Improvement of the kernel minimum squared error model for fast feature extraction

The kernel minimum squared error (KMSE) expresses the feature extractor as a linear combination of all the training samples in the high-dimensional kernel space. To extract a feature from a sample, KMSE should calculate as many kernel functions as the training samples. Thus, the computational efficiency of the KMSE-based feature extraction procedure is inversely proportional to the size of the training sample set. In this paper, we propose an efficient kernel minimum squared error (EKMSE) model for two-class classification. The proposed EKMSE expresses each feature extractor as a linear combination of nodes, which are a small portion of the training samples. To extract a feature from a sample, EKMSE only needs to calculate as many kernel functions as the nodes. As the nodes are commonly much fewer than the training samples, EKMSE is much faster than KMSE in feature extraction. The EKMSE can achieve the same training accuracy as the standard KMSE. Also, EKMSE avoids the overfitting problem. We implement the EKMSE model using two algorithms. Experimental results show the feasibility of the EKMSE model.     

Adaptive local kriging to retrieve slant-range surface motion maps of the Wenchuan earthquake

Kriging is a widely used technique for raster data interpolation from point samples, such as in the generation of digital elevation models and geochemical maps. The quality of the result depends on both spatial distribution of the sampled values and nature of the semivariogram model, which fits an empirical global function to the sample data set to predict values at the unknown locations. However, such a semivariogram model may not be suitable for data sets with complex local trends in spatial distribution, such as those observed in differential interferometric synthetic aperture radar (DInSAR) data of the Wenchuan earthquake. Here we propose a modified kriging method, adaptive local kriging (ALK), for the retrieval of data lost through decoherence in Advanced Land Observing Satellite (ALOS) phased array L-band synthetic aperture radar (PALSAR) DInSAR data, within the intensely deformed fault zone of the 2008 Wenchuan earthquake. In ALK, a series of dynamic linear local semivariogram models is used rather than a global semivariogram for the whole data set. The localized adaptive approach ensures accurate interpolation in the areas of good DInSAR data with small decoherence gaps and avoids drastic errors in the extensive decoherence gaps; the overall value prediction is thus significantly improved, as confirmed by comparison with the original DInSAR data and fidelity verification experiments.     

Evaluation of spatio-temporal variations in chlorophyll-a in Lake Naivasha,Kenya: remote-sensing approach

Restoration of the ecosystem services and functions of lakes requires an understanding of the turbidity dynamics in order to arrive at informed environmental management decisions. The understanding of the spatio-temporal dynamics of turbidity requires frequent monitoring of the turbidity components such as chlorophyll-a concentration. In this study, we explored the use of Moderate Resolution Imaging Spectroradiometer Aqua (MODIS-Aqua) satellite data in studying the spatio-temporal changes in chlorophyll-a concentration in Lake Naivasha, a turbid tropical system. The temporal trend of chlorophyll-a concentration over the study period in the lake was also evaluated. The temporal trend assessment was achieved through the removal of periodic seasonal interference using Seasonal-Trend decomposition based on the LOESS (Local Regression) procedure. The resultant chlorophyll-a concentration maps derived from MODIS-Aqua satellite data give an indication of the monthly spatial variation in chlorophyll-a concentration from 2002 to 2012. The results of regression analyses between satellite-derived chlorophyll-a and in situ measurements reveal a high level of precision, but with a measureable bias with the satellite underestimating actual in situ measurements (R² = 0.65, P < 0.001). Although the actual values of the chlorophyll-a concentrations are underestimated, the significant relationship between satellite-derived chlorophyll-a and in situ measurements provides reliable information for studying spatial variations and temporal trends. In 2009 and 2010, it was difficult to detect chlorophyll-a from the MODIS-Aqua imagery, and this coincided with a period of the lowest water levels in Lake Naivasha. An inverse relationship between de-seasoned water level and chlorophyll-a concentration was evident. This study shows that MODIS-Aqua satellite data provide useful information on the spatio-temporal variations in Lake Naivasha, which is useful in establishing general trends that are more difficult to determine through routine ground measurements.     

Occupational posture exposure among construction electricians

The aim of this study is to record, objectively describe and compare working postures of Brazilian and Norwegian construction electricians. Postures of the upper arms, head, and neck during work and breaks were quantified by means of inclinometry for a representative sample of 12 Brazilian and 12 Norwegian electricians in the construction industry during a full work-shift. Despite that differences were found between specific work-related factors, Brazilian and Norwegian workers revealed a high and very similar level and pattern of postures for all evaluated body regions, suggesting that results could be extrapolated to other electricians. Upper arm elevation was high and similar for both groups and head flexion and extension was pronounced compared to other occupations. Thus, extreme postures were identified for construction electricians in both countries, with similar exposure pattern also for defined tasks (planning, support and wiredraw), suggesting that this job is risky regardless organizational differences.     

Neurocognitive signs in prodromal Huntington disease.

Objective: PREDICT-HD is a large-scale international study of people with the Huntington disease (HD) CAG-repeat expansion who are not yet diagnosed with HD. The objective of this study was to determine the stage in the HD prodrome at which cognitive differences from CAG-normal controls can be reliably detected. Method: For each of 738 HD CAG-expanded participants, we computed estimated years to clinical diagnosis and probability of diagnosis in 5 years based on age and CAG-repeat expansion number (Langbehn, Brinkman, Falush, Paulsen, & Hayden, 2004). We then stratified the sample into groups: NEAR, estimated to be ≤9 years; MID, between 9 and 15 years; and FAR, ≥15 years. The control sample included 168 CAG-normal participants. Nineteen cognitive tasks were used to assess attention, working memory, psychomotor functions, episodic memory, language, recognition of facial emotion, sensory–perceptual functions, and executive functions. Results: Compared with the controls, the NEAR group showed significantly poorer performance on nearly all of the cognitive tests and the MID group on about half of the cognitive tests (p = .05, Cohen's d NEAR as large as ?1.17, MID as large as ?0.61). One test even revealed significantly poorer performance in the FAR group (Cohen's d = ?0.26). Individual tasks accounted for 0.2% to 9.7% of the variance in estimated proximity to diagnosis. Overall, the cognitive battery accounted for 34% of the variance; in comparison, the Unified Huntington's Disease Rating Scale motor score accounted for 11.7%. Conclusions: Neurocognitive tests are robust clinical indicators of the disease process prior to reaching criteria for motor diagnosis of HD. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

黄民奇：这是“制程工业”!

从代理、到自己投入半导体尖端制程设备研发，汉辰科技董事长黄民奇，日前在捐赠先进半导体制程设备予台湾国研院奈米组件实验室的仪式后，很多人都说，半导体尖端制程设备研发是精密机械工业，但他认为，应该称之为“制程工业”才对!     

Effects of hostility on ambulatory blood pressure and mood during daily living in healthy adults.

This study (a) tested the effects of hostile attributes on ambulatory blood pressure (BP), heart rate, and mood monitored repeatedly over 3 days in 100 healthy men and women and (b) determined whether the cardiovascular effects of trait hostility were moderated by mood. Multilevel random-coefficients regression analyses showed that hostile individuals exhibited higher systolic and diastolic BP and rated their current moods as more negative and less positive throughout the monitoring. Individuals low in hostility exhibited high BP only during the few occasions when they experienced negative mood. However, these patterns were true only when participants were classified by Potential for Hostility ratings from the Structured Interview (R. H. Rosenman, 1978), not by the Cynical Hostile Attitudes score derived from the Cook-Medley scale. Results provide convergent and ecological validity of interview rating of hostility and illuminate one possible dynamic mechanism by which overt hostile behaviors might contribute to the rates of increased cardiovascular morbidity and mortality. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Typed Abstract State Machines for data-intensive applications

This paper focuses on extensions of Abstract State Machines (ASMs) for the benefit of a refinement calculus in the area of data-intensive applications, in particular data warehouses and on-line analytical processing. The extensions lead to a typed version of ASMs and a more specific notion of strong data refinement, which incorporates the preservation of information content by means of schema dominance. Each typed ASM can be effectively translated into an equivalent ordinary one. Providing typed ASMs helps to exploit the existing logical formalisms used in data-intensive applications to define a ground model. Furthermore, strong data refinement helps to set up a refinement-based development method that uses provably correct standard refinement rules.     

Progression into and out of binge drinking among high school students.

The current study examined binge drinking among high school students over an academic year. Adolescent drinkers (N?=?621; 58% female) were grouped into 4 trajectories: drinkers (35%), increasers (14%), decreasers (16%), and persistent binge drinkers (35%). Prospective analyses indicated several factors that predicted escalation and de-escalation of binge drinking. Increasers were more likely to regularly use alcohol and cigarettes at a younger age than drinkers. Compared with decreasers, persistent binge drinkers reported regular alcohol and marijuana use at younger ages. Lower levels of perceived student drinking appeared to be a protective factor for onset of binge drinking. The results highlight the need to study precursors to the naturally occurring fluctuations in binge drinking and suggest factors that may accentuate the risk of binge drinking. (PsycINFO Database Record (c) 2010 APA, all rights reserved)