April 2024 arXiv papers — page 73

Samali Ghosh, Suvam Pal, Gourab Kumar Sar, Dibakar Ghosh

Swarmalators are entities that swarm through space and sync in time and are potentially considered to replicate the complex dynamics of many real-world systems. So far, the internal dynamics of swarmalators have been taken as a phase oscillator inspired by the Kuramoto model. Here, for the first time, we examine the internal dynamics utilizing an amplitude o

Peaker Guo, Patrick Eades, Anthony Wirth, Justin Zobel

Knowing which strings in a massive text are significant -- that is, which strings are common and distinct from other strings -- is valuable for several applications, including text compression and tokenization. Frequency in itself is not helpful for significance, because the commonest strings are the shortest strings. A compelling alternative is net frequenc

Kavita Yadav, Yuya Tanaka, Kotaro Hirose, Masahiro Adachi

We have investigated the thermoelectric and thermal behaviour of Fe-V-W-Al based thin films prepared using radio frequency magnetron sputtering technique at different base pressures (0.1 ~ 1.0 X 10-2 Pa) and on different substrates (n, p and undoped Si). Interestingly, at lower base pressure, formation of bcc type of Heusler structure was observed in deposit

Jiangyi Deng, Shengyuan Pang, Yanjiao Chen, Liangming Xia

Instead of building deep learning models from scratch, developers are more and more relying on adapting pre-trained models to their customized tasks. However, powerful pre-trained models may be misused for unethical or illegal tasks, e.g., privacy inference and unsafe content generation. In this paper, we introduce a pioneering learning paradigm, non-fine-tu

Xiao Zhang, Gosse Bouma, Johan Bos

Current open-domain neural semantics parsers show impressive performance. However, closer inspection of the symbolic meaning representations they produce reveals significant weaknesses: sometimes they tend to merely copy character sequences from the source text to form symbolic concepts, defaulting to the most frequent word sense based in the training distri

Nanying Yang, Ilya Gorshkov

Let $G$ be a finite group and $N(G)$ be the set of its conjugacy class sizes excluding~$1$. Let us define a directed graph $\Gamma(G)$, the set of vertices of this graph is $N(G)$ and the vertices $x$ and $y$ are connected by a directed edge from $x$ to $y$ if $x$ divides $y$ and $N(G)$ does not contain a number $z$ different from $x$ and $y$ such that $x$ d

Haksun Son, Song Min Kim

The VR industry is one of the most promising industries for the near future, as it can provide a more immersive connection between people and the virtual world. Currently, VR devices interact with people using inconvenient controllers or cameras that perform poorly in dark environments. Interaction through millimeter-wave wearable devices has the potential t

Jakub Woźny, Piotr Jaworski, Damian Jelito, Marcin Pitera

We present a novel data-oriented statistical framework that assesses the presumed Gaussian dependence structure in a pairwise setting. This refers to both multivariate normality and normal copula goodness-of-fit testing. The proposed test clusters the data according to the 20/60/20 rule and confronts conditional covariance (or correlation) estimates on the o

Bruno Laurini, Nicola Cantisani, Wilson R. Leal da Silva, Yi Zong

This article explores the electrification in clay calcination, proposing a dynamic model and energy management strategy for the integration of electrified calcination plants into sustainable power grids. A theoretical dynamic modeling of the electrified calcination process is introduced, aiming at outlining temperature profiles and energy usage - thus explor

Grzegorz Rypeść, Grzegorz Kurzejamski

This work introduces a novel end-to-end approach for estimating extrinsic parameters of cameras in multi-camera setups on real-life sports fields. We identify the source of significant calibration errors in multi-camera environments and address the limitations of existing calibration methods, particularly the disparity between theoretical models and actual s

Yang Hong, Yinfei Li, Xiaojun Qiao, Rui Li

Learning effective representations for Chinese characters presents unique challenges, primarily due to the vast number of characters and their continuous growth, which requires models to handle an expanding category space. Additionally, the inherent sparsity of character usage complicates the generalization of learned representations. Prior research has expl

Yacouba Boubacar Maïnassara, Bruno Saussereau

In this paper we derive the asymptotic distribution of normalized residual empirical autocovariances and autocorrelations under weak assumptions on the noise. We propose new portmanteau statistics for vector autoregressive moving-average (VARMA) models with uncorrelated but non-independent innovations by using a self-normalization approach. We establish the

Shayne Longpre, Robert Mahari, Naana Obeng-Marnu, William Brannon

New capabilities in foundation models are owed in large part to massive, widely-sourced, and under-documented training data collections. Existing practices in data collection have led to challenges in tracing authenticity, verifying consent, preserving privacy, addressing representation and bias, respecting copyright, and overall developing ethical and trust

Matteo Beccaria

We consider 4d $\mathcal N=4$ $U(N)$ SYM and the leading giant graviton correction to the Schur defect 2-point functions of $\frac{1}{2}$-BPS Wilson lines in rank-$k$ symmetric and antisymmetric representations. We study in particular the large $k$ limit for the symmetric case and the regime $1\ll k \ll N$ in the antisymmetric one. We present exact results f

Denis Donadel, Francesco Marchiori, Luca Pajola, Mauro Conti

Recent advancements in Artificial Intelligence, and particularly Large Language Models (LLMs), offer promising prospects for aiding system administrators in managing the complexity of modern networks. However, despite this potential, a significant gap exists in the literature regarding the extent to which LLMs can understand computer networks. Without empiri

Nadège Polette, Olivier Le Maître, Pierre Sochala, Alexandrine Gesret

This paper proposes an effective treatment of hyperparameters in the Bayesian inference of a scalar field from indirect observations. Obtaining the joint posterior distribution of the field and its hyperparameters is challenging. The infinite dimensionality of the field requires a finite parametrization that usually involves hyperparameters to reflect the li

V. N. Antonov, D. A. Kukusta, L. V. Bekenov

We have investigated the electronic structure of the transition metal oxide Ca3Ru2O7 within density functional theory using the generalized gradient approximation while considering strong Coulomb correlations in the framework of the fully relativistic spin-polarized Dirac linear muffin-tin orbital band-structure method. Ca3Ru2O7 can be classified as a Mott i

Dhanurdhar Bajpai, Laura Baudis, Viacheslav Belov, Elisabetta Bossio

The MONUMENT experiment measures ordinary muon capture (OMC) on isotopes relevant for neutrinoless double-beta (0$\nu\beta\beta$) decay and nuclear astrophysics. OMC is a particularly attractive tool for improving the theoretical description of 0$\nu\beta\beta$ decay. It involves similar momentum transfers and allows testing the virtual transitions involved

Yacouba Boubacar Maïnassara, Othman Kadmiri, Bruno Saussereau

We establish the asymptotic behaviour of the sum of squared residuals autocovariances and autocorrelations for the class of multi-variate power transformed asymmetric models. We then derive a portmanteau test. We establish the asymptotic distribution of the proposed statistics. These asymptotic results are illustrated by Monte Carlo experiments. An applicati

Yacouba Boubacar Maïnassara, Eugen Ursu

This article develops the asymptotic distribution of the least squares estimator of the model parameters in periodicvector autoregressive time series models (hereafter PVAR) with uncorrelated but dependent innovations. When theinnovations are dependent, this asymptotic distributions can be quite different from that of PVAR models with in-dependent and identi

Tobias Betz, Long Wen, Fengjunjie Pan, Gemb Kaljavesi

The automotive industry is transitioning from traditional ECU-based systems to software-defined vehicles. A central role of this revolution is played by containers, lightweight virtualization technologies that enable the flexible consolidation of complex software applications on a common hardware platform. Despite their widespread adoption, the impact of con

Wei Wang, Zheng Zhang, Gui-Xin Tang, Tao Wang

We propose to synthesize tunable periodic gauge fields via Floquet engineering cold alkaline-earth atoms in one-dimensional optical lattice. The artificial magnetic flux is designed to emerge during the combined process of Floquet photon assisted tunneling and internal state transitions. By varying initial phases of driving protocol, our proposal presents th

Mokhtar Ben Henda

The digital document evolves rapidly and spectacularly in its structure and information content conveyed on networks and information systems. Generally understood as a neutral support for information carrying a semantic value, the digital document nevertheless carries parameters that denote certain cultural and linguistic values specific to its creator. This

Raghavendra Ramachandra, Narayan Vetrekar, Sushma Venkatesh, Savita Nageshker

Facial biometrics are an essential components of smartphones to ensure reliable and trustworthy authentication. However, face biometric systems are vulnerable to Presentation Attacks (PAs), and the availability of more sophisticated presentation attack instruments such as 3D silicone face masks will allow attackers to deceive face recognition systems easily.

Tomohiko Mizutani

Hyperspectral imaging technology has a wide range of applications, including forest management, mineral resource exploration, and Earth surface monitoring. A key step in utilizing this technology is endmember extraction, which aims to identify the spectral signatures of materials in observed scenes. Theoretical studies suggest that self-dictionary methods us

Aravinda Reddy PN, Raghavendra Ramachandra, Krothapalli Sreenivasa Rao, Pabitra Mitra

Face-morphing attacks are a growing concern for biometric researchers, as they can be used to fool face recognition systems (FRS). These attacks can be generated at the image level (supervised) or representation level (unsupervised). Previous unsupervised morphing attacks have relied on generative adversarial networks (GANs). More recently, researchers have

Jihao Dong, Renjie Pan, Hua Yang

Human-Object Interaction (HOI) detection aims to localize human-object pairs and comprehend their interactions. Recently, two-stage transformer-based methods have demonstrated competitive performance. However, these methods frequently focus on object appearance features and ignore global contextual information. Besides, vision-language model CLIP which effec

Aashay Pandharpatte, Pritam Halder, Aditi Sen De

In a measurement-induced continuous-time quantum walk, we address the problem of detecting a particle in a subspace, instead of a fixed position. In this configuration, we develop an approach of bright and dark states based on the unit and vanishing detection probability respectively for a particle-detection in the subspace. Specifically, by employing the ra

Sylvie Boldo, François Clément, David Hamelin, Micaela Mayero

The goal of this contribution is to provide worksheets in Coq for students to learn about divisibility and binomials. These basic topics are a good case study as they are widely taught in the early academic years (or before in France). We present here our technical and pedagogical choices, the numerous exercises we developed and a small experiment we conduct

Jieyu Zheng, Hong Zhang, Le Tian, Zhuo Zhang

Dilithium is a lattice-based digital signature scheme standardized by the NIST post-quantum cryptography (PQC) project. In this study, we focus on developing efficient sparse polynomial multiplication implementations of Dilithium for ARM Cortex-M4 and Apple M2, which are both based on the ARM architecture. The ARM Cortex-M4 is commonly utilized in resource-c

Zhongyi Lin, Ning Sun, Pallab Bhattacharya, Xizhou Feng

Characterizing and predicting the training performance of modern machine learning (ML) workloads on compute systems with compute and communication spread between CPUs, GPUs, and network devices is not only the key to optimization and planning but also a complex goal to achieve. The primary challenges include the complexity of synchronization and load balanci

Rohit Juneja, Trishul Dhalia, Amita Das

Recent studies have shown direct ion heating (vashistha2020new,Juneja_2023) by lasers EM (Electromagnetic) wave interacting with a plasma threaded by an external uniform magnetic field. The EM wave frequency was near the lower hybrid (LH) resonance frequency. The LH resonance occurs at the edge of the pass band of the magnetized dispersion relation. The grou

Ioan Doré Landau, Dariusz Bismor, Tudor-Bogdan Airimitoaie, Bernard Vau

The paper explores the use of dynamic adaptation gain/step size (DAG) for improving the adaptation transient performance of variable step-size LMS (VS-LMS) adaptation algorithms. A generic form for the implementation of the DAG within the VS-LMS algorithms is provided. The properties of the VS-LMS algorithms using dynamic adaptation gain are discussed in det

Dafa Wardana, Masashi Chiba, Kohei Hayashi

Distinguishing a core and a cusp within dark matter halos is complexified by the existence of mass-anisotropy degeneracy, where various combinations of velocity anisotropy ($\beta$) and inner density slope ($\gamma$) yield similar observational signatures. We construct a dynamical model that incorporates the 4th-order velocity moments to alleviate this chall

Yang Deng, Lizi Liao, Zhonghua Zheng, Grace Hui Yang

Recent research on proactive conversational agents (PCAs) mainly focuses on improving the system's capabilities in anticipating and planning action sequences to accomplish tasks and achieve goals before users articulate their requests. This perspectives paper highlights the importance of moving towards building human-centered PCAs that emphasize human needs

Chandrima Mallick, Ushnanshu Dutta, Moumita Bhowmik, Greeshma M. Mohan

We have performed three-dimensional thunderstorm real simulations using the two-moment and three-moment bulk microphysics schemes in the Weather Research and Forecasting (WRF) model. We have analyzed three cases to understand the potential differences between the double-moment (Morrison-2M) and National Taiwan University triple-moment (NTU-3M) microphysics p

Sergei P. Maydanyuk, Li-Ping Zou, Peng-Ming Zhang

We study electromagnetic form factors of protons in proton-nucleus scattering via analysing of experimental cross-sections of accompanying bremsstrahlung photons. A new bremsstrahlung model for proton-nucleus scattering is developed, where a main focus is given on incoherent bremsstrahlung that has not been considered previously. In analysis we choose experi

Georges Le Bellier, Nicolas Audebert

Earth Observation imagery can capture rare and unusual events, such as disasters and major landscape changes, whose visual appearance contrasts with the usual observations. Deep models trained on common remote sensing data will output drastically different features for these out-of-distribution samples, compared to those closer to their training dataset. Det

Zibo Wang, Haichao Ji, Yifei Zhu, Dan Wang

The escalating influx of data generated by networked edge devices, coupled with the growing awareness of data privacy, has restricted the traditional data analytics workflow, where the edge data are gathered by a centralized server to be further utilized by data analysts. To continue leveraging vast edge data to support various data-incentive applications, c

A. K. Somakumar, Y. Zhydachevskyy, D. Wlodarczyk, S. S Haider

A comprehensive photoluminescence and mechanoluminescence analysis of ZnS:Mn2+ nano-phosphor with zinc blende structure is presented. The sample containing quantum dot-sized nanocrystallites were synthesized by the chemical precipitation method and shows excellent orange luminescence at ambient conditions related to the 4T1->6A1 transition. The sample shows

Ningning Zhu, Dongpo Hu, Huili Bi

In this work, we investigate a reaction-diffusion system in which both species are influenced by self-diffusion. Due to Hopf's boundary lemma, we obtain the boundedness of the classical solution of the system. By considering a particular function, we provide a complete characterization of the parameter ranges such that coexisting solutions of the system do n

Nina Voronova, Anna Grudinina, Riccardo Panico, Dimitris Trypogeorgos

Macroscopic coherence in quantum fluids allows the observation of interference effects in their wavefunctions, and enables applications such as superconducting quantum interference devices based on Josephson tunneling. The Josephson effect manifests in both fermionic and bosonic systems, and has been well studied in superfluid helium and atomic Bose-Einstein

Shinji Tanimoto

Best simultaneous approximation (BSA) for finitely or infinitely many functions are considered under the uniform norm and other important norms. Characterization theorems for a BSA from a finite-dimensional subspace are obtained by a generalized minimax theorem. From the characterization theorem a strong unicity theorem is also deduced for a BSA.

Nicholas Phat Nguyen

This article provides a geometric representation for the well-known isomorphism between the special orthogonal group of an isotropic quadratic space of dimension 3 and the group of projective transformations of a projective line. This geometric representation depends on the theory of inversive transformations in dimension 1 as outlined in the 2021 article Pr

ATLAS Collaboration

A search for the non-resonant production of Higgs boson pairs in the $HH\rightarrow b\bar{b}\tau^+\tau^-$ channel is performed using 140 fb$^{-1}$ of proton-proton collisions at a centre-of-mass energy of $13$ TeV recorded by the ATLAS detector at the CERN Large Hadron Collider. The analysis strategy is optimised to probe anomalous values of the Higgs boson

Hongzhi Qi, Hanfei Liu, Jianqiang Li, Qing Zhao

In the social media, users frequently express personal emotions, a subset of which may indicate potential suicidal tendencies. The implicit and varied forms of expression in internet language complicate accurate and rapid identification of suicidal intent on social media, thus creating challenges for timely intervention efforts. The development of deep learn

Joy Morris, Pablo Spiga

Let $R$ be a group and let $S$ be a subset of $R$. The Haar graph $\mathrm{Haar}(R,S)$ of $R$ with connection set $S$ is the graph having vertex set $R\times\{-1,1\}$, where two distinct vertices $(x,-1)$ and $(y,1)$ are declared to be adjacent if and only if $yx^{-1}\in S$. The name Haar graph was coined by Toma\v{z} Pisanski in one of the first investigati

Man M. Ho, Elham Ghelichkhan, Yosep Chong, Yufei Zhou

Latent Diffusion Models (LDMs) can generate high-fidelity images from noise, offering a promising approach for augmenting histopathology images for training cancer grading models. While previous works successfully generated high-fidelity histopathology images using LDMs, the generation of image tiles to improve prostate cancer grading has not yet been explor

Sandra Johnson, Kerrie Mengersen, Patrick O'Callaghan, Anders L. Madsen

Immediate settlement, or single-slot finality (SSF), is a long-term goal for Ethereum. The growing active validator set size is placing an increasing computational burden on the network, making SSF more challenging. EIP-7251 aims to reduce the number of validators by giving stakers the option to merge existing validators. Key to the success of this proposal

Fumiyoshi Kobayashi, Shota Nagayama

Neutral atom arrays manipulated with optical tweezers are promising candidates for fault-tolerant quantum computers due to their advantageous properties, such as scalability, long coherence times, and optical accessibility for communication. A significant challenge to overcome is the presence of non-Pauli errors, specifically erasure errors and leakage error

Shi Pan, Yong Cai, Yun-Song Piao

The violation of the null energy condition (NEC) may play a crucial role in enabling a scalar field to climb over high potential barriers, potentially significant in the very early universe. We propose a single-field model where the universe sequentially undergoes a first stage of slow-roll inflation, NEC violation, and a second stage of slow-roll inflation.

Z. Neishabouri, K. Azizi, H. R. Moshfegh

We investigate the semileptonic decay of $\Omega_b\to\Omega_c~{\ell}\bar\nu_{\ell}$ in three lepton channels. To this end, we use QCD sum rule method in three point framework to calculate the form factors defining the matrix elements of these transitions. Having calculated the form factors as building blocks, we calculate the decay widths and branching fract

Dren Fazlija, Arkadij Orlov, Johanna Schrader, Monty-Maximilian Zühlke

With an ever-increasing reliance on machine learning (ML) models in the real world, adversarial examples threaten the safety of AI-based systems such as autonomous vehicles. In the image domain, they represent maliciously perturbed data points that look benign to humans (i.e., the image modification is not noticeable) but greatly mislead state-of-the-art ML

Yuan Zang, Tian Yun, Hao Tan, Trung Bui

Do vision-language models (VLMs) pre-trained to caption an image of a "durian" learn visual concepts such as "brown" (color) and "spiky" (texture) at the same time? We aim to answer this question as visual concepts learned "for free" would enable wide applications such as neuro-symbolic reasoning or human-interpretable object classification. We assume that t

Flor Ortiz, Eva Lagunas, Almoatssimbillah Saifaldawla, Mahdis Jalali

Global communications have undergone a paradigm shift with the rapid expansion of low-earth orbit (LEO) satellite constellations, offering a new space era of reduced latency and ubiquitous, high-speed broadband internet access. However, the fast developments in LEO orbits pose significant challenges, particularly the coexistence with geostationary earth orbi

Man M. Ho, Shikha Dubey, Yosep Chong, Beatrice Knudsen

The Frozen Section (FS) technique is a rapid and efficient method, taking only 15-30 minutes to prepare slides for pathologists' evaluation during surgery, enabling immediate decisions on further surgical interventions. However, FS process often introduces artifacts and distortions like folds and ice-crystal effects. In contrast, these artifacts and distorti

Peng Chen, Jun Jing

We propose a quantum metrology protocol based on a two-step joint evolution of the probe system and an ancillary qubit and quantum measurement. With a proper initial state of the ancillary qubit and an optimized evolution time, the quantum Fisher information (QFI) about the phase parameter encoded in the probe system is found to be determined by the expectat

Jianliang He, Han Zhong, Zhuoran Yang

We study infinite-horizon average-reward Markov decision processes (AMDPs) in the context of general function approximation. Specifically, we propose a novel algorithmic framework named Local-fitted Optimization with OPtimism (LOOP), which incorporates both model-based and value-based incarnations. In particular, LOOP features a novel construction of confide

Yidong Bai, Toshiharu Sugawara

In multi-agent reinforcement learning, decentralized execution is a common approach, yet it suffers from the redundant computation problem. This occurs when multiple agents redundantly perform the same or similar computation due to overlapping observations. To address this issue, this study introduces a novel method referred to as locally centralized team tr

Tony Metger, Alexander Poremba, Makrand Sinha, Henry Yuen

Uniformly random unitaries, i.e. unitaries drawn from the Haar measure, have many useful properties, but cannot be implemented efficiently. This has motivated a long line of research into random unitaries that "look" sufficiently Haar random while also being efficient to implement. Two different notions of derandomisation have emerged: $t$-designs are random

K. Nakagawa, A. Hariki, T. Okauchi, H. Fujiwara

We present Ir $4f$ core-level hard-x-ray photoemission spectroscopy (HAXPES) experiments conducted across antiferromagnetic (AFM) ordering transition in Ruddlesden-Popper iridates Sr$_2$IrO$_4$ and Sr$_3$Ir$_2$O$_7$. The Ir $4f$ spectra exhibit distinct changes between the AFM and paramagnetic (PM) phases, with the spectral difference $I_\text{PM}-I_\text{AF

Katsuhiro Tanaka, Takuya Nomoto, Ryotaro Arita

We investigate the functionality of the $\mathrm{Cr}$-doped $\mathrm{RuO_{2}}$ as an electrode of the magnetic tunnel junction (MTJ), motivated by the recent experiment showing that $\mathrm{Cr}$-doping into the rutile-type $\mathrm{RuO_{2}}$ will be an effective tool to control its antiferromagnetic order and the resultant magnetotransport phenomena easily.

Francesc Castella

These are expanded notes for the mini-course given by the author at the 2022 ICTS workshop `Elliptic curves and the special values of $L$-functions'.

Nishant Chaudhary, Mihir Raj, Richik Bhattacharjee, Anmol Srivastava

This paper presents a demonstration of the developed prototype showcasing a way to preserve the Intangible Cultural Heritage of Uttarakhand, India. Aipan is a traditional art form practiced in the Kumaon region in the state of Uttarakhand. It is typically used to decorate floors and walls at places of worship or entrances of homes and is considered auspiciou

Shanmin Wang, Hui Shuai, Qingshan Liu, Fei Wang

In this paper, we propose a new Multimodal Representation Learning (MRL) method for Multimodal Sentiment Analysis (MSA), which facilitates the adaptive interaction between modalities through Cooperative Sentiment Agents, named Co-SA. Co-SA comprises two critical components: the Sentiment Agents Establishment (SAE) phase and the Sentiment Agents Cooperation (

Yuriy Yerin, Stefan-Ludwig Drechsler, A. A. Varlamov, Mario Cuoco

We consider nonreciprocal supercurrent effects in Josephson junctions based on multiband superconductors with a pairing structure that can break time-reversal symmetry. We demonstrate that a nonreciprocal supercurrent can be generally achieved by the cooperation of interband superconducting phase mismatch and interband scattering as well as by multiband phas

Nikolay A. Olkhovsky, Leonid B. Sokolinsky

The article presents a new method of linear programming, called the surface movement method. This method constructs an optimal objective path on the surface of the feasible polytope from the initial boundary point to the point at which the optimal value of the objective function is achieved. The optimality of the path means moving in the direction of maximum

Sibo Gai, Donglin Wang

In this work, we propose a new setting of continual learning: data-incremental continual offline reinforcement learning (DICORL), in which an agent is asked to learn a sequence of datasets of a single offline reinforcement learning (RL) task continually, instead of learning a sequence of offline RL tasks with respective datasets. Then, we propose that this n

Jie Wang, Zhihai Wang, Xijun Li, Yufei Kuang

Cutting planes (cuts) play an important role in solving mixed-integer linear programs (MILPs), which formulate many important real-world applications. Cut selection heavily depends on (P1) which cuts to prefer and (P2) how many cuts to select. Although modern MILP solvers tackle (P1)-(P2) by human-designed heuristics, machine learning carries the potential t

Banasree Sadhukhan, Anders Bergman, Patrik Thunström, Manuel Pereiro Lopez

YMn$_6$Sn$_6$ consists of two types of Mn-based kagome planes stacked along $c$-axis having a complex magnetic interactions. We report a spin reconstruction in YMn$_6$Sn$_6$ from ferromagnet (FM) into a combination of two incommensurate spin spirals (SSs) originating from two different type of Mn kagome planes driven by frustrated magnetic exchanges along th

Gaurav N. Gadbail, Sanjay Mandal, P. K. Sahoo

The increase of discrepancy in the standard procedure to choose the arbitrary functional form of the Lagrangian $f(Q)$ motivates us to solve this issue in modified theories of gravity. In this regard, we investigate the Gaussian process (GP), which allows us to eliminate this issue in a $f(Q)$ model-independent way. In particular, we use the 57 Hubble measur

Boyang Yang, Haoye Tian, Jiadong Ren, Hongyu Zhang

Within the realm of software engineering, specialized tasks on code, such as program repair, present unique challenges, necessitating fine-tuning Large language models~(LLMs) to unlock state-of-the-art performance. Fine-tuning approaches proposed in the literature for LLMs on program repair tasks generally overlook the need to reason about the logic behind c

Heqi Peng, Yunhong Wang, Ruijie Yang, Beichen Li

Adversarial example detection, which can be conveniently applied in many scenarios, is important in the area of adversarial defense. Unfortunately, existing detection methods suffer from poor generalization performance, because their training process usually relies on the examples generated from a single known adversarial attack and there exists a large disc

Danqing Ma, Meng Wang, Ao Xiang, Zongqing Qi

This study proposes a multi-modal fusion framework Multitrans based on the Transformer architecture and self-attention mechanism. This architecture combines the study of non-contrast computed tomography (NCCT) images and discharge diagnosis reports of patients undergoing stroke treatment, using a variety of methods based on Transformer architecture approach

Tianfu Wang, Qilin Fan, Chao Wang, Long Yang

Virtual network embedding (VNE) is an essential resource allocation task in network virtualization, aiming to map virtual network requests (VNRs) onto physical infrastructure. Reinforcement learning (RL) has recently emerged as a promising solution to this problem. However, existing RL-based VNE methods are limited by the unidirectional action design and one

V. G. Bardakov, T. A. Kozlovskaya, P. P. Sokolov, K. V. Zimireva

We find connection between relative Rota--Baxter operators and usual Rota--Baxter operators. We prove that any relative Rota--Baxter operator on a group $H$ with respect to $(G, \Psi)$ defines a Rota--Baxter operator on the semi-direct product $H\rtimes_{\Psi} G$. On the other side, we give condition under which a Rota--Baxter operator on the semi-direct pro

Solvi Arnold, Reiji Suzuki, Takaya Arita, Kimitoshi Yamazaki

Advanced biological intelligence learns efficiently from an information-rich stream of stimulus information, even when feedback on behaviour quality is sparse or absent. Such learning exploits implicit assumptions about task domains. We refer to such learning as Domain-Adapted Learning (DAL). In contrast, AI learning algorithms rely on explicit externally pr

Zixuan Gong, Qi Zhang, Guangyin Bao, Lei Zhu

Decoding natural visual scenes from brain activity has flourished, with extensive research in single-subject tasks and, however, less in cross-subject tasks. Reconstructing high-quality images in cross-subject tasks is a challenging problem due to profound individual differences between subjects and the scarcity of data annotation. In this work, we proposed

Alan Yang, Tara Mina, Grace Gao

Optimizing the correlation properties of spreading codes is critical for minimizing inter-channel interference in satellite navigation systems. By improving the codes' correlation sidelobes, we can enhance navigation performance while minimizing the required spreading code lengths. In the case of low earth orbit (LEO) satellite navigation, shorter code lengt

Zi-Long Li, Xiao-Hui Li, Yuan Wan

We study theoretically the nonlinear optical response of disordered electrons in the regime of weak (anti)localization. Our analytical and numerical calculations reveal that, in orthogonal/symplectic class systems, two consecutive, phase coherent optical pulses generates an electric current echo that appears after the second pulse, and at a time equal to the

Darshan Prabhu, Sai Ganesh Mirishkar, Pankaj Wasnik

Self-supervised learned (SSL) models such as Wav2vec and HuBERT yield state-of-the-art results on speech-related tasks. Given the effectiveness of such models, it is advantageous to use them in conventional ASR systems. While some approaches suggest incorporating these models as a trainable encoder or a learnable frontend, training such systems is extremely

Eric Vincent Galeta, Ayman A. Nada, Sabah M. Ahmed, Victor Parque

The safety and accuracy of robotic navigation hold paramount importance, especially in the realm of soft continuum robotics, where the limitations of traditional rigid sensors become evident. Encoders, piezoresistive, and potentiometer sensors often fail to integrate well with the flexible nature of these robots, adding unwanted bulk and rigidity. To overcom

Pengdeng Li, Shuxin Li, Xinrun Wang, Jakub Cerny

Pursuit-evasion games (PEGs) model interactions between a team of pursuers and an evader in graph-based environments such as urban street networks. Recent advancements have demonstrated the effectiveness of the pre-training and fine-tuning paradigm in PSRO to improve scalability in solving large-scale PEGs. However, these methods primarily focus on specific

Vandad Davoodnia, Saeed Ghorbani, Alexandre Messier, Ali Etemad

We introduce SkelFormer, a novel markerless motion capture pipeline for multi-view human pose and shape estimation. Our method first uses off-the-shelf 2D keypoint estimators, pre-trained on large-scale in-the-wild data, to obtain 3D joint positions. Next, we design a regression-based inverse-kinematic skeletal transformer that maps the joint positions to po

Sheng Wang, Ge Sun, Fulong Ma, Tianshuai Hu

Evaluating and training autonomous driving systems require diverse and scalable corner cases. However, most existing scene generation methods lack controllability, accuracy, and versatility, resulting in unsatisfactory generation results. Inspired by DragGAN in image generation, we propose DragTraffic, a generalized, interactive, and controllable traffic sce

Chaehyeon Lee, Jonathan Heiss, Stefan Tai, James Won-Ki Hong

Verifiable decentralized federated learning (FL) systems combining blockchains and zero-knowledge proofs (ZKP) make the computational integrity of local learning and global aggregation verifiable across workers. However, they are not end-to-end: data can still be corrupted prior to the learning. In this paper, we propose a verifiable decentralized FL system

Ci Xue, Anthony Remijan, Alexandre Faure, Emmanuel Momjian

At centimeter wavelengths, single-dish observations have suggested that the Sagittarius (Sgr) B2 molecular cloud at the Galactic Center hosts weak maser emission from several organic molecules, including CH$_2$NH, HNCNH, and HCOOCH$_3$. However, the lack of spatial distribution information of these new maser species has prevented us from assessing the excita

Yixuan Zhang, Mugeng Liu, Haoyu Wang, Yun Ma

WebAssembly (abbreviated as Wasm) was initially introduced for the Web but quickly extended its reach into various domains beyond the Web. To create Wasm applications, developers can compile high-level programming languages into Wasm binaries or manually convert equivalent textual formats into Wasm binaries. Regardless of whether it is utilized within or out

Javier Silva-Farfán, Francisco Förster, Takashi J. Moriya, L. Hernández-García

We report an analysis of a sample of 186 spectroscopically confirmed Type II supernova (SN) light curves (LCs) obtained from a combination of Zwicky Transient Facility (ZTF) and Asteroid Terrestrial-impact Last Alert System (ATLAS) observations. We implement a method to infer physical parameters from these LCs using hydrodynamic models that take into account

Guillermo Federico Umbricht, Diana Rubio

In this article, the problem of identifying the source term in transport processes given by a complete parabolic equation is studied mathematically from noisy measurements taken at an arbitrary fixed time. The problem is solved analytically with Fourier techniques and it is shown that this solution is not stable. Three single parameter families of regulariza

Tatsuya Miura, Glen Wheeler

The free elastic flow is the $L^2$-gradient flow for Euler's elastic energy, or equivalently the Willmore flow with translation invariant initial data. In contrast to elastic flows under length penalisation or preservation, it is more challenging to study the free elastic flow's asymptotic behavior, and convergence for closed curves is lost. In this paper, w

Haiyang Miao, Pan Tang, Weirang Zuo, Qi Wei

In the sixth-generation (6G), with the further expansion of array element number and frequency bands, the wireless communications are expected to operate in the near-field region. The near-field radio communications (NFRC) will become crucial in 6G communication systems. The new mid-band (6-24 GHz) is the 6G potential candidate spectrum. In this paper, we wi

Hoang H. Nguyen, Chenwei Zhang, Ye Liu, Natalie Parde

Naively assuming English as a source language may hinder cross-lingual transfer for many languages by failing to consider the importance of language contact. Some languages are more well-connected than others, and target languages can benefit from transferring from closely related languages; for many languages, the set of closely related languages does not i

Derek Knowles, Grace Gao

Numerous methods have been proposed for global navigation satellite system (GNSS) receivers to detect faulty GNSS signals. One such fault detection and exclusion (FDE) method is based on the mathematical concept of Euclidean distance matrices (EDMs). This paper outlines a greedy approach that uses an improved Euclidean distance matrix-based fault detection a

Adam B. Watts, Luca Cortese, Barbara Catinella, Amelia Fraser-McKelvie

Stellar feedback-driven outflows are important regulators of the gas-star formation cycle. However, resolving outflow physics requires high resolution observations that can only be achieved in very nearby galaxies, making suitable targets rare. We present the first results from the new VLT/MUSE large program MAUVE (MUSE and ALMA Unveiling the Virgo Environme

Xin Zhang

Baxter's $T-Q$ relation for the periodic spin-$\frac12$ XYZ chain is studied. We extensively perform numerical calculations for the $T-Q$ relation and the Bethe ansatz equations. Numerical based hypotheses are then proposed to answer some open questions regarding Baxter's $T-Q$ relation and the XYZ chain.

D. J. Strozzi, H. Sio, G. B. Zimmerman, J. D. Moody

The use of magnetic fields to improve the performance of hohlraum-driven implosions on the National Ignition Facility (NIF) is discussed. The focus is on magnetically insulated inertial confinement fusion (ICF), where the primary field effect is to reduce electron-thermal and alpha-particle loss from the compressed hotspot (magnetic pressure is of secondary

Xinyu Liu, Hai Zhang

In this paper, we study the problem of learning one-dimensional Gaussian mixture models (GMMs) with a specific focus on estimating both the model order and the mixing distribution from independent and identically distributed (i.i.d.) samples. This paper establishes the optimal sampling complexity for model order estimation in one-dimensional Gaussian mixture

Huilin Yin, Jiaxiang Li, Pengju Zhen, Jun Yan

Trajectory prediction is critical for the safe planning and navigation of automated vehicles. The trajectory prediction models based on the neural networks are vulnerable to adversarial attacks. Previous attack methods have achieved high attack success rates but overlook the adaptability to realistic scenarios and the concealment of the deceits. To address t

Xiao-Yin Liu, Guotao Li, Xiao-Hu Zhou, Xu Liang

Accurate recognition of human motion intention (HMI) is beneficial for exoskeleton robots to improve the wearing comfort level and achieve natural human-robot interaction. A classifier trained on labeled source subjects (domains) performs poorly on unlabeled target subject since the difference in individual motor characteristics. The unsupervised domain adap