Additive Spanners and (alpha, beta)-Spanners

An (alpha, beta)-spanner of an unweighted graph G is a subgraph H that distorts distances in G up to a multiplicative factor of a and an additive term beta. It is well known that any graph contains a (multiplicative) (2k - 1, 0)-spanner of size O(n(1+1/k)) and an (additive) (1, 2)-spanner of size O(n(3/2)). However no other additive spanners are known to exist. In this article we develop a couple of new techniques for constructing (alpha, beta)-spanners. Our first result is an additive (1, 6)-spanner of size O(n(4/3)). The construction algorithm can be understood as an economical agent that assigns costs and values to paths in the graph, purchasing affordable paths and ignoring expensive ones, which are intuitively well approximated by paths already purchased. We show that this path buying algorithm can be parameterized in different ways to yield other sparseness-distortion tradeoffs. Our second result addresses the problem of which (alpha, beta)-spanners can be computed efficiently, ideally in linear time. We show that, for any k, a (k, k - 1)-spanner with size O(kn(1+1/k)) can be found in linear time, and, further, that in a distributed network the algorithm terminates in a constant number of rounds. Previous spanner constructions with similar performance had roughly twice the multiplicative distortion.

Incentive mechanism to control customer buy-down behaviour

Airlines have successfully practiced revenue management over the past four decades and enhanced their revenue. Most of the traditional models that are applied assume that customers buying a high-fare class ticket will not purchase a low-fare class ticket even if it is available. This is not a very realistic assumption and has led to revenue leakage due to customers exhibiting buy-down behaviour. This paper aims at devising a suitable incentive mechanism that would incite the customer to reveal his nature. This helps in reducing revenue leakage. We show that the proposed incentive mechanism is profitable to both the buyer and seller and hence ensures the buyers participation in the mechanism. Journal of the Operational Research Society (2011) 62, 1566-1573. doi:10.1057/jors.2010.57 Published online 11 August 2010

Mechanism design problems in carbon economics

Reduction of carbon emissions is of paramount importance in the context of global warming and climate change. Countries and global companies are now engaged in understanding systematic ways of solving carbon economics problems, aimed ultimately at achieving well defined emission targets. This paper proposes mechanism design as an approach to solving carbon economics problems. The paper first introduces carbon economics issues in the world today and next focuses on carbon economics problems facing global industries. The paper identifies four problems faced by global industries: carbon credit allocation (CCA), carbon credit buying (CCB), carbon credit selling (CCS), and carbon credit exchange (CCE). It is argued that these problems are best addressed as mechanism design problems. The discipline of mechanism design is founded on game theory and is concerned with settings where a social planner faces the problem of aggregating the announced preferences of multiple agents into a collective decision, when the actual preferences are not known publicly. The paper provides an overview of mechanism design and presents the challenges involved in designing mechanisms with desirable properties. To illustrate the application of mechanism design in carbon economics,the paper describes in detail one specific problem, the carbon credit allocation problem.

Extracting Three Dimensional Surface Model of Human Kidney from the Visible Human Data Set using Free Software

Three dimensional digital model of a representative human kidney is needed for a surgical simulator that is capable of simulating a laparoscopic surgery involving kidney. Buying a three dimensional computer model of a representative human kidney, or reconstructing a human kidney from an image sequence using commercial software, both involve (sometimes significant amount of) money. In this paper, author has shown that one can obtain a three dimensional surface model of human kidney by making use of images from the Visible Human Data Set and a few free software packages (ImageJ, ITK-SNAP, and MeshLab in particular). Images from the Visible Human Data Set, and the software packages used here, both do not cost anything. Hence, the practice of extracting the geometry of a representative human kidney for free, as illustrated in the present work, could be a free alternative to the use of expensive commercial software or to the purchase of a digital model.

CAVVA: Computational Affective Video-in-Video Advertising

Advertising is ubiquitous in the online community and more so in the ever-growing and popular online video delivery websites (e. g., YouTube). Video advertising is becoming increasingly popular on these websites. In addition to the existing pre-roll/post-roll advertising and contextual advertising, this paper proposes an in-stream video advertising strategy-Computational Affective Video-in-Video Advertising (CAVVA). Humans being emotional creatures are driven by emotions as well as rational thought. We believe that emotions play a major role in influencing the buying behavior of users and hence propose a video advertising strategy which takes into account the emotional impact of the videos as well as advertisements. Given a video and a set of advertisements, we identify candidate advertisement insertion points (step 1) and also identify the suitable advertisements (step 2) according to theories from marketing and consumer psychology. We formulate this two part problem as a single optimization function in a non-linear 0-1 integer programming framework and provide a genetic algorithm based solution. We evaluate CAVVA using a subjective user-study and eye-tracking experiment. Through these experiments, we demonstrate that CAVVA achieves a good balance between the following seemingly conflicting goals of (a) minimizing the user disturbance because of advertisement insertion while (b) enhancing the user engagement with the advertising content. We compare our method with existing advertising strategies and show that CAVVA can enhance the user's experience and also help increase the monetization potential of the advertising content.