Reviewerly/exHarmony huggingface dataset:
Efficient inversion with complex geostatistical priors using normalizing flows and variational inference

&amp;lt;p&amp;gt;&amp;lt;span&amp;gt;We propose a&amp;lt;/span&amp;gt;&amp;lt;span&amp;gt;n&amp;lt;/span&amp;gt;&amp;lt;span&amp;gt; approach for solving geophysical inverse problems which significantly reduces computational costs as compared to Markov chain Monte Carlo (MCMC) methods while providing enhanced uncertainty quantification as compared to efficient gradient-based deterministic methods. The propose&amp;lt;/span&amp;gt;&amp;lt;span&amp;gt;d&amp;lt;/span&amp;gt;&amp;lt;span&amp;gt; approach relies on variational inference (VI), which seeks to approximate the unnormalized posterior distribution parametrically for a given family of distributions by solving an optimization problem. Although prone to bias if the family of distributions is too limited, VI provides a computationally-efficient approach that scales well to high-dimensional problems. To enhance the expressiveness of the parameterized posterior in the context of geophysical inverse problems, we use a combination of VI and inverse autoregressive flows (IAF), a type of normalizing flows that has been shown to be efficient for machine learning tasks. The &amp;lt;/span&amp;gt;&amp;lt;span&amp;gt;IAF&amp;lt;/span&amp;gt;&amp;lt;span&amp;gt; consist&amp;lt;/span&amp;gt;&amp;lt;span&amp;gt;s&amp;lt;/span&amp;gt;&amp;lt;span&amp;gt; of invertible neural transport maps transforming an initial density of random variables into a target density, in which the mapping of each instance is conditioned on previous ones. In the combined VI-IAF routine, the approximate distribution is parameterized by the IAF, therefore, the potential expressiveness of the unnormalized posterior is determined by the architecture of the network. The parameters of the IAF are learned by minimizing the Kullback-Leibler divergence between the approximated posterior, which is obtained from samples drawn from a standard normal distribution that are pushed forward through the IAF, and the target posterior distribution. We test this approach on problems in which complex geostatistical priors are described by latent variables within a deep generative model (DGM) of the adversarial type. Previous results have concluded that inversion based on gradient-based optimization techniques perform poorly in this setting because of the high nonlinearity of the generator. Preliminary results involving linear physics suggest that the VI-IAF routine can recover the true model and provides high-quality uncertainty quantification at a low computational &amp;lt;/span&amp;gt;&amp;lt;span&amp;gt;cost&amp;lt;/span&amp;gt;&amp;lt;span&amp;gt;. As a next step, we will consider cases where the forward model is nonlinear and include comparison against standard MCMC sampling. As most of the inverse problem nonlinearity arises from the DGM generator, we do not expect significant differences in the quality of the approximations with respect to the linear physics case.&amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt;
QUALITATIVE USER-GENERATED CONTENT ANALYSIS AS A METHODOLOGY TO LEARN ABOUT INDUSTRIES’ STRENGTHS AND WEAKNESSES IN UNIVERSITY LESSONS

Market research topics and methodologies are gaining presence in the most syllabus of different types of University degrees. Market research-related subjects are becoming more and more important in marketing and business management degrees but also in tourism, engineering, history, and art degrees, among others. Today’s students will probably become business managers of different types of companies commercializing various products and services such as hotels, restaurants, or cultural products and services. Because of this, they must learn how to apply quantitative and qualitative market research techniques to know about customer needs. Traditionally, quantitative methodologies have prevailed in both research and university curriculums. However, the popularization of Information and Communication Technologies has made qualitative research more necessary than ever before. Most customers express their reviews and opinions on digital platforms such as Booking, Tripadvisor, or Trivago. And this is one of the most credible and influential sources of information for other potential customers. Because of this, it is so important that future business managers learn how to analyse the valuable information provided by customers through this type of platform. A practical exercise was proposed to a group of tourism students to identify substantial aspects and improvement areas in the management of tourism companies by using qualitative user-generated content analysis. As a result of a knowledge co-creation process, lecturer-students, the joint content analysis of all positive and negative comments compilated by all the students let us reach the following main conclusions. On the one hand:(I) free details personalization, (II) good value for money, (III) offers and promotions, (IV) location, or (V) ease of booking are some of the most times positively mentioned aspects. On the other hand:(I) room cleaning, (II) soundproofing, (III) staff attention, and (IV) menu options are the main reasons for complaining. This topic identification has critical managerial implications for improving the level of satisfaction of customers of tourism companies.
Nondominated Policy-Guided Learning in Multi-Objective Reinforcement Learning

Control intelligence is a typical field where there is a trade-off between target objectives, and researchers in this field have longed for artificial intelligence that achieves the target objectives. Multi-objective deep reinforcement learning was sufficient to satisfy this need. In particular, multi-objective deep reinforcement learning methods based on policy optimization are leading the optimization of control intelligence. However, multi-objective reinforcement learning has difficulties when finding various Pareto optimals of multi-objectives due to the greedy nature of reinforcement learning. We propose a method of policy assimilation to solve this problem. This method was applied to MO-V-MPO, one of preference-based multi-objective reinforcement learning, to increase diversity. The performance of this method has been verified through experiments in a continuous control environment.
An Comparative Analysis of Plate Poem and Palindrome Poem : Focusing on Plate Poem and Xuanjitu

This thesis examines the origins of Plate Poem and Palindrome Poem and inquires the effect of these two poetic systems on the future generation, while comparing the characteristics of those systems in creation through Xuanjitu, showing the example of Palindrome Poem.<BR> The difference between Plate Poem and Xuanjitu is that their origin, systems, and ways of reading are different. the spiral way of reading in Plate Poem originated from the influence of Buddhism, pursuing blessings and peace, while Xuanjitu stemmed from Palindrome Poem, where forms and meanings of it fit neatly into any reading part, from the first letter to the last letter.<BR> There is something in common that Plate Poem and Palindrome Poem have. The poets of both works are female writers and those contents are characterized by literature written about women
Multi-Scale Curriculum Learning for Efficient Automatic Whole Slide Image Segmentation

Deep learning becomes a powerful tool for multiple tasks in computational pathology and has achieved remarkable performance in automatic tumor segmentation of whole slide images (WSIs). Curriculum learning is an effective learning strategy that trains a deep neural network from easy to hard samples and has been applied to the problem of tumor segmentations in WSIs. However, it requires measuring the “difficulty” of histopathological images by pathologists, which is challenging. Here we propose a curriculum learning strategy that does not require additional difficulty labeling based on the conjecture that high resolution labeling is more challenging than low resolution labeling. Our multi-scale curriculum learning strategy allows us to control the difficulty of segmentation labels for more efficient training. Our experiments on the PAIP prostate cancer dataset validate the effectiveness of our proposed multi-scale curriculum learning strategy, showing improved performance in tumor segmentation qualitatively and quantitatively.
Fast and Accurate Deep Learning-Based Framework for 3D Multi-Object Detector for Autonomous Vehicles

With the ongoing development of hardware infrastructure, deep learning has a solid cornerstone to get into various practical applications. Specifically, for the autonomous vehicles field, the 3D surrounding objects need to be localized correctly. Many 3D detector works are proposed. However, most of them remain huge computations due to high dimensionality and densely of point clouds. Hence, this work proposes a fast and accurate 3D multi-object detection framework to balance between speed and accuracy efficiently. Experiments on the challenging KITTI dataset show that this work outperforms other methods by significant margins while running at 62 FPS on a single GTX-1080 GPU.
Reconstructions of North Hemisphere summer temperature based on tree-ring proxies using linear and machine learning methods

&amp;lt;p&amp;gt;In order to improve the climate reconstruction quality and better understand last millennium temperature variability, a reservoir computing (RC) method: Echo State Network (ESN) is applied for the reconstruction of the North Hemisphere summer seasonal temperature. ESN, a specialized type of recurrent neural network method, belongs to the family of machine learning methods, which is suitable for mapping complex systems with chaotic dynamics, for instance the hemisphere temperature variability. ESN is the widely implementation of RC and employs a structure with neuron-like nodes and recurrent connections, the internal reservoir, to handle the sequential data. It consists of three layers: input layer, reservoir layer and output layer; a randomly generated reservoir in ESN preserves a set of nonlinear transformations of the input data and a linear regression criterion is employed for its training process to optimize the parameters. ESN could provide an alternative nonlinear machine learning method that might improve the prediction or reconstruction skills of paleoclimate. In this context, we first conduct pseudoproxy experiments (PPEs) using three different Earth System Models (ESM), including Community Climate System Model CCSM4, the Max-Planck-Institute climate model MPI-ESM-P and the Community Earth System Model CESM1-CAM5. Two classical multivariable linear regression methods, Principal component regression and Canonical correlation analysis, are also employed as a benchmark. Among the three models providing climate simulations of the past millennium, both derived spatial and temporal reconstruction results based on PPEs demonstrate that ESN could capture more variance than other two classical methods, and could potentially achieve paleo-temperature reconstruction improvements. This suggests that the ESN machine learning method could be an alternative method for paleoclimate analysis.&amp;lt;/p&amp;gt;
An advanced modeling environment based on a hybrid AI-OR approach

This chapter introduces knowledge-based model building. It describes the KONWERK tool-box and shows the modeling of optimization tasks with KONWERK. In many cases the modeling task is the most time-consuming part. The modeling process can be considered as the “transformation” of a given problem from a human formulation into a form which can be processed and solved by a machine. Modeling is in general not just a straight forward transformation but often some kind of art. model builder, who wants to develope an OR model accurately representing all important facts of the real world, almost needs to be an allround genie. In order to reduce the pollution of the river and to improve the water quality of the river system there are treatments to be implemented at the emission points. The knowledge base of KONWERK is divided into distinct types of knowledge about the domain, one or several objectives and the intended solution procedure.
Numerical simulation of the Lagrangian transport of aerosols of various genesis in urban conditions

&amp;lt;p&amp;gt;This work is devoted to the development of a numerical model of the transport of aerosol particles in the atmospheric boundary layer, as well as its application in idealized cases and studies with a realistic urban surface. Air quality and the distribution of pollutants is one of the major urban problems, and measurement methods can be limited in the complex geometry of the city, which motivates the development of modeling methods.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The model uses the Lagrangian approach to modeling, taking into account the size and mass of each particle, the possibility of aerosol deposition and their collision with various surfaces. The particle motion equation takes into account various parameters of the atmosphere: wind direction and speed, turbulent characteristics. The influence of turbulence on the motion of aerosols can be taken into account in the model using several parametrizations &amp;amp;#8211; stochastic Lagrangian models of zero and first order. It is possible to simulate a huge number of particles at the same time. The algorithm is implemented in the C++ programming language.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The model can be used as a separate tool that requires information about the state of the atmosphere as input data - these can be measurement data, results of hydrodynamic modeling, analytically given values. Numerous experiments have been carried out in this mode. The model was verified on exact analytical solutions for light and heavy particles, on the data of field measurements of the concentrations of dust and sand particles. Calculations were carried out in conditions of idealized geometry of buildings (urban canyons) and in conditions of real urban development. For this, input data from RANS and LES models were used.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The developed algorithm can also be used as a module connected to hydrodynamic models. In this mode, it is possible to use the input data on atmospheric parameters with the maximum resolution in time and space. By connecting to the LES model, high-resolution simulations of aerosol transport in realistic urban environments were performed.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The work is supported by Russian Ministry of Science and Higher Education, agreement No. 075-15-2021-574 (megagrant leaded by M.Kulmala in Moscow State University, WP4), No. 075-15-2019-1621, by RSF grant 21-17-00249, by RFBR grants 20-05-00776 and 19-05-50110.&amp;lt;/p&amp;gt;
Development and application of two-dimensional reflective waveguide technology

With the development of augmented reality technology, In the future, what kind of technology can meet the needs of users for performance? Consumer AR is coming, two-dimensional reflective waveguide technology can be expected in the future. Compared with one-dimensional pupil dilation, it has many advantages, such as a larger field of view, a smaller optical machine, and a thinner body. In 2021, Lingxi AR has successfully developed a two-dimensional optical module with a 60° field of view, and vigorously promoted mass production. It will promote the rapid development of consumer AR.
TEACHER SELF-EFFICACY FOR BEHAVIOR MANAGEMENT CONSIDERING EDUCATION FOR TEACHING PUPILS WITH DISABILITIES

The paper investigates the relationship between some factors related to teacher education for teaching pupils with disabilities and teacher self-efficacy for behaviour management. The research has two aims. The first is to define the latent structure of teachers' characteristics related to their education for teaching pupils with disabilities during initial education, and the second is to define the latent structure of those characteristics related to their education for teaching pupils with disabilities through in-service training. The survey included 177 teachers and it was part of the pilot study related to teachers’ self-assessment of self-efficacy for inclusive education in Istria County, Croatia. Behavioural management refers to those activities that teachers undertake to facilitate academic and socioemotional learning, and its goal is not only to maintain order in the classroom for easier adoption of information but also to encourage the social development of pupils. Teacher self-efficacy for behaviour management refers to a teacher's assessment of their own ability to successfully manage students' behaviour in the classroom, especially when faced with difficulties, such as group work, setting class rules, or controlling disruptive behaviours. The Self-Efficacy for Behaviour Management Subscale, which is an integral part of the Self-Efficacy for Inclusive Practice Questionnaire, was used as a measuring instrument. One-way and multivariate analysis of variance and discriminant analysis were used in the data processing. The results indicate the existence of statistically significant differences in self-efficacy for behaviour management between groups of teachers who did not attend any courses focused on teaching pupils with disabilities during their studies, those who attended one or two such courses, and those who attended three or more courses. In addition, statistically significant differences in self-efficacy for behaviour management were found in teachers who after their studies did not participate in in-service training focused on teaching pupils with disabilities, those who were trained for up to 20 hours, and those who were trained for 20 and more hours. Discriminant analysis showed the existence of two discriminant functions that significantly differentiate respondents in terms of the number of courses related to inclusive education they attended during initial education and the number of hours of in-service training to teach pupils with disabilities. The scientific contribution of the results is reflected in obtaining results that offer valuable insight into understanding the relationship of individual components of self-efficacy for behavioural management with factors that could affect it. The applicative value lies in the establishment of guidelines on the possibilities of enriching the professional development of teachers and building their capacities for inclusive teaching, both through initial education and through in-service training.
Real-Time Implementations of Background Subtraction for IoT Applications

Detection of moving objects is often the first stage in many computer vision applications through the use of static or moving cameras. Subsequently, the separation process between the background and the foreground is carried out using the background subtraction approach. In the state of the art, it has been cited that the application of machine learning as well as signal processing offers promising results in the face of the challenges to overcome in test video sequences. However, the fundamental objective is that algorithms based on the background subtraction approach can be used in real applications under the context of IoT. In this chapter, we analyze different background subtraction strategies developed over time with efficient results in real-time applications. Thus, we survey Graphics Processing Unit (GPU) implementations, embedded implementations in smart cameras and systems, and the development of specific architectures using digital signal processing (DSP), field programmable gate array (FPGA), and very large-scale integration (VLSI) technologies. Second, we review fog computing and edge computing strategies employed for global video systems in the IoT context. Finally, we provide perspectives.
Improving the Accuracy of Person Re- Identification by Mining Semantic Features and Applying New Attention Mechanism

Person re-identification performance has progressed greatly in recent years, and this technique has been applied to many fields. While many person re-identification algorithms are devoted to building complex network structures for robustness and accuracy, this approach often places a high demand on hardware and software support. To overcome the performance-complexity trade-off, in this paper we propose a Semantic Attention Network(SA-Net), which does not need a complex network while bringing clear performance gain. Firstly, we design a lightweight semantic segmentation subnetwork, which can effectively remove the background redundant information. As a result, in the process of person re-identification, only the specific pedestrian features can be obtained, and the network can pay more attention to pedestrian parts rather than to the background. Secondly, considering the importance of attention mechanism in person re-identification tasks and to integrate the pedestrian features better, we apply the typical attention mechanism proposed in [5] and improve the module to integrate the persons' channel feature faster. The proposed Semantic Attention network has been evaluated on two publicly available benchmark datasets to show its superior performance over the state-of-the-art methods. The top-1 average accuracy of our method on the Market-1501 dataset reaches 96.35%. Compared the typical person re-identification model: Attentive but Diverse Network [3], the top-1 accuracy of our theory increases by 0.75% on Market-1501 dataset and 0.97% on DukeMTMC-reID dataset.
Development of Real-Time Traffic-Object and Traffic-Sign Detection Models Applied for Autonomous Intelligent Vehicles

Technologies for detecting traffic objects are an essential requirement for any applications in autonomous intelligent vehicles. In this work, models for detecting traffic objects were developed. Based on the existing datasets and the pre-trained models, fine-tuning techniques were applied to achieve trained models with higher accuracies even for the very challenging test data. The traffic object detection was developed based on the pre-trained YOLOv5s model. Two approaches were introduced for the traffic sign detection task. The so-called tiling technique incorporated with the YOLOv5s model was exploited in the first approach. In the second approach, a combination of the RetinaFace model for the localization and the MobileNetV1-SSD for the classification was employed. The experimental results show that all developed models release a very high rate of accuracy with a maximum AP50 of 75.9% for object detection and mAP50 of 64.2% for sign detection. Models developed via the second approach have twofold advantages in terms of accuracy and computational efficiency, which allows to deploy practical applications.