Self-Organizing Flows in Social Networks

Social networks offer users new means of accessing information, essentially relying on "social filtering", i.e. propagation and filtering of information by social contacts. The sheer amount of data flowing in these networks, combined with the limited budget of attention of each user, makes it difficult to ensure that social filtering brings relevant content to the interested users. Our motivation in this paper is to measure to what extent self-organization of the social network results in efficient social filtering. To this end we introduce flow games, a simple abstraction that models network formation under selfish user dynamics, featuring user-specific interests and budget of attention. In the context of homogeneous user interests, we show that selfish dynamics converge to a stable network structure (namely a pure Nash equilibrium) with close-to-optimal information dissemination. We show in contrast, for the more realistic case of heterogeneous interests, that convergence, if it occurs, may lead to information dissemination that can be arbitrarily inefficient, as captured by an unbounded "price of anarchy". Nevertheless the situation differs when users' interests exhibit a particular structure, captured by a metric space with low doubling dimension. In that case, natural autonomous dynamics converge to a stable configuration. Moreover, users obtain all the information of interest to them in the corresponding dissemination, provided their budget of attention is logarithmic in the size of their interest set

Size Does Matter (in P2P Live Streaming)

Optimal dissemination schemes have previously been studied for peer-to-peer live streaming applications. Live streaming being a delay-sensitive application, fine tuning of dissemination parameters is crucial. In this report, we investigate optimal sizing of chunks, the units of data exchange, and probe sets, the number peers a given node probes before transmitting chunks. Chunk size can have significant impact on diffusion rate (chunk miss ratio), diffusion delay, and overhead. The size of the probe set can also affect these metrics, primarily through the choices available for chunk dissemination. We perform extensive simulations on the so-called random-peer, latest-useful dissemination scheme. Our results show that size does matter, with the optimal size being not too small in both cases

Hardware Pioneers: Harnessing the Impact Potential of Technology Entrepreneurs

Billions of households across the world live without conveniences such as electric lighting, flush toilets, and sanitary sewerage systems. Products such as milk chilling machines and solar home systems can have a significant impact on lives and livelihoods of people living in poverty in developing countries. Hardware pioneers—inventors and entrepreneurs creating breakthrough products tailored to the needs of these populations—are pushing the frontiers of technology and business to create and scale innovative hardware technologies.Numerous case studies within the report illustrate how these pioneers face many of the same challenges of any entrepreneur but with the added complexity of developing hardware and scaling in remote areas with scarce resources. There is a significant opportunity for actors across sectors to strategically leverage their resources in order to support the journeys of these hardware pioneers, from initial inspiration to ultimate impact at scale.Top TakeawaysHardware pioneers lack the right supports in the critical Pioneer Gap stages when they are blueprinting, validating, and preparing their models. In the early stages, these needs range from patient capital to prototyping facilities. Later on, issues such as distribution, financing, servicing, and quality standards become more important.Critically, because the success of hardware pioneers depends on the successful blending of both business and technology skills, those working closely with pioneer teams also need to bring the right combination of these skills, and this unfortunately is rare in the impact enterprise ecosystem.There is need to not just support hardware pioneers directly but also to assemble the other needed parts of the ecosystem, from last-mile specialist companies that help pioneers reach and serve their target markets to the programs and institutions that are helping to spark the initial impulse that gets pioneers started on their journey in the right way.New ideas can have an ultimate impact that is much greater than that of the original pioneer alone through a transfer of the idea to a more scale-ready partner or just through adoption and adaptation of the idea by follower entrepreneurs. We believe that there is great impact potential in supporting these more networked pathways for scaling

Hardware Pioneers: Harnessing the Impact Potential of Technology Entrepreneurs, Executive Summary

Technology has been a powerful driver of humanity's development over the past few centuries. It continues to hold great potential to help us live longer and in better health, as well as raising our productivity and standards of living.Yet many of these benefits remain out of the reach of the global poor.While people living in the developed world have enjoyed the benefits of electric lighting since the late 1800s, nearly 1.3 billion poor households in sub-Saharan Africa and South Asia still live in the dark today. Without electricity, many poor households are not able to make use of household appliances common in the richer world, like refrigerators, televisions,or computers.Modern sanitation technologies -- flush toilets and sewerage systems -- are another example. These technologies have existed for hundreds of years but are still not available to 2.4 billion people around the world. This lack of sanitation infrastructure leads to contamination of water sources across large stretches of South Asia and Africa. Water-borne diseases such as cholera, typhoid, and dysentery claim 3.4 million lives every year.Modern machinery and information technology have also changed the way we work. In advanced economies, large commercial farms enjoy the benefit of improved seed varieties, farm machinery, modern irrigation systems, and post-harvest storage systems. In stark contrast, many smallholder farmers in developing countries lack similar solutions and struggle to improve their crop yields and livelihoods.Against this challenging backdrop, inventors and entrepreneurs are developing new breakthrough products tailored to the needs of the global poor. These hardware pioneers are helping to improve lives and livelihoods by pushing the frontiers of technology and business. They are bringing reliable electricity to remote villages, safe drinking water to neglected slums, productivity gains to struggling smallholder farmers, and life-saving health services to sick children

Optimal Content Replication and Request Matching in Large Caching Systems

We consider models of content delivery networks in which the servers are constrained by two main resources: memory and bandwidth. In such systems, the throughput crucially depends on how contents are replicated across servers and how the requests of specific contents are matched to servers storing those contents. In this paper, we first formulate the problem of computing the optimal replication policy which if combined with the optimal matching policy maximizes the throughput of the caching system in the stationary regime. It is shown that computing the optimal replication policy for a given system is an NP-hard problem. A greedy replication scheme is proposed and it is shown that the scheme provides a constant factor approximation guarantee. We then propose a simple randomized matching scheme which avoids the problem of interruption in service of the ongoing requests due to re-assignment or repacking of the existing requests in the optimal matching policy. The dynamics of the caching system is analyzed under the combination of proposed replication and matching schemes. We study a limiting regime, where the number of servers and the arrival rates of the contents are scaled proportionally, and show that the proposed policies achieve asymptotic optimality. Extensive simulation results are presented to evaluate the performance of different policies and study the behavior of the caching system under different service time distributions of the requests.Comment: INFOCOM 201

Analyzing the Efficacy of an LLM-Only Approach for Image-based Document Question Answering

Recent document question answering models consist of two key components: the vision encoder, which captures layout and visual elements in images, and a Large Language Model (LLM) that helps contextualize questions to the image and supplements them with external world knowledge to generate accurate answers. However, the relative contributions of the vision encoder and the language model in these tasks remain unclear. This is especially interesting given the effectiveness of instruction-tuned LLMs, which exhibit remarkable adaptability to new tasks. To this end, we explore the following aspects in this work: (1) The efficacy of an LLM-only approach on document question answering tasks (2) strategies for serializing textual information within document images and feeding it directly to an instruction-tuned LLM, thus bypassing the need for an explicit vision encoder (3) thorough quantitative analysis on the feasibility of such an approach. Our comprehensive analysis encompasses six diverse benchmark datasets, utilizing LLMs of varying scales. Our findings reveal that a strategy exclusively reliant on the LLM yields results that are on par with or closely approach state-of-the-art performance across a range of datasets. We posit that this evaluation framework will serve as a guiding resource for selecting appropriate datasets for future research endeavors that emphasize the fundamental importance of layout and image content information

Comparative Studies on Antimicrobial and Antifungal Efficacy from Bixa Orellana L., Lantana Camara L., Stachytarpheta Jamaicensis (l.)vahl., Hyptis Suaveolens (l.) Poit.with Triclosan

The aim of the present study was to assess the Antimicrobial and Antifungal activities of the Phenolic leaf extracts of Bixa orellana L., Lantana camara L and Stachytarpheta jamaicensis (L.) Vahl. Hyptis suaveolens (L.) Piot. and the Triclosan, a chlorinated aromatic compound with antibacterial and antifungal properties used in common house hold and personal care products and to compare household and personal care products and to compare their effectiveness against 4 bacterial strains - 2 Gram Positive strains – Staphylococcus aurens and Bacillus substitis and 2 Gram negative strains – Escherischia coli and Pseudomonas fluorescens and 3 Fungi- Aspergillus niger, Aspergillus flavus and Mucor Sp., by Agar well diffusion Assay. The phenolic extracts of all the 4 plants showed Maximum (80-100%), Relative inhibition against Pseudomonas fluorescence, Moderate inhibition (30-70%) against Staphylococcus aurens and Bacillus substilis and least inhibition (30-47%) against Escherischia coli, while, the Antifungal efficacy of all the 4 Phenolic plant extracts were observed to be effective at the concentration ranging from 70-300 µg. The plant phenolic extracts for Antimicrobial and Antifungal properties were compared with Standard Triclosan, a chlorinated compound. Our studies showed that the phenolic components of plant origin for antibacterial activity were equivalent to Triclosan with the same concentration, while for antifungal activity slightly higher concentrations could be a better alternative and hence there could be a substitution for Triclosan by Plant Phenolic Extracts used in house hold and personal care products, in future days to come

Multi Type Mean Field Reinforcement Learning

Mean field theory provides an effective way of scaling multiagent reinforcement learning algorithms to environments with many agents that can be abstracted by a virtual mean agent. In this paper, we extend mean field multiagent algorithms to multiple types. The types enable the relaxation of a core assumption in mean field games, which is that all agents in the environment are playing almost similar strategies and have the same goal. We conduct experiments on three different testbeds for the field of many agent reinforcement learning, based on the standard MAgents framework. We consider two different kinds of mean field games: a) Games where agents belong to predefined types that are known a priori and b) Games where the type of each agent is unknown and therefore must be learned based on observations. We introduce new algorithms for each type of game and demonstrate their superior performance over state of the art algorithms that assume that all agents belong to the same type and other baseline algorithms in the MAgent framework.Comment: Paper to appear in the Proceedings of International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS) 2020. Revised version has some typos correcte