Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks

Over the last decade, Convolutional Neural Network (CNN) models have been highly successful in solving complex vision problems. However, these deep models are perceived as "black box" methods considering the lack of understanding of their internal functioning. There has been a significant recent interest in developing explainable deep learning models, and this paper is an effort in this direction. Building on a recently proposed method called Grad-CAM, we propose a generalized method called Grad-CAM++ that can provide better visual explanations of CNN model predictions, in terms of better object localization as well as explaining occurrences of multiple object instances in a single image, when compared to state-of-the-art. We provide a mathematical derivation for the proposed method, which uses a weighted combination of the positive partial derivatives of the last convolutional layer feature maps with respect to a specific class score as weights to generate a visual explanation for the corresponding class label. Our extensive experiments and evaluations, both subjective and objective, on standard datasets showed that Grad-CAM++ provides promising human-interpretable visual explanations for a given CNN architecture across multiple tasks including classification, image caption generation and 3D action recognition; as well as in new settings such as knowledge distillation.Comment: 17 Pages, 15 Figures, 11 Tables. Accepted in the proceedings of IEEE Winter Conf. on Applications of Computer Vision (WACV2018). Extended version is under review at IEEE Transactions on Pattern Analysis and Machine Intelligenc

Delay analysis of the IEEE 802.11bd EDCA with repetitions

We analyse the performance of the IEEE 802.11bd MAC protocol, with Enhanced Distributed Channel Access (EDCA) and repeated transmissions, in terms of the MAC access delay of packets pertaining to safety-related events. We outline Markov chain models for the contention mechanism of priority-based access categories, and derive the associated steady-state probabilities. Using these probabilities, we characterise the delay experienced by the packet in the MAC layer. Further, we characterise the reliability of the protocol in terms of the likelihood that a packet is delivered within a critical time interval. Numerical computations are conducted to understand the impact of various system parameters on the MAC access delay. The analysis indicates that the MAC access delay depends on various system parameters, some of which are influenced by the traffic scenario and nature of safety-critical events. Motivated by this, we used our analysis to study the delay and reliability of the 802.11bd MAC protocol specific to the context of platooning of connected vehicles subject to interruptions by human-driven motorised two wheelers. We observe that while the delay performance of the protocol is as per the QoS requirements of the standard, the protocol may not be reliable for this specific application. Our study suggests that it is desirable to co-design vehicular communication protocols with prevalent safety-related traffic applications

Flow Shape Design for Microfluidic Devices Using Deep Reinforcement Learning

Microfluidic devices are utilized to control and direct flow behavior in a wide variety of applications, particularly in medical diagnostics. A particularly popular form of microfluidics -- called inertial microfluidic flow sculpting -- involves placing a sequence of pillars to controllably deform an initial flow field into a desired one. Inertial flow sculpting can be formally defined as an inverse problem, where one identifies a sequence of pillars (chosen, with replacement, from a finite set of pillars, each of which produce a specific transformation) whose composite transformation results in a user-defined desired transformation. Endemic to most such problems in engineering, inverse problems are usually quite computationally intractable, with most traditional approaches based on search and optimization strategies. In this paper, we pose this inverse problem as a Reinforcement Learning (RL) problem. We train a DoubleDQN agent to learn from this environment. The results suggest that learning is possible using a DoubleDQN model with the success frequency reaching 90% in 200,000 episodes and the rewards converging. While most of the results are obtained by fixing a particular target flow shape to simplify the learning problem, we later demonstrate how to transfer the learning of an agent based on one target shape to another, i.e. from one design to another and thus be useful for a generic design of a flow shape.Comment: Neurips 2018 Deep RL worksho

Algebraic properties of binomial edge ideals of Levi graphs associated with curve arrangements

In this article, we study algebraic properties of binomial edge ideals associated with certain plane curve arrangements via their Levi graphs. Using combinatorial properties of the Levi graphs, we discuss the Cohen-Macaulayness of binomial edge ideals of some curve arrangements in the complex projective plane like the $d$-arrangement of curves and the conic-line arrangement. We also discuss the existence of certain induced cycles in the Levi graph of these arrangements and obtain lower bounds for the regularity of powers of the corresponding binomial edge ideals.Comment: 16 pages, comments and suggestions are welcom

Transactional politics and humanitarian crisis: lessons for policy from the political marketplace framework

This memo summarizes observations and conclusions from the Conflict Research Programme/World Peace Foundation research on the political marketplace framework and humanitarian crises. It draws upon seven country-specific case studies (Democratic Republic of Congo, Nigeria, Somalia, South Sudan, Sudan, Syria and Yemen) and on theoretical and cross-cutting analysis. Its primary focus is on food insecurity and famine, though other forms of humanitarian crisis (for instance, displacement) are also considered

A study on premenstrual syndrome among adolescent girl students in an urban area of West Bengal

Background: Previous studies in India reported a prevalence of premenstrual syndrome to be 20% in a general population and severe symptoms in 8%. The present study was conducted to study the socio-demographic characteristics of adolescent school girls, to estimate the proportion of premenstrual syndrome among them and to find out factors associated with premenstrual syndrome, if any.Methods: It was a cross-sectional descriptive study conducted at a Kolkata city. Data were collected from the students of Class IX to XII in the classroom using pre-tested pre-designed self-administered questionnaire. Total 278 students were included in the study. Data analysis was done with the help of SPSS version 20.0.Results: The mean age of the students was 15.61 years ± 1.3. 54% of girls reported to have PMS. Out of the affective symptoms in ACOG criteria depression was by 45.7%, anger by 61.2%, irritability by 88.1%, anxiety by 51.8%, confusion by 46.4%, rejection by 24.8, breast pain by 22.7, abdominal distension by 37.5%, headache by 40.6% and swelling of limbs by 5% of girls.Conclusions: Proper medical care and psychological counselling should be sought earlier for increased blood flow during menstruation and dysmenorrhoea to get rid of PMS in adolescent girls