Miracle-dz/UniFAR-FTUs huggingface dataset:
Dual task interference during gait in people with Parkinson disease: effects of motor versus cognitive secondary tasks.

BACKGROUND AND PURPOSE Exacerbation of movement disorders while doing 2 tasks (dual task performance) is a characteristic feature of Parkinson disease (PD). The aim of this investigation was to identify whether the type of secondary task (motor or cognitive) determined the severity of dual task interference. SUBJECTS AND METHODS Footstep patterns for 15 people with PD and 15 comparison subjects without PD were compared when they walked: (1) at a self-selected speed, (2) while simultaneously performing a motor task (coin transference), and (3) while simultaneously performing a cognitive task (digit subtraction). Gait speed, stride length, cadence, and the percentage of the gait cycle in double-limb stance (DS) were examined with a computerized stride analyzer. RESULTS When there was no second task, the mean stride length was less in the group with PD (1.29 m) than in the comparison group (1.51 m), and the mean gait speed was less in the group with PD (71.47 m/min) than in the comparison group (87.29 m/min). The mean cadence was less in the group with PD (110.79 steps/min) than in the comparison group (115.81 steps/min). The percentage of the gait cycle in DS was greater in the group with PD (33.38%) than in the comparison group (31.21%). Both groups reduced their stride length and speed when they had to change from unitask performance to dual task performance and DS increased. For the group with PD, cadence also decreased. For both groups, the type of secondary task had a negligible effect on the performance decrement. DISCUSSION AND CONCLUSION Although the performance of simultaneous motor or cognitive tasks compromised gait in people with PD, the type of secondary task was not a major determinant of the severity of dual task interference.
Testing alternative mechanisms of evolutionary divergence in an African rain forest passerine bird

Abstract Models of speciation in African rain forests have stressed either the role of isolation or ecological gradients. Here we contrast patterns of morphological and genetic divergence in parapatric and allopatric populations of the Little Greenbul, Andropadus virens, within different and similar habitats. We sampled 263 individuals from 18 sites and four different habitat types in Upper and Lower Guinea. We show that despite relatively high rates of gene flow among populations, A. virens has undergone significant morphological divergence across the savanna–forest ecotone and mountain–forest boundaries. These data support a central component of the divergence‐with‐gene‐flow model of speciation by suggesting that despite large amounts of gene flow, selection is sufficiently intense to cause morphological divergence. Despite evidence of isolation based on neutral genetic markers, we find little evidence of morphological divergence in fitness‐related traits between hypothesized refugial areas. Although genetic evidence suggests populations in Upper and Lower Guinea have been isolated for over 2 million years, morphological divergence appears to be driven more by habitat differences than geographic isolation and suggests that selection in parapatry may be more important than geographic isolation in causing adaptive divergence in morphology.
AUV Positioning Method Based on Tightly Coupled SINS/LBL for Underwater Acoustic Multipath Propagation

This paper researches an AUV (Autonomous Underwater Vehicle) positioning method based on SINS (Strapdown Inertial Navigation System)/LBL (Long Base Line) tightly coupled algorithm. This algorithm mainly includes SINS-assisted searching method of optimum slant-range of underwater acoustic propagation multipath, SINS/LBL tightly coupled model and multi-sensor information fusion algorithm. Fuzzy correlation peak problem of underwater LBL acoustic propagation multipath could be solved based on SINS positional information, thus improving LBL positional accuracy. Moreover, introduction of SINS-centered LBL locating information could compensate accumulative AUV position error effectively and regularly. Compared to loosely coupled algorithm, this tightly coupled algorithm can still provide accurate location information when there are fewer than four available hydrophones (or within the signal receiving range). Therefore, effective positional calibration area of tightly coupled system based on LBL array is wider and has higher reliability and fault tolerance than loosely coupled. It is more applicable to AUV positioning based on SINS/LBL.
Interoperable context sharing in an ontology-enabled collaboration framework

With the rapid growth of network-enabled environment, the range of intelligent application services is widely expanding with continuous attempts to fully utilize highly accurate personal or environmental information. In parallel, a variety of professional knowledge data have been built up and provided to the public in the form of semantically linked ontology files. This phenomenon encourages us to focus on developing more flexible data mixing bowl, where the individual data are used to support their inherent functions and associated with different data to trigger inter-related complex services. This research has been evolved from the existing collaboration framework for a context-aware environment by integrating ontology gateway, the XOnt agent, as its ontology-enabled context-aware knowledge option. The ontology-enabled semantic reasoning scheme includes the XOntology combined with the Context-Aware Inference (CAI) model of the existing collaboration framework. Based on the properties of the ontology and behavioral activities of individual instances of the classes, the XOnt agent semantically evaluates current situation and controls the proposed ontology-enabled collaboration framework.
Orchestrating Bulk Data Transfers across Geo-Distributed Datacenters

As it has become the norm for cloud providers to host multiple datacenters around the globe, significant demands exist for inter-datacenter data transfers in large volumes, e.g., migration of big data. A challenge arises on how to schedule the bulk data transfers at different urgency levels, in order to fully utilize the available inter-datacenter bandwidth. The Software Defined Networking (SDN) paradigm has emerged recently which decouples the control plane from the data paths, enabling potential global optimization of data routing in a network. This paper aims to design a dynamic, highly efficient bulk data transfer service in a geo-distributed datacenter system, and engineer its design and solution algorithms closely within an SDN architecture. We model data transfer demands as delay tolerant migration requests with different finishing deadlines. Thanks to the flexibility provided by SDN, we enable dynamic, optimal routing of distinct chunks within each bulk data transfer (instead of treating each transfer as an infinite flow), which can be temporarily stored at intermediate datacenters to mitigate bandwidth contention with more urgent transfers. An optimal chunk routing optimization model is formulated to solve for the best chunk transfer schedules over time. To derive the optimal schedules in an online fashion, three algorithms are discussed, namely a bandwidth-reserving algorithm, a dynamically-adjusting algorithm, and a future-demand-friendly algorithm, targeting at different levels of optimality and scalability. We build an SDN system based on the Beacon platform and OpenFlow APIs, and carefully engineer our bulk data transfer algorithms in the system. Extensive real-world experiments are carried out to compare the three algorithms as well as those from the existing literature, in terms of routing optimality, computational delay and overhead.
The Evolution of a Kernel-Based Distance Metric for k-NN Regression

k-Nearest Neighbours (k-NN) is a well understood and widely-used approach to classification and regression problems. In many cases, s uch applica- tions of k-NN employ the standard Euclidean distance metric for the determina- tion of the set of nearest neighbours to a particular test data sample. This paper investigates the use of a data-driven evolutionary approach, named KTree, for the automatic construction of a kernel-based distance metric as an alternative to Eu- clidean distance. The key idea behind this approach is that a different dis tance metric is generated for a particular data domain. The performance of k-NN with the standard Euclidean distance measure is compared with that of k-NN based on a kernel-based distance metric evolved by KTree. This comparison is based on experiments on both synthetic and real-world datasets.
TCP smart framing: a segmentation algorithm to reduce TCP latency

TCP Smart Framing, or TCP-SF for short, enables the Fast Retransmit/Recovery algorithms even when the congestion window is small. Without modifying the TCP congestion control based on the additive-increase/multiplicative-decrease paradigm, TCP-SF adopts a novel segmentation algorithm: while Classic TCP always tries to send full-sized segments, a TCP-SF source adopts a more flexible segmentation algorithm to try and always have a number of in-flight segments larger than 3 so as to enable Fast Recovery. We motivate this choice by real traffic measurements, which indicate that today's traffic is populated by short-lived flows, whose only means to recover from a packet loss is by triggering a Retransmission Timeout. The key idea of TCP-SF can be implemented on top of any TCP flavor, from Tahoe to SACK, and requires modifications to the server TCP stack only, and can be easily coupled with recent TCP enhancements. The performance of the proposed TCP modification were studied by means of simulations, live measurements and an analytical model. In addition, the analytical model we have devised has a general scope, making it a valid tool for TCP performance evaluation in the small window region. Improvements are remarkable under several buffer management schemes, and maximized by byte-oriented schemes.
Learning from negative example in relevance feedback for content-based image retrieval

In this paper, we address some issues related to the combination of positive and negative examples to perform more efficient image retrieval. We analyze the relevance of negative example and how it can be interpreted. Then we propose a new relevance feedback model that integrates both positive and negative examples. First, a query is formulated using positive example, then negative example is used to refine the system's response. Mathematically, relevance feedback is formulated as an optimization of intra and inter variances of positive and negative examples.
Probabilistic similarity networks

Normative expert systems have not become commonplace because they have been difficult to build and use. Over the past decade, however, researchers have developed the influence diagram, a graphical representation of a decision maker's beliefs, alternatives, and preferences that serves as the knowledge base of a normative expert system. Most people who have seen the representation find it intuitive and easy to use. Consequently, the influence diagram has overcome significantly the barriers to constructing normative expert systems. Nevertheless, building influence diagrams is not practical for extremely large and complex domains. In this book, I address the difficulties associated with the construction of the probabilistic portion of an influence diagram, called a knowledge map, belief network, or Bayesian network. I introduce two representations that facilitate the generation of large knowledge maps. In particular, I introduce the similarity network, a tool for building the network structure of a knowledge map, and the partition, a tool for assessing the probabilities associated with a knowledge map. I then use these representations to build Pathfinder, a large normative expert system for the diagnosis of lymph-node diseases (the domain contains over 60 diseases and over 100 disease findings). In an early version of the system, I encoded the knowledge of the expert using an erroneous assumption that all disease findings were independent, given each disease. When the expert and I attempted to build a more accurate knowledge map for the domain that would capture the dependencies among the disease findings, we failed. Using a similarity network, however, we built the knowledge-map structure for the entire domain in approximately 40 hours. Furthermore, the partition representation reduced the number of probability assessments required by the expert from 75,000 to 14,000.
Wavelet Decomposition and Adaboost Feature Weighting for Facial Expression Recognition

In order to accomplish subject-independent facial expression recognition, a facial expression recognition approach based on wavelets decomposition and adaboost feature weighting is presented in this paper. At first, wavelet is adopted to decompose images into several bands of frequency images from which the LBP features are extracted. Then adaboost is introduced to learn the dichotomy-dependent weights for SVM classification because different image region has different contribution when dichotomizing different expression pairs. Finally, we compare the recognition accuracy with several other popular expression recognition paradigms. The results show that the proposed improvements in this paper have promoted the performance of facial expression recognition prominently.
Patterns in Contemporary Canadian Picture Books: Radical Change in Action

This comprehensive qualitative examination of two groups of Canadian picture books, 57 titles published in 2005 and 120 titles published in 2015, offers comparative data that demonstrate patterns related to authors, illustrators, characterization, genres, audiences, and particular elements of Radical Change. Following book collection, content analysis was conducted with a consideration of Dresang’s notion that books for children are evolving with respect to forms and formats, perspectives, and boundaries. Our process for analysis was developed from Berg’s framework of systematic content analysis based on predetermined as well as emerging categories. There is much recent research exploring particular content in children’s literature, supporting the central importance of literature in the classroom and community. Comparative Canadian studies across decades, however, are rare, and are increasingly important as a way to track and describe the changes that are taking place with respect to books for young people. It is interesting that in both 2005 and 2015, picture books tended to feature children as protagonists, with the highest number of books from the 2005 set utilizing the fantasy genre (at 34%) or realistic fiction (at 28%) and the highest number of books from the 2015 set occurring in non-fiction (at 34%, up from 16% in 2005) or fantasy (at 31%). Historical fiction in both years presented comparatively low, at 12% and 3%, respectively. Findings of this study support and extend the notion of Radical Change. The research team noted marked innovations within the 2015 group related to forms and formats, boundaries, and perspectives. Of particular note are the increasing numbers of books that present Indigenous content and perspectives. While many of the changes appearing in Canadian picture books between 2005 and 2015 might be predicted through the standard categories of Radical Change (Dresang, 1999), other findings also emerged that suggest new Radical Change considerations. Continuing to examine children’s literature as artifacts of a culture can illuminate particular aspects of that culture and offer opportunities to engage authors, illustrators, and publishers in filling gaps where particular perspectives or topics are missing. Advocacy is important as children’s literature continues to be a source of tension for what it portrays and presents as well as its missing voices. A knowledge of patterns and trends in relation to available content and resources supports classroom practice as well as encourages classroom research and further explorations of the evolving landscape of children's books.
Estimating C-CAPM and the equity premium over the frequency domain

Abstract In this paper we estimate the single-factor Consumption Capital Asset Pricing Model (C-CAPM) over the frequency domain. We modify the standard two-step methodology (Fama and French 1992) to account for the spectral properties of consumption risk and we find that its lower frequencies explain up to 98% of the cross-sectional variation of expected returns and that the equity premium puzzle is eliminated. These results are robust to the definitions of the variables, the sample span and the set of portfolios utilized, and the maturity of interest rates.
Recommendations for real‐time speech MRI

Real‐time magnetic resonance imaging (RT‐MRI) is being increasingly used for speech and vocal production research studies. Several imaging protocols have emerged based on advances in RT‐MRI acquisition, reconstruction, and audio‐processing methods. This review summarizes the state‐of‐the‐art, discusses technical considerations, and provides specific guidance for new groups entering this field. We provide recommendations for performing RT‐MRI of the upper airway. This is a consensus statement stemming from the ISMRM‐endorsed Speech MRI summit held in Los Angeles, February 2014. A major unmet need identified at the summit was the need for consensus on protocols that can be easily adapted by researchers equipped with conventional MRI systems. To this end, we provide a discussion of tradeoffs in RT‐MRI in terms of acquisition requirements, a priori assumptions, artifacts, computational load, and performance for different speech tasks. We provide four recommended protocols and identify appropriate acquisition and reconstruction tools. We list pointers to open‐source software that facilitate implementation. We conclude by discussing current open challenges in the methodological aspects of RT‐MRI of speech. J. MAGN. RESON. IMAGING 2016;43:28–44.
Adoption and implementation of information technology in Bali's local government: A comparison between single level path analyses using PLSPATH 3.01 and AMOS 4 and Multilevel Path Analyses using MPLUS 2.01

Recent advances in information technology have aroused much interest among policy-makers, the business sector, the media and the academic world in developed countries. However, very little is known about the obstacles to accessing information technology (IT) and the diffusion and use of information technologies in developing countries, particularly the low-income economies. This research stems from the issues described above. A variety of factors may influence the outcome of adopting and implementing IT in local governments. These factors range from technological and institutional to personal, social and economic factors, and include not only the information on variables gathered at the employee level but also questions regarding the characteristics of each organization involved in the study. In addition, there are also some district characteristics collected through various secondary data sources. Hence the data files contain information obtained at three different levels, namely individual level, organizational level, and district level. In addition to identifying various factors that may affect each step of IT adoption processes, this paper also addresses more technical issues involved in determining units of analysis and choosing the proper analytical methods since the structure of the data reflects the hierarchical nature. Three different statistical packages were used to analyse the data. The exploratory path analyses were done using PLSPATH 3.01 both at individual level and organizational level separately. The results of these analyses were then compared to the AMOS 4 results. AMOS 4 could only be used to analyze the path model at the individual level due to the small number of cases at the organizational level. Based on these results, two level models were developed and further analysed with MPLUS 2.01.
Sub pixel level arrangement of spatial dependences to improve classification accuracy

The colors in universe have sharp boundaries everybody is aware of specifically wherever a color starts and wherever it ends and that any color communicates the details about the targets in the scene in a much better way and that this detailed information can be used to further polish the interpretation of an imaging system. In this paper, the proposed subpixel level arrangements of spatial dependences provide super resolved landuse landcover information using the output of soft classified fractional values. The output of soft classifier satisfies the constraint of non-negativity and sum to 1 instead of whatever their “natural” total is of fractional abundance within the pixels. This phenomenon is also discussed while defining mixed pixels, the pixels at boundary contain both the colors in a proportion so that the pixel appears the color different from either of two. This paper main goal is to extract the information from mixed pixels and subpixel analysis with the subpixel level arrangements of spatial dependences to get the super resolved information.
Sentiment Classification in Chinese Microblogs: Lexicon-based and Learning-based Approaches

Sentiment classification in Chinese microblogs is more challenging than that of Twitter for numerous reasons. In this paper, two kinds of approaches are proposed to classify opinionated Chinese- microblog posts: 1) lexicon-based approaches combining Simple Sentiment Word-Count Method with 3 Chinese sentiment lexicons, 2) machine learning models with multiple features. According to our experiment, lexicon-based approaches can yield relatively fine results and machine learning classifiers outperform both the majority baseline and lexicon-based approaches. Among all the machine learning-based approaches, Random Forests works best and the results are satisfactory.
Stories of the sharing economy: Policy narratives surrounding the entry of transportation network companies into four mid-sized American cities

ABSTRACT New transportation businesses such as Uber and Lyft have recently entered metropolitan markets across the US. Because they do not fit into existing regulatory and policy frameworks, and their entrance into markets is sometimes abrupt or illegal, these transportation network companies (TNCs) engender widely varying reactions from city governments. These reactions are captured in different stories that develop about TNCs when they enter a market. Using content analysis, elements of the narrative policy framework (NPF), and application of Mark Bevir and R.A. W. Rhodes decentered theory of governance, this study explores the narratives that develop about TNCs and how those narratives may influence policy making. Prominent themes that emerged from an analysis of city council meetings in four mid-sized American communities include safety, leveling the playing field, and market considerations. There was also an identifiable causal linkage between the types of stories councilmembers told, and the solutions they proposed in deliberations. Finally, this article makes a compelling argument for the compatibility of the NPF and Bevir and Rhodes interpretivist tradition by pointing out the similarities among the two, and demonstrating ways in which they can effectively dovetail in narrative analysis.