Miracle-dz/UniFAR-FTUs huggingface dataset:
A randomised controlled trial of cognitive-behavioural therapy for women with problematic menopausal hot flushes: MENOS 2 trial protocol

Objectives Hot flushes and night sweats (HF/NS) are experienced by 60–70% of menopausal women and are problematic for approximately 20–25%. Potential health risks associated with hormone-replacement therapy (HT) have led to a significant decline in HT use. There is therefore a need for safe, effective and evidence-based alternative treatments for menopausal symptoms. Previous exploratory work suggests that cognitive–behavioural therapy (CBT) is acceptable and effective for women with HF/NS during natural menopause and following breast-cancer treatment. This randomised controlled trial compares the effectiveness of Group CBT and Guided Self-Help CBT with no treatment control in reducing HF/NS Problem Rating and Frequency at post-treatment (main outcome) and at 6 months postrandomisation. Methods and analysis 120 women, with 10 or more HF/NS a week for a month, are recruited from GP surgeries and the local community of South London. They are randomised to either 4 weeks of Group CBT, 4 weeks guided Self-Help CBT or no treatment control. Participants attend a clinical interview, and complete baseline questionnaire measures of HF/NS Problem Rating and Frequency (primary outcomes), mood, quality of life, self-esteem, hot-flush beliefs and behaviours, optimism and somatic amplification, and undergo 24 h sternal skin conductance monitoring (SCC—physiological measure of HF/NS) (secondary outcomes). Post-treatment measures (SSC, questionnaires and use of medical services) are collected 6–8 weeks later and follow-up measures (questionnaires and a use of medical services measure) at 6 months postrandomisation. Ethics and dissemination Ethical registration was granted by King's College London Research Ethics Committee (ref: PNM/08/09-42). All participants provide written informed consent. If treatment is successful, a Group CBT training manual and training sessions for health professionals, and a Self-Help CBT book will be published. Other CBT delivery options will also be examined (including Computerised Self-Help CBT and Group CBT workshops). Clinical Trial Registration Number ISRCTN57302613.
Multi-Armed Bandits with Betting

In this paper we consider an extension where the gambler has, at each round, K coins available for play, and the slot machines accept bets. If the player bets m coins on a machine, then the machine will return m times the payoff of that round. It is important to note that betting m coins on a machine results in obtaining a single sample from the rewards distribution of that machine (multiplied by m), not m independent samples. At each round, the gambler must divide all of his or hersK coins among the machines in such a way as to maximize the total expected payoff.
Icord-vi -international Conference on Operational Research for Development Icord-vi -international Conference on Operational Research for Development Multi-objective Evolutionary Algorithm for the Transit Network Design Problem

The transit network design problem (TNDP) aims to find a set of routes and corresponding frequencies for an urban public transportation system. We model the TNDP as a multi-objective combinatorial optimization problem whose resolution involves finding a Pareto front that represents different trade-off levels between opposite objectives, the travel and waiting times and the required fleet of buses. We propose a Multi-Objective Evolutionary Algorithm (MOEA-TNDP) which has three algorithmic components, 1) an algorithm that generates a pool of candidate routes, 2) an assignment model that evaluates a given solution and checks the fulfillment of constraints, 3) an evolutionary process based on the Non-Dominated Sorting Genetic Algorithm version II that produces an approximated Pareto front. MOEA-TNDP is tested with the case of Rivera (a medium/small size city of 65000 inhabitants), taking its real solution and the solutions generated by the GRASP-TNDP algorithm as a reference for comparison.
Disjunctures and contradictions: The role of measurement in poverty representation, policy, and development practice

The disjunctures between development policy and practice are well articulated in the literature. One focus of this conversation is the inherent tension between the partial and contingent nature of policy interactions and the need to operationalize practice to get things done. Often lost in these discussions, however, is the critical role played by poverty measurement in creating this tension. As a multidimensional construct and a policy concern, poverty has been ascribed many meanings such as a threshold of income, a lack of freedom, or the result of unequal power relationships. These discursive understandings, however, are not well integrated into the practice of poverty measurement. The disconnect is in part due to the fact that measurement tools inherently simplify the meaning of poverty. Moreover, for operational feasibility, practitioners must choose between measures, privileging one definition over another. Yet, the processes of simplification and selection conceal important social understandings that a multidimensional definition of poverty is meant to capture. This research is concerned with the practice of poverty measurement. It looks at how the procedural act of collecting poverty data diverges from the popular discourse on poverty conceptualization and measurement; and, in turn, how this discourse diverges from experiences on the ground. I argue that how the development community conceptualizes poverty influences the practice poverty measurement which in turn influences the discourse, all of which help shape our understanding of what it means to be poor, and ultimately how we address poverty alleviation. To explore these recursive issues, I take the reader through the complex methodological steps of poverty measurement from the perspective of the practitioner and then analyze the process through the eyes of researcher. My study focuses on a single village in rural Kenya that currently receives aid from the UN World Food Program. Using a household survey and focus group discussions, I construct and compare four poverty measures commonly used by development aid agencies. My findings indicate that each measure defines a different set of households as poor and that the measures are related by the degree to which they capture a local or internalized understanding of poverty.
Mean Revesion In Equity Prices : Evidence From International Stock Markets

This article develops a continuous-time mean reverting model for stock prices that reconciles many of the emirical . ndings. The continuous time parameters ofour model are recovered by an exact discretization method. Using national stock index data from 7 countries we . nd strong evidence of mran reversion, evenat daily frequencies. Monte Carlo simulations support our emirical results.
Adaptive harmonic time-frequency decomposition of audio using shift-invariant PLCA

Numerous methods have been developed for the time-frequency analysis and smart decomposition of audio signals. However, these techniques are not consistently suitable for real music signals where each note presents continuous variations of both pitch and spectral envelope. This paper presents a new model for analyzing the harmonic structures of an audio signal that can jointly handle those two types of variations. Each note in a constant-Q transform is modeled as a weighted sum of narrowband parametric spectra, and positive deconvolution is performed to estimate the model parameters, in the framework of probabilistic latent component analysis. The algorithm has been tested in a task of monopitch estimation. The very promising results highlight the reliability and the robustness of the model.
Non-clinical development of CER-001

Cardiovascular disease remains the most pressing healthcare issue for the developed world and is becoming so for developing countries. There are no currently approved therapies that can rapidly reduce the burden of unstable, inflamed plaque in the overall coronary vascular bed. High-density lipoprotein (HDL) has multiple actions that could lead to plaque stabilization, such as rapid removal of large quantities of cholesterol from the vasculature through the process of reverse lipid transport, improvement in endothelial function, protection against oxidative damage, and reduction in inflammation. Short-term infusion of HDL-mimetics in animal models as well as in humans has shown promising effects on the plaque size and morphology. Cerenis Therapeutics has developed CER-001, a negatively charged lipoprotein complex consisting of phospholipid and recombinant human apoA-I that mimics the structure and function of natural HDL. Three clinical trials using CER-001 infusions have demonstrated improvements in the carotid wall thickness of patients with familial hypercholesterolaemia and in patients with hypo-alphalipoproteinaemia, as well as an impact on coronary plaque burden measured by intravascular ultrasonography at the lowest tested dose (3 mg/kg) in post-ACS patients. Here, we reviewed the non-clinical data leading to the demonstration that CER-001 is a full HDL mimetic.
Safeguarding and Accessing Drama as Intangible Cultural Heritage

Storytelling, especially in the form called drama, is pervasive across cultures and ages. Though much attention has been devoted to the preservation of the physical supports of drama (e.g., films and tapes), there is a widespread acknowledgment that such tangible heritage is the expression of an intangible notion of drama. This article introduces drama as a form of intangible cultural heritage and presents a solution for its preservation in terms of a formal encoding through a computational ontology. After the review of a formal representation of drama, called Drammar, developed in previous works, we show how an abstraction of drama can be encoded into a digital item. We also show how the method proposed is compliant with the major initiatives for the documentation of cultural heritage, namely CIDOC-CRM and FRBR. Finally, we test the applicability of our solution by showing how the major tenets of two well-known theories of drama can be encoded in Drammar and presenting the results of a focus group of drama scholars and practitioners who have accessed the encoding through a visualization system. The encoding and visualization system have turned out to be a promising support for teaching and investigating drama.
Towards the Application of Big Data: A New Way to make Data Driven Healthcare Decision

era of Big Data, large amounts of both structured and unstructured (also semi-structured) data are growing exponentially because of using social media, smart phones, archives and remote sensors. Most of these (approx. 80-90% data) are unstructured, commonly found in textual form and difficult to process using conventional database and software techniques. These large amounts of unstructured as well as complex data are giving new opportunities to the business organizations, governments, and researchers. Successful harnessing of Big Data provides valuable insights; improves data-driven decision making in healthcare, manufacturing, education, stock marketing, banking and insurance, agriculture, weather forecasting, travel and transportation, telecommunication, sports, and entertainment. This paper aims to emphasize on Big Data application and demonstrates that the analysis of healthcare data paves the way to make a better healthcare decision, reduce cost, and raise healthcare consciousness. The experimental analysis has been accomplished with a proposed methodology based on existing Text Mining and Natural Language Processing (NLP) techniques.
Title Driving Timing Convergence of FPGA Designs throughMachine Learning and Cloud Computing

Machine learning and cloud computing techniques can help accelerate timing closure for FPGA designs without any modification to original RTL code. RTL is generally frozen closer to system delivery target to avoid injecting new unforeseen bugs or significantly affecting design characteristics. In these circumstances, developers trying to close timing are either at the mercy of random trials through placement seed exploration or through vendor-provided design space exploration tools that run a few compilation trials with changes to the CAD tool options (or parameters). Instead, we propose evaluating multiple CAD runs in parallel on the cloud, supported by a Bayesian learning and classification framework for generating multiple CAD parameter combinations most likely to help attain timing closure. We maintain a database of FPGA CAD tool parameters (input) along with associated variations in timing slack (output) to enable the learning process. A key engineering resource we use here is cheap and abundant parallelism made possible through cloud computing frameworks such as the Google Compute Engine. Across a range of open-source benchmarks, we show that learning helps improve total negative slack (TNS) scores by 10.5⇥ (geomean) when compared to a single baseline run of Quartus 14.1 and by 7⇥ (geomean) when compared to Altera Quartus 14.1 Design Space Explorer (DSE).
Enhancing community detection by using local structural information

Many real-world networks, such as gene networks, protein–protein interaction networks and metabolic networks, exhibit community structures, meaning the existence of groups of densely connected vertices in the networks. Many local similarity measures in the networks are closely related to the concept of the community structures, and may have a positive effect on community detection in the networks. Here, various local similarity measures are used to extract local structural information, which is then applied to community detection in the networks by using the edge-reweighting strategy. The effect of the local similarity measures on community detection is carefully investigated and compared in various networks. The experimental results show that the local similarity measures are crucial for the improvement of community detection methods, while the positive effect of the local similarity measures is closely related to the networks under study and applied community detection methods.
Stem Cells and Their Mediators – Next Generation Therapy for Bronchopulmonary Dysplasia

Bronchopulmonary dysplasia (BPD) remains a major complication of premature birth. Despite great achievements in perinatal medicine over the past decades, there is no treatment for BPD. Recent insights into the biology of stem/progenitor cells have ignited the hope of regenerating damaged organs. Animal experiments revealed promising lung protection/regeneration with stem/progenitor cells in experimental models of BPD and led to first clinical studies in infants. However, these therapies are still experimental and knowledge on the exact mechanisms of action of these cells is limited. Furthermore, heterogeneity of the therapeutic cell populations and missing potency assays currently limit our ability to predict a cell product’s efficacy. Here, we review the therapeutic potential of mesenchymal stromal, endothelial progenitor, and amniotic epithelial cells for BPD. Current knowledge on the mechanisms behind the beneficial effects of stem cells is briefly summarized. Finally, we discuss the obstacles constraining their transition from bench-to-bedside and present potential approaches to overcome them.
Process control and instrumentation of SeSWRS based hydrogen cooling system of thermal power stations: A 2×660 MW unit

The paper presents a ‘Seven Stage Warm Redundant Structured’ (SeSWRS) hydrogen cooling system (HCS) for generators cooling in a typical thermal power station. The proposed system is equally reliable for steam and gas turbine based generating sections. System reliability of the proposed SeSWRS model is evaluated using MATLAB. The process control and instrumentation of the system is designed and simulated on Factory-Talk ViewSE, a supervisory control and data acquisition (SCADA) platform by Rockwell automation. The process model is designed as per specifications guided by IEEE C37.1-2007; IEEE 1413-2010, and ISA 100a.
Book Reviews: Cognitive Science: An Introduction

For instance, for a direct causal relationship, once the simple sentences that express the ACT and RESULT have been chosen, the choice of syntactic structure, the ordering of information, and the number of sentences still remain. Moreover, not all combinations of these choices yield acceptable texts. The discourse grammar encodes the acceptable choice combinations for the semantic relationship. Thus, in order to build a generation system which is able to handle some particular semantic relation, one must first do a detailed linguistic analysis to find the simple sentences that could be used to convey the information (and encode this in the lexicon grammar), and next do another linguistic analysis to see how these simple sentences can be combined, ordered, and syntactically presented so as to convey the semantic relationship (and encode this in the discourse grammar). Once the analysis has been done, the generation system can use these two grammars to do generation. It is the case, however, that the two grammars encode choices that are mutually dependent. Thus a choice in one will limit the available choices in the other. The priority of these decisions can only be determined within a particular domain. In applying this generation model to several domains, Danlos is extremely thorough and insightful. While one would hope that the domain dependence that Danlos advocates is not necessary, her analysis is quite convincing. Throughout the book she points out areas where "general principles" used by others must actually be operationalized in a very domain-dependent fashion. Thus the usefulness of such principles is called into question. In all I found the book to be most interesting. As a computer scientist I found the book's linguistic analysis very helpful. It forced me to look at generation fi'om a new point of view. I would expect that linguists will have a similar reaction because of the book's strong commitment to processing. I believe that Danlos has been able to successfully straddle the fence that lies between these two fields. In doing so, she has made a real contribution to both.
Node-attribute Graph Layout for Small-World Networks

Small-world networks are a very commonly occurring type of graph in the real-world, which exhibit a clustered structure that is not well represented by current graph layout algorithms. In many cases we also have information about the nodes in such graphs, which are typically depicted on the graph as node colour, shape or size. Here we demonstrate that these attributes can instead be used to layout the graph in high-dimensional data space. Then using a dimension reduction technique, targeted projection pursuit, the graph layout can be optimised for displaying clustering. The technique out-performs force-directed layout methods in cluster separation when applied to a sample, artificially generated, small-world network.
Human motion reconstruction based on inertial motion sensors

Recently, the opportunity to have a smart human motion tracking systems represents an interesting instrument in a wide range of advanced application, such as gaming, augmented reality, telemedicine, rehabilitation and human machine interaction applications. In this research, we developed a Digital Suit (DS) for a horseback riding robot system named HRB-1. It gathers human motion data during horse riding. From these data, posture of the trainee can be corrected into that of expert. The system configuration and its usage are discussed on this paper.
Effectiveness of EEG-Biofeedback on Attentiveness, Working Memory and Quantitative Electroencephalography on Reading Disorder

Objective: Cognitive factors are the important correlates of reading disorder and their impairments are established in children with reading disorder. Neurofeedback as an intervention has been reported to be useful in improvement of cognitive deficits. The present study aimed to determine the effectiveness of this treatment on attentiveness and working memory and related electroencephalographic (EEG) changes in children with reading disorder. Methods: In this single subject study, six children with reading disorder aged 8-10 years old completed twenty 30-minunt sessions of treatment. Continuous performance task, the digit span subscale of the 3rd edition of Wechsler Intelligence Scale for Children (WISC-III) and quantitative electroencephalography were used to evaluate the changes at pre and post-treatment. The data were evaluated by visual inspection of the graph, the mean percentage improvement and signal detection measures. Results: The results showed improvements in attention and working memory. Furthermore, EEG analysis did not show notable changes in the power of the targeted bands (delta, theta, and beta), rather the normalization of coherence was explicit in theta band at T3-T4, delta band at Cz-Fz, beta band at Cz-Fz, Cz-Pz and Cz-C4. Conclusions: These significant changes in coherence are possible indications of the connectivity between frontal and posterior association and integration between sensory and motor areas that explain the improvements in attention and working memory. Declaration of interest: None.
Co-culture of Yeast Antagonists of Fusarium Head Blight and their Effect onDisease Development in Wheat

Multistrain mixtures of biocontrol agents which can reduce plant disease to a greater extent than the individual strains of the mixture, commonly, are prepared by blending separately produced fermentation products. Co-cultivation of strains to equivalent biomass yields would provide mixture advantages without incWTing the cost disadvantages of multiple fermentation and processing protocols. Fusariwn Head Blight (FHB) antagonists Cryp/ococcusflovescens OR 182.9 (NRRL Y-302l6), C. oureusOR 71.4 (NRRL Y-302l3) and C. aureus OR 181.1 (NRRL Y -30215), were gro\Vll in twoand three-strain co-cultures to assess the quality and efficacy of the fermentation end products produced. Final cell COlllltS of component strains of all co-cultures produced were equivalent when plated on a medilllll that contained the trisaccharide melezitose as a sole carbon source and produced colonies of strain-distinguishable sizes. Co-cultures of C. flavescens OH 182.9 and C. aureus OH 71.4 significantly reduced FHB disease severity (32%, p = 0.05, DUllllett's t-test) when averaged across four greenhouse studies. In wheat field trials, biomass from co-cultures of these two strains reduced FHB incidence in some cases but rarely other FHB disease parameters (p = 0.05, Bonferoni mean separation). Relative Performance Index (RPI) analysis of the overall effect of treatments at both field sites revealed that treatment with the OH 71.4 and OH 182.9 co-culture significantly reduced FHB, as evidence by a higher RPI value than for the control, while the individual strains did not. The potential for obtaining superior efficacy and cost benefits with multi-strain cultures of biocontrol agents justifies additional research effort.
Representing the Visibility Structure of a Polyhedral Terrein Through a Horizon Map

Abstract We present a model for describing the visibility of a polyhedral terrain from a fixed viewpoint, based on a collection of nested horizons. We briefly introduce the concepts of mathematical and digital terrain models, and some background notions for visibility problems on terrains. Then, we define horizons on a polyhedral terrain, and introduce a visibility model, that we call the horizon map. We present a construction algorithm and a data structure for encoding the horizon map, and show how it can be used for solving point visibility queries with respect to a fixed viewpoint.