Miracle-dz/UniFAR-FTUs huggingface dataset:
Cross-Platform App Recommendation by Jointly Modeling Ratings and Texts

Over the last decade, the renaissance of Web technologies has transformed the online world into an application (App) driven society. While the abundant Apps have provided great convenience, their sheer number also leads to severe information overload, making it difficult for users to identify desired Apps. To alleviate the information overloading issue, recommender systems have been proposed and deployed for the App domain. However, existing work on App recommendation has largely focused on one single platform (e.g., smartphones), while it ignores the rich data of other relevant platforms (e.g., tablets and computers). In this article, we tackle the problem of cross-platform App recommendation, aiming at leveraging users’ and Apps’ data on multiple platforms to enhance the recommendation accuracy. The key advantage of our proposal is that by leveraging multiplatform data, the perpetual issues in personalized recommender systems—data sparsity and cold-start—can be largely alleviated. To this end, we propose a hybrid solution, STAR (short for “croSs-plaTform App Recommendation”) that integrates both numerical ratings and textual content from multiple platforms. In STAR, we innovatively represent an App as an aggregation of common features across platforms (e.g., App’s functionalities) and specific features that are dependent on the resided platform. In light of this, STAR can discriminate a user’s preference on an App by separating the user’s interest into two parts (either in the App’s inherent factors or platform-aware features). To evaluate our proposal, we construct two real-world datasets that are crawled from the App stores of iPhone, iPad, and iMac. Through extensive experiments, we show that our STAR method consistently outperforms highly competitive recommendation methods, justifying the rationality of our cross-platform App recommendation proposal and the effectiveness of our solution.
The Negligible Influence of Chronic Obesity on Hospitalization, Clinical Status, and Complications in Elective Posterior Lumbar Interbody Fusion

Background. Posterior lumbar interbody fusion (PLIF) is a common surgical treatment for degenerative spinal instability, but many surgeons consider obesity a contraindication for elective spinal fusion. The aim of this study was to analyze whether obesity has any influence on hospitalization parameters, change in clinical status, or complications. Methods. In this prospective study, regression analysis was used to analyze the influence of the body mass index (BMI) on operating time, postoperative care, hospitalization time, type of postdischarge care, change in paresis or sensory deficits, pain level, wound complications, cerebrospinal fluid leakage, and implant complications. Results. Operating time increased only 2.5 minutes for each increase of BMI by 1. The probability of having a wound complication increased statistically with rising BMI. Nonetheless, BMI accounted for very little of the variation in the data, meaning that other factors or random chances play a much larger role. Conclusions. Obesity has to be considered a risk factor for wound complications in patients undergoing elective PLIF for degenerative instability. However, BMI showed no significant influence on other kinds of peri- or postoperative complications, nor clinical outcomes. So obesity cannot be considered a contraindication for elective PLIF.
The Organization and Performance of a TREAT-Based Production System Compiler

An ensemble of techniques that compile OPS5 production system programs to executable machine code is described and an increase in the execution speed of production system programs by two orders of magnitude over the commonly used list processing (LISP)-based OPS5 system is demonstrated. The computer is based on the TREAT incremental match algorithm. A version of the TREAT algorithm, formulated in relational algebra, is presented. The compiler employs optimization techniques derived from relational database system. The combination of the TREAT algorithm and the compiling techniques reduces the proportion of time spent in the match phase below the 'greater than 90%' figure often cited by developers of other production system environments. >
Normalization of total variability matrix for i-vector/PLDA speaker verification

Gaussian PLDA with uncertainty propagation is effective for i-vector based speaker verification. The idea is to propagate the uncertainty of i-vectors caused by the duration variability of utterances to the PLDA model. However, a limitation of the method is the difficulty of performing length normalization on the posterior covariance matrix of an i-vector. This paper proposes a method to avoid performing length normalization on i-vectors in Gaussian PLDA modeling so that uncertainty propagation can be directly applied without transforming the posterior covariance matrices of i-vectors. Instead of performing length normalization on i-vectors independently, the proposed method normalizes the column vectors of the total variability matrix. Because the i-vectors of all utterances are derived from the same normalized total variability matrix, they will be subject to the same degree of normalization, thereby avoiding the undesirable distortion introduced by the utterance-dependent length-normalization process. Experimental results on both NIST 2010 and 2012 SREs demonstrate that the proposed method achieves a performance similar to (and in some situations better than) that of Gaussian PLDA with length normalization. The method has the potential of improving the performance of uncertainty propagation for i-vector/PLDA speaker verification.
Maternal Death Due to Amniotic Fluid Embolism: A National Study in France

BACKGROUND: A structured definition of amniotic fluid embolism (AFE) based on 4 criteria was recently proposed for use in research by the Society for Maternal-Fetal Medicine (SMFM) and the Amniotic Fluid Embolism Foundation. The main objective of this study was to review all AFE-related maternal deaths in France during 2007–2011 according to the presence or not of all these 4 diagnostic criteria. METHODS: Maternal deaths due to AFE were identified by the national experts committee of the French Confidential Enquiry into Maternal Deaths during 2007–2011 (n = 39). The maternal mortality ratio for AFE was calculated. We applied the structured definition proposed by the Society for Maternal-Fetal Medicine and the Amniotic Fluid Embolism Foundation to AFE-related maternal deaths identified by the national experts committee. Characteristics of women, pregnancies and deliveries; clinical and biological features of AFE; and specific laboratory tests used were described by the presence or not of all 4 diagnostic criteria. Management of obstetric hemorrhage and quality of care according to the experts were also described. RESULTS: The maternal mortality ratio from AFE was 0.95/100,000 live births (95% confidence interval, 0.67–1.3). Detailed clinical data were collected for 36 women who died from AFE: 21 (58%) had all 4 proposed diagnostic criteria and 15 (42%) had 1 or more missing criterion. Documented early disseminated intravascular coagulopathy was missing for 14 women, and 2 women exhibited more than 1 missing criterion. Ten of the 15 women with missing criteria had clinical coagulopathy, with standard hemostasis tests performed in only 3. Specific diagnostic examinations for AFE were performed in similar proportions by the presence or not of all diagnostic criteria. Opportunities to improve care included timely performance of indicated hysterectomy (n = 13) and improved transfusion practices (n = 9). In the context of maternal cardiac arrest, for 5 of 13 women, fetal extraction was performed within 5 minutes. CONCLUSIONS: The structured definition of AFE for research studies would exclude more than one-third of AFE-related maternal deaths identified by the national experts committee. Inclusion of clinical coagulopathy as a diagnostic criterion for AFE would reduce this proportion to 14%. There is still room for improvement in the management of obstetric hemorrhage and timely fetal extraction in the context of maternal cardiac arrest, frequently observed in AFE-related maternal death.
Optimal and robust control and estimation of linear paths to transition

Optimal and robust control theories are used to determine effective, estimator-based feedback control rules for laminar plane channel flows that effectively stabilize linearly unstable flow perturbations at Re=10 000 and linearly stable flow perturbations, characterized by mechanisms for very large disturbance amplification, at Re=5000. Wall transpiration (unsteady blowing/suction) with zero net mass flux is used as the control, and the flow measurement is derived from the wall skin friction. The control objective, beyond simply stabilizing any unstable eigenvalues (which is relatively easy to accomplish), is to minimize the energy of the flow perturbations created by external disturbance forcing. This is important because, when mechanisms for large disturbance amplification are present, small-amplitude external disturbance forcing may excite flow perturbations with sufficiently large amplitude to induce nonlinear flow instability. The control algorithms used in the present work account for system disturbances and measurement noise in a rigorous fashion by application of modern linear control techniques to the discretized linear stability problem. The disturbances are accounted for both as uncorrelated white Gaussian processes ([Hscr ]2 or ‘optimal’ control) and as finite ‘worst case’ inputs which are maximally detrimental to the control objective ([Hscr ]∞ or ‘robust’ control). Root loci and transient energy growth analyses are shown to be inadequate measures to characterize overall system performance. Instead, appropriately defined transfer function norms are used to characterize all systems considered in a consistent and relevant manner. In order to make a parametric study tractable in this high-dimensional system, a convenient new scaling to the estimation problem is introduced such that three scalar parameters {γ, α, [lscr ]} may be individually adjusted to achieve desired closed-loop characteristics of the resulting systems. These scalar parameters may be intuitively explained, and are defined such that the resulting control equations retain the natural dual structure between the control parameter, [lscr ], and the estimation parameter, α. The performance of the present systems with respect to these parameters is thoroughly investigated, and comparisons are made to simple proportional schemes where appropriate.
Short term effects of temperature on risk of myocardial infarction in England and Wales: time series regression analysis of the Myocardial Ischaemia National Audit Project (MINAP) registry

Objective To examine the short term relation between ambient temperature and risk of myocardial infarction. Design Daily time series regression analysis. Setting 15 conurbations in England and Wales. Participants 84 010 hospital admissions for myocardial infarction recorded in the Myocardial Ischaemia National Audit Project during 2003-6 (median 57 events a day). Main outcome measures Change in risk of myocardial infarction associated with a 1°C difference in temperature, including effects delayed by up to 28 days. Results Smoothed graphs revealed a broadly linear relation between temperature and myocardial infarction, which was well characterised by log-linear models without a temperature threshold: each 1°C reduction in daily mean temperature was associated with a 2.0% (95% confidence interval 1.1% to 2.9%) cumulative increase in risk of myocardial infarction over the current and following 28 days, the strongest effects being estimated at intermediate lags of 2-7 and 8-14 days: increase per 1°C reduction 0.6% (95% confidence interval 0.2% to 1.1%) and 0.7% (0.3% to 1.1%), respectively. Heat had no detrimental effect. Adults aged 75-84 and those with previous coronary heart disease seemed more vulnerable to the effects of cold than other age groups (P for interaction 0.001 or less in each case), whereas those taking aspirin were less vulnerable (P for interaction 0.007). Conclusions Increases in risk of myocardial infarction at colder ambient temperatures may be one driver of cold related increases in overall mortality, but an increased risk of myocardial infarction at higher temperatures was not detected. The risk of myocardial infarction in vulnerable people might be reduced by the provision of targeted advice or other interventions, triggered by forecasts of lower temperature.
In vitro functional effects of XPC gene rare variants from bladder cancer patients

The XPC gene is involved in repair of bulky DNA adducts formed by carcinogenic metabolites and oxidative DNA damage, both known bladder cancer risk factors. Single nucleotide polymorphisms (SNPs) in XPC have been associated with increased bladder cancer risk. Recently, rarer genetic variants have been identified but it is difficult to ascertain which are of functional importance. During a mutation screen of XPC in DNA from 33 bladder tumour samples and matched blood samples, we identified five novel variants in the patients’ germ line DNA. In a case–control study of 771 bladder cancer cases and 800 controls, c.905T>C (Phe302Ser), c.1177C>T (Arg393Trp), c. *156G>A [3′ untranslated region (UTR)] and c.2251-37C>A (in an intronic C>G SNP site) were found to be rare variants, with a combined odds ratio of 3.1 (95% confidence interval 1.0–9.8, P = 0.048) for carriage of one variant. The fifth variant was a 2% minor allele frequency SNP not associated with bladder cancer. The two non-synonymous coding variants were predicted to have functional effects using analytical algorithms; a reduced recruitment of GFP-tagged XPC plasmids containing either c.905T>C or c.1177C>T to sites of 408 nm wavelength laser-induced oxidative DNA damage was found in vitro. c. *156G>A appeared to be associated with reduced messenger RNA stability in an in vitro plasmid-based assay. Although the laser microbeam assay is relevant to a range of DNA repair genes, our 3′ UTR assay based on Green fluorescent protein(GFP) has widespread applicability and could be used to assess any gene. These assays may be useful in determining which rare variants are functional, prior to large genotyping efforts.
Knockout of a difficult‐to‐remove CHO host cell protein, lipoprotein lipase, for improved polysorbate stability in monoclonal antibody formulations

While the majority of host cell protein (HCP) impurities are effectively removed in typical downstream purification processes, a small population of HCPs are particularly challenging. Previous studies have identified HCPs that are challenging for a variety of reasons. Lipoprotein lipase (LPL)—a Chinese hamster ovary (CHO) HCP that functions to hydrolyze esters in triglycerides—was one of ten HCPs identified in previous studies as being susceptible to retention in downstream processing. LPL may degrade polysorbate 80 (PS‐80) and polysorbate 20 (PS‐20) in final product formulations due to the structural similarity between polysorbates and triglycerides. In this work, recombinant LPL was found to have enzymatic activity against PS‐80 and PS‐20 in a range of solution conditions that are typical of mAb formulations. LPL knockout CHO cells were created with CRISPR and TALEN technologies and resulting cell culture harvest fluid demonstrated significantly reduced polysorbate degradation without significant impact on cell viability when compared to wild‐type samples. Biotechnol. Bioeng. 2017;114: 1006–1015. © 2016 Wiley Periodicals, Inc.
Raptor Nest Decorations Are a Reliable Threat Against Conspecifics

Black kite nest decorations are honest signals of individual and territory quality. Individual quality is often signaled by phenotypic flags, such as bright plumage patches in birds. Extended phenotype signals can similarly show quality, but in these cases the signals are external to the individual, often taking the form of objects scavenged from the environment. Through multiple manipulative experiments, we showed that objects used for nest decoration by a territorial raptor, the black kite (Milvus migrans), act as reliable threats to conspecifics, revealing the viability, territory quality, and conflict dominance of the signaler. Our results suggest that animal-built structures may serve as signaling devices much more frequently than currently recognized.
Heterogeneity in the Activity of Mexican Helicobacter pylori Strains in Gastric Epithelial Cells and Its Association with Diversity in the cagA Gene

ABSTRACT Helicobacter pylori CagA is translocated into gastric epithelial cells by a type IV secretion system and interacts with the Src homology 2 phosphatase, altering cell morphology. Multiple EPIYA motifs in CagA are associated with increased activity in cells and with gastric cancer. The aim of this work was to study the heterogeneity in activity in cells of multiple H. pylori single colonies isolated from a single patient and its association with polymorphism in cagA. The presence of cagA, cagE, cagT, and cag10 was studied with 318 H. pylori isolates from the antra and corpora of 18 patients. AGS gastric epithelial cells were infected with 75 isolates, and interleukin-8 (IL-8) secretion, cytoskeletal changes, CagA translocation, and tyrosine phosphorylation were measured. The cagA 3′-variable region was sequenced for 30 isolates to determine the number and types of EPIYA motifs. Isolates from an individual stomach were usually genetically related and had quantitatively similar phenotypic effects on cells (IL-8 induction and cytoskeletal changes). However, strains from different patients with similar CagA EPIYA motif patterns varied widely in these phenotypes. Among isolates with an EPIYA-ABC pattern, the phenotype was variable: IL-8 induction ranged from 200 to 1,200 pg/ml, and morphological changes occurred in 20 to 70% of cells. In several cases, cagA sequence diversity appeared to explain the lack of CagA activity, as isolates with an EPIYA-ACC pattern or a modified B motif had reduced cell activity. cag pathogenicity island-positive H. pylori isolates displayed a high level of heterogeneity in the capacity to induce IL-8 secretion and morphological changes; an absent or modified B motif was associated with low activity.
Hybrid Fragmentation to Preserve Data Privacy for SaaS

SaaS is a novel software model that data and applications of service are outsourced to service provider. Although SaaS model offers many benefits for small and medium enterprises, data privacy issue is the most challenge for the development of SaaS. In this paper we propose a new hybrid fragmentation approach which is different from traditional data encryption to protect data. We define three kinds of privacy constraints to support finger-grained privacy customization. We also give a heuristic hybrid fragmentation algorithm which considers query efficiency to produce a hybrid fragmentation. We make some experiments to analyze our approach in the paper.
Face Recognition Using Nonlinear Regression

Face recognition algorithms mainly differ in how to represent the probe face image using the training data. As the state-of-the-art face recognition algorithm, linear regression computes a reconstruction matrix from the images of each subject and then approximates the probe face image using the reconstruction matrix. However, the performance of this linear algorithm is limited due to the nonlinear structure of the face images which is caused by variations in illumination, expression, pose and occlusion. To overcome the problem, in this paper we propose a kernel-based nonlinear regression algorithm for effective face recognition. Because of the high (even infinite) dimensionality of the nonlinear transformation function, it is infeasible to directly calculate the corresponding reconstruction matrix and therefore is unable to explicitly approximate the probe image. With the help of kernel trick, we tackle this difficulty by embedding the nonlinear regression in the stage of computing the distance between the probe image and the approximated probe image. The proposed nonlinear regression classification algorithm is evaluated on several popular standard databases under a number of classical evaluation protocols that have been reported in the face recognition literature. A comparative study with linear regression classification approach and several other algorithms shows the superiority of the proposed approach.
Rapid unwinding of triplet repeat hairpins by Srs2 helicase of Saccharomyces cerevisiae

Expansions of trinucleotide repeats cause at least 15 heritable human diseases. Single-stranded triplet repeat DNA in vitro forms stable hairpins in a sequence-dependent manner that correlates with expansion risk in vivo. Hairpins are therefore considered likely intermediates during the expansion process. Unwinding of a hairpin by a DNA helicase would help protect against expansions. Yeast Srs2, but not the RecQ homolog Sgs1, blocks expansions in vivo in a manner largely dependent on its helicase function. The current study tested the idea that Srs2 would be faster at unwinding DNA substrates with an extrahelical triplet repeat hairpin embedded in a duplex context. These substrates should mimic the relevant intermediate structure thought to occur in vivo. Srs2 was faster than Sgs1 at unwinding several substrates containing triplet repeat hairpins or another structured loop. In contrast, control substrates with an unstructured loop or a Watson–Crick duplex were unwound equally well by both enzymes. Results with a fluorescently labeled, three-way junction showed that Srs2 unwinding proceeds unabated through extrahelical triplet repeats. In summary, Srs2 maintains its facile unwinding of triplet repeat hairpins embedded within duplex DNA, supporting the genetic evidence that Srs2 is a key helicase in Saccharomyces cerevisiae for preventing expansions.
An intelligent clustering forecasting system based on change-point detection and artificial neural networks: application to financial economics

Suggests a new clustering forecasting system to integrate change-point detection and artificial neural networks. The basic concept of the proposed model is to obtain intervals divided by change points, to identify them as change-point groups, and to involve them in the forecasting model. The proposed models consist of two stages. The first stage (the clustering neural network modeling stage) detects successive change points in a data set and forecasts the change-point group with backpropagation neural networks (BPN). In this stage, three change-point detection methods are applied and compared: (1) the parametric method, (2) the nonparametric approach, and (3) the model-based approach. The next stage is to forecast the final output with a BPN. Through an application to financial economics, we compare the proposed models with a neural network model alone and, in addition, determine which of the three change-point detection methods can perform better. This article then examines the predictability of the integrated neural network model based on change-point detection.