nils-herrmann/clean_scirepeval huggingface dataset:
Contextualized trajectory parsing with spatiotemporal graph.

This work investigates how to automatically parse object trajectories in surveillance videos, which aims at jointly solving three subproblems: 1) spatial segmentation, 2) temporal tracking, and 3) object categorization. We present a novel representation spatiotemporal graph (ST-Graph) in which: 1) Graph nodes express the motion primitives, each representing a short sequence of small-size patches over consecutive images, and 2) every two neighbor nodes are linked with either a positive edge or a negative edge to describe their collaborative or exclusive relationship of belonging to the same object trajectory. Phrasing the trajectory parsing as a graph multicoloring problem, we propose a unified probabilistic formulation to integrate various types of context knowledge as informative priors. An efficient composite cluster sampling algorithm is employed in search of the optimal solution by exploiting both the collaborative and the exclusive relationships between nodes. The proposed framework is evaluated over challenging videos from public datasets, and results show that it can achieve state-of-the-art tracking accuracy.
Single depth intra coding mode in 3D-HEVC

As the state-of-the-art video coding standard, High Efficiency Video Coding (HEVC) has been finalized in January 2013. To support the coding of multiple views and associated depth data, the development of the HEVC extension for 3D video coding is also about to be finalized. New coding unit (CU) level coding tools are added to the HEVC design to improve the compression capabilities for both video views and depth data. In this paper, we introduce a new coding mode termed as “Single Depth Intra Coding Mode” to efficiently code the smooth area within a depth map. The concept of single depth mode is to simply reconstruct a coding block with a single depth value based on block merging scheme under the HEVC quad-tree based block partitioning. The proposed single depth mode has been adopted into the working draft and 3D-HEVC test model 12 (HTM-12.0). The experimental results evaluated under the common test conditions (CTC) for 3D-HEVC show that the proposed technique achieves up to 5% bit-rate saving for depth coding and on average 0.3% bit-rate saving regarding the synthesis results over the texture and depth bit-rate with decoding time reduced by 4%.
Towards Computational Baby Learning: A Weakly-Supervised Approach for Object Detection

Intuitive observations show that a baby may inherently possess the capability of recognizing a new visual concept (e.g., chair, dog) by learning from only very few positive instances taught by parent(s) or others, and this recognition capability can be gradually further improved by exploring and/or interacting with the real instances in the physical world. Inspired by these observations, we propose a computational model for weakly-supervised object detection, based on prior knowledge modelling, exemplar learning and learning with video contexts. The prior knowledge is modeled with a pre-trained Convolutional Neural Network (CNN). When very few instances of a new concept are given, an initial concept detector is built by exemplar learning over the deep features the pre-trained CNN. The well-designed tracking solution is then used to discover more diverse instances from the massive online weakly labeled videos. Once a positive instance is detected/identified with high score in each video, more instances possibly from different view-angles and/or different distances are tracked and accumulated. Then the concept detector can be fine-tuned based on these new instances. This process can be repeated again and again till we obtain a very mature concept detector. Extensive experiments on Pascal VOC-07/10/12 object detection datasets [9] well demonstrate the effectiveness of our framework. It can beat the state-of-the-art full-training based performances by learning from very few samples for each object category, along with about 20,000 weakly labeled videos.
Data-Driven Scene Understanding with Adaptively Retrieved Exemplars

This article investigates a data-driven approach for semantic scene understanding, without pixelwise annotation or classifier training. The proposed framework parses a target image in two steps: first, retrieving its exemplars (that is, references) from an image database, where all images are unsegmented but annotated with tags; second, recovering its pixel labels by propagating semantics from the references. The authors present a novel framework making the two steps mutually conditional and bootstrapped under the probabilistic Expectation-Maximization (EM) formulation. In the first step, the system selects the references by jointly matching the appearances as well as the semantics (that is, the assigned labels) with the target. They process the second step via a combinatorial graphical representation, in which the vertices are superpixels extracted from the target and its selected references. Then they derive the potentials of assigning labels to one vertex of the target, which depend upon the graph edges that connect the vertex to its spatial neighbors of the target and to similar vertices of the references. The proposed framework can be applied naturally to perform image annotation on new test images. In the experiments, the authors validated their approach on two public databases, and demonstrated superior performance over the state-of-the-art methods in both semantic segmentation and image annotation tasks.
Performance Evaluations of an Ant Colony Optimization Routing Algorithm for Wireless Sensor Networks

Previous research of ant-based routing algorithms mainly focused on energy efficiency. However, how ant-based routing algorithms perform in other metrics has been neglected. In this research, we compare the packet delivery ratio, the end-to-end delay and the first packet arrival time of an ant colony optimization (ACO) routing algorithm with three routing protocols commonly used in MANET, namely, AODV, DSDV, and DSR, in the wireless sensor network setting. We found that the ACO is not only energy efficient but also has the best performance in both the packet delivery ratio and the first packet arrival time compared to other routing algorithms. In some cases, it might not perform well in the end-to-end delay.
A novel approach to guidance and control system design using genetic-based fuzzy logic model

The proportional navigation guidance law usually guides traditional aerodynamically controlled missiles in the terminal phase. Such design approaches possess little flexibility on the improvement of engagement performance robustness. The Takagi-Sugeno (T-S) fuzzy logic theory and the gain scheduling technique are presented to design guidance and control (GC) parameters over coupled flight conditions. A genetic algorithm also serves as the computing device to determine the consequent function parameters of the fuzzy rules. The proposed design paradigm offers a way to implement simple but robust solutions that cover a wide range of aerodynamic variations. Complete simulation results illustrate the engagement performance and applicability of this design.
Clothes Co-Parsing Via Joint Image Segmentation and Labeling With Application to Clothing Retrieval

This paper aims at developing an integrated system for clothing co-parsing (CCP), in order to jointly parse a set of clothing images (unsegmented but annotated with tags) into semantic configurations. A novel data-driven system consisting of two phases of inference is proposed. The first phase, referred as “image cosegmentation,” iterates to extract consistent regions on images and jointly refines the regions over all images by employing the exemplar-SVM technique [1]. In the second phase (i.e., “region colabeling”), we construct a multiimage graphical model by taking the segmented regions as vertices, and incorporating several contexts of clothing configuration (e.g., item locations and mutual interactions). The joint label assignment can be solved using the efficient Graph Cuts algorithm. In addition to evaluate our framework on the Fashionista dataset , we construct a dataset called the SYSU-Clothes dataset consisting of 2098 high-resolution street fashion photos to demonstrate the performance of our system. We achieve 90.29%/88.23% segmentation accuracy and 65.52%/63.89% recognition rate on the Fashionista and the SYSU-Clothes datasets, respectively, which are superior compared with the previous methods. Furthermore, we apply our method on a challenging task, i.e., cross-domain clothing retrieval: given user photo depicting a clothing image, retrieving the same clothing items from online shopping stores based on the fine-grained parsing results.
Fabrication and Tests of a 1 MJ HTS Magnet for SMES

In this paper, the design, fabrication and tests of a I MJ HTS magnet for SMES was presented. The magnet was fabricated by BSSCO tapes, it consisted of 44 pancakes, the inner diameter of the winding is 400 mm, the outer diameter is 568 mm and the height is 648 mm, the inductance is 6.38 H and the rated operation current is 560 A at 4.2 K. The tests show the magnet could be operated at the design parameters. The magnet will be used for the 1 MJ/500 kVA SMES unit which will be demonstrated at a 10 kV substation in Beijing.
Decentralized navigation of multiple agents based on ORCA and model predictive control

This paper presents a decentralized strategy for collision-free navigation of multiple agents. This strategy combines the Optimal Reciprocal Collision Avoidance (ORCA) algorithm and Model Predictive Control (MPC). Concretely, each agent applies the decentralized ORCA algorithm to compute the collision-avoiding velocities with respect to its neighbors. The derived velocities serve as constraints of a MPC problem whose solution provides the optimal control input that can ensure optimal motion of the agent. The states predicted from the agents' dynamic models are used in the ORCA algorithm to compute the ORCA velocity regions in future steps. This ORCA-MPC combined approach doesn't need a priori the preferred velocity of each agent in comparison to the traditional ORCA algorithm and its existing variants. Simulation results illustrate the effectiveness of the proposed method, and show that this new algorithm can reduce velocity vibrations in the traditional ORCA algorithm.
Study and implementation of the LED headlight driver with auto-start function in specific location

Conventional automatic headlight devices determine whether to switch vehicle headlights on or off according to the brightness of the environment. These devices cannot automatically switch on headlights during daytime unless the headlights are required in specific road conditions (e.g. mountainous or foggy areas), thus compromising the safety of road users and drivers. This study develops a light-emitting diode (LED) headlight system that integrates the global positioning system (GPS) and a controller area network (CAN) bus communication interface. This system uses the GPS system to analyse the geographic location and then determines whether to switch on the LED headlight. To reduce the system complexity, a CAN bus is used to limit the number of control wires required. The LED headlight driver also uses an interleaved boost converter that has low conduction losses and input ripple currents, which improves the conversion efficiency of the headlight drivers and extends the battery lifetime. The feasibility of the proposed technology is verified using experimental results.
Investigating 3-D Model and Part Information for Improving Content-Based Vehicle Retrieval

Content-based vehicle retrieval in unconstrained environment plays an important role in surveillance systems. However, due to large variations in viewing angle/position, illumination, occlusion, and background, traditional vehicle retrieval is extremely challenging. We approach this problem in a different way by rectifying vehicles from disparate views into the same reference view and searching the vehicles based on informative parts such as grille, lamp, and wheel. To extract those parts, we fit 3-D vehicle models to a 2-D image using active shape model. In the experiments, we compare different 3-D model fitting approaches and verify that the impact of part rectification on the content-based vehicle retrieval performance is significant. We propose a model fitting approach with weighted Jacobian system which leverages the prior knowledge of part information and shows promising improvements. Then, we use pyramid histogram of orientation feature to describe rectified parts (e.g., grille, wheel, lamp). We compute mean average precision of vehicle retrieval with L1 distance on NetCarShow300 dataset, a new challenging dataset we construct. We conclude that it benefits more from the fusion of informative rectified parts (e.g., grille, lamp, wheel) than a whole vehicle image described by SIFT feature for content-based vehicle retrieval.
Magnetic behaviors of HTS bulk components in hybrid magnetic circuit

Melt texturing large-grain HTS material has evolved in the last few years into a technology capable of producing large quantities of high-performance superconducting bulk materials. Such materials are used for developing novel engineering devices. In order to clarify magnetic behaviors of HTS bulks in a magnetic circuit, magnetization, magnetic relaxation and the levitation force were measured for the HTS combined bulks with and without soft iron. Experimental observations and theoretical analyzes indicate that the induced proximity current would modify flux diffusion and the screening-current distribution inside, resulting in the change of magnetic behaviors such as the trapped field and the levitation force. Well-designed soft-iron layers would be helpful to enhance the magnetic stability and strengthen the HTS combined bulks. An alternative method is provided to optimize flux configurations in HTS electromagnetic devices.
Summarizing While Recording: Context-Based Highlight Detection for Egocentric Videos

In conventional video summarization problems, contexts (e.g., scenes, activities) are often fixed or in a specific structure (e.g., movie, sport, surveillance videos). However, egocentric videos often include a variety of scene contexts as users can bring the cameras anywhere, which makes these conventional methods not directly applicable, especially because there is limited memory storage and computing power on the wearable devices. To resolve these difficulties, we propose a context-based highlight detection method that immediately generates summaries without watching the whole video sequences. In particular, our method automatically predicts the contexts of each video segment and uses a context-specific highlight model to generate the summaries. To further reduce computational and storage cost, we develop a joint approach that simultaneously optimizes the context and highlight models in an unified learning framework. We evaluate our method on a public Youtube dataset, demonstrating our method outperforms state-of-the-art approaches. In addition, we show the utility of our joint approach and early prediction for achieving competitive highlight detection results while requiring less computational and storage cost.
Effect of operating conditions on emission concentration of PAHs during fluidized bed air gasification of biomass

With the concern of future energy use, hydrogen is taken as an important role in energy alternatives. In addition, biomass air gasification by using fluidized bed is an efficient and advanced method for hydrogen production. Despite of energy product generation, some pollutants such as PAHs easily formed in gasification process because of tar decomposition. Accordingly, this study provides a fundamental work of emission behavior of PAHs generated under different operating conditions such as temperature, air factor, and addition of catalysts. The experimental result shows the emission concentration of PAHs decreases with the operating temperature increases, but PAHs increases with increasing of air factor. Besides, addition of CaO resulting a reduce influence on PAHs emission, but Al2O3 added in fluidized bed cause bimodal particle size segregate and reduce fluidized bed quality that leads PAHs formation during biomass air gasification.
Improvement of Freight Train Timetable for Single-Track Railway System

A new method is proposed for single-track railway freight train timetable with two-stage solution of train scheduling problem. The method describes train scheduling problem in matrix with the “signaling block” concept. The matrix represents entire block occupancy of trains between inter-stations and internal-stations while decomposing individual train occupancy, making optimization method more flexible in design and use. The “two-stage solution” refers to that of train timetable with “conflict solution” and “timetable optimization.” At the beginning, related data for trains are entered to build the initial timetable, which is then transferred and saved as the matrix format. All conflicts between interstations and internal-stations are located; solutions are made with manual scheduling to generate a feasible timetable. In the second stage, departure and stop time of freight trains, and passenger trains if necessary, are adjusted with Fuzzy system. Before optimization, storage is made in ascending amount of extra-time percent in freight train schedule. Execution is made subject to pre-ordered results during optimization for computer program acceleration of current manual spotting solution to find reasonable and feasible solution. The two-stage method also takes less time than the regular railway timetable design methods in cost saving of manual labor and providing higher operation performance and efficiency.
Synthesising gene clock with toggle switch and oscillator.

The usefulness of a genetic clock lies in its role to stimulate a sequence of logic reactions for sequential biological circuits. A clock signal is a periodic square wave, its amplitude alternates at a steady frequency between fixed minimal and maximal levels. Transition between the minimum and the maximum is instantaneous for an ideal square wave; however, the function is unrealisable in physical bio-systems. This research develops a new genetic clock generator based on a genetic oscillator, in which, a sine wave generator is adopted as a signal oscillator. It is shown that combination of a genetic oscillator with a toggle switch is able to generate clock signals forming an efficient way to generate a near square wave. In silico study confirms the proposed idea.
A Novel Analog Integrated Circuit Design Course Covering Design, Layout, and Resulting Chip Measurement

This work describes a novel, first-year graduate-level analog integrated circuit (IC) design course. The course teaches students analog circuit design; an external manufacturer then produces their designs in three different silicon chips. The students, working in pairs, then test these chips to verify their success. All work is completed within one semester, and the grading cycle in the most recent offering of the course extended from September 2007 to February 2008, when there were 10 students enrolled. The manufacturer's shuttle cycle is 3.5 months. Most students in the course have only a college-level electronics background. The manufacturing process is Taiwan Semiconductor Manufacturing Company's (TSMC) 0.35 ¿m CMOS Mixed-Signal 2P4M Polycide 3.3/5 V. The three successful chips consist of a voltage controlled oscillator, a high-performance differential amplifier, and a temperature-independent voltage reference generator. Section VI describes assessment and student feedback as well as proposed course improvement.
Transmission of power and data for contactless dynamic charging

The major drawback of the battery charging in a electric vehicle 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 or vehicle ID code, etc. can be mutually transferred between the grid and vehicle. This work applies inductive power transfer (IPT) to complete the charging system. The proposed charging system can monitor the operating status on the secondary side at all times. It can also adjust charging current depending on the monitoring voltage or curent of battery; furthermore, the propsoed charging system can stop charging if any contingency, such as overvoltage or overcurrent, occurs. Therefore, the proposed charging system maintains an efficient and safely charging operaiton.
Development of a 630 kVA Three-Phase HTS Transformer With Amorphous Alloy Cores

This paper describes design and operation of a three-phase HTS power transformer with capacity of 630 kVA operated in liquid nitrogen of 77 K for primary/secondary voltages of 10.5 kV/0.4 kV. The windings were wound by hermetic stainless steel-enforced multifilamentary Bi2223/Ag tapes. The structures of primary and secondary windings are solenoid and double-pancake respectively. Cryostat is made from electrical insulating materials with room temperature bore for commercial amorphous alloy core. Fundamental characteristics of the transformer are obtained by standard short-circuit and no-load tests. The ac losses were calculated and measured by method of conventional transformer. The HTS power transformer successfully operated in a live power grid.
A constructive courseware design for nursing education: Using intramuscular injection as a case

Learning is the major human activity which can make people to accommodate the environment or the society better. The effectiveness of learning is one important research issue in orange technology. The learning effectiveness of nurse education is more important than the other types of educations. In this paper, we proposed a work which develops a simulation-based digital courseware for intramuscular injection training in nurse education, to improve the learning effectiveness of practical nursing care training. The design of courseware is guided by the constructivism. In addition, the scenario simulation and game are involved to enhance the learning motivation. Moreover, the course has been successfully applied on the nursing students in Taiwan.
Two-Dimensional Numerical Simulation for GaAs MESFET with Heavy Doping Effects

Two-dimensional semiconductor simulation program is a necessary tool for modem semiconductor device design. A new and general simulation program of GaAs MESFET considering heavily doped effects is developed. The effects of heavy doping on the device characteristics of the heavily doped GaAs MESFET are analyzed. It is found that the conventional simulation programs of MESFET face the serious problem of accuracy when the concentration of dopants gets higher. A self-consistent model for describing the electron behaviour in heavily doped semiconductor devices is used. The model allows conveaient treatment of the Poisson's equation, steady-state continuity equations, and transport equations with the heavy doping effects. The bandgap narrowing effects associate with the actual bandgap narrowing effect and the influence of Fermi-Dirac statistics. All the effects are e-Tressed in terms of two band model parameters, the effective bandgap shrinkage, AEp and the effective asymetry fzctor, A. The activity mefficient y can be a useful quantity to represent the nonideality. Two-dimecsional simulation is carried out by taking the heavy doping effects. The results show that the effects have an influence of the increase of the built-in potential. The negative shifts of I-V curve, the slight decrease of transconductance, and the Iittle increase of the threshold voltage are observed as compared to the results of the conventional twodimensional simulation. The Qeneral two-dimensional simulation program can be used well for the design of GaAs and Si MESm circuit of VLSI.