Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying Coarse-grained Independent Causal Mechanisms with Self-supervision

Among the most effective methods for uncovering high dimensional unstructured data’s generating mechanisms are techniques based on disentangling and learning independent causal mechanisms. However, to identify the disentangled model, previous methods need additional observable variables or do not provide identifiability results. In contrast, this work aims to design an identifiable generative model that approximates the underlying mechanisms from observational data using only self-supervision. Specifically, the generative model uses a degenerate mixture prior to learn mechanisms that generate or transform data. We outline sufficient conditions for an identifiable generative model up to three types of transformations that preserve a coarse-grained disentanglement. More-over, we propose a self-supervised training method based on these identifiability conditions. We validate our approach on MNIST, FashionMNIST, and Sprites datasets, showing that the proposed method identifies disentangled models – by visualization and evaluating the downstream predictive model’s accuracy under environment shifts.
Teachers’ Roles and Game Designers’ Perspectives in Educational Game Design Process

INTRODUCTION Games have become essential in primary education to engage and motivate learners. Teachers’ versatile use of games and educational games (EG) applauses the ability to enjoy learning. Games include ‘all games’ that deliver some in-game skills and allow learners to transmit the knowledge gained during play in a classroom situation (Gee 2003, 2007; Rick and Weber 2010). Teachers' beliefs in providing real-world concepts through game scenarios allow learners to relate and achieve learning outcomes of the subject, acknowledging teachers’ competencies, their professional development knowledge, and perceiving the context, which leads to changing their beliefs towards delivering knowledge practices in published work (Skott 2001; Stipek et al. 2001; Beavis et al. 2014; Stieler-Hunt and Jones 2015a, b). Despite positive results, this urges the need for game designers’ perspectives on a similar level.
Capital structure and firm performance moderated by information asymmetry

We analyzed the influence of the capital structure, moderated by the information asymmetry, on the profitability of public companies in the Brazilian market. We analyzed 106 companies in quarterly periods from 2011 to 2020. We submitted this information to panel data analysis, indicating that the increase in indebtedness reduces the return on assets (ROA) and the return on equity (ROE). We found that information asymmetry acts as a potentializing factor in the negative effect of indebtedness on ROA and ROE. Our results corroborate and contribute to the pecking order theory since publicly traded companies in the Brazilian market have lower profitability as they increase their indebtedness. Also, the results help investors to understand the impact of indebtedness on the profitability of companies with different levels of information asymmetry.
Structural equation modeling in the analysis of the satisfaction of those responsible for tourist accommodation to the performance of Portuguese municipalities

Despite a decline at the end of 2019 after years of continuous growth, the tourism industry is expected to keep driving Portugal’s regional and national economic growth. Hospitality is one of the most economically relevant sectors. Its existence enables the region to host tourists and, in certain cases, to boost the region’s dynamism. Thus, using structural equation modeling as a methodology, it was analyzed how the allocation of resources made by local authorities to attract tourists contributes to the overall satisfaction of those responsible for tourist accommodation. It was concluded that the methodology used was adequate to validate the model initially proposed and that the privileged factor with the greatest impact was publicizing/promotion. However, events organized or supported by municipalities influenced the overall satisfaction of those responsible for tourist accommodation.
Implementation of Convolutional Neural Networks for Predicting Vortical Flow Field

Multi-objective optimization should be conducted in a design process given that airfoil design entails a number of aerodynamic requirements, such as high lift, low drag, and stall characteristics [1]. These aerodynamic requirements can be obtained through flow field analysis using computational fluid dynamics (CFD) simulations. However, CFD simulations require a lot of time and expensive computation in the air foil design process. Recently, deep learning [2] has received considerable attention in the field of fluids. Therefore, some researchers are committed to predicting airfoil flow fields. Afshar et al. [3] proposed an approximation model based on convolution neural networks (CNN) to predict the velocity and pressure field for new geometries under new flow conditions. Sekar et al. [4] used CNN to parameterize the airfoil image with some useful parameters. In this research, we propose an airfoil flow field prediction model that uses deep learning technology instead of CFD simulation. Among the various deep learning models, pix2pix method [5] for image-to-image transformation has been selected.
Algorithm for Disposable White Circular Tableware Surface Defects Detection

An image processing algorithm for detecting disposable circular tableware surface defects is proposed.Proposed algorithm firstly uses morphological method to extract tableware's boundary,and corresponding foreground pixels of this boundary are using by least-squares method to fit a circle curve.Center and radius of the circle can be found and used to segment the tableware image into different detection regions.Then proposed algorithm detects segmented regions separately and finally finds out the defects.Codes are developed using VC++6.0. Major functions are commented in detail.Testing results show that the proposed algorithm acts satisfactory performance.
The Immune Profile of Major Dysmood Disorder: Proof of Concept and Mechanism Using the Precision Nomothetic Psychiatry Approach

Major depressive disorder and a major depressive episode (MDD/MDE) are characterized by activation of the immune-inflammatory response system (IRS) and the compensatory immune-regulatory system (CIRS). In MDD/MDE, recent precision nomothetic psychiatry studies discovered a new endophenotype class namely Major Dysmood Disorder, a new pathway phenotype namely reoccurrence of illness (ROI), and a new model of the phenome of depression. The aim of the present study is to examine the association between ROI, the phenome of depression, and MDMD features and IRS, CIRS, M1, T helper (Th)1, Th2, Th17, Tregulatory, and growth factor (GF) profiles. Culture supernatants of unstimulated and stimulated (5 microg/mL of PHA and 25 microg/mL of LPS) diluted whole blood of 30 MDD/MDE patients and 20 controls were assayed for cytokines/GF using the LUMINEX assay. MDMD was characterized by increased M1, Th1, Th2, Th17, Treg, IRS, CIRS, neurotoxicity and GF profiles. Factor analysis shows that ROI features and immune-GF profiles may be combined into one a new pathway phenotype (an extracted latent vector). ROI, lifetime and recent suicidal behaviors, and severity of depression are significantly associated with immunotoxicity and GF profiles. Around 80.0 percent of the variance in the phenome is predicted by ROI and neurotoxicity or the IRS / CIRS ratio. The molecular pathways underpinning ROI-associated sensitization of immune/growth networks are transmembrane receptor protein kinase triggered STAT protein phosphorylation, TLR/NF-kappaB, JAK-STAT, and the main proliferation/survival PI3K/Akt/RAS/MAPK pathway. In conclusion, MDMD heightened immune responses are the consequence of ROI-associated sensitization combined with immunostimulatory triggers.
Dynamics of Two-components Mooring Line in Deep Water

The offshore floating structures in deep water are usually moored by multi-component cables or chains.The restoring force is induced by the nonlinear effect of environment loads,the motion of the floating structure as well as the geometry of mooring lines.The mooring lines made from different materials push the multi-component mooring system into the deeper water while bring a big challenge to the design work with the dynamic computation.A method for the dynamics analysis is put forward,which combined the finite element method and the design wave theory including the effect of the motion of the moored structure at the fairlead point.The computation results of different segments are compared while the results from the two-component mooring line are also compared with the single component one.
A ship target dynamic recognition method based on image recognition and VR technology

Effective identification of ship identity is an important means of water traffic management. Aiming at the technical limitations of AIS, radar and other means, a ship automatic recognition method based on machine learning is proposed, and a water traffic visualization system integrating multi-source recognition technology is realized based on VR technology. The test shows that the method and system can solve the technical problems of ship identification caused by data easy to be tampered with, difficult to distinguish massive targets and large obstacles, and provide an effective method for navigation support in key waters.
Astrometric and Photometric Verification of Faint Blue White Dwarfs in the Gaia Catalog of nearby Stars

The Gaia catalog of nearby stars (GCNS) divided all objects with parallaxes >10 mas into GCNS-selected and GCNS-rejected 100 pc samples. Below the white dwarf (WD) sequence in the complete GCNS color–magnitude diagram (CMD), at Gabs > 14.7 + 4.7(G − RP), there appear 60 GCNS-selected faint blue white dwarfs (FBWDs). However this CMD region is also populated by 411 GCNS-rejected objects, mainly from crowded regions toward the Galactic center and the Magellanic Clouds. The WD catalog of Gentile et al. lists only 47 GCNS-selected but also 8 GCNS-rejected objects. I confirm 59 of the GCNS-selected but none of the GCNS-rejected objects as FBWDs from visual inspection and a proper motion check using additional optical sky surveys. Hence FBWDs form an additional branch in the CMD. Compared to the full GCNS-selected 100 pc sample, FBWDs have relatively high proper motions and tangential velocities. They represent interesting targets for studies of ultracool or infrared-faint, and possibly also ultramassive WDs.
A Method of Integrated Energy Metering Simulation Data Generation Algorithm Based on Variational Autoencoder WGAN

With the development of integrated energy systems, the penetration rate of all types of distributed power sources continues to increase. In a grid oriented to integrated energy metering, the metering business of different energy sources needs to fully consider the dynamic characteristics of different user loads. This paper aims at the generation of large-scale integrated energy metering data in large-scale integrated energy digital simulation technology, starting from supporting the construction of an integrated energy metering simulation system, combining various business applications and artificial intelligence technology under the background of the energy Internet. For the requirements of energy metering simulation, a method of integrated energy metering simulation data generation based on variational autoencoder WGAN is proposed, and the effect of the method proposed in this paper is verified through actual metering scene data.
Research on Multi-Objective Optimization Method of Edge Cloud Computing Virtual Machine Placement

With the emergence of demand for massive computing tasks in the edge cloud of the city, the disordered computing in the edge cloud leads to the high energy consumption of CPU computing and the problem of too long time delay caused by the blockage of computing tasks. This has become the first technical difficulty to solve in the construction of edge cloud. The virtual machine placement method is optimized by computing energy consumption and computing delay. First, the LRR physical host screening model is constructed to detect the status of the physical hosts and form a list of migrated physical hosts; secondly, the MMT time scale model is constructed to generate the list of migrated virtual machines; finally, the GA algorithm is used to place the virtual machines. The simulation results show that this algorithm can reduce the CPU energy consumption of the edge cloud center by 20. 21% and the time delay by 16. 11%. This optimization method has a good theoretical guidance effect on the construction of cloud computing.
X-Ray-induced Diffusion and Mixing in Layered Astrophysical Ices

Ice in cold cosmic environments is expected to be organized in a bilayered structure of polar and apolar components. The initial water-rich layer is embedded in an icy CO envelope, which provides the feedstock for methanol formation through hydrogenation. These two components are thought to be physically segregated, unless an increase in temperature favors mobility and reactivity within the ice. We present new and robust evidence of X-ray-induced diffusion within interstellar ice analogues at very low temperatures, leading to an efficient mixing of the molecular content of the ice. The results of our study have two main implications. First, molecular mixing enhances chemical reactions from which complex organic species, including many of prebiotic interest, are formed. Second, diffusion drives the desorption of species that would otherwise remain buried near the surface of dust, thus enhancing their abundances in the gas, where they can be detected in the radio-wave domain. Such a scenario may have implications for the chemical history of ices in protoplanetary disks, in particular in the early stages of their life.
A fault-tolerance control method of load frequency based on reverse behavior reconstruction

The access of a high proportion of renewable energy and the large-scale application of open sharing communication network are the two main characteristics of modern interconnected power grid. However, the tight coupling between the cyber and physical subsystem makes the malicious network attack and sudden physical failure more likely to cause the collapse of the interconnected power grid. Therefore, a reverse behavior reconstruction strategy is proposed, which does not need to obtain a priori fault information and only depends on the input and output measurements of the system, so as to realize fault tolerance as much as possible. The simulation experiments show that the proposed strategy can achieve the frequency fault tolerance and has better practicability and applicability under complex and diverse fault conditions.
Celebrity Politicians, Digital Campaigns, and Performances of Political Legitimacy in Indonesia’s 2019 Elections

This article looks into celebrity politicians’ social media campaigns during Indonesia’s 2019 elections. It examines the visual political communication strategies of celebrity politicians, particularly how they use their fame and celebrity status to make claims as political candidates and how they transform their fame into political legitimacy through social media. In the process of establishing their political legitimacy, celebrity politicians deploy “affective power. ” While conventional politicians may use similar strategies on social media, the celebrity politicians’ online performance styles are foundational in how the celebrities construct and maintain their often-precarious political legitimacy to the public. Three patterns emerged. First, celebrity politicians used images of them surrounded by people to indicate their celebrity status and political legitimacy. Second, they highlighted their work ethic and commitment to the public cause. Third, they used visual markers of allegiance to the presidential candidates to claim their political legitimacy. This article contributes to the study of celebrity politicians by extending our understanding of the performance and construction of political legitimacy by celebrity politicians on social media.
Elucidation of the effect of fast pyrolysis and hydrothermal liquefaction on the physico-chemical properties of bio-oil from southern yellow pine biomass as a chemical feedstock

Bio-oils obtained from southern yellow pine biomass from two thermochemical conversion processes, fast pyrolysis (FP) and hydrothermal liquefaction (HTL), were investigated. The effects of FP and HTL on the physical and chemical properties of the bio-oils were characterized. The HTL and FP bio-oil yields were 67 and 36 wt%, respectively. The results indicated that the physical properties of the HTL bio-oil and FP bio-oil were similar; however, there were variations in the composition of the bio-oils from the same biomass. The pH values of the FP and HTL bio-oils were 2.3 and 2.8, respectively. From the GC-MS (gas chromatography–mass spectrometry) analysis, esterified chemical compounds were prevalent in the HTL bio-oil, while phenols and phenolic derivatives were found in both bio-oils. The 31P-NMR (phosphorous nuclear magnetic resonance) analysis of the bio-oils further revealed that both FP and HTL bio-oils are rich in phenolic OH and aliphatic OH functionalities, which could serve as a potential bio-polyol.
Experiments in Automatic Gaze Estimation for the Lookit Developmental Research Platform

A recent MIT-developed crowdsourcing platform known as Lookit has signiﬁcantly accelerated the rate at which developmental psychologists can collect video data of infant subjects. With Lookit, researchers can obtain footage of infants’ gaze directions when shown diﬀerent images and sounds, allowing them to draw important conclusions about early childhood understanding. Unfortunately, data collected from Lookit must be manually annotated with which direction (left, right, or away from screen) that infants are looking before inference can be performed. We experiment with CNN-based models that automatically classify infant gaze directions from Lookit videos to try to reduce the overhead caused by human annotation, and compare our results to an existing gaze detection module. We ﬁnd that while custom gaze estimation methods perform better than existing out-of-the-box solutions, the accuracy does not meet the high threshold necessary to be immediately useful to researchers using Lookit.
Analysis of Failure mechanism for Beams Strengthened by Steel Plate

One group of experimental beams strengthened by steel plate was designed for researching the ultimate, bearing capacity. The shear stress and the normal stress at the end of steel plate have been investigated by means of elastic mechanics analytical solution . and the developing coefficients of crack have been provided for analyzing the bearing capacity. Two different kinds of failure patterns have been observed and the failure of strengthened beam is attributed to the principal stress at the end of steel plate.
A Global Optimization Algorithm of Localization for Huge Sculpture Surface Blank

Blanks of sculptured surface usually do not exist positioning references. In order to improve the blank positioning, a global optimization algorithm of localization for huge sculpture surface blank is presented. The algorithm included two steps. The first step is prelocation, translate and rotate the blank to get the largest overlap of the projections of theoretical surface and blank on the false plane. The second step is the optimization of objective function by means of simulated annealing algorithm to get the best localization. The technique is applied to the positioning of a hydroelectric turbine blade. Results show that it is effective and the final positioning result is excellent.
Development of Evaluation Method for Startup Operation of Nuclear Power Plants

Autonomous operation has been one of the main concerns due to the advantage of excluding human error in industrial fields. In the case of nuclear power plants (NPPs), there are some automated systems to assist operators for safety. Such a system is mainly composed of algorithms to maintain specific variables during power operation or to automatically activate safety functions in case of an emergency situation. However, the overall operation flow must be performed by humans, which has resulted in an increased operator workload in startup and shutdown operations where temperature and pressure change dynamically. In case of startup operation, operating tasks are conducted by human operators manually, taking more than 20 hours to finish. In the previous work, the framework to develop autonomous system for startup and shutdown operation was suggested [1]. In this paper, evaluation method for startup operation of NPPs is suggested by quantifying operation performance. As an application, artificial intelligence (AI) model was implemented to two operating blocks and a plant parameter prediction model. Since the action was selected through the exploited method for the convergence of reinforcement learning (RL) in the training stage, the evaluation of the suboptimal action is not learned as detailed as the best action. Nevertheless, in a complex multi-agent environment, the influence between agents cannot be ignored. This is why the evaluation method is needed regardless of RL training.
COVID-19 and thyroid disease: clinical course and prognosis

The aim is to analyze current scientific data on the prevalence of thyroid dysfunction in patients with COVID-19 and to evaluate the relationship between possible complications of COVID-19 and vaccination. Materials and methods. Open digital archive of journal articles on biomedical and biological sciences of the National Institutes of Health (USA), developed by the National Center for Biotechnological Information of the National Medical Library (USA) – PubMed, Google Academy and Academic Journals. Results. The development of thyroid diseases in SARS-CoV-2 may be associated with various mechanisms of its damage, including an excessive immune response, infection-induced immunodeficiency, or direct cell damage due to significant tissue tropism and high affinity of SARS-CoV-2 to thyroid tissue. Possible mechanisms of formation of post-vaccination dysfunction of the thyroid gland are proposed. Conclusions. Thyroid hormone deficiency is associated with an increased risk of adverse events and in-hospital mortality of COVID-19 and depended on the age of the patient. The severe course of Covid-19 is characterized by an increase in the prevalence of TT secondary to destructive or inflammatory thyroiditis. Thyroid-related thyrotoxicosis exacerbates the clinical course of comorbidities and long-term consequences, such as autoimmune hypothyroidism, which occurs in patients of all ages and with any severity of COVID-19. The prevalence of TD in patients with COVID-19 varies from 13 to 64 %. 2. The presence of thyroid dysfunction is positively correlated with the clinical severity of COVID-19. Patients with confirmed thyroid disease should receive COVID-19 vaccine to reduce the risk of morbidity and mortality from COVID-19 infection