December 2024 arXiv papers — page 24

Peng Liu, Tiantian Mao, Ruodu Wang

Choquet capacities and integrals are central concepts in decision making under ambiguity or model uncertainty, pioneered by Schmeidler. Motivated by risk optimization problems for quantiles under ambiguity, we study the subclass of Choquet integrals, called Choquet quantiles, which generalizes the usual (probabilistic) quantiles, also known as Value-at-Risk

Timo Spinde, Fei Wu, Wolfgang Gaissmaier, Gianluca Demartini

Marking biased texts is a practical approach to increase media bias awareness among news consumers. However, little is known about the generalizability of such awareness to new topics or unmarked news articles, and the role of machine-generated bias labels in enhancing awareness remains unclear. This study tests how news consumers may be trained and pre-bunk

Xiang Huang, Jiayu Shen, Shanshan Huang, Sitao Cheng

Semantic parsing, which converts natural language questions into logic forms, plays a crucial role in reasoning within structured environments. However, existing methods encounter two significant challenges: reliance on extensive manually annotated datasets and limited generalization capability to unseen examples. To tackle these issues, we propose Targeted

Subeen Lee, Jiyeon Han, Soyeon Kim, Jaesik Choi

Deep generative models are proficient in generating realistic data but struggle with producing rare samples in low density regions due to their scarcity of training datasets and the mode collapse problem. While recent methods aim to improve the fidelity of generated samples, they often reduce diversity and coverage by ignoring rare and novel samples. This st

Xiaoyang Liu, Boran Wen, Xinpeng Liu, Zizheng Zhou

Spatio-temporal Human-Object Interaction (ST-HOI) understanding aims at detecting HOIs from videos, which is crucial for activity understanding. However, existing whole-body-object interaction video benchmarks overlook the truth that open-world objects are diverse, that is, they usually provide limited and predefined object classes. Therefore, we introduce a

Qi An, Jiao Wang, Zequn Niu, Nana Zhang

In this study, we focus on computing the projection onto the $\ell_p$ quasi-norm ball, which is challenging due to the non-convex and non-Lipschitz nature inherent in the $\ell_p$ quasi-norm with $0<p<1$. We propose a novel localized approximation method that yields a Lipschitz continuous concave surrogate function for the $\ell_p$ quasi-norm with improved a

Congcong Zheng, Ping Xu, Kun Wang, Zaichen Zhang

Genuinely entangled subspaces (GESs) are valuable resources in quantum information science. Among these, the three-qubit GHZ-W GES, spanned by the three-qubit Greenberger-Horne-Zeilinger (GHZ) and W states, is a universal and crucial entangled subspace resource for three-qubit systems. In this work, we develop two adaptive verification strategies, the XZ str

Hiroshi Ishii, Yoshitaro Tanaka

This paper considers the approximation of spatial convolution with a given radial integral kernel. Previous studies have demonstrated that approximating spatial convolution using a system of partial differential equations (PDEs) can eliminate the analytical difficulties arising from integral formulations in one-dimensional space. In this paper, we establish

Xuan Zhou, Xiang Shi, Lele Zhang, Chen Chen

To improve the efficiency of warehousing system and meet huge customer orders, we aim to solve the challenges of dimension disaster and dynamic properties in hyper scale multi-robot task planning (MRTP) for robotic mobile fulfillment system (RMFS). Existing research indicates that hierarchical reinforcement learning (HRL) is an effective method to reduce the

Shuokai Pan, Gerti Tuzi, Sudarshan Sreeram, Dibakar Gope

Despite the revolutionary breakthroughs of large-scale text-to-image diffusion models for complex vision and downstream tasks, their extremely high computational and storage costs limit their usability. Quantization of diffusion models has been explored in recent works to reduce compute costs and memory bandwidth usage. To further improve inference time, fas

Meiqi Wu, Kaiqi Huang, Yuanqiang Cai, Shiyu Hu

Air-writing is a challenging task that combines the fields of computer vision and natural language processing, offering an intuitive and natural approach for human-computer interaction. However, current air-writing solutions face two primary challenges: (1) their dependency on complex sensors (e.g., Radar, EEGs and others) for capturing precise handwritten t

Dmitry Bryukhov

This paper extends approach developed in a recent author's paper on analytic models of potential fields in inhomogeneous media. New three-dimensional analytic models of potential vector fields in some layered media are constructed. Properties of various analytic models in Cartesian and cylindrical coordinates in $\mathbb R^3$ are compared. The original prope

Miaomiao Dai, Qianyu Zhou, Lizhuang Ma

Style transfer aims to generate a new image preserving the content but with the artistic representation of the style source. Most of the existing methods are based on Transformers or diffusion models, however, they suffer from quadratic computational complexity and high inference time. RWKV, as an emerging deep sequence models, has shown immense potential fo

Masashi Wakaiki

Let $T$ be a power-bounded linear operator on a Hilbert space $X$, and let $S$ be a bounded linear operator from another Hilbert space $Y$ to $X$. We investigate the non-exponential rate of decay of $\|T^nS\|$ as $n \to \infty$. First, when $X = Y$ and $S$ commutes with $T$, we characterize the decay rate of $\|T^nS\|$ in terms of the growth rate of $\|(\lam

Junjie Hu, Shuyong Gao, Lingyi Hong, Qishan Wang

Recent research in subject-driven generation increasingly emphasizes the importance of selective subject features. Nevertheless, accurately selecting the content in a given reference image still poses challenges, especially when selecting the similar subjects in an image (e.g., two different dogs). Some methods attempt to use text prompts or pixel masks to i

Santanu Maity, Mousa Alrubayan, Ishmael Apachigwao, Dhruvil Solanki

The light scattering experiment establishes a relationship between refractive index fluctuations and fractal dimension in weakly scattering tissue-like media. Based on the box-counting approach, an analytical model is developed and shows that the fractal dimension has a functional dependency on the structural disorder or refractive index fluctuation for shor

Weichen Yu, Ziyan Yang, Shanchuan Lin, Qi Zhao

In text-to-image (T2I) generation, a prevalent training technique involves utilizing Vision Language Models (VLMs) for image re-captioning. Even though VLMs are known to exhibit hallucination, generating descriptive content that deviates from the visual reality, the ramifications of such caption hallucinations on T2I generation performance remain under-explo

Nicholas Wolczynski, Maytal Saar-Tsechansky, Tong Wang

Despite advances in AI's performance and interpretability, AI advisors can undermine experts' decisions and increase the time and effort experts must invest to make decisions. Consequently, AI systems deployed in high-stakes settings often fail to consistently add value across experts and organizations and can even diminish the value that experts alone provi

Zijian Liu, Zhengyuan Zhou

Recently, the study of heavy-tailed noises in first-order nonconvex stochastic optimization has gotten a lot of attention since it was recognized as a more realistic condition as suggested by many empirical observations. Specifically, the stochastic noise (the difference between the stochastic and true gradient) is considered to have only a finite $\mathfrak

Chuhan Sun, Zipeng Wang

This paper has two purposes. First, we show that the classical Stein-Weiss inequality is true for p=1. Second, by considering a family of strong fractional integral operators whose kernels have singularity on every coordinate subspace, we extend this end-point result to the multi-parameter setting.

Jiayang Xie, Yutao Shen, Haiyan Cen

Advancements in unmanned aerial vehicle (UAV) remote sensing with spectral imaging enable efficient assessment of critical agronomic traits. However, existing reflectance calibration or generation methods suffer from limited prediction accuracy and practical flexibility. This study explores reliable and cost-efficient methods for the accurate conversion of d

Somsubhra Ghosh, Mainak Palit, Sujan Maity, Subhadeep Datta

We present a fully analytical model of hybridization between magnon, and phonons observed experimentally in magneto-Raman scattering in van der Waals (vdW) antiferromagnets (AFM). Here, the representative material, FePS3, has been shown to be a quasi-two-dimensional-Ising antiferromagnet, with additional features of spin-phonon coupling in the Raman spectra

Simon Salamon, Ragini Singhal

The algebra of exterior differential forms on a regular 3-Sasakian 7-manifold is investigated, with special reference to nearly-parallel $G_2$ 3-forms. This is applied to the study of 3-forms invariant under cohomogeneity-one actions by $SO(4)$ on the 7-sphere and on Berger's space $SO(5)/SO(3)$.

Ben Goertzel

We provide a comparative analysis of the deduction, induction, and abduction formulas used in Probabilistic Logic Networks (PLN) and the Non-Axiomatic Reasoning System (NARS), two uncertain reasoning frameworks aimed at AGI. One difference between the two systems is that, at the level of individual inference rules, PLN directly leverages both term and relati

Zhiyu Zhu, Jiayu Zhang, Zhibo Jin, Huaming Chen

The interpretability of deep neural networks is crucial for understanding model decisions in various applications, including computer vision. AttEXplore++, an advanced framework built upon AttEXplore, enhances attribution by incorporating transferable adversarial attack methods such as MIG and GRA, significantly improving the accuracy and robustness of model

Surangika Ranathungaa, Shravan Nayak, Shih-Ting Cindy Huang, Yanke Mao

Neural Machine Translation (NMT) systems built on multilingual sequence-to-sequence Language Models (msLMs) fail to deliver expected results when the amount of parallel data for a language, as well as the language's representation in the model are limited. This restricts the capabilities of domain-specific NMT systems for low-resource languages (LRLs). As a

Tatsuya Matsuki, Shun Furusawa, Katsuhiko Suzuki

We study the nuclear compositions in the central region of a core-collapse supernova, assuming the existence of dineutrons ($^2n$) and tetraneutrons ($^4n$). At 100~ms after core bounce, ${}^2n$ and ${}^4n$ are more abundant than deuterons within radii of approximately 100 and 50~km, respectively. Compared to the model ignoring the existence of ${}^2n$ and $

Yuanfei Huang, Chengyu Liu, Xiang Zhou

The score function for the diffusion process, also known as the gradient of the log-density, is a basic concept to characterize the probability flow with important applications in the score-based diffusion generative modelling and the simulation of It\^{o} stochastic differential equations. However, neither the probability flow nor the corresponding score fu

Y. Fuwa, T. Hasegawa, K. Hirota, T. Hoshino

The ``neutron lifetime puzzle'' arises from the discrepancy between neutron lifetime measurements obtained using the beam method, which measures decay products, and the bottle method, which measures the disappearance of neutrons. To resolve this puzzle, we conducted an experiment using a pulsed cold neutron beam at J-PARC. In this experiment, the neutron lif

Xudong Cai, Yongcai Wang, Zhaoxin Fan, Deng Haoran

Photo-realistic scene reconstruction from sparse-view, uncalibrated images is highly required in practice. Although some successes have been made, existing methods are either Sparse-View but require accurate camera parameters (i.e., intrinsic and extrinsic), or SfM-free but need densely captured images. To combine the advantages of both methods while address

Hyunwoo Cho, Sung Woong Cho, Hyeontae Jo, Hyung Ju Hwang

Differential equations (DEs) are crucial for modeling the evolution of natural or engineered systems. Traditionally, the parameters in DEs are adjusted to fit data from system observations. However, in fields such as politics, economics, and biology, available data are often independently collected at distinct time points from different subjects (i.e., repea

Yogeesh Reddy Yerrababu, Satya N. Majumdar, Benjamin Guiselin, Tridib Sadhu

We present a class of stochastic processes in which the large deviation functions of time-integrated observables exhibit singularities that relate to dynamical phase transitions of trajectories. These illustrative examples include Brownian motion with a death rate or in the presence of an absorbing wall, for which we consider a set of empirical observables s

Swati Chowdhuri, Satadip Saha, Samadrita Karmakar, Ankur Chanda

The prevailing educational methods predominantly rely on traditional classroom instruction or online delivery, often limiting the teachers' ability to engage effectively with all the students simultaneously. A more intrinsic method of evaluating student attentiveness during lectures can enable the educators to tailor the course materials and their teaching s

Sugumi Kanno, Hiroki Matsui, Shinji Mukohyama

Ho\v{r}ava-Lifshitz gravity (to be precise, its projectable version) is recognized as a renormalizable, unitary, and asymptotically free quantum field theory of gravity. Notably, one of its cosmological predictions is that it can produce scale-invariant primordial density fluctuations and primordial gravitational waves without relying on inflation. In this p

Zhe Yang, Yichang Zhang, Yudong Wang, Ziyao Xu

Large Language Models (LLMs) can correct their self-generated responses, but a decline in accuracy after self-correction is also witnessed. To have a deeper understanding of self-correction, we endeavor to decompose, evaluate, and analyze the self-correction behaviors of LLMs. By enumerating and analyzing answer correctness before and after self-correction,

Hua Farn, Hsuan Su, Shachi H Kumar, Saurav Sahay

Fine-tuning large language models (LLMs) for downstream tasks often leads to catastrophic forgetting, notably degrading the safety of originally aligned models. While some existing methods attempt to restore safety by incorporating additional safety data, the quality of such data typically falls short of that used in the original alignment process. Moreover,

Chanon Puttanawarut, Romen Samuel Wabina, Nat Sirirutbunkajorn

Background: Radiation pneumonitis is a side effect of thoracic radiation therapy. Recently, machine learning models with radiomic features have improved radiation pneumonitis prediction by capturing spatial information. To further support clinical decision-making, this study explores the role of post hoc uncertainty quantification methods in enhancing model

Koustav Ghosal, Abhranta Panigrahi, Arnav Chavan, ArunSingh

Seismic full waveform inversion (FWI) has seen promising advancements through deep learning. Existing approaches typically focus on task-specific models trained and evaluated in isolation that lead to limited generalization across different geological scenarios. In this work we introduce a task-agnostic foundational model for FWI that captures general featur

Shiyao Li, Yingchun Hu, Xuefei Ning, Xihui Liu

Vision-Language Models (VLMs) have enabled a variety of real-world applications. The large parameter size of VLMs brings large memory and computation overhead which poses significant challenges for deployment. Post-Training Quantization (PTQ) is an effective technique to reduce the memory and computation overhead. Existing PTQ methods mainly focus on large l

Yu-Cheng Shaw, Hsiu-Chuan Hsu, J. S. You

Strong light-matter interaction has been demonstrated feasible for controlling phases of matter. In this work, the interplay with disorder is studied and rich phenomena are demonstrated. Specifically, the topological phases of the disordered longer-range Su-Schrieffer-Heeger (SSH) model coupled with cavity photons are studied numerically. It is found that ca

Zhaolong Ling, Honghui Peng, Yiwen Zhang, Debo Cheng

Local causal discovery aims to learn and distinguish the direct causes and effects of a target variable from observed data. Existing constraint-based local causal discovery methods use AND or OR rules in constructing the local causal skeleton, but using either rule alone is prone to produce cascading errors in the learned local causal skeleton, and thus impa

Kentarô Yamamoto

A new axiom for rules for claims problems is introduced. It strengthens a condition studied in supply chain literature, which forces rules to disincentivize order inflation under capacity allocation and retail competition. The relevance of the axiom is further demonstrated by one of the main results of the present article: it characterizes the weighted const

Xiaotao Hu, Mingkai Jia, Xiaoyang Guo, Qian Zhang

Recent successes in autoregressive (AR) generation models, such as the GPT series in natural language processing, have motivated efforts to replicate this success in visual tasks. Some works attempt to extend this approach to autonomous driving by building video-based world models capable of generating realistic future video sequences and predicting ego stat

Jing Li, Bo Wang

Recent advancements in scene text spotting have focused on end-to-end methodologies that heavily rely on precise location annotations, which are often costly and labor-intensive to procure. In this study, we introduce an innovative approach that leverages only transcription annotations for training text spotting models, substantially reducing the dependency

Taichiro Takagi

Inspired by the study of the minimal excludant in integer partitions by G.E. Andrews and D. Newman, we introduce a pair of new partition statistics, sqrank and rerank. They are related to a polynomial bosonic form of statistical configuration sums for an integrable cellular automaton. For all nonnegative integers $n$, we prove that the partitions of $n$ on w

Zhichao Zhou, Ruijing Fang, Zhen Zhang, Xiaoyu Wang

Valley-contrasting Hall transport conventionally relies on the inversion symmetry breaking in two-dimensional systems, which greatly limits the selection range of valley materials. In particular, while monolayer transition metal dichalcogenides have been widely utilized as a well-known class of valley materials in valleytronics, the centrosymmetric nature hi

Juan José Fernández-Durán, María Mercedes Gregorio-Domínguez

Fern\'andez-Dur\'an (2004) developed a family of circular distributions based on nonnegative trigonometric sums (NNTS) which is flexible for modeling datasets exhibiting multimodality and asymmetry. Many datasets involving angles in the natural sciences, such as animal movement in biology, are expected to exhibit reflective symmetry with respect to a central

Xudong Mou, Xiaohan Zhang, Tiejun Wang, Tianyu Wo

Redundant manipulators, with their higher Degrees of Freedom (DoFs), offer enhanced kinematic performance and versatility, making them suitable for applications like manufacturing, surgical robotics, and human-robot collaboration. However, motion planning for these manipulators is challenging due to increased DoFs and complex, dynamic environments. While tra

Syeda Amina Shabbir, Frank Fei Yun, Muhammad Nadeem, Xiaolin Wang

The development of quantum materials and the tailoring of their functional properties is of fundamental interest in materials science. Here, a new design concept is proposed for the robust quantum anomalous Hall effect via entropy engineering in 2D magnets. As a prototypical example, the configurational entropy of monolayer transition metal trihalide VCl$_3$

Zexun Jiang, Yafang Shi, Maoxu Li, Hongjiang Xiao

In this paper, we introduce a multi-agent simulation framework Casevo (Cognitive Agents and Social Evolution Simulator), that integrates large language models (LLMs) to simulate complex social phenomena and decision-making processes. Casevo is designed as a discrete-event simulator driven by agents with features such as Chain of Thoughts (CoT), Retrieval-Aug

Pengyu Han, Zeyi Liu, Xiao He, Steven X. Ding

With the increasing complexity of industrial production systems, accurate fault diagnosis is essential to ensure safe and efficient system operation. However, due to changes in production demands, dynamic process adjustments, and complex external environmental disturbances, multiple operating conditions frequently arise during production. The multi-condition

Jie Zhang, Xiangkui Cao, Zhouyu Han, Shiguang Shan

Large Vision-Language Models (LVLMs) exhibit impressive potential across various tasks but also face significant privacy risks, limiting their practical applications. Current researches on privacy assessment for LVLMs is limited in scope, with gaps in both assessment dimensions and privacy categories. To bridge this gap, we propose Multi-PA, a comprehensive

Ioannis Bilionis, Ricardo C. Berrios, Luis Fernandez-Luque, Carlos Castillo

Machine Learning (ML) algorithms are vital for supporting clinical decision-making in biomedical informatics. However, their predictive performance can vary across demographic groups, often due to the underrepresentation of historically marginalized populations in training datasets. The investigation reveals widespread sex- and age-related inequities in chro

Shunpu Tang, Ruichen Zhang, Yuxuan Yan, Qianqian Yang

Semantic communication (SemCom) is an emerging paradigm aiming at transmitting only task-relevant semantic information to the receiver, which can significantly improve communication efficiency. Recent advancements in generative artificial intelligence (GenAI) have empowered GenAI-enabled SemCom (GenSemCom) to further expand its potential in various applicati

Snehal Mandal, Biplab Biswas, Suvankar Purakait, Anupam Roy

Quantum computation based on superconducting circuits utilizes superconducting qubits with Josephson tunnel junctions. Engineering high-coherence qubits requires materials optimization. In this work, we present two superconducting thin film systems, grown on silicon (Si), and one obtained from the other via annealing. Cobalt (Co) thin films grown on Si were

Chengyang Ye, Yunzhi Zhuge, Pingping Zhang

Recently, deep learning based methods have revolutionized remote sensing image segmentation. However, these methods usually rely on a pre-defined semantic class set, thus needing additional image annotation and model training when adapting to new classes. More importantly, they are unable to segment arbitrary semantic classes. In this work, we introduce Open

Mingyuan Jiu, Hailong Zhu, Hichem Sahbi

Multi-label classification is a challenging task in pattern recognition. Many deep learning methods have been proposed and largely enhanced classification performance. However, most of the existing sophisticated methods ignore context in the models' learning process. Since context may provide additional cues to the learned models, it may significantly boost

Asahi Hentona, Jun Baba, Shiki Sato, Reina Akama

User willingness is a crucial element in the sales talk process that affects the achievement of the salesperson's or sales system's objectives. Despite the importance of user willingness, to the best of our knowledge, no previous study has addressed the development of automated sales talk dialogue systems that explicitly consider user willingness. A major ba

Zhilei Shu, Ruili Feng, Yang Cao, Zheng-Jun Zha

Live animation has gained immense popularity for enhancing online engagement, yet achieving high-quality, real-time, and stable animation with diffusion models remains challenging, especially on consumer-grade GPUs. Existing methods struggle with generating long, consistent video streams efficiently, often being limited by latency issues and degraded visual

Kensuke Aihara, Akira Imakura, Keiichi Morikuni

Lanczos-type solvers for large sparse linear systems often exhibit large oscillations in the residual norms. In finite precision arithmetic, large oscillations increase the residual gap (the difference between the recursively updated residual and the explicitly computed residual) and a loss of attainable accuracy of the approximations. This issue is addresse

Zicheng Wang, Zhen Zhao, Luping Zhou, Parashkev Nachev

Brain decoding aims to reconstruct original stimuli from fMRI signals, providing insights into interpreting mental content. Current approaches rely heavily on subject-specific models due to the complex brain processing mechanisms and the variations in fMRI signals across individuals. Therefore, these methods greatly limit the generalization of models and fai

Xian-zhong Zhao, Zi-dong Gao, Dong-lin Lei

Let ${\cal K}_1(G)$ denote the inverse subsemigroup of ${\cal K}(G)$ consisting of all right cosets of all non-trivial subgroups of $G$. This paper concentrates on the study of the group $\Sigma({\cal K}_1(G))$ of all units of the completion of ${\cal K}_1(G)$. The characterizations and the representations of $\Sigma({\cal K}_1(G))$ are given when $G$ is a p

Dong-lin Lei, Jin-xing Zhao, Xian-zhong Zhao

In this paper subcentral (resp., central) idempotent series and composition subcentral (resp., central) idempotent series in an inverse semigroup are introduced and investigated. It is shown that if $S=EG$ is a factorizable inverse monoids with semilattice $E$ of idempotents and the group $G$ of units such that the natural connection $\theta$ is a dual isomo

Masahiro Ibe, Satoshi Shirai, Keiichi Watanabe

The Froggatt-Nielsen (FN) mechanism successfully explains the hierarchical structure of fermion Yukawa couplings by introducing a U(1) flavor symmetry with distinct charge assignments for different fermion generations. While some FN charge assignments have been proposed, their evaluation has largely relied on heuristic approaches. This paper systematically i

Yunhao Li, Xiang Liu, Xiaodong Wang, Xin Yuan

In this paper, we explore the potential of Snapshot Compressive Imaging (SCI) technique for recovering the underlying 3D scene structure from a single temporal compressed image. SCI is a cost-effective method that enables the recording of high-dimensional data, such as hyperspectral or temporal information, into a single image using low-cost 2D imaging senso

Shiwen Ni, Hao Cheng, Min Yang

Legal question answering (QA) has attracted increasing attention from people seeking legal advice, which aims to retrieve the most applicable answers from a large-scale database of question-answer pairs. Previous methods mainly use a dual-encoder architecture to learn dense representations of both questions and answers. However, these methods could suffer fr

Chunmeng Liu, Changjiang Bu

In this paper, we study the spectral radius of the clique tensor A(G) associated with a graph G. This tensor is a higher-order extensions of the adjacency matrix of G. A lower bound of the clique number is given via the spectral radius of A(G). It is an extension of Nikiforov's spectral bound and tighter than the bound of Nikiforov in some classes of graphs.

Bobo Hua, Florentin Münch, Haohang Zhang

For a bounded Lipschitz domain $\Sigma$ in a Riemannian surface $M$ satisfying certain curvature condition, we prove that $$\mu_{3-\beta_1} \leq \lambda_{1},$$ where $\mu_k$ ($\lambda_k$ resp.) is the $k$-th Neumann (Dirichlet resp.) Laplacian eigenvalue on $\Sigma$ and $\beta_1$ is the first Betti number of $\Sigma.$ If $\Sigma$ is smooth and simply connect

Zhengdong Li

In everyday life, photographs taken with a camera often suffer from motion blur due to hand vibrations or sudden movements. This phenomenon can significantly detract from the quality of the images captured, making it an interesting challenge to develop a deep learning model that utilizes the principles of adversarial networks to restore clarity to these blur

Zhengdong Li

In the wake of disruptive IoT technologies generating massive amounts of diverse data, Machine Learning (ML) will play a crucial role in bringing intelligence to Internet of Things (IoT) networks. This paper provides a comprehensive analysis of the current state of resource allocation within IoT networks, focusing specifically on two key categories: Low-Powe

Zechen Guo, Daxiong Sun, Peisheng Huang, Xuandong Sun

Large-scale superconducting quantum computers require massive numbers of high-performance cryogenic low-noise amplifiers (cryo-LNA) for qubit readout. Here we present a C-Band monolithic microwave integrated circuit (MMIC) cryo-LNA for this purpose. This cryo-LNA is based on 150 nm GaAs pseudomorphic high electron mobility transistor (pHEMT) process and impl

Ali Pakzad, Farjana Siddiqua

This paper addresses two significant drawbacks of an eddy viscosity turbulence model: the issue of excessive dissipation relative to energy input and the lack of a universal parameter specification. Considering the Baldwin-Lomax model with backscatter effects, we first prove the existence and uniqueness of global weak solutions under mild conditions. Our nex

Wenkang Xu, An Liu, Min-jian Zhao, Giuseppe Caire

This work considers a spatial non-stationary channel tracking problem in broadband extremely large-scale multiple-input-multiple-output (XL-MIMO) systems. In the case of spatial non-stationary, each scatterer has a certain visibility region (VR) over antennas and power change may occur among visible antennas. Concentrating on the temporal correlation of XL-M

Aditya Agarwal, Rahul Deshpande

The increase in wall-pressure fluctuations with increasing friction Reynolds number ($Re_{\tau}$) of a turbulent boundary layer (TBL) is well known in the literature. However, very few studies have investigated the $Re_{\tau}$-variation of the source terms of the pressure fluctuations, which are solely a function of the spatial velocity gradients within the

Huiqi Xue, Xiu-Hao Deng

The optimization of robust quantum control is often tailored to specific tasks and suffers from inefficiencies due to the complexity of cost functions. Our recent findings indicate a highly effective methodology for the engineering of quantum gates by initiating the process with a robust control configuration of any arbitrary gate. We first introduce the Qua

Koushik R. Das, Sudipta Dutta

We investigate electron transport through azulene molecule with four distinct electrode contact geometries using the non-equilibrium Green's function formalism within the tight-binding Hamiltonian. Employing the Q-matrix approach, we analyze quantum interference (QI) among the molecular orbitals in each contact configuration. Our results reveal distinct tran

Pengxing Feng, Hing Cheung So

Active noise control typically employs adaptive filtering to generate secondary noise, where the least mean square algorithm is the most widely used. However, traditional updating rules are linear and exhibit limited effectiveness in addressing nonlinear environments and nonstationary noise. To tackle this challenge, we reformulate the active noise control p

Jingze Ding, Zijian Zhou, Xiaodan Shao, Bingli Jiao

Integrated sensing and communication (ISAC) is emerging as a pivotal technology for next-generation wireless networks. However, existing ISAC systems are based on fixed-position antennas (FPAs), which inevitably incur a loss in performance when balancing the trade-off between sensing and communication. Movable antenna (MA) technology offers promising potenti

Aniket Shashikant Dhole

This MS thesis outlines my contributions to the closed loop control and system integration of two robotic platforms: 1) Aerobat, a flapping wing robot stabilized by air jets, and 2) Harpy, a bipedal robot equipped with dual thrusters. Both systems share a common theme of the integration of posture manipulation and thrust vectoring to achieve stability and co

Ted Edward Holmberg, Elias Ioup, Mahdi Abdelguerfi

This paper addresses the challenge of multi-agent path planning for efficient data collection in dynamic, uncertain environments, exemplified by autonomous underwater vehicles (AUVs) navigating the Gulf of Mexico. Traditional greedy algorithms, though computationally efficient, often fall short in long-term planning due to their short-sighted nature, missing

Takaaki Ishii, Takaki Matsumoto, Kanta Nakano, Ryosuke Suda

We study oscillons in a real scalar field theory in a (3+1)-dimensional AdS space with global coordinates. The initial configuration is given by a Gaussian shape with an appropriate core size as in Minkowski spacetime. The solution exhibits a long lifetime. In particular, since the AdS space can be seen as a box, the recurrence phenomenon can be observed und

Vaikunth M, Dejey D, Vishaal C, Balamurali S

Helmet detection is crucial for advancing protection levels in public road traffic dynamics. This problem statement translates to an object detection task. Therefore, this paper compares recent You Only Look Once (YOLO) models in the context of helmet detection in terms of reliability and computational load. Specifically, YOLOv8, YOLOv9, and the newly releas

Dongqi Sun

In this paper, we give the definition of the annulus complex of a handlebody and use the combinatorial methods to prove its connectivity.

Jukka Ruohonen

Assertions are a classical and typical software development technique. These are extensively used also in operating systems and their kernels, including the Linux kernel. The paper fills a gap in existing knowledge by empirically examining the longitudinal evolution of assertion use in the Linux kernel. According to the results, the use of assertions that ca

Chandravardhan Singh Raghaw, Aryan Yadav, Jasmer Singh Sanjotra, Shalini Dangi

Objective: To develop a novel deep learning framework for the automated segmentation of colonic polyps in colonoscopy images, overcoming the limitations of current approaches in preserving precise polyp boundaries, incorporating multi-scale features, and modeling spatial dependencies that accurately reflect the intricate and diverse morphology of polyps. Met

Mingsheng Ying, Li Zhou, Gilles Barthe

In this paper, we investigate the fundamental laws of quantum programming. We extend a comprehensive set of Hoare et al.'s basic laws of classical programming to the quantum setting. These laws characterise the algebraic properties of quantum programs, such as the distributivity of sequential composition over (quantum) if-statements and the unfolding of nest

J. Chen, S. D. Ahipaşaoğlu, N. Zhang, Y. Yang

We extend the classical mean-variance (MV) framework and propose a robust and sparse portfolio selection model incorporating an ellipsoidal uncertainty set to reduce the impact of estimation errors and fixed transaction costs to penalize over-diversification. In the literature, the MV model under fixed transaction costs is referred to as the sparse or cardin

Jiaxi Nie

Chv\'atal showed that for any tree $T$ with $k$ edges the Ramsey number $R(T,n)=k(n-1)+1$ ("Tree-complete graph Ramsey numbers." Journal of Graph Theory 1.1 (1977): 93-93). For $r=3$ or $4$, we show that, if $T$ is an $r$-uniform non-trivial tight tree, then the hypergraph Ramsey number $R(T,n)=\Theta(n^{r-1})$. The 3-uniform result comes from observing a co

Rômulo Damasclin Chaves dos Santos, Jorge Henrique de Oliveira Sales

This paper introduces a novel theoretical framework for identifying Lagrangian Coherent Structures (LCS) in manifolds with non-constant curvature, extending the theory to Finsler manifolds. By leveraging Riemannian and Finsler geometry, we generalize the deformation tensor to account for geodesic stretching in these complex spaces. The main result demonstrat

Sayan Kar

We study how a charged particle moving in a uniform magnetic field along its standard circular path (cyclotron motion) reacts to a short-duration, homogeneous, uniform electric field pulse injected in the plane perpendicular to the magnetic field. A `permanent' change in the radius of the initial circle and a shift of its centre is noted at later times, afte

Jaehoon Cho, Minjung Yoo, Jini Yang, Sunok Kim

We address the challenge of single-image de-raining, a task that involves recovering rain-free background information from a single rain image. While recent advancements have utilized real-world time-lapse data for training, enabling the estimation of consistent backgrounds and realistic rain streaks, these methods often suffer from computational and memory

Shuichi Yokoyama

The general theory of relativity is currently established as the most precise theory of gravity supported by observations, and its application is diverse ranging from astronomy to cosmology, while its application to astrophysics has been restricted only to compact stars due to the assumption that the Newtonian approximation is sufficient for celestial bodies

Yiyuan Liang, Zhiying Yan, Liqun Chen, Jiahuan Zhou

Vision-centric autonomous driving systems require diverse data for robust training and evaluation, which can be augmented by manipulating object positions and appearances within existing scene captures. While recent advancements in diffusion models have shown promise in video editing, their application to object manipulation in driving scenarios remains chal

Xuewei Li, Zhenzhen Nie, Mei Yu, Zijian Zhang

Instance features in images exhibit spurious correlations with background features, affecting the training process of deep neural classifiers. This leads to insufficient attention to instance features by the classifier, resulting in erroneous classification outcomes. In this paper, we propose a data augmentation method called Spurious Correlations Guided Syn

Kai Murai, Kazunori Nakayama

We explore a concrete realization of a Nelson-Barr model addressing the strong CP problem with suppressed unfavorable corrections. This model has a scalar field that spontaneously breaks discrete symmetry, and its phase component can naturally be relatively light, which we call the Nelson-Barr axion. It has both a tree-level potential and the QCD instanton-i

Kevin Putra Santoso, Anny Yuniarti, Dwiyasa Nakula, Dimas Prihady Setyawan

Generative AI has transformed the animation industry. Several models have been developed for image-to-image translation, particularly focusing on converting real-world images into anime through unpaired translation. Scenimefy, a notable approach utilizing contrastive learning, achieves high fidelity anime scene translation by addressing limited paired data t

Yuebing Zhou, Jiawei Hu, Hongwei Yu

We study the transition rates of an atom rotating in a circular orbit, which is coupled with fluctuating electromagnetic fields in vacuum. We find that when the rotational angular velocity exceeds the transition frequency of the atom, the excitation rate can reach the same order of magnitude as the emission rate, even with an extremely low centripetal accele

Jumpei Kato, Kaito Wada, Kosuke Ito, Naoki Yamamoto

Simulating open quantum systems is an essential technique for understanding complex physical phenomena and advancing quantum technologies. Some quantum algorithms simulate Lindblad dynamics exponentially accurately, i.e., they achieve logarithmically short circuit depth in terms of accuracy, but they need to coherently encode all possible jump operators with

Cristian Villa-Pérez, Luis J. Mendoza-Herrera, Jesica M. J. Santillán, David Muñetón Arboleda

Metal nanostructures have received significant attention in recent years owing to their peculiar physical and chemical properties, which span a wide range of practical applications in a variety of scientific and engineering fields. Herein, we report a study of the optical properties and application in the field enhancement of silver nanoplates (Ag-Nplates) o

J. M. Joel Ong

Existing asteroseismic rotational measurements assume that stars rotate around a single axis. However, tidal torques from misaligned companions, or their possible engulfment, may bring the rotational axis of a star's envelope out of alignment with its core, breaking azimuthal symmetry. I derive perturbative expressions for asteroseismic signatures of such hi