Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of corticofugal fibre involvement in subcortical stroke

Objective To correlate motor deficit with involvement of corticofugal fibres in patients with subcortical stroke. The descending motor corticofugal fibres originate from the primary motor cortex (M1), dorsal and ventral premotor area (PMdv) and supplementary motor area (SMA). Design Retrospective study. Setting Single tertiary teaching hospital. Participants 57 patients (57% men) with subcortical infarcts on MRI (2009–2011) were included. The mean age was 64.3±14.4 years. Interventions None. Primary and secondary outcome measures National Institute of Health Stroke Scale subscores for arm and leg motor deficit at 90 days. Results An area under the receiver operating characteristics curve (AUC) for the volume of overlap with infarct (and M1/PMdv/SMA fibres) and motor outcome was calculated. The AUC for the association with arm motor deficit from M1 fibres involvement was 0.80 (95% CI 0.66 to 0.94), PMdv was 0.76 (95% CI 0.61 to 0.91) and SMA was 0.73 (95% CI 0.58 to 0.88). The AUC for leg motor deficit from M1 fibres involvement was 0.69 (95% CI 0.52 to 0.85), PMdv was 0.67 (95% CI 0.50 to 0.85), SMA was 0.66 (95% CI 0.48 to 0.84). Conclusions Following subcortical stroke, the correlations between involvement of the corticofugal fibres for upper and lower limbs motor deficit were variable. A poor motor outcome was not universal following subcortical stroke.
Inherited Arrhythmias: A National Heart, Lung, and Blood Institute and Office of Rare Diseases Workshop Consensus Report About the Diagnosis, Phenotyping, Molecular Mechanisms, and Therapeutic Approaches for Primary Cardiomyopathies of Gene Mutations Affecting Ion Channel Function

The National Heart, Lung, and Blood Institute and Office of Rare Diseases at the National Institutes of Health organized a workshop (September 14 to 15, 2006, in Bethesda, Md) to advise on new research directions needed for improved identification and treatment of rare inherited arrhythmias. These included the following: (1) Na+ channelopathies; (2) arrhythmias due to K+ channel mutations; and (3) arrhythmias due to other inherited arrhythmogenic mechanisms. Another major goal was to provide recommendations to support, enable, or facilitate research to improve future diagnosis and management of inherited arrhythmias. Classifications of electric heart diseases have proved to be exceedingly complex and in many respects contradictory. A new contemporary and rigorous classification of arrhythmogenic cardiomyopathies is proposed. This consensus report provides an important framework and overview to this increasingly heterogeneous group of primary cardiac membrane channel diseases. Of particular note, the present classification scheme recognizes the rapid evolution of molecular biology and novel therapeutic approaches in cardiology, as well as the introduction of many recently described diseases, and is unique in that it incorporates ion channelopathies as a primary cardiomyopathy in consensus with a recent American Heart Association Scientific Statement.
Advanced framework for illumination invariant traffic density estimation

CCTV cameras are becoming a common fixture at the roadside. Their use varies from traffic monitoring to security surveillance. In this paper an advanced two-stage framework for estimating vehicular traffic density on a road segment is presented. The proposed approach is computationally efficient and robust to varying illumination. The method is novel because it combines state-of-the-art image processing techniques with a simple traffic model in order to increase robustness. Experimental results have shown that the proposed framework can achieve higher performance than existing state-of-the-art techniques under conditions of varying illumination.
The development of a model of initial and ongoing training for the international teacher

International schools, loosely defined as those involving students, teachers, curricula and/or languages not from the host country, form a significant sector of education worldwide, with numbers estimated at more than three thousand and predicted to grow rapidly over the next decade. Of equal significance is their role in developing students who are internationally-minded, often explicitly included in school mission statements, and increasingly accepted as a central educational imperative rather than a desirable but peripheral option. It is generally acknowledged that teacher effectiveness has a profound influence on student learning, and it follows therefore that teachers who model international-mindedness are also an important part of the equation. Yet, as the number of international schools increases, appropriately-prepared teachers are becoming increasingly scarce. This thesis identifies key issues facing international teachers, each well documented in literature and research, and increasingly evident in educational policy and strategy at national and international levels, yet found to be lacking in teacher preparation. It is my contention, therefore, that the substantial and systematic inclusion of these issues in initial teacher education programmes is absolutely essential and I investigate how the theoretical commitments of policy statements can be converted into practical provision of appropriate preparation for international teachers. By considering literature and research, survey data and anecdotal evidence from the international education community, I attempt to describe (though not define) international teachers, the international nature of the education they provide to their students, and the type of preparation they need in order to do so. Based on the key issues identified, I propose seven domains of knowledge and skills deemed essential for teacher preparation, synthesised into a potential model for systematic implementation within individual schools and programmes, or within whole educational systems at state or national level.
The Latent Bernoulli-Gauss Model for Data Analysis

We present a new latent-variable model employing a Gaussian mixture integrated with a feature selection procedure (the Bernoulli part of the model) which together form a "Latent Bernoulli-Gauss" distribution. The model is applied to MAP estimation, clustering, feature selection and collaborative filtering and fares favorably with the state-of-the-art latent-variable models.
Algorithm Convergence For Inertial Navigation System Alignment

algorithm for estimating variances is investigated. It is established that if the variance estimates and the error covariances are initialized appropriately, the sequence of variance iterates will be monotonically nonincreasing. Under prescribed conditions, the variance estimates will converge to the actual values. An inertial navigation application is discussed in which performance depends on accurately estimating the process variances.
Haptic assistance to improve computer access for motion-impaired users

This paper describes a pilot experiment which examines the effectiveness of using force-feedback technology to assist motion-impaired computer users in target selection tasks. Two measures to evaluate the effectiveness of force feedback assistance are proposed. The radius of curvature of the cursor path and the cursor speed along the path can capture differences in cursor movements that might be lost in a single measure such as time to target. Experimental results indicate that force-feedback implemented as nondirectional viscous damping has a beneficial effect for some, improving times to target by up to 50%. Future work includes developing other forms of haptic assistance, developing new measures for evaluation, and investigating the implications of kinaesthesic impairment.
Change and Identity in Complex Systems

Complex systems are dynamic and may show high levels of variability in both space and time. It is often difficult to decide on what constitutes a given complex system, i.e., where system boundaries should be set, and what amounts to substantial change within the system. We discuss two central themes: the nature of system definitions and their ability to cope with change, and the importance of system definitions for the mental metamodels that we use to describe and order ideas about system change. Systems can only be considered as single study units if they retain their identity. Previous system definitions have largely ignored the need for both spatial and temporal continuity as essential attributes of identity. After considering the philosophical issues surrounding identity and system definitions, we examine their application to modeling studies. We outline a set of five alternative metamodels that capture a range of the basic dynamics of complex systems. Although Holling's adaptive cycle is a compelling and widely applicable metamodel that fits many complex systems, there are systems that do not necessarily follow the adaptive cycle. We propose that more careful consideration of system definitions and alternative metamodels for complex systems will lead to greater conceptual clarity in the field and, ultimately, to more rigorous research.
The Underrepresentation of Black Males in IT Higher Education: a Conceptual Framework for Understanding Individual Differences

Among the many research challenges in studying underrepresented groups in IT is understanding the appropriate theories to apply and much needed analysis at the individual level. This paper presents findings of two studies, which coupled with the application of the Individual Differences Theory of Gender and IT, led to a conceptual framework being developed to delve deeper into the understanding of Black males in IT higher education. The use of the conceptual framework will lead to a better understanding of the factors impacting underrepresented groups participation in IT.
Impaired Comprehension of Nonliteral Language in Tourette Syndrome

ObjectiveTo investigate theory of mind and the understanding of nonliteral language in patients with Tourette syndrome (TS). BackgroundIn TS, striatal dysfunction could affect the functioning of the frontal cortex. Changes in frontal functioning could lead to impairments in theory of mind: the understanding of mental states, such as beliefs, emotions, and intentions. Poor understanding of a speaker's mental state may also impair interpretation of their nonliteral remarks. MethodIn this study, patients with TS and healthy controls completed tasks to assess their understanding of sarcasm, metaphor, indirect requests, and theory of mind. These tasks were the Pragmatic Story Comprehension Task, the Hinting task, and a faux pas task. Inhibitory ability was also assessed through the use of the Hayling task and a black and white Stroop test. ResultsPatients with TS exhibited significant impairment on the faux pas task and Pragmatic Story Comprehension Task despite limited evidence of inhibitory impairment. ConclusionTS may be associated with changes in theory of mind.
Multi-dimensional evolutionary feature synthesis for content-based image retrieval

Low-level features (also called descriptors) play a central role in content-based image retrieval (CBIR) systems. Features are various types of information extracted from the content and represent some of its characteristics or signatures. However, especially the (low-level) features, which can be extracted automatically usually lack the discrimination power needed for accurate description of the image content and may lead to a poor retrieval performance. In order to efficiently address this problem, in this paper we propose a multidimensional evolutionary feature synthesis technique, which seeks for the optimal linear and non-linear operators so as to synthesize highly discriminative set of features in an optimal dimension. The optimality therein is sought by the multi-dimensional particle swarm optimization method along with the fractional global-best formation technique. Clustering and CBIR experiments where the proposed feature synthesizer is evolved using only the minority of the image database, demonstrate a significant performance improvement and exhibit a major discrimination between the features of different classes.
Universidade De Lisboa

This dissertation presents work on optimization of information retrieval methods used on web search. The work had two purposes: i) development of the research on efficient methods for document indexing and relevance ranking, based on a local partitioning of documents distributed architecture, and ii) geographic queries processing, focusing on indexing and ranking using spatial metrics. The results were incorporated in a new version of Sidra, the indexing system of the Tumba! search engine.
Piggyback CrowdSensing (PCS): energy efficient crowdsourcing of mobile sensor data by exploiting smartphone app opportunities

Fueled by the widespread adoption of sensor-enabled smartphones, mobile crowdsourcing is an area of rapid innovation. Many crowd-powered sensor systems are now part of our daily life -- for example, providing highway congestion information. However, participation in these systems can easily expose users to a significant drain on already limited mobile battery resources. For instance, the energy burden of sampling certain sensors (such as WiFi or GPS) can quickly accumulate to levels users are unwilling to bear. Crowd system designers must minimize the negative energy side-effects of participation if they are to acquire and maintain large-scale user populations. To address this challenge, we propose Piggyback CrowdSensing (PCS), a system for collecting mobile sensor data from smartphones that lowers the energy overhead of user participation. Our approach is to collect sensor data by exploiting Smartphone App Opportunities -- that is, those times when smartphone users place phone calls or use applications. In these situations, the energy needed to sense is lowered because the phone need no longer be woken from an idle sleep state just to collect data. Similar savings are also possible when the phone either performs local sensor computation or uploads the data to the cloud. To efficiently use these sporadic opportunities, PCS builds a lightweight, user-specific prediction model of smartphone app usage. PCS uses this model to drive a decision engine that lets the smartphone locally decide which app opportunities to exploit based on expected energy/quality trade-offs. We evaluate PCS by analyzing a large-scale dataset (containing 1,320 smartphone users) and building an end-to-end crowdsourcing application that constructs an indoor WiFi localization database. Our findings show that PCS can effectively collect large-scale mobile sensor datasets (e.g., accelerometer, GPS, audio, image) from users while using less energy (up to 90% depending on the scenario) compared to a representative collection of existing approaches.
A Cloud-Based Black-Box Solar Predictor for Smart Homes

The popularity of rooftop solar for homes is rapidly growing. However, accurately forecasting solar generation is critical to fully exploiting the benefits of locally generated solar energy. In this article, we present two machine-learning techniques to predict solar power from publicly available weather forecasts. We use these techniques to develop SolarCast, a cloud-based web service that automatically generates models that provide customized site-specific predictions of solar generation. SolarCast utilizes a “black box” approach that requires only (1) a site’s geographic location and (2) a minimal amount of historical generation data. Since we intend SolarCast for small rooftop deployments, it does not require detailed site- and panel-specific information, which owners may not know, but instead automatically learns these parameters for each site. We evaluate the accuracy of SolarCast’s different algorithms on two publicly available datasets, each containing over 100 rooftop deployments with a variety of attributes (e.g., climate, tilt, orientation, etc.). We show that SolarCast learns a more accurate model using much less data (∼1 month) than prior SVM-based approaches, which require ∼3 months of data. SolarCast also provides a programmatic API, enabling developers to integrate its predictions into energy efficiency applications. Finally, we present two case studies of using SolarCast to demonstrate how real-world applications can leverage its predictions. We first evaluate a “sunny” load scheduler, which schedules a dryer’s energy usage to maximally align with a home’s solar generation. We then evaluate a smart solar-powered charging station, which can optimally charge the maximum number of electric vehicles (EVs) on a given day. Our results indicate that a representative home is capable of reducing its grid demand up to 40% by providing a modest amount of flexibility (of ∼5 hours) in the dryer’s start time with opportunistic load scheduling. Further, our charging station uses SolarCast to provide EV owners the amount of energy they can expect to receive from solar energy sources.
The Molecular Mechanisms of Zinc Neurotoxicity and the Pathogenesis of Vascular Type Senile Dementia

Zinc (Zn) is an essential trace element that is abundantly present in the brain. Despite its importance in normal brain functions, excess Zn is neurotoxic and causes neurodegeneration following transient global ischemia and plays a crucial role in the pathogenesis of vascular-type dementia (VD). We have investigated the molecular mechanisms of Zn-induced neurotoxicity using immortalized hypothalamic neurons (GT1–7 cells) and found that carnosine (β-alanyl histidine) and histidine (His) inhibited Zn2+-induced neuronal death. A DNA microarray analysis revealed that the expression of several genes, including metal-related genes (metallothionein and Zn transporter 1), endoplasmic reticulum (ER)-stress related genes (GADD34, GADD45, and p8), and the calcium (Ca)-related gene Arc (activity-related cytoskeleton protein), were affected after Zn exposure. The co-existence of carnosine or His inhibited the expression of GADD34, p8, and Arc, although they did not influence the expression of the metal-related genes. Therefore, ER-stress and the disruption of Ca homeostasis may underlie the mechanisms of Zn-induced neurotoxicity, and carnosine might be a possible drug candidate for the treatment of VD.
A Novel Approach for Outlier Detection using Rough Entropy

Outlier detection is an important task in data mining and its applications. It is defined as a data point which is very much different from the rest of the data based on some measures. Such a data often contains useful information on abnormal behavior of the system described by patterns. In this paper, a novel method for outlier detection is proposed among inconsistent dataset. This method exploits the framework of rough set theory. The rough set is defined as a pair of lower approximation and upper approximation. The difference between upper and lower approximation is defined as boundary. Some of the objects in the boundary region have more possibility of becoming outlier than objects in lower approximations. Hence, it is established that the rough entropy measure as a uniform framework to understand and implement outlier detection separately on class wise consistent (lower) and inconsistent (boundary) objects. An example shows that the Novel Rough Entropy Outlier Detection (NREOD) algorithm is effective and suitable for evaluating the outliers. Further, experimental studies show that NREOD based technique outperformed, compared with the existing techniques. Key-Words: Data Mining, Outlier, Rough Set, Classification, Pattern recognition
Towards Adaptive Management: The Strategies of Policy Entrepreneurs to Direct Policy Change

ABSTRACT Growing awareness of the complexities and uncertainties in water governance has put into question the existing paradigms in water management. Increasingly, more flexible, integrated and adaptive policies (“soft solutions”) are promoted. Because of the growing need to adapt, the understanding of how to effect and direct policy change will become increasingly important. This paper focuses on the micro-level of policy-making and on individual change agents, which we describe here as “policy entrepreneurs”. Policy entrepreneurs have received increasing attention in political science during the past decade. Yet, the understanding of the actions that policy entrepreneurs can take to create policy windows and facilitate policy changes, remains limited. In this paper, the key question we seek to address is concerns the strategies that policy entrepreneurs employ in their efforts to pursue policy change. The paper builds on both theoretical exploration and detailed empirical field research in the area of water governance in the Netherlands. Our field research is included in-depth interviews and a focus group with policy entrepreneurs. We conclude that policy entrepreneurs employ numerous strategies that can be grouped in four categories: (1) attention and support-seeking strategies, in order to demonstrate the significance of a problem and to convince a wide range of participants about their preferred policy; (2) linkage strategies, because policy entrepreneurs are mostly unable to accomplish their objectives alone and therefore link with other parties in coalitions, projects, ideas, and policy games; (3) relational management strategies, as the relational factor is critical in policy change trajectories; and finally (4) arena strategies, to influence the time and the place wherein policy entrepreneurs play their policy game. Our study suggest that by employing the listed strategies when the “time is right”, the development of policy streams and consequently their coupling can, to some extent, be influenced and steered. In other words, we suggest that policy entrepreneurs can, to a degree, create a window of opportunity, and therefore direct policy change.
Survey on Traffic Redundancy and Elimination Approach for Reducing Cloud Bandwidth and Costs

Cloud computing is a fast growing field which is arguably a new computing paradigm. In cloud computing, computing resources are provided as services over the internet and users can access resources on based on their payments. Transmission cost plays an important role when trying to minimize cloud cost. However for server specific TRE approach it is difficult to handle the traffic efficiently and it doesn’t suites for the cloud environment because of high processing costs. In this paper we give a survey on the new traffic redundancy technique known as novel-TRE also known as receiver based TRE. This novel-TRE has significant features like detecting the redundancy at the client, repeats appear in chains, matches incoming chunks with a previously received chunk chain or local file and sending to the server for predicting the future data and no need of server to continuously maintain client state.
Notice of RetractionStudy on the theme park design method based-on GPS investigation

By using the GPS walking experimental method, the selected activities in different spatial behavior of human being was investigated in-depth. Make sure the change of spatial behavior trajectory and speed at different space limited condition with limited condition changing, and then grasp the relationship between human being actions and the limited space. Combining the investigation and research with the planning and design of theme park, the theory was used in some real cases.