Fuzzy Interval-Valued Multi Criteria Based Decision Making for Ranking Features in Multi-Modal 3D Face Recognition

Soodamani Ramalingam, 'Fuzzy interval-valued multi criteria based decision making for ranking features in multi-modal 3D face recognition', Fuzzy Sets and Systems, In Press version available online 13 June 2017. This is an Open Access paper, made available under the Creative Commons license CC BY 4.0 https://creativecommons.org/licenses/by/4.0/This paper describes an application of multi-criteria decision making (MCDM) for multi-modal fusion of features in a 3D face recognition system. A decision making process is outlined that is based on the performance of multi-modal features in a face recognition task involving a set of 3D face databases. In particular, the fuzzy interval valued MCDM technique called TOPSIS is applied for ranking and deciding on the best choice of multi-modal features at the decision stage. It provides a formal mechanism of benchmarking their performances against a set of criteria. The technique demonstrates its ability in scaling up the multi-modal features.Peer reviewedProo

3D Face Recognition: Feature Extraction Based on Directional Signatures from Range Data and Disparity Maps

In this paper, the author presents a work on i) range data and ii) stereo-vision system based disparity map profiling that are used as signatures for 3D face recognition. The signatures capture the intensity variations along a line at sample points on a face in any particular direction. The directional signatures and some of their combinations are compared to study the variability in recognition performances. Two 3D face image datasets namely, a local student database captured with a stereo vision system and the FRGC v1 range dataset are used for performance evaluation

"Endogenous" Relative Concerns: The Impact of Workers' Characteristics on Status and Pro ts in the Firm

This paper explores the rationality of status concerns amongst co-workers and the impact of such rational status concerns on a firm's profits. We find that it is individually rational for agents in a firm to develop and exhibit status concerns. Workers are, by their choices of status concerns, able to transfer surplus from the the rm to themselves. Further, relative concerns are shaped by the relative strengths and weaknesses of the workers in the firm. Finally, a firm's profit is reduced (relative to the benchmark moral-hazard model) by workers who exhibit such "endogenous" relative concerns.status, incentives, endogenous preferences, surplus transfer, profits

The relevance of irrelevant information in the dictator game

We examine the sensitivity of the dictator game to information provided to subjects. We investigate if individuals internalize completely irrelevant information about players when making allocation decisions. Subjects are provided with their score and the scores of recipients on a quiz prior to making decisions in multiple dictator games. Quiz scores have no bearing on the game or on players' endowments and hence represent extraneous information. We find that dictators reward good performance on the quiz. We find that information that is irrelevant for the game might nevertheless be relevant for choices. Our results highlight the extreme sensitivity of the dictator game to information and context.dictator game, experiment, irrelevant information, context

Design of a lunar oxygen production plant

To achieve permanent human presence and activity on the moon, oxygen is required for both life support and propulsion. Lunar oxygen production using resources existing on the moon will reduce or eliminate the need to transport liquid oxygen from earth. In addition, the co-products of oxygen production will provide metals, structural ceramics, and other volatile compounds. This will enable development of even greater self-sufficiency as the lunar outpost evolves. Ilmenite is the most abundant metal-oxide mineral in the lunar regolith. A process involving the reaction of ilmenite with hydrogen at 1000 C to produce water, followed by the electrolysis of this water to provide oxygen and recycle the hydrogen has been explored. The objective of this 1990 Summer Faculty Project was to design a lunar oxygen-production plant to provide 5 metric tons of liquid oxygen per year from lunar soil. The results of this study describe the size and mass of the equipment, the power needs, feedstock quantity and the engineering details of the plant

Empirical Bounds on Linear Regions of Deep Rectifier Networks

We can compare the expressiveness of neural networks that use rectified linear units (ReLUs) by the number of linear regions, which reflect the number of pieces of the piecewise linear functions modeled by such networks. However, enumerating these regions is prohibitive and the known analytical bounds are identical for networks with same dimensions. In this work, we approximate the number of linear regions through empirical bounds based on features of the trained network and probabilistic inference. Our first contribution is a method to sample the activation patterns defined by ReLUs using universal hash functions. This method is based on a Mixed-Integer Linear Programming (MILP) formulation of the network and an algorithm for probabilistic lower bounds of MILP solution sets that we call MIPBound, which is considerably faster than exact counting and reaches values in similar orders of magnitude. Our second contribution is a tighter activation-based bound for the maximum number of linear regions, which is particularly stronger in networks with narrow layers. Combined, these bounds yield a fast proxy for the number of linear regions of a deep neural network.Comment: AAAI 202

Empirical Formulation of Highway Traffic Flow Prediction Objective Function Based on Network Topology

Accurate Highway road predictions are necessary for timely decision making by the transport authorities. In this paper, we propose a traffic flow objective function for a highway road prediction model. The bi-directional flow function of individual roads is reported considering the net inflows and outflows by a topological breakdown of the highway network. Further, we optimise and compare the proposed objective function for constraints involved using stacked long short-term memory (LSTM) based recurrent neural network machine learning model considering different loss functions and training optimisation strategies. Finally, we report the best fitting machine learning model parameters for the proposed flow objective function for better prediction accuracy.Peer reviewe

3D Face Synthesis with KINECT

This work describes the process of face synthesis by image morphing from less expensive 3D sensors such as KINECT that are prone to sensor noise. Its main aim is to create a useful face database for future face recognition studies.Peer reviewe