Miracle-dz/UniFAR-FTUs huggingface dataset:
Auditing of Building Information Modeling Application in Decoration Engineering Projects in China

In China’s construction industry, it is a normal practice to separately subcontract the decoration engineering part from construction engineering, and Building Information Modeling (BIM) is also done separately. Application of BIM in decoration engineering should be integrated with other disciplines, but Chinese current practice makes this very difficult and complicated. Currently, there are three barriers in the auditing of BIM application in decoration engineering in China: heavy workload; scarcity of qualified professionals; and lack of literature concerning audit contents, standards, and methods. Therefore, it is significant to perform research on what (contents) should be evaluated, in which phase, and by whom (professional qualifications) in BIM application in decoration construction so that the application of BIM can be promoted in a better manner. Based on this consideration, four principles of BIM auditing are proposed: Comprehensiveness of information, accuracy of data, aesthetic attractiveness of appearance, and scheme optimization. In the model audit, three methods should be used: Collision, observation, and contrast. In addition, BIM auditing at six stages is discussed and a checklist for work items and results to be submitted is proposed. This checklist can be used for reference by decoration project participants. Keywords—Audit, evaluation, dimensions, methods, standards, building information modeling application, decoration engineering projects.
Data Weighted Training Strategies for Grammatical Error Correction

Abstract Recent progress in the task of Grammatical Error Correction (GEC) has been driven by addressing data sparsity, both through new methods for generating large and noisy pretraining data and through the publication of small and higher-quality finetuning data in the BEA-2019 shared task. Building upon recent work in Neural Machine Translation (NMT), we make use of both kinds of data by deriving example-level scores on our large pretraining data based on a smaller, higher-quality dataset. In this work, we perform an empirical study to discover how to best incorporate delta-log-perplexity, a type of example scoring, into a training schedule for GEC. In doing so, we perform experiments that shed light on the function and applicability of delta-log-perplexity. Models trained on scored data achieve state- of-the-art results on common GEC test sets.
A Design of Intelligent and Autonomous Public Transportation System by Co-evolution

Nowadays every big city has its own public transportation system, like the one with citybuses as we can see everywhere all over the world. This article describes a design of such system. Usually, public vehicles follow the pre-set courses with a pre-set schedule determined by human. Our goal is to make each of these vehicles determine autonomously and intelligently by themselves their appropriate behavior such as, what time should it start; which course should it follow; how long should it stay at one stop, and so on. The design will be by co-evolution between the system of transportation versus traffic environment. The latter includes three items: (i) a map of roads which is created randomly at the start; (ii) not-so-intelligent-but-rather-with-random-movement people, like us; and (iii) cars with not so intelligent behavior either. The transportation system evolves to be more and more intelligent while environment evolves to be more and more difficult to be explored.
The Størmer problem for an aligned rotator

The effective potential energy of the particles in the field of rotating uniformly magnetized celestial body is investigated. The axis of rotation coincides with the axis of the magnetic field. Electromagnetic field of the body is composed of a dipole magnetic and quadrupole electric fields. The geometry of the trapping regions is studied as a function of the magnetic field magnitude and the rotation speed of the body. Examples of the potential energy topology for different values of these parameters are given. The main difference from the classical St{\o}rmer problem is that the single toroidal trapping region predicted by St{\o}rmer is divided into equatorial and off-equatorial trapping regions. Applicability of the idealized model of a rotating uniformly magnetized sphere with a vacuum magnetosphere to real celestial bodies is discussed.
Gr upSM Older Adults with down Syndrome Benefit from Assisted Cycling Therapy : Implications for Physical Activity , Fitness and Daily Function

Older adults with Down syndrome are at greatly increased risk for dementia, Alzheimer’s disease, and functional decline compared to the general populations. Physical activity and fitness is associated with a lower risk of these ailments and greater independence in regards to activities of daily living. This study examined the effects of exercise on aerobic walking capacity and leisure physical activity levels in adults with Down syndrome (mean age = 39 years). All participants completed 8 weeks of stationary cycling exercise on 3 days per week. Four participants underwent Assisted Cycling Therapy (ACT), which meant that they cycled with the assistance of an electric motor which augmented their cadence to 135% of their voluntary cadence. The remaining four participants completed Voluntary Cycling (VC) and cycled without the motor at their own self-selected cadence and a light to moderate intensity. A reduction in leisure physical activity levels from preto post-test was observed in the VC group while the ACT group maintained their physical activity levels. The ACT group displayed improved aerobic walking capacity after the intervention as measured with the 6 minute walk test while the walking capacity of the VC group did not change. These findings are discussed in regards to the activitystat hypothesis and Simon D Holzapfel*, Shannon D R Ringenbach, Rachel O Ganger, Elizabeth D Gomez and Lucas Parker School of Nutrition and Health Promotion College of Health Solutions, Arizona State University, USA *Corresponding author: Simon D Holzapfel, School of Nutrition and Health Promotion College of Health Solutions, Arizona State University, 425 N. 5th Street, Phoenix, AZ 85004, USA, Tel: 423-329-3038; Email: Simon.holzapfel@asu.edu Published Date: January 26, 2016
Calibrating Mg ii-based Black Hole Mass Estimators Using Low-to-high-luminosity Active Galactic Nuclei

We present single-epoch black hole mass (MBH) estimators based on the rest-frame ultraviolet (UV) Mg ii 2798 Å and optical Hβ 4861 Å emission lines. To enlarge the luminosity range of active galactic nuclei (AGNs), we combine the 31 reverberation-mapped AGNs with relatively low luminosities from Bahk et al., 47 moderate-luminosity AGNs from Woo et al., and 425 high-luminosity AGNs from the Sloan Digital Sky Survey. The combined sample has a monochromatic luminosity at 5100 Å in the range erg s−1, over the range 5.5 < log(MBH/M⊙) < 9.5. Based on the fiducial mass from the line dispersion or FWHM of Hβ paired with the continuum luminosity at 5100 Å, we calibrate the best-fit parameters in the black hole mass estimators using the Mg ii line. We find that the differences in the line profiles between Mg ii and Hβ have significant effects on calibrating the UV MBH estimators. By exploring the systematic discrepancy between the UV and optical MBH estimators as a function of AGN properties, we suggest adding a correction term in the equation for the UV mass estimator. We also find a ∼0.1 dex bias in the MBH estimation due to the difference in the spectral slope in the range 2800–5200 Å. Depending on whether the selection of MBH estimator is based on either line dispersion or FWHM and either continuum or line luminosity, the derived UV mass estimators show ≳0.1 dex intrinsic scatter with respect to the fiducial Hβ-based MBH.
An Approach for Early Diagnosis of CardioVascular Disease Using Modified Decision Tree

Abstarct: The heart disease is a major cause of mortality and death-rate in modern society. Medical assessment is very important but the labyrinthine task should be performed efficiently and accurately. So, in this era of computing and intelligence it is an easy yet complicated task to estimate the probability of disease on the basis of data and fact provided to the system. With the growing research on heart disease predicting system, it has become important to categorize the research outcomes and provide readers with an overview of the existing heart disease prediction techniques in each category. The data mining algorithms are typically used to identify the disease that occurs in original from the database So in this research work, we have used Decision Tree for the detection of Cardiovascular Disease.
Learning Insulin-Glucose Dynamics in the Wild

We develop a new model of insulin-glucose dynamics for forecasting blood glucose in type 1 diabetics. We augment an existing biomedical model by introducing time-varying dynamics driven by a machine learning sequence model. Our model maintains a physiologically plausible inductive bias and clinically interpretable parameters -- e.g., insulin sensitivity -- while inheriting the flexibility of modern pattern recognition algorithms. Critical to modeling success are the flexible, but structured representations of subject variability with a sequence model. In contrast, less constrained models like the LSTM fail to provide reliable or physiologically plausible forecasts. We conduct an extensive empirical study. We show that allowing biomedical model dynamics to vary in time improves forecasting at long time horizons, up to six hours, and produces forecasts consistent with the physiological effects of insulin and carbohydrates.
Flexible Tag Layouts for the AprilTag Fiducial System

We propose the idea of flexible tag layouts for visual fiducial systems. In contrast to traditional square tags, flexible tag layouts allow circular, annular, or other shapes as desired. Layout flexibility also allows the data density of square shaped tags to be increased at the cost of a minor decrease in detection distance and pose estimation accuracy by decreasing the size of the detection border. In addition, we describe a detector that is faster and has higher recall than either the AprilTag 2 or the ArUco detectors.
Evaluation Models of Guidance and Counseling Service Based Cipp in Senior High School

This research specifically aimed to develop the evaluation models of guidance and counseling service based CIPP in school. This research included as the kind of research and development. The used technique to collect the data in this development research of models consist questionnaire expert judgment and FGD (focused group discussion). This FGD activity involves counselor and supervisor of guidance and counseling in SMA in Semarang city. The structure model of evaluation of guidance and counseling service based CIPP to be: (a) rational, (b) definition, (c) purpose, (d) assumption, (e) intervention target, (f) steps, (g) competency and role of school counselor and (h) system support. Based on Wilcoxon analysis, Zcalculation was reach 3,18 whereas Z-table with significance level 5% got Z-table 1,96. So, the value of Zcalculation > Z-table and can be assumed that evaluation models of guidance and counseling service based CIPP was effective developed to increase the competency of counselor in evaluation service of guidance and counseling. Therefore counselor can take a decision accurately about changes which must be done in order to increase the quality of guidance and counseling in school. For the next research, socialization and training of evaluation models of guidance and counseling which is based CIPP needs to be followed-up with the program of matter refreshment and techniques in more effective implementation.
Computer-Aided Comparative Assessment of Inflammatory Lesions in MRI of the Spine in Axial Spondyloarthritis Patients

Detection of inflammatory lesions in magnetic resonance imaging (MRI) of the spine in axial Spondyloarthritis (SpA) patients is a labor-intensive task. Current scoring methods often require an expert reader to visually assess 23 vertebral units (VU) on multiple MRI slices. Furthermore, monitoring of progressive changes consists of a manual search and alignment of the 23 VUs in both baseline and follow-up scans, followed by side-by-side comparison of two images. We present a semiautomated framework for comparative visualization of inflammatory lesions in MRI of the spine in axial SpA patients. The first part of our method localizes and segments the VUs using a fully automatic algorithm based on quasi-periodicity of vertebral column intensity profile and tissue class probability maps. The second part uses these segmentations to align baseline and follow-up scans by deriving a locally rigid transform from a pre-computed deformable registration. To facilitate visual comparison, the co-registered baseline and follow-up images are fused in a single color-encoded difference image. This allows an expert reader to efficiently interpret progressive changes while focusing on just one image. Experiments in which an expert reader used the proposed framework to score inflammatory changes in axial SpA patients were performed. Results showed strong agreement with ground truth scores for cases of full lesion occurrence/remission.
NuI-Go: Recursive Non-Local Encoder-Decoder Network for Retinal Image Non-Uniform Illumination Removal

Retinal images have been widely used by clinicians for early diagnosis of ocular diseases. However, the quality of retinal images is often clinically unsatisfactory due to eye lesions and imperfect imaging process. One of the most challenging quality degradation issues in retinal images is non-uniform which hinders the pathological information and further impairs the diagnosis of ophthalmologists and computer-aided analysis. To address this issue, we propose a non-uniform illumination removal network for retinal image, called NuI-Go, which consists of three Recursive Non-local Encoder-Decoder Residual Blocks (NEDRBs) for enhancing the degraded retinal images in a progressive manner. Each NEDRB contains a feature encoder module that captures the hierarchical feature representations, a non-local context module that models the context information, and a feature decoder module that recovers the details and spatial dimension. Additionally, the symmetric skip-connections between the encoder module and the decoder module provide long-range information compensation and reuse. Extensive experiments demonstrate that the proposed method can effectively remove the non-uniform illumination on retinal images while well preserving the image details and color. We further demonstrate the advantages of the proposed method for improving the accuracy of retinal vessel segmentation.
Kernel Embedding Based Variational Approach for Low-Dimensional Approximation of Dynamical Systems

Abstract Transfer operators such as Perron–Frobenius and Koopman operator play a key role in modeling and analysis of complex dynamical systems, which allow linear representations of nonlinear dynamics by transforming the original state variables to feature spaces. However, it remains challenging to identify the optimal low-dimensional feature mappings from data. The variational approach for Markov processes (VAMP) provides a comprehensive framework for the evaluation and optimization of feature mappings based on the variational estimation of modeling errors, but it still suffers from a flawed assumption on the transfer operator and therefore sometimes fails to capture the essential structure of system dynamics. In this paper, we develop a powerful alternative to VAMP, called kernel embedding based variational approach for dynamical systems (KVAD). By using the distance measure of functions in the kernel embedding space, KVAD effectively overcomes theoretical and practical limitations of VAMP. In addition, we develop a data-driven KVAD algorithm for seeking the ideal feature mapping within a subspace spanned by given basis functions, and numerical experiments show that the proposed algorithm can significantly improve the modeling accuracy compared to VAMP.
Brain drain and brain gain in Russia: analyzing international mobility of researchers by discipline using Scopus bibliometric data

We study international mobility in academia with a focus on migration of researchers to and from Russia. Using all Scopus publications from 1996 to 2020, we analyze bibliometric data from over half a million researchers who have published with a Russian affiliation address at some point in their careers. Migration of researchers is observed through the changes in their affiliation addresses. For the first time, we analyze origins and destinations of migrant researchers with respect to their fields and performance and compute net migration rates based on incoming and outgoing flows. Our results indicate that while Russia has been a donor country in the late 1990s and early 2000s, it has experienced a relatively symmetric circulation of researchers in more recent years. Using subject categories of publications, we quantify the impact of migration on each field of scholarship. Our analysis shows that Russia has suffered a net loss in almost all disciplines and more so in neuroscience, decision sciences, dentistry, biochemistry, and mathematics. For economics and environmental science, there is a relatively balanced circulation of researchers to and from Russia. Our substantive results reveal new aspects of international mobility in academia and its impact on a national science system which speak directly to policy development. Methodologically, our new approach of handling big data can be adopted as a framework of analysis for studying scholarly migration in other countries.
Nightlife Tourism: A Blessing or a Curse for Host Communities?

Gemmayzeh is located in Beirut Central District, known to be one of the most famous night attractions in the Lebanese capital. It is also a special attraction for domestic visitors and tourists. However, despite its competitive nightlife advantages in the Lebanese tourism industry, its local residents are not convinced with the way tourism is being developed in this area. The lack of cooperation among the different potential tourism stakeholders and the absence of effective regulations and policies have affected the traditions and heritage of the local residents especially that this specific region is one of the oldest in Beirut with many historical and cultural buildings. All these vibes have created negative impacts on the community’s well-being. The absence of tourism planning is affecting most of the Lebanese tourism attractions and it is getting worse with time especially in the case of Gemmayzeh whose residents have been involved in direct conflicts with visitors and are trying to avoid the negative impacts on their children at times. Therefore, this study aims to review the impacts of tourism development on Gemmayzeh’s local residents, to assess and analyze the type of involvement of local communities in tourism planning and development and to explore the attitudes and perceptions of local residents towards tourism development and its impacts on the community. Out of 1,400 permanent local residents in Gemmayzeh, 50 respondents (representing 5% of the target population) were selected and surveyed during January, 2014. Results have revealed that the lack of involvement of local residents in tourism planning and development has negatively affected their perception towards tourism development. Although this issue is affecting the local community, results revealed that the majority of respondents, mainly young ones, have no problem with nightlife as a type of tourism but with the way it has been developed for years. Furthermore, respondents suggested the development of other forms of tourism such as cultural tourism which can have a better impact on all stakeholders.
HoliCity: A City-Scale Data Platform for Learning Holistic 3D Structures

We present HoliCity, a city-scale 3D dataset with rich structural information. Currently, this dataset has 6,300 real-world panoramas of resolution $13312 \times 6656$ that are accurately aligned with the CAD model of downtown London with an area of more than 20 km$^2$, in which the median reprojection error of the alignment of an average image is less than half a degree. This dataset aims to be an all-in-one data platform for research of learning abstracted high-level holistic 3D structures that can be derived from city CAD models, e.g., corners, lines, wireframes, planes, and cuboids, with the ultimate goal of supporting real-world applications including city-scale reconstruction, localization, mapping, and augmented reality. The accurate alignment of the 3D CAD models and panoramas also benefits low-level 3D vision tasks such as surface normal estimation, as the surface normal extracted from previous LiDAR-based datasets is often noisy. We conduct experiments to demonstrate the applications of HoliCity, such as predicting surface segmentation, normal maps, depth maps, and vanishing points, as well as test the generalizability of methods trained on HoliCity and other related datasets. HoliCity is available at this https URL.
A Comparison of Vibro-Acoustic Modelling Techniques for Resonance Changer Tuning

A resonance changer is a device which may be installed in the propulsion system of a marine vessel to reduce the axial vibration transmission from the propeller to the hull. This device is a hydraulic system incorporated in the thrust bearing, which operates analogously to a Helmholtz resonator. Previous work by the authors has examined optimisation of the resonance changer parameters to reduce the acoustic signature of maritime platforms. In those studies, prediction of the low frequency axisymmetric structure-borne noise was carried out using both analytical and fully coupled finite-element/boundary-element models. This paper further develops the analytical model, by including a description of the propeller radiation and its coupling with the hull. The sound power is expressed as the combination of sound radiation from the hull and the direct dipole sound radiation from the propeller. Results calculated from both models are presented for comparison and discussed.
Individual Treatment Effect Estimation in a Low Compliance Setting

Individual Treatment Effect (ITE) estimation is an extensively researched problem, with applications in various domains. We model the case where there is heterogeneous non-compliance to a randomly assigned treatment, a typical situation in health (because of non-compliance to prescription) or digital advertising (because of competition and ad blockers for instance). The lower the compliance, the more the effect of treatment prescription, or individual prescription effect (IPE), signal fades away and becomes hard to capture. We propose a new approach to estimate IPE that takes advantage of observed compliance information to prevent signal fading. Using the Structural Causal Model framework and do-calculus, we define a general mediated causal effect setting under which our proposed estimator soundly recovers the IPE, and study its asymptotic variance. Finally, we conduct extensive experiments on both synthetic and real-world datasets that highlight the benefit of the approach, which consistently improves state-of-the-art in low compliance settings.