nils-herrmann/clean_scirepeval huggingface dataset:
Blood rheology and hemodynamics.

Blood is a two-phase suspension of formed elements (i.e., red blood cells [RBCs], white blood cells [WBCs], platelets) suspended in an aqueous solution of organic molecules, proteins, and salts called plasma. The apparent viscosity of blood depends on the existing shear forces (i.e., blood behaves as a non-Newtonian fluid) and is determined by hematocrit, plasma viscosity, RBC aggregation, and the mechanical properties of RBCs. RBCs are highly deformable, and this physical property significantly contributes to aiding blood flow both under bulk flow conditions and in the microcirculation. The tendency of RBCs to undergo reversible aggregation is an important determinant of apparent viscosity because the size of RBC aggregates is inversely proportional to the magnitude of shear forces; the aggregates are dispersed with increasing shear forces, then reform under low-flow or static conditions. RBC aggregation also affects the in vivo fluidity of blood, especially in the low-shear regions of the circulatory system. Blood rheology has been reported to be altered in various physiopathological processes: (1) Alterations of hematocrit significantly contribute to hemorheological variations in diseases and in certain extreme physiological conditions; (2) RBC deformability is sensitive to local and general homeostasis, with RBC deformability affected by alterations of the properties and associations of membrane skeletal proteins, the ratio of RBC membrane surface area to cell volume, cell morphology, and cytoplasmic viscosity. Such alterations may result from genetic disorders or may be induced by such factors as abnormal local tissue metabolism, oxidant stress, and activated leukocytes; and (3) RBC aggregation is mainly determined by plasma protein composition and surface properties of RBCs, with increased plasma concentrations of acute phase reactants in inflammatory disorders a common cause of increased RBC aggregation. In addition, RBC aggregation tendency can be modified by alterations of RBC surface properties because of RBC in vivo aging, oxygen-free radicals, or proteolytic enzymes. Impairment of blood fluidity may significantly affect tissue perfusion and result in functional deteriorations, especially if disease processes also disturb vascular properties.
Predicting online-purchasing behaviour

This empirical study investigates the contribution of different types of predictors to the purchasing behaviour at an online store. We use logit modelling to predict whether or not a purchase is made during the next visit to the website using both forward and backward variable-selection techniques, as well as Furnival and Wilson’s global score search algorithm to find the best subset of predictors. We contribute to the literature by using variables from four different categories in predicting online-purchasing behaviour: (1) general clickstream behaviour at the level of the visit, (2) more detailed clickstream information, (3) customer demographics, and (4) historical purchase behaviour. The results show that predictors from all four categories are retained in the final (best subset) solution indicating that clickstream behaviour is important when determining the tendency to buy. We clearly indicate the contribution in predictive power of variables that were never used before in online purchasing studies. Detailed clickstream variables are the most important ones in classifying customers according to their online purchase behaviour. In doing so, we are able to highlight the advantage of e-commerce retailers of being able to capture an elaborate list of customer information.
A graph based approach to scientific paper recommendation

When looking for recently published scientific papers, a researcher usually focuses on the topics related to her/his scientific interests. The task of a recommender system is to provide a list of unseen papers that match these topics. The core idea of this paper is to leverage the latent topics of interest in the publications of the researchers, and to take advantage of the social structure of the researchers (relations among researchers in the same field) as reliable sources of knowledge to improve the recommendation effectiveness. In particular, we introduce a hybrid approach to the task of scientific papers recommendation, which combines content analysis based on probabilistic topic modeling and ideas from collaborative filtering based on a relevance-based language model. We conducted an experimental study on DBLP, which demonstrates that our approach is promising.
Development of an Improved P&O Algorithm Assisted Through a Colony of Foraging Ants for MPPT in PV System

The perturb and observe (P&O) algorithm is a simple and efficient technique, and is one of the most commonly employed maximum power point (MPP) tracking (MPPT) schemes for photovoltaic (PV) power-generation systems. However, under partially shaded conditions (PSCs), P&O method miserably fails to recognize global MPP (GMPP) and gets trapped in one of the local MPPs (LMPPs). This paper proposes ant-colony-based search in the initial stages of tracking followed by P&O method. In such a hybrid approach, the global search ability of ant-colony optimization (ACO) and local search capability of P&O method are integrated to yield faster and efficient convergence. A theoretical analysis of the static and dynamic convergence behavior of the proposed algorithm is presented together with computed and measured results.
Design and Analysis of 6-DOF Triple Scissor Extender Robots With Applications in Aircraft Assembly

A new type of parallel robot mechanism with an extendable structure is presented, and its kinematic properties and design parameters are analyzed. The triple scissor extender (TSE) is a six-degree-of-freedom robotic mechanism for reaching high ceilings and positioning an end effector. Three scissor mechanisms are arranged in parallel, with the bottom ends coupled to linear slides, and the top vertex attached to an end effector plate. Arbitrary positions and orientations of the end effector can be achieved through the coordinated motion of the six linear actuators located at the base. By changing key geometric parameters, the TSE's design can yield a specific desired workspace volume and differential motion behavior. A general kinematic model for diverse TSEs is derived, and the kinematic properties, including workspace, singularity, and the Jacobian singular values, are evaluated. From these expressions, four key design parameters are identified, and their sensitivity upon the workspace volume and the Jacobian singular values is analyzed.A case study in autonomous aircraft assembly is presented using the insights gained from the design parameter studies.
Dynamic processor allocation for adaptively parallel work-stealing jobs

This thesis addresses the problem of scheduling multiple, concurrent, adaptively parallel jobs on a multiprogrammed shared-memory multiprocessor. Adaptively parallel jobs are jobs for which the number of processors that can be used without waste varies during execution. We focus on the specific case of parallel jobs that are scheduled using a randomized work-stealing algorithm, as is used in the Cilk multithreaded language. We begin by developing a theoretical model for two-level scheduling systems, or those in which the operating system allocates processors to jobs, and the jobs schedule their threads on the processors. To analyze the performance of a job scheduling algorithm, we model the operating system as an adversary. We show that a greedy scheduler achieves an execution time that is within a factor of 2 of optimal under these conditions. Guided by our model, we present a randomized work-stealing algorithm for adaptively parallel jobs, algorithm WSAP, which takes a unique approach to estimating the processor desire of a job. We show that attempts to directly measure a job’s instantaneous parallelism are inherently misleading. We also describe a dynamic processor-allocation algorithm, algorithm DP, that allocates processors to jobs in a fair and efficient way. Using these two algorithms, we present the design and implementation of Cilk-AP, a two-level scheduling system for adaptively parallel workstealing jobs. Cilk-AP is implemented by extending the runtime system of Cilk. We tested the Cilk-AP system on a shared-memory symmetric multiprocessor (SMP) with 16 processors. Our experiments show that, relative to the original Cilk system, Cilk-AP incurs negligible overhead and provides up to 37% improvement in throughput and 30% improvement in response time in typical multiprogramming scenarios. This thesis represents joint work with Charles Leiserson and Kunal Agrawal of the Supercomputing Technologies Group at MIT’s Computer Science and Artificial Intelligence Laboratory.  Charles E. Leiserson Title: Professor
Hybrid CPU-GPU Framework for Network Motifs

Massively parallel architectures such as the GPU are becoming increasingly important due to the recent proliferation of data. In this paper, we propose a key class of hybrid parallel graphlet algorithms that leverages multiple CPUs and GPUs simultaneously for computing k-vertex induced subgraph statistics (called graphlets). In addition to the hybrid multi-core CPU-GPU framework, we also investigate single GPU methods (using multiple cores) and multi-GPU methods that leverage all available GPUs simultaneously for computing induced subgraph statistics. Both methods leverage GPU devices only, whereas the hybrid multicore CPU-GPU framework leverages all available multi-core CPUs and multiple GPUs for computing graphlets in large networks. Compared to recent approaches, our methods are orders of magnitude faster, while also more cost effective enjoying superior performance per capita and per watt. In particular, the methods are up to 300 times faster than a recent state-of-the-art method. To the best of our knowledge, this is the first work to leverage multiple CPUs and GPUs simultaneously for computing induced subgraph statistics.
Cost-Effective Resource Provisioning for MapReduce in a Cloud

This paper presents a new MapReduce cloud service model, Cura, for provisioning cost-effective MapReduce services in a cloud. In contrast to existing MapReduce cloud services such as a generic compute cloud or a dedicated MapReduce cloud, Cura has a number of unique benefits. First, Cura is designed to provide a cost-effective solution to efficiently handle MapReduce production workloads that have a significant amount of interactive jobs. Second, unlike existing services that require customers to decide the resources to be used for the jobs, Cura leverages MapReduce profiling to automatically create the best cluster configuration for the jobs. While the existing models allow only a per-job resource optimization for the jobs, Cura implements a globally efficient resource allocation scheme that significantly reduces the resource usage cost in the cloud. Third, Cura leverages unique optimization opportunities when dealing with workloads that can withstand some slack. By effectively multiplexing the available cloud resources among the jobs based on the job requirements, Cura achieves significantly lower resource usage costs for the jobs. Cura's core resource management schemes include cost-aware resource provisioning, VM-aware scheduling and online virtual machine reconfiguration. Our experimental results using Facebook-like workload traces show that our techniques lead to more than 80 percent reduction in the cloud compute infrastructure cost with upto 65 percent reduction in job response times.
Fail-Stop Failure Algorithm-Based Fault Tolerance for Cholesky Decomposition

Cholesky decomposition is a widely used algorithm to solve linear equations with symmetric and positive definite coefficient matrix. With large matrices, this often will be performed on high performance supercomputers with a large number of processors. Assuming a constant failure rate per processor, the probability of a failure occurring during the execution increases linearly with additional processors. Fault tolerant methods attempt to reduce the expected execution time by allowing recovery from failure. This paper presents an analysis and implementation of a fault tolerant Cholesky factorization algorithm that does not require checkpointing for recovery from fail-stop failures. Rather, this algorithm uses redundant data added in an additional set of processes. This differs from previous works with algorithmic methods as it addresses fail-stop failures rather than fail-continue cases. The proposed fault tolerance scheme is incorporated into ScaLAPACK and validated on the supercomputer Kraken. Experimental results demonstrate that this method has decreasing overhead in relation to overall runtime as the matrix size increases, and thus shows promise to reduce the expected runtime for Cholesky factorizations on very large matrices.
Optimal base station deployment for small cell networks with energy-efficient power control

In this paper, how to optimally deploy base station density in a small cell network with energy-efficient power control was investigated. Base stations (BSs) and users form two independent Poisson point processes (PPPs) in the network. Since user-centric cell association may lead to void cells that do not have any users, the power of each BS is controlled in either all-on or on-off mode depending on whether its cell is void or not. The average cell rates for each power control mode are first found and their corresponding energy efficiency is also characterized. The optimal BS density that maximizes the energy efficiency under a given user density is theoretically proved to exist and its value can be found numerically. Both analytical and simulated results indicate that on-off power control is significantly superior to all-on power control in terms of energy efficiency if BSs are deployed based on their optimal energy-efficient density.
Autonomous Vehicle Testing and Validation Platform: Integrated Simulation System with Hardware in the Loop*

With the development of autonomous driving, offline testing remains an important process allowing low-cost and efficient validation of vehicle performance and vehicle control algorithms in multiple virtual scenarios. This paper aims to propose a novel simulation platform with hardware in the loop (HIL). This platform comprises of four layers: the vehicle simulation layer, the virtual sensors layer, the virtual environment layer and the Electronic Control Unit (ECU) layer for hardware control. Our platform has attained multiple capabilities: (1) it enables the construction and simulation of kinematic car models, various sensors and virtual testing fields; (2) it performs a closed-loop evaluation of scene perception, path planning, decision-making and vehicle control algorithms, whilst also having multi-agent interaction system; (3) it further enables rapid migrations of control and decision-making algorithms from the virtual environment to real self-driving cars. In order to verify the effectiveness of our simulation platform, several experiments have been performed with self-defined car models in virtual scenarios of a public road and an open parking lot and the results are substantial.
Mixtures of ARMA Models for Model-Based Time Series Clustering

Clustering problems are central to many knowledge discovery and data mining tasks. However, most existing clustering methods can only work with fixed-dimensional representations of data patterns. In this paper, we study the clustering of data patterns that are represented as sequences or time series possibly of different lengths. We propose a model-based approach to this problem using mixtures of autoregressive moving average (ARMA) models. We derive an expectation-maximization (EM) algorithm for learning the mixing coefficients as well as the parameters of the component models. The algorithm can determine the number of clusters in the data automatically. Experiments were conducted on a number of simulated and real datasets. Results from the experiments show that our method compares favorably with another method recently proposed by others for similar time series clustering problems.
3DIC/TSV process developments by printing technologies

We have examined printing technology to be adaptable to TSV forming process such as isolation layer forming and via fill. The result shows good possibility. Isolation layer forming by silica paste fill had been confirmed to adaptable for CD 1.5μm to 15μm. Printing process in vacuum chamber by squeezing followed by centrifugal processing is a candidate even though it should be improved on fill-rate. We chose Nano-Function material for TSV fill which was initially developed for power semiconductor attachment. They filled into via also using vacuum circumstances. We confirmed good results to fill via array by the paste using glass substrate with 50μm pitch, 20-25μm diameter and 100μm depth.
The traumatic stress response in child maltreatment and resultant neuropsychological effects

Child maltreatment is a pervasive problem in our society that has long-term detrimental consequences to the development of the affected child such as future brain growth and functioning. In this paper, we surveyed empirical evidence on the neuropsychological effects of child maltreatment, with a special emphasis on emotional, behavioral, and cognitive process–response difficulties experienced by maltreated children. The alteration of the biochemical stress response system in the brain that changes an individual’s ability to respond efficiently and efficaciously to future stressors is conceptualized as the traumatic stress response. Vulnerable brain regions include the hypothalamic–pituitary–adrenal axis, the amygdala, the hippocampus, and prefrontal cortex and are linked to children’s compromised ability to process both emotionally-laden and neutral stimuli in the future. It is suggested that information must be garnered from varied literatures to conceptualize a research framework for the traumatic stress response in maltreated children. This research framework suggests an altered developmental trajectory of information processing and emotional dysregulation, though much debate still exists surrounding the correlational nature of empirical studies, the potential of resiliency following childhood trauma, and the extent to which early interventions may facilitate recovery.
Authentication of Bulbus Fritillariae Cirrhosae by RAPD-Derived DNA Markers

Bulbus Fritillariae is the most commonly used antitussive herb in China. Eleven species of Fritillaria are recorded as Bulbus Fritillariae in the Chinese Pharmacopoeia. Bulbus Fritillariae Cirrhosae is a group of six Fritillaria species with higher efficiency and lower toxicity derived mainly from wild sources. Because of their higher market price, five other Fritillaria species are often sold deceptively as Bulbus Fritillariae Cirrhosae in the herbal market. To ensure the efficacy and safety of medicinal herbs, the authentication of botanical resources is the first step in quality control. Here, a DNA based identification method was developed to authenticate the commercial sources of Bulbus Fritillariae Cirrhosae. A putative DNA marker (0.65 kb) specific for Bulbus Fritillariae Cirrhosae was identified using the Random Amplified Polymorphic DNA (RAPD) technique. A DNA marker representing a Sequence Characterized Amplified Region (SCAR) was developed from a RAPD amplicon. The SCAR marker was successfully applied to differentiate Bulbus Fritillariae Cirrhosae from different species of Fritillaria. Additionally, the SCAR marker was also useful in identifying the commercial samples of Bulbus Fritillariae Cirrhosae. Our results indicated that the RAPD-SCAR method was rapid, accurate and applicable in identifying Bulbus Fritillariae Cirrhosae at the DNA level.
Data-Driven 3-D Human Body Customization With a Mobile Device

It is more convincing for users to have their own 3-D body shapes in the virtual fitting room when they shop clothes online. However, existing methods are limited for ordinary users to efficiently and conveniently access their 3-D bodies. We propose an efficient data-driven approach and develop an android application for 3-D body customization. Users stand naturally and their photos are taken from front and side views with a handy phone camera. They can wear casual clothes like a short-sleeved/long-sleeved shirt and short/long pants. First, we develop a user-friendly interface to semi-automatically segment the human body from photos. Then, the segmented human contours are scaled and translated to the ones under our virtual camera configurations. Through this way, we only need one camera to take photos of human in two views and do not need to calibrate the camera, which satisfy the convenience requirement. Finally, we learn body parameters that determine the 3-D body from dressed-human silhouettes with cascaded regressors. The regressors are trained using a database containing 3-D naked and dressed body pairs. Body parameters regression only costs 1.26 s on an android phone, which ensures the efficiency of our method. We invited 12 volunteers for tests, and the mean absolute estimation error for chest/waist/hip size is 2.89/1.93/2.22 centimeters. We additionally use 637 synthetic data to evaluate the main procedures of our approach.
A redundancy-removing feature selection algorithm for nominal data

Computations to obtain the mutual information that is in nominal data are usually difficult, and there will always be more redundancy in a nominal dataset, which means that an efficient nominal-data feature selection method is difficult to find. In this paper, a nominal-data feature selection method based on mutual information without data transformation, called the redundancy-removing more relevance less redundancy algorithm, is proposed. By forming several new information-related definitions and the corresponding computational methods, the proposed method can compute the information-related amount of nominal data directly. Furthermore, by creating a new evaluation function that considers both the relevance and the redundancy globally, the new feature selection method can evaluate the importance of each nominal-data feature. Although the presented feature selection method takes commonly used MIFS-like forms, it is capable of handling high-dimensional datasets without expensive computations. We perform extensive experimental comparisons of the proposed algorithm and other methods using three benchmarking nominal datasets with two different classifiers. The experimental results demonstrate the average advantage of the presented algorithm over the well-known NMIFS algorithm in terms of the feature selection and classification accuracy, which indicates that the proposed method has a promising performance.
Smart pneumatic artificial muscle actuator with embedded microfluidic sensing

We describe the design, fabrication, and characterization of a novel pneumatic artificial muscle actuator with embedded contraction sensing. The muscle is composed of three main components: elastomer air chamber, embedded Kevlar threads, and a helical microchannel filled with a liquid conductor. When the air chamber is inflated with compressed air, the constrained length of the Kevlar threads causes the muscle to contract in the axial direction. During this contraction, the microchannel can detect the shape change of the muscle by sensing, the expansion of the air chamber. This sensing capability increases the controllability of pneumatic muscles. A novel manufacturing method is proposed to embed Kevlar threads and a helical microchannel in an elastomer tube. Then, a liquid metal is injected into the microchannel to make a soft sensor that can detect the geometrical change of the muscle. The muscle prototype was characterized to demonstrate its actuation and sensing capability.