April 2024 arXiv papers — page 111

Tomáš Sourada, Jana Straková, Rudolf Rosa

We focus on morphological inflection in out-of-vocabulary (OOV) conditions, an under-researched subtask in which state-of-the-art systems usually are less effective. We developed three systems: a retrograde model and two sequence-to-sequence (seq2seq) models based on LSTM and Transformer. For testing in OOV conditions, we automatically extracted a large data

Rongguang Ye, Wei-Bin Kou, Ming Tang

Fairness in federated learning has emerged as a critical concern, aiming to develop an unbiased model among groups (e.g., male or female) of diverse sensitive features. However, there is a trade-off between model performance and fairness, i.e., improving model fairness will decrease model performance. Existing approaches have characterized such a trade-off b

Dylan Hyatt-Denesik, Afrouz Jabal Ameli, Laura Sanita

Flexible network design deals with building a network that guarantees some connectivity requirements between its vertices, even when some of its elements (like vertices or edges) fail. In particular, the set of edges (resp. vertices) of a given graph are here partitioned into safe and unsafe. The goal is to identify a minimum size subgraph that is 2-edge-con

You-You Lin, Ji-Ying Wang, Ailin Zhang

There are three $S$ and seven $P$ fully charmed $[cc][\bar c\bar c]$ tetraquarks, the mass spectrum from $1S$ to $2P$ excitations is calculated in a non-relativistic quark potential model. In the calculation, the interactions among four internal quarks/antiquark are approximated as a dominant color interaction between a diquark and an antidiquark, and a resi

Wei Zhang, Zihao Wang, Jie Fan, Hao Wu

The Gromov-Wasserstein distance is a notable extension of optimal transport. In contrast to the classic Wasserstein distance, it solves a quadratic assignment problem that minimizes the pair-wise distance distortion under the transportation of distributions and thus could apply to distributions in different spaces. These properties make Gromov-Wasserstein wi

The Tien Mai

The problem of estimating a matrix based on a set of its observed entries is commonly referred to as the matrix completion problem. In this work, we specifically address the scenario of binary observations, often termed as 1-bit matrix completion. While numerous studies have explored Bayesian and frequentist methods for real-value matrix completion, there ha

Bor-Shiun Wang, Chien-Yi Wang, Wei-Chen Chiu

Recent advancements in post-hoc and inherently interpretable methods have markedly enhanced the explanations of black box classifier models. These methods operate either through post-analysis or by integrating concept learning during model training. Although being effective in bridging the semantic gap between a model's latent space and human interpretation,

Yaohua Sun, Jianfeng Zhu, Mugen Peng

To achieve ubiquitous wireless connectivity, low earth orbit (LEO) satellite networks have drawn much attention. However, effective beam management is challenging due to time-varying cell load, high dynamic network topology, and complex interference situations. In this paper, under inter-satellite handover frequency and satellite-terrestrial/inter-beam inter

Jiyang Li, Lechao Cheng, Zhangye Wang, Tingting Mu

Cinemagraph is a unique form of visual media that combines elements of still photography and subtle motion to create a captivating experience. However, the majority of videos generated by recent works lack depth information and are confined to the constraints of 2D image space. In this paper, inspired by significant progress in the field of novel view synthe

Chengpei Xu, Hao Fu, Long Ma, Wenjing Jia

Localizing text in low-light environments is challenging due to visual degradations. Although a straightforward solution involves a two-stage pipeline with low-light image enhancement (LLE) as the initial step followed by detector, LLE is primarily designed for human vision instead of machine and can accumulate errors. In this work, we propose an efficient a

Chenming Shang, Hengyuan Zhang, Hao Wen, Yujiu Yang

The multimodal deep neural networks, represented by CLIP, have generated rich downstream applications owing to their excellent performance, thus making understanding the decision-making process of CLIP an essential research topic. Due to the complex structure and the massive pre-training data, it is often regarded as a black-box model that is too difficult t

Mengfan Ma, Mingyu Xiao, Tian Bai, Xin Cheng

In the one-dimensional facility assignment problem, m facilities and n agents are positioned along the real line. Each agent will be assigned to a single facility to receive service. Each facility incurs a building cost, which is shared equally among the agents utilizing it. Additionally, each agent independently bears a connection cost to access a facility.

Xian-Peng Zhang

Superconducting diode effects (SDEs) occur in systems with asymmetric critical supercurrents $|I^c_+|\neq |I^c_-|$ yielding dissipationless flow in one direction $(e.g., +)$, while dissipative transport in the opposite direction $(-)$. Here we investigate the SDE in a phase-biased $\phi$ Josephson junction with a double-barrier resonant-tunneling InAs nanowi

Zoi Lygizou, Dimitris Kalles

Current trust and reputation models continue to have significant limitations, such as the inability to deal with agents constantly entering or exiting open multi-agent systems (open MAS), as well as continuously changing behaviors. Our study is based on CA, a previously proposed decentralized computational trust model from the trustee's point of view, inspir

Reshmi Roy, Arup Biswas, Arnab Pal

Performance modeling is a key issue in queuing theory and operation research. It is well-known that the length of a queue that awaits service or the time spent by a job in a queue depends not only on the service rate, but also crucially on the fluctuations in service time. The larger the fluctuations, the longer the delay becomes and hence, this is a major h

Jianfeng Zhu, Yaohua Sun, Mugen Peng

Low earth orbit (LEO) satellite communication based on 3GPP standard is seen as a promising solution to rolling out communication services in areas without terrestrial base stations. However, due to the fast movement of satellites and large beam footprint size, the existing 5G timing advance (TA) estimation mechanism cannot be directly applied when global na

Jianfeng Zhu, Yaohua Sun, Mugen Peng

Low earth orbit (LEO) satellite communication networks have been considered as promising solutions to providing high data rate and seamless coverage, where satellite beam management plays a key role. However, due to the limitation of beam resource, dynamic network topology, beam spectrum reuse, time-varying traffic arrival and service continuity requirement,

Yuwei Tang, Zhenyi Lin, Qilong Wang, Pengfei Zhu

Recently, pre-trained vision-language models (e.g., CLIP) have shown great potential in few-shot learning and attracted a lot of research interest. Although efforts have been made to improve few-shot ability of CLIP, key factors on the effectiveness of existing methods have not been well studied, limiting further exploration of CLIP's potential in few-shot l

Arik Avagyan, Emanuel Knill, Scott Glancy

Gaussian states are ubiquitous in quantum optics and information processing, and it is essential to have effective tools for their characterization. One such tool is a photon-number-resolving detector, and the simplest configuration involves counting the total number of photons in the state to be characterized. This motivates the following question: What pro

Kartik K Iyer, Kalobaran Maiti, S. Rayaprol, B A Chalke

We report the results of dc susceptibility and heat capacity measurements on the (ball-milled) nanocrystalline rare-earth (R) ternary compound, crystallizing in Gd4RhIn type, cubic Dy4RhAl compound. The bulk form of this compound has been known to undergo antiferromagnetic ordering at (TN=) 18 K with concomitant cluster spin glass anomalies. The present stud

Rodrigo A. González, Siqi Pan, Cristian R. Rojas, James S. Welsh

Refined instrumental variable methods have been broadly used for identification of continuous-time systems in both open and closed-loop settings. However, the theoretical properties of these methods are still yet to be fully understood when operating in closed-loop. In this paper, we address the consistency of the simplified refined instrumental variable met

Gebhard Böckle, Chun-Yin Hui

Let $\rho_\ell$ be a semisimple $\ell$-adic representation of a number field $K$ that is unramified almost everywhere. We introduce a new notion called weak abelian direct summands of $\rho_\ell$ and completely characterize them, for example, if the algebraic monodromy of $\rho_\ell$ is connected. If $\rho_\ell$ is in addition $E$-rational for some number fi

Jan Eisner, Lenka Zalabová

We describe the quaternionic Heisenberg group in the dimension $7$ as a matrix group. We study the local control of a compatible left-invariant control system. We describe the impact of symmetries of the corresponding sub-Riemannian structure on the optimality of geodesics.

Hao Guo, Peixue Jiang, Xiaofeng Ma, Boxing Hu

This study introduces an order-lifted inversion/retrieval method for implementing high-order schemes within the framework of an unstructured-mesh-based finite-volume method. This method defines a special representation called the data order-lifted inversion of neighbor cells (DOLINC) differential, which transforms the degrees of freedom of wide templates int

Qinghe Ma, Jian Zhang, Lei Qi, Qian Yu

Both limited annotation and domain shift are prevalent challenges in medical image segmentation. Traditional semi-supervised segmentation and unsupervised domain adaptation methods address one of these issues separately. However, the coexistence of limited annotation and domain shift is quite common, which motivates us to introduce a novel and challenging sc

Francesco G. Blanco, Enrico Russo, Maurizio Palesi, Davide Patti

Currently, there is a growing trend of outsourcing the execution of DNNs to cloud services. For service providers, managing multi-tenancy and ensuring high-quality service delivery, particularly in meeting stringent execution time constraints, assumes paramount importance, all while endeavoring to maintain cost-effectiveness. In this context, the utilization

Abhijnan Nath, Huma Jamil, Shafiuddin Rehan Ahmed, George Baker

Event coreference resolution (ECR) is the task of determining whether distinct mentions of events within a multi-document corpus are actually linked to the same underlying occurrence. Images of the events can help facilitate resolution when language is ambiguous. Here, we propose a multimodal cross-document event coreference resolution method that integrates

Nan Cui, Xiaodong Gu, Beijun Shen

Learning code representations has been the core prerequisite of many software engineering tasks such as code clone detection and code generation. State-of-the-art program representation techniques mainly utilize pre-trained language models (PLMs) such as CodeBERT. A Transformer encoder is firstly pre-trained on a large-scale code corpus to acquire general kn

Daniele Ancora, Alessandro Zunino, Giuseppe Vicidomini, Alvaro H. Crevenna

Confocal laser scanning microscopy (CLSM) stands out as one of the most widely used microscopy techniques, thanks to its three-dimensional imaging capability and its sub-diffraction spatial resolution, achieved through the closure of a pinhole in front of a single-element detector. However, the pinhole also rejects useful photons and beating the diffraction

Zhi-Guo He, Xiao-Bo Jin, Bernd A. Kniehl, Rong Li

Within the framework of nonrelativistic-QCD factorization, we calculate both the next-to-leading-order relativistic and QCD corrections to prompt $J/\psi$ pair production, with feeddown from $\psi(2S)$ mesons, via photon-photon collisions at future $e^+e^-$ colliders including the Future Circular Lepton Collider (FCC-ee), the Circular Electron Positron Colli

Gordan Krekovic, Antonio Poscic, Dejan Grba

Awareness about the immense impact that artificial intelligence (AI) might have or already has made on the social, economic, political, and cultural realities of our world has become part of the mainstream public discourse. Attributes such as ethical, responsible, or explainable emerge as associative and descriptive nominal references in guidelines that infl

Shiyao Wang, Xiuping Liu, Charlie C. L. Wang, Jian Liu

Learning the skill of human bimanual grasping can extend the capabilities of robotic systems when grasping large or heavy objects. However, it requires a much larger search space for grasp points than single-hand grasping and numerous bimanual grasping annotations for network learning, making both data-driven or analytical grasping methods inefficient and in

Yunan Wang, Chuxiong Hu, Yujie Lin, Zeyang Li

Most existing necessary conditions for optimal control based on adjoining methods require both state and costate information, yet the unobservability of costates for a given feasible trajectory impedes the determination of optimality in practice. This paper establishes a novel theoretical framework for time-optimal control of controllable linear systems with

Masayo Fujimura, Oona Rainio, Matti Vuorinen

We prove an identity which connects the visual angle metric $v_{\mathbb{H}^2}$ and the hyperbolic metric $\rho_{\mathbb{H}^2}$ of the upper half plane $\mathbb{H}^2$. The proof is based on geometric arguments and uses computer algebra methods for formula manipulation. We also prove a sharp H\"older continuity result for quasiregular mappings with respect to

Ji-Cai Liu

Motivated by Berkovich's nine $q$-binomial identities involving the Legendre symbol $(\frac{d}{3})$, we establish a unified form of $q$-binomial identities of this type through a combinatorial approach. This unified form includes Berkovich's nine identities as special cases. Many such identities can be also deduced from this unified form.

Ayush Thakur, Raghav Gupta

The relentless pursuit of enhancing Large Language Models (LLMs) has led to the advent of Super Retrieval-Augmented Generation (Super RAGs), a novel approach designed to elevate the performance of LLMs by integrating external knowledge sources with minimal structural modifications. This paper presents the integration of Super RAGs into the Mistral 8x7B v1, a

Xinzhe Zheng, Sijie Ji, Yipeng Pan, Kaiwen Zhang

Inertial tracking is vital for robotic IoT and has gained popularity thanks to the ubiquity of low-cost inertial measurement units and deep learning-powered tracking algorithms. Existing works, however, have not fully utilized IMU measurements, particularly magnetometers, nor have they maximized the potential of deep learning to achieve the desired accuracy.

Wei Zou, Ziyuan Zhuang, Xiang Geng, Shujian Huang

Paraphrase generation strives to generate high-quality and diverse expressions of a given text, a domain where diffusion models excel. Though SOTA diffusion generation reconciles generation quality and diversity, textual diffusion suffers from a truncation issue that hinders efficiency and quality control. In this work, we propose \textit{L}atent \textit{D}i

Otto Brookes, Majid Mirmehdi, Hjalmar Kuhl, Tilo Burghardt

We show that chimpanzee behaviour understanding from camera traps can be enhanced by providing visual architectures with access to an embedding of text descriptions that detail species behaviours. In particular, we present a vision-language model which employs multi-modal decoding of visual features extracted directly from camera trap videos to process query

Yang Hu, Jinxia Zhang, Kaihua Zhang, Yin Yuan

Camouflaged object detection (COD) remains a challenging task in computer vision. Existing methods often resort to additional branches for edge supervision, incurring substantial computational costs. To address this, we propose the Co-Supervised Spotlight Shifting Network (CS$^3$Net), a compact single-branch framework inspired by how shifting light source ex

Zhenglong Li, Vincent Tam

In recent years, deep or reinforcement learning approaches have been applied to optimise investment portfolios through learning the spatial and temporal information under the dynamic financial market. Yet in most cases, the existing approaches may produce biased trading signals based on the conventional price data due to a lot of market noises, which possibl

A. Movaghar, R. Chiodi, M. Oevermann, O. Desjardins

The interaction between turbulence and surface tension is studied numerically using the one-dimensional-turbulence (ODT) model. ODT is a stochastic model simulating turbulent flow evolution along a notional one-dimensional line of sight by applying instantaneous maps that represent the effects of individual turbulent eddies on property fields. It provides af

Gabriel Marin-Sanchez, David Amaro

Even a minor boost in solving combinatorial optimization problems can greatly benefit multiple industries. Quantum computers, with their unique information processing capabilities, hold promise for delivering such enhancements. The Filtering Variational Quantum Eigensolver (F-VQE) is a variational hybrid quantum algorithm designed to solve combinatorial opti

Thinh Nguyen

Both the USA TST 2008 and the ELMO Shortlist 2013 suggested two issues that are connected to fixed points. These problems provide a strong linkage between the various attributes of specific points in a triangle. In this article, we will first investigate various theorems concerning the fixed points that have been presented, and then we will demonstrate how t

Pallavi Basavaraju, Shrinath Hadimani, Sachindranath Jayaraman

We derive some localization and perturbation results for coneigenvalues of quaternion matrices. In localization results, we derive Ger\v{s}gorin type theorems for right and left coneigenvalues of quaternion matrices. We prove that certain coneigenvalues lie in the union of Ger\v{s}gorin balls, in contrast to the complex situation where all eigenvalues lie in

Sambal Shikhar, Anupam Sobti

Detecting various types of stresses (nutritional, water, nitrogen, etc.) in agricultural fields is critical for farmers to ensure maximum productivity. However, stresses show up in different shapes and sizes across different crop types and varieties. Hence, this is posed as an anomaly detection task in agricultural images. Accurate anomaly detection in agric

Takuya Mizoguchi, Seiji Matsumoto, Minoru Biyajima

The L3 Collaboration reported data on 2-jet and 3-jet Bose--Einstein correlations (BECs) with the results obtained through the $\tau$-model in 2011. In this study, we analyze these correlations using the conventional formula with the Gaussian long-range correlation (${\rm CF_I\times LRC_{(Gauss)}}$). The estimated ranges of interactions for 2-jet and 3-jet,

Pantelis S. Apostolopoulos, Noeleen Naidoo

The existence of a set of $10$ Intrinsic Conformal Symmetries, which acts on three-dimensional hypersurfaces (spacelike or timelike), leads to the existence of two distinct families of 5D geometries. These models represent the general solutions of the bulk field equations where their energy-momentum tensor, includes only two components: a negative cosmologic

Johan Edstedt, Georg Bökman, Zhenjun Zhao

In this paper, we analyze and improve into the recently proposed DeDoDe keypoint detector. We focus our analysis on some key issues. First, we find that DeDoDe keypoints tend to cluster together, which we fix by performing non-max suppression on the target distribution of the detector during training. Second, we address issues related to data augmentation. I

Shanpeng Li, Donatello Telesca, Harley I. Kornblum, David Nathanson

In cancer research, leveraging patient-derived xenografts (PDXs) in pre-clinical experiments is a crucial approach for assessing innovative therapeutic strategies. Addressing the inherent variability in treatment response among and within individual PDX lines is essential. However, the current literature lacks a user-friendly statistical power analysis tool

Yidan Liu, Jun Yue, Shaobo Xia, Pedram Ghamisi

As a newly emerging advance in deep generative models, diffusion models have achieved state-of-the-art results in many fields, including computer vision, natural language processing, and molecule design. The remote sensing (RS) community has also noticed the powerful ability of diffusion models and quickly applied them to a variety of tasks for image process

C. Zhang, Z. Zhang, P. Zhang, J. Zhou

It was previously observed that colliding liquid droplets in a gaseous medium tend to bounce off at elevated gas pressure up to about 12 atm. In this letter, we extended the droplet collision experiment to up to 41 atm for the first time and reported a noticeable discovery that the tendency is flattened off at higher pressures. The colliding droplets stop bo

Xiangtian Bu, Haitao Liu, Yuanchang Li

Despite being successfully synthesized [Zhang $et$ $al.$, Nat. Mater. \textbf{20}, 1073 (2021)], the monolayer structure of stable hexagonal TiO$_2$ is unknown, and it is not even clear whether it can exist in a freestanding form. Through first-principles calculations, we have identified two previously uncharted stable structures, namely, distorted 1$\times$

Zhuyang Xie, Yan Yang, Jie Wang, Xiaorong Liu

Multimodal video sentiment analysis aims to integrate multiple modal information to analyze the opinions and attitudes of speakers. Most previous work focuses on exploring the semantic interactions of intra- and inter-modality. However, these works ignore the reliability of multimodality, i.e., modalities tend to contain noise, semantic ambiguity, missing mo

Alain Kraus

Let $\overline{\mathbb{Q}}$ be an algebraic closure of $\mathbb{Q}$ and $\mathbb{Q}^{tr}$ be the subfield of $\overline{\mathbb{Q}}$ obtained by taking the union of all totally real number fields. For any prime $p\geq 3$, let $F_p/\mathbb{Q}$ be the Fermat curve of equation $x^p+y^p+z^p=0$. In 1996, Pop has shown that the field $\mathbb{Q}^{tr}$ is large. In

Qi Zhao, M. Salman Asif, Zhan Ma

The primary focus of Neural Representation for Videos (NeRV) is to effectively model its spatiotemporal consistency. However, current NeRV systems often face a significant issue of spatial inconsistency, leading to decreased perceptual quality. To address this issue, we introduce the Pyramidal Neural Representation for Videos (PNeRV), which is built on a mul

Zihao Song

We consider the global well-posedness and decay rates for solutions of 3D incompressible micropolar equation in the critical Besov space. Spectrum analysis allows us to find not only parabolic behaviors of solutions, but also damping effect of angular velocity in the low frequencies. Based on this observation, we establish the global well-posedness with more

Anowar Shaikh, Shubhalaxmi Rath, Sadhana Dash, Binata Panda

We have studied the charge and the heat transport properties of a hot and dense QCD matter by solving the relativistic Boltzmann transport equation using a novel approximation method. Following the recently developed novel relaxation time approximation (RTA) model, we have proposed a novel Bhatnagar-Gross-Krook (BGK) model with a modified collision integral

Zihao Song

In this paper, we study the Navier-Stokes-Korteweg equations governed by the evolution of compressible fluids with capillarity effects. We first investigate the global well-posedness of solution in the critical Besov space for large initial data. Contrary to pure parabolic methods in Charve, Danchin and Xu \cite{CDX}, we also take the strong dispersion due t

Binghua Li, Jie Mao, Zhe Sun, Chao Li

Automated diagnosis with artificial intelligence has emerged as a promising area in the realm of medical imaging, while the interpretability of the introduced deep neural networks still remains an urgent concern. Although contemporary works, such as XProtoNet and MProtoNet, has sought to design interpretable prediction models for the issue, the localization

Weidong Guo, Hantao Zhang, Shouhong Wan, Bingbing Zou

Accurate segmentation of metastatic lymph nodes in rectal cancer is crucial for the staging and treatment of rectal cancer. However, existing segmentation approaches face challenges due to the absence of pixel-level annotated datasets tailored for lymph nodes around the rectum. Additionally, metastatic lymph nodes are characterized by their relatively small

Zhenwei Wang, Qiule Sun, Bingbing Zhang, Pengfei Wang

Few-shot learning has been successfully applied to medical image classification as only very few medical examples are available for training. Due to the challenging problem of limited number of annotated medical images, image representations should not be solely derived from a single image modality which is insufficient for characterizing concept classes. In

Praveen Mathil, Jitender Kumar

Let $R$ be a ring with unity. The clean graph $\text{Cl}(R)$ of a ring $R$ is the simple undirected graph whose vertices are of the form $(e,u)$, where $e$ is an idempotent element and $u$ is a unit of the ring $R$ and two vertices $(e,u)$, $(f,v)$ of $\text{Cl}(R)$ are adjacent if and only if $ef = fe =0$ or $uv = vu=1$. In this manuscript, for a commutativ

Yun Ma, Yihong Wu, Pengkun Yang

We consider the problem of approximating a general Gaussian location mixture by finite mixtures. The minimum order of finite mixtures that achieve a prescribed accuracy (measured by various $f$-divergences) is determined within constant factors for the family of mixing distributions with compactly support or appropriate assumptions on the tail probability in

Oem Trivedi, Robert J. Scherrer

We explore the asymptotic future evolution of holographic dark energy (HDE) models, in which the density of the dark energy is a function of a cutoff scale $L$. We develop a general methodology to determine which models correspond to future big rip, little rip, and pseudo-rip (de Sitter) evolution, and we apply this methodology to a variety of well-studied H

Katja Fahrion, Torsten Böker, Michele Perna, Tracy L. Beck

We present a detailed study of the centre of NGC4654, a Milky Way-like spiral galaxy in the Virgo cluster that has been reported to host a double stellar nucleus, thus promising a rare view of ongoing star cluster infall into a galaxy nucleus. Analysing JWST NIRSpec integral-field spectroscopic data and Hubble Space Telescope imaging of the inner 330 $\times

Yanzeng Li, Cheng Zeng, Jialun Zhong, Ruoyu Zhang

Simulated Patients (SPs) play a crucial role in clinical medical education by providing realistic scenarios for student practice. However, the high cost of training and hiring qualified SPs, along with the heavy workload and potential risks they face in consistently portraying actual patients, limit students' access to this type of clinical training. Consequ

M. S. S. Manasa, Kali Krishna Kota, Praful D. Mankar, Harpreet S. Dhillon

We consider a multi-user multiple input single output (MU-MISO) system assisted by a reconfigurable intelligent surface (RIS). For such a system, we aim to optimally select the RIS phase shifts and precoding vectors for maximizing the effective rank of the weighted channel covariance matrix which in turn improves the channel capacity. For a low-complex trans

Ajmal PS, Ditto PS, Jithin VG

Intellecta dataset emerges as an innovative synthetic dataset, engineered to enhance the cognitive processing capabilities of contemporary language models. With a composition of 11.53 billion tokens, integrating 8.01 billion tokens of synthetic data with 3.52 billion tokens of rich textbook data, Intellecta is crafted to foster advanced reasoning and compreh

Jiaoying Pei

Traditional finance and macroeconomic models usually assume people can form rational expectations or reach them via a learning path by minimizing prediction errors. The recent Reference Model Based Learning (RMBL) model provides a new perspective: It hypothesizes that people minimize surprises instead of errors. Following the spirit of Simon's "satisficing"

Jingxuan Zhang, Zhengping Zhu, Limin Wang, Ruifeng Hu

In this work, we study the causality of near-wall inner and outer turbulent motions. The inner motions are defined as the self-sustained near-wall cycle, and the outer motions as those living in the logarithmic layer exhibiting footprints on the near-wall region. Causal inference with three typical methods is performed, i.e. transfer entropy, information flo

Lennart Ante, Aman Saggu, Benjamin Schellinger, Friedrich Wazinksi

This paper investigates the potential of blockchain-based fan tokens, a class of crypto asset that grants holders access to voting on club decisions and other perks, as a mechanism for stimulating democratized decision-making and fan engagement in the sports and esports sectors. By utilizing an extensive dataset of 3,576 fan token polls, we reveal that fan t

Omar Mustafa

We study the Klein-Gordon (KG) oscillators in a spinning cosmic string spacetime and an external magnetic field. The corresponding KG-equation is shown to admit a solution in the form of the confluent hypergeometric functions/polynomials. Consequently, the corresponding energies are shown to be given in a quadratic equation of a delicate nature that has to b

Sriganapathy Raghav, Suranjana Ghosh, Jayanta Bera, Utpal Roy

Circular atomtronics is known to exhibit a uniform ground state, unlike elliptical atomtronics. In elliptical atomtronics, the matter wave tends to accumulate along the semimajor edges during its time dynamics, which we depict by the survival function. Consequently, the dynamical time scales become coupled to the eccentricity, making the dynamics nontrivial

Anna Knezevic

Enhancing the existing solution for pricing of fixed income instruments within Black-Karasinski model structure, with neural network at various parameterisation points to demonstrate that the method is able to achieve superior outcomes for multiple calibrations across extended projection horizons.

Xiaoli Li, Nan Zheng, Jie Shen

We construct new higher-order implicit-explicit (IMEX) schemes using the generalized scalar auxiliary variable (GSAV) approach for the Landau-Lifshitz equation. These schemes are linear, length preserving and only require solving one elliptic equation with constant coefficients at each time step. We show that numerical solutions of these schemes are uniforml

Gang Liao, Ye Liu, Jianjun Chen, Daniel J. Abadi

The past two decades have witnessed significant success in applying columnar storage to data warehousing and analytics. However, the rapid growth of machine learning poses new challenges. This paper presents Bullion, a columnar storage system tailored for machine learning workloads. Bullion addresses the complexities of data compliance, optimizes the encodin

Xue Feng, Thomas Strohmer

Autoencoders are important generative models that, among others, have the ability to interpolate image sequences. However, interpolated images are usually not semantically meaningful.In this paper, motivated by dynamic optimal transport, we consider image interpolation as a mass transfer problem and propose a novel regularization term to penalize non-smooth

Yinqiu Liu, Hongyang Du, Dusit Niyato, Jiawen Kang

Mobile AI-Generated Content (AIGC) has achieved great attention in unleashing the power of generative AI and scaling the AIGC services. By employing numerous Mobile AIGC Service Providers (MASPs), ubiquitous and low-latency AIGC services for clients can be realized. Nonetheless, the interactions between clients and MASPs in public mobile networks, pertaining

Xuefei Cao, Shijia Wang, Yongdao Zhou

Approximate Bayesian computation (ABC) is a class of Bayesian inference algorithms that targets for problems with intractable or {unavailable} likelihood function. It uses synthetic data drawn from the simulation model to approximate the posterior distribution. However, ABC is computationally intensive for complex models in which simulating synthetic data is

Zhihao He, Chen Ma, Jiannong Wang, Iam Keong Sou

Manganese telluride (MnTe) has garnered strong interest recently for its antiferromagnetic semiconductor properties, which are promising for applications in spintronics, data storage, and quantum computing. In this study, we discovered that the deposition of FeTe at 300oC onto zinc-blende MnTe (ZB-MnTe) via molecular beam epitaxy (MBE) results in a phase tra

Yifan Qiao, Shanxiu He, Yingrui Yang, Parker Carlson

This paper revisits cluster-based retrieval that partitions the inverted index into multiple groups and skips the index partially at cluster and document levels during online inference using a learned sparse representation. It proposes an approximate search scheme with two parameters to control the rank-safeness competitiveness of pruning with segmented maxi

Si-Qi Liu, Yuewei Wang, Youjin Zhang

We define a certain extension of the Ablowitz-Ladik hierarchy, and prove that this extended integrable hierarchy coincides with the topological deformation of the Principal Hierarchy of a generalized Frobenius manifold with non-flat unity.

Yibo Zhong, Yao Zhou

Low-rank adaptation (LoRA) has shifted the paradigm of adapting pre-trained Vision Transformers (ViT), achieving great efficiency by updating only a subset of tailored parameters to approximate weight updates. However, the multi-head design of the self-attention mechanism, with the heads working in parallel in the computation flow, exhibiting similar visual

Shan Gao, Amit K. Chakraborty, Russell Greiner, Mark A. Lewis

Forecasting the occurrence and absence of novel disease outbreaks is essential for disease management. Here, we develop a general model, with no real-world training data, that accurately forecasts outbreaks and non-outbreaks. We propose a novel framework, using a feature-based time series classification method to forecast outbreaks and non-outbreaks. We test

Kai Tang, Jin Chen

Remote sensing change detection (CD) is a pivotal technique that pinpoints changes on a global scale based on multi-temporal images. With the recent expansion of deep learning, supervised deep learning-based CD models have shown satisfactory performance. However, CD sample labeling is very time-consuming as it is densely labeled and requires expert knowledge

Chuchu Chen, Tonghe Dang, Jialin Hong, Guoting Song

This paper investigates longtime behaviors of the $\theta$-Euler-Maruyama method for the stochastic functional differential equation with superlinearly growing coefficients. We focus on the longtime convergence analysis in mean-square sense and weak sense of the $\theta$-Euler-Maruyama method, the convergence of the numerical invariant measure, the existence

David Maranto

As spacecraft journey further from Earth with more complex missions, systems of greater autonomy and onboard intelligence are called for. Reducing reliance on human-based mission control becomes increasingly critical if we are to increase our rate of solar-system-wide exploration. Recent work has explored AI-based goal-oriented systems to increase the level

James M. Rogers, Matthew J. Frost, Lisa DeBeer-Schmitt

The CG-2 beamline at the High Flux Isotope Reactor (HFIR) exhibits a notable discrepancy between observed count rates and the count rates we would expect based on a Monte-Carlo neutron ray-trace simulation. These simulations consistently predict count rates approximately five times greater than those observed in four separate experimental runs involving diff

Guoqi Ma, Prabhakar R. Pagilla, Swaroop Darbha

In this paper, we investigate the selection of time headway to ensure robust string stability in connected and autonomous vehicle platoons in the presence of signal noise in Vehicle-to-Vehicle (V2V) communication. In particular, we consider the effect of noise in communicated vehicle acceleration from the predecessor vehicle to the follower vehicle on the se

Yue Zhou, Barbara Di Eugenio, Brian Ziebart, Lisa Sharp

Health coaching helps patients identify and accomplish lifestyle-related goals, effectively improving the control of chronic diseases and mitigating mental health conditions. However, health coaching is cost-prohibitive due to its highly personalized and labor-intensive nature. In this paper, we propose to build a dialogue system that converses with the pati

Jinhao Pan, Ziwei Zhu, Jianling Wang, Allen Lin

Collaborative filtering (CF) based recommendations suffer from mainstream bias -- where mainstream users are favored over niche users, leading to poor recommendation quality for many long-tail users. In this paper, we identify two root causes of this mainstream bias: (i) discrepancy modeling, whereby CF algorithms focus on modeling mainstream users while neg

Henry Peng Zou, Gavin Heqing Yu, Ziwei Fan, Dan Bu

In e-commerce, accurately extracting product attribute values from multimodal data is crucial for improving user experience and operational efficiency of retailers. However, previous approaches to multimodal attribute value extraction often struggle with implicit attribute values embedded in images or text, rely heavily on extensive labeled data, and can eas

Mengnan Qi, Yufan Huang, Yongqiang Yao, Maoquan Wang

Large language models (LLMs) has experienced exponential growth, they demonstrate remarkable performance across various tasks. Notwithstanding, contemporary research primarily centers on enhancing the size and quality of pretraining data, still utilizing the next token prediction task on autoregressive transformer model structure. The efficacy of this task i

T. Emil Rivera-Thorsen, J. Chisholm, B. Welch, J. R. Rigby

We report the detection of a population of Wolf-Rayet (WR) stars in the Sunburst Arc, a strongly gravitationally lensed galaxy at redshift $z=2.37$. As the brightest known lensed galaxy, the Sunburst Arc has become an important cosmic laboratory for studying star and cluster formation, Lyman $\alpha$ radiative transfer, and Lyman Continuum (LyC) escape. Here

A. T. James, E. R. Williams

These notes have been adapted from an undergraduate course given by Professor Alan James at the University of Adelaide from around 1965 and onwards. This adaption has put a focus on the definition of projection matrices and the sweep operator. These devices were at the heart of the development of the statistical package Genstat which initially focussed on th

Yuhang Tan, Jiecheng Yang, Hairong Zheng, Dong Liang

As a multi-contrast X-ray computed tomography (CT) imaging system, the grating-based Talbot-Lau interferometer is able to generate the absorption contrast and differential phase contrast (DPC) images concurrently. However, experiments found that the absorption CT (ACT) images have better spatial resolution, i.e., higher modulation transfer function (MTF), th

A. Gunawan, H. S. Ramadhan, I. Prasetyo

Using the BPS Lagrangian method, we obtain a distinct set of Bogomolny equations for the Cho-Maison monopoles from the bosonic sector of a regularized electroweak theory. In the limit of $n\rightarrow\infty$ of the permittivity regulator, $\epsilon\left(\rho^n\right)$, the mass of the monopole can be estimated to be $M_W\sim3.56$ TeV. This value is within th

Tingtao Zhou, John F. Brady

Mechanical properties of disordered materials are governed by their underlying free energy landscape. In contrast to external fields, embedding a small fraction of active particles within a disordered material generates non-equilibrium internal fields, which can help to circumvent kinetic barriers and modulate the free energy landscape. In this work, we inve

Guoqi Ma, Prabhakar R. Pagilla, Swaroop Darbha

In this paper, we investigate the effect of signal delay in communicated information in connected and autonomous vehicles. In particular, we relate this delay's effect on the selection of the time headway in predecessor-follower type vehicle platooning with a constant time headway policy (CTHP). We employ a CTHP control law for each vehicle in the platoon by