A tutorial survey of reinforcement learning

This paper gives a compact, self-contained tutorial survey of reinforcement learning, a tool that is increasingly finding application in the development of intelligent dynamic systems. Research on reinforcement learning during the past decade has led to the development of a variety of useful algorithms. This paper surveys the literature and presents the algorithms in a cohesive framework.

Genetic algorithms: a survey

Genetic algorithms provide an alternative to traditional optimization techniques by using directed random searches to locate optimal solutions in complex landscapes. We introduce the art and science of genetic algorithms and survey current issues in GA theory and practice. We do not present a detailed study, instead, we offer a quick guide into the labyrinth of GA research. First, we draw the analogy between genetic algorithms and the search processes in nature. Then we describe the genetic algorithm that Holland introduced in 1975 and the workings of GAs. After a survey of techniques proposed as improvements to Holland's GA and of some radically different approaches, we survey the advances in GA theory related to modeling, dynamics, and deception

Soft condensed matter: A survey and a proposal

Pn this perspective on the increasingly important field of soft condensed matter science, I make the case for a concentrated multidisciplinary effort to develop the area in India. I base my arguments on its demonstrated potential for new 'emergent' phenomena, interesting table-top experiments, and applications.

Status Survey of the Malabar Pied Hornbill in the Dandeli Region, Northern Western Ghats, India

The Malabar Pied Hornbill, Anthracoceros coronatus, is a near threatened species, endemic to the tropical deciduous forests of central and southern India and Sri Lanka. The Dandeli region in Karnataka (India) is believed to be the last stronghold of this species in the Western Ghats biodiversity hotspot. Being a rapidly developing area with a growing human population, the threats to this species and their habitat are mounting, especially due to a large number of hydroelectric projects and habitat fragmentation caused by paper and plywood industries. This study evaluated the change in population status of the Malabar Pied Hornbill over a 23 year period and defined priorities for the long term conservation and monitoring of hornbills in Dandeli. Encounter rates of hornbills were also analysed in relation to the density and species richness of trees and fruiting trees, basal area, canopy cover and distance from river. Hornbill encounters were not significantly different compared to the earlier study carried out by Reddy in 1988, but were significantly different across the five sites in the current study. Higher numbers of hornbills were encountered closer to the river, but these results were only marginally significant. The mean numbers of hornbills recorded at the two roost sites identified in Dandeli were 26 +/- 4.47 (n=16 counts) and 31.78 +/- 3.53 (n=14 counts) respectively. The study also helped build local awareness about the species, train local Forest Department staff in monitoring hornbills and develop a management plan for its conservation.

Anti-malarial herbal remedies of northeast India, Assam: An ethnobotanical survey

Ethnopharmacological relevance: Malaria is a serious public health problem in the north-eastern region of India including Assam, in view of development of chloroquine resistant Plasmodium falciparum. There is need for alternative and affordable therapy. Aim of the study: This study was conducted to document indigenous knowledge, usage customs and practices of medicinal plant species traditionally used by the residents of Sonitpur district of Tezpur, Assam to treat malaria and its associated symptoms. Materials and methods:A total of 50 randomly selected sampling represented by male (38.76%) and female respondents (12.24%) were interviewed using a semi-structured questionnaire. Results: The present ethno-botanical survey revealed 22 species of plants belonging to 17 botanical families were reported to be used exclusively in this region for the treatment of malaria. Verbenaceae (three species), Menispermaceae (two species), and Acanthaceae (two species) botanical families represented the species that are most commonly cited in this survey work and the detailed use of plants has been collected and described. Conclusions: The most serious threat to the existing knowledge and practice on traditional medicinal plants included cultural change, particularly the influence of modernization and lack of interests shown by the next younger generations were the main problems reported by the informants during the field survey. Hence, the proper documentation of traditional medicinal plants being used as anti-malarial agents and related indigenous knowledge held by the tribal community is an important approach to control the spread of vector-borne diseases like malaria reported in this survey work. (C) 2010 Elsevier Ireland Ltd. All rights reserved.

An ethnobotanical survey of medicinal plants in the Eastern Himalayan zone of Arunachal Pradesh, India

Aim of the study: The medicinal plants are integral source of easily available remedy used in rural healthcare system. This study was conducted among three major ethnic groups namely the Nocte, the Nyishi and the Adi in the Eastern Himalayan region of Arunachal Pradesh to evaluate their comparative knowledge on medicinal plants. Materials and methods: The three remote districts of Arunachal Pradesh namely the Tirap, the Dibang Valley and the Papum Pare were surveyed through interviewing of randomly selected 237 participants using semi-structured questionnaire and regular field visits to selected districts. Results: We recorded the traditional use of 74 medicinal plants species belonging to 41 taxonomic plant families used for treating a total of 25 different diseases/ailments. The informant consensus factor (ICF) values demonstrated that local people tend to agree more with each other in terms of the plants used to treat malaria (0.71), jaundice (0.62), urological problems (0.56), dermatological disorders (0.45), pain (0.30), and respiratory disorder (0.33), and while the general health (0.15) and gastro-intestinal disorders category (0.28) were found low ICF values. Conclusion: Of the total 74 species recorded, the highest number of medicinal plants (36 species) was reported from the Adi of Lower Dibang Valley followed by the Nocte of the Tirap (25 species) and the Nyishi ethnic groups of Papum Pare districts (13 species). In the present study, we found that the men, elder people and illiterate ones had better knowledge on medicinal plants as compared to women, younger and literate people. Findings of this documentation study can be used as an ethnopharmacological basis for selecting plants for future phytochemical and pharmaceutical studies. (C) 2010 Elsevier Ireland Ltd. All rights reserved.

Artificial intelligence approaches to network management: recent advances and a survey

With increased number of new services and users being added to the communication network, management of such networks becomes crucial to provide assured quality of service. Finding skilled managers is often a problem. To alleviate this problem and also to provide assistance to the available network managers, network management has to be automated. Many attempts have been made in this direction and it is a promising area of interest to researchers in both academia and industry. In this paper, a review of the management complexities in present day networks and artificial intelligence approaches to network management are presented. Published by Elsevier Science B.V.

Techniques for realistic visualization of fluids: A survey

Visualization of fluids has wide applications in science, engineering and entertainment. Various methodologies Of visualizing fluids have evolved which emphasize on capturing different aspects of the fluids accurately. In this survey the existing methods for realistic visualization of fluids are reviewed. The approaches are classified based on the key concept they rely on for fluid modeling. This classification allows for easy selection of the method to be adopted for visualization given an application. It also enables identification of alternative techniques for fluid modeling.

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.

Fuzzy Clustering Based Ad Recommendation for TV Programs

Advertisements(Ads) are the main revenue earner for Television (TV) broadcasters. As TV reaches a large audience, it acts as the best media for advertisements of products and services. With the emergence of digital TV, it is important for the broadcasters to provide an intelligent service according to the various dimensions like program features, ad features, viewers’ interest and sponsors’ preference. We present an automatic ad recommendation algorithm that selects a set of ads by considering these dimensions and semantically match them with programs. Features of the ad video are captured interms of annotations and they are grouped into number of predefined semantic categories by using a categorization technique. Fuzzy categorical data clustering technique is applied on categorized data for selecting better suited ads for a particular program. Since the same ad can be recommended for more than one program depending upon multiple parameters, fuzzy clustering acts as the best suited method for ad recommendation. The relative fuzzy score called “degree of membership” calculated for each ad indicates the membership of a particular ad to different program clusters. Subjective evaluation of the algorithm is done by 10 different people and rated with a high success score.

Pathogen-specific TLR2 Protein Activation Programs Macrophages to Induce Wnt-beta-Catenin Signaling

Innate immunity recognizes and resists various pathogens; however, the mechanisms regulating pathogen versus non-pathogen discrimination are still imprecisely understood. Here, we demonstrate that pathogen-specific activation of TLR2 upon infection with Mycobacterium bovis BCG, in comparison with other pathogenic microbes, including Salmonella typhimurium and Staphylococcus aureus, programs macrophages for robust up-regulation of signaling cohorts of Wnt-beta-catenin signaling. Signaling perturbations or genetic approaches suggest that infection-mediated stimulation of Wnt-beta-catenin is vital for activation of Notch1 signaling. Interestingly, inducible NOS (iNOS) activity is pivotal for TLR2-mediated activation of Wnt-beta-catenin signaling as iNOS(-/-) mice demonstrated compromised ability to trigger activation of Wnt-beta-catenin signaling as well as Notch1-mediated cellular responses. Intriguingly, TLR2-driven integration of iNOS/NO, Wnt-beta-catenin, and Notch1 signaling contributes to its capacity to regulate the battery of genes associated with T(Reg) cell lineage commitment. These findings reveal a role for differential stimulation of TLR2 in deciding the strength of Wnt-beta-catenin signaling, which together with signals from Notch1 contributes toward the modulation of a defined set of effector functions in macrophages and thus establishes a conceptual framework for the development of novel therapeutics.

Characterization of Clean and Fouled Rail Track Ballast Subsurface Using Seismic Surface Survey Method: Model and Field Studies

The efficiency of track foundation material gradually decreases due to insufficient lateral confinement, ballast fouling, and loss of shear strength of the subsurface soil under cyclic loading. This paper presents characterization of rail track subsurface to identify ballast fouling and subsurface layers shear wave velocity using seismic survey. Seismic surface wave method of multi-channel analysis of surface wave (MASW) has been carried out in the model track and field track for finding out shear wave velocity of the clean and fouled ballast and track subsurface. The shear wave velocity (SWV) of fouled ballast increases with increase in fouling percentage, and reaches a maximum value and then decreases. This character is similar to typical compaction curve of soil, which is used to define optimum and critical fouling percentage (OFP and CFP). Critical fouling percentage of 15 % is noticed for Coal fouled ballast and 25 % is noticed for clayey sand fouled ballast. Coal fouled ballast reaches the OFP and CFP before clayey sand fouled ballast. Fouling of ballast reduces voids in ballast and there by decreases the drainage. Combined plot of permeability and SWV with percentage of fouling shows that after critical fouling point drainage condition of fouled ballast goes below acceptable limit. Shear wave velocities are measured in the selected location in the Wollongong field track by carrying out similar seismic survey. In-situ samples were collected and degrees of fouling were measured. Field SWV values are more than that of the model track SWV values for the same degree of fouling, which might be due to sleeper's confinement. This article also highlights the ballast gradation widely followed in different countries and presents the comparison of Indian ballast gradation with international gradation standards. Indian ballast contains a coarser particle size when compared to other countries. The upper limit of Indian gradation curve matches with lower limit of ballast gradation curves of America and Australia. The ballast gradation followed by Indian railways is poorly graded and more favorable for the drainage conditions. Indian ballast engineering needs extensive research to improve presents track conditions.

Radio-Optical Imaging of ATLBS Survey

We present the radio-optical imaging of ATLBS, a sensitive radio survey (Subrahmanyan et al. 2010). The primary aim of the ATLBS survey is to image low-power radio sources which form the bulk of the radio source population to moderately high red-shifts (z similar to 1.0). The accompanying multiband optical and near infra-red observations provide information about the hosts and environments of the radio sources. We give here details of the imaging of the radio data and optical data for the ATLBS survey.

A survey of temporal data mining

Data mining is concerned with analysing large volumes of (often unstructured) data to automatically discover interesting regularities or relationships which in turn lead to better understanding of the underlying processes. The field of temporal data mining is concerned with such analysis in the case of ordered data streams with temporal interdependencies. Over the last decade many interesting techniques of temporal data mining were proposed and shown to be useful in many applications. Since temporal data mining brings together techniques from different fields such as statistics, machine learning and databases, the literature is scattered among many different sources. In this article, we present an overview of techniques of temporal data mining.We mainly concentrate on algorithms for pattern discovery in sequential data streams.We also describe some recent results regarding statistical analysis of pattern discovery methods.

Petri net based performance modeling for effective DVFS for multithreaded programs

Dynamic Voltage and Frequency Scaling (DVFS) is a very effective tool for designing trade-offs between energy and performance. In this paper, we use a formal Petri net based program performance model that directly captures both the application and system properties, to find energy efficient DVFS settings for CMP systems, that satisfy a given performance constraint, for SPMD multithreaded programs. Experimental evaluation shows that we achieve significant energy savings, while meeting the performance constraints.