nils-herrmann/clean_scirepeval huggingface dataset:
Stream Flow Painting from Real Images Using Anisotropic Band-Pass Filter

A new non-photorealistic rendering algorithm is proposed for creating artistic painting with soft stream flow from natural color images. The algorithm consists mainly of two stages, that is, a revised bilateral filter called trilateral filter is firstly applied to original color image for creating drawings using gradient information and then a DoG-like band-pass filter is adapted for generating soft stream flow along the eigenvectors and therefore the image is smoothed along curved stream lines. The proposed trilateral filter is an extension of bilateral filter by incorporating gradient space. On the other hand, DoG-like band-pass filter is designed by applying eigenvectors and eigenvalues of a structure tensor matrix calculated at each pixel. Our approach effectively preserves image main structures while smoothing image regions in an anisotropic way. Even in regions with lower contrast, stream flow-like potential structures are also well produced due to a gradient relaxation. The experiments demonstrate that the proposed algorithm works well and produces good and pleasant visual results.
A data protection model for fog computing

Cloud computing has established itself as an alternative IT infrastructure and service model. However, as with all logically centralized resource and service provisioning infrastructures, cloud does not handle well local issues involving a large number of networked elements (IoTs) and it is not responsive enough for many applications that require immediate attention of a local controller. Fog computing preserves many benefits of cloud computing and it is also in a good position to address these local and performance issues because its resources and specific services are virtualized and located at the edge of the customer premise. However, data security is a critical challenge in fog computing especially when fog nodes and their data move frequently in its environment. This paper addresses the data protection and the performance issues by 1) proposing a Region-Based Trust-Aware (RBTA) model for trust translation among fog nodes of regions, 2) introducing a Fog-based Privacy-aware Role Based Access Control (FPRBAC) for access control at fog nodes, and 3) developing a mobility management service to handle changes of users and fog devices' locations. The implementation results demonstrate the feasibility and the efficiency of our proposed framework.
Pains in Parkinson disease—many syndromes under one umbrella

Pain is a nonmotor symptom that substantially affects the quality of life of at least one-third of patients with Parkinson disease (PD). Interestingly, patients with PD frequently report different types of pain, and a successful approach to distinguish between these pains is required so that effective treatment strategies can be established. Differences between these pains are attributable to varying peripheral pain mechanisms, the role of motor symptoms in causing or amplifying pain, and the role of PD pathophysiology in pain processing. In this Review, we propose a four-tier taxonomy to improve classification of pain in PD. This taxonomy assigns nociceptive, neuropathic and miscellaneous pains to distinct categories, as well as further characterization into subcategories. Currently, treatment of pain in PD is based on empirical data only, owing to a lack of controlled studies. The facultative symptom of 'dopaminergically maintained pain' refers to pain that benefits from antiparkinson medication. Here, we also present additional pharmacological and nonpharmacological treatment approaches, which can be targeted to a specific pain following classification using our taxonomy.
A Novel Lifecycle Framework for Semantic Web Service Annotation Assessment and Optimization

Semantic annotation plays an important role for semantic-aware web service discovery, recommendation and composition. In recent years, many approaches and tools have emerged to assist in semantic annotation creation and analysis. However, the Quality of Semantic Annotation (QoSA) is largely overlooked despite of its significant impact on the effectiveness of semantic-aware solutions. Moreover, improving the QoSA is time-consuming and requires significant domain knowledge. Therefore, how to verify and improve the QoSA has become a critical issue for semantic web services. In order to facilitate this process, this paper presents a novel lifecycle framework aiming at QoSA assessment and optimization. The QoSA is formally defined as the success rate of web service invocations, associated with a verification framework. Based on a local instance repository constructed from the execution information of the invocations, a two-layer optimization method including a local-feedback strategy and a global-feedback one is proposed to improve the QoSA. Experiments on real-world web services show that our framework can gain 65.95%~148.16% improvement in QoSA, compared with the original annotation without optimization.
On the Sample Complexity of the Linear Quadratic Regulator

education Ph.D. candidate, EECS, University of California, Berkeley, Spring 2019 (Expected). Advised by Prof. Benjamin Recht. S.M., EECS, Massachusetts Institute of Technology, Spring 2014. Advised by Prof. Samuel Madden.  Fast Transactions for Multicore In-Memory Databases. B.A., Computer Science, University of California, Berkeley, Fall 2010. B.S., Mechanical Engineering, University of California, Berkeley, Fall 2010.
Explosive Emission Properties of Cathode Materials in Relativistic Electron Beam Generation

Relativistic electron beam generation studies have been carried out in LIA-400 system through explosive electron emission for various cathode materials. This paper presents the emission properties of different cathode materials at peak diode voltages varying from 10 to 220 kV and at peak current levels from 0.5 to 2.2 kA in a single pulse duration of 160-180 ns. The cathode materials used are graphite, stainless steel, and red polymer velvet. The perveance data calculated from experimental waveforms are compared with 1-D Child Langmuir formula to obtain the cathode plasma expansion velocity for various cathode materials. Various diode parameters are subject to shot to shot variation analysis. Velvet cathode proves to be the best electron emitter because of its lower plasma expansion velocity and least shot to shot variability.
Inductance calculations for PM machines with concentrated windings

The usage of three phase permanent magnet (PM) machines with concentrated coil fractional pitch double layer windings, proofs to be very cost-effective for range extenders in the automotive sector. However, the number of possible slot pole combinations for these machine types is countless. This paper presents an analytical method for calculating the inductance components of these electrical machine types. This method can be used for calculations of all possible slot pole combinations. It does so by first deriving the machine optimal winding configuration. The machine winding configuration is then used to set up an armature reaction flux model. From the armature reaction flux the different inductance components, including the self-inductance with its main and leakage component and the mutual-inductance component can be determined. The analytical model is used for the inductance calculations of two prototype generators for the Peec-Power range extender. The results are compared to FEM calculations. An accurate analytical model is the result.
A Multi-component View of Digital Forensics

We are living in a world where there is an increasing need for evidence in organizations. Good digital evidence is becoming a business enabler. Very few organizations have the structures (management and infrastructure) in place to enable them to conduct cost effective, low-impact and fficient digital investigations . Digital Forensics (DF) is a vehicle that organizations use to provide good and trustworthy evidence and processes. The current DF models concentrate on reactive investigations, with limited reference to DF readiness and live investigations. However, organizations use DF for other purposes for example compliance testing. The paper proposes that DF consists of three components: Pro-active (ProDF), Active (ActDF) and Re-active (ReDF). ProDF concentrates on DF readiness and the proactive responsible use of DF to demonstrate good governance and enhance governance structures. ActDF considers the gathering of live evidence during an ongoing attack with a limited live investigation element whilst ReDF deals with the traditional DF investigation. The paper discusses each component and the relationship between the components.
Variable frequency and constant frequency modulation techniques for GaN based MHz H-bridge PFC

This paper explores two modulation techniques for power factor corrector (PFC) based on critical conduction mode (CRM) and proposes a new modulation technique which has the benefits of CRM and allows quasi constant switching frequency also. The converter is designed for MHz range switching frequency as high frequency reduces the size of the EMI filter. However at high frequency, the switching losses become the dominant losses making soft switching a necessity. CRM allows zero current switching (ZCS) turn-on but it's not able to achieve zero voltage switching (ZVS) turn-on when the input voltage is greater than half of the output voltage. To achieve ZVS turn-on over the entire mains cycle, triangular current mode (TCM) was proposed by Marxgut, C. et ] but both these methods have the drawback of variable switching frequency. The new method proposed modifies TCM so that the switching frequency is quasi constant and ZVS turn-on is also achieved over the entire mains cycle. Based on analytical loss model of Cascode GaN transistor, the efficiency of the three modulation techniques is compared. Also, the parameters of the EMI filter required are compared based on simulated noise measurement. As variable switching frequency is not preferred in three phase systems, the quasi constant frequency approach finds its benefits in three phase PFC.
Potential of Natural Products in the Inhibition of Adipogenesis through Regulation of PPARγ Expression and/or Its Transcriptional Activity

Obesity is a global health problem characterized as an increase in the mass of adipose tissue. Adipogenesis is one of the key pathways that increases the mass of adipose tissue, by which preadipocytes mature into adipocytes through cell differentiation. Peroxisome proliferator-activated receptor γ (PPARγ), the chief regulator of adipogenesis, has been acutely investigated as a molecular target for natural products in the development of anti-obesity treatments. In this review, the regulation of PPARγ expression by natural products through inhibition of CCAAT/enhancer-binding protein β (C/EBPβ) and the farnesoid X receptor (FXR), increased expression of GATA-2 and GATA-3 and activation of the Wnt/β-catenin pathway were analyzed. Furthermore, the regulation of PPARγ transcriptional activity associated with natural products through the antagonism of PPARγ and activation of Sirtuin 1 (Sirt1) and AMP-activated protein kinase (AMPK) were discussed. Lastly, regulation of mitogen-activated protein kinase (MAPK) by natural products, which might regulate both PPARγ expression and PPARγ transcriptional activity, was summarized. Understanding the role natural products play, as well as the mechanisms behind their regulation of PPARγ activity is critical for future research into their therapeutic potential for fighting obesity.
The Positive Relationships of Playfulness With Indicators of Health, Activity, and Physical Fitness

Adult playfulness is a personality trait that enables people to frame or reframe everyday situations in such a way that they experience them as entertaining, intellectually stimulating, or personally interesting. Earlier research supports the notion that playfulness is associated with the pursuit of an active way of life. While playful children are typically described as being active, only limited knowledge exists on whether playfulness in adults is also associated with physical activity. Additionally, existing literature has not considered different facets of playfulness, but only global playfulness. Therefore, we employed a multifaceted model that allows distinguishing among Other-directed, Lighthearted, Intellectual, and Whimsical playfulness. For narrowing this gap in the literature, we conducted two studies addressing the associations of playfulness with health, activity, and fitness. The main aim of Study 1 was a comparison of self-ratings (N = 529) and ratings from knowledgeable others (N = 141). We tested the association of self- and peer-reported playfulness with self- and peer-reported physical activity, fitness, and health behaviors. There was a good convergence of playfulness among self- and peer-ratings (between r = 0.46 and 0.55, all p < 0.001). Data show that both self- and peer-ratings are differentially associated with physical activity, fitness, and health behaviors. For example, self-rated playfulness shared 3% of the variance with self-rated physical fitness and 14% with the pursuit of an active way of life. Study 2 provides data on the association between self-rated playfulness and objective measures of physical fitness (i.e., hand and forearm strength, lower body muscular strength and endurance, cardio-respiratory fitness, back and leg flexibility, and hand and finger dexterity) using a sample of N = 67 adults. Self-rated playfulness was associated with lower baseline and activity (climbing stairs) heart rate and faster recovery heart rate (correlation coefficients were between -0.19 and -0.24 for global playfulness). Overall, Study 2 supported the findings of Study 1 by showing positive associations of playfulness with objective indicators of physical fitness (primarily cardio-respiratory fitness). The findings represent a starting point for future studies on the relationships between playfulness, and health, activity, and physical fitness.
Vital Role of Complement Receptor 2 ( CR 2 / CD 21 ) Along With Other Proteins in the Pathogenesis of Systemic Lupus Erythematosus

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease and its pathogenesis is unknown. SLE is regulated by complement receptors, proteins and antibodies such as complement receptor 2 (CR2/CD21), anti-dsDNA antibodies, Cysteine p Guanidine DNA (CpG DNA), toll-like receptor 9 (TLR9), interluekin-6 (IL-6), and interferon(IFN-α). Upon activation of plasmacytoid dendritic cells by bacterial CpG DNA or synthetic CpG ODN, these ligands binds to the cell surface CR2 and TLR9 to generate pro inflammatory cytokines via through NF-kB. In this, binding of these ligands induces releases of IFN-α from the plasmacytoid dendritic cells which further binds to IFN-α 1 & 2 receptors present on B cells. This binding was not completely blocked by an anti-IFNαR1 inhibitory antibody, indicating that the released IFN-α may partially binds to the CR2 present on the surface of B cells. IFN-α and IL-6 released from B cells was partially blocked by anti-CR2 inhibitory mAb171. These studies suggested that the cell surface CR2 partially involved in binding these ligands to generate pro inflammatory cytokines. More importantly these CpG DNA or CpG ODN predominantly binds to the cell surface/cellular TLR9 on B cells in order to induce the release of IL-6 and IFN-α, and other pro-inflammatory cytokines. This review describes how the bacterial CpG DNA/CpG motif/ CpG ODN regulate the innate immune system through B cell surface CR2 and TLR9 in B cell signaling.
Prediction of Grain Size , Precipitation and Crack Susceptibility in Continuous Casting

The formation of precipitates, especially along austenite grain boundaries, greatly affects the formation of transverse cracks on the surface of continuous-cast steel. The steel composition and cooling history influences the formation of precipitates, and the higher temperature and corresponding larger grain growth rate under oscillation marks or surface depressions also have an important effect on crack problems. This paper develops a model to predict and track the amount, composition and size distribution of precipitates and the grain size in order to predict the susceptibility of different steel grades to ductility problems during continuous casting processes. The results are important for controlled cooling of microalloyed steels to prevent cracks and assure product quality.
Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach

Short-term passenger demand forecasting is of great importance to the ondemand ride service platform, which can incentivize vacant cars moving from over-supply regions to over-demand regions. The spatial dependences, temporal dependences, and exogenous dependences need to be considered simultaneously, however, which makes short-term passenger demand forecasting challenging. We propose a novel deep learning (DL) approach, named the fusion convolutional long short-term memory network (FCL-Net), to address these three dependences within one end-to-end learning architecture. The model is stacked and fused by multiple convolutional long short-term memory (LSTM) layers, standard LSTM layers, and convolutional layers. The fusion of convolutional techniques and the LSTM network enables the proposed DL approach to better capture the spatiotemporal characteristics and correlations of explanatory variables. A tailored spatially aggregated random forest is employed to rank the importance of the explanatory variables. The ranking is then used for feature selection. The proposed DL approach is applied to the short-term forecasting of passenger demand under an on-demand ride service platform in Hangzhou, China. Experimental results, validated on real-world data provided by DiDi Chuxing, show that the FCL-Net achieves better predictive performance than traditional approaches in∗Corresponding author Email address: chenxiqun@zju.edu.cn (Xiqun (Michael) Chen)  2017 ar X iv :1 70 6. 06 27 9v 1 [ cs .A I] 2 0 Ju n 20 17 cluding both classical time-series prediction models and neural network based algorithms (e.g., artificial neural network and LSTM). Furthermore, the consideration of exogenous variables in addition to passenger demand itself, such as the travel time rate, time-of-day, day-of-week, and weather conditions, is proven to be promising, since it reduces the root mean squared error (RMSE) by 50.9%. It is also interesting to find that the feature selection reduces 30% in the dimension of predictors and leads to only 0.6% loss in the forecasting accuracy measured by RMSE in the proposed model. This paper is one of the first DL studies to forecast the short-term passenger demand of an on-demand ride service platform by examining the spatio-temporal correlations.
MHTN: Modal-adversarial Hybrid Transfer Network for Cross-modal Retrieval

Cross-modal retrieval has drawn wide interest for retrieval across different modalities (such as text, image, video, audio, and 3-D model). However, existing methods based on a deep neural network often face the challenge of insufficient cross-modal training data, which limits the training effectiveness and easily leads to overfitting. Transfer learning is usually adopted for relieving the problem of insufficient training data, but it mainly focuses on knowledge transfer only from large-scale datasets as a single-modal source domain (such as ImageNet) to a single-modal target domain. In fact, such large-scale single-modal datasets also contain rich modal-independent semantic knowledge that can be shared across different modalities. Besides, large-scale cross-modal datasets are very labor-consuming to collect and label, so it is significant to fully exploit the knowledge in single-modal datasets for boosting cross-modal retrieval. To achieve the above goal, this paper proposes a modal-adversarial hybrid transfer network (MHTN), which aims to realize knowledge transfer from a single-modal source domain to a cross-modal target domain and learn cross-modal common representation. It is an end-to-end architecture with two subnetworks. First, a modal-sharing knowledge transfer subnetwork is proposed to jointly transfer knowledge from a single modality in the source domain to all modalities in the target domain with a star network structure, which distills modal-independent supplementary knowledge for promoting cross-modal common representation learning. Second, a modal-adversarial semantic learning subnetwork is proposed to construct an adversarial training mechanism between the common representation generator and modality discriminator, making the common representation discriminative for semantics but indiscriminative for modalities to enhance cross-modal semantic consistency during the transfer process. Comprehensive experiments on four widely used datasets show the effectiveness of MHTN.
Power gating: Circuits, design methodologies, and best practice for standard-cell VLSI designs

Power Gating has become one of the most widely used circuit design techniques for reducing leakage current. Its concept is very simple, but its application to standard-cell VLSI designs involves many careful considerations. The great complexity of designing a power-gated circuit originates from the side effects of inserting current switches, which have to be resolved by a combination of extra circuitry and customized tools and methodologies. In this tutorial we survey these design considerations and look at the best practice within industry and academia. Topics include output isolation and data retention, current switch design and sizing, and physical design issues such as power networks, increases in area and wirelength, and power grid analysis. Designers can benefit from this tutorial by obtaining a better understanding of implications of power gating during an early stage of VLSI designs. We also review the ways in which power gating has been improved. These include reducing the sizes of switches, cutting transition delays, applying power gating to smaller blocks of circuitry, and reducing the energy dissipated in mode transitions. Power Gating has also been combined with other circuit techniques, and these hybrids are also reviewed. Important open problems are identified as a stimulus to research.
Deep Neural Networks versus Naive Bayes Classifiers for Identifying Informative Tweets during Disasters

Traditional machine learning techniques have shown promising results in automating the process of identifying useful information in crisis-related data posted through micro-blogging services such as Twitter. More recently, deep learning techniques have also shown promise in the area of disaster response. In this paper, we focus on understanding the e ectiveness of deep neural networks by comparison with the e ectiveness of standard classifiers that use carefully engineered features. Specifically, we design various feature sets (based on tweet content, user details and polarity clues) and use these feature sets individually or in various combinations, with Naïve Bayes classifiers. Furthermore, we develop neural models based on Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) with handcrafted architectures. We compare the two types of approaches in the context of identifying informative tweets posted during disasters, and show that the deep neural networks, in particular the CNN networks, are more e ective for the task considered.