nils-herrmann/clean_scirepeval huggingface dataset:
Ambrisentan for the treatment of pulmonary arterial hypertension: results of the ambrisentan in pulmonary arterial hypertension, randomized, double-blind, placebo-controlled, multicenter, efficacy (ARIES) study 1 and 2.

Ambrisentan is a propanoic acid-based, A-selective endothelin receptor antagonist for the once-daily treatment of pulmonary arterial hypertension.



Ambrisentan in Pulmonary Arterial Hypertension, Randomized, Double-Blind, Placebo-Controlled, Multicenter, Efficacy Study 1 and 2 (ARIES-1 and ARIES-2) were concurrent, double-blind, placebo-controlled studies that randomized 202 and 192 patients with pulmonary arterial hypertension, respectively, to placebo or ambrisentan (ARIES-1, 5 or 10 mg; ARIES-2, 2.5 or 5 mg) orally once daily for 12 weeks. The primary end point for each study was change in 6-minute walk distance from baseline to week 12. Clinical worsening, World Health Organization functional class, Short Form-36 Health Survey score, Borg dyspnea score, and B-type natriuretic peptide plasma concentrations also were assessed. In addition, a long-term extension study was performed. The 6-minute walk distance increased in all ambrisentan groups; mean placebo-corrected treatment effects were 31 m (P=0.008) and 51 m (P<0.001) in ARIES-1 for 5 and 10 mg ambrisentan, respectively, and 32 m (P=0.022) and 59 m (P<0.001) in ARIES-2 for 2.5 and 5 mg ambrisentan, respectively. Improvements in time to clinical worsening (ARIES-2), World Health Organization functional class (ARIES-1), Short Form-36 score (ARIES-2), Borg dyspnea score (both studies), and B-type natriuretic peptide (both studies) were observed. No patient treated with ambrisentan developed aminotransferase concentrations >3 times the upper limit of normal. In 280 patients completing 48 weeks of treatment with ambrisentan monotherapy, the improvement from baseline in 6-minute walk at 48 weeks was 39 m.



Ambrisentan improves exercise capacity in patients with pulmonary arterial hypertension. Improvements were observed for several secondary end points in each of the studies, although statistical significance was more variable. Ambrisentan is well tolerated and is associated with a low risk of aminotransferase abnormalities.
Accelerating Low Bit-Width Deep Convolution Neural Network in MRAM

Deep Convolution Neural Network (CNN) has achieved outstanding performance in image recognition over large scale dataset. However, pursuit of higher inference accuracy leads to CNN architecture with deeper layers and denser connections, which inevitably makes its hardware implementation demand more and more memory and computational resources. It can be interpreted as ‘CNN power and memory wall’. Recent research efforts have significantly reduced both model size and computational complexity by using low bit-width weights, activations and gradients, while keeping reasonably good accuracy. In this work, we present different emerging nonvolatile Magnetic Random Access Memory (MRAM) designs that could be leveraged to implement ‘bit-wise in-memory convolution engine’, which could simultaneously store network parameters and compute low bit-width convolution. Such new computing model leverages the ‘in-memory computing’ concept to accelerate CNN inference and reduce convolution energy consumption due to intrinsic logic-in-memory design and reduction of data communication.
Clinical Applications of Circulating Tumor Cells and Circulating Tumor DNA as Liquid Biopsy.

"Liquid biopsy" focusing on the analysis of circulating tumor cells (CTC) and circulating cell-free tumor DNA (ctDNA) in the blood of patients with cancer has received enormous attention because of its obvious clinical implications for personalized medicine. Analyses of CTCs and ctDNA have paved new diagnostic avenues and are, to date, the cornerstones of liquid biopsy diagnostics. The present review focuses on key areas of clinical applications of CTCs and ctDNA, including detection of cancer, prediction of prognosis in patients with curable disease, monitoring systemic therapies, and stratification of patients based on the detection of therapeutic targets or resistance mechanisms.



The application of CTCs and ctDNA for the early detection of cancer is of high public interest, but it faces serious challenges regarding specificity and sensitivity of the current assays. Prediction of prognosis in patients with curable disease can already be achieved in several tumor entities, particularly in breast cancer. Monitoring the success or failure of systemic therapies (i.e., chemotherapy, hormonal therapy, or other targeted therapies) by sequential measurements of CTCs or ctDNA is also feasible. Interventional studies on treatment stratification based on the analysis of CTCs and ctDNA are needed to implement liquid biopsy into personalized medicine. Cancer Discov; 6(5); 479-91.
The spread of fake news by social bots

The massive spread of fake news has been identified as a major global risk and has been alleged to influence elections and threaten democracies. Communication, cognitive, social, and computer scientists are engaged in efforts to study the complex causes for the viral diffusion of digital misinformation and to develop solutions, while search and social media platforms are beginning to deploy countermeasures. However, to date, these efforts have been mainly informed by anecdotal evidence rather than systematic data. Here we analyze 14 million messages spreading 400 thousand claims on Twitter during and following the 2016 U.S. presidential campaign and election. We find evidence that social bots play a key role in the spread of fake news. Accounts that actively spread misinformation are significantly more likely to be bots. Automated accounts are particularly active in the early spreading phases of viral claims, and tend to target influential users. Humans are vulnerable to this manipulation, retweeting bots who post false news. Successful sources of false and biased claims are heavily supported by social bots. These results suggest that curbing social bots may be an effective strategy for mitigating the spread of online misinformation.
The METIS 5G Architecture: A Summary of METIS Work on 5G Architectures

During the last two years, the METIS project ("Mobile and wireless communications Enablers for the Twenty-twenty Information Society") has been conducting research on 5G-enabling technology components. This paper provides a summary of METIS work on 5G architectures. The architecture description is presented from different viewpoints. First, a functional architecture is presented that may lay a foundation for development of first novel 5G network functions. It is based on functional decomposition of most relevant 5G technology components provided by METIS. The logical orchestration & control architecture depicts the realization of flexibility, scalability and service orientation needed to fulfil diverse 5G requirements. Finally, a third viewpoint reveals deployment aspects and function placement options for 5G.
Analyzing the Behavior of Visual Question Answering Models

Recently, a number of deep-learning based models have been proposed for the task of Visual Question Answering (VQA). The performance of most models is clustered around 60-70%. In this paper we propose systematic methods to analyze the behavior of these models as a first step towards recognizing their strengths and weaknesses, and identifying the most fruitful directions for progress. We analyze the best performing models from two major classes of VQA models – with-attention and without-attention and show the similarities and differences in the behavior of these models. Our behavior analysis reveals that despite recent progress, today’s VQA models are “myopic” (tend to fail on sufficiently novel instances), often “jump to conclusions” (converge on a predicted answer after ‘listening’ to just half the question), and are “stubborn” (do not change their answers across images).
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size

Recent research on deep convolutional neural networks (CNNs) has focused primarily on improving accuracy. For a given accuracy level, it is typically possible to identify multiple CNN architectures that achieve that accuracy level. With equivalent accuracy, smaller CNN architectures offer at least three advantages: (1) Smaller CNNs require less communication across servers during distributed training. (2) Smaller CNNs require less bandwidth to export a new model from the cloud to an autonomous car. (3) Smaller CNNs are more feasible to deploy on FPGAs and other hardware with limited memory. To provide all of these advantages, we propose a small CNN architecture called SqueezeNet. SqueezeNet achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters. Additionally, with model compression techniques, we are able to compress SqueezeNet to less than 0.5MB (510× smaller than AlexNet). The SqueezeNet architecture is available for download here: https://github.com/DeepScale/SqueezeNet
Paleobiological perspectives on early eukaryotic evolution.

Eukaryotic organisms radiated in Proterozoic oceans with oxygenated surface waters, but, commonly, anoxia at depth. Exceptionally preserved fossils of red algae favor crown group emergence more than 1200 million years ago, but older (up to 1600-1800 million years) microfossils could record stem group eukaryotes. Major eukaryotic diversification ~800 million years ago is documented by the increase in the taxonomic richness of complex, organic-walled microfossils, including simple coenocytic and multicellular forms, as well as widespread tests comparable to those of extant testate amoebae and simple foraminiferans and diverse scales comparable to organic and siliceous scales formed today by protists in several clades. Mid-Neoproterozoic establishment or expansion of eukaryophagy provides a possible mechanism for accelerating eukaryotic diversification long after the origin of the domain. Protists continued to diversify along with animals in the more pervasively oxygenated oceans of the Phanerozoic Eon.
IMPACTS OF DAMS ON FLOW REGIMES IN THREE HEADWATER SUBBASINS OF THE COLUMBIA RIVER BASIN , UNITED STATES

We compared long-term changes in flow regimes resulting from climate change with those resulting from dams in three matched pairs of natural and modified headwater subbasins of the Columbia River. Based on the analysis of 12 flow-regime metrics, we found that damming had minimal effect on most quantity of flow metrics, but major effect on timing of flow metrics, especially those representing ‘‘spring runoff.’’ In all modified subbasins, ‘‘spring runoff’’ metrics occurred much earlier than natural flow (up to 44 days earlier for April-July flows). Storage capacity modulated the magnitude of timing of flow-metric changes, with the largest storage capacity leading to the most change. However, even in subbasins with low storage capacity, we found significant change in most timing of flow metrics. We also found that damming, especially in subbasins with higher storage capacity, overwhelmed climate variability in all basins for most flow metrics. This shows that reservoir operations need to be modified to more closely match the natural timing of flow regimes to promote positive ecologic response in modified rivers, even in basins where quantity of flow metrics have not changed substantially as a result of damming.
Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

Protein contacts contain key information for the understanding of protein structure and function and thus, contact prediction from sequence is an important problem. Recently exciting progress has been made on this problem, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction.



This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual neural networks. The first residual network conducts a series of 1-dimensional convolutional transformation of sequential features; the second residual network conducts a series of 2-dimensional convolutional transformation of pairwise information including output of the first residual network, EC information and pairwise potential. By using very deep residual networks, we can accurately model contact occurrence patterns and complex sequence-structure relationship and thus, obtain higher-quality contact prediction regardless of how many sequence homologs are available for proteins in question.



Our method greatly outperforms existing methods and leads to much more accurate contact-assisted folding. Tested on 105 CASP11 targets, 76 past CAMEO hard targets, and 398 membrane proteins, the average top L long-range prediction accuracy obtained by our method, one representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints but without any force fields can yield correct folds (i.e., TMscore>0.6) for 203 of the 579 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them, respectively. Our contact-assisted models also have much better quality than template-based models especially for membrane proteins. The 3D models built from our contact prediction have TMscore>0.5 for 208 of the 398 membrane proteins, while those from homology modeling have TMscore>0.5 for only 10 of them. Further, even if trained mostly by soluble proteins, our deep learning method works very well on membrane proteins. In the recent blind CAMEO benchmark, our fully-automated web server implementing this method successfully folded 6 targets with a new fold and only 0.3L-2.3L effective sequence homologs, including one β protein of 182 residues, one α+β protein of 125 residues, one α protein of 140 residues, one α protein of 217 residues, one α/β of 260 residues and one α protein of 462 residues. Our method also achieved the highest F1 score on free-modeling targets in the latest CASP (Critical Assessment of Structure Prediction), although it was not fully implemented back then.



http://raptorx.uchicago.edu/ContactMap/.
Motivating Factors for Adult Learners in Higher Education

All learners learn best when they are motivated; so do adults. Hence, the way to ensure success of students in higher education is first to know what motivates and sustains them in the learning process. Based on a study of 203 university students, this paper presents eight top most motivating factors for adult learners in higher education. These include quality of instruction; quality of curriculum; relevance and pragmatism; interactive classrooms and effective management practices; progressive assessment and timely feedback; self-directedness; conducive learning environment; and effective academic advising practices. The study concludes that these eight factors are critical to eliciting or enhancing the will power in students in higher education toward successful learning. The implications for practice and further research are also discussed.
Fast bilateral filtering for the display of high-dynamic-range images

We present a new technique for the display of high-dynamic-range images, which reduces the contrast while preserving detail. It is based on a two-scale decomposition of the image into a base layer, encoding large-scale variations, and a detail layer. Only the base layer has its contrast reduced, thereby preserving detail. The base layer is obtained using an edge-preserving filter called the bilateral filter. This is a non-linear filter, where the weight of each pixel is computed using a Gaussian in the spatial domain multiplied by an influence function in the intensity domain that decreases the weight of pixels with large intensity differences. We express bilateral filtering in the framework of robust statistics and show how it relates to anisotropic diffusion. We then accelerate bilateral filtering by using a piecewise-linear approximation in the intensity domain and appropriate subsampling. This results in a speed-up of two orders of magnitude. The method is fast and requires no parameter setting.
Threat Modeling for Cloud Data Center Infrastructures

Cloud computing has undergone rapid expansion throughout the last decade. Many companies and organizations have made the transition from tra­ ditional data centers to the cloud due to its flexibility and lower cost. However, traditional data centers are still being relied upon by those who are less certain about the security of cloud. This problem is highlighted by the fact that there only exist limited efforts on threat modeling for cloud data centers. In this paper, we conduct comprehensive threat modeling exercises based on two representative cloud infrastructures using several popular threat modeling methods, including attack surface, attack trees, attack graphs, and security metrics based on attack trees and attack graphs, respectively. Those threat modeling efforts provide cloud providers practical lessons and means toward better evaluating, understanding, and improving their cloud infrastructures. Our results may also imbed more con­ fidence in potential cloud tenants by providing them a clearer picture about po­ tential threats in cloud infrastructures and corresponding solutions.
Parsing clothing in fashion photographs

In this paper we demonstrate an effective method for parsing clothing in fashion photographs, an extremely challenging problem due to the large number of possible garment items, variations in configuration, garment appearance, layering, and occlusion. In addition, we provide a large novel dataset and tools for labeling garment items, to enable future research on clothing estimation. Finally, we present intriguing initial results on using clothing estimates to improve pose identification, and demonstrate a prototype application for pose-independent visual garment retrieval.
Mining Social Media Content for Crime Prediction

Social media provides increasing opportunities for users to voluntarily share their thoughts and concerns in a large volume of data. While user-generated data from each individual may not provide considerable information, when combined, they include hidden variables, which may convey significant events. In this paper, we pursue the question of whether social media context can provide socio-behavior "signals" for crime prediction. The hypothesis is that crowd publicly available data in social media, in particular Twitter, may include predictive variables, which can indicate the changes in crime rates. We developed a model for crime trend prediction where the objective is to employ Twitter content to identify whether crime rates have dropped or increased for the prospective time frame. We also present a Twitter sampling model to collect historical data to avoid missing data over time. The prediction model was evaluated for different cities in the United States. The experiments revealed the correlation between features extracted from the content and crime rate directions. Overall, the study provides insight into the correlation of social content and crime trends as well as the impact of social data in providing predictive indicators.
Multi-Scale Dense Convolutional Networks for Efficient Prediction

This paper studies convolutional networks that require limited computational resources at test time. We develop a new network architecture that performs on par with state-of-the-art convolutional networks, whilst facilitating prediction in two settings: (1) an anytime-prediction setting in which the network’s prediction for one example is progressively updated, facilitating the output of a prediction at any time; and (2) a batch computational budget setting in which a fixed amount of computation is available to classify a set of examples that can be spent unevenly across “easier” and “harder” examples. Our network architecture uses multi-scale convolutions and progressively growing feature representations, which allows for the training of multiple classifiers at intermediate layers of the network. Experiments on three image-classification datasets demonstrate the efficacy of our architecture, in particular, when measured in terms of classification accuracy as a function of the amount of compute available.
Development of an Autonomous Vehicle for High-Speed Navigation and Obstacle Avoidance

This paper introduces the autonomous vehicle Pharos, which participated in the 2010 Autonomous Vehicle Competition organized by Hyundai-Kia motors. Pharos was developed for high-speed on/off-road unmanned driving avo iding diverse patterns of obstacles. For the high speed travel ing up to 60 Km/h, long range terrain perception, real-time path planning and high speed vehicle motion control algorithms are developed. This paper describes the major hardware and software components of our vehicle.
INVITED PAPER Special Section on Analog Circuits and Related Topics Low Voltage Analog Circuit Design Techniques: A Tutorial

Low voltage (LV) analog circuit design techniques are addressed in this tutorial. In particular, (i) technology considerations; (ii) transistor model capable to provide performance and power tradeoffs; (iii) low voltage implementation techniques capable to reduce the power supply requirements, such as bulk-driven, floating-gate, and self-cascode MOSFETs; (iv) basic LV building blocks; (v) multi-stage frequency compensation topologies; and (vi) fully-differential and fully-balanced systems. key words: analog circuits, amplifiers, transistor model, bulkdriven, floating-gate, self-cascode, NGCC frequency compensation, fully-differential and fully-balanced systems.
Using Operations Research to Reduce Delays for Healthcare

The Institute of Medicine identified “timeliness” as one of six key “aims for improvement” in its most recent report on quality. Yet patient delays remain prevalent, resulting in dissatisfaction, adverse clinical consequences, and often, higher costs. This tutorial describes several areas in which patients routinely experience significant and potentially dangerous delays and presents operations research (OR) models that have been developed to help reduce these delays, often at little or no cost. I also describe the difficulties in developing and implementing models as well as the factors that increase the likelihood of success. Finally, I discuss the opportunities, large and small, for using OR methodologies to significantly impact practices and policies that will affect timely access to healthcare.
Analysis and Prediction of Student’s Academic Performance in University Courses

Management of huge amount of data has always been a matter of concern. With the increase in awareness towards education, the amount of data in educational institutes is also increasing. The increasing growth of educational databases, have given rise to a new field of data mining, known as Educational Data Mining (EDM). With the help of this one can predict the academic performance of a student that can help the students, their instructors and also their guardians to take necessary actions beforehand to improve the future performance of a student. This paper deals with the implementation of ID3 decision tree algorithm to build a predictive model based on the previous performances of a student. The dataset used in this paper is the semester data of the students of a private institute of India. Rapidminer, an open source software platform is used to obtain the results.