Performance evaluation of AR4 Climate Models in simulating daily precipitation over the Indian region using skill scores

The ability of Coupled General Circulation Models (CGCMs) participating in the Intergovernmental Panel for Climate Change's fourth assessment report (IPCC AR4) for the 20th century climate (20C3M scenario) to simulate the daily precipitation over the Indian region is explored. The skill is evaluated on a 2.5A degrees x 2.5A degrees grid square compared with the Indian Meteorological Department's (IMD) gridded dataset, and every GCM is ranked for each of these grids based on its skill score. Skill scores (SSs) are estimated from the probability density functions (PDFs) obtained from observed IMD datasets and GCM simulations. The methodology takes into account (high) extreme precipitation events simulated by GCMs. The results are analyzed and presented for three categories and six zones. The three categories are the monsoon season (JJASO - June to October), non-monsoon season (JFMAMND - January to May, November, December) and for the entire year (''Annual''). The six precipitation zones are peninsular, west central, northwest, northeast, central northeast India, and the hilly region. Sensitivity analysis was performed for three spatial scales, 2.5A degrees grid square, zones, and all of India, in the three categories. The models were ranked based on the SS. The category JFMAMND had a higher SS than the JJASO category. The northwest zone had higher SSs, whereas the peninsular and hilly regions had lower SS. No single GCM can be identified as the best for all categories and zones. Some models consistently outperformed the model ensemble, and one model had particularly poor performance. Results show that most models underestimated the daily precipitation rates in the 0-1 mm/day range and overestimated it in the 1-15 mm/day range.

No-reference image quality assessment using modified extreme learning machine classifier

In this paper, we present a machine learning approach to measure the visual quality of JPEG-coded images. The features for predicting the perceived image quality are extracted by considering key human visual sensitivity (HVS) factors such as edge amplitude, edge length, background activity and background luminance. Image quality assessment involves estimating the functional relationship between HVS features and subjective test scores. The quality of the compressed images are obtained without referring to their original images ('No Reference' metric). Here, the problem of quality estimation is transformed to a classification problem and solved using extreme learning machine (ELM) algorithm. In ELM, the input weights and the bias values are randomly chosen and the output weights are analytically calculated. The generalization performance of the ELM algorithm for classification problems with imbalance in the number of samples per quality class depends critically on the input weights and the bias values. Hence, we propose two schemes, namely the k-fold selection scheme (KS-ELM) and the real-coded genetic algorithm (RCGA-ELM) to select the input weights and the bias values such that the generalization performance of the classifier is a maximum. Results indicate that the proposed schemes significantly improve the performance of ELM classifier under imbalance condition for image quality assessment. The experimental results prove that the estimated visual quality of the proposed RCGA-ELM emulates the mean opinion score very well. The experimental results are compared with the existing JPEG no-reference image quality metric and full-reference structural similarity image quality metric.

Mutations in STIL, Encoding a Pericentriolar and Centrosomal Protein, Cause Primary Microcephaly

Primary microcephaly (MCPH) is an autosomal-recessive congenital disorder characterized by smaller-than-normal brain size and mental retardation. MCPH is genetically heterogeneous with six known loci: MCPH1-MCPH6. We report mapping of a novel locus, MCPH7, to chromosome 1p32.3-p33 between markers D1S2797 and D1S417, corresponding to a physical distance of 8.39 Mb. Heterogeneity analysis of 24 families previously excluded from linkage to the six known MCPH loci suggested linkage of five families (20.83%) to the MCPH7 locus. In addition, four families were excluded from linkage to the MCPH7 locus as well as all of the six previously known loci, whereas the remaining 15 families could not be conclusively excluded or included. The combined maximum two-point LOD score for the linked families was 5.96 at marker D1S386 at theta = 0.0. The combined multipoint LOD score was 6.97 between markers D1S2797 and D1S417. Previously, mutations in four genes, MCPH1, CDK5RAP2, ASPM, and CENPJ, that code for centrosomal proteins have been shown to cause this disorder. Three different homozygous mutations in STIL, which codes for a pericentriolar and centrosomal protein, were identified in patients from three of the five families linked to the MCPH7 locus; all are predicted to truncate the STIL protein. Further, another recently ascertained family was homozygous for the same mutation as one of the original families. There was no evidence for a common haplotype. These results suggest that the centrosome and its associated structures are important in the control of neurogenesis in the developing human brain.

Within-Die Gate Delay Variability Measurement Using Reconfigurable Ring Oscillator

We report the design and characterization of a circuit technique to measure the on-chip delay of an individual logic gate (both inverting and noninverting) in its unmodified form. The test circuit comprises of digitally reconfigurable ring oscillator (RO). The gate under test is embedded in each stage of the ring oscillator. A system of linear equations is then formed with different configuration settings of the RO, relating the individual gate delay to the measured period of the RO, whose solution gives the delay of the individual gates. Experimental results from a test chip in 65-nm process node show the feasibility of measuring the delay of an individual inverter to within 1 ps accuracy. Delay measurements of different nominally identicall inverters in close physical proximity show variations of up to 28% indicating the large impact of local variations. As a demonstration of this technique, we have studied delay variation with poly-pitch, length of diffusion (LOD) and different orientations of layout in silicon. The proposed technique is quite suitable for early process characterization, monitoring mature process in manufacturing and correlating model-to-hardware.