Meta-QoS performance of earliest-deadline-first and rate-monotonic scheduling of smoothed video data in a client-server environment

In this paper we present an extensive performance study of two modified EDF and RM scheduling algorithms which are enhanced to provide quality of service (QoS) guarantees for smoothed video data. With a probabilistic definition of QoS, we incorporate admission control conditions into the two algorithms. Furthermore, we also include a counter-based scheduling module as the core scheduling mechanism which adaptively adjusts the actual QoS levels assigned to requests. Our theoretical analysis of the two enhanced algorithms, called QEDF and QRM, shows that the QRM algorithm is more robust than the QEDF algorithm for different workload and utilization conditions. We also propose to use a new metric called meta-QoS to quantify the overall performance of a packet scheduler given a set of simultaneous requests. In our experiments, we find that the QRM algorithm can sustain a rather stable level of meta-QoS even when the workload and utilization levels are increased. On the other hand, the QEDF algorithm is found to be less desirable for a high level of utilization and a large number of requests.published_or_final_versio

Drawing-Based Automatic Dementia Screening Using Gaussian Process Markov Chains

Screening tests play an important role for early detection of dementia. Among those widely used screening tests, drawing tests have gained much attention in clinical psychology. Traditional evaluation of drawing tests totally relies on the appearance of drawn picture, but does not consider any time-dependent behaviour. We demonstrated that the processing speed and direction can reflect the decline of cognitive function, and thus may be useful for disease screening. We proposed a model of Gaussian process Markov chains (GPMC) to study the complex associations within the drawing data. Specifically, we modeled the process of drawing in a state-space form, where a drawing state is composed of drawing direction and velocity with consideration of the processing time. For temporal modeling, our scope focused more on discrete-time Markov chains on continuous state space. Because of the short processing time of picture drawing, we applied higher-order of Markov chains to model long-term temporal correlation across drawing states. Gaussian process regression was used for universal function approximation to flexibly infer the state transition function. With Gaussian process prior to the distribution of function space, we could encode high-level function properties such as noisiness, smoothness and periodicity. We also derived an efficient training mechanism for complex Gaussian process regression on bivariate Markov chains. With GPMC, we present an optimal decision rule based on Bayesian decision theory. We applied our proposed method to a drawing test for dementia screening, i.e. interlocking pentagon-drawing test. We tested our models with 256 subjects who are aged from 65 to 95. Finally, comparing to the traditional methods, our models showed remarkable improvement in drawing test for dementia screening

The Application of Image Recognition and Machine Learning to Capture Readings of Traditional Blood Pressure Devices: A Platform to Promote Population Health Management to Prevent Cardiovascular Diseases

Digital solutions for Blood Pressure Monitoring (or Telemonitoring) have sprouted in recent years, innovative solutions are often connected to the Internet of Things (IoT), with mobile health (mHealth) platform. However, clinical validity, technology cost and cross-platform data integration remain as the major barriers for the application of these solutions. In this paper, we present an IoT-based and AI-embedded Blood Pressure Telemonitoring (BPT) system, which facilitates home blood pressure monitoring for individuals. The highlights of this system are the machine learning techniques to enable automatic digits recognition, with F1 score of 98.5%; and the cloud-based portal developed for automated data synchronization and risk stratification. Positive feedbacks on trial implementation are received from three clinics. The overall system architecture, development of machine learning model in digit identification and cloud-based telemonitoring are addressed in this paper, alongside the followed implications

Derivation and Analysis of Dynamic Handwriting Features as Clinical Markers of Parkinson’s Disease

Parkinson’s Disease (PD) is a complex neurodegenerative disorder that is challenging to diagnose. Recent research has demonstrated predictive value in the analysis of dynamic handwriting features for detecting PD, however, consensus on clinically-useful features is yet to be reached. Here we explore and evaluate secondary kinematic handwriting features hypothesized to be diagnostically relevant to Parkinson’s Disease using a publicly-available Spiral Drawing Test PD dataset. Univariate and multivariate analysis was performed on derived features. Classification outcome was determined using logistic regression models with 10-fold cross validation. Feature correlation was based on model specificity and sensitivity. Variations in grip angle, instantaneous acceleration and pressure indices were found to have high predictive potential as clinical markers of PD, with combined classification accuracy of above 90%. Our results show that the significance of secondary handwriting features and recommend the feature expansion step for hypothesis generation, comparative evaluation of test types and improved classification accuracy

Data Visualization on Global Trends on Cancer Incidence An Application of IBM Watson Analytics

Visual analytics is widely used to explore data patterns and trends. This work leverages cancer data collected by World Health Organization (WHO) across over a hundred of cancer registries worldwide. In this study, we present a visual analytics platform, IBM Watson Analytics, to explore the patterns of global cancer incidence. We included 26 cancers from different geographic regions. An interactive interface was applied to plot a choropleth map to show global cancer distribution, and line charts to demonstrate historical cancer trends over 29 years. Subgroup analyses were conducted for different age groups. With real-time interactive features, we can easily explore the data with a selection of any cancer type, gender, age group, or geographical region. This platform is running on the cloud, so it can handle data in huge volumes, and is assessable by any computer connected to the Internet

Raw and Cooked Vegetable Consumption and Risk of Cardiovascular Disease:a Study of 400,000 Adults in UK Biobank

OBJECTIVES: Higher levels of vegetable consumption have been associated with a lower risk of cardiovascular disease (CVD), but the independent effect of raw and cooked vegetable consumption remains unclear. METHODS: From the UK Biobank cohort, 399,586 participants without prior CVD were included in the analysis. Raw and cooked vegetable intakes were measured with a validated dietary questionnaire at baseline. Multivariable Cox regression was used to estimate the associations between vegetable intake and CVD incidence and mortality, adjusted for socioeconomic status, health status, and lifestyle factors. The potential effect of residual confounding was assessed by calculating the percentage reduction in the likelihood ratio (LR) statistics after adjustment for the confounders. RESULTS: The mean age was 56 years and 55% were women. Mean intakes of raw and cooked vegetables were 2.3 and 2.8 tablespoons/day, respectively. During 12 years of follow-up, 18,052 major CVD events and 4,406 CVD deaths occurred. Raw vegetable intake was inversely associated with both CVD incidence (adjusted hazard ratio (HR) [95% CI] for the highest vs. lowest intake: 0.89 [0.83–0.95]) and CVD mortality (0.85 [0.74–0.97]), while cooked vegetable intake was not (1.00 [0.91–1.09] and 0.96 [0.80–1.13], respectively). Adjustment for potential confounders reduced the LR statistics for the associations of raw vegetables with CVD incidence and mortality by 82 and 87%, respectively. CONCLUSIONS: Higher intakes of raw, but not cooked, vegetables were associated with lower CVD risk. Residual confounding is likely to account for much, if not all, of the observed associations. This study suggests the need to reappraise the evidence on the burden of CVD disease attributable to low vegetable intake in the high-income populations