May 2024 arXiv papers — page 137

Pablo Bastante, Andres Castellanos-Gomez

This teaching article describes a simple and low-cost methodology for studying electrical transport and constructing basic sensor devices using everyday stationery items, including pencils, paper, and a handheld multimeter. The approach is designed for high school and undergraduate teachers and offers an easy-to-implement, hands-on method for teaching fundam

Fernando Argentieri, Przemysław Berk, Frank Trujillo

We prove ergodicity of a class of infinite measure preserving systems, called skew-products. More precisely, we consider systems of the form \[ {T_f}:{[0, 1) \times \mathbb{R}}\to{[0, 1) \times \mathbb{R}},\quad {T_f(x, t)}:={(T(x), t+f(x))}, \] where $T$ is an interval exchange transformation and $f$ is a piece-wise constant function with a finite number of

Yunxiao Zhang, Zixiong Wang, Zihan Zhao, Rui Xu

Shape manipulation is a central research topic in computer graphics. Topology editing, such as breaking apart connections, joining disconnected ends, and filling/opening a topological hole, is generally more challenging than geometry editing. In this paper, we observe that the saddle points of the signed distance function (SDF) provide useful hints for alter

Peizhuo Li, Tuanfeng Y. Wang, Timur Levent Kesdogan, Duygu Ceylan

Data driven and learning based solutions for modeling dynamic garments have significantly advanced, especially in the context of digital humans. However, existing approaches often focus on modeling garments with respect to a fixed parametric human body model and are limited to garment geometries that were seen during training. In this work, we take a differe

Takara Kondo

We determine the automorphism groups of the orbifold vertex operator algebras associated with the coinvariant lattices of isometries of the Leech lattice in the conjugacy classes 3C, 5C, 11A and 23A. These orbifold vertex operator algebras appear in a classification given by C.H. Lam and H. Shimakura.

Z. Cao, P. G. Jonker, D. R. Pasham, S. Wen

A tidal disruption event (TDE) may occur when a star is torn apart by the tidal force of a black hole (BH). Eventually, an accretion disc is thought to form out of stellar debris falling back towards the BH. If the star's orbital angular momentum vector prior to disruption is not aligned with the BH spin angular momentum vector, the disc will be tilted with

Femke B. Gelderblom, Tron V. Tronstad, Iván López-Espejo

Understanding degraded speech is demanding, requiring increased listening effort (LE). Evaluating processed and unprocessed speech with respect to LE can objectively indicate if speech enhancement systems benefit listeners. However, existing methods for measuring LE are complex and not widely applicable. In this study, we propose a simple method to evaluate

Daphne Theodorakopoulos, Frederic Stahl, Marius Lindauer

Hyperparameter optimization plays a pivotal role in enhancing the predictive performance and generalization capabilities of ML models. However, in many applications, we do not only care about predictive performance but also about additional objectives such as inference time, memory, or energy consumption. In such multi-objective scenarios, determining the im

Meixia Geng, Qingjie Yang, Chaouki Kasmi, J. Kim Welford

The Oceanus Procellarum region, characterized by its vast basaltic plains and pronounced volcanic activity, serves as a focal point for understanding the volcanic history of the Moon. Leveraging the Gravity Recovery and Interior Laboratory (GRAIL) mission data, we imaged the magmatic structures beneath the Oceanus Procellarum region. Our 3D density models un

Qingyang Li, Yihang Zhang, Zhidong Jia, Yannan Hu

It is an interesting question Can and How Large Language Models (LLMs) understand non-language network data, and help us detect unknown malicious flows. This paper takes Carpet Bombing as a case study and shows how to exploit LLMs' powerful capability in the networking area. Carpet Bombing is a new DDoS attack that has dramatically increased in recent years,

Priyanshu Gupta, Shashank Kirtania, Ananya Singha, Sumit Gulwani

The popularity of Large Language Models (LLMs) have unleashed a new age ofLanguage Agents for solving a diverse range of tasks. While contemporary frontier LLMs are capable enough to power reasonably good Language agents, the closed-API model makes it hard to improve in cases they perform sub-optimally. To address this, recent works have explored ways to imp

Asaf Cassel, Haipeng Luo, Aviv Rosenberg, Dmitry Sotnikov

In many real-world applications, it is hard to provide a reward signal in each step of a Reinforcement Learning (RL) process and more natural to give feedback when an episode ends. To this end, we study the recently proposed model of RL with Aggregate Bandit Feedback (RL-ABF), where the agent only observes the sum of rewards at the end of an episode instead

Natsuki Katayama, Yoshihiko Susuki

The Koopman operator framework holds promise for spectral analysis of nonlinear dynamical systems based on linear operators. Eigenvalues and eigenfunctions of the Koopman operator, so-called Koopman eigenvalues and Koopman eigenfunctions, respectively, mirror global properties of the system's flow. In this paper we perform the Koopman analysis of the singula

J. L. Velasco, I. Calvo, F. J. Escoto, E. Sánchez

In omnigeneous magnetic fields, charged particles are perfectly confined in the absence of collisions and turbulence. For this reason, the magnetic configuration is optimized to be close to omnigenity in any candidate for a stellarator fusion reactor. However, approaching omnigenity imposes severe constraints on the spatial variation of the magnetic field. I

G. B. Sushko, A. V. Korol, A. V. Solov'yov

This paper presents a quantitative analysis of the impact of inelastic collisions of ultra-relativistic electrons and positrons, passing through oriented crystalline targets, on the channeling efficiency and on the intensity of the channeling radiation. The analysis is based on the numerical simulations of the channeling process performed using the MBNExplor

Andrei T. Patrascu

I describe the engineered decoherence of a qubit state by means of an environment formed out of a neurally architected material. Such a material is a material that can adjust its inner properties in the same way a neural network is adjusting its weights, subject to a built-in cost function. Such a material is naturally found in biological structures (like a

Alexander C. Tyner, Alexander V. Balatsky

The increasing financial and environmental cost of many inorganic materials has motivated study into organic and "green" alternatives. However, most organic compounds contain a large number of atoms in the primitive unit cell, posing a significant barrier to high-throughput screening for functional properties. In this work, we attempt to overcome this challe

Max Behrens, Maryam Farhadizadeh, Angelika Rohde, Alexander Rühle

In clinical settings, we often face the challenge of building prediction models based on small observational data sets. For example, such a data set might be from a medical center in a multi-center study. Differences between centers might be large, thus requiring specific models based on the data set from the target center. Still, we want to borrow informati

Alessandro Paghi, Giacomo Trupiano, Giorgio De Simoni, Omer Arif

Superconducting circuits based on hybrid InAs Josephson Junctions (JJs) play a starring role in the design of fast and ultra-low power consumption solid-state quantum electronics and exploring novel physical phenomena. Conventionally, 3D substrates, 2D quantum wells (QWs), and 1D nanowires (NWs) made of InAs are employed to create superconducting circuits wi

Fuad Kittaneh, Ali Zamani

We study the concepts of Birkhoff--James orthogonality and parallelism in Hilbert space operators, induced by the operator radius norm $w_{\rho}(\cdot)$. In particular, we completely characterize Birkhoff--James orthogonality and parallelism with respect to $w_{\rho}(\cdot)$. As an application of the results presented, we obtain a well-known characterization

Rasmus Sibbern Frederiksen, Thomas Gundgaard Mulvad, Israel Leyva-Mayorga, Tatiana Kozlova Madsen

Low Earth orbit (LEO) satellite mega-constellations with hundreds or thousands of satellites and inter-satellite links (ISLs) have the potential to provide global end-to-end connectivity. Furthermore, if the physical distance between source and destination is sufficiently long, end-to-end routing over the LEO constellation can provide lower latency when comp

Shuo Liu, Di Yao, Lanting Fang, Zhetao Li

Detecting anomaly edges for dynamic graphs aims to identify edges significantly deviating from the normal pattern and can be applied in various domains, such as cybersecurity, financial transactions and AIOps. With the evolving of time, the types of anomaly edges are emerging and the labeled anomaly samples are few for each type. Current methods are either d

Tatsuki Odake, Satoshi Yoshida, Mio Murao

Recent developments have revealed deterministic and exact protocols for performing complex conjugation, inversion, and transposition of a general $d$-dimensional unknown unitary operation using a finite number of queries to a black-box unitary operation. In this work, we establish analytical lower bounds for the query complexity of unitary inversion, transpo

David Bucher, Nico Kraus, Jonas Blenninger, Michael Lachner

Benchmarking the performance of quantum optimization algorithms is crucial for identifying utility for industry-relevant use cases. Benchmarking processes vary between optimization applications and depend on user-specified goals. The heuristic nature of quantum algorithms poses challenges, especially when comparing to classical counterparts. A key problem in

Ruixi Lin, Yang You

Even as we engineer LLMs for alignment and safety, they often uncover biases from pre-training data's statistical regularities (from disproportionate co-occurrences to stereotypical associations mirroring human cognitive biases). This leads to persistent, uneven class accuracy in classification and QA. Such per-class accuracy disparities are not inherently r

Daniel Cutting, Frédéric A. Dreyer, David Errington, Constantin Schneider

We introduce IgDiff, an antibody variable domain diffusion model based on a general protein backbone diffusion framework which was extended to handle multiple chains. Assessing the designability and novelty of the structures generated with our model, we find that IgDiff produces highly designable antibodies that can contain novel binding regions. The backbon

Kaushik Dey, Satheesh K. Perepu, Abir Das, Pallab Dasgupta

Intent Management Function (IMF) is an integral part of future-generation networks. In recent years, there has been some work on AI-based IMFs that can handle conflicting intents and prioritize the global objective based on apriori definition of the utility function and accorded priorities for competing intents. Some of the earlier works use Multi-Agent Rein

Shaoshuai Chu, Alexander Kurganov, Ruixiao Xin

The low-dissipation central-upwind (LDCU) schemes have been recently introduced in [A. Kurganov and R. Xin, J. Sci. Comput., 96 (2023), Paper No. 56] as a modification of the central-upwind (CU) schemes from [{\sc A. Kurganov and C. T. Lin, Commun. Comput. Phys., 2 (2007), pp. 141-163}]. The LDCU schemes achieve much higher resolution of contact waves and ma

Michael Kohler, Adam Krzyzak, Benjamin Walter

Image classification based on over-parametrized convolutional neural networks with a global average-pooling layer is considered. The weights of the network are learned by gradient descent. A bound on the rate of convergence of the difference between the misclassification risk of the newly introduced convolutional neural network estimate and the minimal possi

Lvchang Li, Haichou Li

In the present paper, we study the boundedness and compactness of Toeplitz operators and Berezin-type operators between different weighted Bergman spaces over tubular domains in $\mathbb{C}^n$. We establish their connection with Carleson measures and provide some characterizations.

Katja Höflich, Krzysztof Mackosz, Chinmai S. Jureddy, Aleksei Tsarapkin

Direct electron beam writing is a powerful tool for fabricating complex nanostructures in a single step. The electron beam locally cleaves the molecules of an adsorbed gaseous precursor to form a deposit, similar to 3D printing but without the need for a resist or development step. Here, we employ for the first time a silver $\beta$-diketonate precursor for

Chunlong Sun, Mengmeng Zhang, Zhidong Zhang

This work considers the inverse dynamic source problem arising from the time-domain fluorescence diffuse optical tomography (FDOT). We recover the dynamic distributions of fluorophores in biological tissue by the one single boundary measurement in finite time domain. We build the uniqueness theorem of this inverse problem. After that, we introduce a weighted

Hung Tuan Le, Long Truong To, Manh Trong Nguyen, Kiet Van Nguyen

Fact-checking is essential due to the explosion of misinformation in the media ecosystem. Although false information exists in every language and country, most research to solve the problem mainly concentrated on huge communities like English and Chinese. Low-resource languages like Vietnamese are necessary to explore corpora and models for fact verification

Debolina Deb, Gopalakrishnan Sai Gautam

Na-ion batteries (NIBs) are increasingly looked at as a viable alternative to Li-ion batteries due to the abundance, low cost, and thermal stability of Na-based systems. To improve the practical utilization of NIBs in applications, it is important to boost the energy and power densities of the electrodes being used, via discovery of novel candidate materials

Kazuhiro Seki, Yuta Kikuchi, Tomoya Hayata, Seiji Yunoki

Complex quantum many-body dynamics spread initially localized quantum information across the entire system. Information scrambling refers to such a process, whose simulation is one of the promising applications of quantum computing. We demonstrate the Hayden-Preskill recovery protocol and the interferometric protocol for calculating out-of-time-ordered corre

Ryo Takahashi

We establish two expansions of the Potts model partition function of a graph. One is along the deletions of a graph, a rewritten formula given in Biggs (1977). The other is along the contractions of a graph. Then, we specialize the partition function to the chromatic or flow polynomial by the M\"obius inversion formula, and prove two known equations of the t

Marco Spanghero, Filip Geib, Ronny Panier, Panos Papadimitratos

Global Navigation Satellite System (GNSS) receivers provide ubiquitous and precise position, navigation, and time (PNT) to a wide gamut of civilian and tactical infrastructures and devices. Due to the low GNSS received signal power, even low-power radiofrequency interference (RFI) sources are a serious threat to the GNSS integrity and availability. Nonethele

Qing-Zeng Yan, Ji Yang, Yang Su, Yan Sun

In this work, we report a study on the relationship between flux and intensity for molecular clouds. Our analysis is established on high-quality CO images from the Milky Way Imaging Scroll Painting (MWISP) project. The new flux-intensity relation characterizes the flux variation of molecular clouds above specific intensity levels. We found that the flux-inte

Elena Merdjanovska, Ansar Aynetdinov, Alan Akbik

Available training data for named entity recognition (NER) often contains a significant percentage of incorrect labels for entity types and entity boundaries. Such label noise poses challenges for supervised learning and may significantly deteriorate model quality. To address this, prior work proposed various noise-robust learning approaches capable of learn

Jing Xu, Zhan Wang, Fan Yang, Ning Kang

Congestion control plays a pivotal role in large-scale data centers, facilitating ultra-low latency, high bandwidth, and optimal utilization. Even with the deployment of data center congestion control mechanisms such as DCQCN and HPCC, these algorithms often respond to congestion sluggishly. This sluggishness is primarily due to the slow notification of cong

M Rosalin, K. A. Irshad, Boby Joseph, Prachi Telang

The pyrochlore iridates, A$_2$Ir$_2$O$_7$, show a wide variety of structural, electronic, and magnetic properties controlled by the interplay of different exchange interactions, which can be tuned by external pressure. In this work, we report pressure-induced phase transitions at ambient temperature using synchrotron-based X-ray diffraction (up to ~ 20 GPa)

Dionysia Danai Brilli, Evangelos Georgaras, Stefania Tsilivaki, Nikos Melanitis

Assistive technologies for the visually impaired have evolved to facilitate interaction with a complex and dynamic world. In this paper, we introduce AIris, an AI-powered wearable device that provides environmental awareness and interaction capabilities to visually impaired users. AIris combines a sophisticated camera mounted on eyewear with a natural langua

Marco Becattini, Davide Borsatti, Armir Bujari, Laura Carnevali

Digital twin networks (DTNs) serve as an emerging facilitator in the industrial networking sector, enabling the management of new classes of services, which require tailored support for improved resource utilization, low latencies and accurate data fidelity. In this paper, we explore the intersection between theoretical recommendations and practical implicat

Yuchen Guo, Martin Shepperd, Ning Li

Context: Software defect prediction utilizes historical data to direct software quality assurance resources to potentially problematic components. Effort-aware (EA) defect prediction prioritizes more bug-like components by taking cost-effectiveness into account. In other words, it is a ranking problem, however, existing ranking strategies based on classifica

Andrii Tytarenko

Care-giving and assistive robotics, driven by advancements in AI, offer promising solutions to meet the growing demand for care, particularly in the context of increasing numbers of individuals requiring assistance. This creates a pressing need for efficient and safe assistive devices, particularly in light of heightened demand due to war-related injuries. W

D. Zhu, F. L. Zhang, J. L. Chen

We study the dissipative dynamics of interferometric power as a discordlike measure in Markovian environments, such as dephasing, depolarizing, and generalized amplitude damping. Moreover, we compare the dynamics of interferometric power and entanglement by choosing proper initial conditions. Our study shows that in all cases where the sudden death of entang

Haoyu Ren, Xue Li, Darko Anicic, Thomas A. Runkler

Recent advances in Tiny Machine Learning (TinyML) empower low-footprint embedded devices for real-time on-device Machine Learning. While many acknowledge the potential benefits of TinyML, its practical implementation presents unique challenges. This study aims to bridge the gap between prototyping single TinyML models and developing reliable TinyML systems i

Hakan Yekta Yatbaz, Mehrdad Dianati, Konstantinos Koufos, Roger Woodman

The deep neural network (DNN) models are widely used for object detection in automated driving systems (ADS). Yet, such models are prone to errors which can have serious safety implications. Introspection and self-assessment models that aim to detect such errors are therefore of paramount importance for the safe deployment of ADS. Current research on this to

Leon Gerard, Michael Scherbela, Halvard Sutterud, Matthew Foulkes

Deep-Learning-based Variational Monte Carlo (DL-VMC) has recently emerged as a highly accurate approach for finding approximate solutions to the many-electron Schr\"odinger equation. Despite its favorable scaling with the number of electrons, $\mathcal{O}(n_\text{el}^{4})$, the practical value of DL-VMC is limited by the high cost of optimizing the neural ne

Tommaso Cremaschi, Viola Giovannini, Jean-Marc Schlenker

We provide conditions under which a Riemann surface $X$ is the asymptotic boundary of a convex co-compact hyperbolic manifold, homeomorphic to a handlebody, of negative renormalized volume. We prove that this is the case when there are on $X$ enough closed curves of short enough hyperbolic length.

Olivia Griffin, Jerry Sun

In this paper, we consider pluractional markers in Kaqchikel, Karuk, and Yurok. Like Balinese, each of these languages marks one type of pluractionality via reduplication, and a different type of pluractionality via non-reduplicative affixation. This paper serves as a proof-of-concept for applying model-theoretic approaches to language as a lens that can hel

Khac-Hoang Ngo, Johan Östman, Alexandre Graell i Amat

Local mutual-information privacy (LMIP) is a privacy notion that aims to quantify the reduction of uncertainty about the input data when the output of a privacy-preserving mechanism is revealed. We study the relation of LMIP with local differential privacy (LDP), the de facto standard notion of privacy in context-independent (CI) scenarios, and with local in

Dehong Kong, Siyuan Liang, Wenqi Ren

Object detection techniques for Unmanned Aerial Vehicles (UAVs) rely on Deep Neural Networks (DNNs), which are vulnerable to adversarial attacks. Nonetheless, adversarial patches generated by existing algorithms in the UAV domain pay very little attention to the naturalness of adversarial patches. Moreover, imposing constraints directly on adversarial patche

Congjia Chen, Xiaoyu Jia, Yanhong Zheng, Yufu Qu

Point cloud registration is a fundamental task for estimating rigid transformations between point clouds. Previous studies have used geometric information for extracting features, matching and estimating transformation. Recently, owing to the advancement of RGB-D sensors, researchers have attempted to combine visual and geometric information to improve regis

Ryoi Ohashi, Jushin Tei, Yukio Tanaka, Takeshi Mizushima

Identifying the superconducting gap symmetry and topological signatures in the putative spin-triplet superconductor $\text{UTe}_2$ is an important issue. Especially, a smoking-gun detection scheme for Majorana surface states hallmarking topological superconductivity in $\text{UTe}_2$ is still lacking. In this study, we examine the surface spin susceptibility

Zhong-Xia Shang, Zi-Han Chen, Cai-Sheng Cheng

Fighting against noise is crucial for NISQ devices to demonstrate practical quantum applications. In this work, we give a new paradigm of quantum error mitigation based on the vectorization of density matrices. Different from the ideas of existing quantum error mitigation methods that try to distill noiseless information from noisy quantum states, our propos

Satoshi Masuya

The present study explores a problem that can be resolved by employing the notion of a partially defined cooperative game, yet cannot by using a restricted game. The following situation is considered: First, it is assumed that the worth of the grand and singleton coalitions are known. It takes some amount of costs to obtain worth of unknown coalitions. If it

Camelia Oprea, Mike Grüne, Mateusz Buglowski, Lena Olivier

With the digitalization of health care systems, artificial intelligence becomes more present in medicine. Especially machine learning shows great potential for complex tasks such as time series classification, usually at the cost of transparency and comprehensibility. This leads to a lack of trust by humans and thus hinders its active usage. Explainable arti

Paolo Fittipaldi, Kentaro Teramoto, Naphan Benchasattabuse, Michal Hajdušek

Satellite quantum communication is a promising way to build long distance quantum links, making it an essential complement to optical fiber for quantum internetworking beyond metropolitan scales. A satellite point to point optical link differs from the more common fiber links in many ways, both quantitative (higher latency, strong losses) and qualitative (no

David Coudert, Mónika Csikós, Guillaume Ducoffe, Laurent Viennot

For any set system $H=(V,R), \ R \subseteq 2^V$, a subset $S \subseteq V$ is called \emph{shattered} if every $S' \subseteq S$ results from the intersection of $S$ with some set in $\R$. The \emph{VC-dimension} of $H$ is the size of a largest shattered set in $V$. In this paper, we focus on the problem of computing the VC-dimension of graphs. In particular,

Muhammad Nadeem, Ahmad F. Taha

This paper deals with the joint reduction of the number of dynamic and algebraic states of a nonlinear differential-algebraic equation (NDAE) model of a power network. The dynamic states depict the internal states of generators, loads, renewables, whereas the algebraic ones define network states such as voltages and phase angles. In the current literature of

Panyut Sriwirote, Wei Qi Leong, Charin Polpanumas, Santhawat Thanyawong

Automatic dependency parsing of Thai sentences has been underexplored, as evidenced by the lack of large Thai dependency treebanks with complete dependency structures and the lack of a published systematic evaluation of state-of-the-art models, especially transformer-based parsers. In this work, we address these problems by introducing Thai Universal Depende

Giovanni Interdonato, Stefano Buzzi

We investigate the non-orthogonal coexistence between the ultra-reliable low-latency communication (URLLC) and the enhanced mobile broadband (eMBB) in the downlink of a cell-free massive multiple-input multiple-output (MIMO) system. We provide a unified information-theoretic framework that combines a finite-blocklength analysis of the URLLC error probability

Quentin Bramas, Jean-Romain Luttringer, Pascal Mérindol

Segment routing (SR) offers precise control over the paths taken: it specifies a list of detours, called segments, in IP packets. However, the number of detours that can be specified is limited by the hardware. When calculating segment lists, it is therefore necessary to limit their size. Although solutions have been proposed for calculating these lists, the

Manon Ballu, Bastien Mirmand, Thomas Badr, Hélène Perrin

We report on an experimental platform based on an atom chip encompassing a coplanar waveguide which enables the manipulation of a quantum gas of sodium atoms with strong microwave fields. We describe the production with this setup of a very elongated degenerate quantum gas with typically 10^6 atoms, that can be prepared all along the cross-over from the thre

Christoph Netsch, Till Schöpe, Benedikt Schindele, Joyam Jayakumar

Machine Learning (ML) based prognostics and health monitoring (PHM) tools provide new opportunities for manufacturers to operate and maintain their equipment in a risk-optimized manner and utilize it more sustainably along its lifecycle. Yet, in most industrial settings, data is often limited in quantity, and its quality can be inconsistent - both critical f

Fengchuang Xing, Xiaowen Shi, Yuan-Gen Wang, Chunsheng Yang

Unveiling the real appearance of retouched faces to prevent malicious users from deceptive advertising and economic fraud has been an increasing concern in the era of digital economics. This article makes the first attempt to investigate the face retouching reversal (FRR) problem. We first collect an FRR dataset, named deepFRR, which contains 50,000 StyleGAN

Jean-Baptiste Quélène, Didier Pohl, David Bitauld, Karim Hassan

Fiber Bragg Grating (FBG) sensors offer multiple benefits in comparison with electronic sensors due to their compactness, electromagnetic immunity as well as their resistance to harsh environments and their multiplexing capabilities. Structural Health Monitoring (SHM) is one of the various potential industrial applications that could take full advantage of t

Ziwei Zhao, Fake Lin, Xi Zhu, Zhi Zheng

Last year has witnessed the considerable interest of Large Language Models (LLMs) for their potential applications in recommender systems, which may mitigate the persistent issue of data sparsity. Though large efforts have been made for user-item graph augmentation with better graph-based recommendation performance, they may fail to deal with the dynamic gra

Florence Débarre, Michael Worobey

In a short text published as Letter to the Editor of the Journal of the Royal Statistical Society Series A, Weissman (2024) argues that the finding that early COVID-19 cases without an ascertained link to Wuhan's Huanan Seafood Wholesale market resided on average closer to the market than cases epidemiologically linked to it, reveals "major proximity ascerta

David Senjaya, Supakchai Ponglertsakul

In this letter, we present a novel exact scalar quasibound states solutions in the extremal Reissner-Norstr\"om black hole background. We start with the construction of the governing covariant relativistic scalar field equation, the Klein-Gordon equation in the extremal Reissner-Norstr\"om black hole background and applying the separation of variables anzat.

Francesco Trainotti, Steven W. B. Klaassen, Tomaz Bregar, Daniel J. Rixen

High quality measurements are paramount to a successful application of experimental techniques in structural dynamics. The presence of noise and disturbances can significantly distort the information stored in the data and, if not adequately treated, may result in erroneous findings and misleading predictions. A common technique to filter out noise relies on

Riccardo Ferrazzoli, Dmitry Prokhorov, Niccolò Bucciantini, Patrick Slane

Supernova remnants (SNRs) provide insights into cosmic-ray acceleration and magnetic field dynamics at shock fronts. Recent X-ray polarimetric measurements by the Imaging X-ray Polarimetry Explorer (IXPE) have revealed radial magnetic fields near particle acceleration sites in young SNRs, including Cassiopeia A, Tycho, and SN 1006. We present here the spatia

Zhaocong Liu, Jie Huang

The problem of the distributed Nash equilibrium seeking for aggregative games has been studied over strongly connected and weight-balanced static networks and every time strongly connected and weight-balanced switching networks. In this paper, we further study the same problem over jointly connected and weight-balanced networks. The existing approaches criti

G. A. Bobkov, K. A. Bokai, M. M. Otrokov, A. M. Bobkov

The discovery of 2D materials opens up unprecedented opportunities to design new materials with specified properties. In many cases, the design guiding principle is based on one or another proximity effect, i.e. the nanoscale-penetration of electronic correlations from one material to another. In a few layer van der Waals (vdW) heterostructures the proximity

Marta Ewa Lech, Sune Lehmann, Jonas L. Juul

Since the creation of the Billboard Hot 100 music chart in 1958, the chart has been a window into the music consumption of Americans. Which songs succeed on the chart is decided by consumption volumes, which can be affected by consumer music taste, and other factors such as advertisement budgets, airplay time, the specifics of ranking algorithms, and more. S

Yiqun Duan, Xianda Guo, Zheng Zhu, Zhen Wang

Current multi-modality driving frameworks normally fuse representation by utilizing attention between single-modality branches. However, the existing networks still suppress the driving performance as the Image and LiDAR branches are independent and lack a unified observation representation. Thus, this paper proposes MaskFuser, which tokenizes various modali

Michael Kiermaier, Jonathan Mannaert, Alfred Wassermann

In 1982, Cameron and Liebler investigated certain "special sets of lines" in PG(3,q), and gave several equivalent characterizations. Due to their interesting geometric and algebraic properties, these "Cameron-Liebler line classes" got much attention. Several generalizations and variants have been considered in the literature, the main directions being a vari

Lazaro Janier Gonzalez-Soler, Maciej Salwowski, Christian Rathgeb, Daniel Fischer

Tattoos have been used effectively as soft biometrics to assist law enforcement in the identification of offenders and victims, as they contain discriminative information, and are a useful indicator to locate members of a criminal gang or organisation. Due to various privacy issues in the acquisition of images containing tattoos, only a limited number of dat

Valerio Belcamino, Miwa Takase, Mariya Kilina, Alessandro Carfì

This work aims to tackle the intent recognition problem in Human-Robot Collaborative assembly scenarios. Precisely, we consider an interactive assembly of a wooden stool where the robot fetches the pieces in the correct order and the human builds the parts following the instruction manual. The intent recognition is limited to the idle state estimation and it

Juhwan Lee, Jisu Kim

This study addresses the hallucination problem in large language models (LLMs). We adopted Retrieval-Augmented Generation(RAG) (Lewis et al., 2020), a technique that involves embedding relevant information in the prompt to obtain accurate answers. However, RAG also faced inherent issues in retrieving correct information. To address this, we employed the Dens

Qingyang Mo, Riyi Zheng, Cuicui Lu, Xueqin Huang

Band crossing points, such as Weyl and Dirac points, play a crucial role in the topological classification of materials and guide the exploration of exotic topological phases. The Berry dipole, a three-dimensional band crossing point beyond the Chern class, hosts a dipolar Berry curvature field and gives rise to numerous nontrivial quantum geometric effects.

Gaoyuan Cheng, Xianxin Song, Zhonghao Lyu, Jie Xu

This paper studies the exploitation of networked integrated sensing and communications (ISAC) to support low-altitude economy (LAE), in which a set of networked ground base stations (GBSs) transmit wireless signals to cooperatively communicate with multiple authorized unmanned aerial vehicles (UAVs) and concurrently use the echo signals to detect the invasio

Marius-F. Danca, Guanrong Chen

In this paper, the Parameter Switching (PS) algorithm is used to approximate numerically attractors of a Hopfield Neural Network (HNN) system. The PS algorithm is a convergent scheme designed for approximating attractors of an autonomous nonlinear system, depending linearly on a real parameter. Aided by the PS algorithm, it is shown that every attractor of t

Oscar Randal-Williams

We prove a new kind of homological stability theorem for automorphism groups of finitely-generated projective modules over Dedekind domains, which takes into account all possible stabilisation maps between these, rather than only stabilisation by the free module of rank 1. We show the same kind of stability holds for Clausen and Jansen's reductive Borel--Ser

Mikuláš Zindulka

We consider sums of Hurwitz class numbers of the type $\sum_{t \equiv m \pmod{M}}H(4p-t^2)$, where $M$ is composite. For $M=6$ and $8$, we show that these sums can be expressed in terms of coefficients of CM cusp forms. This leads to explicit formulas which depend on the expression of $p$ in the form $x^2+ny^2$.

Imteaz Rahaman, Hunter D. Ellis, Kathy Anderson, Michael A. Scarpulla

Rutile Germanium Dioxide (GeO2) has been recently theoretically identified as an ultrawide bandgap (UWBG) semiconductor with bandgap 4.68 eV similar to Ga2O3 but having bipolar dopability and ~2x higher electron mobility, Baliga figure of merit (BFOM) and thermal conductivity than Ga2O3. Bulk crystal growth is rapidly moving towards making large sized native

Asma Mezrag, Zoltan Muzsnay

In this paper, we investigate the holonomy group of $n$-dimensional projective Finsler metrics of constant curvature. We establish that in the spherically symmetric case, the holonomy group is maximal, and for a simply connected manifold it is isomorphic to $Diff_o({\mathbb S^{n-1}})$, the connected component of the identity of the group of smooth diffeomorp

Yuki Yada, Hayato Yamana

Personalized news recommendations are essential for online news platforms to assist users in discovering news articles that match their interests from a vast amount of online content. Appropriately encoded content features, such as text, categories, and images, are essential for recommendations. Among these features, news categories, such as tv-golden-globe,

Andrey V. Galichin, Mikhail Pautov, Alexey Zhavoronkin, Oleg Y. Rogov

While Deep Neural Networks (DNNs) have demonstrated remarkable performance in tasks related to perception and control, there are still several unresolved concerns regarding the privacy of their training data, particularly in the context of vulnerability to Membership Inference Attacks (MIAs). In this paper, we explore a connection between the susceptibility

Chee Sien Wong, Benediktus Madika, Jiwon Yeom, Youngwoo Choi

Here, we present an EfficientNet-B0-based model to directly predict multiple properties of lithium manganese nickel oxides (LMNO) using their crystal structure images. The model is supposed to predict the energy above the convex hull, bandgap energy, crystal systems, and crystal space groups of LMNOs. In the last layer of the model, a linear function is used

Bjørn Pedersen, Maisha Islam, Doris Tove Kristoffersen, Lars Ailo Bongo

This paper investigates the feasibility of using pre-trained generative Large Language Models (LLMs) to automate the assignment of ICD-10 codes to historical causes of death. Due to the complex narratives often found in historical causes of death, this task has traditionally been manually performed by coding experts. We evaluate the ability of GPT-3.5, GPT-4

Jiejia Liu, Sifan Wang, Hai Jin, Qian Wang

Diffuse X-ray Explorer (DIXE) is a proposed X-ray spectroscopic survey experiment for the China Space Station. Its detector assembly (DA) contains the transition edge sensor (TES) microcalorimeter and readout electronics based on the superconducting quantum interference device (SQUID) on the cold stage. The cold stage is thermally connected to the ADR stage,

Siyu Zou, Ting Li, Jiandong Zhu

This paper investigates the synchronization problems for general high-dimensional linear networks over finite fields. By using the technique of linear transformations and invariant subspaces for linear spaces over finite fields, several necessary and sufficient conditions for the synchronization of high-dimensional linear networks over finite fields are prop

Varul Srivastava, Sujit Gujar

Distributed consensus protocols reach agreement among $n$ players in the presence of $f$ adversaries; different protocols support different values of $f$. Existing works study this problem for different adversary types (captured by threat models). There are three primary threat models: (i) Crash fault tolerance (CFT), (ii) Byzantine fault tolerance (BFT), an

Jia Hu, Nuoheng Zhang, Haoran Wang, Tenglong Jiang

Longitudinal-only platooning methods are facing great challenges on running mobility, since they may be impeded by slow-moving vehicles from time to time. To address this issue, this paper proposes a vehicles swarming method coupled both longitudinal and lateral cooperation. The proposed method bears the following contributions: i) enhancing driving mobility

Jia Hu, Shuhan Wang, Yiming Zhang, Haoran Wang

Human-Lead Cooperative Adaptive Cruise Control (HL-CACC) is regarded as a promising vehicle platooning technology in real-world implementation. By utilizing a Human-driven Vehicle (HV) as the platoon leader, HL-CACC reduces the cost and enhances the reliability of perception and decision-making. However, state-of-the-art HL-CACC technology still has a great

Jiongjie Wang, Jiang Xiao

The S-parameter $S_{21}$ is widely used to characterize the resonant properties of various systems. However, we demonstrates that the reliability of $S_{21}$ as a true indicator of system resonances depends heavily on the specific probing setup employed. While point-probe or weak probes preserve the integrity of the $S_{21}$ spectrum and accurately reflect s

Yumeng Yin, Aurelien Trichet, Jiangrui Qian, Jason Smith

Shape measurement of rod-shaped particles in fluids is an outstanding challenge with applications in characterising synthetic functional nanoparticles and in early warning detection of rod-shaped pathogens in water supplies. However, it is challenging to achieve accurate and real-time measurements at a single particle scale in solution with existing methods.

Mingyue Lei, Haoran Wang, Lu Xiong, Jaehyun

Cooperative and Adaptive Cruise Control (CACC) is widely focused to enhance driving fuel-efficiency by maintaining a close following gap. The ecology of CACC could be further enhanced by adapting to the rolling terrain. However, current studies cannot ensure both planning optimality and computational efficiency. Firstly, current studies are mostly formulated