Miracle-dz/UniFAR-FTUs huggingface dataset:
Formation of membrane networks in vitro by kinesin-driven microtubule movement

Certain intracellular organelles such as the endoplasmic reticulum (Terasaki, M., L. B. Chen, and K. Fujiwara. 1986. J. Cell Biol. 103:1557-1568) and lysosomes (Swanson, J., A. Bushnell, and S. C. Silverstein. Proc. Natl. Acad. Sci. USA. 84:1921-1925) form tubular networks that are closely aligned with microtubules. Here we describe the formation of polygonal networks composed of interconnected membrane tubules that occurs when a preparation of microtubule affinity-purified squid kinesin is combined with microtubules and ATP on a glass surface. The membrane, which is a minor contaminant in the microtubule affinity- purified kinesin preparation, binds to microtubules translocating along kinesin-coated glass surfaces. Force exerted by kinesin upon the microtubule is transmitted to the membrane and a tubular extension of the membrane is produced. As the membrane tubule elongates, membrane tension exerts an opposing force upon the translocating microtubule that can alter its direction of movement by dissociating or partially dissociating the microtubule from the kinesin-coated surface. Membrane tubules that come in contact appear to fuse with one another, and thus give rise to two-dimensional polygonal networks of tubules that have similar features to endoplasmic reticulum networks in cells. Artificial liposomes composed of dimyristoylphosphatidylcholine and yolk phosphatidylglycerol also form stable tubular structures when subjected to shear forces, but do not interact with microtubules or form polygonal networks, suggesting that such phenomena may require membrane- associated proteins. These findings indicate that kinesin generates sufficient force to form tubular membrane extensions in vitro and suggest that this microtubule-based motility protein may also be responsible for creating tubular membrane networks within cells.
A spatial scan statistic for nonisotropic two‐level risk cluster

Spatial scan statistic methods are commonly used for geographical disease surveillance and cluster detection. The standard spatial scan statistic does not model any variability in the underlying risks of subregions belonging to a detected cluster. For a multilevel risk cluster, the isotonic spatial scan statistic could model a centralized high‐risk kernel in the cluster. Because variations in disease risks are anisotropic owing to different social, economical, or transport factors, the real high‐risk kernel will not necessarily take the central place in a whole cluster area. We propose a spatial scan statistic for a nonisotropic two‐level risk cluster, which could be used to detect a whole cluster and a noncentralized high‐risk kernel within the cluster simultaneously. The performance of the three methods was evaluated through an intensive simulation study. Our proposed nonisotropic two‐level method showed better power and geographical precision with two‐level risk cluster scenarios, especially for a noncentralized high‐risk kernel. Our proposed method is illustrated using the hand–foot–mouth disease data in Pingdu City, Shandong, China in May 2009, compared with two other methods. In this practical study, the nonisotropic two‐level method is the only way to precisely detect a high‐risk area in a detected whole cluster. Copyright © 2011 John Wiley & Sons, Ltd.
A Control Architecture for Autonomous Mobile Robotics

The field of mobile robotics is on the forefront of robotics research around the world. Control architectures for complex autonomous mobile robots have largely settled on hybrid architectures for their suitability at dealing with the opposing forces of planning and reactivity. We present a general, heterogeneous 3-Tier hybrid architecture for control of an autonomous mobile robot and discuss an implementation in the domain of campus navigation. The architecture features a useful organization structure for high-level skills and offers flexible construction options for low-level behavior hierarchies.
Teleconsultation: Application to Provision of Consultation Services for School Consultants

ABSTRACT In this article, we provide critical information on previous research and offer recommendations for establishing and implementing teleconsultation services within a school setting. Specifically, we review (a) the history behind teleconsultation, (b) previous applications of technology and teleconsultation within the school setting, (c) recommended equipment for completing teleconsultation, (d) previously identified benefits of using teleconsultation, (e) previously identified concerns regarding the use of teleconsultation, and (f) the best practices for providing teleconsultation services. Important considerations from current research in the area of teleconsultation are highlighted.
Vision Based Obstacle Recognition Approach of a Power Line Inspection Robot

This paper introduces the research and application situations of the power transmission line inspection robot. It describes the architecture of two–arm rotary mechanism robot and the configuration of control system. The emphasis of this paper is placed on the algorithm of object recognition for those structure-restrained obstacles such as suspending clamps, counterweights, insulators and so on. The experiments of laboratory tests and field applications show that the proposed image recognition approach can achieve accurate results with less memory and computation cost.
StreamJIT

There are many domain libraries, but despite the performance benefits of compilation, domain-specific languages are comparatively rare due to the high cost of implementing an optimizing compiler. We propose commensal compilation, a new strategy for compiling embedded domain-specific languages by reusing the massive investment in modern language virtual machine platforms. Commensal compilers use the host language's front-end, use host platform APIs that enable back-end optimizations by the host platform JIT, and use an autotuner for optimization selection. The cost of implementing a commensal compiler is only the cost of implementing the domain-specific optimizations. We demonstrate the concept by implementing a commensal compiler for the stream programming language StreamJIT atop the Java platform. Our compiler achieves performance 2.8 times better than the StreamIt native code (via GCC) compiler with considerably less implementation effort.
Increasing cognitive load attenuates right arm swing in healthy human walking

Human arm swing looks and feels highly automated, yet it is increasingly apparent that higher centres, including the cortex, are involved in many aspects of locomotor control. The addition of a cognitive task increases arm swing asymmetry during walking, but the characteristics and mechanism of this asymmetry are unclear. We hypothesized that this effect is lateralized and a Stroop word-colour naming task—primarily involving left hemisphere structures—would reduce right arm swing only. We recorded gait in 83 healthy subjects aged 18–80 walking normally on a treadmill and while performing a congruent and incongruent Stroop task. The primary measure of arm swing asymmetry—an index based on both three-dimensional wrist trajectories in which positive values indicate proportionally smaller movements on the right—increased significantly under dual-task conditions in those aged 40–59 and further still in the over-60s, driven by reduced right arm flexion. Right arm swing attenuation appears to be the norm in humans performing a locomotor-cognitive dual-task, confirming a prominent role of the brain in locomotor behaviour. Women under 60 are surprisingly resistant to this effect, revealing unexpected gender differences atop the hierarchical chain of locomotor control.
Subspace method for blind separation of sources in convolutive mixture

For the convolutive mixture, a subspace method to separate the sources is proposed. It is showed that after using only the second order statistic but more sensors than sources, the convolutive mixture can be itentified up to instantaneou mixture. Furthermore, the sources can be separated by any algorithm for instantaneous mixture (based in generally on the fourth order statistics).
Improving SMT for Baltic Languages with Factored Models

This paper reports on implementation and evaluation of English-Latvian and Lithuanian-English statistical machine translation systems. It also gives brief introduction of project scope --Baltic languages, prior implementations of MT and evaluation of MT systems. In this paper we report on results of both automatic and human evaluation. Results of human evaluation show that factored SMT gives significant improvement of translation quality compared to baseline SMT.
A Novel Macroblock Level Rate Control Method for Stereo Video Coding

To compress stereo video effectively, this paper proposes a novel macroblock (MB) level rate control method based on binocular perception. A binocular just-notification difference (BJND) model based on the parallax matching is first used to describe binocular perception. Then, the proposed rate control method is performed in stereo video coding with four levels, namely, view level, group-of-pictures (GOP) level, frame level, and MB level. In the view level, different proportions of bitrates are allocated for the left and right views of stereo video according to the prestatistical rate allocation proportion. In the GOP level, the total number of bitrates allocated to each GOP is computed and the initial quantization parameter of each GOP is set. In the frame level, the target bits allocated to each frame are computed. In the MB level, visual perception factor, which is measured by the BJND value of MB, is used to adjust the MB level bit allocation, so that the rate control results in line with the human visual characteristics. Experimental results show that the proposed method can control the bitrate more accurately and get better subjective quality of stereo video, compared with other methods.
Automatic Detection of the Intima-Media Layer in Ultrasound Common Carotid Artery Image Based on Active Contour Model

The analysis of the carotid artery wall is of paramount importance in clinical practice. Especially, the intima-media thickness is a risk index for some of the most severe acute cerebrovascular pathologies, hence, an accurate segmentation of the different layers of the carotid artery is needed. IMT is usually manually measured on longitudinal B-mode ultrasound images. In the past ten years, many computer-based techniques for intima-media thickness measurement have been proposed to overcome the limits of manual segmentation, but almost all of them require a certain degree of user interaction. In this paper we proposed a novel approach for the completely user-independent segmentation of the common carotid artery wall. Our algorithm is designed for the extraction of the intima and media layers of the distal carotid wall in ultrasound images. It is based on integrated approach consisting of common carotid artery region identification, contour initialization with threshold segmentation, intima-lumen segmentation with Snake, and media-adventitia segmentation with GVF-Snake that enables the automated tracing of the carotid walls. The acoustic impedance's difference is employed to locate common carotid artery region. Finally, the characterization of the algorithm in terms of segmentation error was evaluated with a set of 40 common carotid artery ultrasound images and compared to the segmentation traced by a trained operator. Experiments showed that a mean error lower than 1.3 pixel both on the intima and media layers was obtained, which is comparable to that obtained by means of operator dependent techniques.
The learning organisation concept: A study of non-governmental organisations in Nairobi

The study set to determine the learning organization concept in the non-govemmental organizations in Nairobi. The objectives of the study were to establish the extent to which organization learning concept has been adopted by the Non governmental organizations and the challenges in the implementation of the learning concept among the nongovemmental organizations. The study is important because it basically addresses the search for the ideal organizational dynamics for growth, as it attempts to deal with the never-ending quest by organizations seeking to improve themselves while maintaining a position o f advantage. The study is therefore useful to managers as they will install, implementation and use ideas of the findings of this study in their organizations. Trainers and consultants will use this study to change their methodology to learning concept. This study will also benefit the academicians in the field of project management and employees will change their mindset on training and appreciate the place of informal learning for development growth. The study was survey designed; the population consisted of all NGOs within Nairobi. Stratified sampling procedure was used to select the sample elements of the study. Primary data was collected using semi-structured questionnaires, with four sections. Data was analyzed using descriptive statistical tools of analysis, which included frequencies and percentages.
Professional perceptions of the effectiveness of visual communication systems and their applications for functional communication interventions for individuals with Autism Spectrum Disorder

Background & aims This study investigated the perceptions of educational professionals in regard to the effectiveness of visual communication systems and their applications as a functional communication intervention for individuals with Autism Spectrum Disorder (ASD). Methods One hundred and one individuals from diverse educational backgrounds, school districts, educational services, and various states were surveyed for this study. All participants in this study served individuals with Autism Spectrum Disorder in a clinical and/or school setting. Results The study found that aided augmentative and alternative communication (AAC) systems were widely utilized, and participants perceived these systems as the most effective for individuals with Autism Spectrum Disorder. It also found that the use of low level tech aided augmentative communication systems such as Picture Exchange System and high level tech systems such as voice output systems that were strictly computer based, were dependent on the individual's abilities and needs. Finally, the study found that the use of photography and photo journaling techniques had positive outcomes for individuals diagnosed with Autism Spectrum Disorder and other students in the learning environment. Conclusions The results revealed that the overall consensus of educational professionals that serve individuals with ASD agreed that aided AAC systems were more effective methods to foster and enhance functional communication. In terms of effectiveness of the level of technology utilized within the system, it depends on the needs and abilities of the individual with ASD. Participants, however, did agree that photography and photo journaling techniques may provide positive attributes to all students and not only those diagnosed with ASD. Implications The ability to modify or alter the ways in which AAC systems are created and implemented may address the need to individualize the systems in terms of the needs and abilities of the individual with ASD. The results inform educational practices as they highlight that a majority of the professionals that participated in this study need further professional development with the understanding and application of these systems in order to better understand their benefits and what they have to offer individuals with ASD. The results also provided a lens through the professionals' experiences into the classrooms by providing information from professionals that utilize and implement these systems daily including their successes in aiding communication effectively.
An Echo State Network based pedestrian counting system using wireless sensor networks

A pedestrian counter has a lot of applications like effective resource utilization, planning of service activities and ensuring safety and convenience. The design and implementation of a new intelligent pedestrian counter is presented in this paper. The counter is publicly usable, low cost, easily deployable and scalable. We used off-the-shelf components for our design and the overall cost is less than 200 euro. The counter works in distributed mode and has wireless communication facilities. The hardware platform consists of PIR sensor units that detect the pedestrian movements, the wireless sensor nodes that handle the sensor data acquisition and transmission and the base station computer that process the data. We have trained an echo state network and this recurrent neural network functions as the brain for the the pedestrian counter. A system model was constructed using Simulink for generating the training data for the neural network. A modular layered software framework was designed for processing the pedestrian counter data. The echo state network successfully learned the various motion patterns and the pedestrian counter gave a reasonably good performance of 80.4%. To improve the performance further, redesigning the systems using low cost Active IR distance sensor is suggested.
Predictive Load Balancing in Cloud Computing Environments Based on Ensemble Forecasting

Cloud Computing allows the on-demand usage of computing resources in a pay-as-you-use manner. One major problem for Cloud providers is the trade-off between the huge amount of energy consumption resulting from the non optimal utilisation of their servers, and meeting the service level agreements. Virtualisation allows for a better utilisation of existing servers while maintaining the required quality of service, increasing the return on investment. Consequently, dynamic algorithms are needed that determine an optimal plan for the live migration and allocation of Virtual Machines (VM) during run time. The contribution of this paper is as follows: First, we present our forecast module for time series to future utilisation of VMs. Second, we demonstrate how forecasts of CPU utilisation can be used beneficially in Cloud Computing environments. We propose a novel proactive VM migration policy utilising forecasts(PRUF) in Cloud data centres using an predictive overload detection. It uses short-term VM utilisation forecasts based on an ensemble forecasting approach to estimate which host will be overloaded when the next migration process is triggered. A study in the cloud computing simulation toolkit Cloud Sim shows that our predictive approach reduces the number of service level agreement violations and the performance degradation due to VM migrations compared to VM migration algorithms implemented in Cloud Sim.
Bioinformatics analysis for genome design and synthetic biology

Till the end of June 2005, there are totally around 1500 complete and ongoing genome sequencing projects (see the GOLD at http://www.genomesonline.org/) around the world. Near two hundred and fifty bacterial genomes have already been completely sequenced and other seven hundreds more are currently underway. The availability of the tremendous amount of whole genome sequences has no doubt opened up new avenues for genome-wide innovative genome design and efficient synthetic biology research. With such abundant resources, we do indeed need to develop tools for analyzing these sequences and mining the genomic treasures among these species. By analyzing the whole genomes for similarities/differences and structural features, it has become possible to identify new genetic targets and investigate their roles. Our group is interested in studying the architectural structures of the various genomes by many novel methods used to characterize genome structure in natural living cells and investigating their use in genome design and engineering. To aid this kind of comparative genome analysis, we have developed efficient gene identification and functional annotation pipeline and also constructed tools that can plot numerous measures for all positions in a long DNA sequence and do multiple whole genome comparisons. In this report, we would like to present a package of integrated comparative analysis platform (iCAP) for comparing various genomes from sequence level to pathway level and use locally sequenced bacterial species as examples to present some of the discovered features for genome design.
Practical challenges that arise when clustering the web using spectral methods

This is a report on an implementation of a spectral clustering algorithm for classifying very large internet sites, with special emphasis on the practical problems encountered in developing such a data mining system. Remarkably some of these technical difficulties are due to fundamental issues pertaining to the mathematics involved, and are not treated properly in the literature. Others are inherent to the functions and numerical methods proper to the high level technical computing programming environment that we use. We will point out what these practical challenges are and how to solve them.
Pinning Impulsive Synchronization of Reaction–Diffusion Neural Networks With Time-Varying Delays

This paper investigates the exponential synchronization of reaction–diffusion neural networks with time-varying delays subject to Dirichlet boundary conditions. A novel type of pinning impulsive controllers is proposed to synchronize the reaction–diffusion neural networks with time-varying delays. By applying the Lyapunov functional method, sufficient verifiable conditions are constructed for the exponential synchronization of delayed reaction–diffusion neural networks with large and small delay sizes. It is shown that synchronization can be realized by pinning impulsive control of a small portion of neurons of the network; the technique used in this paper is also applicable to reaction–diffusion networks with Neumann boundary conditions. Numerical examples are presented to demonstrate the effectiveness of the theoretical results.
Object Region Modelling with Eigen-Density , SVM and

Aim of this work is to detect and segment objects such as license plate or human eyes from images with Markov Random Fields (MRFs). MRF is a region based segmentation method that works iteratively in Bayes framework and simulated annealing was used as optimization method. We focused on modelling the MRF regional observation energies with probabilistic distributions. Although simple segmentation problems such as thresholding can be modelled by simple univariate Gaussian distributions, complex textures regions such as license plate or human eye requires the use of multi-dimensional distributions. We suggest using Eigen-Density distributions and Support Vector Machines (SVM) to model observation energies for complex textured regions. We also demonstrate the performance of MRFs for removing the noise and providing smoothness on segmented results and discrimination and generalization power of the SVMs.
The 21 st Annual Conference of the Japanese Neural Network Society ( December , 2011 ) Emotion Recognition from Walk Pattern

Humans can convey emotions through different ways. Gait is one of them. This paper presents a method to recognize emotions from walk pattern. The movements of 4 candidates are recorded by optical motion capture. We use the PCA of the feature vectors computed for each trial to develop an algorithm able to recognize the emotion conveyed in the gait. Keywords―Emotion, Kinematics, Motion capture, Feature vector, PCA.
The rate of convergence in the method of alternating projections

A generalization of the cosine of the Friedrichs angle between two subspaces to a parameter associated to several closed subspaces of a Hilbert space is given. This parameter is used to analyze the rate of convergence in the von Neumann-Halperin method of cyclic alternating projections. General dichotomy theorems are proved, in the Hilbert or Banach space situation, providing conditions under which the alternative QUC/ASC (quick uniform convergence versus arbitrarily slow convergence) holds. Several meanings for ASC are proposed.