Miracle-dz/UniFAR-FTUs huggingface dataset:
Piano playing robot

One of the most familiar musical instrument is Piano, which uses the basic principles of sound, string, damping and vibration to produce sounds. Pianos were, and are, extremely popular instruments for private household ownership, especially among the middle and upper-class. When a key of a piano is hit, series of mechanism converts mechanical energy to sound energy. The beauty of this instrument is that is produces a particular sound when a specific key is pressed for a specific amount of time. The aim of this project is to build a robot which can play piano. It uses a simple DC motor which pulls the finger down and thus presses a key on the keyboard. The finger is then pulled up again with a help of an elastic string. The motors are controlled by a microcontroller which acts as the brain of the system, thus enabling the robot to play various tunes.
Human Capabilities on Video-based Facial Expression Recognition

A lot of promising computer vision research has been conducted in order to automatically recognize facial expressions during the last decade. Some of them achieve high accuracy, however, it has not yet been investigated how accurately humans accomplish this task, which will introduce a comparable measure. Therefore, we conducted a survey on this issue and this paper evaluates the gathered information regarding the recognition rate and the confusion of facial expressions.
Quantifying Policy Options for Reducing Future Coronary Heart Disease Mortality in England: A Modelling Study

Aims To estimate the number of coronary heart disease (CHD) deaths potentially preventable in England in 2020 comparing four risk factor change scenarios. Methods and Results Using 2007 as baseline, the IMPACTSEC model was extended to estimate the potential number of CHD deaths preventable in England in 2020 by age, gender and Index of Multiple Deprivation 2007 quintiles given four risk factor change scenarios: (a) assuming recent trends will continue; (b) assuming optimal but feasible levels already achieved elsewhere; (c) an intermediate point, halfway between current and optimal levels; and (d) assuming plateauing or worsening levels, the worst case scenario. These four scenarios were compared to the baseline scenario with both risk factors and CHD mortality rates remaining at 2007 levels. This would result in approximately 97,000 CHD deaths in 2020. Assuming recent trends will continue would avert approximately 22,640 deaths (95% uncertainty interval: 20,390-24,980). There would be some 39,720 (37,120-41,900) fewer deaths in 2020 with optimal risk factor levels and 22,330 fewer (19,850-24,300) in the intermediate scenario. In the worst case scenario, 16,170 additional deaths (13,880-18,420) would occur. If optimal risk factor levels were achieved, the gap in CHD rates between the most and least deprived areas would halve with falls in systolic blood pressure, physical inactivity and total cholesterol providing the largest contributions to mortality gains. Conclusions CHD mortality reductions of up to 45%, accompanied by significant reductions in area deprivation mortality disparities, would be possible by implementing optimal preventive policies.
LA KETAMINE DOIT-ELLE ETRE ASSOCIEE A LA MORPHINE ?

La kétamine est un vieil agent anesthésique général dont les effets résiduels psychodysleptiques ont limité les indications pour l’anesthésie à certaines circonstances cliniques bien précises. Néanmoins, ses propriétés analgésiques obtenues à des doses dix fois plus faibles que les doses anesthésiques et expliquées par l’inhibition des récepteurs N-méthyl-D-aspartate (NMDA) au niveau du système nerveux central ont réactualisé récemment l’intérêt clinique de cette substance et ouvert la voie au développement d'une nouvelle classe de médicaments analgésiques.
Load-balancing and caching for collection selection architectures

To address the rapid growth of the Internet, modern Web search engines have to adopt distributed organizations, where the collection of indexed documents is partitioned among several servers, and query answering is performed as a parallel and distributed task. Collection selection can be a way to reduce the overall computing load, by finding a trade-off between the quality of results retrieved and the cost of solving queries. In this paper, we analyze the relationship between the collection selection strategy, the effect on load balancing and on the caching subsystem, by exploring the design-space of a distributed search engine based on collection selection. In particular, we propose a strategy to perform collection selection in a load-driven way, and a novel caching policy able to incrementally refine the effectiveness of the results returned for each subsequent cache hit. The combination of load-driven collection selection and incremental caching strategies allows our system to retrieve two thirds of the top-ranked results returned by a baseline centralized index, with only one fifth of the computing workload.
The Underground Economy and the Underdevelopment Trap

A general equilibrium model is proposed which assumes that firms hire both official and unregistered workers as imperfect substitutes, that entrepreneurs differ in their ability to increase the efficiency of official labour, and that this ability is due to heterogeneous original ability and to Marshallian (non-linear) externalities. In equilibrium, smaller firms hire fewer official workers and are less efficient. If externalities increase sufficiently when firms are numerous, two stable equilibria exist where the number and the size of firms, the proportion of official employment, overall output and efficiency are, respectively, small (the trap) and large. The increase of individual ability due to, e.g., educational policies, has positive effects on the equilibrium number of firms, overall output, and labour regularisation. High and evenly distributed entrepreneurship also make it more likely that increased externalities and penalties on the underground economy will have positive effects. These results may contribute to the current debate on the underground economy in the Southern areas of Italy
DNA Image Pro - A Tool for Generating Pixel Patterns using DNA Tile Assembly

Self-assembly is a process found everywhere in the Nature. In particular, it is known that DNA self-assembly is Turing universal. Thus one can do arbitrary computations or build nano-structures using DNA self-assembly. In order to understand the DNA self-assembly process, many mathematical models have been proposed in the literature. In particular, abstract Tile Assembly Model (aTAM) received much attention. In this work, we investigate pixel pattern generation using aTAM. For a given image, a tile assembly system is given which can generate the image by self-assembly process. We also consider image blocks with specific cyclic pixel patterns (uniform shift and non uniform shift) self assembly. A software, DNA Image Pro, for generating pixel patterns using DNA tile assembly is also given.
Enhancing Linear Programming with Motion Modeling for Multi-target Tracking

In this paper we extend the minimum-cost network flow approach to multi-target tracking, by incorporating a motion model, allowing the tracker to better cope with long term occlusions and missed detections. In our new method, the tracking problem is solved iteratively: Firstly, an initial tracking solution is found without the help of motion information. Given this initial set of track lets, the motion at each detection is estimated, and used to refine the tracking solution. Finally, special edges are added to the tracking graph, allowing a further revised tracking solution to be found, where distant track lets may be linked based on motion similarity. Our system has been tested on the PETS S2.L1 and Oxford town-center sequences, outperforming the baseline system, and achieving results comparable with the current state of the art.
Identifying and Utilizing Routines of Human Movement

There are many patterns in the everyday life of a person, some of which are more conscious, others are less. Going to work on weekdays, or visiting friends on every second Saturdays are typically conscious ones, which are natural to human, but hard to recognize for a computer. In this paper we are introducing a set of algorithm proposals, designed to efficiently recognize routines of human movement. The domain of recognition is far beyond daily routines, any frequency of movement repetition can be identified. Based on routines, an optimal meeting point calculation system will be shown, as well as the description of a working prototype, integrating this functionality into a mobile community network.
A Compact Slotted Microstrip Patch Antenna for RFID applications

This paper presents a compact slotted microstrip patch antenna for RFID applications. The antenna is designed to operate at 2.40-2.45 GHz using the Computer Simulation Technology (CST) software. The proposed antenna is designed and fabricated using Flame Retardant 4, FR4 substrate with the design specification dielectric constant (εr) of 4.5 and thicknesses (d) of 1.6mm. In order to minimize the size of the microstrip patch antenna, the proposed antenna contains additional slotted structure attached in the reflector of FR-4 substrate. The new Compact Slotted Microstrip Patch Antenna (CSMPA) reduce by 50% from its conventional size for 2.40 GHz microstrip antenna operated frequency antenna. The antenna produces gains of 2.5 dB at 2.415 GHz with the overall size of antenna about 35 × 26 × 1.67 mm. Simulation and optimization of the presented CSMPA produced a good results which suitable for RFID applications.
Solomonoﬀ Induction

Solomonoff's induction method is an interesting theoretical model of what could be considered a perfect inductive inference system. Furthermore, it can be proven that a whole range of commonly used induction principles are computable approximations or special cases of Solomonoff's method. As such, Solomonoff induction provides us with a powerful and unifying perspective on the many diverse principles and methods that exist to deal with induction problems. The foundations of Solomonoff induction are well rooted in the mathematics of computation and information theory. Indeed, due to his early work on induction, Solomonoff is now considered the father of the field of algorithmic information theory; a field which has exposed many deep connections between topics such as randomness, computability, complexity, chaos and Gödel incom-pleteness. It is perhaps surprising then that in many fields which deal with induction problems, for example statistics, Solomonoff's work on induction is almost completely unknown. It would seem that one of the reasons for this lies in the diverse range of background material demanded of the reader. For example, readers from a statistical background, while familiar with Bayes' theorem and measure theory, are usually unfamiliar with topics such as coding theory, com-putability theory and algorithmic information theory. Similarly readers from other disciplines are not usually equipped with all the necessary background. As such, the first part of this report is spent briefly covering all the necessary background topics. In this way we hope to make the subject available to as wide an audience as possible. In the second part we present the basic essentials of Solomonoff's inductive inference method. The approach taken is not that originally used by Solomonoff but rather we come from the more general perspective of dominant enumerable semi-measures with Solomonoff's prior being a special case. Because the purpose of this report is simply to serve as an introduction to Solomonoff's approach to inductive inference and not as an introduction to general inductive inference theory, it is reasonable to assume that the reader is already familiar with Bayes' theorem and the problems associated with selecting prior distributions. In fact one can view Solomonoff's inference method to be essentially just a general purpose Bayesian inference system with a special information theoretic universal prior. Some effort has gone into keeping this work as small as possible without becoming terse. As such, many interesting connections to other topics and related issues are absent. This has been done …
Adaptation, population viability and colonization-extinction dynamics of Silene tatarica in riparian habitats

Plants in riparian environments have to tolerate disturbances like floods and erosion. In the absence of disturbances, habitats will gradually become unfavorable for early-successional species. This can have fundamental consequences not only for adaptation at the individual level, but also for the viability of local populations as well as for the persistence of the species on a regional scale. Silene tatarica is a rare perennial plant exposed to annual floods of the Oulanka River. After sand burial most plants formed vertical rhizomes and new meristems by bud ramification. The special anatomical features of these fleshy underground structures seemed to allow the plants to be very resilient against fast-flowing water. Seed structure may enhance long-distance dispersal by water. Selection gradients for plant height were positive at the individual and group levels, but for the number of stems they were in opposition, as selection at the group level favored a smaller number of stems. This can be associated with pollination and herbivory, since taller and larger plants often attract more pollinators and mammalian herbivores. At the local level, population growth was most sensitive to the survival and growth of juvenile individuals. The populations showed different fates, and according to stochastic simulations some of them will disappear in the near future. Their colonization and extinction rates varied between the years, but their balance over the study period was positive. Small patches had the highest risk of extinction and recolonization of extinct patches was very rare. Elasticity analysis indicated that the survival of patches made a much greater contribution to patch dynamics than did the production of
The Search for Meaning in Art: Interpretive Styles and Judgments of Quality

This study examined the seach for meaning in art and its relationship to judgments of aesthetic quality. A meaning paradigm was developed in which subjects arranged three items, within sets of paintings and sculptures, in an increasingly meaningful order. Although theme was held constant within each triad, one artwork (A) gave it a more literal (i.e., representational) treatment and the other two (B and C) increasingly explored visual effects (e.g., color, composition, shape). It was hy pothesized that naive subjects would search for recognizable objects and find the literal order (CBA) to be more meaningful, whereas trained subjects would be sensitive to stylistic transformations and choose the visual effects order (ABC). Results of two experiments sup ported the hypotheses, particularly for trained subjects. Judgments of quality were also ex amined. In Experiment 1, a new series of paintings varying in quality were rated by artists. Factor analysis revealed that their judg ments were founded primarily on the tradi tional principle unity-in-variety. In Experiment 2, subjects performed both meaning and qual ity tasks. Results suggest that the discrimi nation of quality was facilitated by a sensitivity to visual effects and reduced by a search for literal meaning. Questionnaire data about ar tistic practices and preferences also sup ported the hypotheses.
Literature Review on Entrepreneurial Network: A Comprehensive Frame

Entrepreneurial network plays an important role in entrepreneurship activities. In recent years, entrepreneurial network becomes hot spot in the area of Entrepreneurship. With the support of “social network theory”, “social exchange theory” and “social cognitive theory”, entrepreneurial network research has been developed to micro-and macro-levels gradually. The purposes of this paper is to analyses the entrepreneurial network in the last ten years, and proposes the research framework of entrepreneurial network, deeply analyses the default of the existing research, then propose prospects about entrepreneurial network in future. And we hope that the Literature Review On Entrepreneurial Network will give some inspiration to researchers.
Antiviral drug‐resistant HBV: Standardization of nomenclature and assays and recommendations for management

Substantial advances have been made in the treatment of chronic hepatitis B in the past decade. Approved treatments for chronic hepatitis B include 2 formulations of interferon and 4 nucleos(t)ide analogues (NAs). Sustained viral suppression is rarely achieved after withdrawal of a 48‐week course of NA therapy, necessitating long, and in many cases, indefinite treatment with increasing risk of development of drug resistance. Antiviral resistance and poor adherence are the most important factors in treatment failure of hepatitis B. Thus, there is a need to standardize nomenclature relating to hepatitis B antiviral resistance, and to define genotypic, phenotypic, and clinical resistance to NA therapy. (HEPATOLOGY 2007;46:254–265.)
Caracterització de la fibromiàlgia i factors pronòstic de resposta al tractament

Objective. Despite showing acceptable psychometric properties, the criterion validity of the original Fibromyalgia Rapid Screening Tool (FiRST) has been called into question for including insufficiently challenging comparison groups. Consequently our objective was to validate a Spanish version of the FiRST including pain disorders more analogous to fibromyalgia. Methods. The FiRST was translated following international standards. Internal consistency and temporal stability were assessed. The ability of the FiRST global score as a screening tool for fibromyalgia (criterion validity) was assessed by logistic regression analysis. To determine the degree to which potential confounders might affect the criterion validity of the FiRST (divergent validity), it was reassessed by hierarchical multivariate logistic regression, entering demographics in a first step, followed by pain, anxiety and depression, catastrophizing, disability and the FiRST global score in a last step. Results. The final sample comprised 257 patients (67% cases of fibromyalgia). The Spanish version of the FiRST showed acceptable internal consistency, reliability and criterion validity. The FiRST was able to discriminate between fibromyalgia and non-fibromyalgia patients even after controlling for the effect of potential confounders. However, both criterion and divergent validity were challenged by a moderate specificity. Conclusion. The Spanish version of the FiRST may be used as a screening tool for fibromyalgia even in those patients whose cognitive style is characterized by catastrophizing about pain and high levels of functional disability, anxiety and depression. The clinical consequences of the moderate specificity shown by this Spanish version of the FiRST are discussed.
Urban–rural disparities in colorectal cancer screening: cross-sectional analysis of 1998–2005 data from the Centers for Disease Control's Behavioral Risk Factor Surveillance Study

Despite the existence of effective screening, colorectal cancer remains the second leading cause of cancer death in the United States. Identification of disparities in colorectal cancer screening will allow for targeted interventions to achieve national goals for screening. The objective of this study was to contrast colorectal cancer screening rates in urban and rural populations in the United States. The study design comprised a cross‐sectional study in the United States 1998–2005. Behavioral Risk Factor Surveillance System data from 1998 to 2005 were the method and data source. The primary outcome was self‐report up‐to‐date colorectal cancer screening (fecal occult blood test in last 12 months, flexible sigmoidoscopy in last 5 years, or colonoscopy in last 10 years). Geographic location (urban vs. rural) was used as independent variable. Multivariate analysis controlled for demographic and health characteristics of respondents. After adjustment for demographic and health characteristics, rural residents had lower colorectal cancer screening rates (48%; 95% CI 48, 49%) as compared with urban residents (54%, 95% CI 53, 55%). Remote rural residents had the lowest screening rates overall (45%, 95% CI 43, 46%). From 1998 to 2005, rates of screening by colonoscopy or flexible sigmoidoscopy increased in both urban and rural populations. During the same time, rates of screening by fecal occult blood test decreased in urban populations and increased in rural populations. Persistent disparities in colorectal cancer screening affect rural populations. The types of screening tests used for colorectal cancer screening are different in rural and urban areas. Future research to reduce this disparity should focus on screening methods that are acceptable and feasible in rural areas.
Neural Mechanisms of Young Adult Sexual Decision-Making and Risk Behavior

Neural Mechanisms of Young Adult Sexual Decision-Making and Risk Behavior by Elizabeth C. Victor Department of Psychology and Neuroscience Duke University Date:_______________________ Approved: ___________________________ Ahmad Hariri, Supervisor ___________________________ Robert Thompson ___________________________ Christina Meade ___________________________ Timothy Strauman An abstract of a dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Psychology and Neuroscience in the Graduate School of Duke University 2015
Mathematical Modelling of Cardiac Electrical Activity Using Bidomain Approach

In recent times, mathematical model of cardiac electrical activity has been recognized as one of the significant approaches capable of revealing diagnostic information about the heart. However, an efficient and accurate mathematical technique required for this modelling is one of the major problems in the field of biomedical research. This work presents mathematical modelling of cardiac electrical activity using bidomain approach. The cardiac electrical activity is best mathematically modelled coupled systems of ordinary differential equations and partial differential equations which are non-linear, stiff, and therefore difficult to solve numerically and implement. Hence, the bidomain model was adopted due to
Health Care Decision Support System for Swine Flu Prediction Using Naïve Bayes Classifier

The healthcare industry collects a huge amount of data which is not properly mined and not put to the optimum use. Discovery of these hidden patterns and relationships often goes unexploited. However there is ongoing research in medical diagnosis which can predict the diseases of the heart, lungs and various tumors based on the past data collected from the patients. Our research focuses on this aspect of Medical diagnosis by learning pattern through the collected data for Swine Flu. This research has developed prototype Intelligent Swine flu Prediction software (ISWPS). We used Naïve Bayes classifier for classifying the patients of swine flu into three categories (least possible, probable or most probable). We have used 17 symptoms of Swine flu and collected 110 symptoms sets from various hospitals and medical practitioners. Using ISWPS, we have achieved an accuracy of nearly 63.33%. It is implemented on the JAVA platform.