Miracle-dz/UniFAR-FTUs huggingface dataset:
External Flows and Development in Bolivia

External resources for a low income country have the potential of accelerating growth but also of creating Dutch Disease effects. This paper analyses the empirical impact of the most important external resources, foreign direct investment, aid, and loans, on the Bolivian economy for the period 1950-2009. We use a multivariate CVAR(2) model, which directly deals with endogeneity issues and allows for a more profound insight into the chain of causality, through the impulse responses. Our results indicate that loans are positive for the Bolivian economy in the long run, through an increase in exports and a decrease in purchasing power of exports however, we did not include total debt. We find that the effect of aid is negative for the Bolivian GDP per capita in the long run, which is explained by the temporary increase in purchasing power of exports, which lead to a permanent loss in the exports. Finally, foreign direct investments lead to a positive response of Bolivian GDP per capita and exports, and a reduction of purchasing power of exports in the long run.
CEA LIST at TRECVID 2012 : Semantic Indexing and instance search

This paper reports the experiments carried out for the semantic indexing (SIN) and the instance search (INS) tasks at TRECVID 2012. For the SIN task, we evaluated two recently proposed features with a simple one-versus-all linear SVM framework. the rst one is a motion histogram based on trajectory vectors. The second one is a bag-of-visterm that take into account the spatial consistency of descriptors. In the INS task, we proposed a descriptors based on local descriptor matching able to scale to the considered corpus. A second contribution for INS consisted in studying several late fusion schemes. Preliminary experiments were conducted on the INS 11 corpus to choose the best strategy, leading to results in the top 5% of past results. While these preliminary results were very promising, 2012 results are above the median of participating runs, but far from reproducing previous year performances. The signicance of the results is thus studied, showing that signicant dif
Anesthesia and Intensive Care Management in a Pregnant Woman with PRES: A Case Report

Posterior reversible encephalopathy syndrome (PRES) is a temporary condition that is diagnosed clinically, neurologically, and radiologically. Its symptoms vary, and nonspecific headaches, confusion, impairment of consciousness, nausea, vomiting, and visual impairment may occur. Acute hypertension often accompanies these symptoms. Patients can also suffer from convulsions, cortical visual impairment, and coma. Diagnosis can be difficult due to focal neurologic signs. Nevertheless, knowing the clinical risk factors can lead to the right diagnosis. It has been reported that this condition may also occur during organ transplantation, immunosuppressive treatment, and autoimmune diseases and chemotherapy, and also patients with eclampsia. In this paper, a 21-year-old, 31-week pregnant woman, who had been diagnosed with PRES and thanks to early diagnosis and treatment had fully recovered and discharged from the intensive care unit, is presented, and the relevant literature is discussed.
The Adoption of the Euro, Choice of Currency Regime and Integration of Payment Systems

1) The Adoption of the Euro by New Member States: Challenges and Vulnerabilities by Michael C. Bonello 2) The Economics of Offshore Financial Services and the Choice of Tax, Currency, and Exchange-Rate Regimes by George M. von Furstenberg 3) Promoting Integration of European Retail Payment Systems: Role of Competition, Cooperation and Regulation (Kari Kemppainen and Sinikka Salo)
Automatic event detection for long-term monitoring of hydrophone data

In this paper, we propose an efficient method for long-term monitoring of a wide variety of marine mammals and human related activities using hydrophone data. The proposed method uses a combination of a two-stage denoising process followed by a new event detection function that estimates temporal predictability. The detection function utilizes long-term and short-term predictions in order to detect various acoustic events from the background noise. The first stage of the denoising process uses temporal decomposition via Empirical Mode Decomposition to improve the correct detection rate, while the second stage uses Wavelet Packet spectral decomposition to reduce the false detection rate. Applied to event detection in NEPTUNE hydrophone recordings, the method demonstrates an accuracy of 95% and an F-measure of 94%.
Low-level features for inpainting quality assessment

The paper presents an attempt to use a machine learning approach for inpainting quality assessment. Inpainting has received a lot of attention in recent years and quality assessment is an important task to evaluate different image reconstruction approaches. We present an approach for objective inpainting quality assessment based on natural image statistics and machine learning techniques. Our method is based on observation that when images are properly normalized or transferred to a transform domain, local descriptors can be modeled by some parametric distributions. The shapes of these distributions are different for non-inpainted and inpainted images. Approach permits to obtain a feature vector strongly correlated with a subjective image perception by a human visual system.
A Draft Memory Model on Spiking Neural Assemblies

A draft memory model (DM) for neural networks with spike propagation delay (SNNwD) is described. Novelty in this approach are that the DM learns immediately, with stimuli presented once, without synaptic weight changes, and without external learning algorithm. Basal on this model is to trap spikes within neural loops. In order to construct the DM we developed two functional blocks, also described herein. The decoder block receives input from a single spikes source and connect it to one among many outputs. The selector block operates in the opposite direction, receiving many spikes sources and connecting one of them to a single output. We realized conceptual proofs by testing the DM in the prime numbers classifying task. This activation-based memory can be used as immediate and short-term memory.
Luria and the Clinical Method

This paper begins with a review of the scientific and technical problems that led Freud to abandon neuroscience and to establish the new discipline of psychoanalysis. That review is followed by a consideration of some developments that took place in the neurosciences after Freud's death, due to the efforts of the Soviet neurologist, A.R. Luria. These developments seem to reverse the historical justification for an ongoing separation between psychoanalysis and neuroscience. Luria's early involvement with psychoanalysis is then described in detail, and its possible influence on his neuropsychological work is considered. The paper ends with the suggestion that a reintegration of psychoanalysis with the neurosciences is now possible, due to the fact that Luria has addressed all the major neuroscientific problems that Freud identified as the stumbling blocks to a viable neurological understanding of mental functioning. A methodological proposal for future neuro-psychoanalytical interdisciplinary research is advanced on the basis of these historical considerations. In this paper I want to show how knowledge of the history (and prehistory) of our discipline can sometimes contribute to the solution of contemporary scientific problems of practical importance.
Distinct Effects of Hedgehog Signaling on Neuronal Fate Specification and Cell Cycle Progression in the Embryonic Mouse Retina

Cell-extrinsic signals can profoundly influence the production of various neurons from common progenitors. Yet mechanisms by which extrinsic signals coordinate progenitor cell proliferation, cell cycle exit, and cell fate choices are not well understood. Here, we address whether Hedgehog (Hh) signals independently regulate progenitor proliferation and neuronal fate decisions in the embryonic mouse retina. Conditional ablation of the essential Hh signaling component Smoothened (Smo) in proliferating progenitors, rather than in nascent postmitotic neurons, leads to a dramatic increase of retinal ganglion cells (RGCs) and a mild increase of cone photoreceptor precursors without significantly affecting other early-born neuronal cell types. In addition, Smo-deficient progenitors exhibit aberrant expression of cell cycle regulators and delayed G1/S transition, especially during the late embryonic stages, resulting in a reduced progenitor pool by birth. Deficiency in Smo function also causes reduced expression of the basic helix-loop-helix transcription repressor Hes1 and preferential elevation of the proneural gene Math5. In Smo and Math5 double knock-out mutants, the enhanced RGC production observed in Smo-deficient retinas is abolished, whereas defects in the G1/S transition persist, suggesting that Math5 mediates the Hh effect on neuronal fate specification but not on cell proliferation. These findings demonstrate that Hh signals regulate progenitor pool expansion primarily by promoting cell cycle progression and influence cell cycle exit and neuronal fates by controlling specific proneural genes. Together, these distinct cellular effects of Hh signaling in neural progenitor cells coordinate a balanced production of diverse neuronal cell types.
Hybrid Scatter Search Algorithm for 4-Color Mapping Problem

In this paper scatter search algorithm has been hybridized with simulated annealing algorithm. Scatter search algorithm has some disadvantages which will be solved by using concepts of simulated annealing algorithm. Simulated annealing algorithm will provide scatter search algorithm with more of exploration for problem search space, this will increase ratio of getting optimal solutions. The hybrid and original scatter search algorithm have been tested by 4-Color Mapping problem. 6 local instances for Middle East maps have been tested and reported in tables. The computational results illustrate that the hybrid Scatter Search algorithm is better than the original Scatter Search algorithm.
You are a Game Bot!: Uncovering Game Bots in MMORPGs via Self-similarity in the Wild

Game bots are a critical threat to Massively Multiplayer Online Role-Playing Games (MMORPGs) because they can seriously damage the reputation and in-game economy equilibrium of MMORPGs. Existing game bot detection techniques are not only generally sensitive to changes in game contents but also limited in detecting emerging bot patterns that were hitherto unknown. To overcome the limitation of learning bot patterns over time, we propose a framework that detects game bots through machine learning technique. The proposed framework utilizes self-similarity to effectively measure the frequency of repeated activities per player over time, which is an important clue to identifying bots. Consequently, we use realworld MMORPG (“Lineage”, “Aion” and “Blade & Soul”) datasets to evaluate the feasibility of the proposed framework. Our experimental results demonstrate that 1) self-similarity can be used as a general feature in various MMORPGs, 2) a detection model maintenance process with newly updated bot behaviors can be implemented, and 3) our bot detection framework is practicable.
A Radar Guided Vision System for Vehicle Validation and Vehicle Motion Characterization

This paper describes a radar-guided monocular vision system that detects, validates, and tracks the preceding vehicle and thus predicts its lane-change intentions. A vision-based lane tracking process is developed to create a stable motion model in order to map the radar targets to image coordinates and consequently generate the region of interest (ROI) to search for a potential preceding vehicle. Model-based object classification algorithms are then applied to validate the existence of a vehicle in this ROI. Once the detected primary target vehicle, which is in the same lane as the host vehicle, is validated, it will be continuously tracked until it leaves the host lane. The spatial-temporal tracking history of the primary target vehicle is used to infer its intention of changing lanes. This prediction results from a characterization process for the primary target motion vector. The radar-vision integrated system has been evaluated on real-world data collected using a test vehicle equipped with a radar sensor, vision sensor, and a host processor.
CTL+FO verification as constraint solving

Expressing program correctness often requires relating program data throughout (different branches of) an execution. Such properties can be represented using CTL+FO, a logic that allows mixing temporal and first-order quantification. Verifying that a program satisfies a CTL+FO property is a challenging problem that requires both temporal and data reasoning. Temporal quantifiers require discovery of invariants and ranking functions, while first-order quantifiers demand instantiation techniques. In this paper, we present a constraint-based method for proving CTL+FO properties automatically. Our method makes the interplay between the temporal and first-order quantification explicit in a constraint encoding that combines recursion and existential quantification. By integrating this constraint encoding with an off-the-shelf solver we obtain an automatic verifier for CTL+FO.
The study on security issues in imprecise databases based on rough set theory

We have shown how the nature of the rough relational database provides some inherent security through its use of non-first normal form structure. Moreover, we provided measures of database security based on information-theoretic measures that allow for the evaluation of numeric measures for entropy. We are currently investigating the extension of this work for fuzzy rough and intuitionist relational databases.
The Effect of Childcare Staffs'(Early Childhood Teachers') Eating Attitude and a Impulsive Aggression Proclivity on their General Mental Health - Around the City, UL SAN, 0GU

This study seeks to analyze the correlation and influence on general mental health of childcare teachers according to their eating habits and impulsive aggressive tendency. For the study, self-filling questionnaire survey was conducted on childcare teachers working in kindergartens in U city from February 15th 2015 to June 20th 2015. For the data analysis, the SPSS WIN 18.0 program was used to conduct frequency analysis, correlation analysis, and linear regression analysis to verify at the significance level of 5%. The results first showed that the sub-factor of eating habit, fasting, has a positive correlation to mental health; on the contrary, the absorption in food, excessive eating, and aggressive characteristics showed not to be statistically significant. Second, the result of verifying the effect of eating habits and compulsive aggressive characteristics on general mental health showed that the socio-demographic characteristic, religion, and the sub-factor of eating habit, and fasting, showed to affect general mental health with statistical significance. The implications of this study are expected to become basic data to improve the eating habits and general mental health of childcare teachers.
Speech samples recognition based on MFCC and vector Quantization

this paper aims to develop speech sample recognition system regardless their meaning by using well known feature extraction algorithm MFCC with the combination of Vector quantization. Because this combination is easy to apply as well as understand where HMMs are more complicate to implement. It gives good results when compare to so many techniques. Here MATLAB is used here for this implementation. Total system is carried out through two phases. First phase is training phase in this MFCC is applied former for feature extraction. Later VQ code book are generated by trained them. In the second phase testing the matched sample is returned as result. This processing is based on calculation of distance between test sample and pre generated code words.
Follistatin-like immunoreactivity in the cytoplasm and nucleus of spermatogenic cells in the rat.

Immunohistochemistry using an antiserum raised against the synthetic follistatin peptide (residues 123-134) was used, in the present study, to detect the stage-specific appearance of immunoreactive follistatin in the rat testis. Follistatin immunoreactivity was not found in Sertoli and Leydig cells, while it was clearly detected in spermatogenic cells. Follistatin-like immunoreactivity was detected in the cytoplasm and nucleus of late pachytene spermatocytes. Although the reaction in the cytoplasm disappeared after meiosis, it continued to be intense in the nucleus from pachytene spermatocytes to round spermatids. This finding indicated that follistatin or its closely related peptide produced in late pachytene spermatocytes migrates from the cytoplasm to the nucleus. We subjected rat testis homogenate to affinity chromatography on a sulfate-cellulofine and anti-follistatin Cys (123-134)-Affi-Gel Hz column followed by reverse-phase HPLC and analyzed the resulting fractions by Western blotting using follistatin antiserum. Three major bands at 57, 45 and 39 kDa or four bands at 52, 44, 39 and 34 kDa were detected in crude preparations from rat testis homogenate, under reducing or non-reducing SDS-PAGE respectively. The protein from rat testis, which was recognized by anti-follistatin (123-134) antiserum, exhibited a characteristic pattern for follistatin on SDS-PAGE, i.e. slower migration under reducing conditions than under non-reducing conditions, suggesting that it was follistatin or its closely related protein. Follistatin or its closely related protein may be a stage-specific modulator of spermatogenesis. Since follistatin-like immunoreactivity was not found in oocytes in any stage of development from embryonic to adult rats, it may act in an event specific to spermatogenesis, such as nuclear condensation.
Update on Human Herpesvirus 6 Biology, Clinical Features, and Therapy

SUMMARY Human herpesvirus 6 (HHV-6) is a betaherpesvirus that is closely related to human cytomegalovirus. It was discovered in 1986, and HHV-6 literature has expanded considerably in the past 10 years. We here present an up-to-date and complete overview of the recent developments concerning HHV-6 biological features, clinical associations, and therapeutic approaches. HHV-6 gene expression regulation and gene products have been systematically characterized, and the multiple interactions between HHV-6 and the host immune system have been explored. Moreover, the discovery of the cellular receptor for HHV-6, CD46, has shed a new light on HHV-6 cell tropism. Furthermore, the in vitro interactions between HHV-6 and other viruses, particularly human immunodeficiency virus, and their relevance for the in vivo situation are discussed, as well as the transactivating capacities of several HHV-6 proteins. The insight into the clinical spectrum of HHV-6 is still evolving and, apart from being recognized as a major pathogen in transplant recipients (as exemplified by the rising number of prospective clinical studies), its role in central nervous system disease has become increasingly apparent. Finally, we present an overview of therapeutic options for HHV-6 therapy (including modes of action and resistance mechanisms).
RBF Network Based Nonlinear Model Reference Adaptive PD Controller Design for Greenhouse Climate

This paper presents a model reference adaptive PD control scheme based on RBF neural network for the greenhouse climate control problem. A model of nonlinear conservation laws of enthalpy and matter between numerous system variables affecting the greenhouse climate is used to validate the proposed control scheme. Compared with the conventional adaptive PD control scheme based on RBF neural network, the proposed scheme has better adaptability, stronger robustness and set-point tracking performance for the complex and nonlinear time-varying greenhouse climate control system, and it may provide a valuable reference to formulate environmental control strategies for actual application in greenhouse production.
Personalized PageRank to a Target Node

Personalalized PageRank uses random walks to determine the importance or authority of nodes in a graph from the point of view of a given source node. Much past work has considered how to compute personalized PageRank from a given source node to other nodes. In this work we consider the problem of computing personalized PageRanks to a given target node from all source nodes. This problem can be interpreted as finding who supports the target or who is interested in the target. We present an efficient algorithm for computing personalized PageRank to a given target up to any given accuracy. We give a simple analysis of our algorithm's running time in both the average case and the parameterized worst-case. We show that for any graph with $n$ nodes and $m$ edges, if the target node is randomly chosen and the teleport probability $\alpha$ is given, the algorithm will compute a result with $\epsilon$ error in time $O\left(\frac{1}{\alpha \epsilon} \left(\frac{m}{n} + \log(n)\right)\right)$. This is much faster than the previously proposed method of computing personalized PageRank separately from every source node, and it is comparable to the cost of computing personalized PageRank from a single source. We present results from experiments on the Twitter graph which show that the constant factors in our running time analysis are small and our algorithm is efficient in practice.
Research on Modeling for Safety Evaluation System of Amusement Ride Based on Fuzzy Neural Network

According to the principles of fuzzy comprehensive evaluation and neural network, the calculation models of safety evaluation system of amusement ride were created. The requirements and functions of safe evaluating system of amusement ride based on fuzzy neural network were analyzed. The Use Case View was created with Unified Modeling Language. According to Model-View-Controller pattern, the classes were abstracted through analyzing system functions and workflows. The object sequence diagram was created according to fuzzy safety evaluation process, and the object sequence diagram of the optimizing process of weights with neural network was created. The system was developed with Visual C++6.0. The user interface of system is sample and easy to operate. Keywords-fuzzy evaluation; neural network; amusement ride; Unified Modeling Language
Cottonseed And Gossypol : Effects On Lactation , Reproduction And Health Of Dairy Cows

Two major types of cottonseed are available in the southwest US, Upland and Pima. Upland is a high-lint type of cottonseed that is generally fed whole, while Pima is a delinted type of cottonseed that is often fed either cracked or ground to improve nutrient utilization. The nutrient composition of the two types of cottonseed differs, and Pima is often considered nutritionally superior because of its higher fat and protein content. In addition, Pima cottonseed contains more gossypol than whole linted Upland cottonseed, and more of its gossypol is represented by the minus (-) isomer.