Miracle-dz/UniFAR-FTUs huggingface dataset:
A randomized, double-blind, placebo-controlled, dose-ranging study of lisdexamfetamine dimesylate augmentation for major depressive disorder in adults with inadequate response to antidepressant therapy

Background: This randomized, double-blind, placebo-controlled study evaluated dose-response relationships of lisdexamfetamine dimesylate when used as augmentation for major depressive disorder in individuals exhibiting inadequate responses to antidepressant monotherapy. Methods: Eligible adults (18–65 years) were assigned to antidepressant monotherapy (escitalopram or venlafaxine extended-release) plus lisdexamfetamine dimesylate-matching placebo during an eight-week single-blind lead-in phase. Participants meeting randomization criteria were randomized (1:1:1:1:1) to eight weeks of lisdexamfetamine dimesylate (10, 30, 50, or 70 mg) or placebo while maintaining antidepressant therapy. Dose-responses for changes from augmentation baseline to week 16/early termination for Montgomery-Åsberg Depression Rating Scale total score (primary efficacy endpoint) and vital signs (systolic and diastolic blood pressure and pulse) were assessed using multiple comparisons procedures with modeling. Results: For Montgomery-Åsberg Depression Rating Scale total score change, no significant dose-responses were observed for any candidate dose-response curve (all p>0.10). In the dose-response evaluable population, least squares mean (90% confidence interval) treatment differences versus placebo for Montgomery-Åsberg Depression Rating Scale total score change at week 16 were −1.4 (−3.9, 1.2), 0.1 (−2.5, 2.7), −0.7 (−3.4, 2.0), and −0.9 (−3.5, 1.6) with 10, 30, 50, and 70 mg lisdexamfetamine dimesylate, respectively. For all vital sign parameters, lisdexamfetamine dimesylate exhibited significant dose-responses for all candidate dose-response curves (all p<0.10), with increases observed as lisdexamfetamine dimesylate dose increased; a linear relationship provided the best fit. Mean±standard deviation changes from augmentation baseline for systolic and diastolic blood pressure and pulse at week 16/early termination were −0.7±9.90 and −0.3±7.24 mm Hg and 0.2±10.57 bpm with placebo and were 1.9±9.47 and 0.8±7.40 mm Hg and 3.6±9.74 bpm with lisdexamfetamine dimesylate (all doses combined). The safety and tolerability profile of lisdexamfetamine dimesylate was consistent with previous studies. Conclusions: Lisdexamfetamine dimesylate augmentation did not provide benefit over placebo in adults with inadequate responses to antidepressant monotherapy based on the assessed efficacy measures.
Partitioning strategies for distributed association rule mining

In this paper a number of alternative strategies for distributed/parallel association rule mining are investigated. The methods examined make use of a data structure, the T-tree, introduced previously by the authors as a structure for organizing sets of attributes for which support is being counted. We consider six different approaches, representing different ways of parallelizing the basic Apriori-T algorithm that we use. The methods focus on different mechanisms for partitioning the data between processes, and for reducing the message-passing overhead. Both ‘horizontal’ (data distribution) and ‘vertical’ (candidate distribution) partitioning strategies are considered, including a vertical partitioning algorithm (DATA-VP) which we have developed to exploit the structure of the T-tree. We present experimental results examining the performance of the methods in implementations using JavaSpaces. We conclude that in a JavaSpaces environment, candidate distribution strategies offer better performance than those that distribute the original dataset, because of the lower messaging overhead, and the DATA-VP algorithm produced results that are especially encouraging.
Multi-Scale Investigation of the Formation and Breakdown of Passive Films on Carbon Steel Rebar in Concrete

The multi-scale investigation presented in this thesis was carried out to understand better the mechanisms of passivation and chloride-induced depassivation of carbon steel reinforcement in concrete. The study consisted of electrochemical experiments (electrochemical impedance spectroscopy, linear polarization resistance, free corrosion potential, anodic polarization), microscopic examinations (scanning electron microscopy, transmission electron microscopy, selected area diffraction, convergent beam electron diffraction), numerical modeling (finite element method), and spectroscopic studies (x-ray photoelectron, energy dispersed x-ray, electron energy loss). Electrochemical and microscopic studies showed that the composition of the pore solution and the surface conditions of the rebar affect the passivity and depassivation of carbon steel in concrete. It was demonstrated that crevices between mill scale and steel may become potential sites for depassivation and pit nucleation. The numerical investigation that was carried out to test this hypothesis confirmed that the ratio of chloride to hydroxide concentrations, Cl /OH , in crevices increased to levels higher than that of the bulk pore solution, making crevices more vulnerable to depassivation. Therefore, it was concluded that the variability associated with reported chloride thresholds might be attributed, at least in part, to the variability in mill scale properties resulting from the variability in manufacturing. The nano-scale microscopic and spectroscopic studies indicated the formation of 4-10 nmthick passive oxide films on carbon steel in simulated concrete pore solutions, and these films consisted of two layers separated with an indistinct border. The inner layer was mainly composed of protective Fe 2+ -rich oxides that are in epitaxial relationship with the underlying steel surface; while the outer layer mostly consisted of (possibly porous) Fe 3+ -rich oxides, through which chlorides can penetrate. It was proposed that, in the presence of chlorides, Fe +2 -rich oxides in the inner layer transform into Fe +3 -rich oxides and potentially become un-protective. Although how this transformation occurs is still subject of future research, there are evidences showing that the process most likely leads to the formation of local anodic and cathodic sites on the steel surface.
The prevalence and risk factors of stroke in patients with chronic schizophrenia

Objective To investigate the stroke risk and risk factors in patients with chronic schizophrenia. Patients and methods This study was a large-sample, cross-sectional survey. A total of 363 patients with chronic schizophrenia were selected from the Changping Traditional Chinese Medicine Hospital, Beijing, in August 2014. The patients were divided into either stroke group or control group based on the presence of stroke. Clinical evaluation included positive and negative syndrome scale assessment and a detailed questionnaire to collect the general information and disease-related conditions. Results The prevalence of stroke was 16.5% (60 cases). Stroke and control groups showed a significant difference in age, sex, smoking, combined medication, doses, negative factor score in positive and negative syndrome scale, body mass index, waist circumference, and systolic blood pressure. Multivariate analysis showed that a number of factors are significantly related to stroke, including age, sex, smoking, combined medication, doses, body mass index, and systolic blood pressure. Conclusion The prevalence of stroke is relatively higher in Chinese patients with chronic schizophrenia. Chronic schizophrenia patients are more likely to suffer from stroke; meanwhile, a number of risk factors were identified, including old age, female sex, smoking history, combined medication with a variety of drugs, high doses, obesity, and high blood pressure.
CIP4, a New COP1 Target, Is a Nucleus-Localized Positive Regulator of Arabidopsis Photomorphogenesis

Arabidopsis COP1 acts within the nucleus to repress photomorphogenesis, and its nuclear abundance is negatively regulated by light. Here, we report the identification of a COP1-interactive partner, CIP4. CIP4 is a nuclear protein and a potent transcription coactivator. Conditional suppression of CIP4 expression resulted in an elongated hypocotyl and reduced chlorophyll content in the light, indicating that CIP4 is required for the promotion of photomorphogenesis. Furthermore, CIP4 was revealed to act downstream of multiple photoreceptors as well as COP1 in mediating light control of development. CIP4 expression is light inducible and regulated by COP1. However, CIP4 does not play a role in mediating the light induction of anthocyanin accumulation. Together with our previous studies of CIP7 and HY5, our data suggest that COP1 interacts directly with and regulates multiple physiological targets, which in turn regulate distinct sets of light-regulated responses.
Present or Future: Optimal Pricing for Spot Instances

The recent years witnessed rapid emergence and proliferation of cloud computing. To fully utilize the compute resources, some cloud operators provide spot resources. Spot resources allow customers to bid on unused capacity. However, pricing policy of spot resources should be carefully designed and the impact on both present and future should be considered. For the present, the cloud provider can set a higher price to gain extra revenue. For the future, higher price will shift more requests with lower prices to later time and reduce the revenue of future. Meanwhile, the quality of service should be considered either since bad QoS will incur loss of potential users. In this paper, we present a demand curve to model the impact of pricing on the present and future revenue. Then we formulate the revenue maximization problem as a time-average optimization problem. Next, since this basic model fails to provide information of service delay, we extend it to a more generalized one that ensures the worst-case delay of user requests. While the future knowledge of arrival requests is unknown, it is necessary to design online algorithms for the optimization problems. We apply Lyapunov optimization framework and design an efficient online algorithm which dose not require any future knowledge of requests arrival. Evaluations based on real-life datacenter workload and Amazon EC2 Spot Price illustrate efficiency of our algorithms.
A Pattern‐Based Approach Using Compound Unit Recognition and Its Hybridization with Rule‐Based Translation

This paper describes a compound unit (CU) recognizer as a pattern‐based approach and its hybridization with rule‐based translation. A compound unit is a combined concept including collocations, idioms, and compound nouns. CU recognition reduces part of speech ambiguities by combining several words into a unit and consequently lessening the parsing load. It also provides pretranslated natural equivalents. Our focus in this paper is to obtain flexibility and efficiency from pattern‐based machine translation, and high‐quality translation by hybridization. A modified trie, our search index structure using “method” strategy is used to manage heterogeneous property of the constituents. Syntactic verification is integrated to obtain precise CU recognition by means of pruning wrongly recognized units that are caused by improper variable hypotheses. The experimental result with verification shows that the precision of CU recognition is increased to 99.69% with 31 CFG rules on the cyclic trie structure for 1,268 Wall Street Journal articles of the Penn Treebank. Another experiment with CU recognition also shows that it raises the understandability of translation for Web documents.
Discrimination of transmission line insulator contamination grades using visible light images

Flashover occurs more easily on contaminated transmission line insulators, which causes great economic losses and has bad effects on power system stability. An accurate and safe detection of insulator contamination grades is required. In this paper, a new method is proposed to discriminate insulator contamination grades using visible light images. Firstly, ZSW-10/4 insulators are smeared in different contamination grades, and their visible light images are shot under illumination ranges from 10,000lux to 100,000lux. Secondly, both software methods and hardware methods are adopted and compared to eliminate the effects of illumination. After image processing, insulator surface color features in several color spaces are calculated. Results of Fisher criterion shows that hardware methods work better in eliminating illumination effects, and mean value of V component in YUV color space is selected for discriminating contamination grades. Finally, BP (Back Propagation) neural networks are established, whose testing accuracy rates are over 90% in discriminating contamination grades. Further, a general formula between mean value of V component and ESDD (Equivalent Salt Deposit Density) is obtained.
Random Field Model for Integration of Local Information and Global Information

This paper presents a proposal of a general framework that explicitly models local information and global information in a conditional random field. The proposed method extracts global image features as well as local ones and uses them to predict the scene of the input image. Scene-based top-down information is generated based on the predicted scene. It represents a global spatial configuration of labels and category compatibility over an image. Incorporation of the global information helps to resolve local ambiguities and achieves locally and globally consistent image recognition. In spite of the model's simplicity, the proposed method demonstrates good performance in image labeling of two datasets.
Face Detection with Facial Features and Gender Classification Based On Support Vector Machine

Abstract This paper presents a novel face detection and gender classification strategy in color images under nonuniform background. This is done by detecting the human skin regions in image given and detecting facial features based on the measurements in pixels. The proposed algorithm converts the RGB image into the YCbCr color space to detect the skin regions in the facial image. But in order to detect facial features the color image is converted in to gray scale image. This method locates the lip region and the mouth region. From this, left and right eyes’ and the nose regions are located. The gender classification method classifies almost all the images with different image sizes. The best classification rate is achieved using Linear Support Vector Machine.
Probabilistic Hindsight : A SAS Macro for Retrospective Statistical Power Analysis

Statistical power analysis is useful from both prospective and retrospective viewpoints. Prospectively, statistical power analysis is used in the planning of research to estimate sample size requirements. In contrast, retrospective power analysis provides an estimate of the statistical power of a hypothesis test after an investigation has been conducted. Several techniques for retrospective power analysis have been suggested in the statistical literature, including both point and interval estimates of power. This paper presents a SAS macro that calculates three point estimators (a plugin estimate, an unbiased estimate and a median unbiased estimate) and a confidence band estimate of the power of a hypothesis test using sample estimates of the noncentrality parameter of the test statistic. The macro was written to evaluate the power of an F test (obtained through an analysis of variance, multiple regression analysis, or any of several multivariate analyses). Inputs to the macro include the obtained value and degrees of freedom of F, the total sample size, and the nominal alpha level of the hypothesis test. Macro outputs include the four estimates of statistical power as well as the sample effect size. The paper provides a demonstration of the SAS/IML‚ code and examples of the application of the code in simulation studies.
Mental Toughness in Competitive Tennis: Relationships with Resilience and Stress

The present study investigated the relationships between mental toughness (MT), resilience, and stress among competitive South African tennis players. A total of 351 tennis players participating at various competitive standards completed the Sports Mental Toughness Questionnaire, the Resilience Scale for Adults, and a modified version of the Recovery-Stress Questionnaire for Athletes. The results indicated that total MT was positively associated with total resilience (r = 0.59), but negatively associated with total stress (r = -0.44). The resilience subscales of perception of self, perception of future, social competence, and social resources, but not family cohesion, significantly predicted total MT (R2 = 0.35). Both total resilience and total MT significantly predicted total stress (R2 = 0.21). Based on the findings, interrelations between MT and resilience are explored, implications outlined, and additional research is suggested to ascertain the contextual relevance and outcomes associated with each construct in sport.
Creating a Cluster Hierarchy under Constraints of a Partially Known Hierarchy

Although clustering under constraints is a current research topic, a hierarchical setting, in which a hierarchy of clusters is the goal, is usually not considered. This paper tries to fill this gap by analyzing a scenario, where constraints are derived from a hierarchy that is partially known in advance. This scenario can be found, e.g., when structuring a collection of documents according to a user specific hierarchy. Major issues of current approaches to constraint based clustering are discussed, especially towards the hierarchical setting. We introduce the concept of hierarchical constraints and continue by presenting and evaluating two approaches using them. The approaches cover the two major fields of constraint based clustering, i.e. instance and metric based constraint integration. Our objects of interest are text documents. Therefore, the presented algorithms are especially fitted to work for these where necessary. Despite showing the properties and ideas of the algorithms in general, we evaluated the case of constraints that are unevenly scattered over the instance space, which is very common for real-world problems but not satisfyingly covered in other work so far.
Preparation of Conductive Nanoink Using Pulsed-Wire-Evaporated Copper Nanoparticles for Inkjet Printing

Pulsed-wire evaporation (PWE) was used to synthesize copper nanoparticles having an average diameter of about 100nm. These were coated with 1-octanethiol (CH3(CH2)7SH) under high vacuum (HV) (5.33 © 10 ¹4 Pa) using vapor self-assembled multilayers (SAMs) to prevent oxidation of the nanoparticles. Conductive nanoink made from the coated nanoparticles was printed on glass. The printed patterns were sintered in hydrogen (99.999vol%) and mixed gas (Ar 95vol %+ H2 5vol%) atmospheres; a high copper line density was achieved. Differential scanning calorimetry (DSC) established that the removal temperature of 1-octanethiol was 143°C, well below the 350°C sintering temperature. Complete removal of 1-octanethiol after sintering was confirmed by X-ray photoelectron spectroscopy (XPS). The resistivity of the hydrogensintered copper sample was 1.74 © 10 ¹7 ³·m. This dry powder fabrication and coating method is an alternative approach to inhibit copper oxidation and form inkjet-printed lines. [doi:10.2320/matertrans.M2012137]
A parallel attribute reduction algorithm based on Affinity Propagation clustering

As information technology is developing rapidly, massive and high dimensional data sets have appeared in abundance. The existing attribute reduction methods are encountering bottleneck problem of timeliness and spatiality. AP(Affinity Propagation) is an efficient and fast clustering algorithm for large dataset compared with the existing clustering algorithms. This paper discusses attribute clustering method in order to reduce attributes and provides a kind of parallel attribute reduction algorithm based on Affinity Propagation (APPAR) clustering. The attribute set is clustered into several subsets by Affinity Propagation algorithm first, and then the reductions of these subsets are proposed concurrently in order to get attribute reduction set of the whole data set. The whole algorithm has been improved in the two sides so as to largely increase the algorithm's speed. Experimental results show that the APPAR method is outperforming traditional attribute reduction algorithm for huge and high dimensional dataset processing.
X-ray topography studies of microdefects in silicon

X–ray topography has been successfully applied to study microdefects with the sizes in the range from well below standard topographic resolution to tenths of micrometers, thus effectively widening applicability of the Lang section topography. The lower value is set by the application of high order asymmetric reflection. Disappearance of the Kato fringes on the section topography is a clear indication of the existence of defects, which are otherwise not detectable as the standard contrast features. The presence of the defects with such sizes was also confirmed by transmission electron microscopy. Microdefects in the higher end of the size spectrum are well covered by Lang traverse and section topography where detailed contrast study is possible for individual defects. Lang traverse and section topography was applied to study specific case of oxygen related defects in silicon crystals.
Approximate Message Passing with Built-in Parameter Estimation for Sparse Signal Recovery

The approximate message passing (AMP) algorithm shows advantage over conventional convex optimization methods in recovering under-sampled sparse signals. AMP is analytically tractable and has a much lower complexity. However, it requires that the true parameters of the input and output channels are known. In this paper, we propose an AMP algorithm with built-in parameter estimation that jointly estimates the sparse signals along with the parameters by treating them as unknown random variables with simple priors. Specifically, the maximum a posterior (MAP) parameter estimation is presented and shown to produce estimations that converge to the true parameter values. Experiments on sparse signal recovery show that the performance of the proposed approach matches that of the oracle AMP algorithm where the true parameter values are known.
Search for gravitational wave ringdowns from perturbed intermediate mass black holes in LIGO-Virgo data from 2005-2010

We report results from a search for gravitational waves produced by perturbed intermediate mass black holes (IMBH) in data collected by LIGO and Virgo between 2005 and 2010. The search was sensitive to astrophysical sources that produced damped sinusoid gravitational wave signals, also known as ringdowns, with frequency $50\le f_{0}/\mathrm{Hz} \le 2000$ and decay timescale $0.0001\lesssim \tau/\mathrm{s} \lesssim 0.1$ characteristic of those produced in mergers of IMBH pairs. No significant gravitational wave candidate was detected. We report upper limits on the astrophysical coalescence rates of IMBHs with total binary mass $50 \le M/\mathrm{M}_\odot \le 450$ and component mass ratios of either 1:1 or 4:1. For systems with total mass $100 \le M/\mathrm{M}_\odot \le 150$, we report a 90%-confidence upper limit on the rate of binary IMBH mergers with non-spinning and equal mass components of $6.9\times10^{-8}\,$Mpc$^{-3}$yr$^{-1}$. We also report a rate upper limit for ringdown waveforms from perturbed IMBHs, radiating 1% of their mass as gravitational waves in the fundamental, $\ell=m=2$, oscillation mode, that is nearly three orders of magnitude more stringent than previous results.