Miracle-dz/UniFAR-FTUs huggingface dataset:
1 INVESTOR PROTECTION , DISCLOSURE REGULATION AND FINANCIAL MARKET : INTERNATIONAL EVIDENCE

In this research, we are interested in financial market regulations protecting minorities across countries and especially disclosure rules of changes in ownership structure. Different disclosure policies around the world are set. The Sarbanes-Oxley act of 2002 in the USA requires the ownership reports and trading by officers, directors and principal securities holders. However, European directives require disclosure of significant crossing of shareholding thresholds. Despite similarities between the US rules regarding disclosure of insiders trading transactions and European large holding directive for crossing of shareholding thresholds in terms of protection of minorities’ requirements, they are two different strategies. The American strategy focuses on the identity of the shareholder “insider” as opposed to European strategy which focuses on the threshold crossed by the shareholder (ownership concentration). However, recent regulatory developments in European Union have aligned insider trading regulation to US requirements in terms of disclosure policies. The questions we address are: Why did European countries adopt the American strategy of insider trading disclosure recently with the market abuse directive? Is it only by imitation, or is the American strategy protecting minorities more efficient than the European one? The objective of this research is to examine the efficiency of the investor protection regulations around the world. So, we compare alternative disclosure policies of the changes in ownership structure under different economy-wide regulatory environments. First, we compare the information content of insider trading disclosure policy for the ten first listed companies in the USA and in France. Second, the study contributes to the corporate governance literature by comparing two disclosure ownership structure policies: the insider trading disclosure and the crossing of shareholder thresholds disclosure. The results of the event study methodology, comparing market reaction to the insider trading and crossing shareholder thresholds in US and France context, shows that while the market doesn’t react to the disclosure of insider trading in American context, the insider trading has a significant effect on the market reaction in French context. On the other hand, the market reacts more strongly to the insider trading filling than to the crossing thresholds.
The Mechanism of Phillyrin from the Leaves of Forsythia suspensa for Improving Insulin Resistance

Three lignans, phillyrin, forsythia ester A, and rosin-β-D-furan glucose, were isolated from Forsythia suspensa which is a famous Traditional Chinese Medicine used for clearing heat and detoxifying, reducing swelling and dispersing knot, and dispersing wind heat. In this study, the effects of phillyrin, forsythia ester A, and rosin-β-D-furan glucose on insulin resistance of 3T3-L1 adipocytes were investigated by the method of glucose oxidase-peroxidase (GOD-POD) and the mechanism was assayed by the method of western blot. The results indicated that phillyrin, forsythia ester A, and rosin-β-D-furan glucose could improve the glucose uptake in 3T3-L1 adipocytes under insulin resistance (IR). Among them, phillyrin showed significant activity in increasing glucose consumption at the concentrations of 100 μM and 200 μM (P < 0.001). The mechanism of improving insulin resistance may be that phillyrin could raise the protein phosphorylation of IRS-1 and Akt and the expression levels of GLUT4 protein.
Poisson Equation Solver Parallelisation for Particle-in-Cell Model

Numerical simulations based on PIC technique like the SPICE2 model developed at IPP ASCR are often used in tokamak plasma physics to investigate the interaction of edge plasma with plasma-facing components. The SPICE2 model has been parallelised with the exception of the Poisson equation solver which considerably slows down the simulations. It is now being upgraded to a parallelised version to be efficient enough to perform more demanding tasks like the ITER tokamak baseline scenario edge plasma whose conditions like high density (up to 10 m3) and low temperature (1–2 eV) result in simulations taking several months to compute. Performance and scaling are compared for different cases in order to choose the optimal candidate for aforementioned applications. Introduction The basis of the particle-in-cell (PIC) technique is to divide the working field into a cell grid. The charge density ρ is then calculated and afterwards its spatial distribution is used in solving the Poisson equation ∆φ = − ρ ǫ0 . (1) The resulting potential of the electric field φ, is then used for calculation of forces that affect moving particles. In the case of square grid with cell dimension h, the discretized equation (1) can be written as φx−1,y + φx+1,y + φx,y−1 + φx,y+1 − 4φx,y h = − ρx,y ǫ0 , (2) with φi,j or ρi,j describing the potential or charge density in the grid cell at position (i, j). This is a system of linear equations for φi,j . It can be solved iteratively or using a direct solver. The system matrix is sparse, therefore suitable linear algebra software can be used. One of such models, SPICE2, is being developed at IPP ASCR as a continuation of collaboration with CEA Cadarache. It is a self-consistent PIC model with cloud-in-cell weighting and leapfrog particle motion. During its initial development, the code contained an iterative solver. Implementation of the package UMFPACK [Davis, 2004] has improved the performance significantly [Komm et al., 2010]. SPICE2 is partially parallelized using domain decomposition and MPI (distributed memory parallelisation scheme), only the Poisson solver is serial. The code has been successfully used to simulate various plasmas and geometries [e.g., Komm et al., 2010, Dejarnac et al., 2010]. However, more demanding simulations such as ITER baseline scenario, which is characterized with small Debye length λD ≈ 10 −7 m in scrapeoff layer, are almost impossible to simulate so far. Because the grid cell size scales with λD, the overall grid dimensions rise up to thousands of cells in each direction when simulating an area with centimeter scale. This leads to proportionally large matrices of system (2) which take a long time to compute. They can also consume large amounts of RAM per solver thread. The task of this paper is to investigate possibilities in parallelisation of the Poisson solver and compare it to the current method. 233 WDS'14 Proceedings of Contributed Papers — Physics, 233–237, 2014. ISBN 978-80-7378-276-4 © MATFYZPRESS
UNIVERSITY-BUSINESS COLLABORATION AS PERCEIVED BY LEADING ACADEMICS : COMPARING AND CONTRASTING THE TWO MOST INNOVATIVE CZECH REGIONS

This article compares the nature of academia-business collaboration in the two most innovative Czech regions, where the respective regional decision-makers and universities’ representatives differ sharply in their approaches towards the commercialization of academic knowledge. An analysis of the nature of collaboration between life-science researchers in two leading Czech universities and private companies has been performed to identify whether targeted support provided at the regional and university levels can make a real difference and can overcome hindrances from the national level. In particular, the article investigates the motivation and approaches of leaders of life-science research teams to cooperation with private companies, the perceived barriers impeding such cooperation, including the strength of demand for innovation in both analyzed regions. The research identified significant differences in the perception of barriers between life scientists in Prague and South Moravia, vindicating the positive role of the South Moravian innovation strategy. Thus, researchers in South Moravia no longer face barriers preventing the emergence of cooperation with the business sector, and instead they are concerned about obstacles that stand in the way of its more intensive development.
Association Modeling and Predicting Chaotic Circuit Data ∗ Wanting

Analyzing chaotic observations generated from some unknown nonlinear dynamics presents significant challenges for modeling the process and predicting future evolutions. We consider time series data measured from an electrical circuit that exhibits chaotic behavior. We compare and contrast the performance of Gaussian process (GP) and neural network (NN) models in short-term prediction and capturing the long-term dynamics. Major difficulties in modeling observations generated by such a physical process include distinguishing and characterizing the model and observational errors. We explore the effects of different types of model and observational errors on the likelihood function of the initial state using data generated under our fitted NN model. Our results show the distinctive effects of model and observational errors on inferring the initial state. In the absence of model error, we obtain exponentially growing information about the initial state for more observations even under temporally correlated observational errors. However, even with a tiny model error much smaller than what is measured physically and otherwise correct dynamics, the likelihood method no longer identifies the true initial state and hence cannot track the system for an indefinitely long time. Nonetheless, for this circuit system, our fitted predictors based on the GP and NN models appear to be promising in capturing the long-term dynamics in the presence of stochastic thermal noise.
Supplemental to – Pushing the Envelope for RGB-based Dense 3 D Hand Pose Estimation via Neural Rendering

where denotes element-wise multiplication and [j]XY extracts the x, y−coordinate values of skeleton joints from j. The Laplacian regularizer LLap enforces spatial smoothness in the mesh v. This helps avoid generating implausible hand meshes as suggested by Kanazawa et al. [4]. Our renderer fRen synthesizes a 2D hand segmentation mask from a mesh by simulating the camera view of x. Algorithm 2 summarizes the testing process.
Use of Waste Materials in Concrete: A review

Millions of tons of waste is produced in the world each year and most of it is not recyclable. Furthermore, recycling waste consumes energy and produces pollution. In addition, accumulation of waste in the suburbs and the disposal of waste are very dangerous for the environment. Using waste material in concrete production is an appropriate method for achieving two goals: eliminating waste and adding positive properties in concrete. Since the green concrete industry is expanding, it is necessary to evaluate concrete that contains waste from all aspects in order to determine its capability. This literature study consists of two parts i.e. the use of waste as a substitute for cement and as a substitute for aggregates. Leading waste material that has been used as substitutes is highlighted and the characteristics of the resulting concrete is evaluated. Among other findings, rubber was found to have improved fire resistance and ductility in concrete and agricultural and PET wastes were successfully used in non-structural concrete, while glass helped to improve thermal stability.
Symbolic Execution and Program Loops

definovane adresy, obsahujici nějake vstupni symboly. Tato nejednoznacnost je pravě zdrojem celeho problemu. V disertaci dale představujeme symbolickou paměť podporujici symbolicke ukazatele. Tato paměť bojuje s problem nejednoznacneho odkazovani do paměti tak, že uklada tak zvane podminěne hodnoty, použiva nový segment--offset--plane model paměti a použiva vyrovnavaci paměť pro efektivni řeseni dotazů na možne kolidovani adres ukazatelů.
Recognition of TCM syndrome types of cardiovascular diseases based on support vector machine and artificial neural networks

Objective To discuss the recognition method of TCM syndrome types of cardiovascular diseases,and provide some methods and evidences for the standardization of TCM syndrome research.Methods The cases of cardiovascular diseases were selected by using unified scale of TCM inquiry.A database of TCM clinical information was established through assigning "yes" to 1 and assigning "no" to 0 according to the information by inquiry.The clinical information and relation among syndrome types were analysed and model was established based on support vector machine(SVM,including two calculation methods: radial basis function and polynomial kernel function) and artificial neural networks(ANN,including two structure networks: ACON and OCON). The accuracy of syndrome deduction of SVM was observed.Results For heart-qi deficiency,heart-yang deficiency,heart-yin deficiency,phlegm turbidity,qi stagnation and blood stasis,common syndrome types of cardiovascular diseases,the recognition accuracy of OCON structure network was the highest(over 60%),and reached 92.4% and 82.9% respectively for heart-qi deficiency and heart-yang deficiency.Conclusion SVM and ANN can provide some objective bases for the recognition of TCM syndromes of cardiovascular diseases,and OCON structure network has a higher recognition accuracy.
The scales of (algorithmic) justice

Every day, governmental and federally funded agencies --- including criminal courts, welfare agencies, and educational institutions --- make decisions about resource allocation using automated decision-making tools (Lecher, 2018; Fishel, Flack, & DeMatteo, 2018). Important factors surrounding the use of these tools are embedded both in their design and in the policies and practices of the various agencies that implement them. As the use of such tools is becoming more common, a number of questions have arisen about whether using these tools is fair, or in some cases, even legal (K.W. v. Armstrong, 2015; ACLU, Outten & Golden LLP, and the Communications Workers of America, 2019).
Characterization of Fair Experiments for Recommender System Evaluation – A Formal Analysis

Recommender system experiments for evaluation involve a considerable number of configuration settings and decisions. Not only has this been a breeding ground for considerable methodological divergence in experimental practice the field, but it raises que issue to what extent different experimental approaches might be equally reliable. In this paper we address the question on the side of experimental fairness, meaning to what extent the outcome of a comparative experiment matches the underlying truth, and is not biased towards favoring a priori a particular algorithmic approach. Upon an abstract unified characterization of experiment configurations, we identify a formal condition for an experiment to be fair and informative. Based on this, we examine common and particular evaluation procedures described in the literature. We show that common offline approaches are subject to strong biases that may compromise the reliability of comparative evaluation results, while we identify alternatives that ensure fairness. We confirm and illustrate some of our theoretical findings in an experiment with partially synthetic data.
International Review on Modelling and Simulations ( IREMOS ) Contents Stability Study Domain with Parameters Fluctuation of Mamdani Type Fuzzy Control Systems

Deficiencies like unbalanced voltages or harmonics in the voltage source could result in problems like excessive losses, over-voltages, mechanical oscillations, and interference with control electronics. Detecting these abnormal conditions in the machine is of great importance in the interaction of the electrical machine and the power grid. In this paper the effect of the unbalanced voltages , harmonics and flicker on the motor performance, have been investigated. Then, monitoring these unhealthy condition using electrical machine parameters is carried out. In this case, motor itself can act as the sensor that detects abnormal conditions. Also this paper investigates the negative effects of unbalanced sinusoidal voltage which always present in the power supply over balanced (inverter supply) non-sinusoidal voltage on the performance of induction motor in terms of line currents, power factor and efficiency. According to the test results and analysis, the negative effects of unbalanced sinusoidal voltage are more than the balanced no sinusoidal voltage on the motor's performance. Copyright © 2010 Praise Worthy Prize S.r. l. All rights reserved.
5455 Exports and Capacity Constraints : Evidence for Several Euro Area Countries

We argue that, under certain conditions, firms consider exports as a substitute for domestic demand. Our econometric model for six euro area countries suggests domestic demand and capacity constraints as additional variables for export equations. We apply the exponential and logistic variant of a smooth transition regression model and find that domestic demand developments are relevant for short-run export dynamics particularly during more extreme stages of the business cycle. A substitutive relationship between domestic and foreign sales can most clearly be found for Spain, Portugal and Italy, providing evidence of the importance of sunk costs and hysteresis in international trade. JEL-Code: F140, C220, C500, C510, F100.
CUSTOMIZABLE FACIAL GESTURE RECOGNITION FOR IMPROVED ASSISTIVE TECHNOLOGY

Digital devices have become an essential part of modern life. However, it is much more difficult for less able-bodied individuals to interact with them. Assistive technology based on facial gestures could potentially enable people with upper limb motor disability to interact with electronic interfaces effectively and efficiently. Previous studies proposed solution that can classify predefined facial gestures. In this study, we build a customizable facial gesture recognition system using the Prototypical Network, an effective solution to the few-shot learning problem. Our second contribution is the insight that since facial gesture recognition is done based on tracked landmarks, a training set can be synthesized using a graphics engine. We show that our model trained using only synthetic faces can perform reasonably well on realistic faces.
Vision Based Transmission Line Broken Strand Detection and Robot Behaviour Planning

Power line maintenance robots are used to replace workers due to the dangerous maintenance operation, and the robot maintenance effect is much related with accurate fault detection and rational behavior planning. With those requirements in mind, a visual method is presented to detect the broken strand fault based on the classification of an image feature. In the visual detection method, image edge gradient histogram is firstly extracted as the image feature, and broken strand detection can be accomplished by the classification of the image feature with support vector machine(SVM) method. On this basis,several robot state vectors are established by combining the broken strand detection result and the information of robot sensors. Based on the current state vector and robotic broken strand repair process, a behavior planning method for broken strand repair is proposed toward complex operations of broken strand return and clamps installation. Experiments are carried out in the laboratory, and results demonstrate the effectiveness of the proposed broken strand detection method and the behavior planning method.
MASSIVE TRAINING ARTIFICIAL IMMUNE RECOGNITION SYSTEM FOR LUNG NODULES DETECTION HANG SEE PHENG

In the early detection and diagnosis of lung nodule, computer aided detection (CAD) has become crucial to assist radiologists in interpreting medical images and decision making. However, some limitations have been found in the existing CAD algorithms for detecting lung nodules, such as imprecision classification due to inaccurate segmentation and lengthy computation time. In this research, Massive Training Artificial Immune Recognition System (MTAIRS) is proposed to detect lung nodules on Computed Tomography (CT) scans. MTAIRS is developed based on the pixel machine learning and artificial immune-based system-Artificial Immune Recognition System (AIRS). Two versions of proposed algorithms have been investigated in the study: MTAIRS 1 and MTAIRS 2. Since segmentation and feature calculation are not implemented in the pixel-based machine learning, the loss of information can be avoided during the data training in MTAIRS 1 and MTAIRS 2. The experiment and analysis find that MTAIRS 1 and MTAIRS 2 have successfully reduced the computation time and accomplished good accuracy in the detection of lung nodules on CT scans compared to other well-known pixel-based classification algorithms. Furthermore, MTAIRS 1 and MTAIRS 2 are investigated to improve their performance in eliminating the false positives. A weighted non-linear affinity function is employed in the training of MTAIRS 1 and MTAIRS 2 to replace Euclidean distance in affinity measurement. The enhanced algorithms named, E-MTAIRS 1 and E-MTAIRS 2 are capable to reduce the false positives in the non-nodule classification while maintaining the accuracy in nodule detection. In order to further provide comparative analysis of pixel-based classification algorithms in lung nodules detection, a pixel-based evaluation method of Kullback Leibler (KL) divergence is proposed in this study. Based on the pixel-based quantitative analysis, MTAIRS 1 performs better in the elimination of false positives, while MTAIRS 2 in lung nodules detection. The average detection accuracy for both MTAIRS algorithms is 95%.
Raman study of the charge-density-wave state in A ( 0 . 3 )

We report a comparative Raman spectroscopic study of the quasione-dimensional charge-density-wave (CDW) systems A 0.3MoO3 (A = K, Rb). Temperatureand polarization-dependent experiments reveal charge-coupled vibrational Raman features. The strongly temperature-dependent collective amplitudon modes in the two materials differ by about 3 cm −1, thus revealing the role of the alkali atom. We discuss the observed vibrational features in terms of the CDW ground state accompanied by a change in the crystal symmetry. A frequency-kink in some modes seen in K 0.3MoO3 betweenT = 80 and 100 K supports the first-order lock-in transition, unlike the case of Rb 0.3MoO3. The unusually sharp Raman lines (limited by the instrumental response) at very low temperatures and their temperature evolution suggests that the decay of the low-energy phonons is strongly influenced by the presence of the temperaturedependent CDW gap. 4 Present address: Department of Chemistry, University of McGill, 801 Sherbrooke St. West, Montreal, QC, H3A 2K6, Canada. 5 Present address: Experimentalphysik 2, Universität Augsburg, 86135 Augsburg, Germany. 6 Author to whom any conrrespondence should be addressed. New Journal of Physics 10 (2008) 023043 1367-2630/08/023043+11$30.00 © IOP Publishing Ltd and Deutsche Physikalische Gesellschaft
Academic Publication Uncertainty and Publishing Behavior: A Game-Theoretic Perspective

This paper incorporates publication uncertainty in a game between researchers and journal editors and examines its effects on quantity and quality of published research. A stylized differential Stackelberg game between journal editors and academic authors is considered, where authors seek to maximize satisfaction from publications, while journal editors try to enhance reputations of their journals. Publication probability depends on the number of academic journals. Results show that greater journal competition, generally leading to reduced publication uncertainty, would increase author payoffs by increasing citations and publications. However, it is not clear whether the quality of published research is enhanced. Thus, changes in competition in publishing markets have the potential to exacerbate the quantity-quantity trade offs in research markets.