Reviewerly/exHarmony huggingface dataset:
Variable selection in finite mixture of median regression models using skew-normal distribution

A regression model with skew-normal errors provides a useful extension for traditional normal regression models when the data involve asymmetric outcomes. Moreover, data that arise from a heterogeneous population can be efficiently analysed by a finite mixture of regression models. These observations motivate us to propose a novel finite mixture of median regression model based on a mixture of the skew-normal distributions to explore asymmetrical data from several subpopulations. With the appropriate choice of the tuning parameters, we establish the theoretical properties of the proposed procedure, including consistency for variable selection method and the oracle property in estimation. A productive nonparametric clustering method is applied to select the number of components, and an efficient EM algorithm for numerical computations is developed. Simulation studies and a real data set are used to illustrate the performance of the proposed methodologies.
Integrated Landsat multispectral imagery and airborne geophysical data for enhanced litho-structural mapping: A case study of Adamawa Massif, north-eastern Nigeria

Landsat-7 ETM + satellite imagery integrated with corrected airborne geophysical data (radiometric, gravity, and aeromagnetic) anomalies maps, and field investigations were used to discriminate lithologies and related structures in Mayo-Belwa and Jada-Ganye-Toungo areas (Adamawa Massif), northeastern Nigeria. Field investigations portray the granite-gneisses as the oldest rock units, and have sequentially been invaded by the syn-, late-to post-collisional Older granites. Subsurface linear structures in the area display dominant NE-SW, NW-SE, NNE-SSW, and ENE-WSW trends, reflective of variations in pre-existing structural orientations, and near-surface expressions of reactivated zones of weakness in the basement crust. The geodynamic systems revealed that most elongated lineaments are expressed as axial part of valleys trending NE-SW, ENE and NNW, forming diagonal grid to the NW-SE stresses. Abnormally high total radioactivity gravitates to areas of deflection (alluvium), and late- and post-tectonic granites. The gravity intensity provides a spectrum of anomalous responses including abnormally high positive gravity (volcanic rocks), moderately high positive gravity (granite-gneisses and late-to post-tectonic and syn-tectonic granites), background gravity and low negative gravity (granite-gneisses, alluvium and proluvium). Aeromagnetic anomalies discriminate the Older granites (intense positive anomalies), granite-gneisses, sediments (moderate positive, near background, and moderate-intensive negative anomalies), syn-tectonic granites (moderate positive anomalies), banded-gneiss and amphibolite (intensive negative anomalies), volcanic rocks (intense negative anomaly), and alluvium/proluvium (low positive anomalies). This present study led to the development of up-to-date and accurate litho-structural and geophysical maps of the geologically-rugged and hardly accessible Adamawa Massif, north-eastern Nigeria and further constrain the geochemical (High-K calc-alkaline, peraluminous) and tectonic (syn-to post-collisional) characteristics of typical Pan-African granitoids.
The Advantages of The Blockchain Supply Chain Under the Influence of the Epidemic --Take the Retail Industry as an Example

Under the development of globalization, the integration of value chain resources and information has become a prerequisite for optimizing the supply chain. Blockchain technology can effectively analyze and optimize the information of various departments in real-time. This paper studies the advantages of blockchain technology in the upgrading and management of the retail supply chain through the case study method. The research conclusion found that blockchain supply chain technology is still in the development stage. Through the analysis of the supply chains of the three companies, it can be concluded that the current blockchain supply chain network is not well established, lacks corresponding digital technology, and requires corresponding legal regulations on the dissemination of information chains.
Technology-driven Virtual Production

The “subversive” technological drive of game engines for virtual film production is gradually being valued by filmmakers. This technical advantage is manifested in the game engine’s (1) real-time rendering of final pixels (speed and image quality), (2) “post-production in advance” and multi-pipeline for project production, (3) open-source code sharing and plug-ins for VFX. Based on the advantages, this article takes the mainstream Unreal Engine (UE) and Unity as the research objects to clarify the application prospects of game engines in four aspects: digital humans with high fidelity, real-time ray tracing in complex scenes, in-camera VFX, and remote collaboration in the post-pandemic era.
Nagare media ingest

New media ingest protocols have been presented recently. SRT and RIST compete with old protocols such as RTMP while the DASH-IF specified an HTTP-based ingest protocol for CMAF formatted media that lends itself towards delivery protocols such as DASH and HLS. Additionally, use cases of media ingest workflows can vary widely. This makes implementing generic and flexible tools for ingest workflows a hard challenge. A monolithic approach limits adoption if the implementation does not fit the use case completely. We propose a new design for ingest servers that splits responsibilities into multiple components running concurrently. This design enables flexible ingest deployments as is discussed for various use cases. We have implemented this design in the open source software Nagare Media Ingest for the new DASH-IF ingest protocol.
Machine learning-based strategies for streaming and experiencing 3DoF virtual reality

This paper contains the research proposal of Quentin Guimard that was presented at the MMSys 2022 doctoral symposium.
Research Proposal: Exploring Quantum Informatics for Middle School Students

New quantum technologies, such as quantum computers and quantum cryptography, have the potential to completely reshape both computer science, and society more broadly. We are at the early stages of this development and have, as computer science educators, the unique opportunity to help shape the future of quantum informatics and its education early on. To date, there is little research on quantum informatics from the perspective of computer science education. With my PhD research, I aim to contribute to shaping this new field of research. I will apply the method of educational reconstruction, to examine if, why and how quantum informatics could be taught to middle school students. Using a design-based approach, I will develop and empirically evaluate educational materials and an exemplary lessons-plan with middle school students. Finally, I want to define how teaching quantum informatics positions itself within computer science education.
A new free viewpoint video dataset and DIBR benchmark

Free viewpoint video (FVV) has drawn great attention in recent years, which provides viewers with strong interactive and immersive experience. Despite the developments made, further progress of FVV research is limited by existing datasets that mostly have too few number of camera views, or static scenes. To overcome the limitations, in this paper, we present a new dynamic RGB-D video dataset with up to 12 views. Our dataset consists of 13 groups of dynamic video sequences that are taken at the same scene, and a group of video sequences of the empty scene. Each group has 12 HD video sequences taken by synchronized cameras and 12 correspondingly estimated depth video sequences. Moreover, we also introduce a FVV synthesis benchmark on the basis of depth image based rendering (DIBR) to help researchers validate their data-driven methods. We hope our work will inspire more FVV synthesis methods with enhanced robustness, improved performance and deeper understanding.
MAINSTREAMING METADATA INTO RESEARCH WORKFLOWS TO ADVANCE REPRODUCIBILITY AND OPEN GEOGRAPHIC INFORMATION SCIENCE

Abstract. Reproducible open science with FAIR data sharing principles requires research to be disseminated with open data and standardised metadata. Researchers in the geographic sciences may benefit from authoring and maintaining metadata from the earliest phases of the research life cycle, rather than waiting until the data dissemination phase. Fully open and reproducible research should be conducted within a version-controlled executable research compendium with registered pre-analysis plans, and may also involve research proposals, data management plans, and protocols for research with human subjects. We review metadata standards and research documentation needs through each phase of the research process to distil a list of features for software to support a metadata-rich open research life cycle. The review is based on open science and reproducibility literature and on our own work developing a template research compendium for conducting reproduction and replication studies. We then review available open source geographic metadata software against these requirements, finding each software program to offer a partial solution. We conclude with a vision for software-supported metadata-rich open research practices intended to reduce redundancies in open research work while expanding transparency and reproducibility in geographic research.
Protecting quantum entanglement in thermal reservoir by detuning

Abstract In this work, the quantum entanglement dynamics of a two-atom system in the thermal reservoir is investigated. An analytical solution of the single atom in the thermal reservoir and the expression of the quantum entanglement of the two-atom system in the thermal reservoir are obtained by the Markovian master equation method. The results showed that the nonzero temperature leads to sudden death of entanglement under the weak coupling limit. The higher the temperature, the faster the decay of entanglement. In particular, the detuning and the smaller spectrum width in the weak atom-reservoir coupling regime can effectively protect the quantum entanglement of the two-atom system in the thermal reservoir under the weak atom-reservoir coupling regime. The corresponding physical explanation is given by the entropy production rare.
Face Reenactment with Generative Landmark Guidance

The identity preserving problem is one of the major obstacles in face reenactment. The problem occurs when the model fails to preserve the detailed information of the source identity, and especially obvious when reenacting different identities. The underlying factors may include the leaking of driver identity, due to theidentity mismatch, and unseen large head poses. In this paper, we propose a novel face reenactment approach via generative landmark coordinates. Specifically, a conditional generative adversarial network is developed to estimate reenacted landmark coordinates for the driving image, which successfully excludes its identity information. These generated coordinates are further injected into the subsequent inference style transferal module to increase the realism of face images. We evaluated our method on the VoxCeleb1 dataset for self-reenactment and the CelebV dataset for reenacting different identities. Extensive experiments demonstrate that our method can produce more realistic reenacted face images.
Stock Trend Forecasting Based on Multi-Information Fusion with Deep Learning

Stock forecasting is the practice of using past data to predict future stock prices. Stock forecasting is effective in assisting financial regulators and investors to make informed decisions. Most previous studies have used the time series of a single stock to forecast. In the capital market, a change in other stock prices also affects a change in the price of a single stock. Therefore, information about a single stock may lead to inaccurate stock price trend prediction. This study proposes a stock price trend forecasting model based on multi-information fusion. This model uses the Graph Convolutional Network (GCN) to extract the spatial features of information affecting the price change of the target stock and uses the Convolutional Neural Network (CNN) to extract the spatial features of the target stock information. Then, the model combines the extracted two spatial features and the Gated Recurrent Unit (GRU) to extract the temporal features and forecast stock price. Finally, the annualized rate of return, classification accuracy, and other related indicators are used for evaluation in the experimental analysis. The results show that the proposed Graph Convolutional Incorporating GRU (GCN-CNN-GRU) hybrid neural network forecasting model offers benefits for estimating the stock price trend.
X-Ray Microtomography Analysis of Urban Soils of the Rostov Region

Urban soils are exposed to transformations of physical properties which are not captured by the conventional approaches due to the presence of impurities that can distort the obtained data. Advanced visualization techniques such as X-ray computed tomography (CT) offer new opportunities of exploring heterogeneity of soil properties at horizon or aggregate scales. The studies of the structure and pore space of different types of urban soils formed on chernozems were carried out using a SkyScan 1172G tomograph. The tomographic study was carried out in the humus-accumulative horizons for six main types of urban soils. The volumetric parameters for the pore space were calculated, and pores orientation and shape parameters were calculated in 2D sections. The results confirmed that the closed pore value was the most informative diagnostic indicator of the urban soilsâ€™ structural condition. All studied soils at the aggregate scales slightly differ from each other in the value of the internal aggregate tomographic porosity. A morphometric analysis of the shape and orientation of fine soil macropores with a diameter of 0.3-2.0 mm also showed significant structural differences between natural soils under woody or grassy vegetation and urban anthropogenically transformed soils. This allows us to conclude that the anthropogenic transformation of the soil structure of chernozems even at extreme urban conditions does not apply to all levels of structural organization of the soil. Moreover, the structure of chernozem soils at the aggregate scales has a pronounced resistance to anthropogenic impact.
Origin of the High Conductivity Anomalies in the Mid-Lower Crust of the Tibetan Plateau: Dehydration Melting of Garnet Amphibolites

High-conductivity anomalies of 0.1–1 S/m are widely distributed in the mid-lower crust of the Tibetan Plateau, which are traditionally explained as the fluid/melt related crustal flow. Dehydration of amphibole-bearing rocks may play an important role in explaining these anomalies. To survey the anomalies’ origin, the electrical conductivities of amphibole-bearing samples are measured at 1.5 GPa and 600–1300 K. Our experiments show that dehydration melting of amphibole occurs at about 1100 K. Before and after dehydration melting, proton conduction and ionic conduction dominate the conduction mechanisms, respectively. High degree partial melting of amphibolites can enhance the bulk conductivity to 1 S/m under the lower-crust conditions beneath the Tibetan Plateau. Combining the calculated thermal gradient in the crust of Tibetan Plateau with the petrology proof and seismic data, dehydration melting of garnet amphibolites likely contributes to the high conductivity anomalies within the Tibetan crust. MW and cube model simulation indicates that the melt fraction is 3.8–36 vol% in the partial molten region.
(Under) Investment in Cyber Skills and Data Protection Enforcement: Evidence from Activity Logs of the UK Information Commissioner's Office

Data breaches account for a significant share of cyber attacks. While they severely impact customers, who lose valuable personal data, they often have a limited effect on the operations of data holding companies. This might lead firms to underinvest in cybersecurity. Does stronger data protection alleviate the effects of these misaligned incentives? We address this question by examining the link between firms' cybersecurity hirings and stronger data protection laws and enforcement. We study two institutional changes that affect the data protection enforcement by the Information Commissioner's Office (ICO) in the UK. The first is the removal of the requirement to prove substantial damage and distress in 2015 that gives greater discretion to the ICO to issue monetary penalties. The second is the enactment of the Data Protection Act 2018, which significantly raises the ceiling of monetary penalties. To examine the effects of these legal changes, we assemble a novel dataset from ICO activity logs that entails more than 5,000 supervisory actions. We construct an index for exposure to ICO enforcement at the three-digit industry level. Combining the sectoral variation with the timing of the legal changes, our paper produces two novel insights. First, we evaluate the effectiveness of two policy instruments, namely frequent monetary penalties and mega-fines. We find that both instruments are effective in increasing investment in cyber skills. While the impact of mega-fines is substantially stronger, it is less homogeneous across firms with different cash balances, ages, and digital technology profiles. Second, despite the effectiveness of stronger data protection laws and enforcement, they have a negative impact on firm dynamics, reducing the entry rate up to 1.4 percentage points and increasing the exit rate by up to 0.9 percentage points.
Panoptic segmentation network based on fusion coding and attention mechanism

Aiming at the problem that the panoptic segmentation network based on coding structure can't accurately extract the detailed information of panoptic images, considering that there are some commonalities between semantic segmentation and instance segmentation tasks, this paper proposes a panoptic segmentation model with multi-feature fusion structure, which generates multi-scale fused feature maps for the panoptic segmentation network, uses the Atrous Spatial Pyramid Pooling to preferentially process the high-level features with rich context information, and then uses the cascade method to splice the low-level features to improve the panoptic segmentation performance of the model. By adding coordinate attention to the ASPP module of the corresponding branch, the perception ability of the model to the contour and instance center is enhanced.
A young teacher in the mirror of change

Increasing requirements for the results of the education system made it necessary for young teachers to search for individual educational routes for professional development. Thus, the subject of the present study is the choice of an individual educational route, and the object of the study is a young teacher.&#x0D; The purpose of this work is to study the choice of an individual educational route by young teachers.&#x0D; Materials and methods. The main methods of this study are the deductive method and the method of pedagogical analysis, which allow a fresh look at traditional problems and established views on the choice of professional development by young teachers. Our scientific analysis of the category of an individual educational route is aimed at a comprehensive study of the essence and tasks of choosing an individual way for young teachers to realize their professional potential.&#x0D; Results. The study showed that the choice of an individual educational route for professional development is influenced by both internal and external factors. In many ways, this choice is determined by the personal characteristics of a young teacher, his interests, directions related to the tasks of his professional development.&#x0D; Findings. An analysis of the results of experimental work proves the objectivity of the perceived choice by teachers of the components of an individual educational route. The choice of components is largely due to systemic changes in the professional and pedagogical activities of teachers.
State response and controllability for con-number-valued systems with applications to a class of quantum systems

In this paper, the exponential function is firstly defined for con-matrices, and some elegant properties are provided. With the exponential function as a tool, the state response of a con-number-valued system is obtained. The concept of controllability for ordinary linear systems is also extended to the case of con-number-valued systems, and a criterion is also provided according to the original coefficient con-matrices for the controllability. In addition, an example of four cavities interacting with each other by two nonlinear crystals is presented to illustrate the theoretical superiority of con-numbered-value systems.
Towards National Identity: Analysis of Children of Purus and Anticipation to Mentawai Future

This article aims to know some problems of children of Purus, a coastal area in Padang city, especially related to factors leading to literary poor imagination of ‘Kelompok Purus Menulis’ members. The experience in manage of children Purus could became guidelines to develop the future of Mentawai's children. They have attended training and mentoring programs in three years organized by Universitas Negeri Padang. But their quality product likes poem is still poor. They tend to imitate others’ works. Few are confident and stand out of box. Why? Data for this article are based on district document, and interview during the training and mentoring activities. After collecting the data, the analysis and written report for the result of research will follow. The analysis will show the poor imagination of Purus children. There are three reasons for that. First, the writer will have an extensive knowledge from reading, observing, and discussing with others. But Purus children rarely read and write. Second, family supporting. Some children of the community come from poor family background and lower level of education that hinder them from growing any better. Third, social environment. The children live in market and tourist area. It makes the children less focus on study.
Improving Deep Mutual Learning via Knowledge Distillation

Knowledge transfer has become very popular in recent years, and it is either based on a one-way transfer method used with knowledge distillation or based on a two-way knowledge transfer implemented by deep mutual learning, while both of them adopt a teacher–student paradigm. A one-way based method is more simple and compact because it only involves an untrained low-capacity student and a high-capacity teacher network in the knowledge transfer process. In contrast, a two-way based method requires more training costs because it involves two or more low-cost network capacities from scratch simultaneously to obtain better accuracy results for each network. In this paper, we propose two new approaches, namely full deep distillation mutual learning (FDDML) and half deep distillation mutual learning (HDDML), and improve convolutional neural network performance. These approaches work with three losses by using variations of existing network architectures, and the experiments have been conducted on three public benchmark datasets. We test our method on some existing KT task methods, showing its performance over related methods.