Dynamic Information Flow Analysis in Ruby

With the rapid increase in usage of the internet and online applications, there is a huge demand for applications to handle data privacy and integrity. Applications are already complex with business logic; adding the data safety logic would make them more complicated. The more complex the code becomes, the more possibilities it opens for security-critical bugs. To solve this conundrum, we can push this data safety handling feature to the language level rather than the application level. With a secure language, developers can write their application without having to worry about data security. This project introduces dynamic information flow analysis in Ruby. I extend the JRuby implementation, which is a widely used implementation of Ruby written in Java. Information flow analysis classifies variables used in the program into different security levels and monitors the data flow across levels. Ruby currently supports data integrity by a tainting mechanism. This project extends this tainting mechanism to handle implicit data flows, enabling it to protect confidentiality as well as integrity. Experimental results based on Ruby benchmarks are presented in this paper, which show that: This project protects confidentiality but at the cost of 1.2 - 10 times slowdown in execution time

Fast Algorithms for Displacement and Low-Rank Structured Matrices

This tutorial provides an introduction to the development of fast matrix algorithms based on the notions of displacement and various low-rank structures

Short Data Gap Filling Algorithm Prototype

This design note describes an algorithm to fill short data gaps encountered in pixel or flux time series using auto-regressive modeling techniques

KAUTILYA: POLITICS, ETHICS AND STATECRAFT

Kautilya was the minister in the Kingdom of Chandragupta Maurya during 317 – 293 B.C. He has been considered as one of the shrewdest ministers of the times and has explained his views on State, War, Social Structures, Diplomacy, Ethics, Politics and Statecraft very clearly in his book called Arthashastra . The Mauryan Empire was larger than the later British India which expanded from the Indian Ocean to Himalayas and upto to Iran in the West. After Alexander left India, this was the most powerful kingdom in India and Kautilya was minister who advised the King. Before Kautilya there were other philosophers in India who composed the Shastras but his work was robust and encompassed all the treaties written earlier. I considered Kautilya for three reasons. Firstly, I wanted to highlight the patterns of thinking in the east which was present long before Machiavelli wrote his “Prince”. Secondly Kautilya’s ideologies on state, statecraft and ethics are very realistic and vastly applicable in today’s context. Thirdly, I feel Kautilya’s work on diplomacy is greatly underrepresented in the western world and it is quite apt to analyze his work in that area. If we compare statesman on the four dimension framework of: War & Peace, Human Rights, International Economic Justice and World Order Kautilya had a strong opinion on all the four aspects. In fact people like Bismark and Woodrow Wilson in recent history had been able to demonstrate their views only on two of the four dimensions. Kautilya’s work is primarily a book of political realism where State is paramount and King shall carry out duties as advised in his book to preserve his state. Kautilya’s work is so deep rooted in realism that he goes to describe the gory and brutal means a King must adopt to be in power. This could have been one reason why Ashoka, the grandson of Chandragupta Maurya whom Kautilya advised renounced violence and war thus taking the path of Dharma or Morals. In this paper, I shall primarily focus on Kautilya’s thoughts on war, diplomacy and ethics. I have devoted a section to compare Kautilya with great philosophers like Plato and later ponder over why Machiavelli’s work looks so abridged and succinct in comparison to Kautilya’s work. Kautilya’s work is then seen in the light of today’s politics and ethics. As Max Weber put it aptly in his lecture, “Politics as a Vocation”, he said Machiavelli’s work was harmless when compared to Kautilya’s Arthashastra.

Finding a most biased coin with fewest flips

We study the problem of learning a most biased coin among a set of coins by tossing the coins adaptively. The goal is to minimize the number of tosses until we identify a coin i* whose posterior probability of being most biased is at least 1-delta for a given delta. Under a particular probabilistic model, we give an optimal algorithm, i.e., an algorithm that minimizes the expected number of future tosses. The problem is closely related to finding the best arm in the multi-armed bandit problem using adaptive strategies. Our algorithm employs an optimal adaptive strategy -- a strategy that performs the best possible action at each step after observing the outcomes of all previous coin tosses. Consequently, our algorithm is also optimal for any starting history of outcomes. To our knowledge, this is the first algorithm that employs an optimal adaptive strategy under a Bayesian setting for this problem. Our proof of optimality employs tools from the field of Markov games

High performance FPGA implementation of the mersenne twister

Efficient generation of random and pseudorandom sequences is of great importance to a number of applications [4]. In this paper, an efficient implementation of the Mersenne Twister is presented. The proposed architecture has the smallest footprint of all published architectures to date and occupies only 330 FPGA slices. Partial pipelining and sub-expression simplification has been used to improve throughput per clock cycle. The proposed architecture is implemented on an RC1000 FPGA Development platform equipped with a Xilinx XCV2000E FPGA, and can generate 20 million 32 bit random numbers per second at a clock rate of 24.234 MHz. A through performance analysis has been performed, and it is observed that the proposed architecture clearly outperforms other existing implementations in key comparable performance metrics

RETROSPECTIVE ANALYSIS OF DYSHORMONGENETIC GOITRE

Dyshormonogenetic goitre is a rare thyroid entity which occurs due to enzymatic deficiency in the physiological process of thyroxin synthesis resulting in goitre formation. This has to be differentiated from iodine deficiency goitres for their similarity in clinical presentation, hormonal profile and on scintigraphy studies. This differentiation is vital for the reason that Dyshormonogenetic goitre (DHGG) needs to be treated with thyroxin while Iodine deficiency disorder (IDD) requires simple dietary iodine supplementation. 

Relative Entropy Relaxations for Signomial Optimization

Signomial programs (SPs) are optimization problems specified in terms of signomials, which are weighted sums of exponentials composed with linear functionals of a decision variable. SPs are non-convex optimization problems in general, and families of NP-hard problems can be reduced to SPs. In this paper we describe a hierarchy of convex relaxations to obtain successively tighter lower bounds of the optimal value of SPs. This sequence of lower bounds is computed by solving increasingly larger-sized relative entropy optimization problems, which are convex programs specified in terms of linear and relative entropy functions. Our approach relies crucially on the observation that the relative entropy function -- by virtue of its joint convexity with respect to both arguments -- provides a convex parametrization of certain sets of globally nonnegative signomials with efficiently computable nonnegativity certificates via the arithmetic-geometric-mean inequality. By appealing to representation theorems from real algebraic geometry, we show that our sequences of lower bounds converge to the global optima for broad classes of SPs. Finally, we also demonstrate the effectiveness of our methods via numerical experiments

Sufficient Dimension Reduction and Modeling Responses Conditioned on Covariates: An Integrated Approach via Convex Optimization

Given observations of a collection of covariates and responses $(Y, X) \in \mathbb{R}^p \times \mathbb{R}^q$, sufficient dimension reduction (SDR) techniques aim to identify a mapping $f: \mathbb{R}^q \rightarrow \mathbb{R}^k$ with $k \ll q$ such that $Y|f(X)$ is independent of $X$. The image $f(X)$ summarizes the relevant information in a potentially large number of covariates $X$ that influence the responses $Y$. In many contemporary settings, the number of responses $p$ is also quite large, in addition to a large number $q$ of covariates. This leads to the challenge of fitting a succinctly parameterized statistical model to $Y|f(X)$, which is a problem that is usually not addressed in a traditional SDR framework. In this paper, we present a computationally tractable convex relaxation based estimator for simultaneously (a) identifying a linear dimension reduction $f(X)$ of the covariates that is sufficient with respect to the responses, and (b) fitting several types of structured low-dimensional models -- factor models, graphical models, latent-variable graphical models -- to the conditional distribution of $Y|f(X)$. We analyze the consistency properties of our estimator in a high-dimensional scaling regime. We also illustrate the performance of our approach on a newsgroup dataset and on a dataset consisting of financial asset prices.Comment: 34 pages, 1 figur

openEyeTrack - a high speed multi-threaded eye tracker for head-fixed applications

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