Miracle-dz/UniFAR-FTUs huggingface dataset:
MARKET EFFICIENCY, FINANCIAL INTEGRATION, AND SHOCK TRANSMISSION (EMPIRICAL EVIDENCE FROM D-8 ECONOMIES)

The specific purpose of the Developing-8 economies is to integrate the endeavours of each member countries to excel economic cooperation and facilitation among the Islamic countries to enhance the investment opportunities and not to put pressure on the geopolitical environment of the world. The purpose of this study is to visualize the market efficiency, financial integration, and shock transmission process in the D-8 Economies for the period from Jan 2011 to Dec 2016. Daily market data is taken for KSE, DSE, TSE, JCI, KLCI, ISE, EGX, and NSE equity markets. Unit Root Test, Serial Correlation Test, Runs Test, and Variance Ratio Test are used to test the market efficiency whereas Johansen Cointegration Test, Granger Causality Test, Vector Error Correction Model, and Impulse Response Test are used to test the financial integration and shock transmission. The performance of the Tehran stock market remained excellent during this study period. Mixed evidence is concluded regarding the market efficiency of D-8 equity markets. It is concluded that there exists a long-run relationship between KSE and DSE. Short-run relationship indicates that KSE has a significant positive short-run relationship with JCI and have a negative relationship with KLCI. Negative asymmetric behaviour is more influential in the negatively skewed markets and negative shocks have a greater effect than the positive shocks. Hence it is concluded that investors can get benefit from the arbitrage process due to market inefficiencies and through the short selling process. There is a need to establish economic equilibrium through the arbitrage process where investors may enjoy the opportunities to excel more economic stream of benefits by noise and market reverting behaviour. The economic integration will increase the cooperation among these economies to establish more growth opportunities in the trade and corporate investments. The implication of the studies suggests and directs to enhance mutual interface and co-operation including Banking and Finance, Human and Social Development, Energy Sector, Development of Rural Population, Science and Technology, Health and Environment, and Agriculture Sector. Moreover, there is a dire need for restructuring the economic and financial bylaws that may ease the financial liberalization and to enhance the dynamics of trading actives among the D-8 economies.
Performance evaluation of deep neural networks for forecasting time-series with multiple structural breaks and high volatility

The problem of automatic and accurate forecasting of time-series data has always been an interesting challenge for the machine learning and forecasting community. A majority of the real-world time-series problems have non-stationary characteristics that make the understanding of trend and seasonality difficult. Our interest in this paper is to study the applicability of the popular deep neural networks (DNN) as function approximators for non-stationary TSF. We evaluate the following DNN models: Multi-layer Perceptron (MLP), Convolutional Neural Network (CNN), and RNN with Long-Short Term Memory (LSTM-RNN) and RNN with Gated-Recurrent Unit (GRU-RNN). These DNN methods have been evaluated over 10 popular Indian financial stocks data. Further, the performance evaluation of these DNNs has been carried out in multiple independent runs for two settings of forecasting: (1) single-step forecasting, and (2) multi-step forecasting. These DNN methods show convincing performance for single-step forecasting (one-day ahead forecast). For the multi-step forecasting (multiple days ahead forecast), we have evaluated the methods for different forecast periods. The performance of these methods demonstrates that long forecast periods have an adverse effect on performance.
Smarter Features, Simpler Learning?

Earlier work on machine learning for automated reasoning mostly relied on simple, syntactic features combined with sophisticated learning techniques. Using ideas adopted in the software verification community, we propose the investigation of more complex, structural features to learn from. These may be exploited to either learn beneficial strategies for tools, or build a portfolio solver that chooses the most suitable tool for a given problem. We present some ideas for features of term rewrite systems and theorem proving problems.
Long-range Prediction of Vital Signs Using Generative Boosting via LSTM Networks

Vital signs including heart rate, respiratory rate, body temperature and blood pressure, are critical in the clinical decision making process. Effective early prediction of vital signs help to alert medical practitioner ahead of time and may prevent adverse health outcomes. In this paper, we suggest a new approach called generative boosting, in order to effectively perform early prediction of vital signs. Generative boosting consists of a generative model, to generate synthetic data for next few time steps, and several predictive models, to directly make long-range predictions based on observed and generated data. We explore generative boosting via long short-term memory (LSTM) for both the predictive and generative models, leading to a scheme called generative LSTM (GLSTM). Our experiments indicate that GLSTM outperforms a diverse range of strong benchmark models, with and without generative boosting. Finally, we use a mutual information based clustering algorithm to select a more representative dataset to train the generative model of GLSTM. This significantly improves the long-range predictive performance of high variation vital signs such as heart rate and systolic blood pressure.
MMGAN: Generative Adversarial Networks for Multi-Modal Distributions

Over the past years, Generative Adversarial Networks (GANs) have shown a remarkable generation performance especially in image synthesis. Unfortunately, they are also known for having an unstable training process and might loose parts of the data distribution for heterogeneous input data. In this paper, we propose a novel GAN extension for multi-modal distribution learning (MMGAN). In our approach, we model the latent space as a Gaussian mixture model with a number of clusters referring to the number of disconnected data manifolds in the observation space, and include a clustering network, which relates each data manifold to one Gaussian cluster. Thus, the training gets more stable. Moreover, MMGAN allows for clustering real data according to the learned data manifold in the latent space. By a series of benchmark experiments, we illustrate that MMGAN outperforms competitive state-of-the-art models in terms of clustering performance.
Methylglyoxal Binding to Bovine Liver Catalase Results in Loss of Activity and Heme Dislocation

Glycation, the non-enzymatic attachment of glucose to protein, is one of the important events in the pathophysiology of diabetes mellitus, Alzheimer’s, Parkinson’s and other diseases. Methylglyoxal (MG), a dicarbonyl compound formed during glycation, monosaccharide autoxidation, and metabolism is elevated during diabetes mellitus. Among other antioxidant enzymes, catalase is important for the defense against oxidative damage. However, antioxidant enzymes including catalase can themselves become targets of non-enzymatic modification by methylglyoxal. In this study, catalase was incubated with increasing concentrations of MG for different time intervals. Structural and functional alterations to catalase were monitored by a variety of approaches, namely, assay of enzyme activity, staining of gels for activity as well as heme, measurement of protein carbonyls and Arg pyrimidine, which is a specific MG modification product. A progressive increase in electrophoretic mobility and detachment of heme from the monomer were observed with increasing concentrations of methylglyoxal. The MG-modified enzyme showed reduced affinity towards the substrate hydrogen peroxide. Molecular modeling studies revealed that MG can access the heme and arginine residues close to it. Thus, the decrease in activity of methylglyoxal-modified catalase may be important in aggravating the severity of secondary complications seen in diabetes mellitus. HIGHLIGHTS •Increase in concentration of methylglyoxal caused a progressive increase in electrophoretic mobility and detachment of heme from the monomer. •MG-modified enzyme showed reduced affinity towards the substrate hydrogen peroxide. •Molecular modeling studies showed that MG can access the heme and arginine residues close to it.
Low Order Finite Element Methods for the Navier-Stokes-Cahn-Hilliard Equations

A computationally efficient, low order finite element formulation is developed for modelling the Navier-Stokes-Cahn-Hilliard equations, which have been established as a promising phase field modelling approach for simulation of immiscible multiphase flows. The present study suggests that traditional Navier-Stokes-Cahn-Hilliard models do not allow for surface tension effects to be neglected due to the presence of the surface tension parameter in the Cahn-Hilliard equation. This motivates the proposed formulation, which allows surface tension effects to be changed without affecting the behaviour of the phase transport. Two methods are proposed: The first uses stabilised SUPG/PSPG linear elements, while the second is based on mixed Taylor-Hood elements. The proposed models are applied to a number of benchmark and example problems, including both capillary regime in which surface tension effects are dominant, and inertial regime in which surface tension effects are negligibly small. All results obtained agree very well with reference solutions.
Computationally Data-Independent Memory Hard Functions

Memory hard functions (MHFs) are an important cryptographic primitive that are used to design egalitarian proofs of work and in the construction of moderately expensive key-derivation functions resistant to brute-force attacks. Broadly speaking, MHFs can be divided into two categories: data-dependent memory hard functions (dMHFs) and data-independent memory hard functions (iMHFs). iMHFs are resistant to certain side-channel attacks as the memory access pattern induced by the honest evaluation algorithm is independent of the potentially sensitive input e.g., password. While dMHFs are potentially vulnerable to side-channel attacks (the induced memory access pattern might leak useful information to a brute-force attacker), they can achieve higher cumulative memory complexity (CMC) in comparison than an iMHF. In this paper, we introduce the notion of computationally data-independent memory hard functions (ciMHFs). Intuitively, we require that memory access pattern induced by the (randomized) ciMHF evaluation algorithm appears to be independent from the standpoint of a computationally bounded eavesdropping attacker --- even if the attacker selects the initial input. We then ask whether it is possible to circumvent known upper bound for iMHFs and build a ciMHF with CMC $\Omega(N^2)$. Surprisingly, we answer the question in the affirmative when the ciMHF evaluation algorithm is executed on a two-tiered memory architecture (RAM/Cache). See paper for the full abstract.
CatGAN: Category-aware Generative Adversarial Networks with Hierarchical Evolutionary Learning for Category Text Generation

Generating multiple categories of texts is a challenging task and draws more and more attention. Since generative adversarial nets (GANs) have shown competitive results on general text generation, they are extended for category text generation in some previous works. However, the complicated model structures and learning strategies limit their performance and exacerbate the training instability. This paper proposes a category-aware GAN (CatGAN) which consists of an efficient category-aware model for category text generation and a hierarchical evolutionary learning algorithm for training our model. The category-aware model directly measures the gap between real samples and generated samples on each category, then reducing this gap will guide the model to generate high-quality category samples. The Gumbel-Softmax relaxation further frees our model from complicated learning strategies for updating CatGAN on discrete data. Moreover, only focusing on the sample quality normally leads the mode collapse problem, thus a hierarchical evolutionary learning algorithm is introduced to stabilize the training procedure and obtain the trade-off between quality and diversity while training CatGAN. Experimental results demonstrate that CatGAN outperforms most of the existing state-of-the-art methods.
Linkage between Farm and Non-farm Sectors and its Impact on Agricultural Production : Evidence from Vietnam

Title Linkage between Farm and Non-farm Sectors and its Impact on Agricultural Production : Evidence from Vietnam [an abstract of dissertation and a summary of dissertation review] Author(s) NGUYEN, THI THUY HANG Citation 北海道大学. 博士(農学) 甲第13757号 Issue Date 2019-09-25 Doc URL http://hdl.handle. net/2115/75944 Rights(URL) https://creativecommons.org/licenses/by/4.0/ Type theses (doctoral abstract and summary of review) Additional Information There are other files related to this item in HUSCAP. Check the above URL. File Information Nguyen_Thi_Thuy_Hang_abstract. pdf (論文内容の要旨)
Reusable neural skill embeddings for vision-guided whole body movement and object manipulation

Both in simulation settings and robotics, there is an ambition to produce flexible control systems that can enable complex bodies to perform dynamic locomotion and natural object manipulation. In previous work, we developed a framework to train locomotor skills and reuse these skills for whole-body visuomotor tasks. Here, we extend this line of work to tasks involving whole body movement as well as visually guided manipulation of objects. This setting poses novel challenges in terms of task specification, exploration, and generalization. We develop an integrated approach consisting of a flexible motor primitive module, demonstrations, an instructed training regime as well as curricula in the form of task variations. We demonstrate the utility of our approach for solving challenging whole body tasks that require joint locomotion and manipulation, and characterize its behavioral robustness. We also provide a high-level overview video, see this https URL .
Interpreting chest X-rays via CNNs that exploit disease dependencies and uncertainty labels

Chest radiography is one of the most common types of diagnostic radiology exams, which is critical for screening and diagnosis of many different thoracic diseases. Specialized algorithms have been developed to detect several specific pathologies such as lung nodule or lung cancer. However, accurately detecting the presence of multiple diseases from chest X-rays (CXRs) is still a challenging task. This paper presents a supervised multi-label classification framework based on deep convolutional neural networks (CNNs) for predicting the risk of 14 common thoracic diseases. We tackle this problem by training state-of-the-art CNNs that exploit dependencies among abnormality labels. We also propose to use the label smoothing technique for a better handling of uncertain samples, which occupy a significant portion of almost every CXR dataset. Our model is trained on over 200,000 CXRs of the recently released CheXpert dataset and achieves a mean area under the curve (AUC) of 0.940 in predicting 5 selected pathologies from the validation set. This is the highest AUC score yet reported to date. The proposed method is also evaluated on the independent test set of the CheXpert competition, which is composed of 500 CXR studies annotated by a panel of 5 experienced radiologists. The performance is on average better than 2.6 out of 3 other individual radiologists with a mean AUC of 0.930, which ranks first on the CheXpert leaderboard at the time of writing this paper.
Molecular Epidemiology of Bovine Tuberculosis in Cattle and its Public Health Implications in Gambella Town and its Surroundings, Gambella Regional State, Ethiopia

A cross sectional study was conducted from December 2014 to May 2015 in Gambella town municipal abattoir and health centers to investigate the prevalence of BTB, isolation and molecular characterization of its causative agents and to assess its public health implications in Gambella, Ethiopia. Postmortem examination, bacteriological culturing, RD deletion typing and spoligotyping were used for investigation. The overall prevalence of BTB in cattle was 13.2% (66/500) on the basis of detailed postmortem examination. Statistical significant difference was observed in the prevalence of BTB among different body conditioned animals (χ2 = 39.105, P=0.000and breeds (χ2 = 24.996, P=0.000). Molecular characterization of 11 mycobacterial isolates from human patients using RD9 deletion typing showed that all were M. tuberculosis and further spoligotyping of the isolated revealed that SIT289, SIT134, SIT1634, SIT142 and one new strain not found in the spoligotype databases.
Determining Fundamental Supply and Demand Curves in a Wholesale Electricity Market

In this paper we develop a novel method of wholesale electricity market modeling. Our optimization-based model decomposes wholesale supply and demand curves into buy and sell orders of individual market participants. In doing so, the model detects and removes arbitrage orders. As a result, we construct an innovative fundamental model of a wholesale electricity market. First, our fundamental demand curve has a unique composition. The demand curve lies in between the wholesale demand curve and a perfectly inelastic demand curve. Second, our fundamental supply and demand curves contain only actual (i.e. non-arbitrage) transactions with physical assets on buy and sell sides. Third, these transactions are designated to one of the three groups of wholesale electricity market participants: retailers, suppliers, or utility companies. To evaluate the performance of our model, we use the German wholesale market data. Our fundamental model yields a more precise approximation of the actual load values than a model with perfectly inelastic demand. Moreover, we conduct a study of wholesale demand elasticities. The obtained conclusions regarding wholesale demand elasticity are consistent with the existing academic literature.
Additive Compositionality of Word Vectors

Additive compositionality of word embedding models has been studied from empirical and theoretical perspectives. Existing research on justifying additive compositionality of existing word embedding models requires a rather strong assumption of uniform word distribution. In this paper, we relax that assumption and propose more realistic conditions for proving additive compositionality, and we develop a novel word and sub-word embedding model that satisfies additive compositionality under those conditions. We then empirically show our model’s improved semantic representation performance on word similarity and noisy sentence similarity.
The Distribution of Patterns and Types of Questions in Genetic Learning Implementing Reading-Questioning-Answering Learning Model

It is important to identify the characteristics of questions in learning activities so teachers understand the material mastery level of students. The study aimed at documenting the patterns and types of questions in genetic lecture using RQA (Reading, Questioning, Answering) learning. The data were collected by documenting all the questions during the learning process, and the sentence structures were analyzed referring to the guide of Krathwohl as the revision form of the Bloom guide. The findings of the study showed that the patterns of questions were not always similar to the questioning skill theory due to the linguistic structure and the understanding in communication during the learning interaction. The most types of questions occurred were understand as much as 40.3% related to cognitive dimension, conceptual as much as 41.1% related to knowledge dimension, and classify as much as 23.4% related to the cross section between cognitive and knowledge dimension. These results are important for the evaluation based of the learning achievements.
Numerical Modeling of Turbine Blade Cooling for Aero Engine Applications with the Use of Surrogate Models

The optimization of the thermodynamic cycles of aero engines has always been in the main targets of engineering efforts for environmental and economic reasons. The aero engine thermodynamic cycle is significantly affected by the selection of the cycle maximum temperature, which at the same time, should be high enough to achieve increased cycle efficiency but also be always kept within turbine blades material temperature limits to ensure turbine blades endurance and integrity. For these reasons, cooling techniques are used where a part of the compressor discharge air is usually used as a cooling medium for the turbine blades. Since this part of air does not participate in the heat addition process inside the combustion chamber its amount should be carefully estimated in order to simultaneously provide cooling air protection for the turbine blades and avoid loss of potential turbine work. Towards this direction, numerical tools such as the one presented in this work are developed. The present work is focused on the development of a surrogate model network for the calculation of turbine blade cooling for aero engine applications. In the analysis the turbine blade is modelled as a heat exchange unit composed of a number of interconnected sub-units. In each sub-unit the effect of turbine blade local geometrical features, such as inner channels length and hydraulic diameter, on heat transfer and pressure losses was incorporated through the use of literature based correlations. The investigations were focused on typical turbine blade conditions for recuperative aero engine applications as presented in Salpingidou et al. (2017). For the cooling mass flow calculations the suggestions of Young and Wilcock (2002a and 2002b) and Wilcock et al. (2005) were taken into consideration. Furthermore, after the development of the surrogate model network, the effect of incorporating heat transfer enhancers on inner flow turbine blade geometry was numerically assessed for two heat transfer augmentation surfaces, such as the ones presented in the work of Alam and Kim (2018), using experimental data available in literature describing their heat transfer and pressure loss characteristics targeting the identification of configurations with promising effect on the aero engine.
A study on competitiveness of sea and island tourism in Vietnam

Article history: Received: September 5 2019 Received in revised format: September 5 2019 Accepted: October 15, 2019 Available online: October 15, 2019 This study selects and employs the model of Dwyer and Kim (2003) [Dwyer, L. & Kim, C. (2003). Destination competitiveness: Determinants and indicators. Current Issues in Tourism, 6(5), 369-414. ] to assess the competitiveness of tourist destinations, in particular, the sea and island tourism in Nghe An province of Vietnam in the general development of sea and island tourism; based on the comparison with other similar destinations and top destinations in Vietnam. The study also indicates strengths and weaknesses of the sea and island tourism in Nghe An. We collect both qualitative and quantitative data and the results show that Nghe An stands in the middle position among five studied localities, reaching a level between neutral and good. Most of the competitive criteria of Nghe An are a little higher than Thanh Hoa and Ha Tinh but much lower than Da Nang and Khanh Hoa. Market demand is viewed as the strongest aspect of the sea and island tourism in Nghe An with criteria brand and the friendly/hospitality of local people, employees and office staff while the results of tourism activities and resources especially natural tourism resources are underestimated. © 2020 by the authors; licensee Growing Science, Canada