Miracle-dz/UniFAR-FTUs huggingface dataset:
Incremental object matching and detection with Bayesian methods and particle filters

This study is about object matching, that is, a problem of locating the corresponding points of an object in an image. Conventional approaches to object matching are batch methods, meaning that the methods first learn the object model from a training set of example images that contain instances of the object, and then use the learned object model to match instances of the same object (or object class) in unseen test images. Such batch learning often leads to computationally heavy learning, at least when the images are incorporated sequentially into the system and large memory requirements. In computer vision, little work has been done in developing incremental object learners. Especially, an incremental object-matching method has – to the best knowledge of the authors – never been introduced. In this paper, the authors present such a method. Our technique finds the corresponding points of similar object instances, appearing in natural greyscale images with arbitrary location, scale and orientation, by processing the images sequentially. The approach is Bayesian and combines the shape and appearance of the corresponding points into the posterior distribution for the location of them. The posterior distribution is recursively sampled with particle filters to locate the most probable corresponding point sets in the image being processed. The results indicate that the matched corresponding points can be used in forming a representation of the object with which instances of the object in novel test images are successfully detected.
Particle creation and vacuum polarisation in an isotropic universe

The particle interpretation of quantised fields in homogeneous isotropic space-time is given which is based on the diagonalisation of the Hamiltonian constructed via the metrical stress-energy tensor (SET). This interpretation and the regularisation procedure of Zeldovich and Starobinsky (1971) allows one to obtain the total renormalised vacuum expectation values of SET which include both vacuum polarisation and non-local terms describing the creation of particles. The case of the explicitly soluble cosmological model with initial singularity which is asymptotically static in the future is considered. The results of the calculation of the total SET in realistic Friedman cosmological models are presented.
Stereoscopic Digital Particle Image Velocimetry for Application in Wind Tunnel Flows

A particle image velocimetry system capable of accurately recovering the out-of-plane velocity component has been realized based on a stereoscopic viewing arrangement. To allow a large viewing angle with long focal length objective lenses, the angular displacement or Scheimpflug imaging configuration is employed in which the image, object and lens planes intersect in a common line. The varying magnification factor associated with this imaging configuration is accounted for using an accurate and simple-to-use calibration procedure based on solving the projection equations for each of the two cameras. A pair of high-resolution cameras, both capable of recording image pairs in the microsecond range, are synchronized to a pulsed Nd-YAG laser. By placing the cameras on either side of the light sheet the favourable light scattering characteristics of micron-sized seeding particles in forward scatter provide images at significantly higher illumination than at normal or backscatter viewing angles. Ultimately designed for use in industrial wind tunnels, the camera system is capable of working with non-symmetric arrangements. It has been successfully tested in a laboratory environment by imaging the unsteady flow field of a vortex ring passing through a laser light sheet. Adaptive processing software capable of dynamically adjusting the sample location of the interrogation windows to the local displacement vector significantly improves data yield. The algorithm requires only the selection of the final window/overlap size. The hierarchical interrogation approach permits the processing of images whose displacement dynamic range exceeds the interrogation window size.
Upwind Algorithm for the Parabolized Navier-Stokes Equations

A new upwind algorithm based on Roe's scheme has been developed to solve the two-dimensional parabolized Navier-Stokes equations. This method does not require the addition of user-specified smoothing terms for the capture of discontinuities such as shock waves. Thus, the method is easy to use and can be applied without modification to a wide variety of supersonic flowfields. The advantages and disadvantages of this adaptation are discussed in relation to those of the conventional Beam-Warming (1978) scheme in terms of accuracy, stability, computer time and storage requirements, and programming effort. The new algorithm has been validated by applying it to three laminar test cases, including flat-plate boundary-layer flow, hypersonic flow past a 15-deg compression corner, and hypersonic flow into a converging inlet. The computed results compare well with experiment and show a dramatic improvement in the resolution of flowfield details when compared with results obtained using the conventional Beam-Warming algorithm.
Data-driven soft sensors in blast furnace ironmaking: a survey

The blast furnace is a highly energy-intensive, highly polluting, and extremely complex reactor in the ironmaking process. Soft sensors are a key technology for predicting molten iron quality indices reflecting blast furnace energy consumption and operation stability, and play an important role in saving energy, reducing emissions, improving product quality, and producing economic benefits. With the advancement of the Internet of Things, big data, and artificial intelligence, data-driven soft sensors in blast furnace ironmaking processes have attracted increasing attention from researchers, but there has been no systematic review of the data-driven soft sensors in the blast furnace ironmaking process. This review covers the state-of-the-art studies of data-driven soft sensors technologies in the blast furnace ironmaking process. Specifically, we first conduct a comprehensive overview of various data-driven soft sensor modeling methods (multiscale methods, adaptive methods, deep learning, etc.) used in blast furnace ironmaking. Second, the important applications of data-driven soft sensors in blast furnace ironmaking (silicon content, molten iron temperature, gas utilization rate, etc.) are classified. Finally, the potential challenges and future development trends of data-driven soft sensors in blast furnace ironmaking applications are discussed, including digital twin, multi-source data fusion, and carbon peaking and carbon neutrality.
Depth of Field Adaptation for Observation of Microscopic Objects by Using Variable Annular Aperture System

High magnification optical systems such as microscopes usually suffer from limited depth-of-field (DOF) problem. This hinders efficient observation of microscopic objects and prevents vision based approaches from being applied to automatic micromanipulation tasks. A DOF extension method using variable annular pupil was proposed by the authors' previous publication, and a tradeoff between the depth extension range and image quality was observed. In this article, this tradeoff is numerically analyzed by a proposed wavelet-based image quality measure, and a proper DOF scheme to provide the best image for the given object distribution is proposed. The problem is reduced to finding the optimum pupil division parameter. The experimental results conducted for various imaging conditions with some MEMS objects verify that the system DOF can be adjusted according to their position in depth direction to provide the best visibility.
Visual interactive image clustering: a target-independent approach for configuration optimization in machine vision measurement

Machine vision measurement (MVM) is an essential approach that measures the area or length of a target efficiently and non-destructively for product quality control. The result of MVM is determined by its configuration, especially the lighting scheme design in image acquisition and the algorithmic parameter optimization in image processing. In a traditional workflow, engineers constantly adjust and verify the configuration for an acceptable result, which is time-consuming and significantly depends on expertise. To address these challenges, we propose a target-independent approach, visual interactive image clustering, which facilitates configuration optimization by grouping images into different clusters to suggest lighting schemes with common parameters. Our approach has four steps: data preparation, data sampling, data processing, and visual analysis with our visualization system. During preparation, engineers design several candidate lighting schemes to acquire images and develop an algorithm to process images. Our approach samples engineer-defined parameters for each image and obtains results by executing the algorithm. The core of data processing is the explainable measurement of the relationships among images using the algorithmic parameters. Based on the image relationships, we develop VMExplorer, a visual analytics system that assists engineers in grouping images into clusters and exploring parameters. Finally, engineers can determine an appropriate lighting scheme with robust parameter combinations. To demonstrate the effectiveness and usability of our approach, we conduct a case study with engineers and obtain feedback from expert interviews.
Automatic discovery of stateful variables in network protocol software based on replay analysis

Network protocol software is usually characterized by complicated functions and a vast state space. In this type of program, a massive number of stateful variables that are used to represent the evolution of the states and store some information about the sessions are prone to potential flaws caused by violations of protocol specification requirements and program logic. Discovering such variables is significant in discovering and exploiting vulnerabilities in protocol software, and still needs massive manual verifications. In this paper, we propose a novel method that could automatically discover the use of stateful variables in network protocol software. The core idea is that a stateful variable features information of the communication entities and the software states, so it will exist in the form of a global or static variable during program execution. Based on recording and replaying a protocol program’s execution, varieties of variables in the life cycle can be tracked with the technique of dynamic instrument. We draw up some rules from multiple dimensions by taking full advantage of the existing vulnerability knowledge to determine whether the data stored in critical memory areas have stateful characteristics. We also implement a prototype system that can discover stateful variables automatically and then perform it on nine programs in ProFuzzBench and two complex real-world software programs. With the help of available open-source code, the evaluation results show that the average true positive rate (TPR) can reach 82% and the average precision can be approximately up to 96%. 网络协议软件通常具有程序路径复杂、状态空间庞大的特点。 程序中往往存在着一些带有状态的关键变量, 用于记录协议状态和会话信息。 这些状态变量一旦处理不当, 很可能违背协议规范, 进而产生逻辑错误, 导致协议软件出现潜在的缺陷或漏洞。 本文针对现有程序分析技术难以发现网络协议软件中的状态变量, 且自动化程度偏低的问题, 提出一种基于重放分析的状态变量识别方法。 考虑到状态变量主要反映着通信双方的参数和程序的状态, 具有这些特征的变量通常会以全局变量或静态变量的形式, 持续存在于进程之中, 该方法通过记录和重放协议软件的执行轨迹, 运用动态插桩技术, 在协议状态和软件状态的变化过程中, 分析内存关键区域的全局变量和静态变量的状态特征, 并结合规则进行筛选判定。 在此基础上, 设计并实现了一套能够自动化发现状态变量的原型系统, 在ProFuzzBench中的9个程序和2个现实中的复杂协议软件上进行了测试。 实验结果显示, 平均真正类率(TPR)可达82%, 平均准确度可达96%左右。
Development and analysis of a fuzzy identifier for nonlinear discrete systems

The paper presents an algorithm for fuzzy logic identification of nonlinear systems. The proposed algorithm uses a linear in parameters identifier to avoid the problem of local minima. The stability of the proposed algorithm is proved and a sufficient condition on system excitation to ensure identifiability is presented. The algorithm is validated by comparing its performance with neural networks and backpropagation trained fuzzy identifiers.
Stepped frequency chirp signal imaging radar jamming using two-dimensional nonperiodic phase modulation

步进线性调频雷达信号可以通过多个窄带子线性调频脉冲合成的方式获取目标高分辨图像。 由于步进线性调频雷达信号对雷达瞬时带宽要求较低, 已成为最常用的宽带雷达波形之一。 本文针对步进线性调频成像雷达对抗问题, 提出一种基于二维非周期相位调制的雷达干扰方法。 所提方法利用非周期相位调制产生高水平旁瓣的独特特性, 可在雷达图像上形成沿距离向与方位向二维分布的压制性干扰, 从而使得目标无法被有效识别。 在此基础上, 讨论了编码码元宽度与占空比等不同调制参数对干扰特性的影响, 并进一步给出相应干扰参数设计准则。 最后, 通过雅克-42飞机数据仿真以及实测无人机数据实验证明所提干扰方法的有效性。 Stepped frequency chirp signal obtains high-resolution radar images by synthesizing multiple narrowband chirp pulses. It has been one of the most commonly used wideband radar waveforms due to its lower demand for radar instant bandwidth. In this paper, we propose a radar jamming method using two-dimensional nonperiodic phase modulation against stepped frequency chirp signal imaging radar. Using the unique property of nonperiodic phase modulation, the proposed method can generate high-level sidelobes that perform as a special blanket jamming along both the range and azimuth directions and make the target unrecognizable. Then, the influence of different modulation parameters, such as the code width and duty ratio, are further discussed. Based on this, the corresponding parameter design principles are presented. Finally, the validity of the proposed method is demonstrated by the Yake-42 plane data simulation and measured unmanned aerial vehicle data experiment.
Mantel's space-time clustering statistic: computing higher moments and a comparison of various data transforms

Mantel proposed a statistic for assessing space-time clustering. He showed how to compute its theoretical expectation and variance and pointed out its asymptotic normality, noting that a simulation approach might be used in insufficiently asymptotic situations. Another approach to this distribution problem is presented by which graduation curves (e.g. Pearson curves) are fitted to the theoretical distribution. This requires the computation of the exact first four moments and the formulae are presented herein. In terms of computer time, this compares favourably with a simulation approach. The analysis of two sets of data confirmed that normality of the test statistic cannot be taken for granted and that a variety of inverse transformations of the time and space data and Knox's method were equally robust.
Calculation of nonlinear deformation of a flexible rod

The class of nonlinear tasks for flexible rods, even in the case of flat deformation, is rather complicated. Closed form solutions are possible only for some special cases. An algorithm of numerical integration of the nonlinear equations of equilibrium of a flat rod by a Runge-Kutta method is considered. The boundary conditions on the extremity of a rod are satisfied approximately. The projection equations are obtained from the common vectorial equations of equilibrium. As solving functions the projections of a transition vector and internal forces vector on global axes, angle of a normal's turn to the rod axis, and internal flexing, are chosen.
Ambition and Its Psychopathologies

Abstract Ambition is a uniquely human, complex personality trait. Although the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition mentions ambition only once, in a peripheral comment concerning narcissistic personality disorder, psychopathological states associated with ambition are commonly encountered in everyday life. Ambition has been linked to narcissism, power, and dominance, but it is distinct from each of these concepts. Although the development of ambition is most strongly influenced by social, cultural, and demographic factors, evidence suggests that genetic and biological factors also contribute. This article describes how ambition-related psychopathologies can manifest as 1) misalignment of ambitions and capabilities; 2) “blind ambition”; 3) distortions associated with bipolar and other mood disorders; 4) amalgams of ambition with Machiavellian traits, psychopathy, narcissism, and sadism (i.e., dark triad/tetrad traits); and 5) amotivation/apathy syndrome deficits associated with general medical and psychiatric conditions. The field requires more robust measures for assessing ambition, further delineation of ambition-related psychopathologies, and effective treatments for these conditions.
The Effectiveness of the Interventions to Reduce Sound Levels in the ICU: A Systematic Review

OBJECTIVES: Excessive noise is ubiquitous in the ICU, and there is growing evidence of the negative impact on work performance of caregivers. This study aims to determine the effectiveness of interventions to reduce noise in the ICU. DATA SOURCES: Databases of PubMed, EMBASE, PsychINFO, CINAHL, and Web of Science were systematically searched from inception to September 14, 2022. STUDY SELECTION: Two independent reviewers assessed titles and abstracts against study eligibility criteria. Noise mitigating ICU studies were included when having at least one quantitative acoustic outcome measure expressed in A-weighted sound pressure level with an experimental, quasi-experimental, or observational design. Discrepancies were resolved by consensus, and a third independent reviewer adjudicated as necessary. DATA EXTRACTION: After title, abstract, and full-text selection, two reviewers independently assessed the quality of each study using the Cochrane’s Risk Of Bias In Nonrandomized Studies of Interventions tool. Data were synthesized according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, and interventions were summarized. DATA SYNTHESIS: After screening 12,652 articles, 25 articles were included, comprising either a mixed group of healthcare professionals (n = 17) or only nurses (n = 8) from adult or PICU settings. Overall, the methodological quality of the studies was low. Noise reduction interventions were categorized into education (n = 4), warning devices (n = 3), multicomponent programs (n = 15), and architectural redesign (n = 3). Education, a noise warning device, and an architectural redesign significantly decreased the sound pressure levels. CONCLUSIONS: Staff education and visual alert systems seem promising interventions to reduce noise with a short-term effect. The evidence of the studied multicomponent intervention studies, which may lead to the best results, is still low. Therefore, high-quality studies with a low risk of bias and a long-term follow-up are warranted. Embedding noise shielding within the ICU-redesign is supportive to reduce sound pressure levels.
A spaceborne lidar for high-resolution topographic mapping of the Earth's surface

In 2007, the National Research Council (NRC) completed its first decadal survey for Earth science at the request of NASA and the US Geological Survey.1 It recommended development of the Lidar Surface Topography (LIST) mission as one of 15 projects to take forward. The mission’s primary objectives are to map global topography and vegetation structure at a spatial resolution of 5m and acquire global coverage within a few years. NASA Goddard Space Flight Center conducted an initial mission-concept study and developed the LIST measurement requirements in 2007. Although most previous spaceborne lidar (light detection and ranging) instruments projected and imaged a single laser spot for altimetry measurements,2–4 the recent Lunar Orbiter Laser Altimeter uses a nonscanned five-beam approach for mapping lunar topography.5 Using multiple laser beams enables surfaceslope measurements and greatly reduces the time needed to globally map surface topography. In 2009, we started a threeyear project to develop and demonstrate technologies for a nextgeneration, efficient, swath-mapping spaceborne laser altimeter. Our objective is to demonstrate key capabilities for a new, highly efficient laser altimeter for the LIST mission. The mission’s lidar needs to generate a swath with 5m pixels 5km wide, image this onto a detector array, and produce a range image that includes the topographic height of the sampled area. This involves measuring through foliage if covered by vegetation and measuring the 3D structure of the vegetation cover. Our pushbroom approach (see Figure 1) uses an area 5km wide composed of 1000 laser beams in a linear array, oriented in the cross-track direction. The divergence of each beam yields a 5m-diameter footprint on the ground from a 400–425km orbit altitude. The spots are contiguous cross-track. At 10kHz laser-pulse rate and a nominal Figure 1. Concept of the Lidar Surface Topography (LIST) satellite generating a 5km swath containing 1000 beam spots at 5m per spot.
Asymptotic analysis of turbulent channel and boundary-layer flow

Asymptotic expansion techniques are used, in the limit of large Reynolds number, to study the structure of fully turbulent shear layers. The relevant Reynolds number characterizes the ratio of the local turbulent stress to the local laminar stress, so that a relatively thick outer defect layer, in which, to lowest order, there is a balance between turbulent stress and convection of momentum, may be distinguished from a relatively thin wall layer, in which, to lowest order, there is a balance between turbulent and laminar stresses. The two cases examined are channel (or pipe) flow and two-dimensional boundary-layer flow with an applied pressure gradient, upstream of any separation. Attention, for these two cases, is confined to the flow of incompressible constant property fluids. Closure is effected through the introduction of an eddy-viscosity model formulated with sufficient generality for most existing models to be special cases. Results are carried to higher orders of approximation to indicate what properties for the friction velocity, integral thicknesses, and velocity profiles, and what conditions for similarity are implied by current eddy-viscosity closures.
Genetic evidence on the occurrence of Channa harcourtbutleri (Annandale, 1918) in Eastern Ghats, India: first report from mainland India

Channa harcourtbutleri (Annandale) was described from Inle Lake (Southern Shan State) in Myanmar, and is currently considered as a valid species in the Channa gachua species-group. Notwithstanding several detailed studies on Channa from India in the recent, none has mentioned the occurrence of C. harcourtbutleri in the Indian mainland. In continuation to the faunal diversity exploration in Eastern Ghats, India, a few specimens in the C. gachua species-group were collected from the river Sabri sub-basin of the river Godavari basin in the East Godavari District of Andhra Pradesh which was identified as C. harcourtbutleri through DNA barcoding. This is a first report on occurrence of the species in the wild in the Eastern Ghats, India.
Freshness prediction for seafood using deep neural network

In this paper, we experimentally verified that deep learning based on Convolutional Neural Network (CNN) is an effective method to judge the freshness of seafood fillets from its images. Currently, freshness of seafood is generally estimated by human’s eyes (connoisseurs) in Japanese fishing industry. However, it requires many years of experience and is a difficult task for new workers due to lack of experience. Furthermore, the Japanese fishing industry is faced with a worker shortage that makes it difficult to pass on their skills to next generation due to an aging and a lack of new workers. Meanwhile, CNN has been achieved a certain success in image recognition areas. If this deep learning can be applied to predict the freshness of fishes, i.e., if the freshness of a subject can be inferred from an image of seafood, it would be a solution for the problems of aging and labor shortage in Japan’s fisheries industry. Therefore, in this paper, we verify that CNN-based deep learning models are effective in estimating/predicting seafood freshness using tuna and squid, which are commonly caught fish in Iwate Prefecture (in Japan) and show that representative CNN models such as ResNet-50 achieved nearly 100% of predicting accuracy from experiments with over 12,000 images.