May 2023 arXiv papers — page 179

Harsha A. Tanti, Abhirup Datta, S. Ananthakrishnan

The estimation of the direction of electromagnetic (EM) waves from a radio source using electrically short antennas is one of the challenging problems in the field of radio astronomy. In this paper we have developed an algorithm which performs better in direction and polarization estimations than the existing algorithms. Our proposed algorithm Snapshot Avera

Mohammad Hossein Bahonar, Reza Ghaderi Zefreh, Rouhollah Amiri

Due to the ever increasing data rate demand of beyond 5G networks and considering the wide range of Orthogonal Frequency Division Multipllexing (OFDM) technique in cellular systems, it is critical to reduce pilot overhead of OFDM systems in order to increase data rate of such systems. Due to sparsity of multipath channels, sparse recovery methods can be expl

Dimpi, Hemant Kumar Singh

Let G = Z2 act on a finite CW-complex X having mod 2 cohomology isomorphic to the product of quaternionic projective space and sphere HPn x Sm, n, m > or = 1. This paper is concerned with the connected fixed point sets and the orbit spaces of free involutions on X.

Tan Pan, Furong Xu, Xudong Yang, Sifeng He

Image retrieval plays an important role in the Internet world. Usually, the core parts of mainstream visual retrieval systems include an online service of the embedding model and a large-scale vector database. For traditional model upgrades, the old model will not be replaced by the new one until the embeddings of all the images in the database are re-comput

G. L. Klimchitskaya, V. M. Mostepanenko

It has been known that the fundamental Lifshitz theory, which is based on first principles of thermal quantum field theory, experiences difficulties when compared with precise measurements of the Casimir force. We analyze the nonconventional fit of response functions of many materials along the imaginary frequency axis to the empirical model of "modified

Daniil Homskiy, Narek Maloyan

In recent years, sentiment analysis has gained significant importance in natural language processing. However, most existing models and datasets for sentiment analysis are developed for high-resource languages, such as English and Chinese, leaving low-resource languages, particularly African languages, largely unexplored. The AfriSenti-SemEval 2023 Shared Ta

Lotem Ikan, David Lagziel

The indoctrination game is a complete-information contest over public opinion. The players exert costly effort to manifest their private opinions in public in order to control the discussion, so that the governing opinion is similar to theirs. Our analysis provides a theoretical foundation for the silent majority and vocal minority phenomena, i.e., we show t

Dominic Breit

We consider the incompressible Navier-Stokes equations in a moving domain whose boundary is prescribed by a function $η=η(t,y)$ (with $y\in\mathbb R^2$) of low regularity. This is motivated by problems from fluid-structure interaction. We prove partial boundary regularity for boundary suitable weak solutions assuming that $η$ is continuous in time with value

Guo-Jun Zhu, Yi-Bin Fang, Zhi-Guo Tao, Ji-Hui Yang

Illumination has been long known to affect semiconductor defect properties during either growth or operating process. Current theories of studying the illumination effects on defects usually have the assumption of unaffected formation energies of neutral defects as well as defect transition energy levels, and use the quasi-Fermi levels to describe behaviors

Ziwei Liu, Wen Chen, Zhendong Li, Jinhong Yuan

In this paper, we investigated the downlink transmission problem of a cognitive radio network (CRN) equipped with a novel transmissive reconfigurable intelligent surface (TRIS) transmitter. In order to achieve low power consumption and high-rate multi-streams communication, time-modulated arrays (TMA) is implemented and users access the network using rate sp

Fen Lyu, Ji-Gui Cheng, En-Wei Liang, Can-Min Deng

The observed spectral shapes variation and tentative bimodal burst energy distribution (E-distribution) of fast radio burst (FRB) 20121102A with the FAST telescope are great puzzles. Adopting the published multifrequency data observed with the FAST and Arecibo telescopes at $L$ band and the GBT telescope at $C$ band, we investigate these puzzles through Mont

Lexuan Xu, Guang Hua, Haijian Zhang, Lei Yu

Most of the artificial lights fluctuate in response to the grid's alternating current and exhibit subtle variations in terms of both intensity and spectrum, providing the potential to estimate the Electric Network Frequency (ENF) from conventional frame-based videos. Nevertheless, the performance of Video-based ENF (V-ENF) estimation largely relies on th

Licheng Wang, Tao Wang, Gang Huang, Ruifeng Yan

With the continuous increase of photovoltaic (PV) penetration, the voltage control interactions between newly installed PV inverters and previously deployed on-load tap-changer (OLTC) transformers become ever more significant. To achieve coordinated voltage regulation, current methods often rely on a decision-making algorithm to fully take over the control o

Fen Lyu, En-Wei Liang

A comparative analysis of the individual bursts between FRB 20190520B and FRB 20121102A is presented by compiling a sample of bursts in multiple wavelengths. It is found that the peak frequency ($ν_p$) distribution of the bursts of FRB 20190520B illustrates four discrete peaks in $\sim1-6$ GHz and their spectral width distribution can be fitted with a log-no

Vittorio Pippi, Silvia Cascianelli, Christopher Kermorvant, Rita Cucchiara

Recent advancements in Deep Learning-based Handwritten Text Recognition (HTR) have led to models with remarkable performance on both modern and historical manuscripts in large benchmark datasets. Nonetheless, those models struggle to obtain the same performance when applied to manuscripts with peculiar characteristics, such as language, paper support, ink, a

Ayşe Elçiboğa Kuday, Ferhat Özok, Erdinç Ulaş Saka

We analyse dark matter in most general form of effective field theory approach. To examine the interactions between weakly interacting massive particles(WIMPs) and Standard Model (SM) particles, we use the six-dimensional EFT mediated by new physics scale $Λ$ at tree level. After implementing a new effective field theory model in FeynRules \cite{Feynrules} W

Shingo Kono, Jiahe Pan, Mahdi Chegnizadeh, Xuxin Wang

Superconducting qubits are one of the most advanced candidates to realize scalable and fault-tolerant quantum computing. Despite recent significant advancements in the qubit lifetimes, the origin of the loss mechanism for state-of-the-art qubits is still subject to investigation. Moreover, successful implementation of quantum error correction requires neglig

Shuai Wan, Pi-Yu Wang, Rui Ma, Zheng-Yu Wang

Generated in high-Q optical microresonators, dissipative Kerr soliton microcombs constitute broadband optical frequency combs with chip sizes and repetition rates in the microwave to millimeter-wave range. For frequency metrology applications such as spectroscopy, optical atomic clocks and frequency synthesizers, octave-spanning soliton microcombs generated

Christoph Hirche, Xinyue Guan, Marco Tomamichel

Bounds on information combining are a fundamental tool in coding theory, in particular when analyzing polar codes and belief propagation. They usually bound the evolution of random variables with respect to their Shannon entropy. In recent work this approach was generalized to Renyi $α$-entropies. However, due to the lack of a traditional chain rule for Reny

C. Menapara, A. K. Rai

N and $Δ$ baryons hold an important place towards understanding the quark dynamics inside hadrons. The hypercentral Constituent Quark Model (hCQM) has been employed in various studies ranging from light to heavy hadrons. In the present article, screened potential has been used to study light baryon resonances. The Regge trajectories have been plotted alongwi

Dan Goreac, Alain Rapaport

We provide a duality result linking the value function for a control problem with supremum cost H under an isoperimetric inequality G $\le$ gmax, and the value function for the same controlled dynamics with cost G and state constraint H $\le$ hmax. This duality is proven for initial conditions at which lower semi-continuity of the value functions can be guar

Peterson Yuhala

With the increasing popularity of Internet of Things (IoT) devices, security concerns have become a major challenge: confidential information is constantly being transmitted (sometimes inadvertently) from user devices to untrusted cloud services. This work proposes a design to enhance security and privacy in IoT based systems by isolating hardware peripheral

Hao Zhang, Meng Yu, Yuzhong Wu, Tao Yu

Deep learning has been recently introduced for efficient acoustic howling suppression (AHS). However, the recurrent nature of howling creates a mismatch between offline training and streaming inference, limiting the quality of enhanced speech. To address this limitation, we propose a hybrid method that combines a Kalman filter with a self-attentive recurrent

Persi Diaconis, Laurent Miclo

For $N\in\mathbb{N}$, let $π_N$ be the law of the number of fixed points of a random permutation of $\{1, 2, ..., N\}$. Let $\mathcal{P}$ be a Poisson law of parameter 1.A classical result shows that $π_N$ converges to $\mathcal{P}$ for large $N$ and indeed in total variation $$\left\Vert π_N-\mathcal{P}\right\Vert_{\mathrm{tv}} \leq \frac{2^N}{(N+1)!}$$ Thi

Binbin Xie, Jia Song, Liangying Shao, Suhang Wu

Keyphrase prediction aims to generate phrases (keyphrases) that highly summarizes a given document. Recently, researchers have conducted in-depth studies on this task from various perspectives. In this paper, we comprehensively summarize representative studies from the perspectives of dominant models, datasets and evaluation metrics. Our work analyzes up to

Ruoyu Feng, Jinming Liu, Xin Jin, Xiaohan Pan

Image coding for machines (ICM) aims to compress images to support downstream AI analysis instead of human perception. For ICM, developing a unified codec to reduce information redundancy while empowering the compressed features to support various vision tasks is very important, which inevitably faces two core challenges: 1) How should the compression strate

Renshen Wang, Yasuhisa Fujii, Alessandro Bissacco

Text reading order is a crucial aspect in the output of an OCR engine, with a large impact on downstream tasks. Its difficulty lies in the large variation of domain specific layout structures, and is further exacerbated by real-world image degradations such as perspective distortions. We propose a lightweight, scalable and generalizable approach to identify

Wei Sun

In this paper, we shall study the boundary case for complex Monge-Ampère type equations under certain geometric assumptions.

Ziheng Cheng, Junzi Zhang, Akshay Agrawal, Stephen Boyd

Laplacian regularized stratified models (LRSM) are models that utilize the explicit or implicit network structure of the sub-problems as defined by the categorical features called strata (e.g., age, region, time, forecast horizon, etc.), and draw upon data from neighboring strata to enhance the parameter learning of each sub-problem. They have been widely ap

Yuxiang An, Dongnan Liu, Weidong Cai

AI-enhanced segmentation of neuronal boundaries in electron microscopy (EM) images is crucial for automatic and accurate neuroinformatics studies. To enhance the limited generalization ability of typical deep learning frameworks for medical image analysis, unsupervised domain adaptation (UDA) methods have been applied. In this work, we propose to improve the

Shang Chai, Liansheng Zhuang, Fengying Yan

Automatic layout generation that can synthesize high-quality layouts is an important tool for graphic design in many applications. Though existing methods based on generative models such as Generative Adversarial Networks (GANs) and Variational Auto-Encoders (VAEs) have progressed, they still leave much room for improving the quality and diversity of the res

Ruizhe Zhang, Xinzhi Zhang

In 2013, Marcus, Spielman, and Srivastava resolved the famous Kadison-Singer conjecture. It states that for $n$ independent random vectors $v_1,\cdots, v_n$ that have expected squared norm bounded by $ε$ and are in the isotropic position in expectation, there is a positive probability that the determinant polynomial $\det(xI - \sum_{i=1}^n v_iv_i^\top)$ has

Md. Mouzakkir Hossain, Sukhendu Ghosh, Harekrushna Behera

This research focuses on the stability analysis of an odd viscosity-induced shear-imposed Newtonian fluid flowing down an inclined slippery bed having an insoluble surfactant at the top of the liquid surface. The Orr-Sommerfeld boundary value problem is developed by applying the normal mode approach to the infinitesimal perturbed fluid flow and solved using

Shitao Xiao, Zheng Liu, Yingxia Shao, Zhao Cao

To better support information retrieval tasks such as web search and open-domain question answering, growing effort is made to develop retrieval-oriented language models, e.g., RetroMAE and many others. Most of the existing works focus on improving the semantic representation capability for the contextualized embedding of the [CLS] token. However, recent stu

Sarita Rosenstock

This paper motivates and develops a framework for understanding how the socio-technical systems surrounding AI development interact with social welfare. It introduces the concept of ``signaling'' from evolutionary game theory and demonstrates how it can enhance existing theory and practice surrounding the evaluation and governance of AI systems.

Zhewen Yang, Changrong Wu, Chen Tian, Zhaochen Zhang

In today's private cloud, the resource of the datacenter is shared by multiple tenants. Unlike the storage and computing resources, it's challenging to allocate bandwidth resources among tenants in private datacenter networks. State-of-the-art approaches are not effective or practical enough to meet tenants' bandwidth requirements. In this paper,

Sankalok Sen

Analysis and extraction of useful information from legal judgments using computational linguistics was one of the earliest problems posed in the domain of information retrieval. Presently, several commercial vendors exist who automate such tasks. However, a crucial bottleneck arises in the form of exorbitant pricing and lack of resources available in analysi

Ritu Garg, Pallavi Gupta, A. Upadhyay

We have employed HQET to give the spin-parity quantum numbers for recently observed bottom strange states $B_{sJ}(6063)$ and $B_{sJ}(6114)$ by LHCb collaborations. By exploring flavour independent parameters $ Δ_{F}^{(c)} =Δ_{F}^{(b)}$ and $ λ_{F}^{(c)} = λ_{F}^{(b)}$, we calculated masses of experimentally missing bottom strange meson states $2S, 1P, 1D$. W

Ruixin Hong, Hongming Zhang, Hong Zhao, Dong Yu

Although large language models demonstrate remarkable question-answering performances, revealing the intermediate reasoning steps that the models faithfully follow remains challenging. In this paper, we propose FAME (FAithful question answering with MontE-carlo planning) to answer questions based on faithful reasoning steps. The reasoning steps are organized

Xiang Zhang, Siddarth Jain, Baichuan Huang, Masayoshi Tomizuka

The skill of pivoting an object with a robotic system is challenging for the external forces that act on the system, mainly given by contact interaction. The complexity increases when the same skills are required to generalize across different objects. This paper proposes a framework for learning robust and generalizable pivoting skills, which consists of th

Greta Panova

Kostka, Littlewood-Richardson, Kronecker, and plethysm coefficients are fundamental quantities in algebraic combinatorics, yet many natural questions about them stay unanswered for more than 80 years. Kronecker and plethysm coefficients lack ``nice formulas'', a notion that can be formalized using computational complexity theory. Beyond formulas and

Luyao Zhang, Fan Zhang

Blockchain enables peer-to-peer transactions in cyberspace without a trusted third party. The rapid growth of Ethereum and smart contract blockchains generally calls for well-designed Transaction Fee Mechanisms (TFMs) to allocate limited storage and computation resources. However, existing research on TFMs must consider the waiting time for transactions, whi

Thomas Tarrants, Andrew Li

The nearby M2 dwarf GJ 3470 has been the target of considerable interest after the discovery of a transiting short-period Neptune-sized planet. Recently, claims regarding the existence of additional transiting planets has gotten some attention, suggesting both the presence of a gas giant in the habitable zone, and that the system hosts a remarkable co-orbita

Dagmara Celik Katreniak, Alexey Khazanov, Omer Moav, Zvika Neeman

Take up of microcredit by the poor for investment in businesses or human capital turned out to be very low. We show that this could be explained by risk aversion, without relying on fixed costs or other forms of non-convexity in the technology, if the investment is aimed at increasing the probability of success. Under this framework, rational risk-averse age

Feodor F. Dragan, Guillaume Ducoffe

We extend known results on chordal graphs and distance-hereditary graphs to much larger graph classes by using only a common metric property of these graphs. Specifically, a graph is called $α_i$-metric ($i\in \mathcal{N}$) if it satisfies the following $α_i$-metric property for every vertices $u,w,v$ and $x$: if a shortest path between $u$ and $w$ and a sho

Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia, Thanasis Pittas

We study principal component analysis (PCA), where given a dataset in $\mathbb{R}^d$ from a distribution, the task is to find a unit vector $v$ that approximately maximizes the variance of the distribution after being projected along $v$. Despite being a classical task, standard estimators fail drastically if the data contains even a small fraction of outlie

Fengmiao Bian, Jian-Feng Cai, Rui Zhang

The low-rank matrix recovery problem often arises in various fields, including signal processing, machine learning, and imaging science. The Riemannian gradient descent (RGD) algorithm has proven to be an efficient algorithm for solving this problem. In this paper, we present a preconditioned Riemannian gradient descent (PRGD) for low-rank matrix recovery. T

Vivek F. Farias, Hao Li, Tianyi Peng, Xinyuyang Ren

Interference is a ubiquitous problem in experiments conducted on two-sided content marketplaces, such as Douyin (China's analog of TikTok). In many cases, creators are the natural unit of experimentation, but creators interfere with each other through competition for viewers' limited time and attention. "Naive" estimators currently used in pr

Lei Jiao, Xuefeng Zhang

TianQin is a proposed space-based gravitational wave detector designed to operate in circular high Earth orbits. As a sequel to [Zhang et al. Phys. Rev. D 103, 062001 (2021)], this work provides an analytical model to account for the perturbing effect of the Earth's gravity field on the range acceleration noise between two TianQin satellites. For such an

Jian Teng, Bhargav Rallabandi, Jesse T. Ault

Solute-surface interactions have garnered considerable interest in recent years as a novel control mechanism for driving unique fluid dynamics and particle transport with potential applications in fields such as biomedicine, the development of microfluidic devices, and enhanced oil recovery. In this study, we will discuss dispersion induced by the diffusioos

Chun-Wei Chen, Linh V. Nguyen, Kabish Wisal, Shuen Wei

The key challenge for high-power delivery through optical fibers is overcoming nonlinear optical effects. To keep a smooth output beam, most techniques for mitigating optical nonlinearities are restricted to single-mode fibers. Moving out of the single-mode paradigm, we show experimentally that wavefront-shaping of coherent input light that is incident on a

Xu Wang, Jun Ma, Jing Li

Coronary CT angiography (CCTA) scans are widely used for diagnosis of coronary artery diseases. An accurate and automatic vessel labeling algorithm for CCTA analysis can significantly improve the diagnostic efficiency and reduce the clinicians'manual efforts. In this paper, we propose a simple vessel labeling method based on the Point Transformer, which

Xiaorui Hu, Tianle Xu, Junyang Zhang, Shoufeng Shen

The generalized hierarchies of compound WKI-SP (Wadati-Konno-Ichikawa and short pulse) equations are presented. The proposed integrable nonlinear equations include the WKI-type equations, the SP-type equations and the compound generalized WKI-SP equations. A chain of hodograph transformations are established to relate the compound WKI-SP equations with the M

Yaqi Shen, Le Hui, Jin Xie, Jian Yang

3D scene flow estimation aims to estimate point-wise motions between two consecutive frames of point clouds. Superpoints, i.e., points with similar geometric features, are usually employed to capture similar motions of local regions in 3D scenes for scene flow estimation. However, in existing methods, superpoints are generated with the offline clustering met

Hui-Ru Ho, Nathan White, Edward Hubbard, Bilge Mutlu

Parent-child interaction is critical for child development, yet parents may need guidance in some aspects of their engagement with their children. Current research on educational math robots focuses on child-robot interactions but falls short of including the parents and integrating the critical role they play in children's learning. We explore how educa

Zhou Yu, Lixiang Zheng, Zhou Zhao, Fei Wu

Building benchmarks to systemically analyze different capabilities of video question answering (VideoQA) models is challenging yet crucial. Existing benchmarks often use non-compositional simple questions and suffer from language biases, making it difficult to diagnose model weaknesses incisively. A recent benchmark AGQA poses a promising paradigm to generat

Zishun Zhou, Liping Ma, Xilong Liu, Zhiqiang Cao

Precise calibration is the basis for the vision-guided robot system to achieve high-precision operations. Systems with multiple eyes (cameras) and multiple hands (robots) are particularly sensitive to calibration errors, such as micro-assembly systems. Most existing methods focus on the calibration of a single unit of the whole system, such as poses between

Jun-Yu Ma, Jia-Chen Gu, Jiajun Qi, Zhen-Hua Ling

This paper describes the system developed by the USTC-NELSLIP team for SemEval-2023 Task 2 Multilingual Complex Named Entity Recognition (MultiCoNER II). A method named Statistical Construction and Dual Adaptation of Gazetteer (SCDAG) is proposed for Multilingual Complex NER. The method first utilizes a statistics-based approach to construct a gazetteer. Sec

Xiuyuan Guo, Ashwin Kallingal Joshy, Benjamin Steenhoek, Wei Le

Static analysis is widely used for software assurance. However, static analysis tools can report an overwhelming number of warnings, many of which are false positives. Applying static analysis to a new version, a large number of warnings can be only relevant to the old version. Inspecting these warnings is a waste of time and can prevent developers from find

Xiao-Long Wang, Min Fang, Gregory J. Herczeg, Yu Gao

We present an analysis of 288 young stellar objects (YSOs) in the Perseus Molecular Cloud that have well defined $g$ and $r$-band lightcurves from the Zwicky Transient Facility. Of the 288 YSOs, 238 sources (83% of our working sample) are identified as variables based on the normalized peak-to-peak variability metric, with variability fraction of 92% for sta

Prasanna Date, Chathika Gunaratne, Shruti Kulkarni, Robert Patton

In many neuromorphic workflows, simulators play a vital role for important tasks such as training spiking neural networks (SNNs), running neuroscience simulations, and designing, implementing and testing neuromorphic algorithms. Currently available simulators are catered to either neuroscience workflows (such as NEST and Brian2) or deep learning workflows (s

Zhuo-Xu Cui, Congcong Liu, Chentao Cao, Yuanyuan Liu

Magnetic resonance imaging (MRI) is known to have reduced signal-to-noise ratios (SNR) at lower field strengths, leading to signal degradation when producing a low-field MRI image from a high-field one. Therefore, reconstructing a high-field-like image from a low-field MRI is a complex problem due to the ill-posed nature of the task. Additionally, obtaining

Sharat Ibrahimpur, Manish Purohit, Zoya Svitkina, Erik Vee

Online caching is among the most fundamental and well-studied problems in the area of online algorithms. Innovative algorithmic ideas and analysis -- including potential functions and primal-dual techniques -- give insight into this still-growing area. Here, we introduce a new analysis technique that first uses a potential function to upper bound the cost of

Peng Ye, Tong He, Shengji Tang, Baopu Li

Residual networks have shown great success and become indispensable in recent deep neural network models. In this work, we aim to re-investigate the training process of residual networks from a novel social psychology perspective of loafing, and further propose a new training scheme as well as three improved strategies for boosting residual networks beyond t

Kwanhyung Lee, Soojeong Lee, Sangchul Hahn, Heejung Hyun

Electronic Health Record (EHR) provides abundant information through various modalities. However, learning multi-modal EHR is currently facing two major challenges, namely, 1) data embedding and 2) cases with missing modality. A lack of shared embedding function across modalities can discard the temporal relationship between different EHR modalities. On the

Hou, Sujuan, Li, Xingzhuo

Logo detection plays an integral role in many applications. However, handling small logos is still difficult since they occupy too few pixels in the image, which burdens the extraction of discriminative features. The aggregation of small logos also brings a great challenge to the classification and localization of logos. To solve these problems, we creativel

Brad Bachu

We show how the well known patterns of masses and interactions that arise from spontaneous symmetry breaking can be determined from an entirely on-shell perspective, that is, without reference to Lagrangians, gauge symmetries, or fields acquiring a vacuum expectation value. To do this, we review how consistent factorization of $2\rightarrow 2$ tree level sca

Collin Sakal, Tingyou Li, Juan Li, Xinyue Li

Introduction. The societal burden of cognitive impairments in China has prompted researchers to develop clinical prediction models aimed at making risk assessments that enable preventative interventions. However, it is unclear which risk factors best predict future cognitive impairment and if predictive ability is consistent across different socioeconomic gr

Shujian Zhang, Chengyue Gong, Lemeng Wu, Xingchao Liu

AI tasks encompass a wide range of domains and fields. While numerous AI models have been designed for specific tasks and applications, they often require considerable human efforts in finding the right model architecture, optimization algorithm, and hyperparameters. Recent advances in large language models (LLMs) like ChatGPT show remarkable capabilities in

Alejandro Ranchal-Pedrosa, Vincent Gramoli

The problem of Byzantine consensus has been key to designing secure distributed systems. However, it is particularly difficult, mainly due to the presence of Byzantine processes that act arbitrarily and the unknown message delays in general networks. Although it is well known that both safety and liveness are at risk as soon as n/3 Byzantine processes fail,

Zhiyuan Liu, Chunjie Cao, Fangjian Tao, Jingzhang Sun

Combining Graph neural networks (GNNs) with contrastive learning for anomaly detection has drawn rising attention recently. Existing graph contrastive anomaly detection (GCAD) methods have primarily focused on improving detection capability through graph augmentation and multi-scale contrast modules. However, the underlying mechanisms of how these modules wo

Samuel L. Krushkal

We establish that the Grunsky norm of any normalized univalent function on the disk is completely determined by the squares of holomorphic abelian differentials (in contrast to the Teichmuller norm, which relates to all integrable holomorphic quadratic differentials). This result has important interesting applications. In particular, it provides an explicit

Shuhang Tan, Zhiling Wang, Yan Zhong

Recent years have witnessed the proliferation of traffic accidents, which led wide researches on Automated Vehicle (AV) technologies to reduce vehicle accidents, especially on risk assessment framework of AV technologies. However, existing time-based frameworks can not handle complex traffic scenarios and ignore the motion tendency influence of each moving o

Ilkin Isler, Debesh Jha, Curtis Lisle, Justin Rineer

In this study, our goal is to show the impact of self-supervised pre-training of transformers for organ at risk (OAR) and tumor segmentation as compared to costly fully-supervised learning. The proposed algorithm is called Monte Carlo Transformer based U-Net (MC-Swin-U). Unlike many other available models, our approach presents uncertainty quantification wit

Ye-Peng Yan, Guo-Jian Wang, Si-Yu Li, Jun-Qing Xia

Primordial B-mode detection is one of the main goals of next-generation cosmic microwave background (CMB) experiments. Primordial B-modes are a unique signature of primordial gravitational waves (PGWs). However, the gravitational interaction of CMB photons with large-scale structures will distort the primordial E modes, adding a lensing B-mode component to t

Yangyang Chen, Pedro Alberto Morettin, Ronaldo Dias, Chang Chiann

This work proposes a new procedure for estimating the non-stationary spatial covariance function for Spatial-Temporal Deformation. The proposed procedure is based on a monotonic function approach. The deformation functions are expanded as a linear combination of the wavelet basis. The estimate of the deformation guarantees an injective transformation. Such t

Yue Xu, Xionghong He, Nu Xu

The nucleon coalescence model is one of the most popular theoretical models for light nuclei production in high-energy heavy-ion collisions. The production of light nuclei $d$, $t$, $^{3}$He, and $^{4}$He is studied using the transport model JAM with a simplified afterburner coalescence at $\sqrt{s_{NN}}=3$ GeV Au+Au collisions. We scan the cut-off of phenom

Xin Guan, Gang Chen

Topological insulator lie at the forefront of condensed matter physics. However strong disorder can destroy the topological states and make all states become localized. In this paper, we investigate the competition between topology and localization in the one-dimensional Su-Schrieffer-Heeger (SSH) model with controllable off-diagonal quasi-periodic modulatio

Juan Zuluaga-Gomez

Breast cancer is one of the most threatening diseases in women's life; thus, the early and accurate diagnosis plays a key role in reducing the risk of death in a patient's life. Mammography stands as the reference technique for breast cancer screening; nevertheless, many countries still lack access to mammograms due to economic, social, and cultural

Dejian Tian, Xunlian Wang

Motivated by the results of static monetary or star-shaped risk measures, the paper investigates the representation theorems in the dynamic framework. We show that dynamic monetary risk measures can be represented as the lower envelope of a family of dynamic convex risk measures, and normalized dynamic star-shaped risk measures can be represented as the lowe

M. Huertas-Company, K. G. Iyer, E. Angeloudi, M. B. Bagley

We analyze the Near Infrared ($\sim0.8-1μ$m) rest-frame morphologies of galaxies with $\log M_*/M_\odot>9$ in the redshift range $0<z<6$, compare with previous HST-based results and release the first JWST-based morphological catalog of $\sim20,000$ galaxies in the CEERS survey. Galaxies are classified into four main broad classes -- spheroid, disk+spheroid,

Qiyuan Chen, J. Uhlmann, Ke Ye

In this paper we derive and analyze an algorithm for inverting quaternion matrices. The algorithm is an analogue of the Frobenius algorithm for the complex matrix inversion. On the theory side, we prove that our algorithm is more efficient that other existing methods. Moreover, our algorithm is optimal in the sense of the least number of complex inversions.

Xian Gong, Claire McFarland, Paul McCarthy, Colin Griffith

Understanding the relationship between emerging technology and research and development has long been of interest to companies, policy makers and researchers. In this paper new sources of data and tools are combined with a novel technique to construct a model linking a defined set of emerging technologies with the global leading R&D spending companies. The r

Faiza Khan Khattak, Vallijah Subasri, Amrit Krishnan, Elham Dolatabadi

Machine Learning Health Operations (MLHOps) is the combination of processes for reliable, efficient, usable, and ethical deployment and maintenance of machine learning models in healthcare settings. This paper provides both a survey of work in this area and guidelines for developers and clinicians to deploy and maintain their own models in clinical practice.

Abed AlRahman Al Makdah, Fabio Pasqualetti

In this work, we derive dynamic output-feedback controllers that render the closed-loop system externally positive. We begin by expressing the class of discrete-time, linear, time-invariant systems and the class of dynamic controllers in the space of input-output behaviors, where a dynamic controller can be expressed as a static behavioral feedback gain. We

Fatemeh Shiri, Teresa Wang, Shirui Pan, Xiaojun Chang

International maritime crime is becoming increasingly sophisticated, often associated with wider criminal networks. Detecting maritime threats by means of fusing data purely related to physical movement (i.e., those generated by physical sensors, or hard data) is not sufficient. This has led to research and development efforts aimed at combining hard data wi

Fernanda Viégas, Martin Wattenberg

This is a speculative essay on interface design and artificial intelligence. Recently there has been a surge of attention to chatbots based on large language models, including widely reported unsavory interactions. We contend that part of the problem is that text is not all you need: sophisticated AI systems should have dashboards, just like all other compli

Ashish Sharma, Kevin Rushton, Inna Wanyin Lin, David Wadden

A proven therapeutic technique to overcome negative thoughts is to replace them with a more hopeful &#34;reframed thought.&#34; Although therapy can help people practice and learn this Cognitive Reframing of Negative Thoughts, clinician shortages and mental health stigma commonly limit people&#39;s access to therapy. In this paper, we conduct a human-centere

Katherine A. Bennett, Seth Redfield, Antonija Oklopčić, Ilaria Carleo

Hot Jupiters orbiting extremely close to their host star may experience atmospheric escape due to the large amounts of high-energy radiation they receive. Understanding the conditions under which this occurs is critical, as atmospheric escape is believed to be a driving factor in sculpting planetary populations. In recent years, the near-infrared 10833 Å hel

Weicong Chen, Chao-Kai Wen, Xiao Li, Shi Jin

The performance of transmission schemes is heavily influenced by the wireless channel, which is typically considered an uncontrollable factor. However, the introduction of reconfigurable intelligent surfaces (RISs) to wireless communications enables the customization of a preferred channel for adopted transmissions by reshaping electromagnetic waves. In this

Ngozi Ihemelandu, Michael D. Ekstrand

A number of information retrieval studies have been done to assess which statistical techniques are appropriate for comparing systems. However, these studies are focused on TREC-style experiments, which typically have fewer than 100 topics. There is no similar line of work for large search and recommendation experiments; such studies typically have thousands

Vasudha Varadarajan, Swanie Juhng, Syeda Mahwish, Xiaoran Liu

While transformer-based systems have enabled greater accuracies with fewer training examples, data acquisition obstacles still persist for rare-class tasks -- when the class label is very infrequent (e.g. < 5% of samples). Active learning has in general been proposed to alleviate such challenges, but choice of selection strategy, the criteria by which rare-c

Syamantak Kumar, Purnamrita Sarkar

Since its inception in 1982, Oja&#39;s algorithm has become an established method for streaming principle component analysis (PCA). We study the problem of streaming PCA, where the data-points are sampled from an irreducible, aperiodic, and reversible Markov chain. Our goal is to estimate the top eigenvector of the unknown covariance matrix of the stationary

Huimei Liu, Moritz M. Hirschmann, George A. Sawatzky, Giniyat Khaliullin

SmB6 is a mixed-valence compound with flat f-electron bands that have a propensity to magnetism. Here, using a realistic Gamma8 quartet model, we investigate the dynamical spin susceptibility and describe the in-gap collective mode observed in neutron scattering experiments. We show that as the Sm valence increases with pressure, the magnetic correlations en

Abigail J. Lee

The J-region Asymptotic Giant Branch (JAGB) method is a standard candle based on the intrinsic luminosities of carbon stars in the near infrared. We directly constrain the impact of metallicity, age, and reddening on the JAGB method. We assess how the mode, skew, and spread of the JAGB star luminosity function change throughout diverse stellar environments i

C. Patrick Royall, Patrick Charbonneau, Marjolein Dijkstra, John Russo

The simplicity of hard spheres as a model system is deceptive. Although the particles interact solely through volume exclusion, that nevertheless suffices for a wealth of static and dynamical phenomena to emerge, making the model an important target for achieving a comprehensive understanding of matter. In addition, while real colloidal suspensions are typic

Zhiyu Lin, Upol Ehsan, Rohan Agarwal, Samihan Dani

Generative Artificial Intelligence systems have been developed for image, code, story, and game generation with the goal of facilitating human creativity. Recent work on neural generative systems has emphasized one particular means of interacting with AI systems: the user provides a specification, usually in the form of prompts, and the AI system generates t

Konrad Anand, Andreas Göbel, Marcus Pappik, Will Perkins

We provide a perfect sampling algorithm for the hard-sphere model on subsets of $\mathbb{R}^d$ with expected running time linear in the volume under the assumption of strong spatial mixing. A large number of perfect and approximate sampling algorithms have been devised to sample from the hard-sphere model, and our perfect sampling algorithm is efficient for

Robert J. Moss, Mykel J. Kochenderfer, Maxime Gariel, Arthur Dubois

Estimating the probability of failure is an important step in the certification of safety-critical systems. Efficient estimation methods are often needed due to the challenges posed by high-dimensional input spaces, risky test scenarios, and computationally expensive simulators. This work frames the problem of black-box safety validation as a Bayesian optimi

Ahmed Mohammed Cherif

In this paper, we give some properties of biharmonic hypersurface in Riemannian manifold has a torse-forming vector field.