Miracle-dz/UniFAR-FTUs huggingface dataset:
The DEBIT trial: an intervention to reduce antipsychotic polypharmacy prescribing in adult psychiatry wards – a cluster randomized controlled trial

Background Clinical guidelines advise against prescribing more than one antipsychotic with limited exceptions. Despite this, surveys continue to report high antipsychotic polypharmacy rates. The aim of the study was to investigate the effectiveness of a multi-faceted intervention in reducing prescribing of antipsychotic polypharmacy on general adult psychiatry wards, compared with guidelines alone. Method A pragmatic cluster randomized controlled trial recruited 19 adult psychiatric units (clusters) from the South West of England. Participants were all ward doctors and nurses. The multi-faceted intervention comprised: an educational/CBT workbook; an educational visit to consultants; and a reminder system on medication charts. Results The odds of being prescribed antipsychotic polypharmacy in those patients prescribed antipsychotic medication was significantly lower in the intervention than control group when adjusted for confounders (OR 0.43, 95% CI 0.21–0.90, p=0.028). There was considerable between-unit variation in polypharmacy rates and in the change in rates between baseline and follow-up (5 months after baseline). Conclusion The intervention reduced levels of polypharmacy prescribing compared to guidelines alone although the effect size was relatively modest. Further work is needed to elicit the factors that were active in changing prescribing behaviour.
Detection and tracking of arbitrary objects in videos

In this work we present and analyse a methodology for detecting and tracking arbitrary objects in cinematic movies and documentaries. This work will bridge the gap between static imaging and the moving picture by using techniques from image segmentation in a video processing application. In particular, we adopt a point based approach to detect and track the temporal movement of arbitrary physical objects. We combine ideas from point tracking algorithms and static image segmentation to enhance the performance of a baseline motion based system. Our proposed methodology will be detecting arbitrary objects by looking for common properties between points of interest placed on edgy and textured areas on frames. We envisage that the points placed on a common object will exhibit similar motion and visual properties. Thus arbitrary physical objects can be detected by clustering points having similar features. We adopt a customised graph based partitioning technique which uses on the degree of connectivity between the nodes to cluster the points of interest. We construct a new benchmark corpus containing 201 annotated objects over 1298 frames in 15 minutes of video composed from three historical documentaries. We compare the performance of motion, visual and positional feature sets and find that the combined representation significantly outperforms the baseline feature sets.
Theorem Proving Support for View Consistency Checking

A formal, mechanically checked specification of the consistency constraints between two views of object-oriented systems are presented. The views, described in the BON modelling language, capture the static architecture of systems via contract-annotated class diagrams, and the dynamic view provided by collaboration diagrams. The constraints are specified as an extension of the BON metamodel, and are implemented in PVS. They ensure that the sequence of messages appearing in the dynamic view is legal, given the pre- and postconditions of methods appearing in the static view. An example of how the PVS theorem prover might be used to verify view consistency is described.
Evaluating performance of long short-term memory recurrent neural networks on intrusion detection data

This paper evaluates the performance of long short-term memory recurrent neural networks (LSTM-RNN) on classifying intrusion detection data. LSTM networks can learn memory and can therefore model data as a time series. LSTM is trained and tested on a processed version of the KDDCup99 dataset. A variety of suitable performance measures are discussed and applied. Our LSTM network structure and parameters are experimentally obtained within a series of experiments presented. Results finally show that LSTM is able to learn all attack classes hidden in the training data. Furthermore we learn that the receiver operating characteristic (ROC) curve and the corresponding area-under-the-curve (AUC) value are well suited for selecting well performing networks.
Using Process Mining Metrics to Measure Noisy Process Fidelity

Background: When teams follow a software development process they do not follow the process consistently. We need a method to measure their fidelity to that process. Objective: To evaluate Rozinat and Aalst's metrics for process conformance to a state based model on noisy data (Rozinat and van der Aalst, 2008). Method: We instructed 14 teams that were developing a software system using Extreme Programming (XP) to record the events of their project (for example writing code, or testing). We calculated the values of the proposed metrics by comparing the data collected to a process model of XP. Results: 13 teams recoded data that we treat as a multiple case study. The fitness metric gave varying results over the teams that corresponded to the number of event types used in the correct order. The appropriateness metrics measured the same values for all teams. Conclusion: The fitness metric is useful for measuring fidelity, but the appropriateness metrics do not measure over fitting well with noisy data. In addition neither metric gave useful information about other aspects like iteration.
Nonparametric inference for ergodic, stationary time series

The setting is a stationary, ergodic time series. The challenge is to construct a sequence of functions, each based on only finite segments of the past, which together provide a strongly consistent estimator for the conditional probability of the next observation, given the infinite past. Ornstein gave such a construction for the case that the values are from a finite set, and recently Algoet extended the scheme to time series with coordinates in a Polish space. The present study relates a different solution to the challenge. The algorithm is simple and its verification is fairly transparent. Some extensions to regression, pattern recognition, and on-line forecasting are mentioned.
Improved parametric method applied to wave estimation based on ship motion responses

Wave measurement technique based on ship motion response is a new technique used in moving vessels. Scholars mainly use parametric method to estimate wave, but the calculation of parametric method is complicated, the calculation time of parametric method is long, and the calculation values of parametric method is difficult to guarantee, The paper works on improving the existing parametric method. First, wave directional spectrum model is separated into wave distribution model and wave power spectrum model in the paper; Second, the parameters of the above two models are calculated separately. The improved parametric method reduces the number of parameters during nonlinear equation optimization process, which is more simple and accurate; finally the improved parametric method is validated by simulation.
Sentiment analysis of Chinese micro-blog using vector space model

In recent years, mining micro-blog becomes a hot research field, especially it may create commercial and political values in a fast changing big data era. This paper investigates the sentiment analysis of Chinese micro-blogs (SACM) using a vector space model. With the analysis of the nature properties of the Chinese micro-blogs, a sentiment analysis system has been proposed by formulating it as a two-type classification problem whether positive sentiment or negative sentiment. To achieve robust results, a preprocessing approach has been developed to remove the emotional unrelated words, transform the traditional expression to simplified one, and unify the punctuation by analyzing the dynamic and complicated micro-blog expressions. Besides, with aids of word segmentation and frequency statistical techniques the vector space model has been formed to generate the sentiment-related micro-blog feature vector. The support vector machine (SVM) has been taken as the classifier for its excellent ability in solving two-class classification problem. Experiments have been carried out to evaluate the proposed sentiment analysis system. Three different databases have been used in word segmentation stage including the emotion dictionary from Dalian University of Technology, CNKI-Hownet emotional dictionary and our self-established dictionary. Experimental results show that the proposed SACM system is able to achieve 80.86% classification accuracy using above databases.
Protocol for Secure Iris Authentication Framework using Cryptography

Data encryption is the key role in current and future technologies. Many public key cryptography algorithms were presented depending on a specific mathematical problem. It is desired to communicate data with high security over insecure network. Due to sensor technology biometrics has lead to rising concerns about the network security, privacy, trust issues and accuracy. Hence, securing biometric template in public network is great challenge. Because once it is compromised or stolen it cannot be revoked. In order to provide remote biometric authentication these systems incorporated with cryptography and primarily focused on template security, revocability, privacy, accuracy. The system is secure in the sense that the biometric are going to use is encrypted using Elliptic curve cryptography (ECC). Proposed system is based on client server architecture in that server is secure. Experimental result shows encryption time using ECC 256-bit key size for iris authentication.
Nutrigenomics and Beef Quality: A Review about Lipogenesis

The objective of the present review is to discuss the results of published studies that show how nutrition affects the expression of genes involved in lipid metabolism and how diet manipulation might change marbling and composition of fat in beef. Several key points in the synthesis of fat in cattle take place at the molecular level, and the association of nutritional factors with the modulation of this metabolism is one of the recent targets of nutrigenomic research. Within this context, special attention has been paid to the study of nuclear receptors associated with fatty acid metabolism. Among the transcription factors involved in lipid metabolism, the peroxisome proliferator-activated receptors (PPARs) and sterol regulatory element-binding proteins (SREBPs) stand out. The mRNA synthesis of these transcription factors is regulated by nutrients, and their metabolic action might be potentiated by diet components and change lipogenesis in muscle. Among the options for dietary manipulation with the objective to modulate lipogenesis, the use of different sources of polyunsaturated fatty acids, starch concentrations, forage ratios and vitamins stand out. Therefore, special care must be exercised in feedlot feed management, mainly when the goal is to produce high marbling beef.
The Effect of Inflammation on Preterm Birth

1.1 General considerations The World Health Organization defines preterm birth as the delivery of an infant between 20 and 37 weeks of gestation (Martin et al., 2005, Beck et al., 2010). Preterm birth (PTB) is a worldwide health problem and remains the leading cause of perinatal morbidity and mortality. PTB rates have been reported as 11% in the United States, and approximately 5 to 7% in Europe (Goldenberg, 2002). In industrialized countries, preterm delivery is responsible for 70% of mortality and 75% of morbidity in the neonatal period, contributing to significant long-term neurodevelopmental problems, pulmonary dysfunction, and visual impairment (Challis et al., 2001; Wen et al., 2004; Iacovidou et al., 2010). In South America, more than 10% of newborns are preterm, and the incidence is between 8% and 10% in Uruguay (Rey et al., 2008; Sosa et al., 2011). Unfortunately, the rate of PTB continues to rise according to recent incidence reports, and is currently 12–13% in the USA (Ananth et al., 2006).
An evaluation of three simple scalable MIM capacitor models

This paper presents an evaluation of three different scalable metal-insulator-metal capacitor models for use in monolithic-microwave integrated-circuit design. The models, including one previously unpublished, are based on transmission-line theory and are easily scalable using only physical dimensions and constants. Comparisons to measured data for several device sizes, up to 45deg in electrical length, show that the three models exhibit similar performance, with a mean deviation between models and S11 measurements that is less than 3%
Twist: A Regulator of Epithelial-Mesenchymal Transition in Lung Fibrosis

Background Several studies have implicated viral infection as an important factor in the pathogenesis of IPF and related fibrotic lung disorders. Viruses are thought to cause epithelial cell injury and promote epithelial-mesenchymal transition (EMT), a process whereby differentiated epithelial cells undergo transition to a mesenchymal phenotype, and considered a source of fibroblasts in the setting of lung injury. We have demonstrated an association between the epithelial injury caused by chronic herpes virus infection with the murine γ-herpes virus, MHV68, and lung fibrosis. We hypothesize that EMT in this model of virus-induced pulmonary fibrosis is driven by the expression of the transcription factor Twist. Methods/Findings In vitro MHV68 infection of murine lung epithelial cells induced expression of Twist, and mesenchymal markers. Stable overexpression of Twist promoted EMT in MLE15 lung epithelial cells. Transient knockdown expression of Twist resulted in preservation of epithelial phenotype after in vitro MHV68 infection. In concordance, high expression of Twist was found in lung epithelial cells of MHV68 infected mice, but not in mock infected mice. Alveolar epithelial cells from lung tissue of idiopathic pulmonary fibrosis (IPF) patients were strongly positive for Twist. These cells demonstrated features of EMT with low expression of E-cadherin and upregulation of the mesenchymal marker N-cadherin. Finally, IPF tissue with high Twist protein levels was also positive for the herpesvirus, EBV. Conclusions/Significance We conclude that Twist contributes to EMT in the model of virus-induced pulmonary fibrosis. We speculate that in some IPF cases, γ-herpes virus infection with EBV might be a source of injury precipitating EMT through the expression of Twist.
Data-driven inverse dynamics for human motion

Inverse dynamics is an important and challenging problem in human motion modeling, synthesis and simulation, as well as in robotics and biomechanics. Previous solutions to inverse dynamics are often noisy and ambiguous particularly when double stances occur. In this paper, we present a novel inverse dynamics method that accurately reconstructs biomechanically valid contact information, including center of pressure, contact forces, torsional torques and internal joint torques from input kinematic human motion data. Our key idea is to apply statistical modeling techniques to a set of preprocessed human kinematic and dynamic motion data captured by a combination of an optical motion capture system, pressure insoles and force plates. We formulate the data-driven inverse dynamics problem in a maximum a posteriori (MAP) framework by estimating the most likely contact information and internal joint torques that are consistent with input kinematic motion data. We construct a low-dimensional data-driven prior model for contact information and internal joint torques to reduce ambiguity of inverse dynamics for human motion. We demonstrate the accuracy of our method on a wide variety of human movements including walking, jumping, running, turning and hopping and achieve state-of-the-art accuracy in our comparison against alternative methods. In addition, we discuss how to extend the data-driven inverse dynamics framework to motion editing, filtering and motion control.
Cosmic evolution of submillimeter galaxies and their contribution to stellar mass assembly

The nature of galaxies selected at submillimeter wavelengths (SMGs, S 850 ≳ 3 mJy), some of the bolometrically most luminous objects at high redshifts, is still elusive. In particular their star formation histories and source of emission are not accurately constrained. In this paper we introduce a new approach to analyse the SMG data. Namely, we present the first self-consistent UV-to-radio spectral energy distribution fits of 76 SMGs with spectroscopic redshifts using all photometric datapoints from ultraviolet to radio simultaneously. We find that they are highly star-forming (median star formation rate 713 M ⊙ yr -1 for SMGs at z > 0.5), moderately dust-obscured (median A v ~ 2 mag), hosting significant stellar populations (median stellar mass 3.7 10 11 M ⊙ ) of which only a minor part has been formed in the ongoing starburst episode. This implies that in the past, SMGs experienced either another starburst episode or merger with several galaxies. The properties of SMGs suggest that they are progenitors of present-day elliptical galaxies. We find that these bright SMGs contribute significantly to the cosmic star formation rate density (~20%) and stellar mass density (~30-50%) at redshifts 2-4. Using number counts at low fluxes we find that as much as 80% of the cosmic star formation at these redshifts took place in SMGs brighter than 0.1 mJy. We find evidence that a linear infrared-radio correlation holds for SMGs in an ' unchanged form up to redshift of 3.6, though its normalization is offset from the local relation by a factor of ~2.1 towards higher radio luminosities. We present a compilation of photometry data of SMGs and determinations of cosmic SFR and stellar mass densities.
Principal Clusters Analysis: Analyzing Web Navigation Using a Multivariate Technique

We present a new statistical approach, called principal clusters analysis, for analyzing millions of user navigations among Web documents. This technique can identify distinct clusters of related information on a given topic. In addition, it can determine which information items within a cluster are useful starting points to explore the topic of the cluster, as well as key documents within the cluster to explore the topic in greater detail. This technique should prove promising in addressing information overload and other knowledge management issues.
MALNUTRITION IN PATIENTS WITH GASTROINTESTINAL CANCER: EFFECTIVENESS OF DIFFERENT DIAGNOSTIC METHODS.

OBJECTIVE to estimate the effectiveness of methods for identifying the risk and/or presence of malnutrition in individuals with gastrointestinal neoplasia. METHODS participated 143 patients with gastrointestinal cancer, cared for in the Oncology Clinic Infirmary of "Hospital Amaral Carvalho" (Jaú-SP). Excluded from the study were patients hospitalized in the intensive care unit, in a terminal state or those who had members amputated; those who had received blood transfusions during the last month; significant clinical bleeding; received endovenous albumin and uncontrolled infection. The nutritional status was classified according to the ratio between Real Weight and Habitual Weight, Body Mass Index, Nutritional Risk Index and Percent Adequacy. As the gold standard method the Scored Patient-Generated Subjective Global Assessment was used. The effectiveness of the methods for detecting the risk for or presence of malnutrition was evaluated. A ROC curve was constructed and its area (AUROC) was estimated. The areas were compared using z statistics. For each method the best cut-off point was established. RESULTS of the patients, 74.1% presented an advanced stage of the disease, and 83.2% were undergoing chemotherapy treatment. All the methods showed adequate discriminatory capacity for detecting the risk of malnutrition and presence of malnutrition. The BMI was significantly better for detecting malnutrition than for the risk of malnutrition. The RW/HW was significantly better for detecting the risk of malnutrition than the other methods. The cut-off points were slightly lower than those recommended for the normative population for the methods RW/HW, NRI and Score %. For the BMI the cut-off point was higher than the recommended for the normative population. CONCLUSION the methods for evaluating nutritional status showed adequate discriminatory capacity for the risk of malnutrition and presence of malnutrition in patients with gastrointestinal cancer.
Regularity properties in the classification program for separable amenable C*-algebras

We report on recent progress in the program to classify separable amenable C∗-algebras. Our emphasis is on the newly apparent role of regularity properties such as finite decomposition rank, strict comparison of positive elements, and Z-stability, and on the importance of the Cuntz semigroup. We include a brief history of the program’s successes since 1989, a more detailed look at the Villadsen-type algebras which have so dramatically changed the landscape, and a collection of announcements on the structure and properties of the Cuntz semigroup.
Data Mining in Incomplete Numerical and Categorical Data Sets: A Neuro Fuzzy Approach

There are many applications dealing with incomplete data sets that take different approaches to making imputations for missing values. Most tackle the problem for numerical input variables in the data set. However, when there are two types of input variables, numerical and categorical, the state of the art has provided no clear solutions. This paper presents a proposal for handling incomplete numerical and categorical data sets using an extension of an existing neuro-fuzzy approach. The method is extensively tested in a real environment in the field of the political election polls.
Estimating exports and imports demand for manufactured goods : The role of FDI . 1

In this paper we examine the joint performance of FDI and trade under a full liberalization process consistent with New Trade Theory models. The testing framework consists of the estimation of demand for exports and imports of manufactured goods for a panel containing the majority of the EU countries as well as the US and Japan. The model includes as explanatory factors, not only the traditional determinants of trade, but also new ones, such as the stock of foreign direct investment (FDI). We apply a variety of panel unit root and cointegration tests to the cases of both homogeneous and heterogeneous panels. Whereas there is no evidence of cointegration when using just the traditional formulation, the results are favorable to the existence of long-run relationships linking the variables of the augmented model. Moreover, the results point mainly to a complementarity relationship between trade and FDI. J.E. L. codes: C23, F14, F21.