Gandhi,Mahatma And The Working Scientist - A Reconciliation

IMAGINE a scientist who is a follower of Mahatma Gandhi. What kind of science can he practice? Would it be different from the kind of science that is being practised? I believe it would be and will illustrate this by constructing Mahatma Gandhi's view on science and scientific research based on his writings on related subjects. To me this implies that science is affected by the scientist's subjective values. I will then trace some of the values behind science as practised today and examine their implications for .he relationship between the scientist and the society. I will also present a case for abandoning the belief that science must be universal and show the relevance of Gandhian concepts to scientists.

Reference annual atmospheres for midlatitude and tropics

Many aerospace and scientific applications require the specification of the atmospheric properties at various locations, UT and seasons for a range of solar and geomagnetic activity. The nature and mechanisms of the lower and upper atmospheres are different so also their models. Further there is a need to match these models and this is accomplished here in a simple way. In the revision of CIRA 1972 in 1986, the reference middle atmospheres is not yet complete. Two annual reference atmospheres from sea level up to 2000 km for the midlatitude and the tropics is proposed. Other monthly reference atmospheres as also the structure of the atmospheric tables to be provided in the above document is also indicated.

Molecular systematics and conservation of the langurs and leaf monkeys of South Asia

Numerous morphology-based classification schemes have been proposed for langurs and leaf monkeys of South Asia but there is very little agreement between them. An incorrect classification scheme when used as a basis for biogeographic studies can support erroneous hypotheses. Further, lack of taxonomic resolution will also confound conservation efforts, given that conservation biologists use traditional morphology-based-classification schemes to prioritize species for conservation. Here, I have revisited recent molecular phylogenetic studies done on langurs and leaf monkeys of South Asia. Results from these studies are in turn used to derive a rational and scientific basis for prioritizing species for conservation. Molecular data support the classification of langurs of the Indian subcontinent-Hanuman, Nilgiri and purple-faced langurs-in the genus Semnopithecus, whereas Phayre's leaf monkey along with other Southeast Asian leaf monkeys form another distinct clade (Trachypithecus). The phylogenetic position of capped and golden langurs remains unresolved. Molecular data suggest that they are closely related to each other but this group might have evolved through past hybridization between Semnopithecus and Trachypithecus. Additionally, genetic data also support the splitting of the so-called Hanuman langurs into at least three species. The scores for taxonomic uniqueness of langurs and leaf monkeys of South Asia were revised using this molecular phylogeny-based classification. According to the revised scores, Phayres leaf monkey and golden langur are priority species for conservation followed by capped and Nilgiri langurs.

The State and Fate of Himalayan Glaciers

Himalayan glaciers are a focus of public and scientific debate. Prevailing uncertainties are of major concern because some projections of their future have serious implications for water resources. Most Himalayan glaciers are losing mass at rates similar to glaciers elsewhere, except for emerging indications of stability or mass gain in the Karakoram. A poor understanding of the processes affecting them, combined with the diversity of climatic conditions and the extremes of topographical relief within the region, makes projections speculative. Nevertheless, it is unlikely that dramatic changes in total runoff will occur soon, although continuing shrinkage outside the Karakoram will increase the seasonality of runoff, affect irrigation and hydropower, and alter hazards.

Quantification of impact of a cricket ball

The cricket is one of most popular games in the Asian subcontinent and its popularity is increasing every day. The issue of replacement of the cricket ball amidst the matches is always an uncomfortable situation for teams, umpires and even supporters. At present the basis of the replacement is solely on the judgement, experience and expertise of the umpires, which is subjective, controversial and debatable. In this paper, we have attempted a new approach to quantify the number of impacts or impact factor of a 4-piece leather ball used in the Intemational one-day and test cricket matches. This gives a more objective and scientific basis/ criteria for the replacement of the ball. Here, we have used a well known and widely used Thermal Infra-Red (TIR) imaging to capture the dynamics of the thermal profice of the cricket ball, which has been heated for about 15 seconds. The idea behind this approach is the simple observation that an old ball (ball with a few impacts) has different thermal signature/profice compared to the that of a new ball. This could be due to the change in the surface profice and internal structure, minor de-shaping, opening of seam etc. The TIR video and its frames, which is inherently noisy, are restored using Hebbian learning based FIR (sic), which performs optimal smoothing in relatively less number of iteration. We have focussed on the hottest region of the ball i.e., the inner core and tracked its thermal profice dynamics. Finally we have used multi layer perceptron model (MLP) to quantify the impact factor with fairly good accuracy.

A cost effective pipelined divider for double precision floating point number

he growth of high-performance application in computer graphics, signal processing and scientific computing is a key driver for high performance, fixed latency; pipelined floating point dividers. Solutions available in the literature use large lookup table for double precision floating point operations.In this paper, we propose a cost effective, fixed latency pipelined divider using modified Taylor-series expansion for double precision floating point operations. We reduce chip area by using a smaller lookup table. We show that the latency of the proposed divider is 49.4 times the latency of a full-adder. The proposed divider reduces chip area by about 81% than the pipelined divider in [9] which is based on modified Taylor-series.

Automatic Compilation of MATLAB Programs for Synergistic Execution on Heterogeneous Processors

MATLAB is an array language, initially popular for rapid prototyping, but is now being increasingly used to develop production code for numerical and scientific applications. Typical MATLAB programs have abundant data parallelism. These programs also have control flow dominated scalar regions that have an impact on the program's execution time. Today's computer systems have tremendous computing power in the form of traditional CPU cores and throughput oriented accelerators such as graphics processing units(GPUs). Thus, an approach that maps the control flow dominated regions to the CPU and the data parallel regions to the GPU can significantly improve program performance. In this paper, we present the design and implementation of MEGHA, a compiler that automatically compiles MATLAB programs to enable synergistic execution on heterogeneous processors. Our solution is fully automated and does not require programmer input for identifying data parallel regions. We propose a set of compiler optimizations tailored for MATLAB. Our compiler identifies data parallel regions of the program and composes them into kernels. The problem of combining statements into kernels is formulated as a constrained graph clustering problem. Heuristics are presented to map identified kernels to either the CPU or GPU so that kernel execution on the CPU and the GPU happens synergistically and the amount of data transfer needed is minimized. In order to ensure required data movement for dependencies across basic blocks, we propose a data flow analysis and edge splitting strategy. Thus our compiler automatically handles composition of kernels, mapping of kernels to CPU and GPU, scheduling and insertion of required data transfer. The proposed compiler was implemented and experimental evaluation using a set of MATLAB benchmarks shows that our approach achieves a geometric mean speedup of 19.8X for data parallel benchmarks over native execution of MATLAB.

Automatic compilation of MATLAB programs for synergistic execution on heterogeneous processors

MATLAB is an array language, initially popular for rapid prototyping, but is now being increasingly used to develop production code for numerical and scientific applications. Typical MATLAB programs have abundant data parallelism. These programs also have control flow dominated scalar regions that have an impact on the program's execution time. Today's computer systems have tremendous computing power in the form of traditional CPU cores and throughput oriented accelerators such as graphics processing units(GPUs). Thus, an approach that maps the control flow dominated regions to the CPU and the data parallel regions to the GPU can significantly improve program performance. In this paper, we present the design and implementation of MEGHA, a compiler that automatically compiles MATLAB programs to enable synergistic execution on heterogeneous processors. Our solution is fully automated and does not require programmer input for identifying data parallel regions. We propose a set of compiler optimizations tailored for MATLAB. Our compiler identifies data parallel regions of the program and composes them into kernels. The problem of combining statements into kernels is formulated as a constrained graph clustering problem. Heuristics are presented to map identified kernels to either the CPU or GPU so that kernel execution on the CPU and the GPU happens synergistically and the amount of data transfer needed is minimized. In order to ensure required data movement for dependencies across basic blocks, we propose a data flow analysis and edge splitting strategy. Thus our compiler automatically handles composition of kernels, mapping of kernels to CPU and GPU, scheduling and insertion of required data transfer. The proposed compiler was implemented and experimental evaluation using a set of MATLAB benchmarks shows that our approach achieves a geometric mean speedup of 19.8X for data parallel benchmarks over native execution of MATLAB.