LLukas22/scidocs huggingface dataset:
Ritonavir-boosted lopinavir plus nucleoside or nucleotide reverse transcriptase inhibitors versus ritonavir-boosted lopinavir plus raltegravir for treatment of HIV-1 infection in adults with virological failure of a standard first-line ART regimen (SECOND-LINE): a randomised, open-label, non-inferio

BACKGROUND
Uncertainty exists about the best treatment for people with HIV-1 who have virological failure with first-line combination antiretroviral therapy of a non-nucleoside analogue (NNRTI) plus two nucleoside or nucleotide analogue reverse transcriptase inhibitors (NtRTI). We compared a second-line regimen combining two new classes of drug with a WHO-recommended regimen.


METHODS
We did this 96-week, phase 3b/4, randomised, open-label non-inferiority trial at 37 sites worldwide. Adults with HIV-1 who had confirmed virological failure (plasma viral load >500 copies per mL) after 24 weeks or more of first-line treatment were randomly assigned (1:1) to receive ritonavir-boosted lopinavir plus two or three NtRTIs (control group) or ritonavir-boosted lopinavir plus raltegravir (raltegravir group). The randomisation sequence was computer generated with block randomisation (block size four). Neither participants nor investigators were masked to allocation. The primary endpoint was the proportion of participants with plasma viral load less than 200 copies per mL at 48 weeks in the modified intention-to-treat population, with a non-inferiority margin of 12%. This study is registered with ClinicalTrials.gov, number NCT00931463.


FINDINGS
We enrolled 558 patients, of whom 541 (271 in the control group, 270 in the raltegravir group) were included in the primary analysis. At 48 weeks, 219 (81%) patients in the control group compared with 223 (83%) in the raltegravir group met the primary endpoint (difference 1·8%, 95% CI -4·7 to 8·3), fulfilling the criterion for non-inferiority. 993 adverse events occurred in 271 participants in the control group versus 895 in 270 participants in the raltegravir group, the most common being gastrointestinal.


INTERPRETATION
The raltegravir regimen was no less efficacious than the standard of care and was safe and well tolerated. This simple NtRTI-free treatment strategy might extend the successful public health approach to management of HIV by providing simple, easy to administer, effective, safe, and tolerable second-line combination antiretroviral therapy.


FUNDING
University of New South Wales, Merck, AbbVie, the Foundation for AIDS Research.
Effect of probiotics on body weight and body-mass index: a systematic review and meta-analysis of randomized, controlled trials.

OBJECTIVE
To assess the efficacy of probiotic therapies on body weight and BMI using a meta-analysis of randomized, controlled trials.


METHODS
Twenty studies with 25 trials (1931 participants with age over 18 years) were included. The pooled WMD was calculated by random effects model.


RESULTS
Probiotic consumption significantly reduced body weight by 0.59 kg (95% CI, 0.30-0.87) and BMI by 0.49 kg/m(2) (95% CI, 0.24-0.74). A greater reduction in BMI was found with multiple species of probiotics. Subgroup analysis of trials with intervention duration ≥8 weeks found a more significant reduction in BMI. Limiting analysis to trials with a baseline BMI ≥25 kg/m(2) showed a greater reduction in BMI.


CONCLUSION
Consuming probiotics could reduce body weight and BMI, with a potentially greater effect when multiple species of probiotics were consumed, the duration of intervention was ≥8 weeks, or the objects were overweight.
A randomized double-blind placebo-controlled pilot study to assess the efficacy of a 24-week topical treatment by latanoprost 0.1% on hair growth and pigmentation in healthy volunteers with androgenetic alopecia.

BACKGROUND
Latanoprost is a prostaglandin analogue used to treat glaucoma. It can cause adverse effects, such as iridial and periocular hyperpigmentation, and eyelash changes including pigmentation and increased thickness, length, and number. Latanoprost has been used to treat eyelash alopecia, but knowledge on its effects on human scalp hair growth is not available.


OBJECTIVE
The primary objectives were to assess the efficacy of latanoprost on hair growth and pigmentation. The secondary objectives were to assess the effect on scalp pigmentation; investigate the treatment duration needed to affect hair growth, hair pigmentation, and scalp pigmentation; and assess safety of latanoprost.


METHODS
Sixteen men with mild androgenetic alopecia (Hamilton II-III) were included. Latanoprost 0.1% and placebo were applied daily for 24 weeks on two minizones on the scalp. Measurements on hair growth, density, diameter, pigmentation, and anagen/telogen ratio were performed throughout the study.


RESULTS
At 24 weeks, an increased hair density on the latanoprost-treated site was observed compared with baseline (n = 16, P < .001) and placebo-treated site (P = .0004).


LIMITATIONS
Only young men with mild androgenetic alopecia were included. The results may not be applicable to other patient groups. Choice of investigational site may have affected the results.


CONCLUSIONS
Latanoprost significantly increased hair density (terminal and vellus hairs) at 24 weeks compared with baseline and the placebo-treated area. Latanoprost could be useful in stimulating hair follicle activity and treating hair loss.
Backtrack Search Algorithms and the Maximal Common Subgraph Problem

SUMMARY Backtrack algorithms are applicable to a wide variety of problems. An efficient but readable version of such an algorithm is presented and its use in the problem of finding the maximal common subgraph of two graphs is described. Techniques available in this application area for ordering and pruning the backtrack search are discussed. This algorithm has been used successfully as a component of a program for analysing chemical reactions and enumerating the bond changes which have taken place.
MnasNet: Platform-Aware Neural Architecture Search for Mobile

Designing convolutional neural networks (CNN) for mobile devices is challenging because mobile models need to be small and fast, yet still accurate. Although significant efforts have been dedicated to design and improve mobile CNNs on all dimensions, it is very difficult to manually balance these trade-offs when there are so many architectural possibilities to consider. In this paper, we propose an automated mobile neural architecture search (MNAS) approach, which explicitly incorporate model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency. Unlike previous work, where latency is considered via another, often inaccurate proxy (e.g., FLOPS), our approach directly measures real-world inference latency by executing the model on mobile phones. To further strike the right balance between flexibility and search space size, we propose a novel factorized hierarchical search space that encourages layer diversity throughout the network. Experimental results show that our approach consistently outperforms state-of-the-art mobile CNN models across multiple vision tasks. On the ImageNet classification task, our MnasNet achieves 75.2% top-1 accuracy with 78ms latency on a Pixel phone, which is 1.8× faster than MobileNetV2 [29] with 0.5% higher accuracy and 2.3× faster than NASNet [36] with 1.2% higher accuracy. Our MnasNet also achieves better mAP quality than MobileNets for COCO object detection. Code is at https://github.com/tensorflow/tpu/ tree/master/models/official/mnasnet.
Reasoning about reasoning by nested conditioning: Modeling theory of mind with probabilistic programs

A wide range of human reasoning patterns can be explained as conditioning in probabilistic models; however, conditioning has traditionally been viewed as an operation applied to such models, not represented in such models. We describe how probabilistic programs can explicitly represent conditioning as part of a model. This enables us to describe reasoning about others’ reasoning using nested conditioning. Much of human reasoning is about the beliefs, desires, and intentions of other people; we use probabilistic programs to formalize these inferences in a way that captures the flexibility and inherent uncertainty of reasoning about other agents. We express examples from game theory, artificial intelligence, and linguistics as recursive probabilistic programs and illustrate how this representation language makes it easy to explore new directions in each of these fields. We discuss the algorithmic challenges posed by these kinds of models and describe how dynamic programming techniques can help address these challenges. 2014 Published by Elsevier B.V.
Lumbar punctures in thrombocytopenic children with cancer.

INTRODUCTION
Lumbar punctures (LP) in patients presenting thrombocytopenia are linked to the possibility of bleeding and spinal hematomas. The minimum platelet count required for the safe performance of spinal procedures is still under discussion. Children with some oncology diseases require routinely lumbar punctures; such patients often present thrombocytopenia, making this group an ideal population to study the association between lumbar puncture in thrombocytopenic patients and complications.


OBJECTIVES
To determine the platelet count of oncology children undergone lumbar punctures and the occurrence of spinal hematomas.


METHOD
Observational longitudinal study. It included a retrospective analysis of electronic medical records of oncology patients submitted to LP for intrathecal chemotherapy between January 2004 and October 2011, carried out at the 'Instituto de Medicina Integral Prof. Fernando Figueira (IMIP)', Recife, Brazil.


RESULTS
We evaluated 9088 lumbar punctures performed in 440 patients. The platelet count distribution before lumbar punctures was as follows: 25 punctures had zero to 10,000 platelets per mm(3), 67 punctures had between 10,000 and 20,000 platelets per mm(3), 88 had between 20,000 and 30,000 platelets per mm(3), 92 punctures had between 30,000 and 40,000 platelets per mm(3), 107 punctures had between 40,000 and 50,000 platelets per mm(3), and 729 punctures had between 50,000 and 100,000 platelets per mm(3). In this series, the incidence of bloody tap was 16.9% (1112 lumbar punctures of 6552 had more than ten erythrocytes). No complications were observed.


CONCLUSIONS
Even in thrombocytopenic patient, an epidural hematoma would be a relatively rare complication following lumbar puncture. Despite the large number of punctures performed on patients with platelet counts below 100,000 mm(-3) (n = 1108), further studies are necessary in order to determine a lower safe platelet count threshold for the performance of lumbar punctures in healthy patients undergoing neuraxial anesthesia.
Exploring Deep Learning Models for Machine Comprehension on SQuAD

This paper explores the use of multiple models in performing question answering tasks on the Stanford Question Answering Database. We first implement and share results of a baseline model using bidirectional long short-term memory (BiLSTM) encoding of question and context followed a simple co-attention model [1]. We then report on the use of match-LSTM and Pointer Net which showed marked improvements in question answering over the baseline model [2]. Lastly, we extend the model by adding Dropout [3] and randomization strategies to account for unknown tokens. Final test score on Codalab under Username: yife, F1: 66.981, EM: 56.845.
Analyzing and predicting news popularity on Twitter

Twitter is not only a social network, but also an increasingly important news media. In Twitter, retweeting is the most important information propagation mechanism, and supernodes (news medias) that have many followers are the most important information sources. Therefore, it is important to understand the news retweet propagation from supernodes and predict news popularity quickly at the very first few seconds upon publishing. Such understanding and prediction will benefit many applications such as social media management, advertisement and interaction optimization between news medias and followers. In this paper, we identify the characteristics of news propagation from supernodes from the trace data we crawled from Twitter. Based on the characteristics, we build a news popularity prediction model that can predict the final number of retweets of a news tweet very quickly. Through trace-driven experiments, we then validate our prediction model by comparing our predicted popularity and real popularity, and show its superior performance in comparison with the regression prediction model. From the study, we found that the average interaction frequency between the retweeters and the news source is correlated with news popularity. Also, the negative sentiment of news has some correlations with retweet popularity while the positive sentiment of news does not have such obvious correlation. Published by Elsevier Ltd.
Efficient Matrix Chain Ordering in Polylog Time

The matrix chain ordering problem is to find the cheapest way to multiply a chain of n matrices, where the matrices are pairwise compatible but of varying dimensions. Here we give several new parallel algorithms including O(lg3 n)-time and n/lgn-processor algorithms for solving the matrix chain ordering problem and for solving an optimal triangulation problem of convex polygons on the common CRCW PRAM model. Next, by using efficient algorithms for computing row minima of totally monotone matrices, this complexity is improved to O(lg2 n) time with n processors on the EREW PRAM and to O(lg2 n lg lgn) time with n/lg lgn processors on a common CRCW PRAM. A new algorithm for computing the row minima of totally monotone matrices improves our parallel MCOP algorithm to O(n lg1.5 n) work and polylog time on a CREW PRAM. Optimal log-time algorithms for computing row minima of totally monotone matrices will improve our algorithm and enable it to have the same work as the sequential algorithm of Hu and Shing [SIAM J. Comput., 11 (1982), pp. 362–373; SIAM J. Comput., 13 (1984), pp. 228–251].
A DECISION TREE APPROACH FOR ACHIEVING HIGH CUSTOMER SATISFACTION AT URBAN INTERCHANGES

1 University of Thessaly, Department of Civil Engineering Pedion Areos, GR-38334 Volos, Greece Ph.: +302421074133, Fax: +302421074131, E-mail: marttsami@gmail.com 2 University of Thessaly, Department of Civil Engineering Pedion Areos, GR-38334 Volos, Greece Ph.: +302421074158, Fax: +302421074131, E-mail: giadamos@civ.uth.gr 3 University of Thessaly, Department of Civil Engineering Pedion Areos, GR-38334 Volos, Greece Ph.: +302421074164, Fax: +302421074131, E-mail: enath@uth.gr 4 Transport and Telecommunication Institute Lomonosova Street 1, LV-1019 Riga, Latvia Ph.: +37167100544, Fax: +37167100660, E-mail: evelina.budilovich@gmail.com 5 Transport and Telecommunication Institute Lomonosova Street 1, LV-1019 Riga, Latvia Ph.: +37167100544, Fax: +37167100660, E-mail: Jackiva.I@tsi.lv 6 University of Thessaly, Department of Civil Engineering Pedion Areos, GR-38334 Volos, Greece Ph.: +302421074133, Fax: +302421074131, E-mail: amagginas@yahoo.gr
Locally Scale-Invariant Convolutional Neural Networks

Convolutional Neural Networks (ConvNets) have shown excellent results on many visual classification tasks. With the exception of ImageNet, these datasets are carefully crafted such that objects are well-aligned at similar scales. Naturally, the feature learning problem gets more challenging as the amount of variation in the data increases, as the models have to learn to be invariant to certain changes in appearance. Recent results on the ImageNet dataset show that given enough data, ConvNets can learn such invariances producing very discriminative features [1]. But could we do more: use less parameters, less data, learn more discriminative features, if certain invariances were built into the learning process? In this paper we present a simple model that allows ConvNets to learn features in a locally scale-invariant manner without increasing the number of model parameters. We show on a modified MNIST dataset that when faced with scale variation, building in scale-invariance allows ConvNets to learn more discriminative features with reduced chances of over-fitting.
Dank Learning: Generating Memes Using Deep Neural Networks

We introduce a novel meme generation system, which given any image can produce a humorous and relevant caption. Furthermore, the system can be conditioned on not only an image but also a user-defined label relating to the meme template, giving a handle to the user on meme content. The system uses a pretrained Inception-v3 network to return an image embedding which is passed to an attention-based deep-layer LSTM model producing the caption inspired by the widely recognized Show and Tell Model. We implement a modified beam search to encourage diversity in the captions. We evaluate the quality of our model using perplexity and human assessment on both the quality of memes generated and whether they can be differentiated from real ones. Our model produces original memes that cannot on the whole be differentiated from real ones. https://github.com/alpv95/MemeProject
Symmetry and Human Facial Attractiveness

Symmetry may act as a marker of phenotypic and genetic quality and is preferred during mate selection in a variety of species. Measures of human body symmetry correlate with attractiveness, but studies manipulating human face images report a preference for asymmetry. These results may reflect unnatural feature shapes and changes in skin textures introduced by image processing. When the shape of facial features is varied (with skin textures held constant), increasing symmetry of face shape increases ratings of attractiveness for both male and female faces. These findings imply facial symmetry may have a positive impact on mate selection in humans. © 1999 Elsevier Science Inc.
HIV incidence and sexually transmitted disease prevalence associated with condom use: a population study in Rakai, Uganda.

OBJECTIVE
Evidence of condom effectiveness for HIV and sexually transmitted disease (STD) prevention is based primarily on high-risk populations. We examined condom effectiveness in a general population with high HIV prevalence in rural Africa.


METHODS
Data were from a randomized community trial in Rakai, Uganda. Condom usage information was obtained prospectively from 17,264 sexually active individuals aged 15-59 years over a period of 30 months. HIV incidence and STD prevalence was determined for consistent and irregular condom users, compared to non-users. Adjusted rate ratios (RR) of HIV acquisition were estimated by Poisson multivariate regression, and odds ratios of STDs estimated by logistic regression.


RESULTS
Only 4.4% reported consistent condom use and 16.5% reported inconsistent use during the prior year. Condom use was higher among males, and younger, unmarried and better educated individuals, and those reporting multiple sex partners or extramarital relationships. Consistent condom use significantly reduced HIV incidence [RR, 0.37; 95% confidence interval (CI), 0.15-0.88], syphilis [odds ratio (OR), 0.71; 95% CI, 0.53-0.94] and gonorrhea/Chlamydia (OR, 0.50; 95% CI, 0.25-0.97) after adjustment for socio-demographic and behavioral characteristics. Irregular condom use was not protective against HIV or STD and was associated with increased gonorrhea/Chlamydia risk (OR, 1.44; 95% CI, 1.06-1.99). The population attributable fraction of consistent use for prevention of HIV was -4.5% (95% CI, -8.3 to 0.0), due to the low prevalence of consistent use in the population.


CONCLUSIONS
Consistent condom use provides protection from HIV and STDs, whereas inconsistent use is not protective. Programs must emphasize consistent condom use for HIV and STD prevention.
Give me a better break: Choosing workday break activities to maximize resource recovery.

Surprisingly little research investigates employee breaks at work, and even less research provides prescriptive suggestions for better workday breaks in terms of when, where, and how break activities are most beneficial. Based on the effort-recovery model and using experience sampling methodology, we examined the characteristics of employee workday breaks with 95 employees across 5 workdays. In addition, we examined resources as a mediator between break characteristics and well-being. Multilevel analysis results indicated that activities that were preferred and earlier in the work shift related to more resource recovery following the break. We also found that resources mediated the influence of preferred break activities and time of break on health symptoms and that resource recovery benefited person-level outcomes of emotional exhaustion, job satisfaction, and organizational citizenship behavior. Finally, break length interacted with the number of breaks per day such that longer breaks and frequent short breaks were associated with more resources than infrequent short breaks.
Bias, underestimation of risk, and loss of statistical power in patient-level analyses of lesion detection

Sensitivity and the false positive rate are usually defined with the patient as the unit of observation, i.e., the diagnostic test detects or does not detect disease in a patient. For tests designed to find and diagnose lesions, e.g., lung nodules, the usual definitions of sensitivity and specificity may be misleading. In this paper we describe and compare five measures of accuracy of lesion detection. The five levels of evaluation considered were patient level without localization, patient level with localization, region of interest (ROI) level without localization, ROI level with localization, and lesion level. We found that estimators of sensitivity that do not require the reader to correctly locate the lesion overstate sensitivity. Patient-level estimators of sensitivity can be misleading when there is more than one lesion per patient and they reduce study power. Patient-level estimators of the false positive rate can conceal important differences between techniques. Referring clinicians rely on a test’s reported accuracy to both choose the appropriate test and plan management for their patients. If reported sensitivity is overstated, the clinician could choose the test for disease screening, and have false confidence that a negative test represents the true absence of lesions. Similarly, the lower false positive rate associated with patient-level estimators can mislead clinicians about the diagnostic value of the test and consequently that a positive finding is real. We present clear recommendations for studies assessing and comparing the accuracy of tests tasked with the detection and interpretation of lesions...
A recursive branch-and-bound algorithm for the rectangular guillotine strip packing problem

A heuristic recursive algorithm for the two-dimensional rectangular strip packing problem is presented. It is based on a recursive structure combined with branch-and-bound techniques. Several lengths are tried to determine the minimal plate length to hold all the items. Initially the plate is taken as a block. For the current block considered, the algorithm selects an item, puts it at the bottom-left corner of the block, and divides the unoccupied region into two smaller blocks with an orthogonal cut. The dividing cut is vertical if the block width is equal to the plate width; otherwise it is horizontal. Both lower and upper bounds are used to prune unpromising branches. The computational results on a class of benchmark problems indicate that the algorithm performs better than several recently published algorithms. 2006 Elsevier Ltd. All rights reserved.