nils-herrmann/clean_scirepeval huggingface dataset:
Discriminative Video Pattern Search for Efficient Action Detection

Actions are spatiotemporal patterns. Similar to the sliding window-based object detection, action detection finds the reoccurrences of such spatiotemporal patterns through pattern matching, by handling cluttered and dynamic backgrounds and other types of action variations. We address two critical issues in pattern matching-based action detection: 1) the intrapattern variations in actions, and 2) the computational efficiency in performing action pattern search in cluttered scenes. First, we propose a discriminative pattern matching criterion for action classification, called naive Bayes mutual information maximization (NBMIM). Each action is characterized by a collection of spatiotemporal invariant features and we match it with an action class by measuring the mutual information between them. Based on this matching criterion, action detection is to localize a subvolume in the volumetric video space that has the maximum mutual information toward a specific action class. A novel spatiotemporal branch-and-bound (STBB) search algorithm is designed to efficiently find the optimal solution. Our proposed action detection method does not rely on the results of human detection, tracking, or background subtraction. It can handle action variations such as performing speed and style variations as well as scale changes well. It is also insensitive to dynamic and cluttered backgrounds and even to partial occlusions. The cross-data set experiments on action detection, including KTH, CMU action data sets, and another new MSR action data set, demonstrate the effectiveness and efficiency of the proposed multiclass multiple-instance action detection method.
Electron beam diagnostics for compact 1.2 GeV booster synchrotron

First operational experience has been gained with the linac and booster diagnostic system during the commissioning of the booster synchrotron at Duke University. The booster was designed and developed as an injector for the storage ring as a part of the High Intensity Gamma-ray Source (HIGS) upgrade of Duke FEL storage ring. Booster beam instrumentation includes: beam charge measurements (Faraday cups, Integrated Current Transformers (ICT), Modular Parametric Current Transformer (MPCT)), beam position monitoring (BPM), betatron tune measurements using synchrotron radiation (SR), transverse profile and temporal beam structure monitoring (insertable screens, striplines, dissector). The diagnostics provided good understanding of electron beam behavior and allowed us to adjust important beam parameters within design specifications. An overview of the diagnostic instrumentation of the Duke Booster synchrotron is given along with measurement examples and discussion of operational experience.
Implementation of Wiki-Based Knowledge Management Systems for Small Research Groups

Due to the soaring of knowledge-based economy and the blooming growth of e-business, the way to effectively accumulate and manage valuable data of knowledge has become an important issue for all the industry. There are many knowledge management systems built for large corporate companies. However, not many open source systems are available for small research communities. Currently, Wiki has not only changed the conception of building knowledge in an on-going process, but also become a popular tool of sharing knowledge worldwide. In this paper, we adopt Wiki tools, Tiddly-Wiki and Media-Wiki, to build two useful platforms as knowledge management systems for small research groups less than twenty members. Furthermore, we also provide integration service via the Update Server, which supports knowledge update, coherence editing, and knowledge sharing in order to satisfy the requirements of personal and group research. Our prototype intends to help students quickly grasp the expertise of their specific domain knowledge as new comers and become experienced researchers after graduation. We try to make this project as a continuous effort to accumulate more useful knowledge in a collaborative manner as required by the researchers.
Design and research of Multi-Agent Control system for central cooling system

Multi-Agent system (MAS) is applied to the intelligent control of the complex system more extensively. This paper carries out the design and research on MAS control system based on the central cooling system of an electric propulsion plant on ship. A Multi-Agent hierarchical control structure which is appropriate for the control of the central cooling system is established. The cooperative method of contract net model is used for the Agents' cooperation. This paper sets up an experimental table that is based on the composition and arrangement of the cooling system, and achieves the confirmation of the control requirement according to the experiment. This research provides foundation for further research of the MAS control to the complex cooling system.
Thermal-Electric Conversion Efficiency of the Dish/AMTEC Solar Thermal Power System in Wind Condition

The dish/AMTEC solar thermal power system is a newly proposed solar energy utilization system that enables the direct thermal-electric conversion. The performance of the solar dish/AMTEC system in wind condition has been theoretically evaluated in addition to that of no-wind condition. Results show that, the variation of the overall efficiency of dish/AMTEC system with the operating temperature in wind condition is very similar to that of no-wind condition. The difference is that, the maximum overall efficiency of the dish/AMTEC system reduces from 20.6% in no-wind condition to 19.0% in the wind condition. Furthermore, the overall efficiency of dish/AMTEC system decreases with increasing wind speed, while the wind direction shows almost no or little effect.
The influence of different length of standard parts on the accuracy of TRL calibration

This paper mainly studies the influence of the connection errors on the accuracy of the calibration. Through theoretical derivation of the simplified error transfer model, the influence of the introduced error can be obtained by repeatedly introducing errors at the transition; through HFSS-Matlab-API script programming, the simulation results via HFSS and Matlab can be obtained respectivity, which two are very consistent with each other. What's more, introducing errors multiple times to the Device Under Test (DUT) during the process of measurement, the coefficient of variations of the measurement results will appear regularly maxima and minima with the length of standard parts and the different values of the resistance of DUTs. Therefore, in the condition that the standard parts and the connection parts of vector network analyzer have tiny difference, according to the values of DUTs and the length of the standard parts that the minimum value of the coefficient of variations corresponding to, a method that how to design suitable length of fixture and standard parts to reduce the calibration errors to the minimum has been proposed. In addition, the actual productions of the circuits have been measured, and the results are very consistent with the simulation results, which have verified the rationality of the design and the reliability of the simulation.
Drought monitoring in Southwest China with HJ data

Drought is a normal, recurring feature of the climate in most parts of china. In recent years, many researchers have found lots of drought monitoring methods with remote sensing data. In this paper, the author firstly analyze advantages and disadvantages of common drought monitoring methods, then study the drought monitoring method meeting HJ satellite condition. At last, the author use HJ data to monitor southwest china drought which happened in the spring of 2010, moreover, analyze the result.
Observations and Measurements of Anomalous Hollow Electron Beams in the Duke Storage Ring

Anomalous hollow electron beams have been recently observed in the Duke storage ring. With a single bunch beam in a lattice with a negative chromaticity, a hollow beam can be created. This beam consists of a solid core beam inside and a large ring beam outside. In this paper, we report the measurements of the hollow beam phenomenon, including its distinct image pattern and spectrum signature, and its evolution with time. By capturing the post-instability bursting beam, the hollow beam is a unique model system for studying transverse instabilities, in particular, the interplay of the wakefield and lattice nonlinearity. The hollow beam can also be used as a tool to study linear and nonlinear particle dynamics in the storage ring.
A Management Object Oriented Solution of Knowledge Management System

With the development of information system, there is an urgent need for enterprises to change from information management to knowledge management. In the network-computing environment, enterprises need to apply the complex, dynamic and integrated, object-oriented knowledge management system. First, this paper analyzed the essence of knowledge and knowledge management, discussed the concept and characteristics of enterprise knowledge portal. On this basis, the article proposed a management object oriented solution of knowledge management system, focusing on the framework of the system, mode of operation and the value of application with a case study. Such a solution can help enterprises to establish open, dynamic and integrated information and knowledge management systems.
Articulate hand motion capturing based on a Monte Carlo Nelder-Mead simplex tracker

This work presents an algorithm for tracking the articulate hand motion in monocular video sequences. The task is challenging due to the high degrees of freedom involved in the hand motion. The complexity can be reduced by considering the natural motion constraints. To take advantage of the constraints, we propose to use a nonparametric representation of the feasible configuration space and employ a Monte Carlo Nelder-Mead simplex search algorithm. The tracker combines the strengths of both sequential Monte Carlo and direct search algorithms. First, its multiple hypotheses nature increases the chance of the simplex method to identify the global maximum. Second, the direct search algorithm produces a set of more representative particles. Experiment results show that this hybrid approach is robust for tracking the hand motion.
Exploiting sparsity in dense optical flow

In this paper we validated that the dense optical flow field is sparse in certain frequency domains, while the flow gradient field is also sparse in image domain. Based on this sparsity prior, the optical flow estimation problem is casted as sparse signal recovery from highly shorted measurements. By minimizing its l1-norm in frequency domain and gradient domain, the model can accurately estimate the dense flow field without other assumptions. Outliers are further identified and removed in the flow denoising process to improve the results. Experiments show that our method significantly outperforms traditional methods based on global or piecewise smoothness priors. Moreover, it can well handle the complexity incurred by motion discontinuities.
A physics based approach for ramping magnet control in a compact booster

At Duke University, a booster synchrotron was recently commissioned as part of the high intensity gamma-ray source (HIGS) upgrade. For the ramping magnet power supply controls, a scheme was developed to present the high level operator interface in terms of the physics quantities of the accelerator, i.e. the effective focusing strength of the magnets. This scheme allows for the nonlinearities of the magnets - a result of the extremely compact footprint of this booster - to be incorporated into the low level software. This facilitates machine studies and simplifies the use of physics modeling. In addition, it simplifies operation, allowing the booster to ramp to any energy from the 0.24 GeV of the injector linac to the 1.2 GeV maximum of the Duke storage ring. The high level of flexibility of this system is further advanced by incorporating the level of tunability typically found in a storage ring control system. Tuning changes made during steady-state operation are automatically propagated to the waveforms which make up the booster ramp. This approach provides a good match to the wide operation modes of the Duke storage ring and its associated free electron laser (FEL), and may be useful for other compact booster synchrotrons.
Abnormal event detection based on trajectory clustering by 2-depth greedy search

Clustering-based approaches for abnormal video event detection have been proven to be effective in the recent literature. Based on the framework proposed in our previous work (F. Jiang et al.,2007), we have developed in this paper a new strategy for unsupervised trajectory clustering. More specifically, an information- based trajectory dissimilarity measure is proposed, based on the Bayesian information criterion (BIC). In order to minimize BIC, the agglomerative hierarchical clustering is applied using a 2-depth greedy search process. This strategy achieves better clustering results compared to the traditional 1-depth greedy search. The increased computational complexity is addressed with several bounds on the trajectory dissimilarity.
Robust real-time human hand localization by self-organizing color segmentation

This paper describes a robust tracking algorithm used to localize a human hand in video sequences. The localization system relies mainly on an automatic color-based segmentation scheme combined with the motion cue. An automatic self-organizing clustering algorithm, is proposed to learn the color clusters unsupervisedly in the HSI space without specifying the number of clusters in advance. The schemes of growing, pruning and merging of 1-D self-organizing map (SOM) are facilitated to find an appropriate number of clusters in the forming stage of SOM. The training and segmentation in our approach is fast enough to make possible real-time applications. This segmentation scheme is capable of tracking multiple objects of different colors simultaneously. A motion cue is employed to focus the attention of the tracking algorithm. This approach is also applied to other tasks such as human face tracking and color indexing. Our localization system implemented on a SGI O2 R10000 workstation is reliable and efficient at 20-30 Hz.
Frequency tunable metamaterials based on ferroelectric substrate

Tunable metamaterials are highly desired in many applications. This paper is a study of tunability of metamaterials based on ferroelectrics. Dielectric constant of ferroelectrics is dependent on a dc bias electric field. The main features distinguishing the ferroelectrics are the extremely low control power consumption and high speed. Proper structures are selected to realize tunability. We present both S-shaped and omega resonators simulation results. Using finite element simulation, varying the permittivity of the substrate from 15 to 30, it can be seen that the resonant frequency is sensitive to the change of substrate permittivity. This study can serve as a guide for designing voltage tunable dielectric substrates for realizing tunable metamaterials or frequency scanning for more applications.
A 3.5–6.8GHz wide-bandwidth DTC-assisted fractional-N all-digital PLL with a MASH ΔΣ TDC for low in-band phase noise

We present a digital-to-time converter (DTC)-assisted fractional-N wide-bandwidth all-digital PLL (ADPLL). It employs a MASH ΔΣ time-to-digital converter (TDC) to achieve low in-band phase noise, and a wide-tuning range digitally-controlled oscillator (DCO). Fabricated in 40nm CMOS, the ADPLL consumes 10.7 mW while outputting 1.73 to 3.38 GHz (after a ÷2 division) and achieves better than -109 dBc/Hz in-band phase noise and 420fsrms integrated jitter.
An adaptive neural network compensator for decoupling of dynamic effects of a macro-mini manipulator

In this paper, an adaptive neural network compensator is proposed to improve the control performance of a macro-mini robotic manipulator, whereby an end effector called the mini manipulator is mounted at the end of an industrial robot called the macro manipulator in robotic literature. With the macro-mini architecture, the manipulator has the advantages of a large workspace due to the industrial robot, as well as fast dynamic response and diverse functions due to the end effector with optimized mechanical design. However, the coupling dynamics between two separated systems will also influence the control performance, especially at the end effector. An adaptive neural network compensator is added to the mini's original control system, and it can estimate and eliminate the dynamic coupling effect coming from the macro system in real-time in an on-line manner. Simulation shows that even if the model of the macro is unknown and its controller unchanged, in the presence of external disturbance at macro manipulator, the dynamic coupling effect can be almost eliminated in both position and force controls by the appended adaptive neural network compensator. Experimental results also demonstrate that as compared to the mini based on feedback linearization with the PID controller, vibration at the end point due to the coupling effect can be obviously suppressed using the adaptive compensator, and the steady state error is also gradually improved.
L1 matting

Natural image matting continues to play a large role in a wide variety of applications. As an ill-posed problem, matting is a very difficult to solve due to its underconstrained nature. Current approaches can require a lot of user input, restrict themselves to a sparse subset of the image, and often make assumptions that are unlikely to hold. In this paper, we pose a way to better satisfy smoothness assumptions of some of these methods utilizing the nonlinear median filter which arises naturally from the L1 norm. The median has the property that it tends to smooth the foreground and background of the image while leaving any edges relatively unaltered. We then show that such an image is often more suitable as input than the original image, even when user interaction is minimal, suggesting that our method is more amenable to automatic matting.
Process integration of 3D Si interposer with double-sided active chip attachments

A double-sided Si passive interposer connecting active dies on both sides for a 3D IC integration is investigated. This interposer is 100μm-thick with 10μm-diameter TSVs (through silicon vias), 3 RDLs (redistribution layer) on its front-side, 2 RDLs on its backside. It supports 2 active dies on its frontside and 1 active die at its backside. The present study focuses on the process integration of the passive interposer, double-sided chip assembly process, and passive electrical characterization.
A digital management system of part processing parameter based on UG

That digital management of part parameters information is not uniform in design, manufacture and detection in digital factory construction. A unified digitization system of part parameters based on UG (Unigraphics NX) is proposed. This digital management system is established under .NET environment, and the user interface is built by C# which realizes part design, inspection planning and processes information collected based on UG. In order to unify part parameters information management in design, manufacture and detection, moreover, the SQL Server database is established for realizing uniform management of the part parameters. This digital management system Effectively improve the processing quality and efficiency.
Decision making on site selection of a civil explosive distribution center based on SOM neural network

Owing to the particularity of civil explosives, the site selection of a civil explosive distribution center has remarkable difference from the site selection of an ordinary distribution center. Particular consideration should be given to the safety condition. SOM (Self-Organizing Map) neural network is used to analyze and process the factors that influence the site selection of a civil explosive distribution center. Optimization and decision are made on multiple candidate solutions. The calculation analysis indicates this method realizes the classification of relations through competition among neurons and can perfectly meet the requirements of the decision making on site selection of a civil explosive distribution center.
Visualisation and analysis of network motifs

Many of the complex networks that occur both in nature and in technology are built up from frequently recurring patterns of basic structural elements. These structural patterns known as motifs play a significant role in the function of the network. Visualisation is a useful tool for understanding the structure in a network. The quality of a visualisation can be significantly improved if it effectively displays these motifs. In this paper we present visualisations designed to highlight motifs detected through analysis. We argue that these visualisations designed to show functionally important subgraphs give a greater insight into the function of the network.
Simulation techniques on complex system of mission driving material support

To study the material support system, build a simulate system is an efficient way. The complicity of the material support system is analyzed, the similarities and differences of the mission for materials, transport system, and support stations to carry out is proposed; then an element mission model for general use is constructed, based on which the multi-level mission model is set up and expressed; characteristics of support stations belong to different echelons or types are analyzed, and a common station model is set up, based on which the structure of a simulation system is proposed. The models for key modules of the system such as material status watching and failure managing module, the database for the simulate system, are set up. At the end of the paper, a simulate system for three-echelon support system is constructed, a scenario is performed to test the simulate system.
Collaborative tracking of multiple targets

Coalescence, meaning the tracker associates more than one trajectories to some targets while loses track for others, is a challenging problem for visual tracking of multiple targets, especially when similar targets move close or present occlusions. Existing approaches that are based on joint data association are confronted by the combinatorial complexity due to the concatenation of the state spaces of individual targets. This paper presents a novel collaborative approach with linear complexity to the coalescence problem. The basic idea is the collaborative inference mechanism, in which the estimate of an individual target is not only determined by its own observation and dynamics, but also through the interaction and collaboration with the estimates of its adjacent targets, which leads to a competition mechanism that enables different targets to compete for the common image observations. The theoretical foundation of the new approach is based on Markov networks. Variational analysis of this Markov network reveals a mean field approximation to the posterior density of each target, therefore provides a computationally efficient way for such a difficult inference problem. In addition, a mean field Monte Carlo (MFMC) algorithm is designed to achieve Bayesian inference by simulating the competition among a set of low dimensional particle filters. Compared with the existing solutions, the proposed new collaborative approach stands out by its effectiveness and low computational cost to the coalescence problem, as pronounced in the extensive experiments.