Miracle-dz/UniFAR-FTUs huggingface dataset:
AN AMPLE ANALYSIS ON EXTENDED LDA MODELS FOR ASPECT BASED REVIEW ANALYSIS

Topic Modeling Algorithms (TMA) widely used in the field of Aspect Based Review Analysis and demonstrated good performance. The prime intuition behind topic models is that each document is a collection of some topics. A topic is a collection of words that represent the topic as a whole. TMA extract the ‘latent’ semantic topics and themes in a collection of documents. Latent Dirichlet Allocation (LDA) is the most popular TMA used in various text mining applications. In this study, we give deep insight on LDA and its combination with other approaches in Opinion Mining or Sentiment Analysis domain. The purpose of this paper is to provide an ample analysis of various extensions and combinations of LDA for optimizing the complete process of review miming.
Lipid Distribution and Rheological Properties of Creamy Custard Pudding Prepared with Egg Yolk and Milk Fat Cream

* Kyoto Prefectural University Graduate school ** Prefectural University of Hiroshima *** Prefectural University of Hiroshima Graduate school Inquiry Graduate school of Comprehensive Scientific Research, Prefectural University of Hiroshima 1-1-71 Ujina-higashi, Minami-ku, Hiroshima 734-8558, Japan TEL 082(251)9807 Lipid Distribution and Rheological Properties of Creamy Custard Pudding Prepared with Egg Yolk and Milk Fat Cream Satomi Nomura* Manami Imaoka** Kazumasa Mizuo*** Sumire Tanaka** Risa Yasui** Sumi Sugiyama***
Polyneuropathy disease forecast in the type 2 diabetes mellitus patients using data mining based approach

Nur Kuban Torun1, Umman Tuğba Şimşek Gürsoy2, Saadet Kader3, M. Burak Oztop4 1Faculty of Economics and Administrative Sciences, Department of Business Administration/Quantitive Methods, Bilecik Seyh Edebali University, Bilecik, 2Faculty of Business Administration, Department of Quantitative Methods, Istanbul University, İstanbul, 3Department of Medical Biochemistry, Bilecik Public Health Laboratory, Bilecik, 4Department of General Surgery Bilecik Public Hospital, Bilecik, Turkey Polyneuropathy disease forecast using data mining based approach Polyneuropathy disease forecast in the type 2 diabetes mellitus patients using data mining based approach
LASER FIELD EFFECTS ON THE INTERBAND TRANSITIONS IN DIFFERENTLY SHAPED QUANTUM WELLS

The intense laser field effects on the interband transitions in GaAs–Ga1xAlxAs quantum wells having power-exponential confinement potentials are investigated within the effective mass and parabolic band approximations. Tthe energy levels, electron and hole wave functions as well as their corresponding overlap integrals and oscillator strength have been calculated. It is shown that the energy levels and radiative decay time associated with the electron interband transitions can be tuned to a large extent by a proper choice of the nanostructure design as well as by varying the laser field intensity. The noticeable increase of the radiative lifetime with the laser parameter can be attributed to the laser fieldinduced separation of the confined electron-hole pair.
Most admired companies ’ performance and CEO compensation during 2007-2009 recession

This study investigates financial performance and compensation of chief executive officer (CEO) of the world’s 50 most admired companies and their matched not-admired peers during 2007-2009 recession. The study has two purposes. The first purpose is to examine whether these 50 high-reputation companies are truly admirable, i.e., outperforming their competitors during this worst recession since World War II. The second purpose is to investigate how well these companies aligned the compensation of their CEO with firms’ performance during this recession. Financial performance is based upon financial statement data, seven financial ratios, and changes in these ratios that measure profitability, solvency and CEO power. There are two significant findings. First, most admired companies had significantly better financial performance, i.e., higher profitability, solvency and CEO power than their matched firms during the recession. Second, most admired companies’ CEO compensation had a stronger correlation with their net income and revenue than that of their matched peers. The first finding has an implication for investors, and the second finding has an implication for other companies to emulate this exemplary compensation practice. This study contributes to corporate reputation literature and is the first one to focus on performance and CEO compensation of these 50 most admired companies during these tough and trying years.
The Application of Magnetic Resonance Imaging T2 Mapping in Detecting Early Knee Joint Injury

The knee is the largest flexion joint of the human body. Its structure is complex, for instance, it can withstand strong leverage, but nevertheless it is extremely vulnerable. In recent years, with the changes in people's lifestyles and increasing pressure in work and life, the risk of knee joint injury, especially the risk of meniscus and articular cartilage damage, has increased significantly. Arthroscopy is the golden standard for the diagnosis of knee injury, but it has certain limitations in the early and convenient diagnosis of the injury. Moreover, although this examination is an invasive operation, it may causes a certain degree of secondary damage to the patient during the examination. Magnetic resonance imaging (MRI) T2 mapping technology is a new MRI technology that is developed in recent years. It can indirectly reflect the structures and histological composition of articular cartilage before the morphological changes of articular cartilage. Therefore, this technique is of great significance for early knee injury diagnosis. In this article, we reviewed the basic physiological and pathological changes before and after knee injury, as well as the conventional knee joint technique, focusing on the imaging principle and diagnostic advantages of the T2 mapping sequence and comparing it with conventional MRI sequences.
The Influence of Heart Rate and its Variations on Image Quality: A Comparative Study of 16- and 64-Slice Multidetector Row Computed Tomography

Purpose: We wanted to investigate the influence of heart rate (HR) and its variations on the CT angiography (CTA) image quality with using 16- and 64-slice multidetector CT (MDCT). Materials and Methods: Of the 200 recruited patients, 100 underwent 16-slice MDCT (M:F=74:26, median age: 55.2 12.8 years) and 100 underwent 64-slice MDCT (M:F=68:32, median age: 58.4 11.6 years). We evaluated the image quality with respect to the HR and the variations in the HR. The images were graded in terms of the quality of vessel depiction with using a four-point scoring system from 1 to 4, where 4 represented the best image quality. In addition, we analyzed the 16- and 64-slice MDCT images in terms of the presence and frequency of stair-step artifacts. Results: The mean HR variation was significantly lower on 64-slice MDCT than that on 16-slice MDCT (5.48 4.20 bpm vs. 10.31 5.90 bpm, respectively, p 0.36 vs. 2.75 0.43, respectively, p 3.2 vs. 0.5 1.04, respectively, p
White-box Compression: Learning and Exploiting Compact Table Representations

We formulate a conceptual model for white-box compression, which represents the logical columns in tabular data as an openly deﬁned function over some actually stored physical columns. Each block of data should thus go accompanied by a header that describes this functional mapping. Because these compression functions are openly deﬁned, database systems can exploit them using query optimization and during execution, enabling e.g. better ﬁlter predicate pushdown. In addition, we show that white-box compression is able to identify a broad variety of new opportunities for compression, leading to much better compression factors. These opportunities are identiﬁed using an automatic learning process that learns the functions from the data. We provide a recursive pattern-driven algorithm for such learning. Finally, we demonstrate the effectiveness of white-box compression on a new benchmark we contribute hereby: the Public BI benchmark provides a rich set of real-world datasets. We believe our basic prototype for white-box compression opens the way for future research into transparent compressed data representations on the one hand and database system architectures that can eﬃciently exploit these on the other, and should be seen as another step into the direction of data management systems that are self-learning and optimize themselves for the data they are deployed on.
The Frequency of Cash Rent Contract Terms Across Regions Region

This article examines contractual practices that are often assumed customary. In particular it examines discreteness in agricultural contracts, and focuses on the distinction between the use of simple discrete fraction terms in cropshare contracts and the nearly continuous payment terms used in cash rent contracts. We show that the pattern of shares is best explained as a response to moral hazard problems spread over large numbers of inputs. A contracting model explains the pattern of shares, the difference in flexibility with cash rent contracts, and the lower bound on shares. Empirical analysis using micro data on over 3,000 contracts are used to test implications of the model. A wide range of support is found for a model based on moral hazard and measurement costs.
Predictive Control Method Based on Adjacent Vector Confinement Technique for a Three-Phase AC-DC Matrix Converter with High Efficiency

A model predictive current control method is proposed to reduce switching losses in an AC-DC matrix converter. In the proposed control strategy, several vectors are selected from among all possible switching vectors for a given location of the input current reference. The switching vector that minimizes the cost function is applied to the converter in the next sampling period. The principle of the proposed method involves clamping the selected switches to stop performing the switching operation to minimize the number of switchings in every sampling cycle. The total efficiency of the AC-DC matrix converter under the proposed strategy is 91.2% whereas that of the conventional strategy is 89.7%. In addition, unity-power-factor operation is guaranteed and smooth and sinusoidal waveforms are achieved. Finally, simulation and experimental results are demonstrated to confirm the validity of the proposed control strategy.
Classifying Computer Science Papers

This paper addresses the problem of classifying academic papers. It is a building block in constructing an advanced scholarly search engine, such as in crawling and recommending papers in a particular area. Our goal is to identify the best classification method for scholarly data, to choose appropriate parameters, and to gauge how accurate academic papers can be classified using document content only. In addition, we also want to find out whether the neural network approach, which has been proven very successful in many other areas, can help in this particular problem. Our experiments are conducted on 160,000 papers from arXiv data set, each is already labeled as either a computer science (CS) paper or a paper in other areas. We experimented with a variety of classification methods, including Multinomial Naive Bayes on unigram and bigram models, and Logistic Regression on distributional representation obtained from sentence2vec. We find that computer science papers can be identified with high accuracy (F1 close to 0.95). The best method is the bigram model using Multinomial Naive Bayes method and point-wise mutual information (PMI) as the feature selection method.
The Effects of Governance on the Financial Reporting Quality of Nonprofit Organizations

Abstract: Various stakeholders use nonprofit financial information for investing, contracting, and regulating decisions, and these decisions can be affected by the quality of the underlying financial information. Using multiple measures of financial reporting governance and reporting quality, we find that higher reporting quality is associated with increased governance. We find that our market-based governance measures have a more consistent effect on nonprofit reporting quality than do our regulatory-based measures. Our findings suggest that attempts to enhance the monitoring and oversight of nonprofits can lead to higher quality financial reports, particularly if those efforts involve market participants such as lenders or donors. ______________________________________________________________________________
Optimization based Neural Network for face Recognition

Intelligent face recognition remains a challenging task when offering uncooperative users as well as in uncontrolled conditions and so received a great deal of attention by the computer perspective research community. In this proposed work, the face with its expression and various movements is recognized. The proposed method includes three phases particularly, pre-processing phase, feature extraction phase and finally classification phase. The feature extraction phase is carried out with the GLCM feature extraction and is followed by the NN classification phase. Moreover, for accurate prediction the Cuckoo Search optimization is included along with the classification phase. The whole proposed methodology is implemented in the working platform of MATLAB and the results were analyzed.
The Celerity Open Source RISC-V Tiered Accelerator Fabric: Fast Architectures and Design Methodologies for Fast Chips

Emerging workloads have extremely strict energy efficiency and performance requirements which are difficult to attain. Increasingly we see that specialized hardware accelerators are necessary to attain these requirements, but accelerator development is time-intensive and their behavior cannot be easily modified to adapt to changing workload properties. These motivates new architectures which can be rapidly constructed to address new application domains, while still leveraging specialized hardware and offering high performance and energy efficiency even as applications evolve posttapeout.
Service System Platforms to improve value co-creation: insights for Translational Medicine

Purpose – The aim of this paper is to analyze how service system platforms can facilitate the value co-creation processes in healthcare context and then to foster the development of new compounds of medical protocols and/or treatments to improve patient’s quality of life (Polese, Capunzo, 2013) according to the translational medicine purposes. We investigated how technological, interconnected, and smart solutions can facilitate the information-sharing processes by enabling researchers, clinicians, industries and patients to interact without the constraints of time, place and space by organizing data and information. Design/Methodology/approach – The work is developed by integrating and applying the theoretical perspectives of Service Science (Maglio, Sphorer, 2008) and Service-Dominant logic (Vargo, Lusch, 2008) to the paradigm of translational medicine. Findings – Translational medicine is a rapidly growing discipline in biomedical and public health research that aims to improve the health of individuals and the community by "translating" findings into diagnostic tools, medicines, procedures, policies and education, using a multi-disciplinary, highly collaborative, "bench-to-bedside" approach (Abraham et al. 2012; Stephen, 2008). It contributes to create value not only for the patient, but also for all involved actors such as clinicians, academic researchers, pharmaceutical industries, investors (Littman et al., 2007). In this sense, according to the Service-Dominant logic approach, service systems platforms could be useful to complete the value co-creation process in the translational medicine systems
Corporate Social Responsibility Information Disclosure and Innovation Sustainability: Evidence from China

We selected the Chinese A-share listed companies during period of 2007 to 2017 as the research subject, and from the perspective of information and reputation effects, we examined the relationship between corporate social responsibility (CSR) information disclosure and innovation sustainability. The results show that CSR information disclosure has a significant positive relationship with innovation sustainability. Analysis of the effects channel suggests that the information effect plays a dominant role; CSR information disclosure can alleviate the information asymmetry between managers and investors, controlling shareholders and minority shareholders, and alleviate the financing constraint problems, thereby improving innovation sustainability. Our findings support the information hypothesis but not the reputation hypothesis. The relationship between CSR information disclosure and innovation sustainability is more significant in non-state-owned companies. The moderating effect shows that managerial stock incentives can strengthen the positive relationship between CSR information disclosure and innovation sustainability. A series of robustness test results show that the conclusions are reliable. The research is important for promoting the fulfillment of CSR, improving corporate innovation, and promoting the healthy development of the capital market.
A better comprehension of “Management of Hyperglycemia in Type 2 Diabetes:A Patient-Centered Approach—Position Statement of the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD)”

" Management of Hyperglycemia in Type 2Diabetes : A Patient-Centered Approach — Position Statement of the American Diabetes Association(ADA) and the European Association for the Study of Diabetes(EASD)" was published in Diabetes Care and Diabetology in April this year.It,first,emphasizes the control of hyperglycemia in the treatment of T2DM;second,sets up the target of control of hyperglycemia flexibly according to natural course of disease,pathological condition,accompanied diseases,age,and life expectancy of the patients;third,once again emphasizes metformin for first-line use;fourth,more specifically discusses the applications,therapeutic effects,and advantages and disadvantages of various insulin preparations,GLP-1,DPP-4inhibitors and GLP-1receptor agonists;fifth,states the individualization treatment;and sixth,underlines that all patients should accept diabetes education aimed at hyperglycemia control.
Optimal Solving of Constrained Path-Planning Problems with Graph Convolutional Networks and Optimized Tree Search

Learning-based methods are growing prominence for planning purposes. However, there are very few approaches for learning-assisted constrained path-planning on graphs, while there are multiple downstream practical applications. This is the case for constrained path-planning for Autonomous Unmanned Ground Vehicles (AUGV), typically deployed in disaster relief or search and rescue applications. In off-road environments, the AUGV must dynamically optimize a source-destination path under various operational constraints, out of which several are difficult to predict in advance and need to be addressed on-line. We propose a hybrid solving planner that combines machine learning models and an optimal solver. More specifically, a graph convolutional network(GCN) is used to assist a branch and bound(B&B) algorithm in handling the constraints. We conduct experiments on realistic scenarios and show that GCN support enables substantial speedup and smoother scaling to harder problems.
Complete dissolution of woody biomass using an ionic liquid

Birch wood meal was added to the ionic liquid 1-ethyl-3-methylimidazolium acetate ([Emim][OAc])/dimethyl sulfoxide (DMSO) to investigate the conditions required for the complete dissolution of the lignocellulose. Cellulose was treated with [Emim][OAc], and its molecular weight distribution was analyzed by size exclusion chromatography as a model compound. The solubilities of lignocellulose were compared under different treatment conditions, such as the ball-milling time of the raw material and the temperature of the [Emim][OAc]/DMSO treatment. The insoluble fraction was analyzed by pyrolysis-gas chromatography-mass spectrometry (Py-GC/MS) to investigate the lignin structure. Cellulose treated with [Emim][OAc] under a nitrogen atmosphere for 1 to 12 h was remarkably depolymerized. It was demonstrated that lignocellulose is easily dissolved when the temperature of [Emim][OAc]/DMSO and the ball-milling times of lignocellulose are increased; a ball-milled sample was completely dissolved in the ionic liquid. The well-dissolved sample had a low cellulose molecular weight. Ball-milling reduced the primary particle size of the sample and facilitated the dissolution and extraction of guaiacyl lignin units.
THE EFFECTS OF ANTECEDENT SOIL MOISTURE ANOMALIES ON TORNADO ACTIVITY IN THE UNITED STATES

Recently there has been increased interest in the ability to forecast severe weather events on a seasonal scale. Being able to forecast events such as the April 2011 outbreak could have beneficial impacts such as increased preparedness at the Federal and local level as well as greater public awareness. To better forecast tornadoes on a seasonal scale, we must first look at the underlying factors that influence the inter-annual variability of both tornado locations and intensity. Soil moisture has been shown, on regional scales, to have an effect on moisture within the boundary layer, and therefore, the potential for deep convection. Previous studies have examined relationships between factors related to soil moisture such as precipitation and evapotranspiration and their effects on tornado climatology at the local and regional scale. This study examined the relationship between antecedent soil moisture and tornado activity in five regions within the United States east of the Rocky Mountains, using two approaches. The first approach used fall and winter soil moisture anomalies as a predictor for spring tornado activity while the second looked at the six months preceding each month in the year. In addition, we also assessed the reliability of our modeled soil moisture dataset by comparing it to the high-resolution Oklahoma Mesonet observational network. __________________________________________________
Effect of the Cross-Sectional Rigidity on the Static and Seismic Behaviour of CSP Culverts

In Canada and around the world there is a growing trend to use wide span soil-steel arch structures as a substitute for the more conventional types of bridges and rigid culverts. CSP culverts (corrugated steel plate culverts) are flexible structures which gain load-bearing capacity by interaction with the surrounding engineered backfill enabling them to carry significant overburden and vehicular loads. In this paper, comprehensive finite element analyses were carried out to study the effect of changing the cross-sectional rigidity on the static and seismic behaviour of CSP culverts. First, the static behaviour of a field case study of a wide span culvert was verified. Then, the full dynamic analyses were utilized using seismic records suitable for the city of Vancouver in Western Canada. Subsequently, the culvert’s cross-sectional properties were changed to examine their effect on the culvert’s performance. Finally, the results of the finite element analyses were compared with the simplified design equations of the Canadian Highway Bridge Design Code (CHBDC). This study shows that the culvert’s cross-sectional rigidity has a significant effect on the soil structure interaction (SSI) between the culvert and the surrounding backfill, which affects the performance and the load carrying capacity of the culvert.