April 2024 arXiv papers — page 72

Edward Bryden, Lizhi Chen

We define a flexible class of Riemmanian metrics on the three-torus. Then, using Stern's inequality relating scalar curvature to harmonic one-forms, we show that any sequence of metrics in this family whose negative part of the scalar curvature tends to zero in $L^2$ norm has a subsequence which converges to some flat metric on the three-torus in the sense o

Kang Zeng, Hao Shi, Jiacheng Lin, Siyu Li

LiDAR-based Moving Object Segmentation (MOS) aims to locate and segment moving objects in point clouds of the current scan using motion information from previous scans. Despite the promising results achieved by previous MOS methods, several key issues, such as the weak coupling of temporal and spatial information, still need further study. In this paper, we

Marco Caponigro, Arianna Vicari

We consider the problem of transporting \nota{one probability measure into another through} the flow of a given driftless control-affine system. Under suitable regularity conditions, the controllability of the system by means of open-loop controls is a sufficient condition for the existence of time-varying feedback controls such that the $1$-time flow of the

Marco Merboldt, Michael Schüler, David Schmitt, Jan Philipp Bange

Recent advances in the field of condensed-matter physics have unlocked the potential to realize and control emergent material phases that do not exist in thermal equilibrium. One of the most promising concepts in this regard is Floquet engineering, the coherent dressing of matter via time-periodic perturbations. However, the broad applicability of Floquet en

Pedro Lauand, Davide Poderini, Rafael Rabelo, Rafael Chaves

Bell's theorem prompts us with a fundamental inquiry: what is the simplest scenario leading to the incompatibility between quantum correlations and the classical theory of causality? Here we demonstrate that quantum non-classicality is possible in a network consisting of only three dichotomic variables, without the need of the locality assumption neither ext

Wei W. Xing, Weijian Fan, Zhuohua Liu, Yuan Yao

Automatic transistor sizing in circuit design continues to be a formidable challenge. Despite that Bayesian optimization (BO) has achieved significant success, it is circuit-specific, limiting the accumulation and transfer of design knowledge for broader applications. This paper proposes (1) efficient automatic kernel construction, (2) the first transfer lea

Sergio Blanes, Nikita Kopylov, Muaz Seydaoğlu

This work presents a new algorithm to compute the matrix exponential within a given tolerance. Combined with the scaling and squaring procedure, the algorithm incorporates Taylor, partitioned and classical Pad\'e methods shown to be superior in performance to the approximants used in state-of-the-art software. The algorithm computes matrix--matrix products a

Nacime Bouziani, Shubhi Tyagi, Joseph Fisher, Jens Lehmann

Extracting structured information from unstructured text is critical for many downstream NLP applications and is traditionally achieved by closed information extraction (cIE). However, existing approaches for cIE suffer from two limitations: (i) they are often pipelines which makes them prone to error propagation, and/or (ii) they are restricted to sentence

Takeshi Odagawa, Sota Yamamoto, Chaoliang Zhang, Kazuki Koyama

We investigate the excitonic species in WS$_{2}$ monolayers transferred onto III-V semiconductor substrates with different surface treatments. When the III-V substrates were covered with amorphous native oxides, negatively charged excitons dominate the spectral weight in low-temperature near-resonance photoluminescence (PL) measurements. However, when the na

Lorenzo Miretti, Sławomir Stańczak

It is generally believed that downlink cell-free networks perform best under centralized implementations where the local channel state information (CSI) acquired by the access-points (AP) is forwarded to one or more central processing units (CPU) for the computation of the joint precoders based on global CSI. However, mostly due to limited fronthaul capabili

Michal Staniaszek, Tobit Flatscher, Joseph Rowell, Hanlin Niu

We give an overview of AutoInspect, a ROS-based software system for robust and extensible mission-level autonomy. Over the past three years AutoInspect has been deployed in a variety of environments, including at a mine, a chemical plant, a mock oil rig, decommissioned nuclear power plants, and a fusion reactor for durations ranging from hours to weeks. The

Myrna C. Silva, Mahtab Dahaghin, Matteo Toso, Alessio Del Bue

We introduce Contrastive Gaussian Clustering, a novel approach capable of provide segmentation masks from any viewpoint and of enabling 3D segmentation of the scene. Recent works in novel-view synthesis have shown how to model the appearance of a scene via a cloud of 3D Gaussians, and how to generate accurate images from a given viewpoint by projecting on it

Marco Zambianco, Claudio Facchinetti, Domenico Siracusa

Cyber deception allows compensating the late response of defenders countermeasures to the ever evolving tactics, techniques, and procedures (TTPs) of attackers. This proactive defense strategy employs decoys resembling legitimate system components to lure stealthy attackers within the defender environment, slowing and/or denying the accomplishment of their g

Fengyi Fu, Shancheng Fang, Weidong Chen, Zhendong Mao

Automatic live video commenting is with increasing attention due to its significance in narration generation, topic explanation, etc. However, the diverse sentiment consideration of the generated comments is missing from the current methods. Sentimental factors are critical in interactive commenting, and lack of research so far. Thus, in this paper, we propo

Akmal Ferdiyan, Reinard Primulando, Qidir Maulana Binu Soesanto, Bayu Dirgantara

We discuss a collider probe of a dark sector model in which the dark matter is charged under a new, hidden $U(1)$ gauge group. In particular, we look for the so-called Darkstrahlung process, in which the final states dark matter radiates a new $Z'$ gauge boson and it manifests as dilepton resonances. This work emphasizes the potential of dilepton final state

Pedro Umpierrez, Victor Arana, Sergio Sancho

A new simulation technique to obtain the synchronized steady-state solutions existing in coupled oscillator systems is presented. The technique departs from a semi-analytical formulation presented in previous works. It extends the model of the admittance function describing each individual oscillator to a piecewise linear one. This provides a global formulat

William Giarè

I review and discuss the possible implications for inflation resulting from considering new physics in light of the Hubble tension. My study is motivated by a simple argument that the constraints on inflationary parameters, most typically the spectral index $n_s$, depend to some extent on the cosmological framework. To avoid broadening the uncertainties resu

Nick Galanis

In the evolving landscape of Federated Learning (FL), a new type of attacks concerns the research community, namely Data Poisoning Attacks, which threaten the model integrity by maliciously altering training data. This paper introduces a novel defensive framework focused on the strategic elimination of adversarial users within a federated model. We detect th

Wenkai Liu, Tao Guan, Bin Zhu, Lili Ju

In the domain of 3D scene representation, 3D Gaussian Splatting (3DGS) has emerged as a pivotal technology. However, its application to large-scale, high-resolution scenes (exceeding 4k$\times$4k pixels) is hindered by the excessive computational requirements for managing a large number of Gaussians. Addressing this, we introduce 'EfficientGS', an advanced a

Javier López-de-la-Cruz, Alexandre N. Oliveira-Sousa

We investigate SIR models with vital dynamics, reinfection, and randomness at the transmission coefficient and recruitment rate. Initially, we conduct an extensive analysis of the autonomous scenario, covering aspects such as local and global well-posedness, the existence and internal structure of attractors, and the presence of gradient dynamics. Subsequent

Dion Linssen, Jim Shih, Morgan MacLeod, Antonija Oklopčić

Atmospheric escape is thought to significantly influence the evolution of exoplanets, especially for sub-Jupiter planets on short orbital periods. Theoretical models predict that hydrodynamic escape could erode the atmospheres of such gaseous planets, leaving only a rocky core. Deriving atmospheric mass-loss rates from observations is necessary to check thes

Ruohan Guo, Weixiang Shen

Accurate state of power (SOP) estimation is of great importance for lithium-ion batteries in safety-critical and power-intensive applications for electric vehicles. This review article delves deeply into the entire development flow of current SOP estimation technology, offering a systematic breakdown of all key aspects with their recent advancements. First,

Zhuo Yao, Wei Wang

Multi-agent pathfinding (MAPF) holds significant utility within autonomous systems, however, the calculation and memory space required for multi-agent path finding (MAPF) grows exponentially as the number of agents increases. This often results in some MAPF instances being unsolvable under limited computational resources and memory space, thereby limiting th

Chetan Arora, Tomas Herda, Verena Homm

Test scenarios are specific instances of test cases that describe actions to validate a particular software functionality. By outlining the conditions under which the software operates and the expected outcomes, test scenarios ensure that the software functionality is tested in an integrated manner. Test scenarios are crucial for systematically testing an ap

Lukas Puts, Daan Lenstra, Kevin Williams, Weiming Yao

Using a rate-equation model we numerically evaluate the carrier concentration and photon number in an integrated two-section semiconductor laser, and analyse its dynamics in three-dimensional phase space. The simulation comprises compact model descriptions extracted from a commercially-available generic InP technology platform, allowing us to model an applie

Chaehyeon Song, Sungho Yoon, Minhyeok Heo, Ayoung Kim

Vision-based ego-lane inference using High-Definition (HD) maps is essential in autonomous driving and advanced driver assistance systems. The traditional approach necessitates well-calibrated cameras, which confines variation of camera configuration, as the algorithm relies on intrinsic and extrinsic calibration. In this paper, we propose a learning-based e

Ruohan Guo, Feng Wang, Cungang Hu, Weixiang Shen

Retired batteries (RBs) for second-life applications offer promising economic and environmental benefits. However, accurate and efficient sorting of RBs with discrepant characteristics persists as a pressing challenge. In this study, we introduce a data driven based electrode aging assessment approach to address this concern. To this end, a number of 15 feat

Valentin Kapitány, Areeba Fatima, Vytautas Zickus, Jamie Whitelaw

Fluorescence lifetime imaging microscopy (FLIM) provides detailed information about molecular interactions and biological processes. A major bottleneck for FLIM is image resolution at high acquisition speeds, due to the engineering and signal-processing limitations of time-resolved imaging technology. Here we present single-sample image-fusion upsampling (Si

Xinlong Ji, Fangneng Zhan, Shijian Lu, Shi-Sheng Huang

Accurately estimating scene lighting is critical for applications such as mixed reality. Existing works estimate illumination by generating illumination maps or regressing illumination parameters. However, the method of generating illumination maps has poor generalization performance and parametric models such as Spherical Harmonic (SH) and Spherical Gaussia

Xu Wang, Fucong Chen, Zefeng Lin, Changhong Yuan

We report our progress in the high-temperature superconductor (HTS) Josephson junction fabrication process founded on using a focused helium ion beam damaging technique and discuss the expected device performance attainable with the HTS multi-junction device technology. Both the achievable high value of characteristic voltage $V_c=I_cR_N$ of Josephson juncti

Wenxuan Zhang, Youssef Mohamed, Bernard Ghanem, Philip H. S. Torr

We propose and study a realistic Continual Learning (CL) setting where learning algorithms are granted a restricted computational budget per time step while training. We apply this setting to large-scale semi-supervised Continual Learning scenarios with sparse label rates. Previous proficient CL methods perform very poorly in this challenging setting. Overfi

Haobai Xue

While computer modeling and simulation are crucial for understanding scientometrics, their practical use in literature remains somewhat limited. In this study, we establish a joint coauthorship and citation network using preferential attachment. As papers get published, we update the coauthorship network based on each paper's author list, representing the co

Mingshang Hu, Renxing Li

In this paper, we introduce $ G $-Bessel processes for a class of $ d $-dimensional $ G $-Brownian motions. Under the condition of dimensionality $ d $, we obtain that the $ G $-Bessel process is the solution of the stochastic differential equation. Furthermore, under the stricter condition of dimensionality, we establish the existence and uniqueness of a so

Dániel Hegedűs, Vince Grolmusz

The correlations of several fundamental properties of human brain connections are investigated in a consensus connectome, constructed from 1064 braingraphs, each on 1015 vertices, corresponding to 1015 anatomical brain areas. The properties examined include the edge length, the fiber number, or edge width, meaning the number of discovered axon bundles formin

Benjamin Fresz, Elena Dubovitskaya, Danilo Brajovic, Marco Huber

This paper investigates the relationship between law and eXplainable Artificial Intelligence (XAI). While there is much discussion about the AI Act, for which the trilogue of the European Parliament, Council and Commission recently concluded, other areas of law seem underexplored. This paper focuses on European (and in part German) law, although with interna

Kento Iida, Andreas Dechant, Takuma Akimoto

Giant diffusion, where the diffusion coefficient of a Brownian particle in a periodic potential with an external force is significantly enhanced by the external force, is a non-trivial non-equilibrium phenomenon. We propose a simple stochastic model of giant diffusion, which is based on a biased continuous-time random walk (CTRW) with flight time. By introdu

Takuya Sera, Izumi Kuwata, Yuki Taya, Noritaka Shimura

This study explores a new method in food development by utilizing AI including generative AI, aiming to craft products that delight the senses and resonate with consumers' emotions. The food ingredient recommendation approach used in this study can be considered as a form of multimodal generation in a broad sense, as it takes text as input and outputs food i

Yi Guo, Fanliu Kong, Xiaoyang Li, Hui Li

Quantization emerges as one of the most promising compression technologies for deploying efficient large models for various real time application in recent years. Considering that the storage and IO of weights take up the vast majority of the overhead inside a large model, weight only quantization can lead to large gains. However, existing quantization schem

C. Lee, T. H. Yoon

We consider a two-mode stabilized squeezed coherent state (SSCS) of light and introduce the $\Pi_{\rm N}$ indicator, a novel measure for characterizing nonclassicality in the resulting EPR-entangled state. Unlike existing methods based on Cauchy-Schwarz or Murihead inequalities, $\Pi_{\rm N}$ leverages analytical solutions to the quantum Langevin equations t

Quentin Gontier, Charles Wiame, François Horlin, Christo Tsigros

This paper focuses on the meta distribution of electromagnetic field exposure (EMFE) experienced by a passive user in a cellular network implementing dynamic beamforming. The meta distribution serves as a valuable tool for extracting fine-grained insights into statistics of individual passive user EMFE across the network. A comprehensive stochastic geometry

Joaquin Cavieres, Paula Moraga, Cole C. Monnahan

Spatial modelling often uses Gaussian random fields to capture the stochastic nature of studied phenomena. However, this approach incurs significant computational burdens (O(n3)), primarily due to covariance matrix computations. In this study, we propose to use a low-rank approximation of a thin plate spline as a spatial random effect in Bayesian spatial mod

Pak-Yeung Chan, Man-Chun Lee, Luke T. Peachey

Motivated by recent work of Deruelle-Schulze-Simon, we study complete weakly PIC1 Ricci flows with Euclidean volume growth coming out of metric cones. We show that such a Ricci flow must be an expanding gradient Ricci soliton, and as a consequence, any metric cone at infinity of a complete weakly PIC1 K\"ahler manifold with Euclidean volume growth is biholom

Qiang He, Tianyi Zhou, Meng Fang, Setareh Maghsudi

Representation rank is an important concept for understanding the role of Neural Networks (NNs) in Deep Reinforcement learning (DRL), which measures the expressive capacity of value networks. Existing studies focus on unboundedly maximizing this rank; nevertheless, that approach would introduce overly complex models in the learning, thus undermining performa

Wenhao Huang, Zhouhong Gu, Chenghao Peng, Zhixu Li

Web scraping is a powerful technique that extracts data from websites, enabling automated data collection, enhancing data analysis capabilities, and minimizing manual data entry efforts. Existing methods, wrappers-based methods suffer from limited adaptability and scalability when faced with a new website, while language agents, empowered by large language m

Lie-Liang Yang

User-centric cell-free (UCCF) wireless networks have a range of distinguished characteristics, which can be exploited for meeting some challenges that the conventional cellular systems are hard to. This chapter is devoted to delivering the fundamentals of wireless communications in UCCF systems, including channel modeling and estimation, uplink (UL) detectio

Alexander Gall, Anja Heim, Patrick Weinberger, Bernhard Fröhler

This work introduces a novel Augmented Reality (AR) approach to visualize material data alongside real objects in order to facilitate detailed material analyses based on spatial non-destructive testing (NDT) data as generated in X-ray computed tomography (XCT) imaging. For this purpose, we introduce a framework that leverages the potential of AR devices, vis

Andrew Ni, Lee Spector

The Traveling Thief Problem is an NP-hard combination of the well known traveling salesman and knapsack packing problems. In this paper, we use symbolic regression to learn useful features of near-optimal packing plans, which we then use to design efficient metaheuristic genetic algorithms for the traveling thief algorithm. By using symbolic regression again

Hesam Hakimnejad, Zohreh Azimifar, Narjes Goshtasbi

Image reconstruction is an essential step of every medical imaging method, including Photoacoustic Tomography (PAT), which is a promising modality of imaging, that unites the benefits of both ultrasound and optical imaging methods. Reconstruction of PAT images using conventional methods results in rough artifacts, especially when applied directly to sparse P

Paul Rosenberger, Moumita Kundu, Andrei Gloskovskii, Christoph Schlueter

Proximity effects allow for the adjustment of magnetic properties in a physically elegant way. If two thin ferromagnetic (FM) films are brought into contact, electronic coupling alters their magnetic exchange interaction at their interface. For a low-TC rare-earth FM coupled to a 3d transition metal FM, even room temperature magnetism is within reach. In add

Rachael Boyd, Corey Bregman, Jan Steinebrunner

We study the classifying space B Diff(M) of the diffeomorphism group of a connected, compact, orientable 3-manifold M. In the case that M is reducible we build a contractible space parametrising the systems of reducing spheres. We use this to prove that if M has non-empty boundary, then B Diff(M rel boundary) has the homotopy type of a finite CW complex. Thi

Avi Hayoun, Veselin Raychev, Jack Hair

Static analysis is a growing application of software engineering, leading to a range of essential security tools, bug-finding tools, as well as software verification. Recent years show an increase of universal static analysis tools that validate a range of properties and allow customizing parts of the scanner to validate additional properties or "static anal

Simon Wietheger, Benjamin Doerr

Despite significant progress in the field of mathematical runtime analysis of multi-objective evolutionary algorithms (MOEAs), the performance of MOEAs on discrete many-objective problems is little understood. In particular, the few existing performance guarantees for classic MOEAs on classic benchmarks are all roughly quadratic in the size of the Pareto fro

David Montero, Miguel D. Mahecha, Francesco Martinuzzi, César Aybar

Accurate quantification of Gross Primary Production (GPP) is crucial for understanding terrestrial carbon dynamics. It represents the largest atmosphere-to-land CO$_2$ flux, especially significant for forests. Eddy Covariance (EC) measurements are widely used for ecosystem-scale GPP quantification but are globally sparse. In areas lacking local EC measuremen

Pablo Biedma, Xiaoyuan Yi, Linus Huang, Maosong Sun

Recent advancements in Large Language Models (LLMs) have revolutionized the AI field but also pose potential safety and ethical risks. Deciphering LLMs' embedded values becomes crucial for assessing and mitigating their risks. Despite extensive investigation into LLMs' values, previous studies heavily rely on human-oriented value systems in social sciences.

Harri Hakula, Antti Rasila

The conjugate function method is an algorithm for numerical computation of conformal mappings for simply and multiply connected domains. In this paper, the conjugate function method is extended to cover conformal mappings between Riemannian surfaces. The main challenge addressed here is the connection between Laplace--Beltrami equations on surfaces and the c

Pritam Bhattacharyya, Abdul Basit, Thorben Petersen, Stephan Rachel

The detection of considerable spin frustration in honeycomb cobalt oxide compounds indicates the presence of sizable Kitaev interactions in these systems, enlarging the pool of Kitaev spin liquid candidates. Several key questions remain to be answered, as basic as the mechanisms behind Kitaev couplings in Co$^{2+}$ $t_{2g}^5e_g^2$ magnets. Analyzing the quan

Marco Mordacci, Davide Ferrari, Michele Amoretti

Classification is particularly relevant to Information Retrieval, as it is used in various subtasks of the search pipeline. In this work, we propose a quantum convolutional neural network (QCNN) for multi-class classification of classical data. The model is implemented using PennyLane. The optimization process is conducted by minimizing the cross-entropy los

Anja Sturm, Moritz Wemheuer

We prove a central limit theorem for a certain class of functions on sparse rank-one inhomogeneous random graphs endowed with additional i.i.d. edge and vertex weights. Our proof of the central limit theorem uses a perturbative form of Stein's method and relies on a careful analysis of the local structure of the underlying sparse inhomogeneous random graphs

Longfei Huang, Shupeng Zhong, Xiangyu Wu, Ruoxuan Li

This report introduces a solution to the Topic 1 Zero-shot Image Captioning of 2024 NICE : New frontiers for zero-shot Image Captioning Evaluation. In contrast to NICE 2023 datasets, this challenge involves new annotations by humans with significant differences in caption style and content. Therefore, we enhance image captions effectively through retrieval a

Ruoyu Li, Qing Li, Tao Lin, Qingsong Zou

Device fingerprinting can be used by Internet Service Providers (ISPs) to identify vulnerable IoT devices for early prevention of threats. However, due to the wide deployment of middleboxes in ISP networks, some important data, e.g., 5-tuples and flow statistics, are often obscured, rendering many existing approaches invalid. It is further challenged by the

Yanjie Zhao, Xinyi Hou, Shenao Wang, Haoyu Wang

The rapid growth and popularity of large language model (LLM) app stores have created new opportunities and challenges for researchers, developers, users, and app store managers. As the LLM app ecosystem continues to evolve, it is crucial to understand the current landscape and identify potential areas for future research and development. This paper presents

Shenao Wang, Yanjie Zhao, Xinyi Hou, Haoyu Wang

The rapid advancement of large language models (LLMs) has revolutionized artificial intelligence, introducing unprecedented capabilities in natural language processing and multimodal content generation. However, the increasing complexity and scale of these models have given rise to a multifaceted supply chain that presents unique challenges across infrastruc

Edan Lerner, Avraham Moriel, Eran Bouchbinder

In addition to Goldstone phonons that generically emerge in the low-frequency vibrational spectrum of any solid, crystalline or glassy, structural glasses also feature other low-frequency vibrational modes. The nature and statistical properties of these modes -- often termed `excess modes' -- have been the subject of decades-long investigation. Studying them

Da Chang, Yu Li

With the rapid development of OCR technology, mixed-scene text recognition has become a key technical challenge. Although deep learning models have achieved significant results in specific scenarios, their generality and stability still need improvement, and the high demand for computing resources affects flexibility. To address these issues, this paper prop

Umberto Morellini

The Dirac vacuum is a non-linear polarisable medium rather than an empty space. This non-linear behaviour starts to be significant for extremely large electromagnetic fields such as the magnetic field on the surface of certain neutron stars. Even though the null temperature case was deeply studied in the past decades, the problem at non-zero temperature need

Lorenzo Botti, Michele Botti, Daniele Antonio Di Pietro, Francesco Carlo Massa

In this work we study the stability, convergence, and pressure-robustness of discretization methods for incompressible flows with hybrid velocity and pressure. Specifically, focusing on the Stokes problem, we identify a set of assumptions that yield inf-sup stability as well as error estimates which distinguish the velocity- and pressure-related contribution

Runqi Kang, Man Jiao, Yu Tong, Yang Liu

We report new experimental results on the search for dark photons based on a near-quantum-limited haloscope equipped with a superconducting cavity. The loaded quality factor of the superconducting cavity is $6\times10^{5}$, so that the expected signal from dark photon dark matter can be enhanced by more than one order compared to a copper cavity. A Josephson

Zepeng Jiang, Weiwei Ni, Yifan Zhang

Conditional Generative Adversarial Networks (CGANs) exhibit significant potential in supervised learning model training by virtue of their ability to generate realistic labeled images. However, numerous studies have indicated the privacy leakage risk in CGANs models. The solution DPCGAN, incorporating the differential privacy framework, faces challenges such

Giacomo Franceschetto, Arno Ricou

Variational quantum algorithms show potential in effectively operating on noisy intermediate-scale quantum devices. A novel variational approach to reinforcement learning has been recently proposed, incorporating linear-optical interferometers and a classical learning model known as projective simulation (PS). PS is a decision-making tool for reinforcement l

Chengwei Qin, Wenhan Xia, Tan Wang, Fangkai Jiao

Analogical reasoning is a unique ability of humans to address unfamiliar challenges by transferring strategies from relevant past experiences. One key finding in psychology is that compared with irrelevant past experiences, recalling relevant ones can help humans better handle new tasks. Coincidentally, the NLP community has also recently found that self-gen

Xiongping Dai, Yuxun Xie

Let $f\colon X\rightarrow Y$ be a continuous surjection of compact Hausdorff spaces. By $$f_*\colon\mathfrak{M}(X)\rightarrow\mathfrak{M}(Y),\ \mu\mapsto \mu\circ f^{-1} \quad{\rm and}\quad 2^f\colon2^X\rightarrow2^Y,\ A\mapsto f[A]$$ we denote the induced continuous surjections on the probability measure spaces and hyperspaces, respectively. In this paper w

Xinfeng Yuan, Siyu Yuan, Yuhan Cui, Tianhe Lin

Large language models (LLMs) have demonstrated impressive performance and spurred numerous AI applications, in which role-playing agents (RPAs) are particularly popular, especially for fictional characters. The prerequisite for these RPAs lies in the capability of LLMs to understand characters from fictional works. Previous efforts have evaluated this capabi

Zhaoxi Mu, Xinyu Yang

The integration of visual cues has revitalized the performance of the target speech extraction task, elevating it to the forefront of the field. Nevertheless, this multi-modal learning paradigm often encounters the challenge of modality imbalance. In audio-visual target speech extraction tasks, the audio modality tends to dominate, potentially overshadowing

Zibin Huang, Jun Xian

In this paper, we propose the Graph-Learning-Dual Graph Convolutional Neural Network called GLDGCN based on the classic Graph Convolutional Neural Network(GCN) by introducing dual convolutional layer and graph learning layer. We apply GLDGCN to the semi-supervised node classification task. Compared with the baseline methods, we achieve higher classification

Weisheng Wang

The Virasoro constraints for moduli spaces of stable torsion free sheaves on a surface with only $(p,p)$-cohomology were recently proved by Bojko-Moreira-Lim. The rank 1 case, which is not restricted to surfaces with only $(p,p)$-cohomology, was established by Moreira. We prove Virasoro constraints for K3 surfaces using Markman monodromy operators, which all

M. Timmermans, G. Dransfield, M. Gillon, A. H. M. J. Triaud

Small planets transiting bright nearby stars are essential to our understanding of the formation and evolution of exoplanetary systems. However, few constitute prime targets for atmospheric characterization, and even fewer are part of multiple star systems. This work aims to validate TOI-4336 A b, a sub-Neptune-sized exoplanet candidate identified by the TES

Zhuohong Li, Fangxiao Lu, Jiaqi Zou, Lei Hu

Land-cover mapping is one of the vital applications in Earth observation, aiming at classifying each pixel's land-cover type of remote-sensing images. As natural and human activities change the landscape, the land-cover map needs to be rapidly updated. However, discovering newly appeared land-cover types in existing classification systems is still a non-triv

Yihao Ding, Kaixuan Ren, Jiabin Huang, Siwen Luo

Document Question Answering (QA) presents a challenge in understanding visually-rich documents (VRD), particularly those dominated by lengthy textual content like research journal articles. Existing studies primarily focus on real-world documents with sparse text, while challenges persist in comprehending the hierarchical semantic relations among multiple pa

I. Marini, P. Popesso, G. Lamer, K. Dolag

The eROSITA will deliver an unprecedented volume of X-ray survey observations, 20-30 times more sensitive than ROSAT in the soft band (0.5-2 keV) and for the first time imaging in the hard band (2-10 keV) including galaxy clusters and groups along with obscured and unobscured AGNs. This calls for a powerful theoretical effort to control the systematics and b

Hisashi Shimodaira

In this paper, we propose a method to improve prediction accuracy of semantic segmentation methods as follows: (1) construct a neural network that has pre-processing layers based on a convolutional autoencoder ahead of a semantic segmentation network, and (2) train the entire network initialized by the weights of the pre-trained autoencoder. We applied this

Leonardo Barcellona, Alberto Bacchin, Matteo Terreran, Emanuele Menegatti

The ability of a robot to pick an object, known as robot grasping, is crucial for several applications, such as assembly or sorting. In such tasks, selecting the right target to pick is as essential as inferring a correct configuration of the gripper. A common solution to this problem relies on semantic segmentation models, which often show poor generalizati

Luca Fabbri

We employ the polar re-formulation of spinor fields to see in a new light their classification into regular and singular spinors, these last also called flag-dipoles, further splitting into the sub-classes of dipoles and flagpoles: in particular, we will study the conditions under which flagpoles may be solutions of the Dirac field equations. We argue for an

Dávid Vass, Emese Tóth, András Szenes, Balázs Bánhelyi

Passive and active targets, implanted with gold nanoprisms, were designed to achieve enhanced and uniform power absorption during two-sided illumination by short laser pulses. The target length was adjusted to match the short laser pulse-length. Capabilities of three different, uniform, single-peaked Gaussian and adjusted, nanoresonator number density distri

Yichong Huang, Xiaocheng Feng, Baohang Li, Yang Xiang

Large language models (LLMs) exhibit complementary strengths in various tasks, motivating the research of LLM ensembling. However, existing work focuses on training an extra reward model or fusion model to select or combine all candidate answers, posing a great challenge to the generalization on unseen data distributions. Besides, prior methods use textual r

Dávid Vass, András Szenes, Péter Zoltán Nagy, Balázs Bánhelyi

Active core-shell nanoresonators were designed in order to achieve large near-field enhancement, large power-outflow and minimal spaser threshold in the pump E-field strength. Gain-metal-dielectric (GMD) and gain-metal-gain (GMG) nanoresonator compositions were optimized with corresponding objective functions. The average local E-field, power-outflow and ext

Guojin Liu, Jianhong Zhou, Hang Su, Biaohong Xiong

Nowadays, network slicing (NS) technology has gained widespread adoption within Internet of Things (IoT) systems to meet diverse customized requirements. In the NS based IoT systems, the detection of equipment failures necessitates comprehensive equipment monitoring, which leads to significant resource utilization, particularly within large-scale IoT ecosyst

Giacomo D'Amicantonio, Egor Bondarau, Peter H. N. de With

Deep learning-based approaches have achieved significant improvements on public video anomaly datasets, but often do not perform well in real-world applications. This paper addresses two issues: the lack of labeled data and the difficulty of explaining the predictions of a neural network. To this end, we present a framework called uTRAND, that shifts the pro

Avinash Anand, Mohit Gupta, Kritarth Prasad, Navya Singla

The rapid progress in the field of natural language processing (NLP) systems and the expansion of large language models (LLMs) have opened up numerous opportunities in the field of education and instructional methods. These advancements offer the potential for tailored learning experiences and immediate feedback, all delivered through accessible and cost-eff

Zhiwei Hu, Víctor Gutiérrez-Basulto, Zhiliang Xiang, Ru Li

Multi-modal entity alignment (MMEA) aims to identify equivalent entity pairs across different multi-modal knowledge graphs (MMKGs). Existing approaches focus on how to better encode and aggregate information from different modalities. However, it is not trivial to leverage multi-modal knowledge in entity alignment due to the modal heterogeneity. In this pape

Peng Liu, Cong Xu, Ming Zhao, Jiawei Zhu

As the last pivotal stage of Recommender System (RS), Multi-Task Fusion (MTF) is responsible for combining multiple scores outputted by Multi-Task Learning (MTL) model into a final score to maximize user satisfaction. Recently, to optimize long-term user satisfaction, Reinforcement Learning (RL) is used for MTF in RSs. However, the existing offline RL algori

Yukang Wei, Yu Bai

Temperature plays a pivotal role in moderating label softness in the realm of knowledge distillation (KD). Traditional approaches often employ a static temperature throughout the KD process, which fails to address the nuanced complexities of samples with varying levels of difficulty and overlooks the distinct capabilities of different teacher-student pairing

Jin Xie, Chenqing Zhu, Songze Li

We focus on the problem of Personalized Federated Continual Learning (PFCL): a group of distributed clients, each with a sequence of local tasks on arbitrary data distributions, collaborate through a central server to train a personalized model at each client, with the model expected to achieve good performance on all local tasks. We propose a novel PFCL fra

Masaharu Ishikawa, Tat-Thang Nguyen

We characterize atypical values at infinity of a real polynomial function of three variables by a certain sum of indices of the gradient vector field of the function restricted to a sphere with a sufficiently large radius. This is an analogy of a result of Coste and de la Puente for real polynomial functions with two variables. We also give a characterizatio

Kailun Chen, Chuanhao Wen, Zhiyong Hou, Huan Yang

We investigate the magnetic-field dependent evolution of the zero-bias conductance peaks (ZBCPs) on the nanoscale bismuth islands grown on the FeTe$_{0.55}$Se$_{0.45}$ substrate. The ZBCPs can be observed throughout the entire region on these islands, and their characteristics align with the signatures of Majorana zero modes. Remarkably, the evolution of ZBC

Giuseppe Gaetano Luciano, Ahmad Sheykhi

Inspired by String T-duality and taking into account the zero-point length correction, $l_0$, to the gravitational potential, we construct modified Friedmann equations by applying the first law of thermodynamics on the apparent horizon of the Friedmann-Robertson-Walker (FRW) Universe. The cosmological viability of this extended scenario is investigated by st

Erwin Brüning, Shigeaki Nagamachi

Using only elementary calculus we prove that the balanced homodyne detector measures the quadrature phase amplitude of a signal. More precisely, after the measurement of photon numbers $l$, the collapse of the composite state of a strong laser beam and a signal approximates the collapse of the signal to the eigen-state of quadrature phase amplitude with eige

Xi Lu, Zhiqing Wei, Ruizhong Xu, Lin Wang

Integrated sensing and communication (ISAC) exhibits notable potential for sensing the unmanned aerial vehicles (UAVs), facilitating real-time monitoring of UAVs for security insurance. Due to the low sensing accuracy of single base stations (BSs), a cooperative UAV sensing method by multi-BS is proposed in this paper to achieve high-accuracy sensing. Specif

Jiazhu Dai, Haoyu Sun

Graph Convolutional Networks (GCNs) have shown excellent performance in dealing with various graph structures such as node classification, graph classification and other tasks. However,recent studies have shown that GCNs are vulnerable to a novel threat known as backdoor attacks. However, all existing backdoor attacks in the graph domain require modifying th

Daniel Kempf, Marius Kurz, Marcel Blind, Patrick Kopper

This work presents GALAEXI as a novel, energy-efficient flow solver for the simulation of compressible flows on unstructured meshes leveraging the parallel computing power of modern Graphics Processing Units (GPUs). GALAEXI implements the high-order Discontinuous Galerkin Spectral Element Method (DGSEM) using shock capturing with a finite-volume subcell appr

Junbiao Pang, Baocheng Xiong, Jiaqi Wu

Crack detection has become an indispensable, interesting yet challenging task in the computer vision community. Specially, pavement cracks have a highly complex spatial structure, a low contrasting background and a weak spatial continuity, posing a significant challenge to an effective crack detection method. In this paper, we address these problems from a v