Miracle-dz/UniFAR-FTUs huggingface dataset:
DURATION MODELS IN USE

duration" and "minimal duration" have The main point in this paper is to been used by many researchers. The describe how duration models actually Klatt duration model [8] has become a are in use. Most obviously we find them standard as will be seen in this auulied in text-to-sueech svstems. We discussion ua~er . aiio find that such'mode1;are slowly introduced in speech understanding systems. We will also discuss the notion of local speech tempo and the need to connect linguistic factors to low-level models. We will also discuss speakerdependent parameters such as vowelconsonant ratio.
Barriers to accessing HIV/AIDS care in North Carolina: Rural and urban differences

Abstract Many HIV-positive individuals face multiple barriers to care and therefore frequently experience unmet medical and support services needs. Rural areas often lack the infrastructure to support the delivery of comprehensive HIV services; however, few studies have examined service barriers faced by rural residents with HIV/AIDS, particularly in the South where two-thirds of people living with HIV/AIDS in rural areas reside. We surveyed North Carolina HIV/AIDS case managers (N=111) employed at state-certified agencies regarding barriers to medical and support services that influence medication adherence for their rural and urban-living clients. For each of the seven barriers assessed (long travel for care, HIV-related stigma, and a lack of transportation; HIV-trained medical practitioners; housing; mental health services and substance abuse treatment), a substantial proportion of case managers (29–67%) reported it was a ‘major problem’. For five of the seven barriers, rural case managers were significantly more likely to identify the barrier as a ‘major problem’. Multivariate analysis revealed that rural case managers and case managers with more female clients reported a greater number of barriers. Because unmet medical and support service needs may result in poorer outcomes for HIV-positive individuals, barriers to these services must be identified and addressed, particularly in rural areas which may be highly underserved.
Performance and Risk Management of Real Estate Enterprises

For the low efficiency and high risk of real estate enterprises in China, we design incentives-supervision model of a real estate enterprise based on principal-agent theory and on some characteristics of the real estate enterprises. Subsequently, in order to improve business performance and lower the risks, we study that how the clients of the real estate enterprises design the compensation for agents and supervisors of different ability levels and risk appetites in the different boom stages of the real estate market. Finally, some policy recommendations are put forwarded.
Challenges when securing manufacturing message service in legacy industrial control systems

The resource constrained legacy embedded systems are widely found in today's Industrial Control System networks. The Manufacturing message service (MMS) protocol is an international messaging service standard for industrial communications with very limited security provisioning. Legacy embedded systems and real-time applications impose challenges to secure the MMS protocol. Analysing the relevant standards such as IEC 62351-4, this paper presents a secure MMS profile by implementing Transport Layer Security (TLS) into the MMS protocol. In this paper, we present a detailed evaluation of the secure MMS profile that include how TLS addresses the vulnerability of the MMS protocol and an analysis of its performance in a low resource embedded system environment. We found that the secure MMS protocol in legacy embedded systems introduces concerns such as high run-time memory usage and considerable increase of the duration of data read and write. Due to these concerns, real-time nature of applications can be compromised.
Induction and Profiling of Strong Multi-Componential Emotions in Virtual Reality

Psychological theories of emotion have often defined an emotion as simultaneous changes in several mental and bodily components. In addition, appraisal theories assume that an appraisal component elicits changes in the other emotion components (e.g., motivational, behavioural, experiential). Neither the componential definition of emotion nor appraisal theory have been systematically translated to paradigms for emotion induction, many of which rely on passive emotion induction without a clear theoretical framework. As a result, the observed emotions are often weak. This study explored the potential of virtual reality (VR) to evoke strong emotions in ecologically valid scenarios that fully engaged the mental and bodily components of the participant. Participants played several VR games and reported on their emotions. Multivariate analyses using hierarchical clustering and multilevel linear modelling showed that participants experienced intense, multi-componential emotions in VR. We identified joy and fear clusters of responses, each involving changes in appraisal, motivation, physiology, feeling, and regulation. Appraisal variables were found to be the most predictive for fear and joy intensities, compared to other emotion components, and were found to explain individual differences in VR scenarios, as predicted by appraisal theory. The results advocate for upgraded methodologies for the induction and analysis of emotion processes.
On the classification of long non-coding RNAs

Long non-coding RNAs (lncRNAs) have been found to perform various functions in a wide variety of important biological processes. To make easier interpretation of lncRNA functionality and conduct deep mining on these transcribed sequences, it is convenient to classify lncRNAs into different groups. Here, we summarize classification methods of lncRNAs according to their four major features, namely, genomic location and context, effect exerted on DNA sequences, mechanism of functioning and their targeting mechanism. In combination with the presently available function annotations, we explore potential relationships between different classification categories, and generalize and compare biological features of different lncRNAs within each category. Finally, we present our view on potential further studies. We believe that the classifications of lncRNAs as indicated above are of fundamental importance for lncRNA studies, helpful for further investigation of specific lncRNAs, for formulation of new hypothesis based on different features of lncRNA and for exploration of the underlying lncRNA functional mechanisms.
The expressibility of languages and relations by word equations

Classically, several properties and relations of words, such as “being a power of the same word” can be expressed by using word equations. This paper is devoted to a general study of the expressive power of word equations. As main results we prove theorems which allow us to show that certain properties of words are not expressible as components of solutions of word equations. In particular, “the primitiveness” and “the equal length” are such properties, as well as being “any word over a proper subalphabet”.
Interactive mining topic evolutionary patterns from internet forums

In many real-world topic detection tasks, the process of the topic detection is often interactive, which means the users are likely to interfere the reason process by expressing their preferences. We proposed an algorithm, iOLDA, and the software framework for interactive topic evolution pattern detection based on Latent Dirichlet Allocation (LDA). To abate those topics not interested or related, it allows the users to add supervised information by adjusting the posterior topic-word distributions at the end of each iteration, which may influence the inference process of the next iteration. Experiments are conducted both on English and Chinese corpus and the results show that the extracted topics capture meaningful themes in the data, and the proposed interaction policies can help to discover better topics.
Extraction of Automatic Search Result Records Using Content Density Algorithm Based on Node Similarity

In this paper, a new method proposed for finding and extracting the SRRs. The method first detects content dense nodes on HTML DOM and then extracts SRRs to suggest a list of candidate HTML DOM nodes for a given single research result Web page instance. Afterwards an evaluation algorithm has been applied to the candidate list to find the best solution without any human interaction and manual process. Experimental results show that the proposed methods are successful for finding and extracting the SRRs.
Automatic MRI brain segmentation using local features, Self-Organizing Maps, and watershed

Image segmentation is the process of partitioning an input image into non-overlapping/disjoint regions. Various methods have been used for image segmentation. Among these methods, watershed-based algorithms have been widely utilized due to their fast computational speed. However, their sensitivity to noise, and also over-segmentation, has made watershed approaches unsuitable for noisy images. In this paper, a novel method for MRI segmentation is proposed. For this purposed, a simple and fast feature extraction method is used. Then, the feature space, is clustered by Self-Organizing Map Neural Networks (SOMNN). After that, an edge map is set up from the clustering result. Finally, watershed transformation is utilized on the edge map. Our algorithm is robust against noise. Although watershed transformation is used in our approach, a region merging and denoising algorithms are not utilized as pre- and post- processing, respectively. This significantly improves the segmentation speed.
Multi-class imagery EEG recognition based on adaptive subject-based feature extraction and SVM-BP classifier

In brain-computer interface (BCI), the classification accuracy significantly drops when the system has multiple motor imagery tasks for different subjects. To improve the classification accuracy and individual adaptability of the system, a new method of feature extraction and classification is presented in this paper for recognizing the four different motor imagery tasks (right hand, left hand, foots and tongue movements). Wavelet packet basis is selected for extracting the frequency bands in which the feature can be classified easily. Then, the EEG feature is extracted from the frequency bands information with One Versus the Rest Common Spatial Patterns (OVR-CSP) algorithm. Furthermore, a hybrid classification model of Support Vector Machines combining with the Back Propagation neural network (SVM-BP) is built to classify the multi-class EEG feature. Experiment results show that the proposed approach achieves better performance than other methods and it has adaptability for different subjects to some extent.
ManetSVM: Dynamic anomaly detection using one-class support vector machine in MANETs

The main goal of one-class classification is to classify one class from remaining feature space. One-class SVM is a kernel based approach which is very fast and precise and therefore is used in different fields such as image processing, protein classification and anomaly detection for statistical learning. There are some approaches suggested for anomaly detection in MANETs that most of them are static and use a predefined model. Due to the dynamic characteristics of MANETs, they cannot be applied to these networks well. In this paper we have proposed a one-class SVM for dynamic anomaly detection in mobile ad-hoc networks with AODV routing protocol, called ManetSVM. The efficiency of ManetSVM for detection of flooding, blackhole, neighbour, rushing, and wormhole attacks has been evaluated. Simulation results show that ManetSVM is able to achieve a better balance between Detection Rate and False alarm Rate in comparison with other dynamic anomaly detection approaches.
compreheNGSive : A Tool for Exploring Next-Gen Sequencing Variants

Next-generation sequencing data is notoriously difficult to process, store, and summarize, let alone analyze with any degree of comprehensiveness. This type of data epitomizes the difficulties that accompany today’s data deluge: its size is massive, and the cost to acquire it is declining sharply, further increasing its abundance [1]. Raw next-generation sequencing (NGS) data must pass through several layers of nontrivial post processing [2]. First, the raw data is assembled into a genome. Next, a variety of attributes are assigned to genomic locations describing the quality of the assembly. And finally, the genome is annotated to assign biologically meaningful concepts to locations in the genome. Currently, these steps require significant competence with command-line utilities and scripting, as well as reading a great deal of documentation that is often insufficient. To add to the complexity most steps, especially annotation, involve their own specific file formats. The time and effort to integrate data from multiple formats and sources can be overwhelming — even a seemingly simple task of assigning individual genomes to meaningful groups becomes difficult. We observed that much of the data is never analyzed because of these complexities. Existing tools for analyzing NGS data are similarly limiting in the amount of data that can be displayed at once. Tools for looking at the data in genomic coordinates, such as the UCSC Genome Browser [3], restrict analysis to comparisons that can be made locally around specific genomic features. The other commonly used method involves filtering the data in a spreadsheet program such as Excel, where scientists must select a small subset of interesting features based on a handful of attributes. With data sets that potentially have millions of points, experts analyze a very small fraction of the data. Though this kind of extreme filtering is necessary for human-driven analysis, no tool provides a global overview of the data to inform this selection. In this design study we are collaborating with a group of genetic epidemiology experts who are using NGS data to study breast cancer [4]. Our work with this group is focused on overcoming many of the challenges for analyzing NGS data, specifically for the biological questions they are tackling. Based on a careful analysis of the problem and needs of the group we are developing an interactive visualization tool, called compreheNGSive, to support the integration and exploration of post processed NGS data. Our contributions are an articulation of a workflow and set of visualization tasks needed by our collaborators, and an early prototype of our system compreheNGSive that supports this workflow and these tasks.
Compliant actuation for energy efficient impedance modulation

Energy efficient compliant actuation is the missing ingredient and key enabler of next-generation autonomous systems, domestic robots, prosthetic devices, orthotic devices, and wearable exoskeletons, to name a few. For all these devices, one would wish to develop actuators enabling wide range impedance modulation with low energy cost. Using conventional and biologically-inspired compliant actuation, previous research led to functional devices but with high energy cost. Here we introduce a minimalistic compliant actuator to realize impedance modulation with low energy cost. Using this actuator we demonstrate stiffness augmentation in human-machine collaboration. We argue that the non-biologically-inspired actuation concept presented here may effectively complement a biological system, by restoring or extending its functionality, with negligible energy cost.
Memory and processing architecture for 3D voxel-based imagery

A versatile voxel-based architecture for 3-D volume visualization, called the Cube architecture, is introduced. A small-scale prototype of the architecture has been realized in hardware and has been operating in true real-time, faster than the alternative voxel systems. The Cube architecture is centered around a 3-D cubic frame buffer, of voxels, and it entertains three processors that access the frame buffer to input sampled and synthetic data, to manipulate the 3-D images, and to project and render them. To cope with the huge quantity of voxels and still perform in real-time, two special features were incorporated within the architecture: a unique skewed memory organization, which permits the retrieval and storage of voxels in parallel, and a multiple-write bus, which speeds up the viewing process. These features allow Cube, for example, to project an image of n/sup 3/ voxels in O(n/sup 2/ log n) time rather than the conventional O(n/sup 3/) time. <<ETX>>
A 'NEW AND SOFT' URBAN PLANNING PARADIGM: THE STRATEGIC SPATIAL PLANNING

A central goal of urban policy and planning is to improve the living conditions and wellbeing of city dwellers. Nevertheless, many people experience a declining quality of life and this is intimately connected with environmental, spatial and socio-economic conditions. Many cities, for example, are experiencing a high incidence of pollution and stress related illnesses linked to poor industrial and transport planning, poor housing quality, underemployment and poverty. Access to basic services and community support is being undermined by changes in the spatial structure of settlements, especially increased dependence on car transport and land-use segregation. Urban and regional planning has a critical role to play in improving people's and cities’ wellbeing, quality of life and futures. Here, “urban planning" is used throughout this paper to describe the process by which the use of land in cities is regulated in the public interest. Governments throughout Europe have established systems intended to achieve this. The evolution of these systems within different cultural and institutional frameworks has led to variation in the terminology used to describe the process. Examples include urbanisme or amenagement du territoire in France, town planning in Great Britain, Raumordnung in Germany, urbanistica in Italy and “sehircilik” in Turkey. This term includes land-use planning, town and country planning, physical planning, urban and regional planning, territorial planning and space management systems. This document focuses primarily on urban issues and therefore uses "urban planning" as an umbrella term. In addition, "urban planning" has the potential to reflect the implications of land-use strategies, policies and programmes for the social, economic and physical environments. On this context, strategic spatial planning is appeared a new and recent approach in planning. So, new urban planning approaches are discussed on and strategic spatial planning approach is explained to be detailed and comprehensive in.
The Space Density of Redshift 5.7 Lyα Emitters: First Constraints from a Multislit Windows Search

We present results from a blind, spectroscopic search for redshift 5.7 Lyα emission-line galaxies at Keck I. Using a band-limiting filter and custom slit mask, we cover the LRIS detector with low-resolution spectra in the 8100-8250 Å atmospheric window, which contains no bright night-sky emission lines. We find nine objects with line fluxes greater than our flux limit of 6 × 10-18 ergs s-1 cm-2 in our ~5.1 arcmin2 field. We rule out a Lyα identification for six of these based on the absence of the continuum break, expected at rest 1215 Å for high-z galaxies and/or the identification of additional emission lines in our follow-up spectra. We find that extremely metal-poor, foreground emission-line galaxies are the most difficult type of interloper to recognize. For the three remaining emission-line objects, we identify a plausible counterpart for each object in a deep V-band image of the field, suggesting that none of them has a continuum break in the i band. Our preliminary conclusion is that our field contains no z = 5.7 Lyα emitters brighter than 0.6L, where L ≡ 3.26 × 1042 ergs s-1. Selecting a field with zero Lyα emitters is marginally consistent with the no-evolution hypothesis—i.e., we expected to recover 2-3 Lyα emitters, assuming that the Lyα luminosity function at redshift 5.7 is the same as it is at redshift 3. Our null result rules out a brightening of L by more than a factor of 1.7 from redshift 3 to 5.7, or, over the same redshift interval, an increase of more than a factor of 2.2 in the number density of Lyα emitters. The paucity of z = 5.7 Lyα emitters raises the question of whether the Lyα-selected population plays a significant role in maintaining the ionization of the intergalactic medium (IGM) at z = 5.7. We find that if the escape fraction of Lyα radiation is less than 0.4fLyc, where fLyc is the escape fraction of Lyman continuum photons, then the star formation rate in the Lyα-emitting population is high enough in the no-evolution model (our upper limit) to maintain the ionization of the IGM at z = 5.7.
Analysis of Multi-Server Round Robin scheduling disciplines

With the need and adoption of link aggregation where multiple links exist between two adjacent nodes in order to increase transmission capacity between them, there arise the problems of service guarantee and fair sharing of multiple servers. Although a lot of significant work has been done for single-server scheduling disciplines in the past years, not much work is available for multi-server scheduling disciplines. In this paper, we present and investigate two round robin based multi-server scheduling disciplines, which are Multi-Server Uniform Round Robin (MS-URR) and Multi-Server Deficit Round Robin (MS-DRR). In particular, we analyze their service guarantees and fairness bounds. In addition, we discuss the misordering problem with MS-DRR and present a bound for its misordering probability.
Dendritic Cell Trafficking and Antigen Presentation in the Human Immune Response to Mycobacterium tuberculosis 1

Mycobacterium tuberculosis (Mtb) is an extraordinarily successful human pathogen, one of the major causes of death by infectious disease worldwide. A key issue for the study of tuberculosis is to understand why individuals infected with Mtb experience different clinical outcomes. To better understand the dynamics of Mtb infection and immunity, we coupled nonhuman primate experiments with a mathematical model we previously developed that qualitatively and quantitatively captures important processes of cellular priming and activation. These processes occur between the lung and the nearest draining lymph node where the key cells mediating this process are the dendritic cells (DC). The nonhuman primate experiments consist of bacteria and cell numbers from tissues of 17 adult cynomolgus macaques (Macaca fascicularis) that were infected with Mtb strain Erdman (∼25 CFU/animal via bronchoscope). The main result of this work is that delays in either DC migration to the draining lymph node or T cell trafficking to the site of infection can alter the outcome of Mtb infection, defining progression to primary disease or latent infection and reactivated tuberculosis. Our results also support the idea that the development of a new generation of treatment against Mtb should optimally elicit a fast DC turnover at the site of infection, as well as strong activation of DCs for maximal Ag presentation and production of key cytokines. This will induce the most protective T cell response.