April 2024 arXiv papers — page 22

Feilong Wang, Qiongrong Ou, Shuyu Zhang

The luminescent mechanism of nano-sized materials with indirect bandgap and parity-forbidden transition has always been a critical issue in breaking through bottlenecks in traditional luminescent materials. The lack of understanding has resulted in great disputes regarding the origin of fluorescence in weak-transition nanocrystals (NCs). This paper proposes

G. Mustafa, Faisal Javed, Arfa Waseem, S. K. Maurya

A gravastar comprises three distinct sections: the interior zone, the middle shell, and its outer region. By considering a specific extended teleparallel gravity model that incorporates conformal Killing vectors and provides the field equations. We observe that the interior part exhibits a repellent force acting on the shell. This is based on the assumption

Jiayin Deng, Zhiqun Hu, Yuxuan Xia, Zhaoming Lu

Roadside perception is a key component in intelligent transportation systems. In this paper, we present a novel three-dimensional (3D) extended object tracking (EOT) method, which simultaneously estimates the object kinematics and extent state, in roadside perception using both the radar and camera data. Because of the influence of sensor viewing angle and l

Ahmed Jellal, Hocine Bahlouli, Michael Vogl

We study an anisotropic cubic Dirac semi-metal subjected to a constant magnetic field. In the case of an isotropic dispersion in the $x$-$y$ plane, with parameters $v_{x}=v_{y}$, it is possible to find exact Landau levels, indexed by the quantum number $n$, using the typical ladder operator approach. Interestingly, we find that the lowest energy level (the z

Mário Pereira

In this paper, we provide a comprehensive, hands-on tutorial on how to apply deductive verification to programs written in OCaml. In particular, we show how one can use the GOSPEL specification language and the Cameleer tool to conduct mostly-automated verification on OCaml code. In our presentation, we focus on two main classes of programs: first, purely fu

Di Wu, Shicai Fan, Xue Zhou, Li Yu

Reconstruction-based methods have been commonly used for unsupervised anomaly detection, in which a normal image is reconstructed and compared with the given test image to detect and locate anomalies. Recently, diffusion models have shown promising applications for anomaly detection due to their powerful generative ability. However, these models lack strict

Anna-Monika Muscas, Daniel Pasca, Cristina Stoica

We study the linear stability of regular $n$-gon rotating equilibria in the $n$-body problem with logarithm interaction. In the presence of a central mass $M$, linear stability is insured if $M$ is bounded below and above by constants depending on the number and mass of the (equal) outer $n$ bodies. Moreover, we provide explicit equations of these bounds. In

Pedro F. Silva Pontes, Minbo Yang

In this paper we are interested in the study of a two-phase problem equipped with the $\Phi$-Laplacian operator $$ \Delta_\Phi u \coloneqq \mbox{div} \left(\phi(|\nabla u|)\dfrac{\nabla u}{|\nabla u|}\right), $$ where $\Phi(s)=e^{s^2}-1$ and $\phi=\Phi'$. We obtain the existence, boundedness, and Log-Lipschitz regularity of the minimizers of the energy funct

Zhongzhen Huang, Kui Xue, Yongqi Fan, Linjie Mu

Large-scale language models (LLMs) have achieved remarkable success across various language tasks but suffer from hallucinations and temporal misalignment. To mitigate these shortcomings, Retrieval-augmented generation (RAG) has been utilized to provide external knowledge to facilitate the answer generation. However, applying such models to the medical domai

Antonio Mastropaolo, Vittoria Nardone, Gabriele Bavota, Massimiliano Di Penta

Generative deep learning (DL) models have been successfully adopted for vulnerability patching. However, such models require the availability of a large dataset of patches to learn from. To overcome this issue, researchers have proposed to start from models pre-trained with general knowledge, either on the programming language or on similar tasks such as bug

Shiva Ghasemi, Denis Gracanin, Mohammad Azab

The integration of brain-computer interfaces (BCIs) into the realm of smart wheelchair (SW) technology signifies a notable leap forward in enhancing the mobility and autonomy of individuals with physical disabilities. BCIs are a technology that enables direct communication between the brain and external devices. While BCIs systems offer remarkable opportunit

Like Xin, Wanqi Yang, Lei Wang, Ming Yang

This paper focuses on unpaired multi-view clustering (UMC), a challenging problem where paired observed samples are unavailable across multiple views. The goal is to perform effective joint clustering using the unpaired observed samples in all views. In incomplete multi-view clustering, existing methods typically rely on sample pairing between views to captu

Hong-Yu Li, Jia-Hao Hu

The rotation curve is significant to research on dark matter and the structure of the Milky Way. But it is a great challenge to measure rotation curve accurately, and different ways obtain distinct results. In this work, we use a DIY small radio telescope to carry out hydrogen 21-cm line observations on campus, and calculate the rotation curve of inner disk

Lindsey Kerbel, Beshah Ayalew, Andrej Ivanco

Emerging data-driven approaches, such as deep reinforcement learning (DRL), aim at on-the-field learning of powertrain control policies that optimize fuel economy and other performance metrics. Indeed, they have shown great potential in this regard for individual vehicles on specific routes or drive cycles. However, for fleets of vehicles that must service a

V. A. Vassiliev

We enumerate all isotopy classes of degree three Morse polynomials ${\mathbb R}^3 \to {\mathbb R}^1$ with nonsingular principal homogeneous parts, proving that there are exactly 37 of them. We also count all 2258 isotopy classes of {\em strictly} Morse polynomials ${\mathbb R}^3 \to {\mathbb R}^1$ of degree three with the maximal possible number (eight) of r

Chenhe Du, Xiyue Lin, Qing Wu, Xuanyu Tian

Limited-angle and sparse-view computed tomography (LACT and SVCT) are crucial for expanding the scope of X-ray CT applications. However, they face challenges due to incomplete data acquisition, resulting in diverse artifacts in the reconstructed CT images. Emerging implicit neural representation (INR) techniques, such as NeRF, NeAT, and NeRP, have shown prom

Ia. A. Filatov, P. I. Gerevenkov, N. E. Khokhlov, A. M. Kalashnikova

We present a concept for selective excitation of magnetostatic surface waves with quasi-discrete spectrum using spatially patterned femtosecond laser pulses inducing either ultrafast change of magnetic anisotropy or inverse Faraday effect. We micromagnetically simulate excitation of the waves with periodically patterned uni- or bipolar laser impact. Such exc

Tahira Shehzadi, Didier Stricker, Muhammad Zeshan Afzal

Document layout analysis involves understanding the arrangement of elements within a document. This paper navigates the complexities of understanding various elements within document images, such as text, images, tables, and headings. The approach employs an advanced Transformer-based object detection network as an innovative graphical page object detector f

Alessandro D. A. M. Spallicci, Abedennour Dib, José A. Helayël-Neto

We set a generalised non-linear Lagrangian, encompassing Born-Infeld and Heisenberg-Euler theories among others. The Lagrangian reduces to the Maxwell Lagrangian at lowest order. The field is composed by a propagating light-wave in an electro-magnetic background. The wave exhibits energy variation when the background is space-time dependent. In the photon de

P. M. Sheverdyaeva, G. Bihlmayer, E. Cappelluti, D. Pacilé

We present a detailed analysis of the electronic properties of graphene/Eu/Ni(111). By using angle and spin-resolved photoemission spectroscopy and ab initio calculations, we show that the Eu-intercalation of graphene/Ni(111) restores the nearly freestanding dispersion of the $\pi\pi^\ast$ Dirac cones at the K point with an additional lifting of the spin deg

Christel Sirocchi, Martin Urschler, Bastian Pfeifer

Interpretable machine learning has emerged as central in leveraging artificial intelligence within high-stakes domains such as healthcare, where understanding the rationale behind model predictions is as critical as achieving high predictive accuracy. In this context, feature selection assumes a pivotal role in enhancing model interpretability by identifying

Lajos Horvath, Lorenzo Trapani, Shixuan Wang

In this paper, we develop two families of sequential monitoring procedure to (timely) detect changes in a GARCH(1,1) model. Whilst our methodologies can be applied for the general analysis of changepoints in GARCH(1,1) sequences, they are in particular designed to detect changes from stationarity to explosivity or vice versa, thus allowing to check for volat

Rodrigo Abadía-Heredia, Adrián Corrochano, Manuel Lopez-Martin, Soledad Le Clainche

This study investigates the generalization capabilities and robustness of purely deep learning (DL) models and hybrid models based on physical principles in fluid dynamics applications, specifically focusing on iteratively forecasting the temporal evolution of flow dynamics. Three autoregressive models were compared: a hybrid model (POD-DL) that combines pro

Zhixiong Huang, Xinying Wang, Chengpei Xu, Jinjiang Li

Underwater scenes intrinsically involve degradation problems owing to heterogeneous ocean elements. Prevailing underwater image enhancement (UIE) methods stick to straightforward feature modeling to learn the mapping function, which leads to limited vision gain as it lacks more explicit physical cues (e.g., depth). In this work, we investigate injecting the

Renato Ferreira Pinto

This paper explores the connection between classical isoperimetric inequalities, their directed analogues, and monotonicity testing. We study the setting of real-valued functions $f : [0,1]^d \to \mathbb{R}$ on the solid unit cube, where the goal is to test with respect to the $L^p$ distance. Our goals are twofold: to further understand the relationship betw

Dan Fretwell, Jenny Roberts

We construct and investigate certain (unbalanced) superalgebra structures on $\text{End}_K(V)$, with $K$ a field of characteristic $0$ and $V$ a finite dimensional $K$-vector space (of dimension $n\geq 2$). These structures are induced by a choice of non-degenerate symmetric bilinear form $B$ on $V$ and a choice of non-zero base vector $w\in V$. After explor

Silviu Balanescu, Mircea Cimpoeas, Thanh Vu

Let $J_{n,m} = (x_1\cdots x_{m},x_2 \cdots x_{m+1},\ldots,x_{n}x_1\cdots x_{m-1})$ be the $m$-path ideal of a cycle of length $n \ge 5$ over a polynomial ring $S = k[x_1,\ldots,x_n]$. Let $t\geq 1$ be an integer. We show that $J_{n,m}^t$ has a linear free resolution and give a precise formula for all of its Betti numbers when $m = n-1, n-2$.

Haijin Ding, Re-Bing Wu, Yu-xi Liu

We propose a method to trap polar molecules with the electrical force induced by the surface acoustic wave (SAW) on piezoelectric materials. In this approach, the electrical force is perpendicular to the moving direction of the polar molecules, and is used to control the positions of trapped polar molecules in the direction orthogonal to the acoustic transmi

Lie Cai, Andre Pfob

Background: Using artificial intelligence (AI) techniques for computational medical image analysis has shown promising results. However, colored images are often not readily available for AI analysis because of different coloring thresholds used across centers and physicians as well as the removal of clinical annotations. We aimed to develop an open-source t

Yubo Feng, Lishuang Li, Yi Xiang, Xueyang Qin

The representation of events in text plays a significant role in various NLP tasks. Recent research demonstrates that contrastive learning has the ability to improve event comprehension capabilities of Pre-trained Language Models (PLMs) and enhance the performance of event representation learning. However, the efficacy of event representation learning based

Weifeng Wei, Jie Wang, Shuqi Deng, Jie Liu

We introduce a high-fidelity neural implicit dense visual Simultaneous Localization and Mapping (SLAM) system, termed DF-SLAM. In our work, we employ dictionary factors for scene representation, encoding the geometry and appearance information of the scene as a combination of basis and coefficient factors. Compared to neural implicit dense visual SLAM method

Yujing Liu, Zongqian Wu, Zhengyu Lu, Ci Nie

Previous graph neural networks (GNNs) usually assume that the graph data is with clean labels for representation learning, but it is not true in real applications. In this paper, we propose a new multi-teacher distillation method based on bi-level optimization (namely BO-NNC), to conduct noisy node classification on the graph data. Specifically, we first emp

Manuel Tonneau, Diyi Liu, Samuel Fraiberger, Ralph Schroeder

Perceptions of hate can vary greatly across cultural contexts. Hate speech (HS) datasets, however, have traditionally been developed by language. This hides potential cultural biases, as one language may be spoken in different countries home to different cultures. In this work, we evaluate cultural bias in HS datasets by leveraging two interrelated cultural

Yilu Chen, Zhijie Cai, Min Wei, Zhifeng Lin

Static IR drop analysis is a fundamental and critical task in chip design since the IR drop will significantly affect the design's functionality, performance, and reliability. However, the process of IR drop analysis can be time-consuming, potentially taking several hours. Therefore, a fast and accurate IR drop prediction is paramount for reducing the overal

Linsen Pei, Yujia Xian, Xiaodan Yan, Charley Schaefer

Membraneless droplets formed through liquid-liquid phase separation (LLPS) play a crucial role in mRNA storage, enabling organisms to swiftly respond to environmental changes. However, the mechanisms underlying mRNA integration and protection within droplets remain unclear. Here, we unravel the role of bacterial aggresomes as stress granules (SGs) in safegua

Virginia Ardévol Martínez, Romeo Rizzi, Abdallah Saffidine, Florian Sikora

For any natural number $d$, a graph $G$ is a (disjoint) $d$-interval graph if it is the intersection graph of (disjoint) $d$-intervals, the union of $d$ (disjoint) intervals on the real line. Two important subclasses of $d$-interval graphs are unit and balanced $d$-interval graphs (where every interval has unit length or all the intervals associated to a sam

Xiaoyu Zhang, Weipeng Jiang, Chao Shen, Qi Li

In recent years, software systems powered by deep learning (DL) techniques have significantly facilitated people's lives in many aspects. As the backbone of these DL systems, various DL libraries undertake the underlying optimization and computation. However, like traditional software, DL libraries are not immune to bugs, which can pose serious threats to us

Mehdi Jadoui, Christophe Blondeau, Emeric Martin, Florent Renac

Advanced Krylov subspace methods are investigated for the solution of large sparse linear systems arising from stiff adjoint-based aerodynamic shape optimization problems. A special attention is paid to the flexible inner-outer GMRES strategy combined with most relevant preconditioning and deflation techniques. The choice of this specific class of Krylov sol

Lenny Jones

A monic polynomial $f(x)\in {\mathbb Z}[x]$ of degree $N$ is called monogenic if $f(x)$ is irreducible over ${\mathbb Q}$ and $\{1,\theta,\theta^2,\ldots ,\theta^{N-1}\}$ is a basis for the ring of integers of ${\mathbb Q}(\theta)$, where $f(\theta)=0$. In this brief note, we prove that there exist exactly three distinct monogenic trinomials of the form $x^4

Youngjoon Hong, Seungchan Ko, Jaeyong Lee

In this paper, we provide a theoretical analysis of a type of operator learning method without data reliance based on the classical finite element approximation, which is called the finite element operator network (FEONet). We first establish the convergence of this method for general second-order linear elliptic PDEs with respect to the parameters for neura

Xiaoshuai Wu, Xin Liao, Bo Ou, Yuling Liu

AI-generated content has accelerated the topic of media synthesis, particularly Deepfake, which can manipulate our portraits for positive or malicious purposes. Before releasing these threatening face images, one promising forensics solution is the injection of robust watermarks to track their own provenance. However, we argue that current watermarking model

Sihem Ben Sassi

Some of them proposed an approach in which involved stakeholders can freely configure the product line without being constrained by the choices made the other ones. The core of any proposed approach in this context focuses on how conflictual situations are resolved. Few works consider stakeholders preferences in their resolution process. However, to generate

Zitong Zhang, Yang Liu, Hao Sun

Data-driven discovery of governing equations has kindled significant interests in many science and engineering areas. Existing studies primarily focus on uncovering equations that govern nonlinear dynamics based on direct measurement of the system states (e.g., trajectories). Limited efforts have been placed on distilling governing laws of dynamics directly

Massimo Bartoletti, Angelo Ferrando, Enrico Lipparini, Vadim Malvone

Smart contracts are an attractive target for attackers, as evidenced by a long history of security incidents. A current limitation of smart contract verification tools is that they are not really effective in expressing and verifying liquidity properties regarding the exchange of crypto-assets: for example, is it true that in every reachable state a user can

Debabrata Jana

Consider the compact quantum group $U_q(2)$, where $q$ is a non-zero complex deformation parameter such that $|q|\neq 1$. Let $C(U_q(2))$ denote the underlying $C^*$-algebra of the compact quantum group $U_q(2)$. We prove that if $q$ is a non-real complex number and $q^\prime$ is real, then the underlying $C^*$-algebras $C(U_q(2))$ and $C(U_{q^\prime}(2))$ a

Tao Meng, Fuchen Zhang, Yuntao Shou, Wei Ai

Efficiently capturing consistent and complementary semantic features in a multimodal conversation context is crucial for Multimodal Emotion Recognition in Conversation (MERC). Existing methods mainly use graph structures to model dialogue context semantic dependencies and employ Graph Neural Networks (GNN) to capture multimodal semantic features for emotion

Yuval Haitman, Oded Bialer

Automotive radars have an important role in autonomous driving systems. The main challenge in automotive radar detection is the radar's wide point spread function (PSF) in the angular domain that causes blurriness and clutter in the radar image. Numerous studies suggest employing an 'end-to-end' learning strategy using a Deep Neural Network (DNN) to directly

David Cushing, Supanat Kamtue, Erin Law, Shiping Liu

In this note, we provide Steinerberger curvature formulas for block graphs, discuss curvature relations between two graphs and the graph obtained by connecting them via a bridge, and show that self-centered Bonnet-Myers sharp graphs are precisely those which are antipodal. We also discuss similarities and differences between Steinerberger and Ollivier Ricci

Emmanuel Tuyishimire

The fourth Industrial Revolution(4IR), together with the COVID-19 pandemic have made a loud call for digitizing diagnosis processes. The world is now convinced that it is imperative to digitize the diagnosis of long standing diseases such as malaria for more efficient treatment and control. It has been seen that malaria control would benefit a lot from digit

Yuntao Shou, Tao Meng, Fuchen Zhang, Nan Yin

Multi-modal Emotion Recognition in Conversation (MERC) has received considerable attention in various fields, e.g., human-computer interaction and recommendation systems. Most existing works perform feature disentanglement and fusion to extract emotional contextual information from multi-modal features and emotion classification. After revisiting the charact

Roger Sewell, Elisabeth Crowe, Sharokh F. Shariat

Shariat et al previously investigated the possibility of predicting from clinical data (including Gleason grade and stage) and preoperative biomarkers, which of any pair of patients would suffer recurrence of prostate cancer first. We wished to establish the extent to which predictions of time of relapse from such a model could be improved upon using Bayesia

Pierre C. Bellec, Kai Tan

This paper investigates the iterates $\hbb^1,\dots,\hbb^T$ obtained from iterative algorithms in high-dimensional linear regression problems, in the regime where the feature dimension $p$ is comparable with the sample size $n$, i.e., $p \asymp n$. The analysis and proposed estimators are applicable to Gradient Descent (GD), proximal GD and their accelerated

Tom Willaert

A defining characteristic of conspiracy texts is that they negotiate power and identity by recontextualizing prior knowledge. This dynamic has been shown to intensify on social media, where knowledge sources can readily be integrated into antagonistic narratives through hyperlinks. The objective of the present chapter is to further our understanding of this

Ziya Ata Yazıcı, İlkay Öksüz, Hazım Kemal Ekenel

Convolutional Neural Networks (CNNs) have become widely adopted for medical image segmentation tasks, demonstrating promising performance. However, the inherent inductive biases in convolutional architectures limit their ability to model long-range dependencies and spatial correlations. While recent transformer-based architectures address these limitations b

Hristina S. Hristova, Svetoslav S. Ivanov, Nikolay V. Vitanov, Andon A. Rangelov

We theoretically propose a type of tunable polarization retarder, which is composed of sequences of half-wave and quarter-wave polarization retarders, allowing operation at broad spectral bandwidth. The constituent retarders are composed of stacked standard half-wave retarders and quarter-wave retarders rotated at designated angles relative to their fast-pol

Fulun Wu, Cai-Zhuang Wang, Kai-Ming Ho, Shunqing Wu

Constraining the melting temperature of iron under Earth's inner core conditions is crucial for understanding core dynamics and planetary evolution. Here, we develop a deep potential (DP) model for iron that explicitly incorporates electronic entropy contributions governing thermodynamics under Earth's core conditions. Extensive benchmarking demonstrates the

Hessah Albanwan

Remote sensing (RS) images are important to monitor and survey earth at varying spatial scales. Continuous observations from various RS sources complement single observations to improve applications. Fusion into single or multiple images provides more informative, accurate, complete, and coherent data. Studies intensively investigated spatial-temporal fusion

The Tien Mai

Reduced rank regression (RRR) is a widely employed model for investigating the linear association between multiple response variables and a set of predictors. While RRR has been extensively explored in various works, the focus has predominantly been on continuous response variables, overlooking other types of outcomes. This study shifts its attention to the

Mohammad Ali S. Afshar, Saeed Noori Gashti, Mohammad Reza Alipour, Jafar Sadeghi

Traditional static methods in phase transition studies, provide good insights into the thermodynamics of black holes. However, they practically lose sight of the dynamic aspects and temporal sequence of events. The Kramer's escape rate, central to our research, offers a somewhat dynamic approach to phase transition. We examine the free energy landscapes for

Fabiano C. Simas, E. da Hora

We examine the evolution of a vacuum configuration when perturbed by an oscillon. We consider the $\phi^4$ scenario with a single scalar field only. For highly excited oscillons, we find that new composite solutions appear. They are formed by multiple antikink-kink pairs and a centered reminiscent oscillon. The overall process gives rise to a resonant struct

Liping Yi, Han Yu, Chao Ren, Heng Zhang

Model-heterogeneous personalized federated learning (MHPFL) enables FL clients to train structurally different personalized models on non-independent and identically distributed (non-IID) local data. Existing MHPFL methods focus on achieving client-level personalization, but cannot address batch-level data heterogeneity. To bridge this important gap, we prop

M. Zeeshan Gul, M. Sharif

This paper investigated the viable traversable wormhole solutions through Karmarkar condition in the context of $f(\mathcal{G},T)$ theory. A static spherical spacetime with anisotropic matter configuration is used to study the wormhole geometry. Karmarkar condition is used to develop a viable shape function for a static wormhole structure. A wormhole geometr

Lichao Wang, Zhihao Yuan, Jinke Ren, Shuguang Cui

Text-to-point-cloud cross-modal localization is an emerging vision-language task critical for future robot-human collaboration. It seeks to localize a position from a city-scale point cloud scene based on a few natural language instructions. In this paper, we address two key limitations of existing approaches: 1) their reliance on ground-truth instances as i

Lei Cheng, Xiaowen Huang, Jitao Sang, Jian Yu

Recently, recommender system has achieved significant success. However, due to the openness of recommender systems, they remain vulnerable to malicious attacks. Additionally, natural noise in training data and issues such as data sparsity can also degrade the performance of recommender systems. Therefore, enhancing the robustness of recommender systems has b

Yixuan Fan, Dehui Wang

In this paper, a new bivariate random coefficient integer-valued autoregressive process based on modified negative binomial operator with dependent innovations is proposed. Basic probabilistic and statistical properties of this model are derived. To estimate unknown parameters, Yule-Walker, conditional least squares and conditional maximum likelihood methods

Madhava Krishna, Bhagesh Gaur, Arsh Verma, Pankaj Jalote

The creation of a Software Requirements Specification (SRS) document is important for any software development project. Given the recent prowess of Large Language Models (LLMs) in answering natural language queries and generating sophisticated textual outputs, our study explores their capability to produce accurate, coherent, and structured drafts of these d

Daryna Dementieva, Valeriia Khylenko, Nikolay Babakov, Georg Groh

The task of toxicity detection is still a relevant task, especially in the context of safe and fair LMs development. Nevertheless, labeled binary toxicity classification corpora are not available for all languages, which is understandable given the resource-intensive nature of the annotation process. Ukrainian, in particular, is among the languages lacking s

Corentin Bodart, Denis Osin

We provide examples of groups with transcendental spectral radius: We first construct finitely presented examples, using links between decidability of the Word Problem and semi-computability of the spectral radius. This argument extends to the exponential growth rate and the asymptotic entropy. We also construct a finitely generated example with decidable Wo

Yizhou Chen, Zeyu Sun, Zhihao Gong, Dan Hao

Currently, smart contract vulnerabilities (SCVs) have emerged as a major factor threatening the transaction security of blockchain. Existing state-of-the-art methods rely on deep learning to mitigate this threat. They treat each input contract as an independent entity and feed it into a deep learning model to learn vulnerability patterns by fitting vulnerabi

Muzzamil Ahmad Eatoo, Nimer Wehbe, Najeh Kharbatia, Xianrong Guo

Water microdroplets containing 100 micromolar HAuCl4 have been shown to reduce gold ions into gold nanoparticles spontaneously. It has been suggested that this chemical transformation is driven by ultrahigh electric fields at the air-water interface, albeit without mechanistic insight. We investigated the fate of several metallic salts in water, methanol, et

Yiming Bao, Xu Zhao, Dahong Qian

Temporal 3D human pose estimation from monocular videos is a challenging task in human-centered computer vision due to the depth ambiguity of 2D-to-3D lifting. To improve accuracy and address occlusion issues, inertial sensor has been introduced to provide complementary source of information. However, it remains challenging to integrate heterogeneous sensor

Giuseppe Favacchio

The total Betti numbers of the toric ideal of a simple graph are, in general, highly sensitive to any small change of the graph. In this paper we look at some combinatorial operations that cause total Betti numbers to change in predictable ways. In particular, we focus on a procedure that preserves these invariants.

Junyi Biana, Weiqi Zhai, Xiaodi Huang, Jiaxuan Zheng

Prevalent solution for BioNER involves using representation learning techniques coupled with sequence labeling. However, such methods are inherently task-specific, demonstrate poor generalizability, and often require dedicated model for each dataset. To leverage the versatile capabilities of recently remarkable large language models (LLMs), several endeavors

Xin Ye, Harald Ruess

Our main result is a polynomial time algorithm for deciding realizability for the GXU sublogic of linear temporal logic. This logic is particularly suitable for the specification of embedded control systems, and it is more expressive than GR(1). Reactive control programs for GXU specifications are represented as Mealy machines, which are extended by the moni

Zhenlan Ji, Daoyuan Wu, Pingchuan Ma, Zongjie Li

Agents based on large language models (LLMs) have demonstrated effectiveness in solving a wide range of tasks by integrating LLMs with key modules such as planning, memory, and tool usage. Increasingly, customers are adopting LLM agents across a variety of commercial applications critical to reliability, including support for mental well-being, chemical synt

Tsimur Hadeliya, Dariusz Kajtoch

We introduce a few-shot benchmark consisting of 7 different classification tasks native to the Polish language. We conducted an empirical comparison with 0 and 16 shots between fine-tuning, linear probing, SetFit, and in-context learning (ICL) using various pre-trained commercial and open-source models. Our findings reveal that ICL achieves the best performa

Vasily V. Strelkov, Margarita A. Khokhlova

We study the phase matching of resonant high-harmonic generation (HHG) and high-order frequency mixing (HFM) in plasma. We solve numerically the propagation equations coupled with the time-dependent Schr\"odinger equation for the nonlinear polarisation. The macroscopic harmonic signal is enhanced in the vicinity of a multiphoton resonance with the transition

Haifeng Yang, Chuanxing Geng, Pong C. Yuen, Songcan Chen

In open-set recognition, existing methods generally learn statically fixed decision boundaries using known classes to reject unknown classes. Though they have achieved promising results, such decision boundaries are evidently insufficient for universal unknown classes in dynamic and open scenarios as they can potentially appear at any position in the feature

Hessah Albanwan

The analysis of time-sequence satellite images is a powerful tool in remote sensing; it is used to explore the statics and dynamics of the surface of the earth. Usually, the quality of multitemporal images is influenced by metrological conditions, high reflectance of surfaces, illumination, and satellite sensor conditions. These negative influences may produ

Victor Quétu, Zhu Liao, Enzo Tartaglione

While deep neural networks are highly effective at solving complex tasks, large pre-trained models are commonly employed even to solve consistently simpler downstream tasks, which do not necessarily require a large model's complexity. Motivated by the awareness of the ever-growing AI environmental impact, we propose an efficiency strategy that leverages prio

Davide Cipollini, Lambert Schomaker

Hierarchical tree structures are common in many real-world systems, from tree roots and branches to neuronal dendrites and biologically inspired artificial neural networks, as well as in technological networks for organizing and searching complex datasets of high-dimensional patterns. Within the class of hierarchical self-organized systems, we investigate th

Fabrizio Colombo, Elodie Pozzi, Irene Sabadini, Brett D. Wick

The path-integral technique in quantum mechanics provides an intuitive framework for comprehending particle propagation and scattering. Calculating the propagator for the Aharonov-Bohm potential fits into the range of potentials in multiply-connected spaces, with the propagator represented through a series expansion. In this paper, we analyze the Schr\"oding

N. V. Porokhov, A. A. Anikanov, A. P. Sirotina, E. A. Pershina

The present study delves into exploring the impact of a thin protective layer of scandium nitride (ScN) on the superconductive properties of thin films of niobium nitride (NbN) generated through reactive magnetron deposition. This is the first time such an investigation has been carried out. The article offers a comprehensive investigation of the morphologic

Chen Xu, Xiaopeng Ye, Wenjie Wang, Liang Pang

Fair re-ranking aims to redistribute ranking slots among items more equitably to ensure responsibility and ethics. The exploration of redistribution problems has a long history in economics, offering valuable insights for conceptualizing fair re-ranking as a taxation process. Such a formulation provides us with a fresh perspective to re-examine fair re-ranki

Zhongze Wang, Haitao Zhao, Jingchao Peng, Lujian Yao

Unpaired image dehazing (UID) holds significant research importance due to the challenges in acquiring haze/clear image pairs with identical backgrounds. This paper proposes a novel method for UID named Orthogonal Decoupling Contrastive Regularization (ODCR). Our method is grounded in the assumption that an image consists of both haze-related features, which

Peng Xu, Haitao Zhang, Shengjun Wu

Attaining high-fidelity two-qubit gates represents a pivotal quantum operation for the realization of large-scale quantum computation and simulation. In this study, we propose a microwave-control protocol for the implementation of a two-qubit gate employing two transmon qubits coupled via a fixed-frequency transmon coupler. This protocol entails applying two

Wenzhen Yue, Xianghua Ying, Ruohao Guo, DongDong Chen

In this paper, we present the Sub-Adjacent Transformer with a novel attention mechanism for unsupervised time series anomaly detection. Unlike previous approaches that rely on all the points within some neighborhood for time point reconstruction, our method restricts the attention to regions not immediately adjacent to the target points, termed sub-adjacent

Luofang Jiao, Jiwei Zhao, Yunting Xu, Tianqi Zhang

With the ever-increasing number of connected vehicles in the fifth-generation mobile communication networks (5G) and beyond 5G (B5G), ensuring the reliability and high-speed demand of cellular vehicle-to-everything (C-V2X) communication in scenarios where vehicles are moving at high speeds poses a significant challenge.Recently, multi-connectivity technology

Xiantong Yang, Mengmeng Zhang

The introduction of generative artificial intelligence (GenAI) into educational practices has been transformative, yet it brings a crucial concern about the potential distortion of users' beliefs. Given the prevalence of GenAI among college students, examining the psychological mechanisms that lead to GenAI distortion from both technological factors and the

Xiaojing Liu, Hongwei Ai, Joshua D. Reiss

We propose a speech enhancement system for multitrack audio. The system will minimize auditory masking while allowing one to hear multiple simultaneous speakers. The system can be used in multiple communication scenarios e.g., teleconferencing, invoice gaming, and live streaming. The ITU-R BS.1387 Perceptual Evaluation of Audio Quality (PEAQ) model is used t

Yuchun Wang, Cheng Gong, Jianwei Gong, Peng Jia

Driving in an off-road environment is challenging for autonomous vehicles due to the complex and varied terrain. To ensure stable and efficient travel, the vehicle requires consideration and balancing of environmental factors, such as undulations, roughness, and obstacles, to generate optimal trajectories that can adapt to changing scenarios. However, tradit

Gwyn Bellamy, Raphaël Paegelow

For $\Gamma$ a finite subgroup of $\mathrm{SL}_2(\mathbb{C})$ and $n \geq 1$, we study the fibers of the Procesi bundle over the $\Gamma$-fixed points of the Hilbert scheme of $n$ points in the plane. For each irreducible component of this fixed point locus, our approach reduces the study of the fibers of the Procesi bundle, as an $(\mathfrak{S}_n \times \Ga

Ching Hang Mak, Shing-Chi Cheung

Context: Bug bisection is a common technique used to identify a revision that introduces a bug or indirectly fixes a bug, and often involves executing multiple revisions of a project to determine whether the bug is present within the revision. However, many legacy revisions often cannot be successfully compiled due to changes in the programming language or t

Tom G. Mackay, Akhlesh Lakhtakia

Closed-form expressions were recently derived for depolarization dyadics for truncated spheres and truncated spheroids, and the formalism was extended to truncated ellipsoids. These results were exploited to develop an implementation of the Maxwell Garnett homogenization formalism for the relative permittivity parameters of homogenized composite mediums (HCM

Niels Swinkels, Carsten Dominik

Planet-forming disks turn from gas-rich, massive disks made of dust and gas into planetary systems containing only small amounts dust produced by collisions between smaller planetary objects like planetesimals, asteroids, or comets. Traditionally we talk about protoplanetary (age $\sim$1 Myr), transitional ($\sim$ 5-10 Myr), and debris disks ($\sim$ 10-hundr

Ge Sun, Milad Shafiee, Peizhuo Li, Guillaume Bellegarda

Animals possess a remarkable ability to navigate challenging terrains, achieved through the interplay of various pathways between the brain, central pattern generators (CPGs) in the spinal cord, and musculoskeletal system. Traditional bioinspired control frameworks often rely on a singular control policy that models both higher (supraspinal) and spinal cord

Shoya Ogawa, Kazuki Yoshida, Yoshiki Chazono, Kazuyuki Ogata

$^6$Li is usually treated as an $\alpha+p+n$ three-body system, and the validity of this picture is important for understanding $^6$Li reactions. The ($p$,$2p$) reaction is a powerful method to study the structure of valence nucleons in $^6$Li. Recently, the new experimental data of the $^{6}$Li($p$,$2p$)$^{5}$He reaction have been obtained and should be ana

Niklas Schlomberg

We present a new greedy rounding algorithm for the Cycle Packing Problem for uncrossable cycle families in planar graphs. This improves the best-known upper bound for the integrality gap of the natural packing LP to a constant slightly less than 3.5. Furthermore, the analysis works for both edge- and vertex-disjoint packing. The previously best-known constan

Jiatong Bai, Feng Shu, Qinghe Zheng, Bo Xu

Due to its excellent performance in rate and resolution, fully-digital (FD) massive multiple-input multiple-output (MIMO) antenna arrays has been widely applied in data transmission and direction of arrival (DOA) measurements, etc. But it confronts with two main challenges: high computational complexity and circuit cost. The two problems may be addressed wel

Kazuma Sawaya, Yoshimasa Uematsu, Masaaki Imaizumi

This study proposes a novel method for estimation and hypothesis testing in high-dimensional single-index models. We address a common scenario where the sample size and the dimension of regression coefficients are large and comparable. Unlike previous approaches, which often overlook the estimation of the unknown link function, we introduce a new method for