1,785 research outputs found
Error Free Perfect Secrecy Systems
Shannon's fundamental bound for perfect secrecy says that the entropy of the
secret message cannot be larger than the entropy of the secret key initially
shared by the sender and the legitimate receiver. Massey gave an information
theoretic proof of this result, however this proof does not require
independence of the key and ciphertext. By further assuming independence, we
obtain a tighter lower bound, namely that the key entropy is not less than the
logarithm of the message sample size in any cipher achieving perfect secrecy,
even if the source distribution is fixed. The same bound also applies to the
entropy of the ciphertext. The bounds still hold if the secret message has been
compressed before encryption.
This paper also illustrates that the lower bound only gives the minimum size
of the pre-shared secret key. When a cipher system is used multiple times, this
is no longer a reasonable measure for the portion of key consumed in each
round. Instead, this paper proposes and justifies a new measure for key
consumption rate. The existence of a fundamental tradeoff between the expected
key consumption and the number of channel uses for conveying a ciphertext is
shown. Optimal and nearly optimal secure codes are designed.Comment: Submitted to the IEEE Trans. Info. Theor
Root Cause Analysis Using Graph Representation of Constraints
When several quantitative variables are related through constraints and objectives, it can be difficult to understand why a certain quantity changes in magnitude after certain changes, or why a certain number seems larger or smaller than expected (as compared to a reference value). Large organizations which seek to optimize very large numbers of parameters to achieve constraints such as supply-demand matching face such problems, where the relationships between variables are controlled by a mixture of human processes and software algorithms. This disclosure describes scalable, flexible frameworks and searching techniques that improve transparency, e.g., enable root-cause analysis, for large families of variables. The techniques enable the understanding of the difference between two data-generating flows (versions) with comparable inputs, intermediate variables, and output variables
Immune-Mediated Drug Induced Liver Injury: A Multidisciplinary Approach
This thesis presents an approach to expose relationships between immune mediated drug induced liver injury (IMDILI) and the three-dimensional structural features of toxic drug molecules and their metabolites. The series of analyses test the hypothesis that drugs which produce similar patterns of toxicity interact with targets within common toxicological pathways and that activation of the underlying mechanisms depends on structural similarity among toxic molecules. Spontaneous adverse drug reaction (ADR) reports were used to identify cases of IMDILI. Network map tools were used to compare the known and predicted protein interactions with each of the probe drugs to explore the interactions that are common between the drugs. The IMDILI probe set was then used to develop a pharmacophore model which became the starting point for identifying potential toxicity targets for IMDILI. Pharmacophore screening results demonstrated similarities between the probe IMDILI set of drugs and Toll-Like Receptor 7 (TLR7) agonists, suggesting TLR7 as a potential toxicity target. This thesis highlights the potential for multidisciplinary approaches in the study of complex diseases. Such approaches are particularly helpful for rare diseases where little knowledge is available, and may provide key insights into mechanisms of toxicity that cannot be gleaned from a single disciplinary study
Immune-Mediated Drug Induced Liver Injury: A Multidisciplinary Approach
This thesis presents an approach to expose relationships between immune mediated drug induced liver injury (IMDILI) and the three-dimensional structural features of toxic drug molecules and their metabolites. The series of analyses test the hypothesis that drugs which produce similar patterns of toxicity interact with targets within common toxicological pathways and that activation of the underlying mechanisms depends on structural similarity among toxic molecules. Spontaneous adverse drug reaction (ADR) reports were used to identify cases of IMDILI. Network map tools were used to compare the known and predicted protein interactions with each of the probe drugs to explore the interactions that are common between the drugs. The IMDILI probe set was then used to develop a pharmacophore model which became the starting point for identifying potential toxicity targets for IMDILI. Pharmacophore screening results demonstrated similarities between the probe IMDILI set of drugs and Toll-Like Receptor 7 (TLR7) agonists, suggesting TLR7 as a potential toxicity target. This thesis highlights the potential for multidisciplinary approaches in the study of complex diseases. Such approaches are particularly helpful for rare diseases where little knowledge is available, and may provide key insights into mechanisms of toxicity that cannot be gleaned from a single disciplinary study
Capacity Analysis of Linear Operator Channels over Finite Fields
Motivated by communication through a network employing linear network coding,
capacities of linear operator channels (LOCs) with arbitrarily distributed
transfer matrices over finite fields are studied. Both the Shannon capacity
and the subspace coding capacity are analyzed. By establishing
and comparing lower bounds on and upper bounds on , various
necessary conditions and sufficient conditions such that are
obtained. A new class of LOCs such that is identified, which
includes LOCs with uniform-given-rank transfer matrices as special cases. It is
also demonstrated that is strictly less than for a broad
class of LOCs. In general, an optimal subspace coding scheme is difficult to
find because it requires to solve the maximization of a non-concave function.
However, for a LOC with a unique subspace degradation, can be
obtained by solving a convex optimization problem over rank distribution.
Classes of LOCs with a unique subspace degradation are characterized. Since
LOCs with uniform-given-rank transfer matrices have unique subspace
degradations, some existing results on LOCs with uniform-given-rank transfer
matrices are explained from a more general way.Comment: To appear in IEEE Transactions on Information Theor
Teletext data change detection and noiseless data compression
Full Channel teletext system is a high speed data broadcasting system. Pages of information are broadcast in a cyclic manner. The detection of data change in the information pages is necessary for data analysis, database update and retransmission. Lossless data compression is also necessary to enhance the data throughput in rebroadcasting and to reduce the storage requirement. Performing data change detection and data compression in real time using a software approach in a small machine is impossible for such high speed data. In this paper, we describe the algorithms that are suitable for hardware implementation for both data change detection and noiseless data compression.published_or_final_versio
Real time teletext broadcast system performance enhancement using ghost rows
Teletext is a one way broadcast system which provides a variety of services to its users. A one way broadcast system is attractive because a single transmission of a piece of information satisfies all potential users of that information. The main drawback of these types of systems is that a user must wait until the required information arrives. Therefore, the access time is an important consideration in the design of broadcast teletext systems. In addition, another important factor, information update delay, is very significant in real time broadcast teletext. The shortening of this delay is important.for real time applications especially for systems broadcasting financial information. In this paper, we propose to use the ghost rows together with the storage capabilities of modern terminals to shorten both the access time and the update delay. A queueing model is developed to analyse the performance of this new system. ©1997 IEEE.published_or_final_versio
On Linear Operator Channels over Finite Fields
Motivated by linear network coding, communication channels perform linear
operation over finite fields, namely linear operator channels (LOCs), are
studied in this paper. For such a channel, its output vector is a linear
transform of its input vector, and the transformation matrix is randomly and
independently generated. The transformation matrix is assumed to remain
constant for every T input vectors and to be unknown to both the transmitter
and the receiver. There are NO constraints on the distribution of the
transformation matrix and the field size.
Specifically, the optimality of subspace coding over LOCs is investigated. A
lower bound on the maximum achievable rate of subspace coding is obtained and
it is shown to be tight for some cases. The maximum achievable rate of
constant-dimensional subspace coding is characterized and the loss of rate
incurred by using constant-dimensional subspace coding is insignificant.
The maximum achievable rate of channel training is close to the lower bound
on the maximum achievable rate of subspace coding. Two coding approaches based
on channel training are proposed and their performances are evaluated. Our
first approach makes use of rank-metric codes and its optimality depends on the
existence of maximum rank distance codes. Our second approach applies linear
coding and it can achieve the maximum achievable rate of channel training. Our
code designs require only the knowledge of the expectation of the rank of the
transformation matrix. The second scheme can also be realized ratelessly
without a priori knowledge of the channel statistics.Comment: 53 pages, 3 figures, submitted to IEEE Transaction on Information
Theor
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