Miracle-dz/UniFAR-FTUs huggingface dataset:
"3rd. Pole" - A Composition Performed via Gestural Cues

rd . Pole" is a musical composition that is performed by a dancer, on a specially designed interface (instrument), based on motion tracking technology. This paper introduces the technical and artistic ideas behind the composition and outlines some of the main conceptual tendencies of my earlier work (8). Several independent components like choreography, instrument design, sound design, formal concepts, etc. were in parallel development throughout the last three years, and came together in this piece. Some of those were already realized in individual projects and are joined now under a new concept of interdependence. A major component of this project however was the implementation of a real-time gesture follower / gesture recognition algorithm applied to full-body motion data. This should be considered as an autonomous module, not allied to this particular project exclusively. Therefore it can be treated and developed independently - as a multi-purpose data mapping strategy - for it has the potential to find further use in any kind of interactive (dance, music, theatre, etc.) performance scenario.
OPTIMIZATION OF FUZZY INTEGRATED TWO-STAGE VENDOR-BUYER INVENTORY SYSTEM

In today’s business a close operation is necessary to decrease the joint total inventory cost. According to Simchi-Levi et al (2000) several international companies have demonstrated that integrating the supply chain has improved the company’s performance and market share. This paper considers an inventory vendor-buyer integrated system in a fuzzy situation by employing the type of fuzzy numbers which are trapezoidal. This model have been developed by using different optimization methods. A full fuzzy model is by using different optimization methods. A fully fuzzy model is developed where the input parameters and the decision variables are fuzzified. The optimal policy for the developed model is determined are fuzzified. The optimal policy for the developed model is determined by using the Lagrangean conditions after the defuzzification of the cost function with the graded mean integration method. The proposed method finds the optimal lotsize for both the vendor and buyer in an integrated two stage supply chain. Numerical examples are provided to highlight the difference between crisp and the fuzzy cases.
Relationship between drizzle rate, liquid water path and droplet concentration at the scale of a stratocumulus cloud system

The recent ACE-2, EPIC and DYCOMS-II field experiments showed that the drizzle precipitation rate of marine stratocumulus scales with the cloud geometrical thickness or liquid water path, and the droplet concentration, when averaged over a domain typical of a GCM grid. This feature is replicated here with large-eddy-simulations using state-of-the-art bulk parameterizations of precipitation formation in stratocumulus clouds. The set of numerical simulations shows scaling relationships similar to the ones derived from the field experiments, especially the one derived from the DYCOMS-II data set. This result suggests that the empirical relationships were not fortuitous and that they reflect the mean effect of cloud physical processes. Such relationships might be more suited to GCM parameterizations of precipitation from shallow clouds than bulk parameterizations of autoconversion, that were initially developed for cloud resolving models.
Learning dynamic shape models for Bayesian tracking

This Thesis addresses appearance-based techniques for modelling complex deformable objects in order to support a detection and tracking process. As example application it considers pedestrian tracking from a moving vehicle. The a-priory knowledge about the object class is captured by a novel probabilistic spatio-temporal shape representation, which consists of a set of distinct linear subspace models and handles continuous as well as discontinuous shape changes. An un-supervised learning algorithm is presented, which fully automatically derives the proposed representation from training sequences of closed contours. In contrast to previous work, no prior feature correspondences are required. The 2D representation is applied to improve the performance of matching systems that correlate using shape templates. The basic idea involves extending an existing set of training shapes with generated “virtual” shapes, in order to improve the representational capability. In the experiments ROC curves demonstrate that generating “virtual” shapes by sampling the linear subspace models of the proposed representation increases the detection rate while simultaneously reducing the false alarm rate of a correlation-based object detection system. A framework for multi-cue 3d object tracking is introduced, which approximates optimal Bayesian tracking by a particle filter and combines the three cues shape, texture and depth, in its observation density function. The framework integrates an independently operating object detection system by means of importance sampling. In the experiments the approach is evaluated on the challenging topic of pedestrian tracking from a moving vehicle in synthetic, rural, and complex urban environments. It is shown on thousands of images, that the proposed approach improves upon a cutting edge pedestrian protection system, that was designed for the European community funded project PROTECTOR.
Mapping Alterations to the Endogenous Elemental Distribution within the Lateral Ventricles and Choroid Plexus in Brain Disorders Using X-Ray Fluorescence Imaging

The choroid plexus and cerebral ventricles are critical structures for the production of cerebral spinal fluid (CSF) and play an important role in regulating ion and metal transport in the brain, however many aspects of its roles in normal physiology and disease states, such as psychiatric illness, remain unknown. The choroid plexus is difficult to examine in vivo, and in situ ex vivo, and as such has typically been examined indirectly with radiolabeled tracers or ex vivo stains, making measurements of the endogenous K+, Cl−, and Ca+ distributions unreliable. In the present study, we directly examined the distribution of endogenous ions and biologically relevant transition metals in the choroid plexus and regions surrounding the ventricles (ventricle wall, cortex, corpus callosum, striatum) using X-ray fluorescence imaging (XFI). We find that the choroid plexus was rich in Cl− and Fe while K+ levels increase further from the ventricle as Cl− levels decrease, consistent with the known role of ion transporters in the choroid plexus CSF production. A polyI:C offspring displayed enlarged ventricles, elevated Cl− surrounding the ventricles, and intraventricular calcifications. These observations fit with clinical findings in patients with schizophrenia and suggest maternal treatment with polyI:C may lead to dysfunctional ion regulation in offspring. This study demonstrates the power of XFI for examining the endogenous elemental distributions of the ventricular system in healthy brain tissue as well as disease models.
Large-eddy simulations and turbulent combustion model analysis of a dual-mode scramjet combustor

Due to their favorable characteristics of reduced payload-cost and high-Mach efficiency, scramjets have been identified as an attractive high-speed propulsion concept (Bertin & Cummings 2003). However, because of stringent operating conditions, accurate and comprehensive physical testings of this type of air-breathing propulsion system have not been feasible, thus preventing the widespread utilization of scramjets. In order to address the lack of reliable test data, numerical simulations provide opportunities to guide the design of scramjet engines. Specifically, the large-eddy simulation (LES) methods have demonstrated promising capabilities to describe key features in supersonic combustion configurations (Fureby 2012; Fulton et al. 2014; Saghafian et al. 2015). To advance the development of numerical techniques for the design of scramjet engines, the National Center for Hypersonic Combined-Cycle Propulsion program (McDaniel et al. 2009) supported a series of dual-mode scramjet experiments, performed at the University of Virginia’s Supersonic Combustion Facility. Non-intrusive diagnostic techniques that have been implemented include focused Schlieren and stereoscopic particle image velocimetry (Rockwell Jr. et al. 2014), coherent anti-Stokes Raman spectroscopy (CARS) (Cutler et al. 2014), and planar laser-induced fluorescence (Johansen et al. 2014), providing measurements of density gradients, velocity fields, hydroxyl radical concentration, temperature, and species mole-fractions. The objective of the current study is to characterize the University of Virginia’s operating point A (UV-A) experiments through a numerical investigation. In this work, a comparative study of two turbulent combustion models, namely the compressible flamelet/progress variable (FPV) model (Pec̆nik et al. 2012) and the laminar finite-rate chemistry (FR) approximation. All simulations are performed with a compressible reacting LES solver, Chris (Brès et al. 2013), and a detailed hydrogen-air reaction mechanism (Burke et al. 2011). To facilitate an objective assessment of the combustion models, all computations are performed with identical grid-quality, subgrid turbulence closure, and time-advancing scheme throughout. The geometry, boundary and operating conditions, and computational setup are presented in the next section. Then, statistical results from the experiment and both simulations are compared, followed by an analysis of the effects of the combustion model by cross-referencing the two numerical datasets and discussions of the evaluations. The paper finishes with conclusions.
Efficient sample generation for scalable meta learning

Meta learning techniques such as cross-validation and ensemble learning are crucial for applying machine learning to real-world use cases. These techniques first generate samples from input data, and then train and evaluate machine learning models on these samples. For meta learning on large datasets, the efficient generation of samples becomes problematic, especially when the data is stored distributed in a block-partitioned representation, and processed on a shared-nothing cluster. We present a novel, parallel algorithm for efficient sample generation from large, block-partitioned datasets in a shared-nothing architecture. This algorithm executes in a single pass over the data, and minimizes inter-machine communication. The algorithm supports a wide variety of sample generation techniques through an embedded user-defined sampling function. We illustrate how to implement distributed sample generation for popular meta learning techniques such as hold-out tests, k-fold cross-validation, and bagging, using our algorithm and present an experimental evaluation on datasets with billions of datapoints.
Offshore Wind Farm Power Control Using HVdc Link

In this paper, a method is presented to control offshore wind farm output power. This method is able to fix the wind farm output power even during wind speed variations. In the proposed method, the offshore wind farm is connected to the onshore grid through the high-voltage dc (HVdc) cable. Moreover, the power control of the wind turbines is achieved by controlling the HVdc convertors. In the proposed system, the generator side convertors have to control the active power absorbed from the wind, and the grid side ones are obtained to control the HVdc link voltage. The control system is based on applying the appropriate modulation index to the voltage source converters. Two control strategies are proposed and analyzed to control wind farm output power. The simulation results illustrate that the proposed method is able to smooth the output power of the offshore wind farms appropriately. The proposed wind farm configuration and the control system are validated by simulations in the MATLAB/Simulink environment.
Bilateral Symmetrical Herpes Zoster in an Immunocompetent 15-Year-Old Adolescent Boy

Herpes zoster is uncommon in immunocompetent children. The bilateral symmetrical occurrence of herpes zoster lesions is extremely rare. We report a 15-year-old immunocompetent Chinese adolescent boy who developed bilateral symmetrical herpes zoster lesions. To our knowledge, the occurrence of bilateral symmetrical herpes zoster lesions in an immunocompetent individual has not been reported in the pediatric literature.
Carboxy-terminal SEKDEL sequences retard but do not retain two secretory proteins in the endoplasmic reticulum

The sequence Ser-Glu-Lys-Asp-Glu-Leu (SEKDEL) has been shown to be a signal which leads to retention of at least two proteins in the endoplasmic reticulum of animal cells (Munro and Pelham, 1987). In this study we tested the function of this signal by appending it to two secretory proteins, rat growth hormone and the alpha subunit of human chorionic gonadotrophin (hCG-alpha). We used oligonucleotide-directed mutagenesis and expression to generate proteins with SEKDEL added to the exact COOH termini and then carried out a detailed analysis of their transport in monkey COS cells. We found that transport was not blocked for either protein, but rather that the half-time for secretion was increased about sixfold for both proteins. Analysis of oligosaccharide processing on hCG-alpha-SEKDEL and indirect immunofluorescence microscopy on cells expressing both proteins was consistent with a retardation of transport between the endoplasmic reticulum and the Golgi apparatus. A change in the last amino acid of the SEKDEL sequence from Leu to Val abolished the retardation almost completely, suggesting a highly specific interaction of the sequence with a receptor. A change in the first amino acid had little or no effect on retardation. We conclude that the SEKDEL signal can have strong effects on reducing the rate of protein exit from the endoplasmic reticulum without generating absolute retention. Presumably other features of protein structure must be important to generate absolute retention.
Thoughts on a Recursive Classifier Graph: a Multiclass Network for Deep Object Recognition

We propose a general multi-class visual recognition model, termed the Classifier Graph, which aims to generalize and integrate ideas from many of today's successful hierarchical recognition approaches. Our graph-based model has the advantage of enabling rich interactions between classes from different levels of interpretation and abstraction. The proposed multi-class system is efficiently learned using step by step updates. The structure consists of simple logistic linear layers with inputs from features that are automatically selected from a large pool. Each newly learned classifier becomes a potential new feature. Thus, our feature pool can consist both of initial manually designed features as well as learned classifiers from previous steps (graph nodes), each copied many times at different scales and locations. In this manner we can learn and grow both a deep, complex graph of classifiers and a rich pool of features at different levels of abstraction and interpretation. Our proposed graph of classifiers becomes a multi-class system with a recursive structure, suitable for deep detection and recognition of several classes simultaneously.
Visual Twitter Analytics: Exploring Fan and Organizer Sentiment During Le Tour de France

— In recent years, Twitter has become a valuable source of information regarding the public perception of products, services, and events. Not only have many sport organizations embraced Twitter as a communication mechanism, the public and collaborative nature of Twitter has allowed fans to communicate with and respond to the organizers, as well as with one another. As a result, Twitter represents a vast wealth of information for understanding fan and sport organization behaviour. However, analyzing such data for the purposes of sport management research is difﬁcult due to the size, temporality, and textual nature of the data. In this paper, we present a visual analytics approach to analyzing the temporally changing sentiment within Twitter, called Vista. Machine learning is used to extract sentiment from individual tweets, which are visually represented using a timeline; interactive tools support exploration and analytical reasoning about the data. A case study is provided to illustrate the types of analyses that can be performed with Vista.
Finite-length EXIT analysis for protograph-coded two-dimensional ISI channels

In this paper, we consider the protograph low-density parity-check (LDPC) codes in two-dimensional inter two-dimensional (2D) intersymbol interference (ISI) channels, which are widely used in modeling ultra-high-density magnetic recoding systems. In particular, we study the convergence and error performance of protograph codes over 2D-ISI channels. We modify the finite-length (FL) extrinsic information transfer (EXIT) algorithm for the sake of facilitating the convergence analysis of protograph codes. Through the FL-EXIT analyses and simulated results, we observe that the regular column-weight-3 (CW-3) code is superior to the protograph codes optimized for one-dimensional (1D) ISI channels, e.g., the 1D-ISI protograph code. Accordingly, the 1D-ISI protograph code may not be served as a good candidate for use in ultra-high-density data storage systems.
Optimized Scalable Hardware Architecture for Modular Addition and Subtraction in Dual-Field

Modular addition and subtraction are applied in every Public Key Cryptography (PKC), such as RSA and ECC. But they are time consuming operations with delay of long carry and borrow propagations when the operands are great numbers. An optimized scalable and unified hardware architecture is proposed in this paper to work with any precision operands for both prime and binary extension finite fields, and modular addition and subtraction are integrated into one single hardware architecture. For obtaining performance results, our work is captured in VerilogHDL and implemented under 0. 18μm CMOS technology. The results indicate that the scalable and unified hardware architecture in our work can work at high clock frequency compared with fixed designs when the operands are large number, and the clock frequency is falling slowly while the width of data path is increasing.
Greedy Flooding in Redoubtable Sensor Networks

In Wireless Sensor Networks, flooding in one of the basic communication primitives. It is used to propagate informations from one node to the entire network. Every node, receiving a piece of information, has to flood it to all its neighborhood. As informations to spread are important, such as fire or intrusion alerts, communications should be secured. In this paper, we present a greedy flooding algorithm for Wireless Sensor Networks secured by a Random Key Pre-distribution model that make them redoubtable. We present two theoretical upper bounds of the time complexity of this algorithm that depend on the network structure. We then use the ViSiDiA platform to implement and simulate our algorithm to validate these theoretical results.
TOWARDS RAPID LANGUAGE PORTABILITY OF SPEECH PROCESSING SYSTEMS

In recent years, more and more speech processing products in several languages have been widely distributed all over the world. This fact reflects the general believe that speech technologies have a huge potential to let everyone participate in today's information revolution and to bridge the language barriers. However, the development of speech processing systems still requires significant skills and resources to be carried out. With some 45006000 languages in the world, the current cost and effort in building speech support is prohibitive to all but the top, most economically viable languages. In order to overcome these limitations, our research centers around the development of new algorithms and tools to rapidly port speech processing systems to new languages. This paper focuses on our approaches to create acoustic models, pronunciation dictionaries, and language models in new languages with only limited or no data resources available in the language of question. For this purpose we developed language independent and language adaptive acoustic models, investigated pronunciation dictionaries which can be directly derived from the written form and propose cross-lingual language model adaptation. The approaches are evaluated on our multilingual text and speech database GlobalPhone which covers more than 15 languages of the world.
The effects of travel experience dimensions on tourist satisfaction and destination loyalty: the case of an island destination

This paper aims to examine the effects of travel experience dimensions on tourist satisfaction and destination loyalty. ,Data were collected from 457 international tourists visiting Phuket through a convenience sampling method. The structural equation modeling approach by AMOS was used to test the effects. ,In case of island destination, beach attraction is not the only factor contributing to tourists’ loyalty, but hospitality of local people also plays an essential component to retain loyal tourists. ,This study examined only one construct (i.e. travel experience) affecting tourist satisfaction and destination loyalty. ,Tourist experience in beach attractions and local people are the key factors to retain royal tourists. ,Quality of beaches and friendliness of local people are important factors to promote island tourism. ,Two key factors of tourist experience were found to affect tourists’ loyalty in the case of island destination: beach attractions and local people.
Probabilistic mutual information based extraction of malignant brain tumors in MR images

Extraction of the malignant tumor region from the brain magnetic resonance (MR) image is a critical task. As the soft tissues of the brain neoplasm has a lot of variation thus the proper extraction of the malignant tumor and segmenting the affected part from brain MR image is the major part of concern. Malignant brain tumors like Central Neuro Cytoma (CNC), Glioblastoma Multiforme (GBM), Gliomas, Intra Ventricular Malignant Mass and Metastasis are concerned as one of the critical brain tumors of medical science therefore the extraction and appropriate segmentation of such tumor from the brain MR image in their early phase of generation is required. In the past innumerable approaches had applied on brain MR imaging system to figure out the proper abnormality region inside the brain image. The literature gives an idea about various clustering and segmentation approaches that help to identify the abnormal region inside imaging systems based on predefined threshold values. While the selection of proper threshold is again a mind blundering. In this paper a new image segmentation algorithm is proposed which clusters the maximum possible abnormality region based on the probabilistic mutual information. Proposed algorithm is free from any initial selection of threshold value and gives significant results in extraction of the malignant tumor region inside brain MR image.
Comparison of Co-located and Remote Collaborative Work using a Stereoscopic Image on Path Steering Task

We compared two types of 3D collaborative work, co-located and remote, on a tabletop display with stereoscopic images on collaborative path steering task with two users. We used IllusionHole, an interactive stereoscopic display for multiple users, that enables each of the users to directly reach out to and point at a particular position on the stereoscopic image. The 3D workspace is simultaneously shared by all users. We carried out a comparative experiment on the path steering task presented on IllusionHole in which two participants cooperatively manipulated a virtual object along a wire. Experimental results show that there was significant difference in favor of co-located situation, and the performance was affected by synchronization difficulties between participants appeared for remote situation. Based on the results, we discuss supporting remote collaborative work with stereoscopic image.