argue that these bounds are better adapted to the Bures distance and hence to the state estimation and learning with the Bures distance rather than that

Azra Seyyedi, Mahdi Bohlouli, SeyedEhsan Nedaaee Oskoee

High connectivity and robustness are critical requirements in distributed networks, as they ensure resilience, efficient communication, and adaptability in dynamic environments. Additionally, optimizing energy consumption is also paramount for ensuring sustainability of networks composed of energy-constrained devices and prolonging their operational lifespan

Ellen Fogh, Gaétan Giriat, Richard Gaal, Luc Testa

The simultaneous application of high magnetic fields and high pressures for controlling magnetic ground states is important for testing our understanding of many-body quantum theory. However, the implementation for neutron scattering experiments presents a technical challenge. To overcome this challenge we present an optimized pressure-cell design with a nov

Delin Zhang, Heshuang Wei, Jinyu Duan, Jiali Chen

Efficiently manipulating the magnetization of van der Waals ferromagnets has attracted considerable interest in developing room-temperature two-dimensional material-based memory and logic devices. Here, taking advantage of the unique properties of the van der Waals ferromagnet as well as promising characteristics of the orbital Hall effect, we demonstrate th

Yuanhao Yue, Chengyu Wang, Jun Huang, Peng Wang

Specializing LLMs in various domain-specific tasks has emerged as a critical step towards achieving high performance. However, the construction and annotation of datasets in specific domains are always very costly. Apart from using superior and expensive closed-source LLM APIs to construct datasets, some open-source models have become strong enough to handle

Izzet Coskun, Abuzer Gündüz

In this paper, we study the existence of a dense orbit for the diagonal $\PGL(n)$ action on self-products of partial flag varieties. We determine when there exists a dense orbit for flag varieties of the form $F(k_1, \dots, k_r; n)^m$ when $k_i = i$ or when $m=3$ and $k_i = \ell + i$ for some $\ell \geq 0$. We also show that certain infinite families of prod

Florian Mayer, Jiří Hlinka

Antiskyrmions, as topological quasi-particles, hold significant promise for spintronics and nanoscale data storage applications. Using molecular dynamics simulations based on effective Hamiltonians, we investigate the thermal stability of antiskyrmion nanodomains in rhombohedral barium titanate. At 1 K, antiskyrmions with a topological charge of -2 emerge as

Fei Gao, Ming Hu, Zhiyu Xie, Peichang Shi

With advancements in AI infrastructure and Trusted Execution Environment (TEE) technology, Federated Learning as a Service (FLaaS) through JointCloud Computing (JCC) is promising to break through the resource constraints caused by heterogeneous edge devices in the traditional Federated Learning (FL) paradigm. Specifically, with the protection from TEE, data

Lei Fan, Dongdong Fan, Zhiguang Hu, Yiwen Ding

We present MANTA, a visual-text anomaly detection dataset for tiny objects. The visual component comprises over 137.3K images across 38 object categories spanning five typical domains, of which 8.6K images are labeled as anomalous with pixel-level annotations. Each image is captured from five distinct viewpoints to ensure comprehensive object coverage. The t

Guoyu Li, Changsheng You, Guanyu Shang, Shaochuan Wu

In this letter, we propose a new conformal array architecture, called extremely large-scale uniform arc array (XL-UAA), to improve near-field communication performance. Specifically,under the non-uniform spherical wavefront channel model, we establish mathematical modeling and performance analysis for XL-UAAs. It is shown that XL-UAAs have larger direction-d

Christophe H. Valahu, Matthew P. Stafford, Zixin Huang, Vassili G. Matsos

Precision metrology underpins scientific and technological advancements. Quantum metrology offers a pathway to surpass classical sensing limits by leveraging quantum states and measurement strategies. However, measuring multiple incompatible observables suffers from quantum backaction, where measurement of one observable pollutes a subsequent measurement of

Florent Hivert

We present a library of formalized results around symmetric functions and the character theory of symmetric groups. Written in Coq/Rocq and based on the Mathematical Components library, it covers a large part of the contents of a graduate level textbook in the field. The flagship result is a proof of the Littlewood-Richardson rule, which computes the structu

K. Górska, A. Horzela, D. Kołaczek, B. J. Spisak

Entanglement of bipartite squeezed states generated by holomorphic Hermite functions of two complex variables is investigated using phase-space approach based on the Wigner distribution function. Orthogonality of the holomorphic Hermite functions implies the relationship between certain real parameter associated with the non-rotational measure in the Bargman

Soyoung An, Kyunghoon Bae, Eunbi Choi, Kibong Choi

This technical report introduces the EXAONE 3.5 instruction-tuned language models, developed and released by LG AI Research. The EXAONE 3.5 language models are offered in three configurations: 32B, 7.8B, and 2.4B. These models feature several standout capabilities: 1) exceptional instruction following capabilities in real-world scenarios, achieving the highe

Jie Lin, I Chiu, Kuan-Chen Wang, Kai-Chun Liu

Electrocardiogram (ECG) signals play a crucial role in diagnosing cardiovascular diseases. To reduce power consumption in wearable or portable devices used for long-term ECG monitoring, super-resolution (SR) techniques have been developed, enabling these devices to collect and transmit signals at a lower sampling rate. In this study, we propose MSECG, a comp

Sebastián Orellana, Leandro Magga, Paolo Gorgi, Hyeokmoon Kweon

Contact centers are crucial in shaping customer experience, especially in industries like airlines where they significantly influence brand perception and satisfaction. Despite their importance, the effect of contact center improvements on business metrics remains uncertain, complicating investment decisions and often leading to insufficient resource allocat

Mingqing Zhang, Haisong Gong, Qiang Liu, Shu Wu

The rapid spread of rumors on social media platforms during breaking events severely hinders the dissemination of the truth. Previous studies reveal that the lack of annotated resources hinders the direct detection of unforeseen breaking events not covered in yesterday's news. Leveraging large language models (LLMs) for rumor detection holds significant prom

Shuren Qi, Fei Wang, Tieyong Zeng, Fenglei Fan

Integrating invariance into data representations is a principled design in intelligent systems and web applications. Representations play a fundamental role, where systems and applications are both built on meaningful representations of digital inputs (rather than the raw data). In fact, the proper design/learning of such representations relies on priors w.r

Zenan Li, Zhi Zhou, Yuan Yao, Yu-Feng Li

A critical question about Large Language Models (LLMs) is whether their apparent deficiency in mathematical reasoning is inherent, or merely a result of insufficient exposure to high-quality mathematical data. To explore this, we developed an automated method for generating high-quality, supervised mathematical datasets. The method carefully mutates existing

Yu Kang, Ge Wang, Xin Yang, Yuda Wang

The development of Large Language Models (LLMs) has created transformative opportunities for the financial industry, especially in the area of financial trading. However, how to integrate LLMs with trading systems has become a challenge. To address this problem, we propose an intelligent trade order recognition pipeline that enables the conversion of trade o

Yaojie Zhang, Tianlun Huang, Weijun Wang, Wei Feng

Traditional point cloud registration (PCR) methods for feature matching often employ the nearest neighbor policy. This leads to many-to-one matches and numerous potential inliers without any corresponding point. Recently, some approaches have framed the feature matching task as an assignment problem to achieve optimal one-to-one matches. We argue that the tr