Miracle-dz/UniFAR-FTUs huggingface dataset:
Validity, Reliability, and Feasibility of Clinical Staging Scales in Dementia

New staging systems of dementia require adaptation of disease management programs and adequate staging instruments. Therefore, we systematically reviewed the literature on validity and reliability of clinically applicable, multidomain, and dementia staging instruments. A total of 23 articles describing 12 staging instruments were identified (N = 6109 participants, age 65-87). Reliability was studied in most (91%) of the articles and was judged moderate to good. Approximately 78% of the articles evaluated concurrent validity, which was good to very good, while discriminant validity was assessed in only 25%. The scales can be applied in ±15 minutes. Clinical Dementia Rating (CDR), Global Deterioration scale (GDS), and Functional Assessment Staging (FAST) have been monitored on reliability and validity, and the CDR currently is the best-evidenced scale, also studied in international perspective, and is available in 14 languages. Taking into account the increasing differentiation of Alzheimer’s disease in preclinical and predementia stages, there is an urgent need for global rating scales to be refined as well.
Hierarchical Performance Analysis on Random Linear Network Coding

Random linear network coding (RLNC) is a promising network coding solution when the network topology information is not fully available to all the nodes. However, in practice, nodes have partial knowledge of the network topology information. Motivated by this, we investigate the performance of RLNC and obtain different upper bounds on the failure probability of RLNC for the network constrained by different partial network topology information. These upper bounds not only improve the existing ones in the literature, but also show that the partial network topology information can bring benefits to the performance analysis of RLNC. On the other hand, it is observed that if more network topology information can be utilized, tighter upper bounds can be obtained, as expected. The upper bounds on two classical networks are compared for demonstration. To obtain a deeper understanding about the performance of RLNC, the asymptotic behavior of RLNC as the field size goes to infinity is also investigated.
An Overview on Implementation of Citation Parser using Knowledge Base Techniques

Use of the bibliographical information of publications available on the Internet is an important task in academic research. Accurate reference metadata extraction for publications is essential for the integration of information from heterogeneous reference sources. It is an essential task in the research paper development to point out references for proper document. Giving a proper acknowledgement to a document or part of document is called citation. A “citation” is the way to tell readers the source from which certain material has come. In general citation implies a relation between part or whole of the cited document and a part or whole of the citing document. To cite a particular document there are several methods. We shall develop a citation tool based on knowledge based citation parsing technique. By developing a current system we shall extract the cited document which is useful for scientific publication or document citing.
Express Paper 3 D Shape from Silhouettes in Water for Online Novel-View Synthesis

This paper is aimed at presenting a new algorithm for full 3D shape reconstruction and online free-viewpoint rendering of objects in water. The key contributions are (1) a new calibration model for the refractive projection, and (2) a new 3D shape reconstruction algorithm based on shape-from-silhouette (SfS) concept. We also propose an online free-viewpoint rendering system as a practical application.
Understanding Review Expertise of Developers: A Reviewer Recommendation Approach Based on Latent Dirichlet Allocation

The code reviewer assignment problem affects the reviewing time of a source code change. To effectively perform the code review process of a software project, the code reviewer assignment problem must be dealt with. Reviewer recommendation can reduce the time required for finding appropriate reviewers for a given source code change. In this paper, we propose a reviewer recommendation approach based on latent Dirichlet allocation (LDA). The proposed reviewer recommendation approach consists of a review expertise generation phase and a reviewer recommendation phase. The review expertise generation phase generates the review expertise of developers for topics of source code changes from the review history of a software project. The reviewer recommendation phase computes the review scores of the developers according to the topic distribution of a given source code change and the review expertise of the developers. In an empirical evaluation of five open source projects, we confirm that the proposed reviewer recommendation approach obtains better average top-10 accuracy than existing reviewer recommendation approaches.
Similar Comment Analysis Using Selector Rating Estimates in Relation to Comments

Though multiple-choice analysis is widely used in questionnaire analyses, free descriptive answers are also used in conjunction with it. In this study, by looking at the individual freely-described comments from the perspective of the questionnaire as a whole and estimating what kind of selectors are selected, we are able to extract similar freely-described comments and generate comments estimated from selector selection examples. The main features of this study are the thinking surrounding the general rating of comments, the generation of feature vectors for convolutional neural networks (CNN), and, in particular, the use of CNN as a filter.
Comparison of tumour morphology and structure from U87 and U118 glioma cells cultured on chicken embryo chorioallantoic membrane

Abstract The objective of this study was the morphological and structural characterisation of glioblastoma multiforme grown in ovo. Glioma cells U87 and U118 were implanted in the chorioallantoic membrane (CAM) of chicken egg. After 10 days of incubation, tumours were resected for further analyses. Culturing two types of glioblastoma tumours from U87 and U118 cell lines has shown a number of differences in their morphology, histology, and ultrastructure. CAM assay proved to be a useful tool for studying glioblastoma growth. The model provides an excellent alternative to current rodent models and could serve as a preclinical screening assay for anticancer molecules. It might increase the speed and efficacy of the development of new drugs for the treatment of glioblastoma.
Evolution of orogenic wedges and continental plateaux: insights from crustal thermal–mechanical models overlying subducting mantle lithosphere

The links between an early phase of orogenesis, when orogens are commonly wedge shaped, and a later phase, with a plateau geometry, are investigated using coupled thermal-mechanical models with uniform velocity subductionboundary conditions applied to the base of the crust, and simple frictional-plastic and viscous rheologies. Models in which rheological properties do not change with depth or temperature are characterized by growth of back-to-back wedges above the subduction zone. Wedge taper is inversely dependent on the Ramberg number (Rm; gravity stress/basal traction); increasing convergence velocity or crustal strength produces narrower and thicker wedges. Models that are characterized by a decrease in crustal viscosity from η c to η b with depth or temperature, leading to partial or full basal decoupling of the crust from the mantle, display more complex behaviour. For models with a moderate viscosity ratio, η b /η c ∼ 10 - 1 , the crustal wedges have dual tapers with a lower taper in the central region and a higher taper at the edges of the deformed crust. A redaction in the viscosity ratio (η b /η c ∼ 10 - 2 ) is sufficient to cause a transition of the central wedge region to a plateau. This transition depends on the basal traction, therefore the thickness of the weak basal layer also affects the transition. Further reduction of the viscosity ratio (η b /η c ∼ 10 - 4 ) leads to full basal decoupling and the development of plateaux in all cases considered. In most models, the plateaux grow laterally at constant thickness between characteristic edge peaks associated with the transitions from coupled to decoupled lower crust. Where the crust is fully decoupled, large-scale model geometries for both depth- and temperature-dependent rheologies are similar with gravity-driven flow concentrated in the low-viscosity region. However, strong lateral temperature gradients within these models, controlled by the interaction of horizontal and vertical thermal advection, diffusion and heterogeneous thickening of the radioactive crustal layer, lead to differences in the velocity and deformation fields between the two cases, particularly at the plateau margins. The results suggest that simple depth-dependent viscosity models may be reasonable approximations for describing the large-scale geometry of fully developed plateaux, but that they are not appropriate for describing the internal features of large orogenic systems or the transition from wedge to plateau geometry.
Information Flow Monitoring System

Vast quantities of data are generated by social networks in seconds. The information generated in a social network is transformed into a flow by the subjects who produce, transmit, and consume it. This flow can be represented in a very complicated directional graph where each subject is represented as a node, and the flow of information is represented as a directed edge. In this paper, we introduce a method of dividing this complex directional graph by user and quantifying the flow of information between and among users based on information flow vectors. We propose a system that can monitor the flow of information in social networks using information flow vectors extracted from social media data. We also introduce an improved skyline algorithm that can respond quickly to a user’s various queries.
Non-linear sliding-mode control of three-phase buck-boost inverter

The development of static power converters capable of transforming DC energy obtained from alternative sources into AC has become one of the main challenges in renewable energy systems. In this context, the buck-boost inverter is advantageous for being capable of providing an AC output voltage higher or lower than the input DC voltage in a single power conversion stage. In this paper, a controller based on the non-linear sliding mode theory is proposed for a three-phase buck-boost inverter to track a desired AC reference voltage. Unlike conventional linear sliding mode controllers that depend on the errors of the state variables, the control law used here depends only on the input and output voltages of the converter without the need to the inductor current measurement, which reduces the system complexity and cost. The proposed controller can not only track the desired reference quickly and accurately, but also achieve a high immunity to external perturbations, such as input voltage and output load disturbances. Several simulation studies are presented in order to investigate the performance of the proposed controller.
Channel estimation and throughput evaluation for 5G wireless communication systems in various scenarios on high speed railways

The fifth generation (5G) wireless communication is currently a hot research topic and wireless communication systems on high speed railways (HSR) are important applications of 5G technologies. Existing studies about 5G wireless systems on high speed railways (HSR) often utilize ideal channel parameters and are usually based on simple scenarios. In this paper, we evaluate the downlink throughput of 5G HSR communication systems on three typical scenarios including urban, cutting and viaduct with three different channel estimators. The channel parameters of each scenario are generated with tapped delay line (TDL) models through ray-tracing simulations, which can be considered as a good match to practical situations. The channel estimators including least square (LS), linear minimum mean square error (LMMSE), and our proposed historical information based basis expansion model (HiBEM). We analyze the performance of the HiBEM estimator in terms of mean square error (MSE) and evaluate the system throughputs with different channel estimates over each scenario. Simulation results are then provided to corroborate our proposed studies. It is shown that our HiBEM estimator outperforms other estimators and that the system throughput can reach the highest point in the viaduct scenario.
Periodontal Tissues, Maxillary Jaw Bone, and Tooth Regeneration Approaches: From Animal Models Analyses to Clinical Applications

This review encompasses different pre-clinical bioengineering approaches for periodontal tissues, maxillary jaw bone, and the entire tooth. Moreover, it sheds light on their potential clinical therapeutic applications in the field of regenerative medicine. Herein, the electrospinning method for the synthesis of polycaprolactone (PCL) membranes, that are capable of mimicking the extracellular matrix (ECM), has been described. Furthermore, their functionalization with cyclosporine A (CsA), bone morphogenetic protein-2 (BMP-2), or anti-inflammatory drugs’ nanoreservoirs has been demonstrated to induce a localized and targeted action of these molecules after implantation in the maxillary jaw bone. Firstly, periodontal wound healing has been studied in an induced periodontal lesion in mice using an ibuprofen-functionalized PCL membrane. Thereafter, the kinetics of maxillary bone regeneration in a pre-clinical mouse model of surgical bone lesion treated with BMP-2 or BMP-2/Ibuprofen functionalized PCL membranes have been analyzed by histology, immunology, and micro-computed tomography (micro-CT). Furthermore, the achievement of innervation in bioengineered teeth has also been demonstrated after the co-implantation of cultured dental cell reassociations with a trigeminal ganglia (TG) and the cyclosporine A (CsA)-loaded poly(lactic-co-glycolic acid) (PLGA) scaffold in the jaw bone. The prospective clinical applications of these different tissue engineering approaches could be instrumental in the treatment of various periodontal diseases, congenital dental or cranio-facial bone anomalies, and post-surgical complications.
Comparing Container-Based Microservices and Workspace as a Service: Which One to Choose?

The concept of microservices has gained increasing popularity since 2014. Almost during the same period, container technology keeps developing and is considered as an excellent way to build microservices-based applications. Mainstream public cloud vendors such as Amazon Web Services, Microsoft Azure, and Google Cloud Platform all provide users with container-based solutions to implementing microservices. Workspace as a Service (WaaS) proposed by An et al. is another approach which uses containers to serve users. Both container-based microservices and WaaS are used to effectively utilize cluster resources via maintaining a number of containers. In this paper, we compare the designing ideas and supporting platforms of these two approaches, which provides a perspective for cluster administrators and users to understand the scenarios where to use them and how to make an appropriate choice to meet their needs. We find that container-based microservices are more suitable for professional IT companies while WaaS fits education and research institutions better.
Analysis and Modelling of FinancialLogarithmic Return Data using Multifractaland Agent-Based Techniques

In recent years physicists have become involved in studying the financial market and the vast data it generates. The constantly-updated streams of information are a perfect testing ground for the hypothesis that the laws of statistical physics might apply to human behaviour. In this thesis, I study two empirical log return time series for the stylised facts of financial data. I then use Multifractal Detrended Fluctuation Analysis to study the empirical log returns for multifractal scaling. I find that extreme events are inimical to the scaling in highly leptokurtic data. I also find that the temporal correlations in the data are crucial to the scaling whereas the shape of its distribution is not as important. I then develop my own agent-based model of the market. With just a few different types of traders operating according to some simple rules, my model generates log returns with many of the statistical properties found in empirical data. The option for traders to opt out of trading is the source of the thin-peaked distribution of the simulated log returns. The distribution of log returns becomes more closely described by a Gaussian at longer lags. This is a consequence of basing the fundamental value of the stock on geometric Brownian motion. Since transition to Gaussianity at long lags is also a feature of empirical log returns, this implies that real traders are also influenced by some geometric Brownian process. Log returns generated by the model also have volatility clustering, are uncorrelated and asymmetrically distributed. I test the log returns generated by my model for their scaling properties and find that they do not have multifractal scaling. This is an interesting result since the simulated log returns do feature other properties of empirical data. I then extend the model in some basic ways to include more heterogeneity. Some limited multifractal scaling is found in the simulated log returns of the extended model. Because the model produces stochastic output, it is extremely difficult to exactly determine the scaling properties. However the results hint at the possibility that the multifractality found in empirical log returns is a consequence of the heterogeneity in both the investment horizons and beliefs of traders in the market.
Collagen-Based Scaffolds for Potential Application of Heart Valve Tissue Engineering

Collagen, glycosaminoglycans and elastin are the major components of heart valves. This work examines the roles of the composition of the collagen-based scaffolds on the microstructures, mechanical properties, and bioactivities. Scaffolds (with up to 80% elastin or 50% chondroitin-4-sulfate) were prepared by freeze drying suspensions of various compositions. The as-prepared scaffolds had mean pore sizes ranging from 115.6 μm to 187.6 μm. Mechanical characterizations showed that the scaffolds behaved as elastomers, and their characteristic mechanical properties were also dependent on the composition. The tensile, compressive, and bending modulus ranged from 39.8 ± 8.8 kPa, 10.2 ± 1.6 kPa, and 25.4 ± 5.1 kPa to 344.6 ± 42.6, 28.3 ± 4.6 kPa, and 78.3 ± 9.7 kPa, respectively. Cardiosphere derived cells were cultured on the scaffolds, and exhibited significant proliferation throughout the course of the experiment. Depending on the precise composition of the scaffold, chondroitin-4-sulfate increased the proliferation rate of the cells while elastin lowered it. The native heart valve has a tri-layer structure with the layers having compositions similar to those used in this study. Thus, the findings from this study will have implications on how to fabricate a more successful scaffold for tissue engineering heart valves.
Measuring Executive-Compensation - Methods And An Application

This paper describes methods for estimating the value of the total annual compensation of top corporate executives. These methods refine those originated by Burgess [1963], Lewellen [1968], and Masson [1969]. Measures of executive compensation are relevant in addressing three current issues. First is the extent to which the compensation effects of different accounting alternatives influence executives' preferences over proposed changes in accounting regulations and their actual selection of financial reporting techniques. Second is whether compensation effects of new financial reporting standards induce managers to change their production and investment decisions. Third is the impact of a change in the terms of executives' compensation contracts on their decisions. '
Learning the Semantics of Discrete Random Variables : Ordinal or Categorical ?

When specifying a probabilistic model of data, the form of the model will typically depend on the spaces in which random variables take their values. In particular, different probability distributions are appropriate for continuous, discrete and binary data. As we respond to ever increasing quantities of data, with increasingly more automatic data analysis techniques, it is necessary to identify these different types of data automatically. While it is trivial to create concise logical rules to distinguish between many different types of data, this cannot be said for choosing between categorical and ordinal data, let alone inferring the ordering. We present some first attempts at this problem and evaluate their performance empirically.
Feature Extraction for Inverse Reinforcement Learning

Equipping an agent with the ability to infer intentions behind observed behavior is a prerequisite for creating truly autonomous robots. By deducing the purpose of the actions of other agents the robot would be able to react in a sensible way and, furthermore, to imitate the strategy on a high level. Inverse Reinforcement Learning (IRL) can be applied to learn a reward function that is consistent with the observed behavior and is, thus, a step towards this overall goal. Some strategies can only be modeled properly by underlying a time-dependent reward function. Maximum Causal Entropy Inverse Reinforcement Learning (MaxCausalEnt-IRL) is a method that can be applied to learn such non-stationary functions. However, it depends on gradient-based optimization and its performance can therefore suffer if too many parameters have to be learned. This can be problematic, since the number of parameters increases significantly if separate reward functions are learned for each time step. Furthermore, since only few time steps might be relevant for the observed task, a second challenge of applying IRL for learning non-stationary reward functions consists in properly extracting such sparseness. This thesis investigates how to meet these practical requirements. A novel approach, IRL-MSD, is developed for that purpose. Unlike some previous IRL methods, Inverse Reinforcement Learning by Matching State Distributions (IRL-MSD) does not aim to match feature counts but instead learns a reward function by matching state distributions. This approach has two interesting properties. Firstly, the features do not have to be defined explicitly but arise naturally from the structure of the observed state distribution. Secondly, it does not require gradient-based optimization. The experiments show that it converges faster than existing IRL methods and properly recovers the goals of a sparse reward function. Therefore, IRL-MSD suggests itself for future research.