nils-herrmann/clean_scirepeval huggingface dataset:
Prasugrel Versus Ticagrelor in Patients With Acute Myocardial Infarction Treated With Primary Percutaneous Coronary Intervention: Multicenter Randomized PRAGUE 18 Study
Venue: Circulation

No randomized head to head comparison of the efficacy and safety of ticagrelor and prasugrel has been published in the 7 years since the higher efficacy of these newer P2Y12 inhibitors were first demonstrated relative to clopidogrel.  This academic study was designed to compare the efficacy and safety of prasugrel and ticagrelor in acute myocardial infarction treated with primary or immediate percutaneous coronary intervention. A total of 1230 patients were randomly assigned across 14 sites to either prasugrel or ticagrelor, which was initiated before percutaneous coronary intervention. Nearly 4% were in cardiogenic shock, and 5.2% were on mechanical ventilation. The primary end point was defined as death, reinfarction, urgent target vessel revascularization, stroke, or serious bleeding requiring transfusion or prolonging hospitalization at 7 days (to reflect primarily the in hospital phase) This analysis presents data from the first 30 days (key secondary end point) The total follow up will be 1 year for all patients and will be completed in 2017.  The study was prematurely terminated for futility. The occurrence of the primary end point did not differ between groups receiving prasugrel and ticagrelor (4.0% and 4.1% respectively; odds ratio, 0.98; 95% confidence interval, 0.55 1.73; P=0.939) No significant difference was found in any of the components of the primary end point. The occurrence of key secondary end point within 30 days, composed of cardiovascular death, nonfatal myocardial infarction, or stroke, did not show any significant difference between prasugrel and ticagrelor (2.7% and 2.5% respectively; odds ratio, 1.06; 95% confidence interval, 0.53 2.15; P=0.864)  This head to head comparison of prasugrel and ticagrelor does not support the hypothesis that one is more effective or safer than the other in preventing ischemic and bleeding events in the acute phase of myocardial infarction treated with a primary percutaneous coronary intervention strategy. The observed rates of major outcomes were similar but with broad confidence intervals around the estimates. These interesting observations need to be confirmed in a larger trial.
Face Landmark Fitting via Optimized Part Mixtures and Cascaded Deformable Model
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence

This paper addresses the problem of facial landmark localization and tracking from a single camera. We present a two stage cascaded deformable shape model to effectively and efficiently localize facial landmarks with large head pose variations. In initialization stage, we propose a group sparse optimized mixture model to automatically select the most salient facial landmarks. By introducing 3D face shape model, we apply procrustes analysis to provide pose aware landmark initialization. In landmark localization stage, the first step uses mean shift local search with constrained local model to rapidly approach the global optimum. The second step uses component wise active contours to discriminatively refine the subtle shape variation. Our framework simultaneously handles face detection, pose robust landmark localization and tracking in real time. Extensive experiments are conducted on both laboratory environmental databases and face in the wild databases. The results reveal that our approach consistently outperforms state of the art methods for face alignment and tracking.
DYNAMIC NETWORK SECURITY PROTECTION ON CLOUD COMPUTING

This paper focuses on DDoS problem and trying to give solution using auto correlation and alert generation methods. Cloud trace back model has efficient and it's dealing with DDoS attacks using back propagation neural network method and finds that the model is useful in tackling Distributed Denial of Service attacks. Distributed denial of service attacks has become more sophisticated as to exploit application layer vulnerabilities. NICE (Network Intrusion Detection and Countermeasure Selection) is used to propose multiphase distributed vulnerability detection for attack measurement, and the countermeasure selection mechanism which is built on attack graph based analytical models and reconfigurable virtual network based countermeasures. The systems and security evaluations demonstrate the efficiency and effectiveness of the solution.
Pregnancy and cardiovascular risk: A review focused on women with heart disease undergoing fertility treatment
Venue: European journal of preventive cardiology

A growing number of women with heart disease are considering pregnancy with the help of assisted reproductive technology (ART) Although an ever increasing amount of knowledge exists on pregnancy in both congenital and acquired heart disease patients, little information is available on fertility treatments specifically in these women. This review seeks to provide an overview of the existing data and explores areas in need of research in this field. Changes in the hormonal environment seen in ART patients initially entail an increase then a decrease in blood pressure and peripheral vascular resistance. A shift in the thrombotic thrombolytic balance towards coagulation is observed. Compared to normal pregnancy, ART conceived pregnancies exhibit an increased adverse event rate for both the mother and foetus, including a higher incidence of hypertensive disorders and an increase in thromboembolic events during the first trimester. Ovarian hyperstimulation syndrome in particular can cause dramatic haemodynamic changes and an increase in upper body thrombosis. Viewing these findings within the context of women with an underlying heart disease reveals indications that maternal and neonatal complications after fertility treatments are higher. Pre pregnancy risk assessment is essential to identifying women with heart disease for whom ART may be dangerous and therefore inappropriate.
Innovations in Utilization of Poor Quality Water for Sustainable Agricultural Production

To meet the requirements of food and other agricultural commodities for the burgeoning population is a big challenge for the agricultural community. With the increasing demand for good quality land and water for urbanization and development projects, agriculture will be pushed more and more to the marginal lands, and the use of poor quality waters for irrigation is inevitable. Groundwater aquifers in the most of arid and semiarid regions are saline, and therefore cultivation of conventional arable crops with saline irrigation has not been considered sustainable in these regions. However, concerted research efforts have shown that the degraded lands can be put to remunerative alternative uses (through agroforestry) including salt tolerant forest and fruit trees, crops, forage grasses, medicinal and aromatic and other high value crops, and adopting appropriate planting (e.g. subsurface planting) and other management techniques (furrow irrigation) Such uses have additional environmental benefits including carbon sequestration and biological reclamation. Agroforestry is not only a necessity for increasing tree cover and hence decreasing pressure on natural forests but also a most desired land use especially for reclaiming and rehabilitating the degraded lands, especially in arid and semiarid rainfed areas underlain with saline groundwater as source of irrigation. In developing countries like India, there seems to be little scope for bringing the fertile lands under forest cover. It may be emphasized that we can bring unproductive wastelands and waterlogged areas under forest cover and take agroforestry tree plantation on non forest community and farmlands utilizing poor quality water including drainage and wastewaters. The long term studies conducted show that salt affected and waterlogged areas and saline water (including seawater) can be utilized satisfactorily in raising forest and fruit tree species with improved techniques, forage grasses, conventional and nonconventional crops, oil yielding crops, aromatic and medicinal plants of high economic value, petro crops, and flower yielding plants.
Hash Learning with Convolutional Neural Networks for Semantic Based Image Retrieval
Venue: PAKDD

Hashing is an effective method of approximate nearest neighbor search (ANN) for the massive web images. In this paper, we propose a method that combines convolutional neural networks (CNN) with hash learning, where the features learned by the former are beneficial to the latter. By introducing a new loss layer and a new hash layer, the proposed method can learn the hash functions that preserve the semantic information and at the same time satisfy the desirable independent properties of hashing. Experiments show that our method outperforms the state of the art methods by a large margin on image retrieval. And the comparisons with baseline models show the effectiveness of our proposed layers.
Fast content based image retrieval using convolutional neural network and hash function
Venue: 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Due to the explosive increase of online images, content based image retrieval has gained a lot of attention. The success of deep learning techniques such as convolutional neural networks have motivated us to explore its applications in our context. The main contribution of our work is a novel end to end supervised learning framework that learns probability based semantic level similarity and feature level similarity simultaneously. The main advantage of our novel hashing scheme that it is able to reduce the computational cost of retrieval significantly at the state of the art efficiency level. We report on comprehensive experiments using public available datasets such as Oxford, Holidays and ImageNet 2012 retrieval datasets.
BLASX: A High Performance Level 3 BLAS Library for Heterogeneous Multi GPU Computing
Venue: ICS

Basic Linear Algebra Subprograms (BLAS) are a set of low level linear algebra kernels widely adopted by applications involved with the deep learning and scientific computing. The massive and economic computing power brought forth by the emerging GPU architectures drives interest in implementation of compute intensive level 3 BLAS on multi GPU systems. In this paper, we investigate existing multi GPU level 3 BLAS and present that 1) issues, such as the improper load balancing, inefficient communication, insufficient GPU stream level concurrency and data caching, impede current implementations from fully harnessing heterogeneous computing resources; 2) and the inter GPU Peer to Peer(P2P) communication remains unexplored. We then present BLASX: a highly optimized multi GPU level 3 BLAS. We adopt the concepts of algorithms by tiles treating a matrix tile as the basic data unit and operations on tiles as the basic task. Tasks are guided with a dynamic asynchronous runtime, which is cache and locality aware. The communication cost under BLASX becomes trivial as it perfectly overlaps communication and computation across multiple streams during asynchronous task progression. It also takes the current tile cache scheme one step further by proposing an innovative 2 level hierarchical tile cache, taking advantage of inter GPU P2P communication. As a result, linear speedup is observable with BLASX under multi GPU configurations; and the extensive benchmarks demonstrate that BLASX consistently outperforms the related leading industrial and academic implementations such as cuBLAS XT, SuperMatrix, MAGMA.
Improving literacy and math instruction at scale in India's primary schools: The case of Pratham's Read India program

Pratham's "Read India" initiative is a large scale intervention to improve basic learning and arithmetic among children in primary school. It was started almost 10 years ago and has evolved considerably over time. Currently, this initiative uses two strategies. The first strategy is to work directly with village communities and local schools to improve children's learning. "Learning camps" are organized in the local school or community for a period of 6 10 days at a time. Local village volunteers help to teach children who are organized in groups by their level of learning. These camps intensive bursts of focused instruction are repeated several times during the year. This model which has been rigourously evaluated shows that children's learning levels improve significantly. The second strategy is to work with the government. This approach is used when school systems want to partner or collaborate with Pratham for improving basic learning. The key element here too is grouping children and teaching them from their level rather than by their grade. This approach also shows promising results. Independent evaluations and randomized control trials conducted on both models have indicated significant impact. Moving between the present set of conditions in India and past lessons, this case describes a decade long journey of efforts to change teaching and learning at the ground level as well the efforts to bring about significant shifts in priority at the system level. The "Read India" case presented here contributes knowledge on strategies under which effective pedagogy can be brought to scale. It also discusses challenges of transforming instructional change in a context of low initial capacity at the school and system levels, where attention to rapid expansion of access to school had kept aside for a long time critical questions about teaching quality and learning outcomes. A second contribution of "Read India" to current knowledge on large scale educational change relates to the role non government actors such as Pratham can play in bringing effective pedagogy to scale to improve student learning.
Using Think Aloud Protocols to Uncover Misconceptions and Improve Developmental Math Instruction: An Exploratory Study

Deficiencies in education continue to escalate around the world. The focus on outcomes assessment has narrowed instructional research and curriculum evaluation to standardized testing in certain subject areas. A prototype for a quantitative literacy assessment instrument was developed with the goal of diagnosing student misconceptions of basic mathematics content and changing instructional practices to undo the misconceptions by applying cognitive psychological theory. Two hundred thirty eight basic math high school students and 209 remedial community college students in New Jersey and New York were administered the instrument, which had been based on coded data from think aloud protocols. The instrument asked students to answer 20 basic mathematics items and, in addition, to evaluate four possible solution strategies. For each item, frequencies of selected solution strategies and the association between strategy selection and performance on the 20 question math test are presented as a means for improving instruction. Follow up research is proposed for determining whether undoing the student misconceptions first before teaching material on a new unit of instruction may yield more positive student outcomes.
Political Participation and Political Repression: Women in Saudi Arabia

In 2015 Saudi Arabian women were for the first time in history granted political space through electoral suffrage. To evaluate whether the new political opening for Saudi Arabian women has improved women's rights and equality in the Kingdom, I sought to conduct interviews to acquire their views and attitudes. In the process my encounters with Saudi Arabian women revealed their fear, cautiousness, and unwillingness to participate politically, which impelled me to discover the relationship between women's political participation and political repression. In the course of this research I learned that political repression inhibits women's political participation, and in Saudi Arabia women remain voiceless despite the new political space political participation did not account for political freedoms. This thesis provides analysis of the relationship by demonstrating the prevalence of Saudi Arabian women's lack of freedom in political participation.
Entropic Fluctuations in Thermally Driven Harmonic Networks

We consider a general network of harmonic oscillators driven out of thermal equilibrium by coupling to several heat reservoirs at different temperatures. The action of the reservoirs is implemented by Langevin forces. Assuming the existence and uniqueness of the steady state of the resulting process, we construct a canonical entropy production functional $S^t$$St which satisfies the Gallavotti Cohen fluctuation theorem. More precisely, we prove that there exists \kappa _c>\frac{1}{2}$kc>12 such that the cumulant generating function of $S^t$$St has a large time limit $e(\alpha $e(a) which is finite on a closed interval \frac{1}{2} \kappa _c,\frac{1}{2}\kappa _c][12 kc,12+kc] infinite on its complement and satisfies the Gallavotti Cohen symmetry $e(1 \alpha =e(\alpha $e(1 a)=e(a) for all \alpha \in \mathbb {R}$aR. Moreover, we show that $e(\alpha $e(a) is essentially smooth, i.e. that $e'\alpha \rightarrow \mp \infty $e'(a) as \alpha \rightarrow \tfrac{1}{2}\mp \kappa _c$$a 12kc. It follows from the Gartner Ellis theorem that $S^t$$St satisfies a global large deviation principle with a rate function I(s) obeying the Gallavotti Cohen fluctuation relation $I( s) I(s)=s$$I( s) I(s)=s for all $s\in \mathbb {R}$sR. We also consider perturbations of $S^t$$St by quadratic boundary terms and prove that they satisfy extended fluctuation relations, i.e. a global large deviation principle with a rate function that typically differs from I(s) outside a finite interval. This applies to various physically relevant functionals and, in particular, to the heat dissipation rate of the network. Our approach relies on the properties of the maximal solution of a one parameter family of algebraic matrix Riccati equations. It turns out that the limiting cumulant generating functions of $S^t$$St and its perturbations can be computed in terms of spectral data of a Hamiltonian matrix depending on the harmonic potential of the network and the parameters of the Langevin reservoirs. This approach is well adapted to both analytical and numerical investigations.
Cluster Based Sorted Neighborhood for Efficient Duplicate Detection
Venue: 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)

Duplicate detection intends to find multiple and syntactically different representations of the same real world entities in a dataset. The naive way of duplicate detection entails a quadratic number of pair wise record comparisons to identify the duplicates. This number of comparisons might take hours even for an average sized dataset. As today's databases grow very fast, different candidate selection methods, such as sorted neighborhood, blocking, canopy clustering and their variations, address this problem by shrinking the comparison space. The volume and velocity of data change require ever faster and more flexible methods of duplicate detection. In particular, they need dynamic indices that can be updated efficiently as new data arrives. We present a novel approach, which combines the idea of cluster based methods with the well known sorted neighborhood method. It carefully filters out irrelevant candidate pairs, which are less likely to yield duplicates, by pre clustering records based not only on their proximity after sorting, but also on their similarity in selected attributes. An empirical evaluation on synthetic and real world datasets shows that our approach improves the overall runtime over existing approaches while maintaining comparable result quality. Meanwhile, it uses a dynamic indices, that in turns make it useful for deduplicating streaming data.
Stochastic network models for logistics planning in disaster relief
Venue: Eur. J. Oper. Res.

Emergency logistics in disasters is fraught with planning and operational challenges, such as uncertainty about the exact nature and magnitude of the disaster, a lack of reliable information about the location and needs of victims, possible random supplies and donations, precarious transport links, scarcity of resources, and so on. This paper develops a new two stage stochastic network flow model to help decide how to rapidly supply humanitarian aid to victims of a disaster within this context. The model takes into account practical characteristics that have been neglected by the literature so far, such as budget allocation, fleet sizing of multiple types of vehicles, procurement, and varying lead times over a dynamic multiperiod horizon. Attempting to improve demand fulfillment policy, we present some extensions of the model via state of art risk measures, such as semideviation and conditional value at risk. A simple two phase heuristic to solve the problem within a reasonable amount of computing time is also suggested. Numerical tests based on the floods and landslides in Rio de Janeiro state, Brazil, show that the model can help plan and organise relief to provide good service levels in most scenarios, and how this depends on the type of disaster and resources. Moreover, we demonstrate that our heuristic performs well for real and random instances.
Monitoring continent wide aerial patterns of bird movements using weather radars

Billions of insects, birds and bats use the aerosphere for migration, dispersive movements or foraging. This enormous movement of biomass plays a key role in ecological connectivity, yet monitoring aerial movements is technically very challenging. Individual tracking devices have been increasingly used over the last decade but these are currently only suitable for relatively large organisms, and the associated costs limits monitoring to a very small sample of the aerial animal community. Radars provide a tool for investigating and quantifying movement patterns for a wide range of flying organisms (birds, bats and insects) across communities and populations. However, research efforts in this field have often been local and uncoordinated. As a network of operational weather radars is continuously recording atmospheric conditions all over Europe, ENRAM (The European Network for the Radar surveillance of Animal Movement) has been recently established to explore the potential for coordinated, large scale studies of the aerial movements of animals (Shamoun Baranes et al. 2014) Here, we present the first outcomes of this collaborative research, and provide details on the visualization of a case study of mass migration of birds tracked using several national weather radars in the Netherlands and Belgium simultaneously. Finally we will also discuss the opportunities that a large sensor network can provide for movement ecology research at the continental scale.
Oil Spill Monitoring in the Italian Waters: COSMO SkyMed Role and Contribution

The Mediterranean Sea, rich in biodiversity and with a large number of endemic species, provides sustenance for millions of people living along its coasts. Due to its position it also represents the natural route between major oil production areas and oil consumers. Its preservation passes also through satellite technologies which are ever and ever playing an increasing role in environmental monitoring. Italy, recognizing its contribution, has decided to invest in this sector with the satellite Mission COSMO SkyMed. Its frequent revisiting time, day and night and all weather acquisition capability, makes it an essential part together with aerial and naval component of the National Contingency Plan (United Nations Conference on Trade and Development, Review in Maritime Transport. Available at http:/unctad.org/en/PublicationsLibrary/rmt2015_en.pdf) to contrast marine oil pollution.