An Estimator Algorithm For Learning Automata With Changing Number Of Actions

In many problems of decision making under uncertainty the system has to acquire knowledge of its environment and learn the optimal decision through its experience. Such problems may also involve the system having to arrive at the globally optimal decision, when at each instant only a subset of the entire set of possible alternatives is available. These problems can be successfully modelled and analysed by learning automata. In this paper an estimator learning algorithm, which maintains estimates of the reward characteristics of the random environment, is presented for an automaton with changing number of actions. A learning automaton using the new scheme is shown to be e-optimal. The simulation results demonstrate the fast convergence properties of the new algorithm. The results of this study can be extended to the design of other types of estimator algorithms with good convergence properties.

Group Actions and Elementary Number Theory

Abstract | In this article the shuffling of cards is studied by using the concept of a group action. We use some fundamental results in Elementary Number Theory to obtain formulas for the orders of some special shufflings, namely the Faro and Monge shufflings and give necessary and sufficient conditions for the Monge shuffling to be a cycle. In the final section we extend the considerations to the shuffling of multisets.

Protumorigenic actions of S100A2 involve regulation of PI3/Akt signaling and functional interaction with Smad3

S100 family of calcium-binding proteins is commonly upregulated in a variety of tumor types and is often associated with tumor progression. Among several S100 members, altered expression of S100A2 is a potential diagnostic and prognostic marker in cancer. Several reports suggest a role for S100A2 in metastasis. Earlier, our studies established regulation of S100A2 by transforming growth factor- (TGF-) and its involvement in TGF--mediated cancer cell invasion and migration. However, the molecular mechanisms of S100A2 protumorigenic actions remain unexplored. In the present study, we demonstrate that overexpression of S100A2 in A549 lung cancer cells induced epithelialmesenchymal transition (EMT) followed by increased invasion, loose colony morphology in soft agar and enhanced Akt phosphorylation (Ser-473). Furthermore, overexpression of S100A2 led to increased tumor growth in immunocompromised mice. In agreement, immunohistochemical examination of resected xenograft tumors established inverse correlation between S100A2 and E-cadherin expression together with activated Akt signaling. Interestingly, our study demonstrates a strong dependence of S100A2 and Smad3 in TGF--induced Hep3B cell EMT and invasion. Most importantly, we demonstrate that these effects of S100A2 are manifested through functional interaction with Smad3, which is enhanced in the presence of high calcium and TGF-. S100A2 stabilizes Smad3 and binds to its C-terminal MH2 domain. Additionally, loss of S100A2 attenuates the transcription of TGF-/Smad3 target genes involved in tumor promotion, such as PA1-1 and vimentin. Collectively, our findings present the first mechanistic details of S100A2 protumorigenic actions and its involvement in TGF--mediated cancer cell invasion and EMT.

A Cause and Effect Analysis of Motion Trajectories for Modeling Actions

An action is typically composed of different parts of the object moving in particular sequences. The presence of different motions (represented as a 1D histogram) has been used in the traditional bag-of-words (BoW) approach for recognizing actions. However the interactions among the motions also form a crucial part of an action. Different object-parts have varying degrees of interactions with the other parts during an action cycle. It is these interactions we want to quantify in order to bring in additional information about the actions. In this paper we propose a causality based approach for quantifying the interactions to aid action classification. Granger causality is used to compute the cause and effect relationships for pairs of motion trajectories of a video. A 2D histogram descriptor for the video is constructed using these pairwise measures. Our proposed method of obtaining pairwise measures for videos is also applicable for large datasets. We have conducted experiments on challenging action recognition databases such as HMDB51 and UCF50 and shown that our causality descriptor helps in encoding additional information regarding the actions and performs on par with the state-of-the art approaches. Due to the complementary nature, a further increase in performance can be observed by combining our approach with state-of-the-art approaches.

Preferences, actions and voting rules

In this paper we address several issues related to collective dichotomous decision-making by means of quaternary voting rules, i.e., when voters may choose between four actions: voting yes, voting no, abstaining and not turning up-which are aggregated by a voting rule into a dichotomous decision: acceptance or rejection of a proposal. In particular we study the links between the actions and preferences of the actors. We show that quaternary rules (unlike binary rules, where only two actions -yes or no- are possible) leave room for "manipulability" (i.e., strategic behaviour). Thus a preference profile does not in general determine an action profile. We also deal with the notions of success and decisiveness and their ex ante assessment for quaternary voting rules, and discuss the role of information and coordination in this context.

Detecting actions of fruit flies

In this thesis we describe a system that tracks fruit flies in video and automatically detects and classifies their actions. We introduce Caltech Fly-vs-Fly Interactions, a new dataset that contains hours of video showing pairs of fruit flies engaging in social interactions, and is published with complete expert annotations and articulated pose trajectory features. We compare experimentally the value of a frame-level feature representation with the more elaborate notion of bout features that capture the structure within actions. Similarly, we compare a simple sliding window classifier architecture with a more sophisticated structured output architecture, and find that window based detectors outperform the much slower structured counterparts, and approach human performance. In addition we test the top performing detector on the CRIM13 mouse dataset, finding that it matches the performance of the best published method.