Towards Eco-Dharma: The Contribution of Gandhian Thought to Ecological Ethics in India

Ecological concern prompts poor and indigenous people of India to consider how a society can ensure both protection of nature and their rightful claim for a just and sustainable future. Previous discussions defended the environment while ignoring the struggles of the poor for sustenance and their religious traditions and ethical values. Mohandas Karamchand Gandhi addressed similar socio-ecological concerns by adopting and adapting traditional religious and ethical notions to develop strategies for constructive, engaged resistance. The dissertation research and analysis verifies the continued relevance of the Gandhian understanding of dharma (ethics) in contemporary India as a basis for developing eco-dharma (eco-ethics) to link closely development, ecology, and religious values. The method of this study is interpretive, analytical, and critical. Françoise Houtart’s social analytical method is used to make visible and to suggest how to overcome social tensions from the perspective of marginalized and exploited peoples in India. The Indian government's development initiatives create a nexus between the eco-crisis and economic injustice, and communities’ responses. The Chipko movement seeks to protect the Himalayan forests from commercial logging. The Narmada Bachao Andolan strives to preserve the Narmada River and its forests and communities, where dam construction causes displacement. The use of Gandhian approaches by these movements provides a framework for integrating ecological concerns with people's struggles for survival. For Gandhi, dharma is a harmony of satya (truth), ahimsa (nonviolence), and sarvodaya (welfare of all). Eco-dharma is an integral, communitarian, and ecologically sensitive ethical paradigm. The study demonstrates that the Gandhian notion of dharma, implemented through nonviolent satyagraha (firmness in promoting truth), can direct community action that promotes responsible economic structures and the well-being of the biotic community and the environment. Eco-dharma calls for solidarity, constructive resistance, and ecologically and economically viable communities. The dissertation recommends that for a sustainable future, India must combine indigenous, appropriate, and small- or medium-scale industries as an alternative model of development in order to help reduce systemic poverty while enhancing ecological well-being.

A Two-step Statistical Approach for Inferring Network Traffic Demands (Revises Technical Report BUCS-2003-003)

Accurate knowledge of traffic demands in a communication network enables or enhances a variety of traffic engineering and network management tasks of paramount importance for operational networks. Directly measuring a complete set of these demands is prohibitively expensive because of the huge amounts of data that must be collected and the performance impact that such measurements would impose on the regular behavior of the network. As a consequence, we must rely on statistical techniques to produce estimates of actual traffic demands from partial information. The performance of such techniques is however limited due to their reliance on limited information and the high amount of computations they incur, which limits their convergence behavior. In this paper we study a two-step approach for inferring network traffic demands. First we elaborate and evaluate a modeling approach for generating good starting points to be fed to iterative statistical inference techniques. We call these starting points informed priors since they are obtained using actual network information such as packet traces and SNMP link counts. Second we provide a very fast variant of the EM algorithm which extends its computation range, increasing its accuracy and decreasing its dependence on the quality of the starting point. Finally, we evaluate and compare alternative mechanisms for generating starting points and the convergence characteristics of our EM algorithm against a recently proposed Weighted Least Squares approach.

SomeCast: A Paradigm for Real-Time Adaptive Reliable Multicast

SomeCast is a novel paradigm for the reliable multicast of real-time data to a large set of receivers over the Internet. SomeCast is receiver-initiated and thus scalable in the number of receivers, the diverse characteristics of paths between senders and receivers (e.g. maximum bandwidth and round-trip-time), and the dynamic conditions of such paths (e.g. congestion-induced delays and losses). SomeCast enables receivers to dynamically adjust the rate at which they receive multicast information to enable the satisfaction of real-time QoS constraints (e.g. rate, deadlines, or jitter). This is done by enabling a receiver to join SOME number of concurrent multiCAST sessions, whereby each session delivers a portion of an encoding of the real-time data. By adjusting the number of such sessions dynamically, client-specific QoS constraints can be met independently. The SomeCast paradigm can be thought of as a generalization of the AnyCast (e.g. Dynamic Server Selection) and ManyCast (e.g. Digital Fountain) paradigms, which have been proposed in the literature to address issues of scalability of UniCast and MultiCast environments, respectively. In this paper we overview the SomeCast paradigm, describe an instance of a SomeCast protocol, and present simulation results that quantify the significant advantages gained from adopting such a protocol for the reliable multicast of data to a diverse set of receivers subject to real-time QoS constraints.

Task-Irrelevant Perceptual Learning Specific to the Contrast Polarity of Motion Stimuli

Studies of perceptual learning have focused on aspects of learning that are related to early stages of sensory processing. However, conclusions that perceptual learning results in low-level sensory plasticity are of great controversy, largely because such learning can often be attributed to plasticity in later stages of sensory processing or in the decision processes. To address this controversy, we developed a novel random dot motion (RDM) stimulus to target motion cells selective to contrast polarity, by ensuring the motion direction information arises only from signal dot onsets and not their offsets, and used these stimuli in conjunction with the paradigm of task-irrelevant perceptual learning (TIPL). In TIPL, learning is achieved in response to a stimulus by subliminally pairing that stimulus with the targets of an unrelated training task. In this manner, we are able to probe learning for an aspect of motion processing thought to be a function of directional V1 simple cells with a learning procedure that dissociates the learned stimulus from the decision processes relevant to the training task. Our results show learning for the exposed contrast polarity and that this learning does not transfer to the unexposed contrast polarity. These results suggest that TIPL for motion stimuli may occur at the stage of directional V1 simple cells.