Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-Time Driver Assistance Systems via Dual Camera Stereo Vision

This paper presents the practical application for a real-time and image-based driver assistance system. Dual-camera stereo vision algorithm for the distance measurement for the detected vehicle and multi-line merge function for the lane detection are implemented via lab-made library and calibrated off-line. A lab-made library developed via programming language is written and compiled for low computing-power consumption and rapidly calculating for the real-time purpose. To save the computing power consumption, some proper region of interest of vehicle detection and lane detection are applied. Finally, this system has been preliminary implemented on a test vehicle and driven on various roads over tens of miles and reach around 4~5 frame per seconds via portable embedded system.
System-Level Simulation for Homogeneous and Heterogeneous Cellular Networks

To examine complex cellular networks, system-level simulation is one possible approach used by various radio planning tools. There is thus an opportunity to provide a comprehensive study including building blocks and simulation flow. In this article, the heterogeneous cellular network in downlink transmissions is simulated by means of system-level approach. Not only small cell deployments but also scheduling techniques are investigated in this simulation. The user throughput distribution is evaluated when varying the small cell placement, number of small cells and scheduling algorithms. Due to the use of small cells and proper scheduler, the user throughput could be significantly enhanced.
Pose-Guided Spatial Alignment and Key Frame Selection for One-Shot Video-Based Person Re-Identification

One-shot video-based person re-identification exploits the unlabeled data by using a single-labeled sample for each individual to train a model and to reduce the need for laborious labeling. Although recent works focusing on this task have made some achievements, most state-of-the-art models are vulnerable to misalignment, pose variation and corrupted frames. To address these challenges, we propose a one-shot video-based person re-identification model based on pose-guided spatial alignment and KFS. First, a spatial transformer sub-network trained using pose-guided regression is employed to perform the spatial alignment. Second, we propose a novel training strategy based on KFS. Key frames with abruptly changing poses are deliberately identified and selected to make the network adaptive to pose variation. Finally, we propose a frame feature pooling method by incorporating long short-term memory with an attention mechanism to reduce the influence of corrupted frames. Comprehensive experiments are presented based on the MARS and DukeMTMC-VideoReID datasets. The mAP values for these datasets reach 46.5% and 68.4%, respectively, demonstrating that the proposed model achieves significant improvements over state-of-the-art one-shot person re-identification methods.
A Phase Shift Control of Minimal Circulating Current and ZVS Turn-On for DAB Converter

Dual active bridge (DAB) is a promising isolated bidirectional dc-dc converter in energy storage system and micro-grids applications. When the input/output voltage gain does not match, a DAB suffers high circulating current and loss of ZVS, which will significantly decrease the efficiency. Theoretical phase shift ratios to minimize the circulating current have been found in literature, however, under light load conditions, this method cannot achieve ZVS-on for the power devices and energy on output capacitance of these devices is dissipated, leading to low efficiency. This paper presents a method to achieve ZVS-on and minimal circulating current at the same time when a DAB operating in Buck mode. Experimental results are given to validate the theoretical analysis.
Relatives’ experiences of loved ones’ donation after circulatory and brain death. A qualitative inquiry

Background: Little is known about the donation-related experiences of relatives of donors after circulatory death. Objective: The goal of this research was to explore such experiences, and to capture donation after circulatory death’s (DCD) distinctive features by contrasting them with experiences of relatives of donors after brain death. Design: An inductive, theory-building approach based on qualitative interviews was used. Participants: Participants were deceased donors’ next-of-kin who were involved in the decision to donate. Results: We found that the assumption of responsibility for the deceased person’s organ donation was accompanied by questions about meaning and guilt for the deciding family member(s). Conclusions: In conclusion, the similarities between the donationrelated experiences of DCD and donation after brain death (DBD) families outweigh the differences. Our recommendations are that health care providers should provide relatives with support and a follow-up to make them feel more confident with the donation decision. Family conferences are an evidence-based tool to improve the care of moribund patients and should be implemented during the donation process and possibly during the follow-up.
Self-regulated learning strategies of adult learners regarding non-native status , gender , and study majors

The number of adult learners is growing rapidly in the US institutions, and these learners have become an important student population in colleges. Therefore, it is important to understand their learning strategies in order to better assist them to achieve academic goals. Meanwhile, US institutions have dramatically increased the number of international English as a Second Language (ESL) students. Thus, to better understand these students’ learning strategies and help them succeed is a need. Therefore, the purpose of this study is to investigate the differences in learning strategies (i.e., cognitive and metacognitive strategies and resource management strategies) of adult learners regarding non-native status (international ESL learner vs. native speaker American learner), gender, and study majors. Two hundred and thirty-six adult learners participated in the study, and results indicated all these three variables have significant influences on these learners’ learning strategies.
Optimum Hull Spacing of a Family of Multihulls

Multihull con gurations are optimised for minimum wave-making using thin-ship theory. Only the number of hulls, and their placement and beams are varied, the total displacement of the vessel and the individual hull length and shape being xed. In particular, optimum con gurations are determined for two, three and four-hulled vessels, with and without longitudinal stagger. An example of use of this theory is given, for a family of multihulls relevant to a high-speed ferry design. Competing designs within this family are assessed over a large speed range, with respect to generated wave amplitude, wave resistance, and total drag.
A study on efficient algorithms for generating virtual wind field usable for real-time animation

We all experience the wind from our everyday life. Wind is invisible. It has no shape, size, smell, taste or sound of its own. Thus, it is the most challenging natural phenomenon to predict accurately. In the field of computer animation, wind is an important element for creating visually plausible motion in natural outdoor scenery. Examples of such motions include the swaying of tree branches in the wind, field of grass blowing in the breeze, the swirling of smoke, the waving of a flag, the flattering of cloth in the wind, etc. Without a turbulent wind field, animations would lack a natural chaotic look, which is an important characteristic of most natural phenomena. A series of natural wind models have been developed since the 1960s to facilitate the study of wind engineering problems. In this thesis, we present a study on wind turbulence models from the field of engineering and their applicability in computer animations for simulating a variety of motions and behaviors of flexible objects in windy conditions. At first, we describe some theoretical aspects of wind turbulence based on measurements of actual winds. Then we describe a simple yet visually convincing simulation of wind fields using our proposed wind models generated from actual wind data. The efficiency and practical utility of the wind field model is implemented in a simulation of grass blowing in the wind. Then, we also present a simple method to generate three-dimensional frozen and non-frozen turbulent wind fields for use in the animation of windinduced motion. Our approach uses 1/f noise to match the characteristics of natural wind. By employing a noise-based approach, the complexity as well as computational cost is reduced. Additionally, by considering key characteristics of actual wind that are applied in the engineering field, our proposed method is also able to produce believable motion results in outdoor wind field simulations. In this thesis, we describe the implementation results of our
The synthesis of linear-phase multirate frequency-response-masking filters

The application of the frequency-response-masking technique for the synthesis of computationally efficient sharp filters has been discussed widely in the literature. However, all of the previously reported frequency-response-masking methods are efficient for synthesizing filters operating in the single rate environment only. In this paper, we extend the frequency response-masking technique into the design of multirate filters. Our method is eminently suitable for the synthesis of anti-aliasing and anti-imagining filters for interpolation and decimation operations.
Coenzyme Q 10 Therapy

For a number of years, coenzyme Q 10 (CoQ 10 ) was known for its key role in mitochondrial bioenergetics; later studies demonstrated its presence in other subcellular fractions and in blood plasma, and extensively investigated its antioxidant role. These 2 functions constitute the basis for supporting the clinical use of CoQ 10 . Also, at the inner mitochondrial membrane level, CoQ 10 is recognized as an obligatory cofactor for the function of uncoupling proteins and a modulator of the mitochondrial transition pore. Furthermore, recent data indicate that CoQ 10 affects the expression of genes involved in human cell signaling, metabolism and transport, and some of the effects of CoQ 10 supplementation may be due to this property. CoQ 10 deficiencies are due to autosomal recessive mutations, mitochondrial diseases, aging-related oxidative stress and carcinogenesis processes, and also statin treatment. Many neurodegenerative disorders, diabetes, cancer, and muscular and cardiovascular diseases have been associated with low CoQ 10 levels as well as different ataxias and encephalomyopathies. CoQ 10 treatment does not cause serious adverse effects in humans and new formuPublished online: March 13, 2014 José A. Sánchez-Alcázar Centro Andaluz de Biología del Desarrollo (CABD) Universidad Pablo de Olavide – Consejo Superior de Investigaciones Científicas Carretera de Utrera Km 1, ES–Sevilla 41013 (Spain) E-Mail jasanalc @ upo.es © 2014 S. Karger AG, Basel 1661–8769/14/0054–0187$39.50/0
The Study of Generative Modeling of Text

Text mining is the task of automatic discovery of new, previously unknown information from unstructured document collections. Vector space or bag of words representation is one of the mainstream descriptions of text, in which each document is a data point in high-dimensional space and order between words is omitted. Generative models are probabilistic representation of data that can be regarded as the generator of observed data. Being probabilistic modelling approaches, a set of methods and criterions are available for model estimation, inference, comparison and selection for generative models. In this paper, we review several existing probabilistic models that are commonly applied to discrete exchangeable collections in English text. We hope this will shed some light on the Chinese text modelling and mining tasks.
EMPIRICAL LIKELIHOOD INFERENCE FOR SURVIVAL RATE REGRESSION WITH MISSING INFORMATION PRINCIPLE

Recently, regression model for the long-term survival prob abilities of patients was proposed, and a semiparametric inference proced ure was developed based on missing information principle. In this paper, we propose an alternative empirical likelihood method. First, we define an estimated empirical l ikelihood ratio for the regression parameter. The limiting distribution of the empir ical likelihood ratio is shown to have a weighted sum of i.i. d. χ 1 ’s. We also define an adjusted empirical likelihood ratio for the regression parameter and the adjusted empiric al likelihood ratio is shown to have a central chi-squared limiting distribution. Confid ence regions for the vector of regression parameter are obtained accordingly. Furthermo r , an extensive simulation study is conducted and it shows the proposed method has bette r cov rage probability. Finally, we use a real data set to illustrate our proposed met hod. 2000 AMS Mathematics Subject Classification:Primary: 62N02; Secondary: 62G20.
Intravenous Immune Globulin for Severe Acute Myocarditis in Children

within 24 hours of PICU admission was not independently associated with increased mechanical ventilation time, length of stay, or mortality(5), though they used a cut off of 200 mg/dL for defining hyperglycemia. Our study patients differ from those in other studies in that we did not have post-surgical patients, while in others they constituted a major proportion. Moreover, a considerable number of children had associated malnutrition. The findings reported here should serve as a caution to the prevalent view supporting the association of hyperglycemia with poor outcome in the PICU.
Mining Opinion Word from Customer Review

Online customer review is considered as a significant informative resource which is useful for both potential customer and product manufacturers. As a result, it is one of the most challenging tasks to mine customer reviews automatically and to provide users with opinion summary. Product features and opinion word play the most important roles in the customers’ opinions mining. In this paper, we dedicate our work to opinion word mining. We proposed an approach for opinion word identification based on the association rule mining algorithm. The method makes full use of co-occurrence syntactic characteristic between product features and opinion word. Firstly, the product feature is identified by two-stage filtering scheme, and secondly the opinion word is extracted through association rule mining. The final experiment results show that the proposed method could not only obtain the product features related to domain characteristics, but identify the opinion word effectively. Meanwhile, our approach possesses much higher precision and recall than Hu’s work.
Load Sharing by Decentralized Control in an Islanded Voltage Source Converter-based Microgrid Considering Fixed Frequency

Background/Objectives: In this paper by utilizing Frequency Tracking electrical loads are shared on Voltage Source Converters and then by applying a Proposed-Supplementary Control System, frequency changes during changing electrical loads inside network is omitted. Methods/Statistical analysis: Using decentralized control in Microgrids and sharing loads on power generation units respect to their nominal capacity are unavoidable approaches to have stable and operational network. Droop control in load sharing is known as practical and significant solution. In droop control, units output frequency, changes proportional to output active power that is not preferable. Simulation results in PSCAD are performance confirmation of proposed control system. Findings: In Proposed-Supplementary Control System, in spite of applying droop control to share loads, nominal frequency of all power generation units are fixed in a floating way while changing output active power Applications/Improvements: Applying Proposed-Supplementary Control System on droop control makes Microgrids appropriate for electric loads which are sensitive to frequency changes.
Subtle and Subliminal Electoral Mobilisation : A Priming Experiment

The act of voting is one of the most widely studied political activities, but at the same time one of the most poorly understood. Although many factors that affect the chance that citizens participate in an election have been identified, the explanatory power of models based on these factors is rather limited. We argue that this may be due to the neglect of subtle psychological processes. We test this idea in two priming experiments. In the first study participants were subtly primed with a word puzzle that included words that either stimulated activism or passivism. In a second experiment participants were primed with similar words, but subliminally. Results showed that subtly primed participants in the activism condition reported stronger voting intentions than those in the passivism condition. However, no such effects were found in the second study. These findings suggest that individuals can be stimulated (or inhibited) to vote through subtle psychological processes. We discuss the implications of our results for the study of voting behaviour as well as campaigns aimed at electoral mobilisation. * Corresponding author Martin Rosema Centre for the Study of Democracy University of Twente P.O. Box 217, 7500 AE Enschede, The Netherlands Phone: +31 53 489 3280 E-mail: m.rosema@utwente.nl Paper to be presented at the ECPR General Conference, Glasgow, 3–6 September 2014. Section: Changing Patterns of Electoral Competition and Voting Behaviour (S006) Panel: Political Participation (P268)
A preliminary users manual for Isabelle

The theorem prover Isabelle and several of its object-logics are described. Where other papers [15] have been concerned with theory, the emphasis here is completely practical: the operations, commands, data structures, and organization of Isabelle. This information could beneflt both users of Isabelle and implementors of other systems.
Vibration-Free Joule-Thomson Cryocoolers for Distributed

This paper reports on an innovative concept for a space-borne Joule-Thomson (J-T) cryocooler that utilizes a continuous-flow compressor to provide cooling to multiple miniature cold heads. The heat transport to each cooling site is accomplished at ambient temperature, allowing large separation distances between cryocooler components and cooling sites with minimal performance impact. The compressor uses non-contacting, gas-lubricated bearings and is a derivative of TRL 9 technology that has demonstrated long life and high reliability. The concept addresses the limitations on life and reliability normally associated with J-T cryocoolers. The key technical challenge is the development of a Low-Specific-Speed (LSS) compressor to match the operating conditions of the J-T cycle. Cycle analysis was carried out to identify the optimum operating conditions as well as the optimum composition of the cycle gas. An LSS compressor and a compact cold head were designed, and their performance, mass, and size are estimated. The cryocooler is designed to provide 10 mW of cooling at 150 K to each of the multiple cold heads. The performance of an LSS impeller is measured and the results are used to correlate a compressor design model. These studies demonstrate that a space-borne J-T cryocooler can be produced to provide efficient cooling at extremely low capacities. The cryocooler is lightweight, compact, extremely reliable, and emits negligible vibration. The scope of this paper is the design of the cryocooler, the optimization of the cycle gas composition, and the preliminary test results for an LSS compressor impeller.
Person authentication using EEG brainwave signals

Studies have shown that the electroencephalogram (EEC) recordings have unique pattern for each individual and thus have potential for biometric ap plications. There are two major problems associated with EEC biometrics. One is that the large EEG features size and the relatively limited EEG data size make it difficult to train a robust model; the other is that the signals from EEG scalp may not be reliable in many situations. Thus in this the sis we proposed new methods for increasing the accuracy and robustness of EEC-based authentication systems. First, to address the concern of the high dimensionality of EEC features, we proposed a novel dimension reduction method of EEG features based on the Fast Johnson-Lindeustrauss Transform (FJLT). We showed that this method has potential of mapping EEG features from a high dimension space to a lower one while keeping discrimination power between the features of subjects. The features we used are Multivariate Autoregressive (mAR) coefficients. We tested this method on a motor task related EEG data set. Second, to increase the reliability of scalp EEC signals, we employed an Independent Component Analysis (ICA)-based approach in our authen tication procedure, with the assumption that EEG recordings are linear combinations of the underlying brain source signals. We estimated the In dependent Components (ICs) from several physical regions on the scalp and determine the Dominating Independent Components (DIC) in the corre sponding regions. Then we extracted the Univariate Autoregressive (AR) coefficients from DICs as features. We tested our algorithm on two data sets, a motor task related EEC data set and an EEC data set of P300 potential. The proposed algorithm appeared to be promising, and when applied to EEG data collected from different days yields better performance
Extruder modelling: a comparison of two paradigms

Two data based modelling paradigms are compared. Using measurements from an industrial plasticating extrusion process, a locally recurrent neural network and a genetic programming algorithm are used to develop inferential models of the polymer viscosity. It is demonstrated that both techniques produce adequate nonlinear dynamic inferential models. However, for this application the genetic programming technique adopted produces models that perform better than the locally recurrent neural network. Moreover, the final model produced by the algorithm has a simple transparent structure.
Chinese land investments in Africa Scale , scope and an empirical analysis

China is accused of ‘land grabbing’ and neo-colonialism because of its FDI investments in Africa. As an exploratory study, this master’s thesis uses macroeconomic data from 48 Sub-Saharan African countries to investigate the host-country determinants of Chinese land investments in that region. Also, differences in investment strategies for Chinese and non-Chinese investors are looked at. Lastly, it is investigated whether China intends to export their agricultural products back to China as a solution for Chinese food insecurity. The results of this study show that Chinese land investments are attracted by large, corrupt, poor, and agricultural countries, that the Chinese investment strategy does not seem to differ much from the non-Chinese strategies, and that there is no clear evidence that China intends to export their agricultural products back to China.