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- A. E. Islam, E. N. Kumar, +6 authors M. A. Alam
- 2007

On-the-fly and Ultra-fast V T are popular characterization techniques for analyzing NBTI degradation. We show that these techniques do not probe the intrinsic NBTI degradation directly and hence require suitable correction. The 'corrected' data allows us to explore the subtlety of relaxation dynamics by various measurements and suggest a theoretical basis… (More)

We test whether two sequences are generated by the same distribution or by two different ones. Unlike previous work, we make no assumptions on the distributions' support size. Additionally, we compare our performance to that of the best possible test. We describe an efficiently-computable algorithm based on pattern maximum likelihood that is near optimal… (More)

- Hirakendu Das, Alexander Vardy
- 2009 IEEE International Symposium on Information…
- 2009

The probability of error in the Koetter-Vardy algebraic soft-decoding algorithm for Reed-Solomon codes is determined by the multiplicity assignment scheme used. A multiplicity assignment scheme converts the reliability matrix Π, consisting of the probabilities observed at the channel output, into a multiplicity matrix M that specifies the algebraic… (More)

We study the problems of classification and closeness testing. A classifier associates a test sequence with the one of two training sequences that was generated by the same distribution. A closeness test determines whether two sequences were generated by the same or by different distributions. For both problems all natural algorithms are symmetric—they make… (More)

- Jayadev Acharya, Hirakendu Das, Ashkan Jafarpour, Alon Orlitsky, Ananda Theertha Suresh
- 2013 IEEE International Symposium on Information…
- 2013

Over the past decade, several papers, e.g., [1-7] and references therein, have considered universal compression of sources over large alphabets, often using patterns to avoid infinite redundancy. Improving on previous results, we prove tight bounds on expected- and worst-case pattern redundancy, in particular closing a decade-long gap and showing that the… (More)

- Jayadev Acharya, Hirakendu Das, Alon Orlitsky
- NIPS
- 2012

The minimax KL-divergence of any distribution from all distributions in a collection P has several practical implications. In compression, it is called redundancy and represents the least additional number of bits over the entropy needed to encode the output of any distribution in P. In online estimation and learning, it is the lowest expected log-loss… (More)

- Jayadev Acharya, Hirakendu Das, Hosein Mohimani, Alon Orlitsky, Shengjun Pan
- 2010 IEEE International Symposium on Information…
- 2010

We describe two algorithms for calculating the probability of m-symbol length-n patterns over k-element distributions, a partition-based algorithm with complexity roughly 2<sup>O(m log m)</sup> and a recursive algorithm with complexity roughly 2<sup>O(m+log n)</sup> with the precise bounds provided in the text. The problem is related to symmetric-polynomial… (More)

- Jayadev Acharya, Hirakendu Das, Olgica Milenkovic, Alon Orlitsky, Shengjun Pan
- 2010 IEEE International Symposium on Information…
- 2010

Motivated by protein sequencing, we consider the problem of reconstructing a string from the compositions of its substrings. We provide several results, including the following. General classes of strings that cannot be distinguished from their substring compositions. An almost complete characterization of the lengths for which reconstruction is possible.… (More)

- Jayadev Acharya, Hirakendu Das, Olgica Milenkovic, Alon Orlitsky, Shengjun Pan
- 2014 IEEE International Symposium on Information…
- 2014

Motivated by the problem of deducing the structure of proteins using mass-spectrometry, we study the reconstruction of a string from the multiset of its substring compositions. We specialize the backtracking algorithm used for the more general turnpike problem for string reconstruction. Employing well known results about transience of random walks in… (More)

- Jayadev Acharya, Hirakendu Das, Alon Orlitsky, Shengjun Pan, Narayana P. Santhanam
- 2010 IEEE International Symposium on Information…
- 2010

We consider the problem of classification, where the data of the classes are generated i.i.d. according to unknown probability distributions. The goal is to classify test data with minimum error probability, based on the training data available for the classes. The Likelihood Ratio Test (LRT) is the optimal decision rule when the distributions are known.… (More)