nils-herrmann/clean_scirepeval huggingface dataset:
Software-only bus encoding techniques for an embedded system

Microprocessors with built-in Liquid Crystal Device (LCD) controllers and equipped with Flash memory are common in mobile computing applications. In the first part of the paper, a software-only encoding technique is proposed to reduce the power consumption of the processor-memory bus when displaying an image on the LCD. Based on the translation mechanism of the LCD controller, our approach is to start with the palette as a coding table for the pixel buffer and then reassign the codes according to the image characteristics. Experimental results prove the efficacy of this approach; power reduction reaches 29% for text-based and 17% for graphics-based images. In the second part of the paper, another software-only encoding technique is presented to reduce the transitions on the processor CompactFlash bus. The device driver in Linux operating system is modified to perform Bus-Invert encoding when the data is read from or written to a Compact Flash file system. With minimal software overhead, the transitions on the bus are reduced by up to 25%.
Development of the World's First HTS Power Substation

With the increasing depletion of fossil fuels and growing environmental pressure, the mankind has got known the need to vigorously develop the renewable energy and the energy-saving technology. The high Tc superconducting (HTS) power technology will be very helpful to enhance the stability, reliability, and efficiency and transmission capacity of the power grid which would be dominated by the renewable energy. In this paper, we will report the installation and operation of a 10 kV HTS power substation which includes a 75 m/1.5 kA HTS power cable, a 10 kV/1.5 kA HTS fault current limiter, a 1 MJ/0.5 MVA high Tc SMES and a 630 kVA/10 kV/0.4 kV HTS power transformer.
Nonlinear current-voltage characteristics of HTS conductor and its application to the operation of SFCL

A proper description of the nonlinear voltage-current relation of the superconductor is necessary for accurate analysis of the superconducting fault current limiters (SFCL). Discrepancies between the experimental results and the prediction of Bean's critical state model were observed. Using a recently found unified nonlinear response function of type-II superconductors, the authors derive the current-voltage characteristic of metal-sheathed HTS conductor and compare it with experiments. Its relation to the operation of SFCL is also discussed.
Recognizing focal liver lesions in contrast-enhanced ultrasound with discriminatively trained spatio-temporal model

The aim of this study is to provide an automatic computational framework to assist clinicians in diagnosing Focal Liver Lesions (FLLs) in Contrast-Enhancement Ultrasound (CEUS). We represent FLLs in a CEUS video clip as an ensemble of Region-of-Interests (ROIs), whose locations are modeled as latent variables in a discriminative model. Different types of FLLs are characterized by both spatial and temporal enhancement patterns of the ROIs. The model is learned by iteratively inferring the optimal ROI locations and optimizing the model parameters. To efficiently search the optimal spatial and temporal locations of the ROIs, we propose a data-driven inference algorithm by combining effective spatial and temporal pruning. The experiments show that our method achieves promising results on the largest dataset in the literature (to the best of our knowledge), which we have made publicly available.
Deep boosting: Layered feature mining for general image classification

Constructing effective representations is a critical but challenging problem in multimedia understanding. The traditional handcraft features often rely on domain knowledge, limiting the performances of exiting methods. This paper discusses a novel computational architecture for general image feature mining, which assembles the primitive filters (i.e. Gabor wavelets) into compositional features in a layer-wise manner. In each layer, we produce a number of base classifiers (i.e. regression stumps) associated with the generated features, and discover informative compositions by using the boosting algorithm. The output compositional features of each layer are treated as the base components to build up the next layer. Our framework is able to generate expressive image representations while inducing very discriminate functions for image classification. The experiments are conducted on several public datasets, and we demonstrate superior performances over state-of-the-art approaches.
3D structure inference by integrating segmentation and reconstruction from a single image

The authors present a hierarchical Bayesian method for inferring the 3D structure of polyhedral man-made objects from a single image by integrating 2D image parsing and 3D reconstruction. In the first stage, the image is parsed into its constituent components - arbitrary shape regions and polygonal shape regions. In the second stage, polygonal shape regions are grouped into man-made polyhedral objects. The 3D structures of these polyhedral objects are further inferred using geometric priors. These two stages are integrated into a Bayesian inference scheme and cooperate to compute the optimal solutions. This method enables the model to correct possible errors and explain ambiguities in the lower level with the help of information from the higher level. The algorithm is applied to the images of indoor scenes, and the experimental results demonstrate satisfactory performance.
Optimisation of train energy-efficient operation for mass rapid transit systems

This study presents a method for optimising train driving strategies of the mass rapid transit systems with the aim to save energy between successive stations. Combinational optimisation techniques are developed to solve the online optimisation problem. To achieve an optimal train driving strategy, train operation modes, including acceleration, cruise and coast modes, in tandem with the speed codes of each section, are determined by the MAX–MIN ant system algorithm. The results of the case study show superior performance in terms of energy consumption in comparison with the previous research. Given that computation time reaches a reasonable level, it becomes legitimate to address online optimisation by using the proposed approach.
Structural analyses of mechanisms with springs in ancient China

Mechanisms with springs were used for variety of purposes from the 2nd century BC in ancient China. Barbed-springs locks and original crossbows are representative types that belong to reconfigurable mechanisms. Several available books with mechanical illustrations introduce the structures and functions of mechanisms with springs in ancient China. However, no historical records describing the appearance or invention of the machinery can be obtained in ancient manuscripts. This work summarizes the authors' research regarding the structural analyses of barbed-springs locks and original crossbows that were described in ancient Chinese books. The historical development of mechanisms with springs in ancient China is briefly introduced first. Then, in viewpoint of modern mechanisms, the functions and components of mechanisms with springs are analyzed and presented.
A Small 1.5 T Persistent Current Operating Test Magnet Using ${\rm MgB}_{2}$  Wire With High $j_{\rm c}$ Joints

Persistent current (PC) operating magnet with bore diameter of about 32 mm was design and developed based on multi- and mono-filament MgB2 wires. The magnet was in a multi-solenoid structure. One pilot coil was wound and joints were fabricated at both ends, connecting it to a superconducting switch for PC operation tests. The magnet was to demonstrate the high field and high temperature performance at different temperatures. Numerical and experimental results demonstrated a promising future of MgB2 wires in low cost magnetic resonance imaging (MRI) systems and high field scientific magnets.
Data-driven scene understanding by adaptive exemplar retrieval

This article studies a data-driven approach for semantically scene understanding, without pixelwise annotation and classifier pre-training. Our framework parses a target image with two steps: (i) retrieving its exemplars (i.e. references) from an image database, where all images are unsegmented but annotated with tags; (ii) recovering its pixel labels by propagating semantics from the references. We present a novel framework making the two steps mutually conditional and bootstrapped under the probabilistic Expectation-maximization (EM) formulation. In the first step, the references are selected by jointly matching their appearances with the target as well as the semantics. We process the second step via a combinatorial graphical representation, in which the vertices are superpixels extracted from the target and its selected references. Then we derive the potentials of assigning labels to one vertex of the target, which depends upon the graph edges that connect the vertex to its spatial neighbors of the target and to its similar vertices of the references. Two steps can be both solved analytically, and the inference is conducted in a self-driven fashion. In the experiments, we validate our approach on two public databases, and demonstrate superior performances over the state-of-the-art methods.
Greetings from the general chairs and program chairs

On behalf of the organizing committee of the 11th IEEE International Conference on Control & Automation (IEEE ICCA 2014), we take great pleasure in extending a warm welcome to each of you attending the conference. IEEE ICCA is a major control event that runs around the world. It aims to create a forum for scientists and practicing engineers throughout the world to get together, present the latest research findings and exchange ideas in the areas of control and automation.
A 2D chamber-free micro droplet generator array controlled by dynamic virtual walls

A chamber-free two-dimensional-array micro droplet generator has been realized by precise time-delayed control of micro bubble arrays as virtual chamber walls. Droplets can be ejected out by the bubbles around the ejection site in specific configuration of excitation, thus replacing physical chamber walls for pressure preservation. The volume, initial speed, and frequency response of the generated droplets was 3.6∼3.8 pL, 15 m/s, and 20 kHz, respectively, meeting the standard of commercial printheads. The micro droplet generator is free of satellite droplets due to the precise control of meniscus that undergoes a “push-pull-push” progress which effectively cuts the liquid column short.
HTS Power Technology for Future DC Power Grid

The growing environmental pressure resulting from the use of fossil fuels is leading mankind to make a transition from the use of traditional energy sources to that of renewable energy based clean energy. Because renewable energy has the feature of instability, it thus brings significant challenges on real-time power balance and power dispatching. Therefore, to secure the power supply, the grid needs to be upgraded by the selection of a reasonable grid structure and operation mode. In this paper, a multiple-level direct current (dc) loop grid, which would be the suitable mode for the future power grid, is suggested. Then, the high-temperature superconducting (HTS) dc power technology such as the HTS dc power cable and dc fault current limiter for the future power grid are discussed. We also report on the test and operation of a 360-m/10-kA HTS dc cable that is being built and would be used for an electrolytic aluminum plant of Zhongfu Group in Henan Province, China.
Synthesising periodic triggering signals with genetic oscillators.

The potential of the clock lies in its role of triggering logic reaction for sequential biological circuits. This research introduces an idea of designing a genetic clock generator by Fourier series based on the genetic oscillators. The authors generalise the design idea using a combination of fundamental sinusoidal signals. Since biochemical reaction of the biological system is extremely slow, however, transition between minimal and maximal levels is instantaneous for an ideal clock signal; it is thus not directly realisable in biological systems. That means it would be hard to directly synthesize a square wave generator for use as a genetic clock. They apply Fourier series to represent a square wave as a finite summation of sinusoidal waves generated by some genetic oscillators with different harmonic oscillating frequencies, in which the amplitude alternates at a constant frequency between the fixed minimal and maximal levels with the same duration of time.
Design and Study on Efficient Triggering Circuit for Reversely Switched Dynistor

The main characteristics of reversely switched dynistor (RSD) are infinite series, long lifetime, turning on in microseconds to nanoseconds, high-current rise rate (100 kA/μs), and ability of passing through large current (several kiloamperes). With these advantages, RSD has a great potential of application in repetition rate pulsed power technology and the future HVdc field. To resolve the problem of residual voltage of RSD caused by insufficient precharge, which leads to nonuniform turn-on of RSD, this paper, based on the conduction mechanism of RSD devices, describes precharge circuit design requirements and establishes a second order circuit model to study the resonant triggering circuit of RSD. It is found that reducing precharge inductor can effectively increase precharge current, resulting in reducing the turn-on voltage of RSD. Finally, design for three improved circuit topologies (full-bridge, intense pulsed, and two-step triggering circuit) are presented and experiments show that these improved circuit topologies can enhance the precharge efficiency and reduce the turn-on voltage of RSD.
Structural equation modeling combined with extension method for evaluating factors affecting location selection of proposed senior daycare facilities

With the tendency of population aging continues in our nation, problems associated with the elderly become major concerns in our society. If daycare centers for seniors can be established to care for the elderly just like daycare facilities for caring pre-school age children, seniors will have a place to enjoy day-time activities and return home to enjoy normal family life. The senior center that allows seniors to enjoyable their remaining years with dignity will be a demanding neighborhood facility. However, the location of such a facility is of great importance for its acceptance by the general public; a location that pleases seniors and their family is expected by all involved. The senior daycare center is a fixed and permanent facility; its location is also the key to affect the return of investors. A properly selected location will maximize the economic benefit while lowering the risks associated with the investment. Hence, in this research, the CFA (Confirmatory Factor Analysis) available in SEM (Structural Equation Modeling) is applied to evaluate important factors that affect the selection of senior daycare center and the degree of suitability for implementing the model to conduct such evaluations. The results on selecting the most appropriate location for establishing a senior daycare center are verified with case study data using the extension method. The combination of structural equation modeling and extension method leads to results that comfort to model evaluation indices when the suitability of individual models and the overall modeling are concerned. Using the extension method allows one to find the most appropriate location based on case study information. This proves that the method implemented in this research is capable of solving problems that are contradictory and incompatible in nature.
Technical analysis on the application of HTS bulk in "Permanent Magnet" motor

HTS materials can be used to develop novel permanent magnet (PM) motor. Based on the experimental and theoretical investigations, it was observed that the multi-impulse magnetization strategy should be selected for the constraints such as the temperature distributions and mechanical strength of the motor. The asymmetrical field distribution of HTS would induce torque ripples and unbalanced force. Proper cares have to be taken to avoid irreversible demagnetization. Resin impregnation and enclosing the rotor in a can/sleeve may provide the rotor enough mechanical strength to enable its high speed applications.
Growth mechanism of Co-2×2 islands on Ag/Ge(111)-√3×√3 surface

Using STM, we have studied a mechanism of Co-2×2 island growth on Ag/Ge(111)-√3×√3 surface. The islands are found to have hcp structure and be oriented with (11–20) face. A model was inferred that the majority of them grow along [0001] crystallographic direction, which in STM images manifests itself by a lateral shift of Co atom rows in the topmost layer compared to that in underlying one.
Integrating Graph Partitioning and Matching for Trajectory Analysis in Video Surveillance

In order to track moving objects in long range against occlusion, interruption, and background clutter, this paper proposes a unified approach for global trajectory analysis. Instead of the traditional frame-by-frame tracking, our method recovers target trajectories based on a short sequence of video frames, e.g., 15 frames. We initially calculate a foreground map at each frame obtained from a state-of-the-art background model. An attribute graph is then extracted from the foreground map, where the graph vertices are image primitives represented by the composite features. With this graph representation, we pose trajectory analysis as a joint task of spatial graph partitioning and temporal graph matching. The task can be formulated by maximizing a posteriori under the Bayesian framework, in which we integrate the spatio-temporal contexts and the appearance models. The probabilistic inference is achieved by a data-driven Markov chain Monte Carlo algorithm. Given a period of observed frames, the algorithm simulates an ergodic and aperiodic Markov chain, and it visits a sequence of solution states in the joint space of spatial graph partitioning and temporal graph matching. In the experiments, our method is tested on several challenging videos from the public datasets of visual surveillance, and it outperforms the state-of-the-art methods.
Integrating multi-stage depth-induced contextual information for human action recognition and localization

Human action recognition and localization is a challenging vision task with promising applications. To tackle this problem, recently developed commodity depth sensor (e.g., Microsoft Kinect) has opened up new opportunities with several developed human motion features based on depth image for action representation. However, how depth information can be effectively adopted in the middle or high level representation in action detection, in particular, the depth induced three dimensional contextual information for modeling interactions between human-human, human-object and human-surroundings has yet been explored. In this paper, we propose a novel action recognition and localization framework which effectively fuses depth-induced contextual information from different levels of the processing pipeline for understanding various interactions. First, depth image is combined with grayscale image for more robust human subject and object detection. Second, three dimensional spatial and temporal relationship among human subjects or objects is represented based on the combination of grayscale and depth images. Third, depth information is further utilized to represent different types of indoor scenes. Finally, we fuse these multiple stage depth-induced contextual information to yield an unified action detection framework. Extensive experiments on a challenging grayscale + depth human action detection benchmark database demonstrate the effectiveness of the depth-induced contextual information and the high detection accuracy of the proposed framework.
Learning Collaborative Sparse Representation for Grayscale-Thermal Tracking.

Integrating multiple different yet complementary feature representations has been proved to be an effective way for boosting tracking performance. This paper investigates how to perform robust object tracking in challenging scenarios by adaptively incorporating information from grayscale and thermal videos, and proposes a novel collaborative algorithm for online tracking. In particular, an adaptive fusion scheme is proposed based on collaborative sparse representation in Bayesian filtering framework. We jointly optimize sparse codes and the reliable weights of different modalities in an online way. In addition, this paper contributes a comprehensive video benchmark, which includes 50 grayscale-thermal sequences and their ground truth annotations for tracking purpose. The videos are with high diversity and the annotations were finished by one single person to guarantee consistency. Extensive experiments against other state-of-the-art trackers with both grayscale and grayscale-thermal inputs demonstrate the effectiveness of the proposed tracking approach. Through analyzing quantitative results, we also provide basic insights and potential future research directions in grayscale-thermal tracking.
Simultaneous Wireless Power/Data Transfer for Electric Vehicle Charging

The major drawback of the battery charging in traditional electric vehicles is the use of plug-in charging devices. The aim of this paper is to propose a wireless battery charging method, in addition to power transfer, data related to battery status, vehicle ID code, or emergency messages can be simultaneously transferred between the grid and vehicle. This work applies inductive power transfer to complete the charging system. The proposed control system can monitor the operating status on the secondary (vehicle) side in real time and adjust charging current depending on the battery status. Furthermore, the proposed mechanism is able to make an immediate stop, if there is any contingency, such as overcharging voltage or current. This will be beneficial to efficient and safety concern during the charging process.
Proposal-Free Network for Instance-Level Object Segmentation

Instance-level object segmentation is an important yet under-explored task. Most of state-of-the-art methods rely on region proposal methods to extract candidate segments and then utilize object classification to produce final results. Nonetheless, generating reliable region proposals itself is a quite challenging and unsolved task. In this work, we propose a Proposal-Free Network (PFN) to address the instance-level object segmentation problem, which outputs the numbers of instances of different categories and the pixel-level information on i) the coordinates of the instance bounding box each pixel belongs to, and ii) the confidences of different categories for each pixel, based on pixel-to-pixel deep convolutional neural network. All the outputs together, by using any off-the-shelf clustering method for simple post-processing, can naturally generate the ultimate instance-level object segmentation results. The whole PFN can be easily trained without the requirement of a proposal generation stage. Extensive evaluations on the challenging PASCAL VOC 2012 semantic segmentation benchmark demonstrate the effectiveness of the proposed PFN solution without relying on any proposal generation methods.