Naxalbari at its Golden Jubilee: Fifty recent books on the Maoist movement in India

There are not many other issues in South Asia that have attracted as much scholarly attention in the last decade as India's Naxalite or Maoist movement. At least 50 scholarly or political books, several novels, and numerous essays have been published since 2007. What we hope to do in this article is to ask why this movement has generated such attention at this moment in time, to analyse the commentaries that have emerged and the questions that have been asked, and also to identify some of the shortfalls in the existing literature and propose some lines of research to be pursued by future scholars

Exploring a Few Good Tuples From a Text Database

Information extraction from text databases is a useful paradigm to populate relational tables and unlock the considerable value hidden in plain-text documents. However, information extraction can be expensive, due to various complex text processing steps necessary in uncovering the hidden data. There are a large number of text databases available, and not every text database is necessarily relevant to every relation. Hence, it is important to be able to quickly explore the utility of running an extractor for a specific relation over a given text database before carrying out the expensive extraction task. In this paper, we present a novel exploration methodology of finding a few good tuples for a relation that can be extracted from a database which allows for judging the relevance of the database for the relation. Specifically, we propose the notion of a good(k,l) query as one that can return any k tuples for a relation among the top-l fraction of tuples ranked by their aggregated confidence scores, provided by the extractor; if these tuples have high scores, the database can be determined as relevant to the relation. We formalize the access model for information extraction, and investigate efficient query processing algorithms for good(k,l) queries, which do not rely on any prior knowledge about the extraction task or the database. We demonstrate the viability of our algorithms using a detailed experimental study with real text databases

Understanding, Estimating, and Incorporating Output Quality Into Join Algorithms For Information Extraction

Information extraction (IE) systems are trained to extract specific relations from text databases. Real-world applications often require that the output of multiple IE systems be joined to produce the data of interest. To optimize the execution of a join of multiple extracted relations, it is not sufficient to consider only execution time. In fact, the quality of the join output is of critical importance: unlike in the relational world, different join execution plans can produce join results of widely different quality whenever IE systems are involved. In this paper, we develop a principled approach to understand, estimate, and incorporate output quality into the join optimization process over extracted relations. We argue that the output quality is affected by (a) the configuration of the IE systems used to process the documents, (b) the document retrieval strategies used to retrieve documents, and (c) the actual join algorithm used. Our analysis considers a variety of join algorithms from relational query optimization, and predicts the output quality –and, of course, the execution time– of the alternate execution plans. We establish the accuracy of our analytical models, as well as study the effectiveness of a quality-aware join optimizer, with a large-scale experimental evaluation over real-world text collections and state-of-the-art IE systems

Understanding, Estimating, and Incorporating Output Quality Into Join Algorithms For Information Extraction

Information extraction (IE) systems are trained to extract specific relations from text databases. Real-world applications often require that the output of multiple IE systems be joined to produce the data of interest. To optimize the execution of a join of multiple extracted relations, it is not sufficient to consider only execution time. In fact, the quality of the join output is of critical importance: unlike in the relational world, different join execution plans can produce join results of widely different quality whenever IE systems are involved. In this paper, we develop a principled approach to understand, estimate, and incorporate output quality into the join optimization process over extracted relations. We argue that the output quality is affected by (a) the configuration of the IE systems used to process the documents, (b) the document retrieval strategies used to retrieve documents, and (c) the actual join algorithm used. Our analysis considers a variety of join algorithms from relational query optimization, and predicts the output quality –and, of course, the execution time– of the alternate execution plans. We establish the accuracy of our analytical models, as well as study the effectiveness of a quality-aware join optimizer, with a large-scale experimental evaluation over real-world text collections and state-of-the-art IE systems

Clinical potential of sensory neurites in the heart and their role in decision-making

The process of decision-making is quite complex involving different aspects of logic, emotion, and intuition. The process of decision-making can be summarized as choosing the best alternative among a given plethora of options in order to achieve the desired outcome. This requires establishing numerous neural networks between various factors associated with the decision and creation of possible combinations and speculating their possible outcomes. In a nutshell, it is a highly coordinated process consuming the majority of the brain’s energy. It has been found that the heart comprises an intrinsic neural system that contributes not only to the decision-making process but also the short-term and long-term memory. There are approximately 40,000 cells present in the heart known as sensory neurites which play a vital role in memory transfer. The heart is quite a mysterious organ, which functions as a blood-pumping machine and an endocrine gland, as well as possesses a nervous system. There are multiple factors that affect this heart ecosystem, and they directly affect our decision-making capabilities. These interlinked relationships hint toward the sensory neurites which modulate cognition and mood regulation. This review article aims to provide deeper insights into the various roles played by sensory neurites in decision-making and other cognitive functions. The article highlights the pivotal role of sensory neurites in the numerous brain functions, and it also meticulously discusses the mechanisms through which they modulate their effects

A Forgotten Adivasi Landscape: Museums and Memory in western India

This article focuses on processes of remembering, forgetting and re-remembering. It examines a fundamental tension between the project of retrieving an adivasi past, initiated by an adivasi museum in rural western India, and the social and material landscape surrounding it, characterised instead by fragmentation and separation from the identity of adivasi. The article reflects on a collaborative research project between the researcher, young adivasi curators and inhabitants of the area adjoining the museum. It shows how, while curators engaged in a project of recuperation, at the same time, they were distancing themselves from their traditional identity by joining reform movements and new religious sects. Processes of memory and forgetting, however, also co-existed. People held multiple identities and the process of retrieving the past also called for transformation and reform. The article is a timely contribution to debates about adivasi identity, social transformation and religious reform. It also offers a reflection on the new role of indigenous museums and their potential to address a ‘crisis of postcolonial memory’ (Werbner 1998). Finally, it contributes to discussions of methodology with a focus on the collaborative process of collecting and its role in eliciting or preventing certain kinds of memories

Acronym-Expansion Recognition and Ranking on the Web

The paper presents a study on large-scale automatic extraction of acronyms and associated expansions from Web data and from the user interactions with this data through Web search engines. We investigate three information sources for extracting and ranking acronym-expansion pairs, as provided by a large-scale search engine: the crawled web documents, the search engine logs, and the search results. We evaluate and compare the acronymexpansion pairs generated from these sources on three dimensions: (1) the precision and recall of each source; (2) the overlap and inclusion among the acronym-expansion sets; and (3) the rank-order correlation of the ordered expansion sets. Our results show that all three data sources play an important role in building a comprehensive up-todate collection of acronym-expansion pairs.

Identifying comparable entities on the web

Web search engines are often presented with user queries that involve comparisons of real-world entities. Thus far, this interaction has typically been captured by users submitting appropriately designed keyword queries for which they are presented a list of relevant documents. Richer interactions that explicitly allow for a comparative analysis of entities represent a new potential direction to improve the search experience. With this in mind, we present an initial step of mining comparable entities from sources of information available to a large-scale Web search engine, namely, search query logs and documents from a Web crawl. Our mining methods generate a diverse set of comparables consisting of entities from a broad class of categories, such as medicines, appliances, electronics, and vacation destinations