Computer-aided diagnosis and prediction of the severity of lung diseases is a promising way to help overburdened medical experts in accelerating and improving their diagnosis. The objective of this work is to investigate the use of deep learning techniques to design a framework for the automatic diagnosis of lung diseases along with the prediction of severity using chest radiographs. We identified input chest radiographs as healthy or belonging to patients with lung disease along with the confidence score of prediction. The unhealthy chest radiograph is further examined to calculate clinical parameters considered in the severity prediction of lung diseases. We calculate clinical parameters such as the extent of lung involvement in disease manifestation, the type of abnormalities present in chest radiographs, and their location in terms of lung zones. We conduct experiments with our in-house Indian database and achieved an accuracy of 95.65% in the classification between healthy and unhealthy chest radiographs. We obtained average precision scores of 0.8128, 1.00, 0.8214, and 0.9650 for the detection of effusion, cavity, lymphadenopathy, and opacity respectively. Experimental results indicated that the proposed framework can be used to provide rapid and cost-effective screening in places where massive traditional testing is not feasible.

This article consists only of a collection of slides from the author's conference presentation.

Cyber-physical control systems involve a discrete computational algorithm to control continuous physical systems. Often the control algorithm uses predictive models of the physical system in its decision making process. However, physical system models suffer from several inaccuracies when employed in practice. Mitigating such inaccuracies is often difficult and have to be repeated for different instances of the physical system. In this paper, we propose a model guided deep learning method for extraction of accurate prediction models of physical systems, in presence of artifacts observed in real life deployments. Given an initial potentially suboptimal mathematical prediction model, our model guided deep learning method iteratively improves the model through a data driven training approach. We apply the proposed approach on the closed loop blood glucose control system. Using this proposed approach, we achieve an improvement over predictive Bergman Minimal Model by a factor of around 100.

Switches today provide a small menu of scheduling algorithms. While we can tweak scheduling parameters, we cannot modify algorithmic logic, or add a completely new algorithm, after the switch has been designed. This paper presents a design for a {\em programmable} packet scheduler, which allows scheduling algorithms---potentially algorithms that are unknown today---to be programmed into a switch without requiring hardware redesign.

Packet scheduling in switches is not programmable; operators only choose among a handful of scheduling algorithms implemented by the manufacturer. In contrast, other switch functions such as packet parsing and header processing are becoming programmable [10, 3, 6]. This paper presents a programmable packet scheduler that allows operators to program a variety of scheduling algorithms.

We study the noncoherent capacity of secret-key agreement with public discussion over independent identically distributed (i.i.d.) Rayleigh fading wireless channels, where neither the sender nor the receivers have access to instantaneous channel state information (CSI). We present two results. At high signal-to-noise ratio (SNR), the secret-key capacity is bounded in SNR, regardless of the number of antennas at each terminal. Second, for a system with a single antenna at both the legitimate and the eavesdropper terminals and an arbitrary number of transmit antennas, the secret-key capacity-achieving input distribution is discrete, with a finite number of mass points. Numerically we observe that at low SNR, the capacity achieving distribution has two mass points with one of them at the origin.

Exception handling is widely regarded as a necessity in programming languages today and almost every programming language currently used for professional software development supports some form of it. However, spreadsheet systems, which may be the most widely used type of "programming language" today in terms of number of users using it to create "programs" (spreadsheets), have traditionally had only extremely limited support for exception handling. Spreadsheet system users range from end users to professional programmers and this wide range suggests that an approach to exception handling for spreadsheet systems needs to be compatible with the equational reasoning model of spreadsheet formulas, yet feature expressive power comparable to that found in other programming languages. We present an approach to exception handling for spreadsheet system users that is aimed at this goal. Some of the features of the approach are new; others are not new, but their effects on the programming language properties of spreadsheet systems have not been discussed before in the literature. We explore these properties, offer our solutions to problems that arise with these properties, and compare the functionality of the approach with that of exception handling approaches in other languages.