Regionalization of precipitation in data sparse areas using large scale atmospheric variables - A fuzzy clustering approach

Delineation of homogeneous precipitation regions (regionalization) is necessary for investigating frequency and spatial distribution of meteorological droughts. The conventional methods of regionalization use statistics of precipitation as attributes to establish homogeneous regions. Therefore they cannot be used to form regions in ungauged areas, and they may not be useful to form meaningful regions in areas having sparse rain gauge density. Further, validation of the regions for homogeneity in precipitation is not possible, since the use of the precipitation statistics to form regions and subsequently to test the regional homogeneity is not appropriate. To alleviate this problem, an approach based on fuzzy cluster analysis is presented. It allows delineation of homogeneous precipitation regions in data sparse areas using large scale atmospheric variables (LSAV), which influence precipitation in the study area, as attributes. The LSAV, location parameters (latitude, longitude and altitude) and seasonality of precipitation are suggested as features for regionalization. The approach allows independent validation of the identified regions for homogeneity using statistics computed from the observed precipitation. Further it has the ability to form regions even in ungauged areas, owing to the use of attributes that can be reliably estimated even when no at-site precipitation data are available. The approach was applied to delineate homogeneous annual rainfall regions in India, and its effectiveness is illustrated by comparing the results with those obtained using rainfall statistics, regionalization based on hard cluster analysis, and meteorological sub-divisions in India. (C) 2011 Elsevier B.V. All rights reserved.

Large rapidity gaps survival probabilities at LHC

We calculate the probability of large rapidity gaps in high energy hadronic collisions using a model based on QCD mini-jets and soft gluon emission down into the infrared region. Comparing with other models we find a remarkable agreement among most predictions.

Generation of Minimum Leakage Input Vectors with Constrained NBTI Degradation

Technology scaling has caused Negative Bias Temperature Instability (NBTI) to emerge as a major circuit reliability concern. Simultaneously leakage power is becoming a greater fraction of the total power dissipated by logic circuits. As both NBTI and leakage power are highly dependent on vectors applied at the circuit’s inputs, they can be minimized by applying carefully chosen input vectors during periods when the circuit is in standby or idle mode. Unfortunately input vectors that minimize leakage power are not the ones that minimize NBTI degradation, so there is a need for a methodology to generate input vectors that minimize both of these variables.This paper proposes such a systematic methodology for the generation of input vectors which minimize leakage power under the constraint that NBTI degradation does not exceed a specified limit. These input vectors can be applied at the primary inputs of a circuit when it is in standby/idle mode and are such that the gates dissipate only a small amount of leakage power and also allow a large majority of the transistors on critical paths to be in the “recovery” phase of NBTI degradation. The advantage of this methodology is that allowing circuit designers to constrain NBTI degradation to below a specified limit enables tighter guardbanding, increasing performance. Our methodology guarantees that the generated input vector dissipates the least leakage power among all the input vectors that satisfy the degradation constraint. We formulate the problem as a zero-one integer linear program and show that this formulation produces input vectors whose leakage power is within 1% of a minimum leakage vector selected by a search algorithm and simultaneously reduces NBTI by about 5.75% of maximum circuit delay as compared to the worst case NBTI degradation. Our paper also proposes two new algorithms for the identification of circuit paths that are affected the most by NBTI degradation. The number of such paths identified by our algorithms are an order of magnitude fewer than previously proposed heuristics.

Reduced Rate Ultra Low Delay Audio Coder using Multistage Vector Quantization

Communication applications are usually delay restricted, especially for the instance of musicians playing over the Internet. This requires a one-way delay of maximum 25 msec and also a high audio quality is desired at feasible bit rates. The ultra low delay (ULD) audio coding structure is well suited to this application and we investigate further the application of multistage vector quantization (MSVQ) to reach a bit rate range below 64 Kb/s, in a scalable manner. Results at 32 Kb/s and 64 Kb/s show that the trained codebook MSVQ performs best, better than KLT normalization followed by a simulated Gaussian MSVQ or simulated Gaussian MSVQ alone. The results also show that there is only a weak dependence on the training data, and that we indeed converge to the perceptual quality of our previous ULD coder at 64 Kb/s.

A Randomized Algorithm for Large Scale Support Vector Learning

We propose a randomized algorithm for large scale SVM learning which solves the problem by iterating over random subsets of the data. Crucial to the algorithm for scalability is the size of the subsets chosen. In the context of text classification we show that, by using ideas from random projections, a sample size of O(log n) can be used to obtain a solution which is close to the optimal with a high probability. Experiments done on synthetic and real life data sets demonstrate that the algorithm scales up SVM learners, without loss in accuracy. 1

INTACTE: An Interconnect Area, Delay, and Energy Estimation Tool for Microarchitectural Explorations

Prior work on modeling interconnects has focused on optimizing the wire and repeater design for trading off energy and delay, and is largely based on low level circuit parameters. Hence these models are hard to use directly to make high level microarchitectural trade-offs in the initial exploration phase of a design. In this paper, we propose INTACTE, a tool that can be used by architects toget reasonably accurate interconnect area, delay, and power estimates based on a few architecture level parameters for the interconnect such as length, width (in number of bits), frequency, and latency for a specified technology and voltage. The tool uses well known models of interconnect delay and energy taking into account the wire pitch, repeater size, and spacing for a range of voltages and technologies.It then solves an optimization problem of finding the lowest energy interconnect design in terms of the low level circuit parameters, which meets the architectural constraintsgiven as inputs. In addition, the tool also provides the area, energy, and delay for a range of supply voltages and degrees of pipelining, which can be used for micro-architectural exploration of a chip. The delay and energy models used by the tool have been validated against low level circuit simulations. We discuss several potential applications of the tool and present an example of optimizing interconnect design in the context of clustered VLIW architectures. Copyright 2007 ACM.

Focussed Crawling with large scale Ordinal Regression Solvers

In this paper we propose a novel, scalable, clustering based Ordinal Regression formulation, which is an instance of a Second Order Cone Program (SOCP) with one Second Order Cone (SOC) constraint. The main contribution of the paper is a fast algorithm, CB-OR, which solves the proposed formulation more eficiently than general purpose solvers. Another main contribution of the paper is to pose the problem of focused crawling as a large scale Ordinal Regression problem and solve using the proposed CB-OR. Focused crawling is an efficient mechanism for discovering resources of interest on the web. Posing the problem of focused crawling as an Ordinal Regression problem avoids the need for a negative class and topic hierarchy, which are the main drawbacks of the existing focused crawling methods. Experiments on large synthetic and benchmark datasets show the scalability of CB-OR. Experiments also show that the proposed focused crawler outperforms the state-of-the-art.