Understanding Anatomy Classification Through Attentive Response Maps

One of the main challenges for broad adoption of deep learning based models such as convolutional neural networks (CNN), is the lack of understanding of their decisions. In many applications, a simpler, less capable model that can be easily understood is favorable to a black-box model that has superior performance. In this paper, we present an approach for designing CNNs based on visualization of the internal activations of the model. We visualize the model's response through attentive response maps obtained using a fractional stride convolution technique and compare the results with known imaging landmarks from the medical literature. We show that sufficiently deep and capable models can be successfully trained to use the same medical landmarks a human expert would use. Our approach allows for communicating the model decision process well, but also offers insight towards detecting biases.Comment: Accepted at ISBI, 201

Deformation due to Mechanical & Electromagnetic Forces in a Magneto-Micropolar Plate irradiated by Thermal Pulsed Laser

The purpose of this paper is to study the elastodynamical interactions in magneto-micropolar thermoelastic half-space considering the effect of hall current, laser heat source and rotation subjected to input ultra-laser heat source. The micropolar theory of thermoelasticity by Eringen (1966) has been used to investigate the problem. Normal mode analysis technique has been used to solve the resulting non–dimensional coupled field equations to obtain displacement, stress components and temperature distribution. Numerical computed results of all the considered variables have been shown graphically to depict the combined effect of hall current, laser heat source and rotation on the phenomena. Some particular cases of interest are also deduced from the present study

Interaction of Laser Beam with Micropolar Thermoelastic Solid

The present investigation deals with the deformation of micropolar generalized thermoelastic solid subjected to thermo-mechanical loading due to thermal laser pulse. Laplace transform and Fourier transform techniques are used to solve the problem. Thermo-mechanical laser interactions are taken as concentrated normal force and thermal source to describe the application of approach. The closed form expressions of normal stress, tangential stress, coupled stress and temperature are obtained in the transferred domain. Numerical inversion technique of Laplace transform and Fourier transform has been implied to obtain the resulting quantities in the physical domain after developing a computer program. The normal stress, tangential stress, coupled stress and temperature are depicted graphically to show the effect of relaxation times. Some particular cases of interest are deduced from the present investigation. Keywords: Pulse Laser, Integral Transform, Thermoelastic, Boundary value Problem

Class Based Strategies for Understanding Neural Networks

One of the main challenges for broad adoption of deep learning based models such as Convolutional Neural Networks (CNN), is the lack of understanding of their decisions. In many applications, a simpler, less capable model that can be easily understood is favorable to a black-box model that has superior performance. Hence, it is paramount to have a mechanism for deep learning models such as deep neural networks to explain their decisions. To resolve this explainability issue, in this thesis the main goal is to explore and develop new class-enhanced support strategies for visualizing and understanding the decision-making process of deep neural networks. In particular, we take a three level approach to provide a holistic framework for explaining deep neural networks predictions. In the first stage (Chapter 3), we first try to answer the question: based on what information neural networks make their decision and how it relates to a human expert's domain knowledge? To this end, we propose to introduce attentive response maps. The attentive response maps are able to show: 1) The locations in the input image that are contributing to decision-making and 2) the level of dominance of such locations. Through various experiments we elaborate how through attention response maps, we are able to visualize the decision-making process of deep neural networks and show where the neural networks were able to or failed to use landmark features similar to a human expert's domain knowledge. In second stage (Chapter 4), we propose a novel end-to-end design architecture for obtaining end-to-end explanations through attentive response maps. Towards the end of this stage, we explore some of the shortcomings of the attentive response maps in failing to explain some of the complex scenarios. In the last stage, (Chapter 5), we try to overcome the shortcomings of the binary attention maps introduced in the first stage. Towards this goal, a CLass-Enhanced Attentive Response (CLEAR) approach was introduced to visualize and understand the decisions made by deep neural networks (DNNs) given a specific input based on spatial support. CLEAR facilitates the visualization of attentive regions and levels of interest of DNNs during the decision-making process. It also enables the visualization of the most dominant classes associated with these attentive regions of interest. As such, CLEAR can mitigate some of the shortcomings of attention response maps-based methods associated with decision ambiguity, and allows for better insights into the decision-making process of DNNs. In the last Chapter of this thesis (Chapter 6), we draw conclusions about the introduced class based explanation strategies and discuss some interesting future directions, including a formulation for class based global explanation that can be used for discovering and explaining the concepts identified by trained deep neural networks using human attribute priors

Effect of biofertilizers on horticultural and yield traits in french bean var. Contender under dry temperate conditions of Kinnaur district of Himachal Pradesh

Kinnaur district is known as the dry temperate zone of Himachal Pradesh and is known for off season and quality production of vegetables.In this district of Himachal Pradesh, Natural farming is mostly done with the minimum use of chemical fertilizers. Farmers are unaware of the judicious use of farm yard manure, and biofertilizers due to which yield of the french bean is very low (50-70 q/ha). French bean is one of the most important vegetables intercropped with apple in Kinnaur District. An experiment was conducted during the summer season of 2011 at the Experimental Farm of Vegetable Research Station, Kalpa, Kinnaur, Himachal Pradesh to study the effect of Rhizobium and Phosphorus Solublizing Bacteria (PSB) on the horticultural and yield traits in french bean var. Contender. Six treatments comprising seed treatments (with and without Rhizobium), seed treatment (with and without PSB) along with the combination of 60 % dose of recommended quantity of Calcium Ammonium Nitrate and 75 % dose of recommended quantity of Single Super Phosphate and organic matter were evaluated in a Randomized Complete Block Design (RCBD) with three replications. The results revealed that T5 treatment, i.e. Rhizobium+ PSB+ Organic matter resulted in more number of pods per plant (20), pod length (18 cm) and pod yield/ha (140 q/ha)