Miracle-dz/UniFAR-FTUs huggingface dataset:
Smoking, HIV, and risk of pregnancy loss

Objective:Cigarette smoking during pregnancy increases risks of poor pregnancy outcomes including miscarriage and stillbirth (pregnancy loss), but the effect of smoking on pregnancy loss among HIV-infected women has not been explored. Here, investigated the impact of smoking on risk of pregnancy loss among HIV-positive and HIV-negative women, and estimated the potential impact of realistic smoking cessation interventions on risk of pregnancy loss among HIV-positive women. Design:We analyzed pregnancy outcomes in HIV-positive and HIV-negative participants in the Women's Interagency HIV Study between 1994 and 2014. Methods:We estimated effects of current smoking at or immediately before pregnancy on pregnancy loss; we controlled for confounding using regression approaches, and estimated potential impact of realistic smoking cessation interventions using a semiparametric g-formula approach. Results:Analysis examined 1033 pregnancies among 659 women. The effect of smoking on pregnancy loss differed dramatically by HIV status: adjusted for confounding, the risk difference comparing current smokers to current nonsmokers was 19.2% (95% confidence limit 10.9–27.5%) in HIV-positive women and 9.7% (95% confidence limit 0.0–19.4%) in HIV-negative women. These results were robust to sensitivity analyses. We estimated that we would need to offer a realistic smoking cessation intervention to 36 women to prevent one pregnancy loss. Conclusion:Smoking is a highly prevalent exposure with important consequences for pregnancy in HIV-positive pregnant women in the United States, even in the presence of potent highly active antiretroviral therapy. This evidence supports greater efforts to promote smoking cessation interventions among HIV-positive women, especially those who desire to become pregnant.
Intensional Concept Graphs as Patterns over Power Context Families

Intension graphs are introduced as an intensional variant of Wille’s concept graphs. Windowed intension graphs are then introduced as formalizations of conjunctive queries. Realizations describe pattern matching over power context families, which have been introduced with concept graphs as representations of relational data using a sequence of formal contexts. Using windowed intension graphs as patterns within pattern structures, we can define concept lattices, where power context families take the role of formal contexts. Relational Context Families, used in Relational Concept Analysis (RCA), correspond to power context families using sorts and only binary relations, and the lattices generated by the RCA algorithm (using wide scaling) can be represented using rooted trees as intents, which are introduced as a subclass of windowed intension graphs. Consequently, projections of the previously introduced pattern structure can be used as an alternative to the RCA algorithm.
Applying Weighted KNN to Word Sense Disambiguation

—One of the major challenges in the process of machine translation is word sense disambiguation (WSD), which is defined as choosing the correct meaning of a multi-meaning word. Supervised learning methods are usually used to solve this problem. The disambiguation task is carried out using the statistics of the translated documents (as training data) or dual corpora of source and target languages. In this paper we present a supervised learning method for WSD, which is based on K-Nearest Neighbor algorithm. As the first step, we extract two sets of features; the set of words that have occurred frequently in the text and the set of words surrounding the ambiguous word. In order to improve the classification accuracy, we propose a feature weighting strategy. We will present the results of evaluating the proposed schemes and illustrate the effect of weighting strategies proposed. The results are encouraging comparing to state of the art.
Learning with Positive and Unlabeled Examples Using Weighted Logistic Regression

The problem of learning with positive and unlabeled examples arises frequently in retrieval applications. We transform the problem into a problem of learning with noise by labeling all unlabeled examples as negative and use a linear function to learn from the noisy examples. To learn a linear function with noise, we perform logistic regression after weighting the examples to handle noise rates of greater than a half. With appropriate regularization, the cost function of the logistic regression problem is convex, allowing the problem to be solved efficiently. We also propose a performance measure that can be estimated from positive and unlabeled examples for evaluating retrieval performance. The measure, which is proportional to the product of precision and recall, can be used with a validation set to select regularization parameters for logistic regression. Experiments on a text classification corpus show that the methods proposed are effective.
Examining the Validity of the Autism Spectrum Rating Scales with a Preschool Population Referred for Special Education Evaluation.

Early identification and intervention of an Autism Spectrum Disorder (ASD) can have beneficial effects that extend into later life. However, currently used instruments have difficulties detecting children who may have an ASD. The current study investigated the utility of a newly published measure, Autism Spectrum Rating Scales (ASRS). Participants included 67 children ages 2 to 5 years old, referred for possible special education services. Participants were divided into two groups: those with an ASD (n = 37) and others suspected of having a general developmental disability (DD) (n = 30). Participants were assessed using the ASRS to examine the instrument’s ability to classify them as having an ASD or a general DD. Additional testing examined the effects various levels of intellectual, adaptive, and language skills have on the ability of the ASRS to classify children. Classification ability and error rates of the ASRS were also examined with regard to base rates and error acceptability by context. Results indicate that with a recommended cut score of 70, the Parent ASRS had an overall hit rate of 64%. The Parent ASRS had a Type I error rate (i.e., false positive) of 16% and a Type II error rate (i.e., false negative) of 19%. For the Teacher ASRS, the hit rate was 62%. The Teacher ASRS had a Type I error rate of 15% and a Type II error rate of 23%. Sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio and negative likelihood ratio of the ASRS were also examined to gain insight into the measure’s utility. ROC Curve analysis determined the area under the curve (AUC) for the ASRS, the most optimal point for sensitivity and specificity. It was concluded that
Exploring the existing category hierarchy to automatically label the newly-arising topics in cQA

This work investigates selecting concise labels for the newly-arising topics in community question answer. Previous methods of generating labels do not take the information of the existing category hierarchy into consideration. The main motivation of our paper is to utilize this information into the label generation process. We propose a general framework to address this problem. Firstly, we map the questions into Wikipedia concept sets, which are more meaningful than terms. Secondly, important concepts are identified to represent the main focus of the newly-arising topics. Thirdly, candidate labels are extracted from Wikipedia category graph. Finally, candidate labels are filtered and reranked by combination of structure information of existing category hierarchy and Wikipedia category graph. The experiments show that in our test collections, about 80% "correct" labels appear in the top ten labels recommended by our system.
A compact multiband MIMO antenna for wireless internet access application

A compact multiple-input-multiple-output (MIMO) antenna of two dual-branch monopoles is proposed. It is loaded with defective ground plane structure (DGS) to improve the isolation between two antenna elements by adding the three dumbel arms between them. Proposed antenna provides three bands which covers 2.4/5.8-GHz WLAN, 2.5/3.5/5.5-GHz WiMAX and the lower UWB band (3.1-4.8 GHz) operation with S11<; -10 dB and S21<; -18 dB. The results show that the MIMO antenna can serve as a phone antenna and it is suitable for integration within portable devices and handheld terminals with total maximum gain of 2.6 dB.
Learning Dynamic Programming with Split-Merge Networks

We consider the learning of algorithmic tasks by mere observation of input-output pairs. Rather than studying this as a black-box discrete regression problem with no assumption whatsoever on the input-output mapping, we concentrate on tasks that are amenable to the principle of divide and conquer, and study what are its implications in terms of learning. This principle creates a powerful inductive bias that we exploit with neural architectures that are defined recursively, by learning two scale-invariant atomic operators: how to split a given input into two disjoint sets, and how to merge two partially solved tasks into a larger partial solution. The scale invariance creates parameter sharing across all stages of the architecture, and the dynamic design creates architectures whose complexity can be tuned in a differentiable manner. As a result, our model is trained by backpropagation not only to minimize the errors at the output, but also to do so as efficiently as possible, by enforcing shallower computation graphs. Moreover, thanks to the scale invariance, the model can be trained only with only input/output pairs, removing the need to know oracle intermediate split and merge decisions. As it turns out, accuracy and complexity are not independent qualities, and we verify empirically that when the learnt complexity matches the underlying complexity of the task, this results in higher accuracy and better generalization in two paradigmatic problems: sorting and finding planar convex hulls. ∗Currently on leave from UC Berkeley 1 ar X iv :1 61 1. 02 40 1v 1 [ cs . L G ] 8 N ov 2 01 6
Prostate cancer and the Will Rogers phenomenon.

BACKGROUND Information on tumor stage and grade are used to assess cancer prognosis and to produce standardized comparisons of end results over time. Changes in the interpretation of classification schemes can alter the apparent distribution of cancer stage or grade in the absence of a true biologic change. Since the introduction of prostate-specific antigen testing, the reported incidence of low-grade prostate cancer has declined. To determine whether this decline is in part a result of Gleason score reclassification during the same time period, we documented the potential impact of reclassification between 1992 and 2002 on clinical outcomes. METHODS A population-based cohort of 1858 men who were < or = 75 years of age at diagnosis of prostate cancer in 1990-1992 was assembled retrospectively from the Connecticut Tumor Registry. Histology slides of the diagnostic prostate tissue were retrieved and reread in 2002-2004 by an experienced pathologist blinded to the original Gleason score readings. Prostate cancer mortality rates for the cohort calculated using the original Gleason score readings were compared with those calculated using the contemporary Gleason score readings. Statistical tests were two sided. RESULTS The contemporary Gleason score readings were statistically significantly higher than the original readings (mean score increased from 5.95 to 6.8; difference = 0.85, 95% confidence interval = 0.79 to 0.91; P < .001). Consequently, the Gleason score-standardized contemporary prostate cancer mortality rate (1.50 deaths per 100 person-years) appeared to be 28% lower than standardized historical rates (2.08 deaths per 100 person-years), even though the overall outcome was unchanged. This apparent improvement in mortality held for all Gleason score categories. CONCLUSIONS In this population, a decline in the reported incidence of low-grade prostate cancers appears to be the result of Gleason score reclassification over the past decade. This reclassification resulted in apparent improvement in clinical outcomes. This finding reflects a statistical artifact known as the Will Rogers phenomenon.
Safe robot arm with safe joint mechanism using nonlinear spring system for collision safety

Collision safety between humans and robots has drawn much attention since service robots are increasingly being used in human environments. A safe robot arm based on passive compliance can usually provide faster and more reliable responses for dynamic collision than an active one involving sensors and actuators. Since both positioning accuracy and collision safety of the robot arm are equally important, a robot arm should have very low stiffness when subjected to a collision force greater than the injury tolerance, but should otherwise maintain very high stiffness. To implement these requirements, a novel safe joint mechanism (SJM-II) which has much smaller size and lighter weight than the previous model, is proposed in this research. The SJM-II has the advantage of nonlinear spring which is achieved using only passive mechanical elements such as linear springs and a double-slider mechanism. Various analyses and experiments on static and dynamic collisions show that stiffness of the SJM-II is kept very high against an external torque less than the predetermined threshold torque, but abruptly drops when the input torque exceeds this threshold, thereby guaranteeing positioning accuracy and collision safety. Furthermore, a robot arm with two SJM-IIs is verified to achieve collision safety in 2D space.
Speeding up large-scale learning with a social prior

Slow convergence and poor initial accuracy are two problems that plague efforts to use very large feature sets in online learning. This is especially true when only a few features are "active" in any training example, and the frequency of activations of different features is skewed. We show how these problems can be mitigated if a graph of relationships between features is known. We study this problem in a fully Bayesian setting, focusing on the problem of using Facebook user-IDs as features, with the social network giving the relationship structure. Our analysis uncovers significant problems with the obvious regularizations, and motivates a two-component mixture-model "social prior" that is provably better. Empirical results on large-scale click prediction problems show that our algorithm can learn as well as the baseline with 12M fewer training examples, and continuously outperforms it for over 60M examples. On a second problem using binned features, our model outperforms the baseline even after the latter sees 5x as much data.
Enhancing K-means Clustering Algorithm with Improved Initial Center

Cluster analysis is one of the primary data analysis methods and k-means is one of the most well known popular clustering algorithms. The k-means algorithm is one of the frequently used clustering method in data mining, due to its performance in clustering massive data sets. The final clustering result of the k- means clustering algorithm greatly depends upon the correctness of the initial centroids, which are selected randomly. The original k-means algorithm converges to local minimum, not the global optimum. Many improvements were already proposed to improve the performance of the k-means, but most of these require additional inputs like threshold values for the number of data points in a set. In this paper a new method is proposed for finding the better initial centroids and to provide an efficient way of assigning the data points to suitable clusters with reduced time complexity. According to our experimental results, the proposed algorithm has the more accuracy with less computational time comparatively original k-means clustering algorithm.
Generalization of Rateless Codes for Unequal Error Protection and Recovery Time: Asymptotic Analysis

In this paper, we propose rateless codes that provide unequal error protection (UEP) property. We analyze the asymptotic properties of these codes under the iterative decoding algorithm. We further verify our work with simulations. The simulation results indicate that the proposed codes have strong UEP property. Moreover, the UEP property does not have a considerable drawback on the overall performance of the codes. We also discuss that the proposed codes can provide unequal recovery time (URT). This means that given a target bit error rate, different parts of information bits can be decoded after receiving different amounts of encoded bits. This implies that the information bits can be recovered in a progressive manner. This URT property may be used for sequential data recovery in video/audio streaming
and Improves Cardiac Function After Myocardial Ischemia and Reperfusion Inhibition of p 38 Mitogen-Activated Protein Kinase Decreases Cardiomyocyte Apoptosis

Xin L. Ma, Sanjay Kumar, Feng Gao, Calvert S. Louden, Bernard L. Lopez, Theodore A. and Improves Cardiac Function After Myocardial Ischemia and Reperfusion Inhibition of p38 Mitogen-Activated Protein Kinase Decreases Cardiomyocyte Apoptosis Print ISSN: 0009-7322. Online ISSN: 1524-4539 Copyright © 1999 American Heart Association, Inc. All rights reserved. is published by the American Heart Association, 7272 Greenville Avenue, Dallas, TX 75231 Circulation doi: 10.1161/01.CIR. 99.13. 1685 1999;99:1685-1691 Circulation. http://circ.ahajournals. org/content/99/13/1685 World Wide Web at: The online version of this article, along with updated information and services, is located on the
Constitutionalism and political order in Libya 2011–2014: three myths about the past and a new constitution

ABSTRACT Libya’s 2011 revolution enabled ordinary citizens and an emerging civil society to voice their demands on a variety of key issues including Libya’s new constitution. Libyans faced the challenge of redefining and re-empowering national political institutions through the establishment of a new constitution. This article analyses a series of constitutional grassroots debates that were led by the Forum for Democratic Libya (FDL), thereby unpacking insights not only into the constitutional process itself but also into the underlying expectations regarding a new political order in the country. We argue that the brief period of relative peace and stability in Libya between 2011 and 2013 presents a ‘golden age’ of constitutional activities that created meaningful interface among Libyan citizens after decades of oppression. We argue however that although constitutional debates allowed for citizen engagement in the process of constitutional development it had little bearing on the outcomes of this process. Citizen demands remained unanswered due to deeply entrenched informal political practices causing activists to face the arduous task of trying to influence a formal process of constitutional development. Priority demands expressed by citizens in our research pointed to the need for Libyan political actors to address the creation of a new system of governance, civil liberties and three regional priorities namely immigration and citizenship in Southern Libya, reconciliation and justice in Western Libya, and natural resources in Eastern Libya. The priorities remain unaddressed in Libya and provide insights of priority issues that will require serious efforts in the future of a stable Libya.
Fast Radiosity Using a Data Parallel Architecture

We present a data parallel algorithm for radiosity. The algorithm was designed to take advantage of large numbers of processors. It has been implemented on the Connection Machine CM2 system and scales linearly in the number of available processors over a wide range. All parts of the algorithm-form-factor computation, visibility determination, adaptive subdivision, and linear algebra solution-execute in parallel with a completely distributed database. Load balancing is achieved through processor allocation and dynamic data structures which reconfigure appropriately to match the granularity of the required calculations.
Data-Transmission Characteristics of Power Cables with Star-Quad Cross-Section

In this paper, the transmission of high-frequency signals for power line communications (PLC) along four-conductor power cables with star-quad cross-section is analyzed. In the derivation, the power cable is treated as a uniform and lossless multiconductor transmission line (MTL), and modal analysis is applied. The cable propagation modes are derived by literal solution of the MTL wave equations and related to the specific structure of the per-unit-length matrix parameters of the cable. The influence of coupling/decoupling schemes on the involved mode propagation paths is analyzed. For different values and configuration of the power cable loads, the signal attenuation (SA) of the PLC channel is evaluated in closed form. It is shown that intra-phase (differential) coupling should be used in order to excite a single propagation mode via balanced connections to a specific pair of phase conductors; under such conditions a two-conductor transmission line (TL) analogy holds. Different three-phase terminal loads and the possible presence of a branch point are considered in order to assess their effect in terms of degradation of the channel SA.
Helios: Web-based Open-Audit Voting

Voting with cryptographic auditing, sometimes called open-audit voting, has remained, for the most part, a theoretical endeavor. In spite of dozens of fascinating protocols and recent ground-breaking advances in the field, there exist only a handful of specialized implementations that few people have experienced directly. As a result, the benefits of cryptographically audited elections have remained elusive. We present Helios, the first web-based, open-audit voting system. Helios is publicly accessible today: anyone can create and run an election, and any willing observer can audit the entire process. Helios is ideal for on-line software communities, local clubs, student government, and other environments where trustworthy, secret-ballot elections are required but coercion is not a serious concern. With Helios, we hope to expose many to the power of open-audit elections.
Better Evaluation Metrics Lead to Better Machine Translation

Many machine translation evaluation metrics have been proposed after the seminal BLEU metric, and many among them have been found to consistently outperform BLEU, demonstrated by their better correlations with human judgment. It has long been the hope that by tuning machine translation systems against these new generation metrics, advances in automatic machine translation evaluation can lead directly to advances in automatic machine translation. However, to date there has been no unambiguous report that these new metrics can improve a state-of-the-art machine translation system over its BLEU-tuned baseline. In this paper, we demonstrate that tuning Joshua, a hierarchical phrase-based statistical machine translation system, with the TESLA metrics results in significantly better human-judged translation quality than the BLEU-tuned baseline. TESLA-M in particular is simple and performs well in practice on large datasets. We release all our implementation under an open source license. It is our hope that this work will encourage the machine translation community to finally move away from BLEU as the unquestioned default and to consider the new generation metrics when tuning their systems.