Reviewerly/exHarmony huggingface dataset:
String Instruments—Plucked

Abstract This chapter provides an introduction to the scientific study of plucked-string instruments, concentrating on aspects that are of direct relevance to performers. Points of similarity and contrast with bowed-string instruments are discussed. A key similarity is in the question of what governs the loudness of any acoustical string instrument. The key contrast is in the manner in which a plucked string vibrates: this is governed by simpler physics than the vibration of a bowed string, so that scientific understanding is more complete. The chapter ends with a discussion of the physics underlying the selection of strings for a plucked instrument. Because of the nature of the subject matter, the organization of the chapter does not separate the background science from the implications for performance. As each successive topic is discussed, implications for performers and teachers are discussed in an integrated way.
Research on the design of hospital navigation system based on augmented reality technology

Based on the development status of augmented reality technology, combined with the needs of hospital construction in the information age, and in view of the inconvenience of navigation indexing in large general hospitals, the basic method of system construction is found by analyzing the characteristics of augmented reality technology and system architecture. Simulate and analyze the behavior of related users, explore the pain points and difficulties of user needs, combine indoor positioning technology, and design action routes for users on the basis of related navigation algorithms, realize virtual information prompts on human-computer interaction interfaces, and provide medical treatment for hospitals. Provide more convenient navigation and guidance services with visitors.
Quantum algorithms for numerical differentiation of expected values with respect to parameters

The quantum algorithms for Monte Carlo integration (QMCI), which are based on quantum amplitude estimation (QAE), speed up expected value calculation compared with classical counterparts and have been widely investigated along with their applications to industrial problems such as financial derivative pricing. In this paper, we consider an expected value of a function of a stochastic variable and a real-valued parameter and how to calculate derivatives of the expectation with respect to the parameter. This problem is related to calculating sensitivities of financial derivatives and so of industrial importance. Based on QMCI and the general-order central difference formula for numerical differentiation, we propose two quantum methods for this problem and evaluate their complexities. The first one, which we call the naive iteration method, simply calculates the formula by iterative computations and additions of the terms in it and then estimates its expected value by QAE. The second one, which we name the sum-in-QAE method, performs the summation of the terms at the same time as the sum over the possible values of the stochastic variable in a single QAE. We see that, depending on the smoothness of the function and the number of qubits available, either of two methods is better than the other. In particular, when the function is nonsmooth or we want to save the qubit number, the sum-in-QAE method can be advantageous.
The Physics of computing and computing with Physics

Abstract This Chapter shows that any physical system performs some type of computation, with the observers providing meaning (semantics) to such a computation. It then introduces an equivalence principle between physical (semantic) information and computation. Finally, it discusses the general criteria shared by all MemComputing machines.
Full Scale Augmented Reality to Support Construction Sequencing Education Case Study

Providing students with hands-on construction experiences enables them to apply conceptual knowledge to practical applications, but the high costs associated with this form of learning limit access to it. Therefore, this paper explores the use of augmented reality (AR) to enable students in a conventional classroom or lab setting to interact with virtual objects similar to how they would if they were physically constructing building components. More specifically, the authors tasked student participants with virtually constructing a wood-framed wall through AR with a Microsoft HoloLens. Participants were video-recorded and their behaviors were analyzed. Subsequently, observed behaviors in AR were analyzed and compared to expected behaviors in the physical environment. It was observed that students performing the tasks tended to mimic behaviors found in the physical environment in how they managed the virtual materials, leveraged physical tools in conjunction with virtual materials, and in their ability to recognize and fix mistakes. Some of the finer interactions observed with the virtual materials were found to be unique to the virtual environment, such as moving objects from a distance. Overall, these findings contribute to the understanding of how AR may be leveraged in classrooms to provide learning experiences that yield similar outcomes to those provided in more resource-intensive physical construction site environments.
A formula for the value of the gravitational constant

Abstract A concise and precise formula for ‘Big G’ has been derived from an electron positron model of vacuum space (the epola model) that regards gravitation as a push force resulting from magnetic spin moments. Extended tests have revealed the significance of the peculiar velocity of the Solar System as a factor within the Fine Structure Constant, by experimentally monitored periodicity of nuclear decay rates. Correlations to gravitational field intensity, indicated by tide heights, also deny a purely random function for nuclear decay. The epola model’s definition of EM waves and their spectral characteristics had indicated possible substructure of the electron as a charged hadronic pion. This study resolves anomalies of the electron by identifying Twist in addition to Spin moments that has exposed an epola mechanism for Quantum Entanglement. When free guest particles interact with the bound particles of the lattice, binding energy density reduction induces gravitational forces and reveals the factors comprising the value of Big G, that further indicate pion sub-structure of leptons with a different fine structure.
Moving Objects Tracking Method Based on Discharged Optical Flow

This study presents the tracking moving object method based on the Lucas-Kanade algorithm with feature points regeneration. The given algorithm provides the time-based ability to scale feature points of a moving object, which reduces the probability of tracking errors. Besides, this method allows to track objects, which have parts with different motion velocities. The Lucas-Kanade method and the tracking method based on the biological regeneration principle were considered in our research. Based on the reviewed methods, a new moving object tracking method with adaptive change in the number of feature points was proposed. The method allows reducing the probability of losing the tracked object when passing through the occlusion area.
Vocal Ensembles

Abstract A typical performance situation of a vocal ensemble or choir consists of a group of singers in a room with listeners. The choir singers on stage interact while they sing, since they also hear the sound of the neighboring singer and react accordingly. From a physical point of view, the choir singers can be regarded as sound sources. The properties of the room influence the sound and the listeners perceive the sound event as a sound receiver. Furthermore, the processes in the choir can also be described acoustically, which affects the overall performance. The room influences the timbre of the sound on their way to the audience, the receiver. Reflection, absorption, diffraction, or refraction influence the timbre in the room. The sound in a performance space can be distinguished between a near field very close to the singer and a far field. The distance at which the far field can be assumed is strongly dependent on the acoustics of the room. Especially for singers within a choir, the differentiation between those sound fields is important for hearing oneself and the other singers. The position of the singers, their directivity, and the seating position of the listener in the audience will have an influence on listener perception. Furthermore, this chapter gives background information on intonation and synchronization aspects, which are most relevant for any vocal ensemble situation. Using this knowledge, intuitive behavior and performance practice can be explained and new adaptations can be suggested for singing in vocal ensembles.
Iterative up and downsampling residual networks for single image super-resolution

With the development of neural networks, the effect of single-image super-resolution is getting better. However, in many models that have been proposed, the processing of the upsampling module for image magnification is relatively simple. In this paper, we propose an iterative up and downsampling residual network. We selected a baseline model, and combined the upsampling sub-module and the downsampling sub-module into the final image upsampling module in an iterative form. This module generates detailed differences of the image by connecting convolution and deconvolution, and then extracts them through the residual method to correct the texture of the upsampled image. The iterative module enhances the effect of correction. Experiments on public datasets prove the effectiveness of the model.
Face Recognition and Detection using Inexact Arithmetic

Face recognition is the technique of recognizing human faces by analyzing images and has found widespread uses in various commercial and security applications. A popular and widely investigated face recognition approach is using principal component analysis (PCA), that has an established reputation of producing a quick and accurate solution. This method however is not cost effective in terms of delay and hardware resources. To mitigate this problem, in this work, an efficient hybrid adder architecture that significantly reduces the area and delay of the hardware, while sacrificing the accuracy only marginally has been developed. Simulations carried out on PCA algorithm by incorporating the proposed adder to compute Eigenfaces achieves a success rate up to 96.5&#x0025;.
Construction of Mathematical Modeling for Teaching Evaluation Index System Based on the Delphi AHP Method

China plays an important role in the global basic education of mathematics. Moreover, China has a lot of educational experience and methods and trains many excellent talents in the field of mathematics. However, there are still various disadvantages in the process of mathematics teaching. The traditional exam-oriented education makes the talent thought of mathematics education lack openness. Teachers also focus on difficult problems, partial questions, and exam questions during teaching. When teaching, teachers cannot contact students' daily life, which has low value. Mathematics teaching methods are too rigid and lack attraction. Most teachers focus on contents in textbooks while students cannot understand the value of mathematics in learning. This results in the decline of most students' interest in mathematics courses. To solve this problem, mathematical modeling teaching is put forward. China has a short application time in this field, and mathematical modeling is complex and systematic. When dealing with mathematical modeling problems, we must be able to skillfully use the basic knowledge learned and also involve the knowledge of other disciplines, so we must realize the integration of the knowledge of various disciplines. In addition, mathematical modeling teaching attaches great importance to students' communication ability, mathematical interpretation ability, and teamwork ability. Teachers can improve students' ability; however, our teachers' abilities in mathematical modeling teaching are uneven and there are no relevant standards. Therefore, it is necessary to establish an evaluation index system to accurately judge teachers' abilities. Based on this, this study uses the Delphi AHP method to construct the evaluation index system and selects four criteria level indexes and 12 index level indexes to judge teachers' mathematical modeling teaching ability.
Drying of green veneer hollow tubes monitored using stereo digital images correlation

The present study is based on the patent FR3052379A1, which consists of rolling and gluing green veneers to form structural size hollow tubes. The objective was to study the drying behavior of circular hollow tubes and investigate the method of crack detection using image correlation from green conditions to equilibrium moisture content. The manufactured tubes are composed of six plies. Three different configurations of global grain angle orientations with respect to the tube longitudinal axis are used: {0, 0, 0}°, {10, 0, 10}°, and {− 10, 0, 10}°. Each grain angle was made up of two plies. The tubes deformation and crack formation due to drying shrinkage were measured using a stereo digital image correlation system. The stereo correlation is suitable for measuring tangential shrinkage and studying the opening and/or closing of cracks on the surface of the tubes subjected to internal moisture variations and blocked deformations. The influence of grain angle was discussed in this paper; however, due to the very few numbers of samples, no conclusive findings can be made.
A mixture distribution approach for assessing genetic impact from twin study

It is challenging to evaluate the genetic impacts on a biologic feature and separate them from environmental impacts. This is usually achieved through twin studies by assessing the collective genetic impact defined by the differential correlation in monozygotic twins vs dizygotic twins. Since the underlying order in a twin, determined by latent genetic factors, is unknown, the observed twin data are unordered. Conventional methods for correlation are not appropriate. To handle the missing order, we model twin data by a mixture bivariate distribution and estimate under two likelihood functions: the likelihood over the monozygotic and dizygotic twins separately, and the likelihood over the two twin types combined. Both likelihood estimators are consistent. More importantly, the combined likelihood overcomes the drawback of mixture distribution estimation, namely, the slow convergence. It yields correlation coefficient estimator of root-n consistency and allows effective statistical inference on the collective genetic impact. The method is demonstrated by a twin study on immune traits.
The Method of Deformed Stars as a Population Algorithm for Global Optimization

In this paper, a new method of deformed stars for global optimization based on the ideas and principles of the evolutionary paradigm was proposed. The two-dimensional case was developed and then extended for n-dimensional case. This method is based on the assumption of rational use of potential solutions groups, which allows increasing the rate of convergence and the accuracy of result. Populations of potential solutions are used to optimize the multivariable function, as well as their transformation, the operations of deformation, rotation and compression. The obtained results of experiments allow us to conclude that the proposed method is applicable to solving problems of finding optimal (suboptimal) values, including non-differentiated functions. The advantages of the developed method in comparison of genetic algorithms, evolutionary strategies and differential evolution as the most typical evolutionary algorithms were shown. The experiments were conducted using several well-known functions for global optimization (Ackley’s function, Rosenbrock’s saddle, Rastrigin’s function).
Design and Evaluation of GAN based Regression Model

Generative Adversarial Networks (GANs) are capable of generating realistic photos of objects, scenes and people that do not exist in real life. This is made possible due to the successful ability of GANs in modeling high dimensional data, handling missing data, providing multi-modal outputs and multi plausible answers. These positive features and capabilities of GANs have spearheaded research in the area of visual modeling using GAN. In this paper, an attempt is made to design a GAN model for solving regression problems. In order to assess the performance evaluation of proposed GAN model for regression problem, four basic functions and seven datasets from standard repositories are employed. It is observed that the proposed GAN model gave satisfactory results and can be employed for various other regression problems too.
GPU Accelerated Collision Detection for Robotic Manipulators

This paper presents a collision detection method for robotic manipulators. In this work, three methods are compared with each other. In the first one, both the segments of the manipulator and the obstacles are modeled with polyhedrons, and the collision between them is detected by checking the feasibility of the union of inequality systems describing each of them. The second one is solving the collision detection problem between meshes and polyhedrons, while the third one uses the same process, but parallelizing it by using GPU. These are general methods to determine which robot configurations are causing collisions, so they can be used in path-planning or in collision-avoidance algorithms as well. The presented GPU-based parallelized method overperformed the previously examined algorithms and it is fast enough to use in online applications.
Deep Learning Techniques for Financial Fraud Detection

Financial fraud is a non-negligible issue in the finance industry and cause considerable cost for government, banks, and ordinary card holders. Classical methods of financial fraud detection demand massive manual work, even if time and economic cost are expensive the accuracy of detection is dissatisfied. However, with the development of Machine Learning technique and cloud computing in recent years provide new methods to detect financial fraud. The most common technique is machine learning there are many research about using machine learning model to detect financial frauds. Use deep leaning to detect financial will significantly reduce the cost and increase the detect accuracy. In the past decade, researchers used different machine learning models and apply both supervised and unsupervised learning to solve problems occur in fraud detection task, but still lack of comprehensive classification and conclusion about financial fraud detection. This review will provide comprehensive analysis on existing methods of fraud detection literature base on the core aspects and general conclusion about the development of the detection methods.
Attention-Based Fine-Grained Hand Shape Recognition for Teaching Musical Instrument

Automatic and professional identification and evaluation makes musical instrument learning more intelligent, thus having significant academic value and broad application prospect. This paper explores a profound integration of machine vision technique into art education in an attempt of enhancing musical instrument performance. The fine-grained image recognition is novelly applied to automatically evaluating proper basic hand shapes, as an exploration of interdisciplinary research. First, this paper formulates a classification criteria and evaluation method by combining the musical instrument pedagogy with image recognition technology. Then, a image dataset for fine-grained hand shapes of Chinese zither (Zheng) performance (CZ-Dataset V2) is established based on free multi-view acquisition. Finally, we propose a fine-grained hand shape image recognition method using attention mechanism and provide a system as an auxiliary for musical instrument education. Experimental results show that the basic instrumental hand shapes can be properly evaluated and the player’s performance can be effectively enhanced through the proposed method of musical instrument performance.
Grassland Data Analysis and Calculation Based on the Internet of Things and Cloud Computing

Grassland productivity monitoring is an important content in grassland management, and it is also an important topic for ecology and grassland academic circles. With the rapid development and maturity of space and information technology, global positioning technology, remote sensing technology and geographic information system have long been applied to the process of grassland productivity monitoring and management. Based on the Internet of Things and cloud computing, this paper has made a detailed dynamic analysis of the ecological environment and animal and plant species of the grassland. The results show that the algorithms of cloud computing and the Internet of Things have improved the statistical efficiency by 7.2% and the accuracy of vegetation analysis by 95%.
Novartis Pharms. Corp. v. Accord Healthcare, Inc.

Biotechnology Law ReportVol. 41, No. 2 Case in PointNovartis Pharms. Corp. v. Accord Healthcare, Inc.United States Court of Appeals of the Federal Circuit, 202221 F.4th 1362Edited for Biotechnology Law Report by Christopher M. HolmanEdited for Biotechnology Law Report by Christopher M. HolmanChristopher M. Holman is a Professor of Law at the University of Missouri–Kansas City School of Law; a Senior Fellow at the Center for Intellectual Property x Innovation Policy (CIP-2) at the Antonin Scalia Law School, George Mason University; and the Executive Editor of Biotechnology Law Report.*The synopsis is original content and the decision itself has been condensed for ease of readability.Search for more papers by this authorPublished Online:12 Apr 2022https://doi.org/10.1089/blr.2022.29268.cmhAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 41Issue 2Apr 2022 InformationCopyright 2022, Mary Ann Liebert, Inc., publishersTo cite this article:Edited for Biotechnology Law Report by Christopher M. Holman.Novartis Pharms. Corp. v. Accord Healthcare, Inc..Biotechnology Law Report.Apr 2022.106-117.http://doi.org/10.1089/blr.2022.29268.cmhPublished in Volume: 41 Issue 2: April 12, 2022Online Ahead of Print:March 31, 2022PDF download