Miracle-dz/UniFAR-FTUs huggingface dataset:
Methodological choices for the clinical development of medical devices

Clinical evidence available for the assessment of medical devices (MDs) is frequently insufficient. New MDs should be subjected to high quality clinical studies to demonstrate their benefit to patients. The randomized controlled trial (RCT) is the study design reaching the highest level of evidence in order to demonstrate the efficacy of a new MD. However, the clinical context of some MDs makes it difficult to carry out a conventional RCT. The objectives of this review are to present problems related to conducting conventional RCTs and to identify other experimental designs, their limitations, and their applications. A systematic literature search was conducted for the period January 2000 to July 2012 by searching medical bibliographic databases. Problems related to conducting conventional RCTs of MDs were identified: timing the assessment, eligible population and recruitment, acceptability, blinding, choice of comparator group, and learning curve. Other types of experimental designs have been described. Zelen’s design trials and randomized consent design trials facilitate the recruitment of patients, but can cause ethical problems to arise. Expertise-based RCTs involve randomization to a team that specializes in a given intervention. Sometimes, the feasibility of an expertise-based randomized trial may be greater than that of a conventional trial. Cross-over trials reduce the number of patients, but are not applicable when a learning curve is required. Sequential trials have the advantage of allowing a trial to be stopped early depending on the results of first inclusions, but they require an independent committee. Bayesian methods combine existing information with information from the ongoing trial. These methods are particularly useful in situations where the number of subjects is small. The disadvantage is the risk of including erroneous prior information. Other types of experimental designs exist when conventional trials cannot always be applied to the clinical development of MDs.
2016 Ieee International Conference on Big Data (big Data) Qed: Groupon's Etl Management and Curated Feature Catalog System for Machine Learning

—In today's technology industry where machine learning has become essential, the effectiveness of algorithms ultimately depends on a robust data pipeline, and fast model prototyping and tuning require easy feature discovery and consumption. Careful management of ETL processes and their produced datasets is key to both model development in the research stage and model execution in the production environment. In this paper we present QED, an ETL management and curated feature catalog system that provides robust, streamlined machine learning pipelines. First, QED promises dynamic, reliable, and timely data delivery to the production pipeline. Its enhanced ETL process persists data from upstream sources in local data stores and ensures their correctness. Second, in contrast to previous systems, QED is capable not only of producing a daily scoring dataset, but also a training dataset with minimized bias by preserving the historical observations of feature values. Third, QED's multiple data store design allows batch process of large datasets as well as fast random access to single records. Finally, its curated feature catalog system enables sharing and reuse of machine learning features. QED serves as the data backend for a variety of machine learning models that provide key insights into the global business, and optimize the daily operations of Groupon.
Dual-Band Wang Shaped Microstrip Patch Antenna for GPS and Bluetooth Application

In this paper, a Wang shape micro strip patch antenna on FR-4 substrate materials has been analyzed and simulated at the 1.04 and 2.24 GHz resonant frequency regime for the GPS and Bluetooth device respectively. The radiation and antenna efficiency are more than 90% and bandwidth is 50.14% and 19% for band 1 and band 2 respectively. The maximum gain is 4.84 dBi to 5.81 dBi for the proposed antenna. This antenna structure is also simulated by using finite element method based simulator IE3D.
Fear of the Fear of the Flu: Assumptions About Media Effects in the 2009 Pandemic

In this article, we present a qualitative study of the Norwegian print news coverage of the 2009 pandemic. In initial research, we found fear to be a notable aspect of the coverage. In studying a relevant subsample in-depth, we discovered that, although there was no sound basis on which to make conclusions about what effects the coverage was having on the public, various actors—including media themselves—came on the mediated scene to express fears that it would create fear and panic. We argue that the mediation of the pandemic can hence be seen as a peculiar case of the “third person effect,” that is, the phenomenon that people tend to believe that other people are more severely influenced by the mass media than what they believe themselves to be. This, we suggest, has some implications for how we think about communication in future pandemics and other crises.
Improving performance and reliability assessments of avionics systems

Model-based development techniques have been successfully introduced into the design process of avionics systems. Examples of these techniques are performance models for digital systems and networks, fault tree models for reliability analysis and 3D-models for equipment allocation and weight assessments. However, current systems development processes usually require separate application of these techniques in order to achieve a well balanced overall solution. This paper presents an improved approach to evaluation of performance and reliability of avionics system designs. Performance and reliability assessments will be combined into a single validation step thus permitting for shorter development cycles. Such an approach is particularly useful during the early stages of development when the system architecture is not yet completely fixed and several alternatives need to be evaluated. It contributes to the maturity of the early system design and provides a good basis for further elaboration of a system architecture.
Analysis of Smoking Patterns and Contexts Among College Student Smokers

Many who smoke in college do so infrequently and smoking conditions are not well understood. We examined smoking patterns among college fraternity and sorority members (N = 207) from a Midwestern university in three successive fall semesters in 2006–2008. Participants completed calendar-assisted retrospective assessments of 30-day smoking at up to five assessment points over 96 days. Overall smoking rates declined over the course of each semester and higher smoking on weekends was observed, with more variability among daily smokers. The most frequent categories of events to cue recall of smoking were socializing, work, and school. Findings can be used to target prevention efforts.
Utilize Old Coordinates: Faster Doubly Stochastic Gradients for Kernel Methods

To address the scalability issue of kernel methods, random features are commonly used for kernel approximation (Rahimi and Recht, 2007). They map the input data to a randomized low-dimensional feature space and apply fast linear learning algorithms on it. However, to achieve high precision results, one might still need a large number of random features, which is infeasible in large-scale applications. Dai et al. (2014) address this issue by recomputing the random features of small batches in each iteration instead of pre-generating for the whole dataset and keeping them in the memory. The algorithm increases the number of random features linearly with iterations, which can reduce the approximation error to arbitrarily small. A drawback of this approach is that the large number of random features slows down the prediction and gradient evaluation after several iterations. We propose two algorithms to remedy this situation by "utilizing" old random features instead of adding new features in certain iterations. By checking the expected descent amount, the proposed algorithm selects "important" old features to update. The resulting procedure is surprisingly simple without enhancing the complexity of the original algorithm but effective in practice. We conduct empirical studies on both medium and large-scale datasets, such as ImageNet, to demonstrate the power of the proposed algorithms.
RoadRunner: automatic data extraction from data-intensive web sites

Data extraction from HTML pages is performed by software modules, usually called wrappers. Roughly speaking, a wrapper identifies and extracts relevant pieces of text inside a web page, and reorganizes them in a more structured format. In the literature there is a number of systems to (semi-)automatically generate wrappers for HTML pages [1]. We have recently investigated for original approaches that aims at pushing further the level of automation of the wrapper generation process. Our main intuition is that, in a dataintensive web site, pages can be classified in a small number of classes, such that pages belonging to the same class share a rather tight structure. Based on this observation, we have studied an novel technique, we call the matching technique [2], that automatically generates a common wrapper by exploiting similarities and differences among pages of the same class. In addition, in order to deal with the complexity and the heterogeneities of real-life web sites, we have also studied several complementary techniques that greatly enhance the effectiveness of matching. Our demonstration presents RoadRunner, our prototype that implements matching and its companion techniques. We have conducted several experiments on pages from real life web sites; these experiences have shown the effectiveness of the approach, as well as the efficiency of the system [2]. The matching technique for wrapper inference [2] is based on an iterative process; at every step, matching works on two objects at a time: (i) an input page, which represented as a list of tokens (each token is either a tag or a text field), and (ii) a wrapper, expressed as a regular expression. The process starts by taking one input page as an initial version of the wrapper; then, the wrapper is matched against the sample and it is progressively refined trying to solve mismatches: a mismatch happens when some token in the sample does not comply to the grammar specified by the wrapper. Mismatches can be solved by generalizing the wrapper. The process succeeds if a common wrapper can be generated by solving all mismatches encountered.
Molecular Line Radiative Transfer in Protoplanetary Disks: Monte Carlo Simulations versus Approximate Methods

We analyze the line radiative transfer in protoplanetary disks using several approximate methods and a well-tested accelerated Monte Carlo code. A low-mass flaring disk model with uniform as well as stratified molecular abundances is adopted. Radiative transfer in low and high rotational lines of CO, C18O, HCO+, DCO+, HCN, CS, and H2CO is simulated. The corresponding excitation temperatures, synthetic spectra, and channel maps are derived and compared to the results of the Monte Carlo calculations. A simple scheme that describes the conditions of the line excitation for a chosen molecular transition is elaborated. We find that the simple LTE approach can safely be applied for the low molecular transitions only, while it significantly overestimates the intensities of the upper lines. In contrast, the full escape probability (FEP) approximation can safely be used for the upper transitions (Jup ≳ 3), but it is not appropriate for the lowest transitions because of the maser effect. In general, the molecular lines in protoplanetary disks are partly subthermally excited and require more sophisticated approximate line radiative transfer methods. We analyze a number of approximate methods, namely, LVG, vertical escape probability (VEP), and vertical one ray (VOR) and discuss their algorithms in detail. In addition, two modifications to the canonical Monte Carlo algorithm that allow a significant speed up of the line radiative transfer modeling in rotating configurations by a factor of 10-50 are described.
Combining Markov Decision Processes with Linear Optimal Controllers

Linear Quadratic Gaussian (LQG) control has a known ana- lytical solution (1) but non-linear problems do not (2). The state of the art method used to nd approximate solutions to non-linear control problems (iterative LQG) (3) carries a large computational cost associated with iterative calculations (4). We propose a novel approach for solving non- linear Optimal Control (OC) problems which combines Reinforcement Learning (RL) with OC. The new algorithm, RLOC, uses a small set of localized optimal linear controllers and applies a Monte Carlo algorithm that learns the mapping from the state space to controllers. We illustrate our approach by solving a non-linear OC problem of the 2-joint arm oper- ating in a plane with two point masses. We show that controlling the arm with the RLOC is less costly than using the Linear Quadratic Regulator (LQR). This nding shows that non-linear optimal control problems can be solved using a novel approach of adaptive RL.
“There’s always stomach on the table and then I gotta write!”: Physical space and learning in fat college women

ABSTRACT Dominant “obesity” discourse research shows a strong correlation between fatness and poor academic outcomes among fat female learners at the postsecondary level. Little attention has been paid, however, to understanding the experiences of fat female learners from their own perspective. Interviews with 13 undergraduate women suggest that fat college women students experience the interactions of their fat bodies with the physical learning environment negatively. Classroom design and furniture contribute to fat women learners experiencing themselves as judged, devalued bodies and incapable learners.
A hybrid method for 3D face localization

Face localization is always a challenging job for a good recognition scheme. If process has to be done for 3D faces, it is quit difficult to localize faces. In this paper we provide a face localization scheme which is based on Average Threshold Value (AVT) and Grayscale Pixel Value Band (GPVB) with the help of Histogram Equalization. We experiment this approach on a synthetic 3D morphable face model of 20 individuals and get 89.51% accuracy among 20 faces with same lighting condition, which is appreciative for further work on same stream.
Ubiquitous Talker: Spoken Language Interaction with Real World Objects

Augmented reality is a research area that tries to embody an electronic information space within the real world through computational devices A crucial issue within thus area is the recognition of real world objects or situations In natural language processing, it is much racier to determine interpietations of utterances even if they art in formed WHEN the context or situation is fixed We therefore introduce robust natural language processing in to a system of augmented reality with situation awareness Based on this idea we have developed a portable system called the Ubiquitous Talker This consists of an LCD display that reflects the scene at which a user is looking as if it is a transparent glass a CCD camera for reognizing real world objects with color bar ID codes a microphone for recognizing a human voice and a speaker which outputs a synthesized voice The Ubiquitous Talker provides its user with some information related to a recognized object by using the display and voice It also accepts requests or questions as voice inputs The user feels as if he/she is talking with the object itself through the system.
Characterization of cDNAs encoding isoforms of hamster 3 (cid:226) -hydroxysteroid dehydrogenase/ (cid:196) 5 (cid:93) 4 isomerase

Western blot analyses of various hamster tissues reveal high levels of expression of 3 (cid:226) hydroxysteroid dehydrogenase (3 (cid:226) -HSD) in adrenal and liver, and moderate levels of expression in kidney. The expression in liver is sexually dimorphic; very high levels of protein are observed in adult male liver but very low levels are seen in the female liver. Three distinct cDNAs encoding isoforms of 3 (cid:226) -HSD were isolated from hamster cDNA libraries. The type 1 isoform is a high-a Y nity dehydrogenase/isomerase expressed in adrenal and male kidney. The type 2 isoform is also a high-a Y nity dehydrogenase/isomerase expressed in kidney and male liver. The type 3 enzyme is a 3-ketosteroid reductase expressed predominantly in kidney. Sequencing of the clones showed that all three are structurally very similar, although types 1 and 2 share the greatest degree of similarity. Immunohistochemical staining for 3 (cid:226) -HSD in the adrenal was found throughout the adrenal cortex. In the kidney staining was conﬁned of cDNAs
Software for Content Analysis – A Review

Software for content analysis divides, according to its intended function, into three major categories. The first set of programs performdictionary-based content analysis . They have the ‘basic handful’ of text analysis functions, involving word counting, sorting, and simple statistical tests. The basic handful are described in the next section. The second set contains development environments . These programs are designed to partially automate the construction of dictionaries, grammars, and other text analysis tools, rather than being analyzers themselves. Development environments are more similar to high-level text-specific programming languages than to freestanding content analysis packages. The third category contains an otation aids. While an annotation aid can often perform some automatic content analysis, it is intended more as an electronic version of the set of marginal notes, cross-references and notepad jottings that a researcher will generate when analyzing a set of texts by hand. The next section describes the basic handful of text analysis functions, and the rest of the paper provides brief descriptions of twenty-one content analysis programs. Some recommendations are made in the conclusion.
Lipin is required for efficient breakdown of the nuclear envelope in Caenorhabditis elegans

The nuclear envelope (NE) is a double lipid bilayer that separates nucleus and cytoplasm. In metazoa, NE breakdown (NEBD) occurs during prophase and NE reformation around segregated chromatids occurs at anaphase-telophase. We identified Caenorhabditis elegans Lipin homologue (called Lpin-1) as an essential factor with roles in endoplasmic reticulum (ER) organization and NEBD. RNAi-mediated downregulation of Lpin-1 had no effect on timely entry into mitosis or on the early steps of NEBD, but Lpin-1 was required for disassembly of the nuclear lamina during late NEBD. This Lpin-1 requirement appears to be separable from the effect of Lpin-1 on the peripheral ER.
Training Neural Networks Using a Genetic Algorithm and an Iterated Pseudo-inverse Feed-forward Networks ?1 If X < ?2 Sx If ?2 X 2

We present a rapid training algorithm for two-layer, feed-forward neu-ral networks. The rst layer of weights is determined by a genetic algorithm , the second layer by an iterated pseudo-inverse. We apply our method to problems such as parity and encoder-decoder (the latter is of the form \N{M{N," with M < N, in which the input and output of the net are identical). The network's form is: y = 1 Vx followed by z = 2 Wy, where x, y, and z are the input, hidden, and output vectors; V and W are the weights matrices; 1 is a piecewise linear function, and 2 is sign. A genetic algorithm evolves V using a tness function based on the net-work's performance after W has been determined by an iterated pseudo-inverse. We study feed-forward neural networks with two layers of weights; i.e., systems, denoted by N{M{K, consisting of an input vector x with N entries, a hidden vector y with M real entries, and an output vector z with K entries. The entries of x are bipolar: 1's. The entries of y and z will be real numbers, although only the algebraic sign of the entries in z will be of interest. Both x and z generally represent truth values. x, y, and z are column vectors. A is an augmentation operator, A : R n ! R n+1 , which prepends a 1.0 for \bias" (this allows all threshold functions to have their thresholds at zero; i.e., thresholds are the same as the other weights). The hidden vector y is a nonlinear function of the input x of the form y = 1 VAx, where V is a real M (N + 1) matrix and 1 is a scalar function, such as tanh (many training algorithms|but not ours|require that 1 be C 1) or a piecewise linear function: 1 (x) = 1 if 2 < x In most of our experiments, the slope s = 1, so 1 is not continuous, and our results for parity problems cannot be compared directly with other approaches. Our output vector z is a linear function of the hidden vector, z = WAy, where
Assistive care loop with electronic maternity records

Surprisingly women-held pregnancy health records (paper based) are still predominantly used in most hospitals in Australia. These records are not standardized as each hospital or state has a slightly different version. Early efforts have been made to standardize pregnancy records and make them available electronically. Electronic record systems do not allow dynamic interaction between users and they are not accessible when users are mobile. This paper describes an assistive maternity care (AMC) system that addresses a number of important issues: 1) transforming a women-held paper-based record for pregnancy care into an electronic maternity record (EMR); 2) investigating mechanisms to make the record active; 3) creating a system whereby details of the pregnant women and their carers can be recorded, updated over wired and wireless networks; and 4) creating a pregnancy care loop over which midwives and doctors and pregnant women under their care can communicate effectively anywhere, anytime for the duration of pregnancy.