December 2024 arXiv papers — page 201

Ting-Ting Duan, Fu-Hu Liu, Khusniddin K. Olimov

After reviewing the sound speeds in various forms and conditions of matter, we investigate the sound speed of hadronic matter that has decoupled from the hot and dense system formed during high-energy collisions. We comprehensively consider factors such as energy loss of the incident beam, rapidity shift of leading nucleons, and the Landau hydrodynamic model

Antoine Moreau, Émilie Sakat, Jean-Paul Hugonin, Téo Mottin

We demonstrate that including the second viscosity of an electron gas in the hydrodynamic model allows for highly accurate modeling of the optical response of heavily doped semiconductors. In our setup, which improves resonance visibility compared to previous approaches, plasmon resonances become more distinct, allowing for a detailed analysis of the underly

José Rui Figueira, Kathrin Klamroth, Michael Stiglmayr, Julia Sudhoff Santos

Multi-objective unconstrained combinatorial optimization problems (MUCO) are in general hard to solve, i.e., the corresponding decision problem is NP-hard and the outcome set is intractable. In this paper we explore special cases of MUCO problems that are actually easy, i.e., solvable in polynomial time. More precisely, we show that MUCO problems with up to

Jana Gierse, Roland Fried

This paper proposes robust estimators of the variogram, a statistical tool that is commonly used in geostatistics to capture the spatial dependence structure of data. The new estimators are based on the highly robust minimum covariance determinant estimator and estimate the directional variogram for several lags jointly. Simulations and breakdown considerati

Qirui Yang, Yinbo Li, Yihao Liu, Peng-Tao Jiang

Existing tone mapping methods operate on downsampled inputs and rely on handcrafted pyramids to recover high-frequency details. These designs typically fail to preserve fine textures and structural fidelity in complex HDR scenes. Furthermore, most methods lack an effective mechanism to jointly model global tone consistency and local contrast enhancement, lea

Christopher G. Best

We compute an asymptotic formula for the mixed second moment of the $\mu$-th and $\nu$-th derivatives of quadratic Dirichlet $L$-functions over monic, irreducible polynomials in the function field setting.

Linh Trinh, Siegfried Mercelis, Ali Anwar

Unmanned Surface Vehicles (USVs) have emerged as a major platform in maritime operations, capable of supporting a wide range of applications. USVs can help reduce labor costs, increase safety, save energy, and allow for difficult unmanned tasks in harsh maritime environments. With the rapid development of USVs, many vision tasks such as detection and segment

Hong Lin, Shixin Wan, Zhongle Xie, Ke Chen

Over the recent years, Shapley value (SV), a solution concept from cooperative game theory, has found numerous applications in data analytics (DA). This paper presents the first comprehensive study of SV used throughout the DA workflow, clarifying the key variables in defining DA-applicable SV and the essential functionalities that SV can provide for data sc

Michael Stadlhofer, Bernhard Thaler, Pascal Heim, Josef Tiggesbäumker

While photodissociation of molecular systems has been extensively studied, the photoinduced formation of chemical bonds remains largely unexplored. Especially for larger aggregates, the electronic and nuclear dynamics involved in the cluster formation process remain elusive. This limitation is rooted in difficulties to prepare reactants at well-defined initi

M. Lemoine, F. Rieger

Recent experimental results from the IceCube detector and their phenomenological interpretation suggest that the magnetized turbulent corona of nearby X-ray luminous Seyfert galaxies can produce $\sim 1-10\,$TeV neutrinos via photo-hadronic interactions. We investigate the physics of stochastic acceleration in these environments in detail and examine the con

MD Raqib Khan, Anshul Negi, Ashutosh Kulkarni, Shruti S. Phutke

Quality degradation is observed in underwater images due to the effects of light refraction and absorption by water, leading to issues like color cast, haziness, and limited visibility. This degradation negatively affects the performance of autonomous underwater vehicles used in marine applications. To address these challenges, we propose a lightweight phase

Weiqiao Shan, Long Meng, Tong Zheng, Yingfeng Luo

Large language models (LLMs) exhibit exceptional performance across various downstream tasks. However, they encounter limitations due to slow inference speeds stemming from their extensive parameters. The early exit (EE) is an approach that aims to accelerate auto-regressive decoding. EE generates outputs from intermediate layers instead of using the whole m

Ashhadul Islam, Abdesselam Bouzerdoum, Samir Brahim Belhaouari

Traditional neural networks employ fixed weights during inference, limiting their ability to adapt to changing input conditions, unlike biological neurons that adjust signal strength dynamically based on stimuli. This discrepancy between artificial and biological neurons constrains neural network flexibility and adaptability. To bridge this gap, we propose a

Miao Meng, Ying Ning Tan, Zi Cong He, Zi Hao Zhong

In recent decades, significant progress has been made in construction and study of individual quantum systems consisting of the basic single matter and energy particles, i.e., atoms and photons, which show great potentials in quantum computation and communication. Here, we demonstrate that the quadruply-bonded Mo$_2$ unit of the complex can trap photons of v

Lasantha Thakshila Wedage, Bernard Butler, Mehmet Can Vuran, Sasitharan Balasubramaniam

This study presents a simulated transceiver with a microstrip patch antenna (MPA) designed to resonate at 150 GHz and embedded in paint. The in-paint MPA (IP-MPA) is designed for the Internet of Paint (IoP) paradigm, which envisions seamless device communication through a paint layer on walls. This study introduces a comprehensive channel model for transceiv

Matthias Georg Mayer, Fabian von der Warth

This paper presents a novel proof that for any convex cone, the size of conically independent generators is at most twice that of minimum cardinality generators. While this result is known for linear spaces, we extend it to general cones through a decomposition into linear and pointed components. Our constructive approach leads to a polynomial-time algorithm

Mridula Vijendran, Jingjing Deng, Shuang Chen, Edmond S. L. Ho

Artificial Intelligence significantly enhances the visual art industry by analyzing, identifying and generating digitized artistic images. This review highlights the substantial benefits of integrating geometric data into AI models, addressing challenges such as high inter-class variations, domain gaps, and the separation of style from content by incorporati

Axel Faes, Ashkan Pirmani, Yves Moreau, Liesbet M. Peeters

Block-Term Tensor Regression (BTTR) has proven to be a powerful tool for modeling complex, high-dimensional data by leveraging multilinear relationships, making it particularly well-suited for applications in healthcare and neuroscience. However, traditional implementations of BTTR rely on centralized datasets, which pose significant privacy risks and hinder

Christopher Damgaard-Carstensen, Torgom Yezekyan, Mark L. Brongersma, Sergey I. Bozhevolnyi

Ultrafast and highly efficient dynamic optical metasurfaces enabling truly spatiotemporal control over optical radiation are poised to revolutionize modern optics and photonics, but their practical realization remains elusive. In this work, we demonstrate highly efficient electro-optic metasurfaces based on quasi-bound states in the continuum (qBIC) operatin

Jianhui Lin, Xiangdong Zhang, Moisés Bravo-Gaete

Recently, two types of solutions to the long-standing issue of general covariance in canonical quantum gravity have been proposed. From the above, a fundamental question arises: which solution is superior? Considering one type of solution with a Cauchy horizon, in the present letter, we explore whether it exhibits properties similar to those of the Reissner-

Shwetha Somasundaram, Anirudh Phukan, Apoorv Saxena

To reduce the latency associated with autoretrogressive LLM inference, speculative decoding has emerged as a novel decoding paradigm, where future tokens are drafted and verified in parallel. However, the practical deployment of speculative decoding is hindered by its requirements for additional computational resources and fine-tuning, which limits its out-o

Younghun Kim, Martin Sevior, Muhammad Usman

The surface code family is a promising approach to implementing fault-tolerant quantum computations. Universal fault-tolerance requires error-corrected non-Clifford operations, in addition to Clifford gates, and for the former, it is imperative to experimentally demonstrate additional resources known as magic states. Another challenge is to efficiently embed

Miao Meng, Ying Ning Tan, Yu Li Zhou, Zi Cong He

The quantization of an optical field is a frontier in quantum optics with implications for both fundamental science and technological applications. Here, we demonstrate that a dinickel complex (Ni$_2$) traps and quantizes classical visible light, behaving as an individual quantum system or the Jaynes Cummings molecule.The composite system forms through coher

Heejin Do, Sangwon Ryu, Jonghwi Kim, Gary Geunbae Lee

With the growing demand to fit fine-grained user intents, faceted query-by-example (QBE), which retrieves similar documents conditioned on specific facets, has gained recent attention. However, prior approaches mainly depend on document-level comparisons using basic indicators like citations due to the lack of facet-level relevance datasets; yet, this limits

Peng-Yu Sun, Hang Zhou, Fu-Quan Dou

Machine learning offers a promising methodology to tackle complex challenges in quantum physics. In the realm of quantum batteries (QBs), model construction and performance optimization are central tasks. Here, we propose a cavity-Heisenberg spin chain quantum battery (QB) model with spin-$j (j=1/2,1,3/2)$ and investigate the charging performance under both

Anian Ruoss, Fabio Pardo, Harris Chan, Bonnie Li

In this paper, we present a benchmark to pressure-test today's frontier models' multimodal decision-making capabilities in the very long-context regime (up to one million tokens) and investigate whether these models can learn from large numbers of expert demonstrations in their context. We evaluate the performance of Claude 3.5 Sonnet, Gemini 1.5 Flash, Gemi

Zhixiang Wang, Xingjun Ma, Yu-Gang Jiang

Physical adversarial patches printed on clothing can enable individuals to evade person detectors, but most existing methods prioritize attack effectiveness over stealthiness, resulting in aesthetically unpleasing patches. While generative adversarial networks and diffusion models can produce more natural-looking patches, they often fail to balance stealthin

Ran Gao, Shuxun Tian, Zhengxiang Li, He Gao

Ultralight bosonic wave dark matter (DM) is preponderantly contesting the conventional cold DM paradigm in predicting diverse and rich phenomena on small scales. For a DM halo made of ultralight bosons, the wave interference naturally induces slow de Broglie time-scale fluctuations of the gravitational potential. In this paper, we first derive an estimation

Cheng-Yu Pai, Yu-Che Tung, Zhen-Ming Huang, Chao-Yu Chen

The widely and commonly adopted upper bound on the set size of aperiodic Z-complementary sets (ZCSs) in the literature has been a conjecture. In this letter, we provide detailed derivations for this conjectured bound. A ZCS is optimal when its set size reaches the upper bound. Furthermore, we propose a new construction of ZCSs based on extended generalized B

Aymen Daghar

In this paper, we introduce the notion of negatively regionally proximal pairs of onto maps which coincides with the set of regionally proximal pair of $f^{-1}$, whenever $f$ is an homeomorphism and we prove the maximal equicontinoues factor for any onto map on a locally connected continuum is monotone. Using this, we prove that if $f$ is a minimal map on a

Pierre Nazé

Kibble-Zurek mechanism is widely known to appear in the transverse-field quantum Ising chain in the thermodynamic limit at zero temperature, having notorious characteristics, like the divergence of its relaxation time. In this work, I present the same effect in a simple system, the driven underdamped Brownian motion. Using linear response theory, I show the

Takayuki Yokoyama, Yasuhiro Tada

Fictitious magnetic fields can be introduced in quantum magnets by the strain engineering and the Aharonov-Casher effect. Here, we study impacts of a uniform fictitious magnetic field and corresponding Landau quantization on the Neel order in the $S=1/2$ Heisenberg model on the square lattice as a prototypical system of quantum magnets. We first analyze the

Vladlen Galetsky, Nilesh Vyas, Alberto Comin, Janis Nötzel

This study explores the feasibility of utilizing quantum error correction (QEC) to generate and store logical Bell states in heralded quantum entanglement protocols, crucial for quantum repeater networks. Two lattice surgery-based protocols (local and non-local) are introduced to establish logical Bell states between distant nodes using an intermediary node.

Jennifer Ding, Eva Jäger, Victoria Ivanova, Mercedes Bunz

Data grows in value when joined and combined; likewise the power of voice grows in ensemble. With 15 UK choirs, we explore opportunities for bottom-up data governance of a jointly created Choral AI Dataset. Guided by a survey of chorister attitudes towards generative AI models trained using their data, we explore opportunities to create empowering governance

Sabine Moehler

Context: The metal-rich bulge globular cluster NGC6388 shows a blue horizontal branch (HB). Helium (He) enrichment, which is correlated with changes in other light elements, might explain this feature. The hot HB stars in the metal-poor globular clusters NGC6397 and NGC6752, instead, show high abundances of heavy elements caused by radiative levitation. Aims

Mona Alawadh, Mahesan Niranjan, Hansung Kim

Semantic Scene Completion (SSC) is a critical task in computer vision, that utilized in applications such as virtual reality (VR). SSC aims to construct detailed 3D models from partial views by transforming a single 2D image into a 3D representation, assigning each voxel a semantic label. The main challenge lies in completing 3D volumes with limited informat

Xiaoguang Han, Yushuang Wu, Luyue Shi, Haolin Liu

MVImgNet is a large-scale dataset that contains multi-view images of ~220k real-world objects in 238 classes. As a counterpart of ImageNet, it introduces 3D visual signals via multi-view shooting, making a soft bridge between 2D and 3D vision. This paper constructs the MVImgNet2.0 dataset that expands MVImgNet into a total of ~520k objects and 515 categories

Yuelei Wang, Jian Zhang, Pengtao Jiang, Hao Zhang

Despite the significant advancements made by Diffusion Transformer (DiT)-based methods in video generation, there remains a notable gap with controllable camera pose perspectives. Existing works such as OpenSora do NOT adhere precisely to anticipated trajectories and physical interactions, thereby limiting the flexibility in downstream applications. To allev

Fereshteh Felegary, Seyed Ali Hosseini Mansoori, Tahere Fallahi Serish, Phongpichit Channuie

The canonical multi-scalar field inflation where the kinetic and potential terms are sum-separable is ruled out by the current observations for the chaotic-type potential $V=\sum_{i} \mu_{i} \phi_{i}^{p}$. This paper explores the non-sum-separable case to validate the chaotic-type potential in the multi-scalar field, incorporating a linear coupling term betw

Hao Li, Xiang Chen, Jiangxin Dong, Jinhui Tang

Despite the significant progress made by all-in-one models in universal image restoration, existing methods suffer from a generalization bottleneck in real-world scenarios, as they are mostly trained on small-scale synthetic datasets with limited degradations. Therefore, large-scale high-quality real-world training data is urgently needed to facilitate the e

M. Veselsky, V. Petousis, P. S. Koliogiannis, Ch. C. Moustakidis

The recent observation of the compact star XTE J1814-338 with a mass of $M=1.2^{+0.05}_{-0.05}~{\rm M_{\odot}}$ and a radius of $R=7^{+0.4}_{-0.4}$ km, together with the HESS J1731-347, which has a mass of $M=0.77^{+0.20}_{-0.17}~{\rm M_{\odot}}$ and a radius of $R=10.4^{+0.86}_{-0.78}$ km, shows they provide evidence for the possible presence of exotic matt

Xinrui Yan, Jiangyan Yi, Jianhua Tao, Yujie Chen

Open environment oriented open set model attribution of deepfake audio is an emerging research topic, aiming to identify the generation models of deepfake audio. Most previous work requires manually setting a rejection threshold for unknown classes to compare with predicted probabilities. However, models often overfit training instances and generate overly c

Jindrich Dunik, Oliver Kost, J. Krejci, Ondrej Straka

This paper focuses on the identification of the process noise density of a linear time-varying system described by the state-space model with the known measurement noise density. A novel method is proposed that enhances the measurement difference method (MDM). The proposed method relies on a refined calculation of the MDM residue, which accounts for both pro

Zhu Liu, Cunliang Kong, Ying Liu, Maosong Sun

Semantic map models (SMMs) construct a network-like conceptual space from cross-linguistic instances or forms, based on the connectivity hypothesis. This approach has been widely used to represent similarity and entailment relationships in cross-linguistic concept comparisons. However, most SMMs are manually built by human experts using bottom-up procedures,

Yonghao Dang, Liyuan Liu, Hui Kang, Ping Ye

Real-time 2D keypoint detection plays an essential role in computer vision. Although CNN-based and Transformer-based methods have achieved breakthrough progress, they often fail to deliver superior performance and real-time speed. This paper introduces MamKPD, the first efficient yet effective mamba-based pose estimation framework for 2D keypoint detection.

Tiago Dias, João Vitorino, Eva Maia, Isabel Praça

Network Intrusion Detection (NID) systems can benefit from Machine Learning (ML) models to detect complex cyber-attacks. However, to train them with a great amount of high-quality data, it is necessary to perform reliable simulations of multiple interacting machines. This paper presents a network simulation solution for the creation of NID datasets with comp

Amber Cassimon, Siegfried Mercelis, Kevin Mets

Recently, a novel paradigm has been proposed for reinforcement learning-based NAS agents, that revolves around the incremental improvement of a given architecture. We assess the abilities of such reinforcement learning agents to transfer between different tasks. We perform our evaluation using the Trans-NASBench-101 benchmark, and consider the efficacy of th

Yichen Wang, Chengcheng Yu

Accurate origin-destination (OD) passenger flow prediction is crucial for enhancing metro system efficiency, optimizing scheduling, and improving passenger experiences. However, current models often fail to effectively capture the asynchronous departure characteristics of OD flows and underutilize the inflow and outflow data, which limits their prediction ac

Balint Varga, Vince Grolmusz

Human braingraphs or connectomes are widely studied in the last decade to understand the structural and functional properties of our brain. In the last several years our research group has computed and deposited thousands of human braingraphs to the braingraph.org site, by applying public structural (diffusion) MRI data from young and healthy subjects. Here

Mikhail Burtsev

Large Language Models demonstrate remarkable mathematical capabilities but at the same time struggle with abstract reasoning and planning. In this study, we explore whether Transformers can learn to abstract and generalize the rules governing Elementary Cellular Automata. By training Transformers on state sequences generated with random initial conditions an

Joshua Owotogbe, Indika Kumara, Willem-Jan Van Den Heuvel, Damian Andrew Tamburri

Organizations, particularly medium and large enterprises, typically rely heavily on complex, distributed systems to deliver critical services and products. However, the growing complexity of these systems poses challenges in ensuring service availability, performance, and reliability. Traditional resilience testing methods often fail to capture the intricate

A. B. Batrakov, S. Yu. Karelin, O. M. Lebedenko, V. S. Mukhin

Experimental data and results of numerical simulations are presented, concerning excitation of narrowband gigahertz-range wave trains in coaxial guiding structures that are partially filled with ferromagnetic material and may involve periodically arranged metal inserts. The experiments performed confirm the possibility of exciting weakly damped electromagnet

Andrzej Indrzejczak

Term-forming operators (tfos), like iota- or epsilon-operator, are technical devices applied to build complex terms in formal languages. Although they are very useful in practice their theory is not well developed. In the paper we provide a proof-theoretic formulation of the general approach to tfos provided independently by several authors like Scott, Hatch

Xiang Li, Yucheng Zhou, Laiping Zhao, Jing Li

Detecting euphemisms is essential for content security on various social media platforms, but existing methods designed for detecting euphemisms are ineffective in impromptu euphemisms. In this work, we make a first attempt to an exploration of impromptu euphemism detection and introduce the Impromptu Cybercrime Euphemisms Detection (ICED) dataset. Moreover,

Miguel Mota-Cruz, João H Santos, José F Macedo, Karima Velasquez

In the ever-evolving landscape of computing, the advent of edge and fog computing has revolutionized data processing by bringing it closer to end-users. While cloud computing offers numerous advantages, including mobility, flexibility and scalability, it introduces challenges such as latency. Fog and edge computing emerge as complementary solutions, bridging

M. Zeeshan Gul, Faisal Javed, M. Sharif, Shalan Alkarni

This research paper investigates the impact of non-metricity and matter source on the geometry of charged spheres in the presence of anisotropic matter configuration. We use a specific model of extended symmetric teleparallel theory to minimize the complexity of the field equations. Moreover, the feasible non-singular solutions are used to examine the interi

Peilin Zhou, Bo Du, Yongchao Xu

Recent trends in cell segmentation have shifted towards universal models to handle diverse cell morphologies and imaging modalities. However, for continuously emerging cell types and imaging techniques, these models still require hundreds or thousands of annotated cells for fine-tuning. We introduce CellSeg1, a practical solution for segmenting cells of arbi

Quentin Kral, Paul Huet, Camille Bergez-Casalou, Philippe Thébault

To date, the most widespread scenario is that the Earth originated without water and was brought to the planet mainly due to impacts by wet asteroids coming from further out in space. However, many uncertainties remain regarding the exact processes that supply water to inner terrestrial planets. This article explores a new mechanism that would allow water to

Aditya Narayan Sankaran, Reza Farahbakhsh, Noel Crespi

Online abusive content detection, particularly in low-resource settings and within the audio modality, remains underexplored. We investigate the potential of pre-trained audio representations for detecting abusive language in low-resource languages, in this case, in Indian languages using Few Shot Learning (FSL). Leveraging powerful representations from mode

Zehuan Wu, Jingcheng Ni, Xiaodong Wang, Yuxin Guo

Generative models have significantly improved the generation and prediction quality on either camera images or LiDAR point clouds for autonomous driving. However, a real-world autonomous driving system uses multiple kinds of input modality, usually cameras and LiDARs, where they contain complementary information for generation, while existing generation meth

Mahmoud Alawashra, Ievgen Vovk, Martin Pohl

Relativistic pair beams produced in the intergalactic medium (IGM) by TeV gamma rays from blazars are expected to generate a detectable GeV-scale electromagnetic cascade, yet this cascade is absent in the observed spectra of hard-spectrum TeV emitting blazars. This suppression is often attributed to weak intergalactic magnetic fields (IGMF) deflecting electr

Thi-Nhu-Quynh Nguyen, Quang-Huy Ho, Duy-Thai Nguyen, Hoang-Minh-Quang Le

Early detection of skin abnormalities plays a crucial role in diagnosing and treating skin cancer. Segmentation of affected skin regions using AI-powered devices is relatively common and supports the diagnostic process. However, achieving high performance remains a significant challenge due to the need for high-resolution images and the often unclear boundar

Samuel T. Baron, Claudio Calosi, Cristian Mariani

We provide a reply to the Argument from Intimacy on behalf of defenders of emergent spacetime in theories of quantum gravity. We argue that if one accepts that spacetime regions are nowhere in the sense that they are locations but do not have locations, then the Argument from Intimacy can be resolved. We go on to consider a problem with this response, namely

A. B. Batrakov, I. N. Onishchenko, S. I. Fedotov, E. G. Glushko

Enhancement of the bremsstrahlung X-ray radiation (BSXR) power generated by the high-current relativistic electron beam (REB) of a pulsed direct-action accelerator TEMP-B is being curried out to use in particular for studying the radiation resistance of walls material of radioactive waste containers. For this the magnetic field in which REB is transported in

Zhuoxiao Li, Shanliang Yao, Taoyu Wu, Yong Yue

While Gaussian Splatting (GS) demonstrates efficient and high-quality scene rendering and small area surface extraction ability, it falls short in handling large-scale aerial image surface extraction tasks. To overcome this, we present ULSR-GS, a framework dedicated to high-fidelity surface extraction in ultra-large-scale scenes, addressing the limitations o

Simon Geirnaert, Iustina Rotaru, Tom Francart, Alexander Bertrand

In a recent paper, we presented the KU Leuven audiovisual, gaze-controlled auditory attention decoding (AV-GC-AAD) dataset, in which we recorded electroencephalography (EEG) signals of participants attending to one out of two competing speakers under various audiovisual conditions. The main goal of this dataset was to disentangle the direction of gaze from t

Bo Pang, Sibo Cheng, Yuhan Huang, Yufang Jin

Predicting the extent of massive wildfires once ignited is essential to reduce the subsequent socioeconomic losses and environmental damage, but challenging because of the complexity of fire behaviour. Existing physics-based models are limited in predicting large or long-duration wildfire events. Here, we develop a deep-learning-based predictive model, Fire-

André Victor Ribeiro Amaral, Adam M. Sykulski, Sophie Fielding, Emma Cavan

Antarctic krill (Euphausia superba) are among the most abundant species on our planet and serve as a vital food source for many marine predators in the Southern Ocean. In this paper, we utilise statistical spatio-temporal methods to combine data from various sources and resolutions, aiming to model krill abundance. Our focus lies in fitting the model to a da

Anna-Maria Halacheva, Yang Miao, Jan-Nico Zaech, Xi Wang

3D scene understanding is a long-standing challenge in computer vision and a key component in enabling mixed reality, wearable computing, and embodied AI. Providing a solution to these applications requires a multifaceted approach that covers scene-centric, object-centric, as well as interaction-centric capabilities. While there exist numerous datasets and a

Huhe Han

In this paper, we provide the upper bound and the lower bound of the sum of the number of spherical face-partition pair of simple spherical polytope $P$ with $p$ facets.

Hiroki Ueda, Yves Joly, Urs Staub

Resonant X-ray diffraction (RXD) provides a unique capability to investigate electronically ordered states in matter. Importantly, circular dichroism in RXD can determine the absolute chirality formed by anisotropic multipole arrangement. Here, we demonstrate that the scattering interference between distinct atomic electric multipoles can create circular con

A. B. Batrakov, S. I. Fedotov, V. G. Korenev, O. M. Lebedenko

Results of research are presented concerning operative modes of a high-voltage (relativistic) pulsed magnetron for the 8 mm wavelength range. Technical solutions are proposed for improving the output system of the device, such as to increase the efficiency of power extraction from the field-particle interaction space. The stability of the magnetron operation

W. Zeng

We theoretically study the transverse charge transport in Josephson junctions based on the tilted Dirac materials with valley-dependent gaps. It is shown that a finite tilt-assisted transverse Josephson Hall current is present under broken time-reversal symmetry. This transverse current is driven by the superconducting phase difference across the junction an

Wenjie Cai, Yi Hu, Xiang Qu, Hui Zhao

The rapid advancements in machine learning have made its application to anomalous diffusion analysis both essential and inevitable. This review systematically introduces the integration of machine learning techniques for enhanced analysis of anomalous diffusion, focusing on two pivotal aspects: single trajectory characterization via machine learning and repr

Sutapa Dey, Amit Tripathi

We investigate the depth of the tensor product of finitely generated modules over local rings. One of the main ingredients of our approach is a lifting construction introduced by Huneke, Jorgensen, and Wiegand. We recover a result of Celikbas, Sadeghi, and Takahashi for local complete intersection rings. Additionally, we provide a negative answer to a questi

Zi-Han Chen, Ming-Cheng Chen, Chao-Yang Lu, Jian-Wei Pan

We propose hardware-efficient schemes for implementing logical H and S gates transversally on rotated surface codes with reconfigurable neutral atom arrays. For logical H gates, we develop a simple strategy to rotate code patches efficiently with two sets of 2D-acousto-optic deflectors (2D-AODs). Our protocol for logical S gates utilizes the time-dynamics of

Vytautas Žalandauskas, Rokas Silkinis, Lasse Vines, Lukas Razinkovas

Understanding the optical properties of color centers in silicon carbide is essential for their use in quantum technologies, such as single-photon emission and spin-based qubits. In this work, first-principles calculations were employed using the r2SCAN density functional to investigate the electronic and vibrational properties of neutral divacancy configura

Paul Mangold, Alain Durmus, Aymeric Dieuleveut, Sergey Samsonov

In this paper, we present a novel analysis of \FedAvg with constant step size, relying on the Markov property of the underlying process. We demonstrate that the global iterates of the algorithm converge to a stationary distribution and analyze its resulting bias and variance relative to the problem's solution. We provide a first-order bias expansion in both

Katarzyna Janocha, Annabel Ling, Alice Godson, Yulia Lampi

Cell and immunotherapy offer transformative potential for treating diseases like cancer and autoimmune disorders by modulating the immune system. The development of these therapies is resource-intensive, with the majority of drug candidates failing to progress beyond laboratory testing. While recent advances in machine learning have revolutionised areas such

Garima Gupta, Jaydev Dabas

This paper discusses the approximate controllability of a fractional differential control problem driven by a nonlinear hemivariational inequality in a Hilbert space. First, we prove the existence of a mild solution for a fractional control inclusion problem which is equivalent to a hemivariational inequality by using the nonsmooth analysis and fixed point t

Nikola Ljubešić, Taja Kuzman, Ivana Filipović Petrović, Jelena Parizoska

This paper introduces the CLASSLA-Express workshop series as an innovative approach to disseminating linguistic resources and infrastructure provided by the CLASSLA Knowledge Centre for South Slavic languages and the Slovenian CLARIN.SI infrastructure. The workshop series employs two key strategies: (1) conducting workshops directly in countries with interes

Namrata Singh, Mahesh Choudhary, A. Gandhi, Aman Sharma

The neutron capture cross-section for the $^{181}$Ta(n,$\gamma$)$^{182}$Ta reaction has been experimentally measured at the neutron energies 0.53 and 1.05 MeV using off-line $\gamma$-ray spectrometry. $^{115}$In(n,n'$\gamma$)$^{115m}$In is used as a reference monitor reaction cross-section. The neutron was produced via the $^{7}$Li(p,n)$^{7}$Be reaction. The

Zhiyan Wang, Zenan Liu, Bin-Bin Mao, Zhe Wang

Quantum Monte Carlo is one of the most promising approaches for dealing with large-scale quantum many-body systems. It has played an extremely important role in understanding strongly correlated physics. However, two fundamental problems, namely the sign problem and general measurement issues, have seriously hampered its scope of application. We propose a un

Ivan DeAndres-Tame, Ruben Tolosana, Pietro Melzi, Ruben Vera-Rodriguez

Synthetic data is gaining increasing popularity for face recognition technologies, mainly due to the privacy concerns and challenges associated with obtaining real data, including diverse scenarios, quality, and demographic groups, among others. It also offers some advantages over real data, such as the large amount of data that can be generated or the abili

Anton Lutsenko, Kevin G. Fripp, Lukáš Flajšman, Andrey V. Shytov

We explore the use of magnonic Fabry-P\'erot resonators as programmable phase shifters for spin-wave computing. The resonator, composed of a yttrium iron garnet (YIG) film coupled with a CoFeB nanostripe, operates through dynamic dipolar coupling, leading to wavelength downconversion and the formation of a magnonic cavity. Using super-Nyquist sampling magnet

Marco Federici, Davide Belli, Mart van Baalen, Amir Jalalirad

While mobile devices provide ever more compute power, improvements in DRAM bandwidth are much slower. This is unfortunate for large language model (LLM) token generation, which is heavily memory-bound. Previous work has proposed to leverage natural dynamic activation sparsity in ReLU-activated LLMs to reduce effective DRAM bandwidth per token. However, more

Shanshan Xiao, Pengzhan Jin, Yifa Tang

In this work, we establish a deformation-based framework for learning solution mappings of PDEs defined on varying domains. The union of functions defined on varying domains can be identified as a metric space according to the deformation, then the solution mapping is regarded as a continuous metric-to-metric mapping, and subsequently can be represented by a

Pang Li, Shahrul Azman Mohd Noah, Hafiz Mohd Sarim

This survey provides an examination of the use of Deep Neural Networks (DNN) in Collaborative Filtering (CF) recommendation systems. As the digital world increasingly relies on data-driven approaches, traditional CF techniques face limitations in scalability and flexibility. DNNs can address these challenges by effectively modeling complex, non-linear relati

Yuhe Ji, Yilun Liu, Feiyu Yao, Minggui He

Log analysis represents a critical sub-domain within AI applications that facilitates automatic approaches to fault and error management of large-scaled software systems, saving labors of traditional manual methods. While existing solutions using large language models (LLMs) show promise, they are limited by a significant domain gap between natural and log l

Pang Li, Shahrul Azman Mohd Noah, Hafiz Mohd Sarim

In this study, we introduce Convolutional Transformer Neural Collaborative Filtering (CTNCF), a novel approach aimed at enhancing recommendation systems by effectively capturing high-order structural information in user-item interactions. CTNCF represents a significant advancement over the traditional Neural Collaborative Filtering (NCF) model by seamlessly

Ilkka Mäkinen

In this article we examine a Hamiltonian constraint operator governing the dynamics of simple quantum states, whose graph consists of a single six-valent vertex, in quantum-reduced loop gravity. To this end, we first derive the action of the Hamiltonian constraint on generic basis states in the Hilbert space of quantum-reduced loop gravity. Specializing to t

Yayoi Abe, Auna Setoh, Gen Yoneda

The vertex coloring problem to find chromatic numbers is known to be unsolvable in polynomial time. Although various algorithms have been proposed to efficiently compute chromatic numbers, they tend to take an enormous amount of time for large graphs. In this paper, we propose a recurrence relation to rapidly obtain the expected value of the chromatic number

L. O. Hryn, Yu. V. Siryk, V. V. Baranov, O. M. Vovk

Eutectics of Sapphire/Yttrium-Aluminum garnet and Ce-doped Sapphire/Yttrium-Aluminum garnet (Ce up to 1 at.%) with a Chinese script microstructure were obtained by the method of horizontal directional crystallization in a carbon-containing reducing atmosphere based on argon gas. The effects of the pooling rate and Ce-dopant concentration on the microstructur

Anna Kuzina, Jakub M. Tomczak

Deep hierarchical variational autoencoders (VAEs) are powerful latent variable generative models. In this paper, we introduce Hierarchical VAE with Diffusion-based Variational Mixture of the Posterior Prior (VampPrior). We apply amortization to scale the VampPrior to models with many stochastic layers. The proposed approach allows us to achieve better perfor

Xiao-Jun Wu, Cai-Jun Zhao, Chun Meng, Hang Wang

The p16/Ki-67 dual staining method is a new approach for cervical cancer screening with high sensitivity and specificity. However, there are issues of mis-detection and inaccurate recognition when the YOLOv5s algorithm is directly applied to dual-stained cell images. This paper Proposes a novel cervical cancer dual-stained image recognition (DSIR-YOLO) model

Davide Gallon, Arnulf Jentzen, Philippe von Wurstemberger

This article provides a mathematically rigorous introduction to denoising diffusion probabilistic models (DDPMs), sometimes also referred to as diffusion probabilistic models or diffusion models, for generative artificial intelligence. We provide a detailed basic mathematical framework for DDPMs and explain the main ideas behind training and generation proce

Ada-Astrid Balauca, Sanjana Garai, Stefan Balauca, Rasesh Udayakumar Shetty

Museums serve as repositories of cultural heritage and historical artifacts from diverse epochs, civilizations, and regions, preserving well-documented collections that encapsulate vast knowledge, which, when systematically structured into large-scale datasets, can train specialized models. Visitors engage with exhibits through curiosity and questions, makin

Jiakai Wang, Pengfei Zhang, Renshuai Tao, Jian Yang

The various post-processing methods for deep-learning-based models, such as quantification, pruning, and fine-tuning, play an increasingly important role in artificial intelligence technology, with pre-train large models as one of the main development directions. However, this popular series of post-processing behaviors targeting pre-training deep models has

Ilkka Mäkinen

We present a systematic approach to the kinematics of quantum-reduced loop gravity, a model originally proposed by Alesci and Cianfrani as an attempt to probe the physical implications of loop quantum gravity. We implement the quantum gauge-fixing procedure underlying quantum-reduced loop gravity by introducing a master constraint operator on the kinematical

Giuseppe Buccheri, Fulvio Corsi, Emilija Dzuverovic

We show that, for a certain class of scaling matrices including the commonly used inverse square-root of the conditional Fisher Information, score-driven factor models are identifiable up to a multiplicative scalar constant under very mild restrictions. This result has no analogue in parameter-driven models, as it exploits the different structure of the scor