nils-herrmann/clean_scirepeval huggingface dataset:
A Modified Proportional Navigation Guidance for Accurate Target Hitting

When a detector sensitive to the target plume IR seeker is used for tracking airborne targets, the seeker tends to follow the target hot point which is a point farther away from the target exhaust and its fuselage. In order to increase the missile effectiveness, it is necessary to modify the guidance law by adding a lead bias command. The resulting guidance is known as target adaptive guidance (TAG). First, the pure proportional navigation guidance (PPNG) in 3-dimensional state is explained in a new point of view. The main idea is based on the distinction between angular rate vector and rotation vector conceptions. The current innovation is based on selection of line of sight (LOS) coordinates. A comparison between two available choices for LOS coordinates system is proposed. An improvement is made by adding two additional terms. First term includes a cross range compensator which is used to provide and enhance path observability, and obtain convergent estimates of state variables. The second term is new concept lead bias term, which has been calculated by assuming an equivalent acceleration along the target longitudinal axis. Simulation results indicate that the lead bias term properly provides terminal conditions for accurate target interception.
Full regeneration of segmental bone defects using porous titanium implants loaded with BMP-2 containing fibrin gels.

Regeneration of load-bearing segmental bone defects is a major challenge in trauma and orthopaedic surgery. The ideal bone graft substitute is a biomaterial that provides immediate mechanical stability, while stimulating bone regeneration to completely bridge defects over a short period. Therefore, selective laser melted porous titanium, designed and fine-tuned to tolerate full load-bearing, was filled with a physiologically concentrated fibrin gel loaded with bone morphogenetic protein-2 (BMP-2). This biomaterial was used to graft critical-sized segmental femoral bone defects in rats. As a control, porous titanium implants were either left empty or filled with a fibrin gels without BMP-2. We evaluated bone regeneration, bone quality and mechanical strength of grafted femora using in vivo and ex vivo µCT scanning, histology, and torsion testing. This biomaterial completely regenerated and bridged the critical-sized bone defects within eight weeks. After twelve weeks, femora were anatomically re-shaped and revealed open medullary cavities. More importantly, new bone was formed throughout the entire porous titanium implants and grafted femora regained more than their innate mechanical stability: torsional strength exceeded twice their original strength. In conclusion, combining porous titanium implants with a physiologically concentrated fibrin gels loaded with BMP-2 improved bone regeneration in load-bearing segmental defects. This material combination now awaits its evaluation in larger animal models to show its suitability for grafting load-bearing defects in trauma and orthopaedic surgery.
Predicting ad click-through rates via feature-based fully coupled interaction tensor factorization

In the real-time bidding (RTB) display advertising ecosystem, when receiving a bid request, the demandside platform (DSP) needs to predict the click-through rate (CTR) for ads and calculate the bid price according to the CTR estimated. In addition to challenges similar to those encountered in sponsored search advertising, such as data sparsity and cold start problems, more complicated feature interactions involving multi-aspects, such as the user, publisher and advertiser, make CTR estimation in RTB more difficult. We consider CTR estimation in RTB as a tensor complement problem and propose a fully coupled interactions tensor factorization (FCTF) model based on Tucker decomposition (TD) to model three pairwise interactions between the user, publisher and advertiser and ultimately complete the tensor complement task. FCTF is a special case of the Tucker decomposition model; however, it is linear in runtime for both learning and prediction. Different from pairwise interaction tensor factorization (PITF), which is another special case of TD, FCTF is independent from the Bayesian personalized ranking optimization algorithm and is applicable to generic third-order tensor decomposition with popular simple optimizations, such as the least square method or mean square error. In addition, we also incorporate all explicit information obtained from different aspects into the FCTF model to alleviate the impact of cold start and sparse data on the final performance. We compare the performance and runtime complexity of our method with Tucker decomposition, canonical decomposition and other popular methods for CTR prediction over real-world advertising datasets. Our experimental results demonstrate that the improved model not only achieves better prediction quality than the others due to considering fully coupled interactions between three entities, user, publisher and advertiser but also can accomplish training and prediction with linear runtime.
Chapter 2 A Taxonomy of CDNs

Content Delivery Networks (CDNs) [79, 97] have received considerable research attention in the recent past. A few studies have investigated CDNs to categorize and analyze them, and to explore the uniqueness, weaknesses, opportunities, and future directions in this field. Peng presents an overview of CDNs [75]. His work describes the critical issues involved in designing and implementing an effective CDN, and surveys the approaches proposed in literature to address these problems. Vakali et al.  present a survey of CDN architecture and popular CDN service providers. The survey is focused on understanding the CDN framework and its usefulness. They identify the characteristics and current practices in the content networking domain, and present an evolutionary pathway for CDNs, in order to exploit the current content networking trends. Dilley et al.  provide an insight into the overall system architecture of the leading CDN, Akamai . They provide an overview of the existing content delivery approaches and describe Akamai’s network infrastructure and its operations in detail. They also point out the technical challenges that are to be faced while constructing a global CDN like Akamai. Saroiu et al.  examine content delivery from the point of view of four content delivery systems: Hypertext Transfer Protocol (HTTP) Web traffic, the Akamai CDN, Gnutella [8, 25], and KaZaa [62, 66] peer-to-peer file sharing systems. They also present significant implications for large organizations, service providers, network infrastructure providers, and general content delivery providers. Kung et al. [60] describe a taxonomy for content networks and introduce a new class of content networks that perform “semantic aggregation and content-sensitive placement” of content. They classify content networks based on their attributes in two dimensions: content aggregation and content placement. Sivasubramanian et al.  identify the issues
EFFECT OF THE BOBATH TECHNIQUE, CONDUCTIVE EDUCATION AND EDUCATION TO PARENTS IN ACTIVITIES OF DAILY LIVING IN CHILDREN WITH CEREBRAL PALSY IN IRAN

1Department of Occupational Therapy, Faculty of Rehabilitation, Tehran University of Medical Sciences, Tehran, 2Department of Epidemiology and Biostatistics, Faculty of Health, Isfahan University of Medical Sciences, Isfahan, 3Department of Paediatrics, Faculty of Medicine, Baqiyatollah University of Medical Sciences, and 4Department of Physiotherapy, Faculty of Rehabilitation, Tehran University of Medical Sciences, Tehran, Iran. Reprint requests and correspondence to: Dr. Leila Dehghan, Department of Occupational Therapy, Faculty of Rehabilitation, Tehran University of Medical Sciences, Piche Shemiran, Tehran, Iran. E-mail: ldehghan@tums.ac.ir EFFECT OF THE BOBATH TECHNIQUE, CONDUCTIVE EDUCATION AND EDUCATION TO PARENTS IN ACTIVITIES OF DAILY LIVING IN CHILDREN WITH CEREBRAL PALSY IN IRAN
Building extraction from multi-source remote sensing images via deep deconvolution neural networks

Building extraction from remote sensing images is of great importance in urban planning. Yet it is a longstanding problem for many complicate factors such as various scales and complex backgrounds. This paper proposes a novel supervised building extraction method via deep deconvolution neural networks (DeconvNet). Our method consists of three steps. First, we preprocess the multi-source remote sensing images provided by the IEEE GRSS Data Fusion Contest. A high-quality Vancouver building dataset is created on pansharpened images whose ground-truth are obtained from the OpenStreetMap project. Then, we pretrain a deep deconvolution network on a public large-scale Massachusetts building dataset, which is further fine-tuned by two band combinations (RGB and NRG) of our dataset, respectively. Moreover, the output saliency maps of the fine-tuned models are fused to produce the final building extraction result. Extensive experiments on our Vancouver building dataset demonstrate the effectiveness and efficiency of the proposed method. To the best of our knowledge, it is the first work to use deconvolution networks for building extraction from remote sensing images.
Gene silencing in X-chromosome inactivation: advances in understanding facultative heterochromatin formation

In female mammals, one of the two X chromosomes is silenced for dosage compensation between the sexes. X-chromosome inactivation is initiated in early embryogenesis by the Xist RNA that localizes to the inactive X chromosome. During development, the inactive X chromosome is further modified, a specialized form of facultative heterochromatin is formed and gene repression becomes stable and independent of Xist in somatic cells. The recent identification of several factors involved in this process has provided insights into the mechanism of Xist localization and gene silencing. The emerging picture is complex and suggests that chromosome-wide silencing can be partitioned into several steps, the molecular components of which are starting to be defined.
Survey on Fraud Ranking Detection in Mobile App Store

From last few years, mobile technology has been received much more attention since it is most popular and basic need of today&apos;s world. Due to the popularity, mobiles are major target for malicious applications. Key challenge is to detect and remove malicious apps from mobiles. Numerous amounts of mobile apps are generated daily so ranking fraud is the one of the major aspects in front of the mobile App market. Ranking fraud refers to fraudulent or vulnerable activities. Main aim of the fraudulent is to knock the fraud mobile apps in the popularity list. Most App developer generates the ranking fraud apps by tricky means like enhancing the apps sales or by simply rating fake apps. Thus, there is need to have novel system to effectively
Cross-Dataset Person Re-identification Using Similarity Preserved Generative Adversarial Networks

Person re-identification (Re-ID) aims to match the image frames which contain the same person in the surveillance videos. Most of the Re-ID algorithms conduct supervised training in some small labeled datasets, so directly deploying these trained models to the real-world large camera networks may lead to a poor performance due to underfitting. The significant difference between the source training dataset and the target testing dataset makes it challenging to incrementally optimize the model. To address this challenge, we propose a novel solution by transforming the unlabeled images in the target domain to fit the original classifier by using our proposed similarity preserved generative adversarial networks model, SimPGAN. Specifically, SimPGAN adopts the generative adversarial networks with the cycle consistency constraint to transform the unlabeled images in the target domain to the style of the source domain. Meanwhile, SimPGAN uses the similarity consistency loss, which is measured by a siamese deep convolutional neural network, to preserve the similarity of the transformed images of the same person. Comprehensive experiments based on multiple real surveillance datasets are conducted, and the results show that our algorithm is better than the state-of-the-art cross-dataset unsupervised person Re-ID algorithms.
Effect of sucralfate on components of mucosal barrier produced by cultured canine epithelial cellsin vitro

The mucous gel maintains a neutral microclimate at the epithelial cell surface, which may play a role in both the prevention of gastroduodenal injury and the provision of an environment essential for epithelial restitution and regeneration after injury. Enhancement of the components of the mucous barrier by sucralfate may explain its therapeutic efficacy for upper gastrointestinal tract protection, repai, and healing. We studied the effect of sucralfate and its major soluble component, sucrose octasulfate (SOS), on the synthesis and release of gastric mucin and surface active phospholipid, utilizing an isolated canine gastric mucous cells in culture. We correlated these results with the effect of the agents on mucin synthesis and secretion utilizing explants of canine fundusin vitro. Sucralfate and SOS significantly stimulated phospholipid secretion by isolated canine mucous cells in culture (123% and 112% of control, respectively.) Indomethacin pretreatment siginificantly inhibited the effect of sucralfate, but not SOS, on the stimulation of phospholipid release. Administration of either sucralfate or SOS to the isolated canine mucous cells had no effect upon mucin synthesis or secretion using a sensitive immunoassay. Sucralfate and SOS did not stimulate mucin release in the canine explants; sucralfate significantly stimulated the synthesis of mucin, but only to 108% of that observed in untreated explants. No increase in PGE2 release was observed after sucralfate or SOS exposure to the isolated canine mucous cells. Our results suggest sucralfate affects the mucus barrier largely in a qualitative manner. No increase in mucin secretion or major effect on synthesis was notd, although a significant increase in surface active phospholipid release was observed. The lack of dose dependency of this effect, along with the results of the PGE2 assay, suggests the drug may act through a non-receptor-mediated mechanism to perturb the cell membrane and release surface active phospholipid. The enhancement of phospholipid release by sucralfate to augment the barrier function of gastric mucus may, in concert with other effects of the drug, strrengthen mucosal barrier function.
Acousto-Ultrasonic Optical Fiber Sensors: Overview and State-of-the-Art

This paper gives a review of acoustic and ultrasonic optical fiber sensors (OFSs). The review covers optical fiber sensing methods for detecting dynamic strain signals, including general sound and acoustic signals, high-frequency signals, i.e., ultrasonic/ultrasound, and other signals such as acoustic emissions, and impact induced dynamic strain. Several optical fiber sensing methods are included, in an attempted to summarize the majority of optical fiber sensing methods used to date. The OFS include single fiber sensors and optical fiber devices, fiber-optic interferometers, and fiber Bragg gratings (FBGs). The single fiber and fiber device sensors include optical fiber couplers, microbend sensors, refraction-based sensors, and other extrinsic intensity sensors. The optical fiber interferometers include Michelson, Mach-Zehnder, Fabry-Perot, Sagnac interferometers, as well as polarization and model interference. The specific applications addressed in this review include optical fiber hydrophones, biomedical sensors, and sensors for nondestructive evaluation and structural health monitoring. Future directions are outlined and proposed for acousto-ultrasonic OFS.
Updates in ANCA-associated vasculitis.

Antineutrophil cytoplasm antibody (ANCA)-associated vasculitides are small-vessel vasculitides that include granulomatosis with polyangiitis (formerly Wegener's granulomatosis), microscopic polyangiitis, and eosinophilic granulomatosis with polyangiitis (Churg-Strauss syndrome). Renal-limited ANCA-associated vasculitides can be considered the fourth entity. Despite their rarity and still unknown cause(s), research pertaining to ANCA-associated vasculitides has been very active over the past decades. The pathogenic role of antimyeloperoxidase ANCA (MPO-ANCA) has been supported using several animal models, but that of antiproteinase 3 ANCA (PR3-ANCA) has not been as strongly demonstrated. Moreover, some MPO-ANCA subsets, which are directed against a few specific MPO epitopes, have recently been found to be better associated with disease activity, but a different method than the one presently used in routine detection is required to detect them. B cells possibly play a major role in the pathogenesis because they produce ANCAs, as well as neutrophil abnormalities and imbalances in different T-cell subtypes [T helper (Th)1, Th2, Th17, regulatory cluster of differentiation (CD)4+ CD25+ forkhead box P3 (FoxP3)+ T cells] and/or cytokine-chemokine networks. The alternative complement pathway is also involved, and its blockade has been shown to prevent renal disease in an MPO-ANCA murine model. Other recent studies suggested strongest genetic associations by ANCA type rather than by clinical diagnosis. The induction treatment for severe granulomatosis with polyangiitis and microscopic polyangiitis is relatively well codified but does not (yet) really differ by precise diagnosis or ANCA type. It comprises glucocorticoids combined with another immunosuppressant, cyclophosphamide or rituximab. The choice between the two immunosuppressants must consider the comorbidities, past exposure to cyclophosphamide for relapsers, plans for pregnancy, and also the cost of rituximab. Once remission is achieved, maintenance strategy following cyclophosphamide-based induction relies on less toxic agents such as azathioprine or methotrexate. The optimal maintenance strategy following rituximab-based induction therapy remains to be determined. Preliminary results on rituximab for maintenance therapy appear promising. Efforts are still under way to determine the optimal duration of maintenance therapy, ideally tailored according to the characteristics of each patient and the previous treatment received.
Service orchestration using enterprise service bus for real-time government executive dashboard system

The increasing implementation of various platforms and technology of information systems also increases the complexity of integration when the integration system is needed. This is just like what has happened in most of government areas. As we have done from a case study in Sleman, a regency of Yogyakarta Indonesia, it has many departments that use different platform and technology on implementing information system. Integration services using point-to-point method is considered to be irrelevant whereas the number of services are growing up rapidly and more complex. So, in this paper we have proposed a service orchestration mechanism using enterprise service bus (ESB) to integrate many services from many departments which used their owned platform and technology of information system. ESB can be the solution of n-to-n integration problem and it strongly supports the implementation of service oriented architecture (SOA). This paper covers the analysis, design and implementation of integration system in government area. Then the result of this integration has been deployed as a single real time executive dashboard system that can be useful for the governance in order to support them on making decision or policy. Functional and performance testing are used to ensure that the implementation of integration does not disrupt other transaction processes.
Likelihood Ratio Gradient Estimation for Stochastic Systems

Consider a computer system having a CPU that feeds jobs to two input/output (I/O) devices having different speeds. Let &thgr; be the fraction of jobs routed to the first I/O device, so that 1 - &thgr; is the fraction routed to the second. Suppose that α = α(&thgr;) is the steady-sate amount of time that a job spends in the system. Given that &thgr; is a decision variable, a designer might wish to minimize α(&thgr;) over &thgr;. Since α(·) is typically difficult to evaluate analytically, Monte Carlo optimization is an attractive methodology. By analogy with deterministic mathematical programming, efficient Monte Carlo gradient estimation is an important ingredient of simulation-based optimization algorithms. As a consequence, gradient estimation has recently attracted considerable attention in the simulation community. It is our goal, in this article, to describe one efficient method for estimating gradients in the Monte Carlo setting, namely the likelihood ratio method (also known as the efficient score method). This technique has been previously described (in less general settings than those developed in this article) in [6, 16, 18, 21]. An alternative gradient estimation procedure is infinitesimal perturbation analysis; see [11, 12] for an introduction. While it is typically more difficult to apply to a given application than the likelihood ratio technique of interest here, it often turns out to be statistically more accurate.
 In this article, we first describe two important problems which motivate our study of efficient gradient estimation algorithms. Next, we will present the likelihood ratio gradient estimator in a general setting in which the essential idea is most transparent. The section that follows then specializes the estimator to discrete-time stochastic processes. We derive likelihood-ratio-gradient estimators for both time-homogeneous and non-time homogeneous discrete-time Markov chains. Later, we discuss likelihood ratio gradient estimation in continuous time. As examples of our analysis, we present the gradient estimators for time-homogeneous continuous-time Markov chains; non-time homogeneous continuous-time Markov chains; semi-Markov processes; and generalized semi-Markov processes. The analysis throughout these sections assumes the performance measure that defines α(&thgr;) corresponds to a terminating simulation.) Finally, we conclude the article with a brief discussion of the basic issues that arise in extending the likelihood ratio gradient estimator to steady-state performance measures.
Low power data processing by elimination of redundant computations

We suggest a new technique to reduce energy consumption in the processor datapath without sacrificing performance by exploiting operand value locality at run time. Data locality is one of the major characteristics of video streams as well as other commonly used applications. We use a cache-like scheme to store a selective history of computation results, and the resultant Te-e-21se leads to power savings. The cache is indexed by the OpeTandS. Based on OUT model, an 8 to 128 entry execution cache TedUCeS power consumption by 20% to 60%.
Modeling wine preferences by data mining from physicochemical properties

We propose a data mining approach to predict human wine taste preferences that is based on easily available analytical tests at the certification step. A large dataset (when compared to other studies in this domain) is considered, with white and red vinho verde samples (from Portugal). Three regression techniques were applied, under a computationally efficient procedure that performs simultaneous variable and model selection. The support vector machine achieved promising results, outperforming the multiple regression and neural network methods. Such model is useful to support the oenologist wine tasting evaluations and improve wine production. Furthermore, similar techniques can help in target marketing by modeling consumer tastes from niche markets.