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Search Results: 1 - 10 of 250 matches for " Sujit Gujar "
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An Optimal Multi-Unit Combinatorial Procurement Auction with Single Minded Bidders
Sujit Gujar,Y Narahari
Computer Science , 2009,
Abstract: The current art in optimal combinatorial auctions is limited to handling the case of single units of multiple items, with each bidder bidding on exactly one bundle (single minded bidders). This paper extends the current art by proposing an optimal auction for procuring multiple units of multiple items when the bidders are single minded. The auction minimizes the cost of procurement while satisfying Bayesian incentive compatibility and interim individual rationality. Under appropriate regularity conditions, this optimal auction also satisfies dominant strategy incentive compatibility.
On Optimal Linear Redistribution of VCG Payments in Assignment of Heterogeneous Objects
Sujit Gujar,Yadati Narahari
Computer Science , 2008,
Abstract: There are p heterogeneous objects to be assigned to n competing agents (n > p) each with unit demand. It is required to design a Groves mechanism for this assignment problem satisfying weak budget balance, individual rationality, and minimizing the budget imbalance. This calls for designing an appropriate rebate function. Our main result is an impossibility theorem which rules out linear rebate functions with non-zero efficiency in heterogeneous object assignment. Motivated by this theorem, we explore two approaches to get around this impossibility. In the first approach, we show that linear rebate functions with non-zero are possible when the valuations for the objects are correlated. In the second approach, we show that rebate functions with non-zero efficiency are possible if linearity is relaxed.
Redistribution Mechanisms for Assignment of Heterogeneous Objects
Sujit Gujar,Yadati Narahari
Computer Science , 2014, DOI: 10.1613/jair.3225
Abstract: There are p heterogeneous objects to be assigned to n competing agents (n > p) each with unit demand. It is required to design a Groves mechanism for this assignment problem satisfying weak budget balance, individual rationality, and minimizing the budget imbalance. This calls for designing an appropriate rebate function. When the objects are identical, this problem has been solved which we refer as WCO mechanism. We measure the performance of such mechanisms by the redistribution index. We first prove an impossibility theorem which rules out linear rebate functions with non-zero redistribution index in heterogeneous object assignment. Motivated by this theorem, we explore two approaches to get around this impossibility. In the first approach, we show that linear rebate functions with non-zero redistribution index are possible when the valuations for the objects have a certain type of relationship and we design a mechanism with linear rebate function that is worst case optimal. In the second approach, we show that rebate functions with non-zero efficiency are possible if linearity is relaxed. We extend the rebate functions of the WCO mechanism to heterogeneous objects assignment and conjecture them to be worst case optimal.
Measures for classification and detection in steganalysis
Sujit Gujar,C E Veni Madhavan
Computer Science , 2009,
Abstract: Still and multi-media images are subject to transformations for compression, steganographic embedding and digital watermarking. In a major program of activities we are engaged in the modeling, design and analysis of digital content. Statistical and pattern classification techniques should be combined with understanding of run length, transform coding techniques, and also encryption techniques.
Multi-Armed Bandit Mechanisms for Multi-Slot Sponsored Search Auctions
Akash Das Sarma,Sujit Gujar,Y. Narahari
Computer Science , 2010,
Abstract: In pay-per click sponsored search auctions which are currently extensively used by search engines, the auction for a keyword involves a certain number of advertisers (say k) competing for available slots (say m) to display their ads. This auction is typically conducted for a number of rounds (say T). There are click probabilities mu_ij associated with each agent-slot pairs. The goal of the search engine is to maximize social welfare of the advertisers, that is, the sum of values of the advertisers. The search engine does not know the true values advertisers have for a click to their respective ads and also does not know the click probabilities mu_ij s. A key problem for the search engine therefore is to learn these click probabilities during the T rounds of the auction and also to ensure that the auction mechanism is truthful. Mechanisms for addressing such learning and incentives issues have recently been introduced and are aptly referred to as multi-armed-bandit (MAB) mechanisms. When m = 1, characterizations for truthful MAB mechanisms are available in the literature and it has been shown that the regret for such mechanisms will be O(T^{2/3}). In this paper, we seek to derive a characterization in the realistic but non-trivial general case when m > 1 and obtain several interesting results.
An Optimal Bidimensional Multi-Armed Bandit Auction for Multi-unit Procurement
Satyanath Bhat,Shweta Jain,Sujit Gujar,Y. Narahari
Computer Science , 2015,
Abstract: We study the problem of a buyer (aka auctioneer) who gains stochastic rewards by procuring multiple units of a service or item from a pool of heterogeneous strategic agents. The reward obtained for a single unit from an allocated agent depends on the inherent quality of the agent; the agent's quality is fixed but unknown. Each agent can only supply a limited number of units (capacity of the agent). The costs incurred per unit and capacities are private information of the agents. The auctioneer is required to elicit costs as well as capacities (making the mechanism design bidimensional) and further, learn the qualities of the agents as well, with a view to maximize her utility. Motivated by this, we design a bidimensional multi-armed bandit procurement auction that seeks to maximize the expected utility of the auctioneer subject to incentive compatibility and individual rationality while simultaneously learning the unknown qualities of the agents. We first assume that the qualities are known and propose an optimal, truthful mechanism 2D-OPT for the auctioneer to elicit costs and capacities. Next, in order to learn the qualities of the agents in addition, we provide sufficient conditions for a learning algorithm to be Bayesian incentive compatible and individually rational. We finally design a novel learning mechanism, 2D-UCB that is stochastic Bayesian incentive compatible and individually rational.
An Incentive Compatible Multi-Armed-Bandit Crowdsourcing Mechanism with Quality Assurance
Shweta Jain,Sujit Gujar,Satyanath Bhat,Onno Zoeter,Y. Narahari
Computer Science , 2014,
Abstract: Consider a requester who wishes to crowdsource a series of identical binary labeling tasks to a pool of workers so as to achieve an assured accuracy for each task, in a cost optimal way. The workers are heterogeneous with unknown but fixed qualities and their costs are private. The problem is to select for each task an optimal subset of workers so that the outcome obtained from the selected workers guarantees a target accuracy level. The problem is a challenging one even in a non strategic setting since the accuracy of aggregated label depends on unknown qualities. We develop a novel multi-armed bandit (MAB) mechanism for solving this problem. First, we propose a framework, Assured Accuracy Bandit (AAB), which leads to an MAB algorithm, Constrained Confidence Bound for a Non Strategic setting (CCB-NS). We derive an upper bound on the number of time steps the algorithm chooses a sub-optimal set that depends on the target accuracy level and true qualities. A more challenging situation arises when the requester not only has to learn the qualities of the workers but also elicit their true costs. We modify the CCB-NS algorithm to obtain an adaptive exploration separated algorithm which we call { \em Constrained Confidence Bound for a Strategic setting (CCB-S)}. CCB-S algorithm produces an ex-post monotone allocation rule and thus can be transformed into an ex-post incentive compatible and ex-post individually rational mechanism that learns the qualities of the workers and guarantees a given target accuracy level in a cost optimal way. We provide a lower bound on the number of times any algorithm should select a sub-optimal set and we see that the lower bound matches our upper bound upto a constant factor. We provide insights on the practical implementation of this framework through an illustrative example and we show the efficacy of our algorithms through simulations.
Perfect Entanglement Transport in Quantum Spin Chain Systems  [PDF]
Sujit Sarkar
Journal of Quantum Information Science (JQIS) , 2011, DOI: 10.4236/jqis.2011.13014
Abstract: We propose a mechanism for perfect entanglement transport in anti-ferromagnetic (AFM) quantum spin chain systems with modulated exchange coupling and also for the modulation of on-site magnetic field. We use the principle of adiabatic quantum pumping process for entanglement transfer in the spin chain systems. We achieve the perfect entanglement transfer over an arbitrarily long distance and a better entanglement transport for longer AFM spin chain system than for the ferromagnetic one. We explain analytically and physically—why the entanglement hops in alternate sites. We find the condition for blocking of entanglement transport even in the perfect pumping situation. Our analytical solution interconnects quantum many body physics and quantum information science.
Vermicompost, the story of organic gold: A review  [PDF]
Sujit Adhikary
Agricultural Sciences (AS) , 2012, DOI: 10.4236/as.2012.37110
Abstract: Earthworm has caught imagination of philosophers like Pascal and Thoreau. Yet its role in the nutrition of agricultural fields has attracted attention of researchers worldwide only in recent decades. Waste management is considered as an integral part of a sustainable society, thereby necessitating diversion of biodegradable fractions of the societal waste from landfill into alternative management processes such as vermicomposting. Earthworms excreta (vermicast) is a nutritive organic fertilizer rich in humus, NPK, micronutrients, beneficial soil microbes; nitrogen-fixing, phosphate solubilizing bacteria, actinomycets and growth hormones auxins, gibberlins & cytokinins. Both vermicompost & its body liquid (vermiwash) are proven as both growth promoters & protectors for crop plants. We discuss about the worms composting technology, its importance, use and some salient results obtained in the globe so far in this review update of vermicompost research.
Mathematical Model to Locate Interference of Blast Waves from Multi-Hole Blasting Rounds  [PDF]
Sujit Kumar Mandal
Engineering (ENG) , 2012, DOI: 10.4236/eng.2012.43019
Abstract: Maximum charge per delay in a blasting round is universally accepted as the influencing parameter to quantify magni-tude of vibration for any distance of concern. However, for any blasting round experimental data reveals that for same charge per delay magnitude of vibration varies with total charge. Considering linear transmission of blast waves, the paper firstly investigates into the influence of explosive weight, blast design parameters and geology of strata on magnitude and characteristics of vibration parameters and thereafter communicates that possibly interference of blast waves generated from same and different holes of a blasting round result into variation in vibration magnitude. The paper lastly developed a mathematical model to evaluate points of interference of blast waves generated from single- and multi-hole blasting round.
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