March 2024 arXiv papers — page 51

Jiarui Hu, Xianhao Chen, Boyin Feng, Guanglin Li

Recently neural radiance fields (NeRF) have been widely exploited as 3D representations for dense simultaneous localization and mapping (SLAM). Despite their notable successes in surface modeling and novel view synthesis, existing NeRF-based methods are hindered by their computationally intensive and time-consuming volume rendering pipeline. This paper prese

Monica La Barbiera, Roya Moghimipor

In this paper, some algebraic invariants of generalized Veronese bi-type ideals are computed. We characterize the unmixed generalized Veronese bi-type ideals and we give a description of their associated prime ideals.

Ivan Sergeev

The contention resolution framework is a versatile rounding technique used as a part of the relaxation and rounding approach for solving constrained submodular function maximization problems. We apply this framework to the hypergraph matching, knapsack, and $k$-column sparse packing problems. In the hypergraph matching setting, we adapt the technique of Guru

Jean-Stefan Koskivirta

We prove that the effective cone of automorphic vector bundles on the Siegel modular variety of rank $n$ in characteristic $p$ at a place of good reduction is encoded by the stack of $G$-zips of Pink--Wedhorn--Ziegler. Specifically, we show that the degree zero cohomology groups of automorphic vector bundles always vanish outside of the zip cone. This result

Carl Lindström, Georg Hess, Adam Lilja, Maryam Fatemi

Neural Radiance Fields (NeRFs) have emerged as promising tools for advancing autonomous driving (AD) research, offering scalable closed-loop simulation and data augmentation capabilities. However, to trust the results achieved in simulation, one needs to ensure that AD systems perceive real and rendered data in the same way. Although the performance of rende

Matthias Bürgler, Daniel Valero, Benjamin Hohermuth, Robert M. Boes

The analysis of bubbly two-phase flows is challenging due to their turbulent nature and the need for intrusive phase-detection probes. However, accurately characterizing these flows is crucial for safely designing critical infrastructure such as dams and their appurtenant structures. The combination of dual-tip intrusive phase-detection probes with advanced

Nikiforos Paraskevopoulos, Carmen G. Almudever, Sebastian Feld

As quantum computing devices increase in size with respect to the number of qubits, two-qubit interactions become more challenging, necessitating innovative and scalable qubit routing solutions. In this work, we introduce beSnake, a novel algorithm specifically designed to address the intricate qubit routing challenges in scalable spin-qubit architectures. U

Parisa Sangtarash, Sedighe Sajadian

One of most important applications of microlensing observations is detecting free-floating planets(FFPs). The time scale of microlensing due to FFPs ($t_{\rm E}$) is short (a few days). Discerning the annual parallax effect in observations from these short-duration events by one observer is barely possible, though their parallax amplitude is larger than that

Debra Boutin, Alice Dean

A geometric graph, $\overline{G}$, is a graph drawn in the plane, with straight line edges and vertices in general position. A geometric homomorphism between two geometric graphs $\overline{G}$, $\overline{H}$ is a vertex map $f:\overline{G}\to\overline{H}$ that preserves vertex adjacency and edge crossings. The geochromatic number of $\overline{G}$, denoted

Serry Sibaee, Omar Najar, Lahouri Ghouti, Anis Koubaa

In this paper we introduce APL (Arabic Programming Language) that uses Large language models (LLM) as semi-compiler to covert Arabic text code to python code then run the code. Designing a full pipeline from the structure of the APL text then a prompt (using prompt engineering) then running the prodcued python code using PyRunner. This project has a three pa

Akhlesh Lakhtakia

A slab made of a dielectric structurally chiral medium (DSCM) strongly reflects the co-handed circularly polarized plane wave, but not the cross-handed circularly polarized plane wave, in a spectral regime called the circular Bragg regime. The effect of inserting a central phase defect in a DSCM slab with a modest number of structural periods is a spectral r

Maria Heuss, Maarten de Rijke, Avishek Anand

While SHAP (SHapley Additive exPlanations) and other feature attribution methods are commonly employed to explain model predictions, their application within information retrieval (IR), particularly for complex outputs such as ranked lists, remains limited. Existing attribution methods typically provide pointwise explanations, focusing on why a single docume

Henning Wachsmuth, Gabriella Lapesa, Elena Cabrio, Anne Lauscher

The computational treatment of arguments on controversial issues has been subject to extensive NLP research, due to its envisioned impact on opinion formation, decision making, writing education, and the like. A critical task in any such application is the assessment of an argument's quality - but it is also particularly challenging. In this position paper,

Krzysztof Gogol, Johnnatan Messias, Deborah Miori, Claudio Tessone

Arbitrage can arise from the simultaneous purchase and sale of the same asset in different markets in order to profit from a difference in its price. This work systematically reviews arbitrage opportunities between Automated Market Makers (AMMs) on Ethereum ZK rollups, and Centralised Exchanges (CEXs). First, we propose a theoretical framework to measure suc

Dianxiang Sun, Wei Ma, Liming Nie, Yang Liu

Rug pulls pose a grave threat to the cryptocurrency ecosystem, leading to substantial financial loss and undermining trust in decentralized finance (DeFi) projects. With the emergence of new rug pull patterns, research on rug pull is out of state. To fill this gap, we first conducted an extensive analysis of the literature review, encompassing both scholarly

Dheepak Khatri, Kenneth Granlund

A theoretical study is performed to analyze the directional response of different types of microphone array designs. 1-D (linear) and 2-D (planar) microphone array types are considered, and the delay and sum beamforming and conventional beamforming techniques are employed to localize the sound source. A non-dimensional parameter, G, is characterized to simpl

Mutlu Cukurova

This paper presents a multi-dimensional view of AI's role in learning and education, emphasizing the intricate interplay between AI, analytics, and the learning processes. Here, I challenge the prevalent narrow conceptualisation of AI as tools, as exemplified in generative AI tools, and argue for the importance of alternative conceptualisations of AI for ach

Xiaoyun Zheng, Liwei Liao, Xufeng Li, Jianbo Jiao

High-quality human reconstruction and photo-realistic rendering of a dynamic scene is a long-standing problem in computer vision and graphics. Despite considerable efforts invested in developing various capture systems and reconstruction algorithms, recent advancements still struggle with loose or oversized clothing and overly complex poses. In part, this is

K. Avinash, S. J. Kalita, R. Ganesh, P. Kaur

It is shown that the dust density regimes in dusty plasma are characterized by two complementary screening processes, (a) the low dust density regime where the Debye screening is the dominant process and (b) the high dust density regime where the Coulomb screening is the dominant process. The Debye regime is characterized by a state where all dust particles

Wenxuan Wu, Xueyuan Chen, Xixin Wu, Haizhou Li

Audio-visual target speech extraction (AV-TSE) is one of the enabling technologies in robotics and many audio-visual applications. One of the challenges of AV-TSE is how to effectively utilize audio-visual synchronization information in the process. AV-HuBERT can be a useful pre-trained model for lip-reading, which has not been adopted by AV-TSE. In this pap

Harold A. Moreno-Franco, Jose-Luis Pérez

We study the optimal bailout dividend problem with transaction costs for an insurance company, where shareholder payouts align with the arrival times of an independent Poisson process. In this scenario, the underlying risk model follows a spectrally negative L\'evy process. Our analysis confirms the optimality of a periodic $(b_{1},b_{2})$-barrier policy wit

Yi Xu, Weiran Shen, Xiao Zhang, Jun Xu

Traditional imitation learning focuses on modeling the behavioral mechanisms of experts, which requires a large amount of interaction history generated by some fixed expert. However, in many streaming applications, such as streaming recommender systems, online decision-makers typically engage in online learning during the decision-making process, meaning tha

Dong Zheng, Zhongxiang Wang

We search for possible pulsar TeV halos among the very-high-energy (VHE) sources reported in different VHE surveys, among which in particular we use the results from the first Large High Altitude Air Shower Observatory (LHAASO) catalog of $\gamma$-ray sources. Six candidates are found. They share similar properties of containing a middle-aged, gamma-ray--bri

Wei Ma, Daoyuan Wu, Yuqiang Sun, Tianwen Wang

Smart contracts are decentralized applications built atop blockchains like Ethereum. Recent research has shown that large language models (LLMs) have potential in auditing smart contracts, but the state-of-the-art indicates that even GPT-4 can achieve only 30% precision (when both decision and justification are correct). This is likely because off-the-shelf

Elmehdi Illi, Marwa Qaraqe

High altitude platforms (HAPs)-aided terrestrial-aerial communication technology based on free-space optical (FSO) and Terahertz (THz) feeder links has been attracting notable interest recently due to its great potential in reaching a higher data rate and connectivity. Nonetheless, the presence of harsh vertical propagation environments and potential aerial

Linzhi Wu, Xingyu Zhang, Yakun Zhang, Changyan Zheng

Lip reading, the process of interpreting silent speech from visual lip movements, has gained rising attention for its wide range of realistic applications. Deep learning approaches greatly improve current lip reading systems. However, lip reading in cross-speaker scenarios where the speaker identity changes, poses a challenging problem due to inter-speaker v

Xin Lin, Rafael Vazquez, Miroslav Krstic

In this paper, we extend our previous work on the power series method for computing backstepping kernels. Our first contribution is the development of initial steps towards a MATLAB toolbox dedicated to backstepping kernel computation. This toolbox would exploit MATLAB's linear algebra and sparse matrix manipulation features for enhanced efficiency; our init

Song-Lin Li, Kathryn Grasha, Mark R. Krumholz, Emily Wisnioski

Galaxies' stellar masses, gas-phase oxygen abundances (metallicity), and star formation rates (SFRs) obey a series of empirical correlations, most notably the mass-metallicity relation (MZR) and fundamental metallicity relation (FZR), which relates oxygen abundance to a combination of stellar mass and SFR. However, due to the difficulty of measuring oxygen a

Franco Vazza, Andrea Botteon

Radio galaxies in clusters of galaxies are prominent reservoirs of magnetic fields and of non-thermal particles, which get mixed with the intracluster medium. We review the observational and theoretical knowledge of the role of these crucial ingredients for the formation of diffuse radio emission in clusters (radio halos, relics, mini halos) and outline the

Guang Lin, Zerui Tao, Jianhai Zhang, Toshihisa Tanaka

Diffusion model (DM) based adversarial purification (AP) has proven to be a powerful defense method that can remove adversarial perturbations and generate a purified example without threats. In principle, the pre-trained DMs can only ensure that purified examples conform to the same distribution of the training data, but it may inadvertently compromise the s

Sriraghavendra Ramaswamy

We present a supervised learning approach for automatic extraction of keyphrases from single documents. Our solution uses simple to compute statistical and positional features of candidate phrases and does not rely on any external knowledge base or on pre-trained language models or word embeddings. The ranking component of our proposed solution is a fairly l

Yejin Kim, Youngbin Lee, Vincent Yuan, Annika Lee

Recommender systems, crucial for user engagement on platforms like e-commerce and streaming services, often lag behind users' evolving preferences due to static data reliance. After Temporal Graph Networks (TGNs) were proposed, various studies have shown that TGN can significantly improve situations where the features of nodes and edges dynamically change ov

Alberto Barchielli

Quantum trajectory techniques have been used in the theory of open systems as a starting point for numerical computations and to describe the monitoring of a quantum system in continuous time. Here we extend this technique and use it to develop a general approach to the dynamics of quantum/classical hybrid systems. By using two coupled stochastic differentia

Fabian Ritter, Sebastian Hack

Performance models are instrumental for optimizing performance-sensitive code. When modeling the use of functional units of out-of-order x86-64 CPUs, data availability varies by the manufacturer: Instruction-to-port mappings for Intel's processors are available, whereas information for AMD's designs are lacking. The reason for this disparity is that standard

Jieao Zhu, Ze Gu, Qian Ma, Linglong Dai

Among various promising candidate technologies for the sixth-generation (6G) wireless communications, recent advances in microwave metasurfaces have sparked a new research area of reconfigurable intelligent surfaces (RISs). By controllably reprogramming the wireless propagation channel, RISs are envisioned to achieve low-cost wireless capacity boosting, cove

Parada T. P. Hutauruk

Nonstandard interaction is expected to be a crucial hint in explaining the experimental data on neutrino scattering off electrons. In this context, the nonstandard interaction vector and axial-vector couplings are needed to be extracted from recent experiments and a few of them are now available in the literature. With these coupling bounds, in this paper, I

Haoyuan Wang, Yue Xue, Xuan Feng, Chao Zhou

We conduct the first comprehensive security study on representative port forwarding services (PFS), which emerge in recent years and make the web services deployed in internal networks available on the Internet along with better usability but less complexity compared to traditional techniques (e.g., NAT traversal techniques). Our study is made possible throu

Hongfu Guo, Wencheng Zou, Zeyu Zhang, Shuishan Zhang

Manifold regularization model is a semi-supervised learning model that leverages the geometric structure of a dataset, comprising a small number of labeled samples and a large number of unlabeled samples, to generate classifiers. However, the original manifold norm limits the performance of models to local regions. To address this limitation, this paper prop

Laurent Mertz, Vahagn Nersesyan, Manuel Rissel

We provide deterministic controllability conditions that imply exponential mixing properties for randomly forced constrained dynamical systems with possibly unbounded state space. As an application, new ergodicity results are obtained for non-smooth models in elasto-plasticity driven by various types of noise, including white noise. It is thereby illustrated

Paolo Vannucci

The problem of obtaining anisotropic auxetic composite laminates, i.e. having a negative Poisson's ratio for at least some directions, is examined in this paper. In particular, the possibility of obtaining auxeticity stacking uni-directional identical plies is considered. It is shown that if the ply is composed by isotropic matrix and fibers, then it is impo

Heyi Zhang, Xin Wang, Zhaopeng Meng, Zhe Chen

Large Language Models (LLMs) has made significant progress in a number of professional fields, including medicine, law, and finance. However, in traditional Chinese medicine (TCM), there are challenges such as the essential differences between theory and modern medicine, the lack of specialized corpus resources, and the fact that relying only on supervised f

Kun Ouyang, Yi Liu, Shicheng Li, Ruihan Bao

Financial prediction from Monetary Policy Conference (MPC) calls is a new yet challenging task, which targets at predicting the price movement and volatility for specific financial assets by analyzing multimodal information including text, video, and audio. Although the existing work has achieved great success using cross-modal transformer blocks, it overloo

Hayato Goto

Standard approaches to quantum error correction for fault-tolerant quantum computing are based on encoding a single logical qubit into many physical ones, resulting in asymptotically zero encoding rates and therefore huge resource overheads. To overcome this issue, high-rate quantum codes, such as quantum low-density parity-check codes, have been studied ove

Jia-Liang Xie, Tao Liu, Yu-Chen Leng, Rui Mei

Angle-resolved polarized Raman (ARPR) spectroscopy provides insights into optical anisotropy and symmetry-related electron-photon/electron-phonon couplings of anisotropic layered materials (ALMs). However, since their discovery over ten years ago, ARPR responses in ALM flakes has exhibited a puzzling dependence on flake thickness, excitation wavelength, and

M. Eisaei, Gh. R. Moghimi

Let $A$ be a Banach algebra with a right identity $u$ such that $uA$ is commutative and semisimple. In this paper, we investigate symmetric bi-derivations of $A$ and detremine their range. We also study symmetric bi-derivations of $A$ with their $k$-centralizing trace. Finally, we prove every symmetric Jordan bi-derivation of $A$ is a symmetric bi-derivation

Congrui Hetang, Haoru Xue, Cindy Le, Tianwei Yue

We propose SAM-Road, an adaptation of the Segment Anything Model (SAM) for extracting large-scale, vectorized road network graphs from satellite imagery. To predict graph geometry, we formulate it as a dense semantic segmentation task, leveraging the inherent strengths of SAM. The image encoder of SAM is fine-tuned to produce probability masks for roads and

Yifan Shi, Yuhui Zhang, Ziyue Huang, Xiaofeng Yang

Federated Split Learning (FSL) is a promising distributed learning paradigm in practice, which gathers the strengths of both Federated Learning (FL) and Split Learning (SL) paradigms, to ensure model privacy while diminishing the resource overhead of each client, especially on large transformer models in a resource-constrained environment, e.g., Internet of

Sangjoon Park, Yongsung Kwon, Hyungjoon Soh, Mi Jin Lee

Predicting temporal patterns across various domains poses significant challenges due to their nuanced and often nonlinear trajectories. To address this challenge, prediction frameworks have been continuously refined, employing data-driven statistical methods, mathematical models, and machine learning. Recently, as one of the challenging systems, shared trans

Xin Gu, Libo Zhang, Fan Chen, Longyin Wen

This paper focuses on understanding the predominant video creation pipeline, i.e., compositional video editing with six main types of editing components, including video effects, animation, transition, filter, sticker, and text. In contrast to existing visual representation learning of visual materials (i.e., images/videos), we aim to learn visual representa

Kyle Bradford

This paper makes the following conjecture: For every prime $p$ there exists a positive integer $x$ with $\left\lceil \frac{p}{4} \right\rceil \leq x \leq \left\lceil \frac{p}{2} \right\rceil$ and a positive divisor $d|x^2$ so that either: (1) $ d \bmod \left( 4x - p \right) \equiv -px$; or (2) $d \leq x$ and $ d \bmod \left( 4x - p \right) \equiv -x$. Furthe

Kanghong Shi, Ian R. Petersen

A hybrid integrator-gain system (HIGS) is a control element that switches between an integrator and a gain, which overcomes some inherent limitations of linear controllers. In this paper, we consider using discrete-time HIGS controllers for the digital control of negative imaginary (NI) systems. We show that the discrete-time HIGS themselves are step-advance

Ioannis Krikidis

In this letter, we study the analogue pre/post-coding vector design for a point-to-point multiple-input multiple-output (MIMO) system with 1-bit phase shifters. Specifically, we focus on the signal-to-noise ratio (SNR) maximization problem which corresponds to a combinatorial NP-hard due to the binary phase resolution. Two classical computation heuristics ar

Mohit Rohida, Alok Shukla, Prakash Vedula

We propose a novel hybrid classical-quantum approach for image processing based on polar Walsh basis functions. Using this approach, we present an algorithm for the removal of the circular banding noise (including Airy pattern noise) and the azimuthal banding noise. This approach is based on a formulation of Walsh basis functions in polar coordinates for ima

Ruibo Wang, Song Zhang, Ping Huang, Donghai Zhang

Recent research that combines implicit 3D representation with semantic information, like Semantic-NeRF, has proven that NeRF model could perform excellently in rendering 3D structures with semantic labels. This research aims to extend the Semantic Neural Radiance Fields (Semantic-NeRF) model by focusing solely on semantic output and removing the RGB output c

Xavier Guidetti, Nathan Mingard, Raul Cruz-Oliver, Yannick Nagel

In material extrusion additive manufacturing, the extrusion process is commonly controlled in a feed-forward fashion. The amount of material to be extruded at each printing location is pre-computed by a planning software. This approach is inherently unable to adapt the extrusion to external and unexpected disturbances, and the quality of the results strongly

Omar Mustafa

We investigate and report the effects of rainbow gravity on the spectroscopic structure of KG-oscillators in a mixed magnetic field (in the sense that it has the usually described as a uniform and a non-uniform magnetic fields, each at a time) introduced by the 4-vector potential $A_\mu=(0,0,A_\varphi,0)$, where $A_\varphi=B_1 r^2/2+B_2 r$, and $B_1$ and $B_

Bo Feng, Chang Hu, Jiyuan Shen, Yaobo Zhang

In this paper we have studied the most general generating function of reduction for one loop integrals with arbitrary tensor structure in numerator and arbitrary power distribution of propagators in denominator. Using IBP relations, we have established the partial differential equations for these generating functions and solved them analytically. These resul

Li Tuobang

Descriptive statistics for parametric models are currently highly sensative to departures, gross errors, and/or random errors. Here, leveraging the structures of parametric distributions and their central moment kernel distributions, a class of estimators, consistent simultanously for both a semiparametric distribution and a distinct parametric distribution,

Cheng Fang, Jinqiao Duan

Establishing appropriate mathematical models for complex systems in natural phenomena not only helps deepen our understanding of nature but can also be used for state estimation and prediction. However, the extreme complexity of natural phenomena makes it extremely challenging to develop full-order models (FOMs) and apply them to studying many quantities of

Qin Liu, Fei Wang, Nan Xu, Tianyi Yan

Performance of large language models (LLMs) may vary with different prompts or instructions of even the same task. One commonly recognized factor for this phenomenon is the model's familiarity with the given prompt or instruction, which is typically estimated by its perplexity. However, finding the prompt with the lowest perplexity is challenging, given the

Taotian Pang, Xingyu Lou, Fei Zhao, Zhen Wu

\textit{Knowledge-aware} recommendation methods (KGR) based on \textit{graph neural networks} (GNNs) and \textit{contrastive learning} (CL) have achieved promising performance. However, they fall short in modeling fine-grained user preferences and further fail to leverage the \textit{preference-attribute connection} to make predictions, leading to sub-optima

M. Eisaei, M. J. Mehdipour, Gh. R. Moghimi

Let $\theta$ be a homomorphism on $L_0^\infty({\Bbb R}^+, \omega)^*$. In this paper, we study left $\theta$-derivations on $L_0^\infty({\Bbb R}^+, \omega)^*$. We show that every left $\theta$-derivation on $L_0^\infty({\Bbb R}^+, \omega)^*$ is always a $\theta$-derivation, and if $\theta$ is isomorphism, then $L_0^\infty({\Bbb R}^+, \omega)^*$ has no non-zer

Qianbiao Liu, Long Liu, Guozhong Xing, Lijun Zhu

Electrical switching of magnetization is central to spintronics. Despite the enormous efforts on the spin torques and the Dzyaloshinskii-Moriya interaction (DMI) effects, some fundamental physics for electrical switching of magnetization is still missing as indicated by a number of remarkable long-standing puzzles. Here, we report the discovery of the long-r

Hao Xiang, Zhaoliang Zheng, Xin Xia, Runsheng Xu

Recent advancements in Vehicle-to-Everything (V2X) technologies have enabled autonomous vehicles to share sensing information to see through occlusions, greatly boosting the perception capability. However, there are no real-world datasets to facilitate the real V2X cooperative perception research -- existing datasets either only support Vehicle-to-Infrastruc

Hongyin Zhu

Graph data, also known as complex network data, is omnipresent across various domains and applications. Prior graph neural network models primarily focused on extracting task-specific structural features through supervised learning objectives, but they fell short in capturing the inherent semantic and structural features of the entire graph. In this paper, w

Han Liu, Jian Zhang, Cen Zhang, Xiaohan Zhang

Static analysis tools have evolved over time to assist in detecting bugs. However, the excessive false warnings can impede developers' productivity and confidence in the tools. Previous research efforts have explored learning-based approaches to identify bug warnings. Nevertheless, their coarse granularity, focusing on either long-term warnings or function-l

Ali Shojaie, Wenyu Chen

Directed acyclic graphs (DAGs) are commonly used to model causal relationships among random variables. In general, learning the DAG structure is both computationally and statistically challenging. Moreover, without additional information, the direction of edges may not be estimable from observational data. In contrast, given a complete causal ordering of the

Dongqi Fu, Zhigang Hua, Yan Xie, Jin Fang

Graph transformer has been proven as an effective graph learning method for its adoption of attention mechanism that is capable of capturing expressive representations from complex topological and feature information of graphs. Graph transformer conventionally performs dense attention (or global attention) for every pair of nodes to learn node representation

Yining Liu, Yanfeng Ouyang

Dockless electric micro-mobility services (e.g., shared e-scooters and e-bikes) have been increasingly popular in the recent decade, and a variety of charging technologies have emerged for these services. The use of charging stations, to/from which service vehicles are transported by the riders for charging, poses as a promising approach because it reduces t

Yao Lu, Jianyang Gu, Xuguang Chen, Saeed Vahidian

Dataset Distillation (DD) is a promising technique to synthesize a smaller dataset that preserves essential information from the original dataset. This synthetic dataset can serve as a substitute for the original large-scale one, and help alleviate the training workload. However, current DD methods typically operate under the assumption that the dataset is u

Takayuki Kihara

In this article, we propose a new classification of $\Sigma^0_2$ formulas under the realizability interpretation of many-one reducibility (i.e., Levin reducibility). For example, ${\sf Fin}$, the decision of being eventually zero for sequences, is many-one/Levin complete among $\Sigma^0_2$ formulas of the form $\exists n\forall m\geq n.\varphi(m,x)$, where $

Zhijie Li, Tianyuan Liu, Wenhui Peng, Zelong Yuan

Predicting the large-scale dynamics of three-dimensional (3D) turbulence is challenging for machine learning approaches. This paper introduces a transformer-based neural operator (TNO) to achieve precise and efficient predictions in the large-eddy simulation (LES) of 3D turbulence. The performance of the proposed TNO model is systematically tested and compar

Yongliang Wang

We derive the general circuit equations and system models directly from four Maxwell's equations and develop the electric-charge-based and magnetic-flux-based analysis methodologies to unify the analyses for both phase-independent circuits such as resistor-inductor-capacitor (RLC) circuits and semiconductor transistor circuits, and phase-dependent circuits s

Ayush Thakur, Rashmi Vashisth

This paper presents a comprehensive study on the unified module for accelerating stable-diffusion processes, specifically focusing on the lcm-lora module. Stable-diffusion processes play a crucial role in various scientific and engineering domains, and their acceleration is of paramount importance for efficient computational performance. The standard iterati

Junbo Wang, Wenhai Liu, Qiaojun Yu, Yang You

Articulated objects are commonly found in daily life. It is essential that robots can exhibit robust perception and manipulation skills for articulated objects in real-world robotic applications. However, existing methods for articulated objects insufficiently address noise in point clouds and struggle to bridge the gap between simulation and reality, thus l

Ryodai Kawaguchi, Shinji Tsujikawa, Yusuke Yamada

We revisit the properties of total time-derivative terms as well as terms proportional to the free equation of motion (EOM) in a Schwinger-Keldysh formalism. They are relevant to the correct calculation of correlation functions of curvature perturbations in the context of inflationary Universe. We show that these two contributions to the action play differen

Kaige Qu, Weihua Zhuang

The emerging data-driven methods based on artificial intelligence (AI) have paved the way for intelligent, flexible, and adaptive network management in vehicular applications. To enhance network management towards network automation, this article presents a digital twin (DT) assisted two-tier learning framework, which facilitates the automated life-cycle man

Tanvir Mahmud, Burhaneddin Yaman, Chun-Hao Liu, Diana Marculescu

As deep neural networks evolve from convolutional neural networks (ConvNets) to advanced vision transformers (ViTs), there is an increased need to eliminate redundant data for faster processing without compromising accuracy. Previous methods are often architecture-specific or necessitate re-training, restricting their applicability with frequent model update

Haichang Yang, Binglin Zeng, Qiuyun Lu, Yaowen Xing

The ability to transfer microdroplets between fluid phases offers numerous advantages in various fields, enabling better control, manipulation, and utilization of small volumes of fluids in pharmaceutical formulations, microfluidics, and lab-on-a-chip devices, single-cell analysis or droplet-based techniques for nanomaterial synthesis. This study focuses on

Yihan Hou, Hao Cui, Rongrong Chen, Wei Zeng

Referents are often used to enhance scale perception in immersive visualizations. Common referent designs include the considerations of referent layout (side-by-side vs. in-situ) and referent size (small vs. medium vs. large). This paper introduces a controlled user study to assess how different referent designs affect the efficiency and accuracy of scale pe

Ilyas Bennia, Yacine Daili, Abdelghani Harrag, Walid Issa

This paper presents a brief comparison for voltage and current controllers implementation in both stationary and dynamic reference frame for a microgrid (MG) application. Diagrams of implementations are reviewed and the simulation results are presented to show the performance of each topology

Eyoel Gebre, Krishna Saxena, Timothy Tran

Image inpainting is the process of taking an image and generating lost or intentionally occluded portions. Inpainting has countless applications including restoring previously damaged pictures, restoring the quality of images that have been degraded due to compression, and removing unwanted objects/text. Modern inpainting techniques have shown remarkable abi

Ziyan Xiong, Bo Chen, Shiyu Huang, Wei-Wei Tu

The advent of deep reinforcement learning (DRL) has significantly advanced the field of robotics, particularly in the control and coordination of quadruped robots. However, the complexity of real-world tasks often necessitates the deployment of multi-robot systems capable of sophisticated interaction and collaboration. To address this need, we introduce the

W. Liu, C. Shu, Z. J. Liu

It is essential to efficiently solve multiscale flows covering the continuum regime to the rarefied regime. The explicit form of Grad's 13 moments distribution function-based moment gas kinetic solver (G13-MGKS) has been proposed in our previous work [Comput. Math. Appl., 137 (2023), pp. 112-125], which demonstrates the potential for efficiently simulating c

Tomonori Kouya

The direct method is one of the most important algorithms for solving linear systems of equations, with LU decomposition comprising a significant portion of its computation time. This study explores strategies to accelerate complex LU decomposition using multiple-precision floating-point arithmetic of the multiple-component type. Specifically, we explore the

Rishabh Agnihotri, Krishnarjun Krishnamoorthy

Let $F$ (over $\mathbb{Q}$) be a totally real number field of narrow class number $1$. We generalize a result of Kohnen on the determination of half integral weight modular forms by their Fourier coefficients supported on squarefree (algebraic) integers. We also give a soft proof that infinitely many Fourier coefficients supported on squarefree integers are

Sakurako Okamoto, Annette M. N. Ferguson, Nobuo Arimoto, Itsuki Ogami

We present the discovery of NGC253-SNFC-dw1, a new satellite galaxy in the remote stellar halo of the Sculptor Group spiral, NGC 253. The system was revealed using deep resolved star photometry obtained as part of the Subaru Near-Field Cosmology Survey that uses the Hyper Suprime-Cam on the Subaru Telescope. Although rather luminous ($\rm{M_{V}} = -11.7 \pm

Toranosuke Matsubara, Akihisa Koga, Atsushi Takano, Yushu Matsushita

Aperiodic crystals constitute a fascinating class of materials that includes incommensurate (IC) modulated structures and quasicrystals (QCs). Although these two categories share a common foundation in the concept of superspace, the relationship between them has remained enigmatic and largely unexplored. Here, we show "any metallic-mean" QCs, surpassing the

Yifei Wang, Chuhong Zhu

Recently, machine learning-based semantic segmentation algorithms have demonstrated their potential to accurately segment regions and contours in medical images, allowing the precise location of anatomical structures and abnormalities. Although medical images are difficult to acquire and annotate, semi-supervised learning methods are efficient in dealing wit

Yuyu Chen, Paul Embrechts, Ruodu Wang

We study the optimal decisions and equilibria of agents who aim to minimize their risks by allocating their positions over extremely heavy-tailed (i.e., infinite-mean) and possibly dependent losses. The loss distributions of our focus are super-Pareto distributions, which include the class of extremely heavy-tailed Pareto distributions. Using a recent result

Hongbin Na

The recent advancements in artificial intelligence highlight the potential of language models in psychological health support. While models trained on data from mental health service platform have achieved preliminary success, challenges persist in areas such as data scarcity, quality, and ensuring a solid foundation in psychological techniques. To address t

Tim Dalby

Another proof that uniformly nonsquare Banach spaces have the fixed point property is presented.

Boyi Li, Weixuan Xia

Recent empirical evidence has highlighted the crucial role of jumps in both price and volatility within the cryptocurrency market. In this paper, we integrate price--volatility co-jumps and volatility short-term dependency into a coherent model framework, featuring fractional stochastic volatility. We particularly focus on inverse options, including the emer

Yucheng Suo, Fan Ma, Linchao Zhu, Yi Yang

We study the zero-shot Composed Image Retrieval (ZS-CIR) task, which is to retrieve the target image given a reference image and a description without training on the triplet datasets. Previous works generate pseudo-word tokens by projecting the reference image features to the text embedding space. However, they focus on the global visual representation, ign

Hao Song, Jiacheng Yao, Zhengxi Li, Shaocong Xu

Over the past few years, federated learning has become widely used in various classical machine learning fields because of its collaborative ability to train data from multiple sources without compromising privacy. However, in the area of graph neural networks, the nodes and network structures of graphs held by clients are different in many practical applica

Shiben Liu, Huijie Fan, Qiang Wang, Xiai Chen

Lifelong Person Re-Identification (LReID) aims to continuously learn from successive data streams, matching individuals across multiple cameras. The key challenge for LReID is how to effectively preserve old knowledge while incrementally learning new information, which is caused by task-level domain gaps and limited old task datasets. Existing methods based

Xiaojun Hou, Jiazheng Xing, Yijie Qian, Yaowei Guo

Multimodal Visual Object Tracking (VOT) has recently gained significant attention due to its robustness. Early research focused on fully fine-tuning RGB-based trackers, which was inefficient and lacked generalized representation due to the scarcity of multimodal data. Therefore, recent studies have utilized prompt tuning to transfer pre-trained RGB-based tra

Lijin Wu, Shanshan Lei, Feilong Liao, Yuanjun Zheng

As the number of IoT devices increases, security concerns become more prominent. The impact of threats can be minimized by deploying Network Intrusion Detection System (NIDS) by monitoring network traffic, detecting and discovering intrusions, and issuing security alerts promptly. Most intrusion detection research in recent years has been directed towards th

Sijia Gu, Ali Mesbah

For large software applications, running the whole test suite after each code change is time- and resource-intensive. Regression test selection techniques aim at reducing test execution time by selecting only the tests that are affected by code changes. However, existing techniques select test entities at coarse granularity levels such as test class, which c

Yanyan Tang, Jie Xiong

In this article, we consider a weighted mean-field control problem with jump-diffusion as its state process. The main difficulty is from the non-Lipschitz property of the coefficients. We overcome this difficulty by an $L_{p,q}$-estimate of the solution processes with a suitably chosen $p$ and $q$. Convex pertubation method combining with the aforementioned