March 2024 arXiv papers — page 46

Hieu The Pham, Eibun Senaha

We study domain wall induced by spontaneously broken $\mathbb{Z}_2$ symmetry and its gravitational wave signature in the standard model with a complex scalar in connection with dark matter physics. In a minimal setup, a linear term of the singlet field is added to the scalar potential as an explicit $\mathbb{Z}_2$ breaking term to make the domain wall unstab

Andrea Campoleoni, Stefan Fredenhagen

These lecture notes provide an introduction to higher-spin gauge theories in three spacetime dimensions, with a focus on their asymptotic symmetries, their holographic description in terms of conformal field theories with W-symmetries as well as on their couplings to scalar matter.

Toyo Taniguchi

The divergence map, an important ingredient in the algebraic description of the Turaev cobracket on a connected oriented compact surface with boundary, is reformulated in the context of non-commutative geometry using a flat connection on the space of 1-forms on a formally smooth associative algebra. We then extend this construction to the case of associative

Chris Verhoek, Jaap Eising, Florian Dörfler, Roland Tóth

A promising step from linear towards nonlinear data-driven control is via the design of controllers for linear parameter-varying (LPV) systems, which are linear systems whose parameters are varying along a measurable scheduling signal. However, the interplay between uncertainty arising from corrupted data and the parameter-varying nature of these systems imp

Elham Baradari and, Ozgur B Akan

Parkinson's disease (PD) is a progressive neurodegenerative disease, and it is caused by the loss of dopaminergic neurons in the basal ganglia (BG). Currently, there is no definite cure for PD, and available treatments mainly aim to alleviate its symptoms. Due to impaired neurotransmitter-based information transmission in PD, molecular communication-based ap

Kohei Noda, Araki Wakiuchi, Yoshihiro Hayashi, Ryo Yoshida

Recent advancements in machine learning have showcased its potential to significantly accelerate the discovery of new materials. Central to this progress is the development of rapidly computable property predictors, enabling the identification of novel materials with desired properties from vast material spaces. However, the limited availability of data reso

Wooyeon Kim

In this paper, we prove a quantitative version of the Oppenheim conjecture for indefinite ternary quadratic forms: for any indefinite irrational ternary quadratic form $Q$ that is not extremely well approxiable by rational forms, and for $a<b$ the number of integral vectors of norm at most $T$ satisfying $a<Q(v)<b$ is asymptotically equivalent to $\big(\math

José Antonio Nájera, Celia Escamilla-Rivera

In this work, we explore new constraints on phantom scalar field cosmologies with a scalar field employing early times catalogues related to CMB measurements, along with the local standard observables, like Supernovae Type Ia (SNIa), $H(z)$ measurements (Cosmic Clocks), and Baryon Acoustic Oscillations (BAO) baselines. In particular, we studied a tracker pha

Xinyuan Ji, Zhaowei Zhu, Wei Xi, Olga Gadyatskaya

Federated Learning (FL) heavily depends on label quality for its performance. However, the label distribution among individual clients is always both noisy and heterogeneous. The high loss incurred by client-specific samples in heterogeneous label noise poses challenges for distinguishing between client-specific and noisy label samples, impacting the effecti

Bo Zhou, Yuyao Sun, Wenbo Liu, Ruixuan Jiao

Force interaction is inevitable when robots face multiple operation scenarios. How to make the robot competent in force control for generalized operations such as multi-tasks still remains a challenging problem. Aiming at the reproducibility of interaction tasks and the lack of a generalized force control framework for multi-task scenarios, this paper propos

Wooyeon Kim

For $d\ge 3$ we first show that the Hausdorff dimension of the set of $A$-divergent on average points in the $(d-1)$-dimensional closed horosphere in the space of $d$-dimensional Euclidean lattices, where $A$ is the group of positive diagonal matrices, is at most $\frac{d-1}{2}$. In particular, this upper bound is sharp for $d=3$. We apply this to compute th

Han Wang, Yanjie Wang, Yongjie Ye, Yuxiang Nie

Multi-modal Large Language Models (MLLMs) have demonstrated their ability to perceive objects in still images, but their application in video-related tasks, such as object tracking, remains understudied. This lack of exploration is primarily due to two key challenges. Firstly, extensive pretraining on large-scale video datasets is required to equip MLLMs wit

Ping Luo, Xiaoge Deng, Ziqing Wen, Tao Sun

Federated Learning (FL) is a distributed machine learning framework in communication network systems. However, the systems' Non-Independent and Identically Distributed (Non-IID) data negatively affect the convergence efficiency of the global model, since only a subset of these data samples are beneficial for model convergence. In pursuit of this subset, a re

Nidhi Kaihnsa, Máté L. Telek

The parameter region of multistationarity of a reaction network contains all the parameters for which the associated dynamical system exhibits multiple steady states. Describing this region is challenging and remains an active area of research. In this paper, we concentrate on two biologically relevant families of reaction networks that model multisite phosp

Mira Khalil, Romain Postoyan, Stéphane Raël, Dragan Nešić

The state of charge (SOC) of lithium-ion batteries needs to be accurately estimated for safety and reliability purposes. For battery packs made of a large number of cells, it is not always feasible to design one SOC estimator per cell due to limited computational resources. Instead, only the minimum and the maximum SOC need to be estimated. The challenge is

Qian Chen, Dongyang Li, Xiaofeng He, Hongzhao Li

The black-box nature of deep learning models in NLP hinders their widespread application. The research focus has shifted to Hierarchical Attribution (HA) for its ability to model feature interactions. Recent works model non-contiguous combinations with a time-costly greedy search in Eculidean spaces, neglecting underlying linguistic information in feature re

Y. Kohsaka, S. Akutagawa, S. Omachi, Y. Iwamichi

Single atomic defects are prominent windows to look into host quantum states because collective responses from the host states emerge as localized states around the defects. Friedel oscillations and Kondo clouds in Fermi liquids are quintessential examples. However, the situation is quite different for quantum spin liquid (QSL), an exotic state of matter wit

Axel Kleidon

The warmer temperatures of global climate change strengthen the water cycle, evaporation and precipitation increase. But the extremes of heavy rain, floods, dry periods and droughts will also increase. How does this fit together? Simple physical considerations show which factors mainly regulate the strength of the water cycle in the Earth system, and how thi

Enis Belgacem, Michele Maggiore, Thomas Moreau

The interaction of a gravitational wave (GW) with an elastic body is usually described in terms of a GW "force" driving the oscillations of the body's normal modes. However, this description is only possible for GW frequencies for which the response of the elastic body is dominated by a few normal modes. At higher frequencies the normal modes blend into a qu

Sourav Bhattacharya

We \emph{propose} a new \emph{invariant} for a \emph{cycle} of an \emph{interval map} $f:[0,1] \to [0,1]$, called its \emph{unfolding number}.

Ning Yu, Zuman Zhang, Hongge Xu, Minxuan Song

In this study, the chemical freeze-out of hadrons, including light-and strange-flavor particles and light nuclei, produced in Au+Au collisions at the Relativistic Heavy Ion Collider (RHIC), was investigated. Using the thermal-FIST thermodynamic statistical model, we analyzed various particle sets: those inclusive of light nuclei, those exclusive to light nuc

Graeme Neil Campbell, Lewis Hill, Pascal Del'Haye, Gian-Luca Oppo

Long range interactions between dark vectorial temporal cavity solitons are induced though the spontaneous symmetry breaking of orthogonally polarized fields in ring resonators. Turing patterns of alternating polarizations form between adjacent solitons, pushing them apart so that a random distribution of solitons along the cavity length reaches equal equili

G. Nikoghosyan, H. S. Nikoghosyan

The quantum states of an ellipsoidal nanocluster of a heterophase system InAs / GaAs are studied using an exact analytical approach, in contrast to the generally accepted theoretical model based on the adiabatic approximation. It is shown that the spectrum of a nanoobject is formed from local groups, consisting of discrete levels, separated by terahertz freq

Sreetama Sarkar, Souvik Kundu, Kai Zheng, Peter A. Beerel

The ubiquity of vision transformers (ViTs) for various edge applications, including personalized learning, has created the demand for on-device fine-tuning. However, training with the limited memory and computation power of edge devices remains a significant challenge. In particular, the memory required for training is much higher than that needed for infere

Marcin Pietroń, Dominik Żurek, Kamil Faber, Roberto Corizzo

Anomaly detection tools and methods enable key analytical capabilities in modern cyberphysical and sensor-based systems. Despite the fast-paced development in deep learning architectures for anomaly detection, model optimization for a given dataset is a cumbersome and time-consuming process. Neuroevolution could be an effective and efficient solution to this

Vlad Stirbu, Arianne Meijer-van de Griend, Jake Muff

Current and near-future quantum computers face resource limitations due to noise and low qubit counts. Despite this, effective quantum advantage can still be achieved due to the exponential nature of bit-to-qubit conversion. However, optimizing the software architecture of these systems is essential to utilize available resources efficiently. Unfortunately,

Elsayed A. H. Elamir

Smooth Estimation of probability density and distribution functions from its sample is an attractive and an important problem that has applications in several fields such as, business, medicine, and environment. This article introduces a simple approach but novel for estimating both functions via beta regression and generalized additive model approaches. The

Philipp Borchert, Jochen De Weerdt, Marie-Francine Moens

Differentiating relationships between entity pairs with limited labeled instances poses a significant challenge in few-shot relation classification. Representations of textual data extract rich information spanning the domain, entities, and relations. In this paper, we introduce a novel approach to enhance information extraction combining multiple sentence r

Jiaojiao Zhang, Linglingzhi Zhu, Mikael Johansson

We introduce a novel differentially private algorithm for online federated learning that employs temporally correlated noise to enhance utility while ensuring privacy of continuously released models. To address challenges posed by DP noise and local updates with streaming non-iid data, we develop a perturbed iterate analysis to control the impact of the DP n

Yan-Ru Bao, Sheng-Qin Feng

Effects of tensor spin polarization (TSP) on the chiral restoration and deconfinement phase transitions are studied in Polyakov loop extended Nambu-Jona-Lasinio (PNJL) model. For chiral phase transition, the higher the polarized degree of quark-antiquark pairs under the strong magnetic field, the higher the phase transition temperature. The TSP corrects the

Yijin Zhou, Fu Li, Yang Li, Youshuo Ji

Recognizing the pivotal role of EEG emotion recognition in the development of affective Brain-Computer Interfaces (aBCIs), considerable research efforts have been dedicated to this field. While prior methods have demonstrated success in intra-subject EEG emotion recognition, a critical challenge persists in addressing the style mismatch between EEG signals f

Tung-Yu Wu, Sheng-Yu Huang, Yu-Chiang Frank Wang

3D visual grounding aims to identify the target object within a 3D point cloud scene referred to by a natural language description. Previous works usually require significant data relating to point color and their descriptions to exploit the corresponding complicated verbo-visual relations. In our work, we introduce Vigor, a novel Data-Efficient 3D Visual Gr

Guo-Jing Tang, Xiao-Dong Chen, Lu Sun, Chao-Heng Guo

3-dB couplers, which are commonly used in photonic integrated circuits for on-chip information processing, precision measurement, and quantum computing, face challenges in achieving robust performance due to their limited 3-dB bandwidths and sensitivity to fabrication errors. To address this, we introduce topological physics to nanophotonics, developing a fr

Nicklas Anttu

High refractive index semiconductor nanowires have recently been demonstrated experimentally as an efficient platform for enhancing the signal in fluorescence-based biosensors. Here, we study through modelling how a vertical GaP nanowire (i) enhances the excitation intensity at the position of the fluorophore attached to the nanowire sidewall, (ii) enhances

Yujin Tang, Peijie Dong, Zhenheng Tang, Xiaowen Chu

Combining CNNs or ViTs, with RNNs for spatiotemporal forecasting, has yielded unparalleled results in predicting temporal and spatial dynamics. However, modeling extensive global information remains a formidable challenge; CNNs are limited by their narrow receptive fields, and ViTs struggle with the intensive computational demands of their attention mechanis

Zifan Wang, Yufei Jia, Lu Shi, Haoyu Wang

Incorporating a robotic manipulator into a wheel-legged robot enhances its agility and expands its potential for practical applications. However, the presence of potential instability and uncertainties presents additional challenges for control objectives. In this paper, we introduce an arm-constrained curriculum learning architecture to tackle the issues in

Lin Tang

In this work, we investigate the stellar metallicities of low surface brightness galaxies (LSBGs) and normal high surface brightness galaxies (HSBGs) in the IllustrisTNG100-1 simulation. LSBGs and HSBGs are classified as galaxies with mean central surface brightness $\mu_{\rm r} > 22.0 \ mag \ arcsec^{-2}$ and $\mu_{\rm r} < 22.0 \ mag \ arcsec^{-2}$, respec

Jincheng Zhong, Shuhui Chen, Chuan Yu

Regular expression matching is the core function of various network security applications such as network intrusion detection systems. With the network bandwidth increases, it is a great challenge to implement regular expression matching for line rate packet processing. To this end, a novel scheme named XAV targeting high-performance regular expression match

Leo C. Y. Ng, Justin Janquart, Hemantakumar Phurailatpam, Harsh Narola

Like light, gravitational waves can be gravitationally lensed by massive astrophysical objects. Strong gravitational lensing by galaxies and galaxy clusters is anticipated to become observable in the coming years. This phenomenon will manifest as multiple copies of the original wave, each exhibiting identical frequency evolution but distinct arrival times, a

Elica Anne Heredia, Shao-Pin Chiu, Ba-Anh-Vu Nguyen, Ruey-Tay Wang

Granular metals offer tailorable electronic properties and play crucial roles in device and sensor applications. We have fabricated a series of nonmagnetic granular CoSi2 thin films and studied the Hall effect and transport properties. We observed a two orders of magnitude enhancement in the Hall coefficient in films fall slightly above the metal-insulator t

Zizhao Hu, Shaochong Jia, Mohammad Rostami

Diffusion models have been widely used for conditional data cross-modal generation tasks such as text-to-image and text-to-video. However, state-of-the-art models still fail to align the generated visual concepts with high-level semantics in a language such as object count, spatial relationship, etc. We approach this problem from a multimodal data fusion per

Tuo Wu, Cunhua Pan, Kangda Zhi, Hong Ren

This paper proposes a novel localization algorithm using the reconfigurable intelligent surface (RIS) received signal, i.e., RIS information. Compared with BS received signal, i.e., BS information, RIS information offers higher dimension and richer feature set, thereby providing an enhanced capacity to distinguish positions of the mobile users (MUs). Additio

Dimity Miller, Niko Sünderhauf, Alex Kenna, Keita Mason

Are vision-language models (VLMs) for open-vocabulary perception inherently open-set models because they are trained on internet-scale datasets? We answer this question with a clear no - VLMs introduce closed-set assumptions via their finite query set, making them vulnerable to open-set conditions. We systematically evaluate VLMs for open-set recognition and

Matthew P Hamilton, Caroline Gao, Jonathan Karnon, Luis Salvador-Carulla

Most health economic analyses are undertaken with the aid of computers. However, the research ethics of implementing health economic models as software (or computational health economic models (CHEMs)) are poorly understood. We propose that developers and funders of CHEMs should adhere to research ethics principles and pursue the goals of: (i) socially accep

Neeloy Chakraborty, Melkior Ornik, Katherine Driggs-Campbell

Autonomous systems are soon to be ubiquitous, spanning manufacturing, agriculture, healthcare, entertainment, and other industries. Most of these systems are developed with modular sub-components for decision-making, planning, and control that may be hand-engineered or learning-based. While these approaches perform well under the situations they were specifi

Haiqiao Wang, Zhuoyuan Wang, Dong Ni, Yi Wang

Deformable image registration plays a crucial role in medical imaging, aiding in disease diagnosis and image-guided interventions. Traditional iterative methods are slow, while deep learning (DL) accelerates solutions but faces usability and precision challenges. This study introduces a pyramid network with the enhanced motion decomposition Transformer (Mode

Eva Lütkebohmert, Julian Sester

We propose a new deep learning approach for the quantification of name concentration risk in loan portfolios. Our approach is tailored for small portfolios and allows for both an actuarial as well as a mark-to-market definition of loss. The training of our neural network relies on Monte Carlo simulations with importance sampling which we explicitly formulate

Bastin Tony Roy Savarimuthu, Surangika Ranathunga, Stephen Cranefield

Software agents, both human and computational, do not exist in isolation and often need to collaborate or coordinate with others to achieve their goals. In human society, social mechanisms such as norms ensure efficient functioning, and these techniques have been adopted by researchers in multi-agent systems (MAS) to create socially aware agents. However, tr

Jie Qiao, Yu Xiang, Zhengming Chen, Ruichu Cai

Count data naturally arise in many fields, such as finance, neuroscience, and epidemiology, and discovering causal structure among count data is a crucial task in various scientific and industrial scenarios. One of the most common characteristics of count data is the inherent branching structure described by a binomial thinning operator and an independent Po

Gayatri Mohan, Umananda Dev Goswami

We consider $f(R)$ modified gravity theory incorporating the chameleon mechanism to address galactic dynamics. By employing the metric formalism and utilizing a conformal transformation, we simplify the field equations and describe the extra degree of freedom $f_{R}$ via a scalar field (scalaron) with chameleonic behavior. A recently proposed $f(R)$ model is

Tuo Wu, Cunhua Pan, Kangda Zhi, Hong Ren

Reconfigurable intelligent surface (RIS)-aided localization systems have attracted extensive research attention due to their accuracy enhancement capabilities. However, most studies primarily utilized the base stations (BS) received signal, i.e., BS information, for localization algorithm design, neglecting the potential of RIS received signal, i.e., RIS inf

Guangqian Yang, Kangrui Du, Zhihan Yang, Ye Du

Alzheimer's disease (AD) is an incurable neurodegenerative condition leading to cognitive and functional deterioration. Given the lack of a cure, prompt and precise AD diagnosis is vital, a complex process dependent on multiple factors and multi-modal data. While successful efforts have been made to integrate multi-modal representation learning into medical

Dingkang Wang, Jingjing Wei, Fanghui Xiao, Xiaopeng Zheng

Based on the partition of parameter space, two algorithms for computing the rational univariate representation of zero-dimensional ideals with parameters are presented in the paper. Unlike the rational univariate representation of zero-dimensional ideals without parameters, the number of zeros of zero-dimensional ideals with parameters under various speciali

Wen Ge, Fujun Du, Lixia Yuan

The morphology of molecular clouds is crucial for understanding their origin and evolution. In this work, we investigate the morphology of the filamentary molecular clouds (filaments for short) using a portion of the $^{12}\text{CO} (J=1-0)$ data from the Milky Way Imaging Scroll Painting (MWISP) project. The data cover an area spanning $104.75^\circ <l< 150

Shawn He, Surangika Ranathunga, Stephen Cranefield, Bastin Tony Roy Savarimuthu

Norms are an important component of the social fabric of society by prescribing expected behaviour. In Multi-Agent Systems (MAS), agents interacting within a society are equipped to possess social capabilities such as reasoning about norms and trust. Norms have long been of interest within the Normative Multi-Agent Systems community with researchers studying

Zhiming Mao, Haoli Bai, Lu Hou, Jiansheng Wei

Prior study shows that pre-training techniques can boost the performance of visual document understanding (VDU), which typically requires models to gain abilities to perceive and reason both document texts and layouts (e.g., locations of texts and table-cells). To this end, we propose visually guided generative text-layout pre-training, named ViTLP. Given a

Zichang Liu

In 1994, Vel\'{a}zquez constructed a countable family of complete hypersurfaces flowing in $\mathbb{R}^{2N}$ $(N\geq 4)$ by mean curvature, each of which develops a type II singularity at the origin in finite time. Later Guo and Sesum showed that for a non-empty subset of Vel\'{a}zquez's solutions, the mean curvature blows up near the origin, at a rate small

Uma Sushmitha Gunturi, Anisha Kumar, Xiaohan Ding, Eugenia H. Rho

In this work, we examine the linguistic signature of online racial microaggressions (acts) and how it differs from that of personal narratives recalling experiences of such aggressions (recalls) by Black social media users. We manually curate and annotate a corpus of acts and recalls from in-the-wild social media discussions, and verify labels with Black wor

Ziyou Liang, Weifeng Liu, Run Wang, Mengjie Wu

In the last few years, the artifact patterns in fake images synthesized by different generative models have been inconsistent, leading to the failure of previous research that relied on spotting subtle differences between real and fake. In our preliminary experiments, we find that the artifacts in fake images always change with the development of the generat

Samuel Cahyawijaya, Holy Lovenia, Pascale Fung

In-context learning (ICL) empowers large language models (LLMs) to perform diverse tasks in underrepresented languages using only short in-context information, offering a crucial avenue for narrowing the gap between high-resource and low-resource languages. Nonetheless, there is only a handful of works explored ICL for low-resource languages with most of the

Zhijun Jiang, Hongjun Xiang, Laurent Bellaiche, Charles Paillard

Electro-optic (EO) effects relate the change of optical constants by low-frequency electric fields. Thanks to the advent of Density Functional Perturbation Theory (DFPT), the EO properties of bulk three-dimensional (3D) materials can now be calculated in an ab initio way. However, the use of periodic boundary conditions in most Density Functional Theory code

Nguyen Duy Cuong, Alexander Y. Kruger, Nguyen Hieu Thao

The paper proposes another extension of the extremal principle. A new extremality model involving collections of arbitrary families of sets is studied. It generalizes the conventional model based on linear translations of given sets as well as its set-valued extensions. This approach leads to a more general and simpler version of fuzzy separation. The new mo

Ziyao Huang, Fan Tang, Yong Zhang, Xiaodong Cun

Despite the remarkable process of talking-head-based avatar-creating solutions, directly generating anchor-style videos with full-body motions remains challenging. In this study, we propose Make-Your-Anchor, a novel system necessitating only a one-minute video clip of an individual for training, subsequently enabling the automatic generation of anchor-style

Se Won Oh, Hyuntae Jeong, Jeong Mook Lim, Seungeun Chung

In 2024, we will hold a research paper competition (the third Human Understanding AI Paper Challenge) for the research and development of artificial intelligence technologies to understand human daily life. This document introduces the datasets that will be provided to participants in the competition, and summarizes the issues to consider in data processing

Joris Mulder

This comment briefly reflects on "Safe Testing" by Gr\"{u}wald et al. (2024). The safety of fractional Bayes factors (O'Hagan, 1995) is illustrated and compared to (safe) Bayes factors based on the right Haar prior.

Dillon Z. Chen, Felipe Trevizan, Sylvie Thiébaux

Current approaches for learning for planning have yet to achieve competitive performance against classical planners in several domains, and have poor overall performance. In this work, we construct novel graph representations of lifted planning tasks and use the WL algorithm to generate features from them. These features are used with classical machine learn

Teodor Knapik, Adolphe Ratiarison, Hasina Razafindralambo

Six time series related to atmospheric phenomena are used as inputs for experiments offorecasting with singular spectrum analysis (SSA). Existing methods for SSA parametersselection are compared throughout their forecasting accuracy relatively to an optimal aposteriori selection and to a naive forecasting methods. The comparison shows that awidespread practi

Yuchen Wang, Yaoyu Liu, Man Zhang, Biying Liu

Nickel-based layered double hydroxides (LDHs) are promising electrode materials in the fields of energy storage (supercapacitors) and conversion (urea oxidation). The rational construction of atomic and electronic structure is crucial for nickel-based LDHs to realize their satisfactory electrochemical performance. Herein, we report a facile, ecofriendly, one

Andrew E. B. Lim, Zhao-Xuan Wei, Hanqin Zhang

We develop a stochastic inventory system which accounts for the limited patience of backlogged customers. While limited patience is a feature that is closer to the nature of unmet demand, our model also unifies the classic backlogging and lost-sales inventory systems which are special cases of the one we propose. We establish the uniform (asymptotic) optimal

Yuchen Wang, Yaoyu Liu, Zuo Chen, Man Zhang

Rational design and cost-effective fabrication of layered double hydroxides (LDHs) nanosheets with extraordinary electrochemical performance is a key challenge for hybrid supercapacitors (HSCs). Herein, we report a facile in situ growth methodology to eco-friendly synthesize hydrophilic NiCo-LDHs nanosheets on biomass waste-derived porous carbon (BC) for rob

Julien Weibel

We prove an ergodic theorem for Markov chains indexed by the Ulam-Harris-Neveu tree over large subsets with arbitrary shape under two assumptions: with high probability, two vertices in the large subset are far from each other and have their common ancestor close to the root. The assumption on the common ancestor can be replaced by some regularity assumption

Junhua Liu, Yong Keat Tan, Bin Fu, Kwan Hui Lim

Multi-turn intent classification is notably challenging due to the complexity and evolving nature of conversational contexts. This paper introduces LARA, a Linguistic-Adaptive Retrieval-Augmentation framework to enhance accuracy in multi-turn classification tasks across six languages, accommodating a large number of intents in chatbot interactions. LARA comb

Chia-Yi Ju, Fu-Hsiang Huang

The behavior of quantum states at exceptional points and at critical points associated with quantum phase transitions is intriguing yet puzzling. In this study, we present an alternative method for obtaining the Berry potentials using the evolution generator along the parameter induced dimension and demonstrate that they are singular at these critical points

Qiushi Nie, Xiaoqing Zhang, Yan Hu, Mingdao Gong

Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse information of images, which may be captured under different times, angles, or modalities. Although several surveys have reviewed the development of medical image registration, these surveys have not systematically summarized methodologies of existing me

Debodeep Banerjee, Stefano Teso, Burcu Sayin, Andrea Passerini

There is growing interest in AI systems that support human decision-making in high-stakes domains (e.g., medical diagnosis) to improve decision quality and reduce cognitive load. Mainstream approaches pair human experts with a machine-learning model, offloading low-risk decisions to the model so that experts can focus on cases that require their judgment. Th

Guillaume Dumas

Property $(TTT)$ was introduced by Ozawa as a strengthening of Kazhdan's property $(T)$ and Burger and Monod's property $(TT)$. In this paper, we improve Ozawa's result by showing that any simple algebraic group of rank $\geq 2$ over a local field has property $(TTT)$. We also show that lattices in a second countable locally compact group inherits property $

Tianwei Zhang, Dong Wei, Mengmeng Zhu, Shi Gu

Self-supervised learning has emerged as a powerful tool for pretraining deep networks on unlabeled data, prior to transfer learning of target tasks with limited annotation. The relevance between the pretraining pretext and target tasks is crucial to the success of transfer learning. Various pretext tasks have been proposed to utilize properties of medical im

Jie Zhang

The ability to form memories is a basic feature of learning and accumulating knowledge. But where is memory information stored in the brain? Within the scientific research community, it is generally believed that memory information is stored in the synapse. However, this widely accepted dogma has been challenged by more and more evidence in recent years. In

Zhiguo Ding

Hybrid non-orthogonal multiple access (H-NOMA) has recently received significant attention as a general framework of multiple access, where both conventional orthogonal multiple access (OMA) and pure NOMA are its special cases. This paper focuses on the application of H-NOMA to ambient Internet of Things (IoT) with energy-constrained devices, where a new bac

Jiaxuan Lu, Fang Yan, Xiaofan Zhang, Yue Gao

As natural image understanding moves towards the pretrain-finetune era, research in pathology imaging is concurrently evolving. Despite the predominant focus on pretraining pathological foundation models, how to adapt foundation models to downstream tasks is little explored. For downstream adaptation, we propose the existence of two domain gaps, i.e., the Fo

T. Asaba, M. Naritsuka, H. Asaeda, Y. Kosuge

Fermionic superfluidity with a nontrivial Cooper-pairing, beyond the conventional Bardeen-Cooper-Schrieffer state, is a captivating field of study in quantum many-body systems. In particular, the search for superconducting states with finite-momentum pairs has long been a challenge, but establishing its existence has long suffered from the lack of an appropr

Qinyao Luo, Silu He, Xing Han, Yuhan Wang

Accurate traffic forecasting is a fundamental problem in intelligent transportation systems and learning long-range traffic representations with key information through spatiotemporal graph neural networks (STGNNs) is a basic assumption of current traffic flow prediction models. However, due to structural limitations, existing STGNNs can only utilize short-r

Lakhan V. Jaybhaye, Moreshwar Tayde, P. K. Sahoo

In the background of $f(R, L_m)$ gravity, this work investigates three distinct dark matter halo profiles to test the possibility of generalised wormhole geometry within the galactic halo regions. The current study aims to accomplish these goals by examining various dark matter profiles including Universal Rotation Curves (URC), Navarro-Frenk-White (NFW) mod

Wei Xu, Junjie Luo, Qi Guo

We present CT-Bound, a robust and fast boundary detection method for very noisy images using a hybrid Convolution and Transformer neural network. The proposed architecture decomposes boundary estimation into two tasks: local detection and global regularization. During the local detection, the model uses a convolutional architecture to predict the boundary st

Maksym Radziwiłł, Niclas Technau

The distribution of the properly renormalized gaps of $\sqrt{n} \,\mathrm{mod}\, 1$ with $n < N$ converges (when $N\rightarrow \infty$) to a non-standard limit distribution, as Elkies and McMullen proved in 2004 using techniques from homogeneous dynamics. In this paper we give an essentially self-contained proof based on the circle method. Our main innovatio

Vincent Colin, Paolo Ghiggini, Ko Honda

We prove the equivalence of the sutured versions of Heegaard Floer homology, monopole Floer homology, and embedded contact homology. As applications we show that the knot versions of Heegaard Floer homology and embedded contact homology are equivalent and that product sutured 3-manifolds are characterized by the fact that they carry an adapted Reeb vector fi

Wai-Keong Mok, Leong-Chuan Kwek, Steven Touzard

Spin ensembles play a pivotal role in various quantum applications such as metrology and simulating many-body physics. Recent research has proposed utilizing spin cat states to encode logical quantum information, with logical lifetimes potentially on the order of seconds, achieved via enhanced collective interactions that scale with system size. We investiga

Mahdieh Dashtbani Moghari, Philip Noonan, David Henry, Roger R Fulton

Even for short protocols (<1 min), head movement can compromise accurate haemodynamic modelling of cerebral CT perfusion (CTP) imaging in acute stroke. Frame-to-frame registration is the most common form of retrospective correction but neglects the fact that motion is continuous, not discrete. By contrast, external tracking devices provide continuous motion

Binh Nguyen, Linh Nguyen, Truong X. Nghiem, Hung La

This paper investigates the problem of informative path planning for a mobile robotic sensor network in spatially temporally distributed mapping. The robots are able to gather noisy measurements from an area of interest during their movements to build a Gaussian Process (GP) model of a spatio-temporal field. The model is then utilized to predict the spatio-t

Fuyilong Ma, Huanhai Xin, Zhiyi Li, Linbin Huang

To satisfy dynamic requirements of power systems, it is imperative for grid-tied inverters to ensure good disturbance rejection performance (DRP) under variable grid conditions. This letter discovers and theoretically proves that for general networks, synthesizing grid-following (GFL) inverters and grid-forming (GFM) inverters can always more effectively ens

Yuchen Wang, Zuo Chen, Man Zhang, Yaoyu Liu

Rational synthesis of robust layered double hydroxides (LDHs) nanosheets for high-energy supercapacitors is full of challenges. Herein, we reported an ultrasonication-assisted strategy to eco-friendly fabricate NiFe-LDHs nanosheets for the enhanced capacitive behavior. The experimental results combined with different advanced characterization tools document

Johan Kristiansson

This paper presents ColonyOS, an open-source meta-operating system designed to improve integration and utilization of diverse computing platforms, including IoT, edge, cloud, and HPC. Operating as an overlay, ColonyOS can interface with a wide range of computing environments, fostering creation of so-called compute continuums. This makes it possible to devel

Abdulaziz Shamsah, Krishanu Agarwal, Shreyas Kousik, Ye Zhao

This study addresses the challenge of bipedal navigation in a dynamic human-crowded environment, a research area that remains largely underexplored in the field of legged navigation. We propose two cascaded zonotope-based neural networks: a Pedestrian Prediction Network (PPN) for pedestrians' future trajectory prediction and an Ego-agent Social Network (ESN)

Gee-Choon Lau, Wai Chee Shiu, K. Premalatha, M. Nalliah

An edge labeling of a connected graph $G = (V, E)$ is said to be local antimagic if there is a bijection $f:E \to\{1,\ldots ,|E|\}$ such that for any pair of adjacent vertices $x$ and $y$, $f^+(x)\not= f^+(y)$, where the induced vertex label $f^+(x)= \sum f(e)$, with $e$ ranging over all the edges incident to $x$. The local antimagic chromatic number of $G$,

Keyaki Ohno, Hirotaka Kameko, Keisuke Shirai, Taichi Nishimura

Geoparsing is the task of estimating the latitude and longitude (coordinates) of location expressions in texts. Geoparsing must deal with the ambiguity of the expressions that indicate multiple locations with the same notation. For evaluating geoparsing systems, several corpora have been proposed in previous work. However, these corpora are small-scale and s

Meng Wei, Zhongnian Li, Peng Ying, Yong Zhou

In multi-label classification, each training instance is associated with multiple class labels simultaneously. Unfortunately, collecting the fully precise class labels for each training instance is time- and labor-consuming for real-world applications. To alleviate this problem, a novel labeling setting termed \textit{Determined Multi-Label Learning} (DMLL)

Chaojie Ji, Yufeng Li, Yiyi Liao

This work tackles the challenging task of achieving real-time novel view synthesis for reflective surfaces across various scenes. Existing real-time rendering methods, especially those based on meshes, often have subpar performance in modeling surfaces with rich view-dependent appearances. Our key idea lies in leveraging meshes for rendering acceleration whi

Zhenzhi Qin, Zhenyu Ming, Defeng Sun, Liping Zhang

Recently, a quaternion tensor product named Qt-product was proposed, and then the singular value decomposition and the rank of a third-order quaternion tensor were given. From a more applicable perspective, we extend the Qt-product and propose a novel multiplication principle for third-order quaternion tensor named gQt-product. With the gQt-product, we intro

Mingyi Zhou, Xiang Gao, Pei Liu, John Grundy

Recent studies show that deployed deep learning (DL) models such as those of Tensor Flow Lite (TFLite) can be easily extracted from real-world applications and devices by attackers to generate many kinds of attacks like adversarial attacks. Although securing deployed on-device DL models has gained increasing attention, no existing methods can fully prevent t

Marvin Klimke, Max Bastian Mertens, Benjamin Völz, Michael Buchholz

Cooperative maneuver planning promises to significantly improve traffic efficiency at unsignalized intersections by leveraging connected automated vehicles. Previous works on this topic have been mostly developed for completely automated traffic in a simple simulated environment. In contrast, our previously introduced planning approaches are specifically des