nils-herrmann/clean_scirepeval huggingface dataset:
Position and orientation estimation of a rigid body: Rigid body localization

Rigid body localization refers to a problem of estimating the position of a rigid body along with its orientation using anchors. We consider a setup in which a few sensors are mounted on a rigid body. The absolute position of the rigid body is not known, but, the relative position of the sensors or the topology of the sensors on the rigid body is known. We express the absolute position of the sensors as an affine function of the Stiefel manifold and propose a simple least-squares (LS) estimator as well as a constrained total least-squares (CTLS) estimator to jointly estimate the orientation and the position of the rigid body. To account for the perturbations of the sensors, we also propose a constrained total least-squares (CTLS) estimator. Analytical closed-form solutions for the proposed estimators are provided. Simulations are used to corroborate and analyze the performance of the proposed estimators.
Inflammation (or synovitis)-driven osteoarthritis: an opportunity for personalizing prognosis and treatment?

The disabling and painful disease osteoarthritis (OA) is the most common form of arthritis. Strong evidence suggests that a subpopulation of OA patients has a form of OA driven by inflammation. Consequently, understanding when inflammation is the driver of disease progression and which OA patients might benefit from anti-inflammatory treatment is a topic of intense research in the OA field. We have reviewed the current literature on OA, with an emphasis on inflammation in OA, biochemical markers of structural damage, and anti-inflammatory treatments for OA. The literature suggests that the OA patient population is diverse, consisting of several subpopulations, including one associated with inflammation. This inflammatory subpopulation may be identified by a combination of novel serological inflammatory biomarkers. Preliminary evidence from small clinical studies suggests that this subpopulation may benefit from anti-inflammatory treatment currently reserved for other inflammatory arthritides.
Software Testing in Cloud Platform: A Survey

Software Testing is a challenging activity for many software engineering projects and it is one of the five main technical activity areas of the software engineering lifecycle that still poses substantial challenges. Testing software requires enough resources and budget to complete it successfully. But most of the organizations face the challenges to provide enough resources to test their software in distributed environment, with different loading level. This leads to severe problem when the software deployed into different client environment and varying user load. Cloud computing is a one of the emerging technology which opens new door for software testing. This paper investigates the software testing in cloud platform which includes cloud testing models, recent research work, commercial tools and research issues.
SpiderFab: An Architecture for Self-Fabricating Space Systems

On-orbit fabrication of spacecraft components can enable space programs to escape the volumetric limitations of launch shrouds and create systems with extremely large apertures and very long baselines in order to deliver higher resolution, higher bandwidth, and higher SNR data. This paper will present results of efforts to investigated the value proposition and technical feasibility of adapting several of the many rapidly-evolving additive manufacturing and robotics technologies to the purpose of enabling space systems to fabricate and integrate significant parts of themselves on-orbit. We will first discuss several case studies for the value proposition for on-orbit fabrication of space structures, including one for a starshade designed to enhance the capabilities for optical imaging of exoplanets by the proposed New World Observer mission, and a second for a long-baseline phased array radar system. We will then summarize recent work adapting and evolving additive manufacturing techniques and robotic assembly technologies to enable automated on-orbit fabrication of large, complex, three-dimensional structures such as trusses, antenna reflectors, and shrouds.
Automatic Generation of Story Highlights

In this paper we present a joint content selection and compression model for single-document summarization. The model operates over a phrase-based representation of the source document which we obtain by merging information from PCFG parse trees and dependency graphs. Using an integer linear programming formulation, the model learns to select and combine phrases subject to length, coverage and grammar constraints. We evaluate the approach on the task of generating “story highlights”—a small number of brief, self-contained sentences that allow readers to quickly gather information on news stories. Experimental results show that the model’s output is comparable to human-written highlights in terms of both grammaticality and content.
The use of relative operating characteristic (ROC) curves in test performance evaluation.

Relative (or receiver) operating characteristic (ROC) curves are a graphical representation of the relationship between sensitivity and specificity of a laboratory test over all possible diagnostic cutoff values. Laboratory medicine has been slow to adopt the use of ROC curves for the analysis of diagnostic test performance. In this tutorial, we discuss the advantages and limitations of the ROC curve for clinical decision making in laboratory medicine. We demonstrate the construction and statistical uses of ROC analysis, review its published applications in clinical pathology, and comment on its role in the decision analytic framework in laboratory medicine.
The effects of an 8-week stabilization exercise program on lumbar movement sense in patients with low back pain.

Lumbar stabilization exercises have gained popularity and credibility in patients with non-acute low back pain. Previous research provides more support to strength/resistance and coordination/stabilisation programs. Some authors also suggest adding strength/resistance training following motor control exercises. However, the effect of such a lumbar stabilization program on lumbar proprioception has never been tested so far. The present study investigated the effects of an 8-week stabilization exercise program on lumbar proprioception in patients with low back pain (LBP) and assessed the 8-week test-retest reliability of lumbar proprioception in control subjects.



Lumbar proprioception was measured before and after an 8-week lumbar stabilization exercise program for patients with LBP. Control subjects participated in the same protocol but received no treatment.



The lumbar proprioception measure showed moderate reliability. Patients with LBP and control subjects demonstrated no differences in lumbar proprioception at baseline. Participants from both groups showed better proprioception following the 8-week interval, demonstrating the presence of learning between testing days.



The improvement of lumbar proprioception seen in both groups was ascribed to motor learning of the test itself. The effect of lumbar stabilization exercises on lumbar proprioception remains unknown because the LBP group did not show lumbar proprioception impairments.
Modelling of solar-wind hybrid renewable energy system architectures

Solar and wind are the most promising among the renewable energy sources. The objective of this paper is to investigate the optimal techno-economically feasible model to mitigate a large scale demand in decentralized scenario. Solar, and wind energy potential in a hybrid scenario is closely assessed with an intermittent support from diesel generator and battery banks. Based on the general usage of appliances in a residential demand for 200 families that comprises of 1000 people a suitable load pattern is developed for this study. Demand in northern Europe predominantly constitutes of heating requirement in the form of water and space heating. The proposed model demonstrates convincing reliability in terms of techno-economical prospective.  HOMER, and Balmorel software environments were used to simulate the models, results are assessed in the paper.
Application of response surface methodology (RSM) to optimize coagulation-flocculation treatment of leachate using poly-aluminum chloride (PAC) and alum.

Coagulation-flocculation is a relatively simple physical-chemical technique in treatment of old and stabilized leachate which has been practiced using a variety of conventional coagulants. Polymeric forms of metal coagulants which are increasingly applied in water treatment are not well documented in leachate treatment. In this research, capability of poly-aluminum chloride (PAC) in the treatment of stabilized leachate from Pulau Burung Landfill Site (PBLS), Penang, Malaysia was studied. The removal efficiencies for chemical oxygen demand (COD), turbidity, color and total suspended solid (TSS) obtained using PAC were compared with those obtained using alum as a conventional coagulant. Central composite design (CCD) and response surface method (RSM) were applied to optimize the operating variables viz. coagulant dosage and pH. Quadratic models developed for the four responses (COD, turbidity, color and TSS) studied indicated the optimum conditions to be PAC dosage of 2g/L at pH 7.5 and alum dosage of 9.5 g/L at pH 7. The experimental data and model predictions agreed well. COD, turbidity, color and TSS removal efficiencies of 43.1, 94.0, 90.7, and 92.2% for PAC, and 62.8, 88.4, 86.4, and 90.1% for alum were demonstrated.
Learning to Index for Nearest Neighbor Search

In this study, we present a novel ranking model based on learning the nearest neighbor relationships embedded in the index space. Given a query point, a conventional nearest neighbor search approach calculates the distances to the cluster centroids, before ranking the clusters from near to far based on the distances. The data indexed in the top-ranked clusters are retrieved and treated as the nearest neighbor candidates for the query. However, the loss of quantization between the data and cluster centroids will inevitably harm the search accuracy. To address this problem, the proposed model ranks clusters based on their nearest neighbor probabilities rather than the query-centroid distances to the query. The nearest neighbor probabilities are estimated by employing neural networks to characterize the neighborhood relationships as a nonlinear function, i.e., the density distribution of nearest neighbors with respect to the query. The proposed probability-based ranking model can replace the conventional distance-based ranking model as a coarse filter for candidate clusters, and the nearest neighbor probability can be used to determine the data quantity to be retrieved from the candidate cluster. Our experimental results demonstrated that implementation of the proposed ranking model for two state-of-the-art nearest neighbor quantization and search methods could boost the search performance effectively in billion-scale datasets.
Large Scale URL-based Classification Using Online Incremental Learning

We address the problem of large-scale topic classification of web pages based on the minimal text available in the URLs. This problem is challenging because of the sparsity of feature vectors that are derived from the URL text, and the typical asymmetry between the cardinality of train and test sets due to non-availability of sufficient sets of annotated URLs for training and very large test sets (e.g., in the case of large-scale focused crawling). We propose an online incremental learning algorithm which addresses these issues. Our experiments based on large publicly available datasets demonstrate an improvement of 0.11 -- 0.12 in terms of F-measure over the baseline algorithms, like Support Vector Machine, in difficult scenarios where the cardinality of train set is just a fraction of that of the test set.
FLOWPAK: Flow-based Ornamental Element Packing

We present a technique for drawing ornamental designs consisting of placed instances of simple shapes. These shapes, which we call elements, are selected from a small library of templates. The elements are deformed to flow along a direction field interpolated from user-supplied strokes, giving a sense of visual flow to the final composition, and constrained to lie within a container region. Our implementation computes a vector field based on user strokes, constructs streamlines that conform to the vector field, and places an element over each streamline. An iterative refinement process then shifts and stretches the elements to improve the composition.
SoftTarget Regularization: An Effective Technique to Reduce Over-Fitting in Neural Networks

Deep neural networks are learning models with a very high capacity and therefore prone to over- fitting. Many regularization techniques such as Dropout, DropConnect, and weight decay all attempt to solve the problem of over-fitting by reducing the capacity of their respective models (Srivastava et al., 2014), (Wan et al., 2013), (Krogh & Hertz, 1992). In this paper we introduce a new form of regularization that guides the learning problem in a way that reduces over- fitting without sacrificing the capacity of the model. The mistakes that models make in early stages of training carry information about the learning problem. By adjusting the labels of the current epoch of training through a weighted average of the real labels, and an exponential average of the past soft-targets we achieved a regularization scheme as powerful as Dropout without necessarily reducing the capacity of the model, and simplified the complexity of the learning problem. SoftTarget regularization proved to be an effective tool in various neural network architectures.
Architecture Compliance Checking of Semantically Rich Modular Architectures: A Comparative Study of Tool Support

Architecture Compliance Checking (ACC) is an approach to verify the conformance of implemented program code to high-level models of architectural design. ACC is used to prevent architectural erosion during the development and evolution of a software system. Static ACC, based on static software analysis techniques, focuses on the modular architecture and especially on rules constraining the modular elements. A semantically rich modular architecture (SRMA) is expressive and may contain modules with different semantics, like layers and subsystems, constrained by rules of different types. To check the conformance to an SRMA, ACC-tools should support the module and rule types used by the architect. This paper presents requirements regarding SRMA support and an inventory of common module and rule types, on which basis eight commercial and non-commercial tools were tested. The test results show large differences between the tools, but all could improve their support of SRMA, what might contribute to the adoption of ACC in practice.
PROJECTED PRINCIPAL COMPONENT ANALYSIS IN FACTOR MODELS.

This paper introduces a Projected Principal Component Analysis (Projected-PCA), which employees principal component analysis to the projected (smoothed) data matrix onto a given linear space spanned by covariates. When it applies to high-dimensional factor analysis, the projection removes noise components. We show that the unobserved latent factors can be more accurately estimated than the conventional PCA if the projection is genuine, or more precisely, when the factor loading matrices are related to the projected linear space. When the dimensionality is large, the factors can be estimated accurately even when the sample size is finite. We propose a flexible semi-parametric factor model, which decomposes the factor loading matrix into the component that can be explained by subject-specific covariates and the orthogonal residual component. The covariates' effects on the factor loadings are further modeled by the additive model via sieve approximations. By using the newly proposed Projected-PCA, the rates of convergence of the smooth factor loading matrices are obtained, which are much faster than those of the conventional factor analysis. The convergence is achieved even when the sample size is finite and is particularly appealing in the high-dimension-low-sample-size situation. This leads us to developing nonparametric tests on whether observed covariates have explaining powers on the loadings and whether they fully explain the loadings. The proposed method is illustrated by both simulated data and the returns of the components of the S&P 500 index.
State of the Art and Research Challenges of new services architecture technologies: Virtualization, SOA and Cloud Computing

The telecommunication world is evolving towards networks and different services. It is necessary to ensure interoperability between different networks to provide seamless and on-demand services. As the number of services and users in Internet Protocol Multimedia Subsystem (IMS) keeps increasing, network virtualization and cloud computing technologies seem to be a good alternative for Mobile Virtual Network Operators (MVNOs) in order to provide better services to customers and save cost and time. Cloud computing known as an IT environment that includes all elements of the IT and network stack, enabling the development, delivery, and consumption of Cloud Services. In this paper, we will present the challenges and issues of these emerging technologies. The first part of this paper describes Cloud computing as the networks of the future. It presents an overview of some works in this area. Some concepts like cloud services and Service oriented Architecture designed to facilitate rapid prototyping and deployment of on demand services that enhance flexibility, communication performance, robustness, and scalability are detailed. The second part exposes SOA and its concept, the third one deals with virtualization. Keywordscloud computing; services; SOA architecture; virtualization, IMS
AFrame: isolating advertisements from mobile applications in Android

Android uses a permission-based security model to restrict applications from accessing private data and privileged resources. However, the permissions are assigned at the application level, so even untrusted third-party libraries, such as advertisement, once incorporated, can share the same privileges as the entire application, leading to over-privileged problems.
 We present AFrame, a developer friendly method to isolate untrusted third-party code from the host applications. The isolation achieved by AFrame covers not only the process/permission isolation, but also the display and input isolation. Our AFrame framework is implemented through a minimal change to the existing Android code base; our evaluation results demonstrate that it is effective in isolating the privileges of untrusted third-party code from applications with reasonable performance overhead.
A Self-Paced Regularization Framework for Multilabel Learning

In this brief, we propose a novel multilabel learning framework, called multilabel self-paced learning, in an attempt to incorporate the SPL scheme into the regime of multilabel learning. Specifically, we first propose a new multilabel learning formulation by introducing a self-paced function as a regularizer, so as to simultaneously prioritize label learning tasks and instances in each iteration. Considering that different multilabel learning scenarios often need different self-paced schemes during learning, we thus provide a general way to find the desired self-paced functions. To the best of our knowledge, this is the first work to study multilabel learning by jointly taking into consideration the complexities of both training instances and labels. Experimental results on four publicly available data sets suggest the effectiveness of our approach, compared with the state-of-the-art methods.
Searching an Encrypted Cloud Meets Blockchain: A Decentralized, Reliable and Fair Realization

Enabling search directly over encrypted data is a desirable technique to allow users to effectively utilize encrypted data outsourced to a remote server like cloud service provider. So far, most existing solutions focus on an honest-but-curious server, while security designs against a malicious server have not drawn enough attention. It is not until recently that a few works address the issue of verifiable designs that enable the data owner to verify the integrity of search results. Unfortunately, these verification mechanisms are highly dependent on the specific encrypted search index structures, and fail to support complex queries. There is a lack of a general verification mechanism that can be applied to all search schemes. Moreover, no effective countermeasures (e.g., punishing the cheater) are available when an unfaithful server is detected. In this work, we explore the potential of smart contract in Ethereum, an emerging blockchain-based decentralized technology that provides a new paradigm for trusted and transparent computing. By replacing the central server with a carefully-designed smart contract, we construct a decentralized privacy-preserving search scheme where the data owner can receive correct search results with assurance and without worrying about potential wrongdoings of a malicious server. To better support practical applications, we introduce fairness to our scheme by designing a new smart contract for a financially-fair search construction, in which every participant (especially in the multiuser setting) is treated equally and incentivized to conform to correct computations. In this way, an honest party can always gain what he deserves while a malicious one gets nothing. Finally, we implement a prototype of our construction and deploy it to a locally simulated network and an official Ethereum test network, respectively. The extensive experiments and evaluations demonstrate the practicability of our decentralized search scheme over encrypted data.
6.1 A 56Gb/s PAM-4/NRZ transceiver in 40nm CMOS

Ultra-high speed data links such as 400GbE continuously push transceivers to achieve better performance and lower power consumption. This paper presents a highly parallelized TRX at 56Gb/s with integrated serializer/deserializer, FFE/CTLE/DFE, CDR, and eye-monitoring circuits. It achieves BER<10−12 under 24dB loss at 14GHz while dissipating 602mW of power.
Mechanisms and applications of the anti-inflammatory effects of photobiomodulation

Photobiomodulation (PBM) also known as low-level level laser therapy is the use of red and near-infrared light to stimulate healing, relieve pain, and reduce inflammation. The primary chromophores have been identified as cytochrome c oxidase in mitochondria, and calcium ion channels (possibly mediated by light absorption by opsins). Secondary effects of photon absorption include increases in ATP, a brief burst of reactive oxygen species, an increase in nitric oxide, and modulation of calcium levels. Tertiary effects include activation of a wide range of transcription factors leading to improved cell survival, increased proliferation and migration, and new protein synthesis. There is a pronounced biphasic dose response whereby low levels of light have stimulating effects, while high levels of light have inhibitory effects. It has been found that PBM can produce ROS in normal cells, but when used in oxidatively stressed cells or in animal models of disease, ROS levels are lowered. PBM is able to up-regulate anti-oxidant defenses and reduce oxidative stress. It was shown that PBM can activate NF-kB in normal quiescent cells, however in activated inflammatory cells, inflammatory markers were decreased. One of the most reproducible effects of PBM is an overall reduction in inflammation, which is particularly important for disorders of the joints, traumatic injuries, lung disorders, and in the brain. PBM has been shown to reduce markers of M1 phenotype in activated macrophages. Many reports have shown reductions in reactive nitrogen species and prostaglandins in various animal models. PBM can reduce inflammation in the brain, abdominal fat, wounds, lungs, spinal cord.