The Experts below are selected from a list of 214809 Experts worldwide ranked by ideXlab platform
Yann Guédon - One of the best experts on this subject based on the ideXlab platform.
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Localizing the latent structure canonical uncertainty: entropy profiles for hidden Markov models
Statistics and Computing, 2016Co-Authors: Jean-baptiste Durand, Yann GuédonAbstract:This paper addresses State inference for hidden Markov models. These models rely on unobserved States, which often have a meaningful interpretation. This makes it necessary to develop diagnostic tools for quantification of State uncertainty. The entropy of the State Sequence that explains an observed Sequence for a given hidden Markov chain model can be considered as the canonical measure of State Sequence uncertainty. This canonical measure of State Sequence uncertainty is not reflected by the classic multidimensional posterior State (or smoothed) probability profiles because of the marginalization that is intrinsic in the computation of these posterior probabilities. Here, we introduce a new type of profiles that have the following properties: (i) these profiles of conditional entropies are a decomposition of the canonical measure of State Sequence uncertainty along the Sequence and makes it possible to localise this uncertainty, (ii) these profiles are unidimensional and thus remain easily interpretable on tree structures. We show how to extend the smoothing algorithms for hidden Markov chain and tree models to compute these entropy profiles efficiently. The use of entropy profiles is illustrated by Sequence and tree data examples.
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Localizing the Latent Structure Canonical Uncertainty: Entropy Profiles for Hidden Markov Models
2012Co-Authors: Jean-baptiste Durand, Yann GuédonAbstract:This report addresses State inference for hidden Markov models. These models rely on unobserved States, which often have a meaningful interpretation. This makes it necessary to develop diagnostic tools for quantification of State uncertainty. The entropy of the State Sequence that explains an observed Sequence for a given hidden Markov chain model can be considered as the canonical measure of State Sequence uncertainty. This canonical measure of State Sequence uncertainty is not reflected by the classic multivariate State profiles computed by the smoothing algorithm, which summarizes the possible State Sequences. Here, we introduce a new type of profiles which have the following properties: (i) these profiles of conditional entropies are a decomposition of the canonical measure of State Sequence uncertainty along the Sequence and makes it possible to localize this uncertainty, (ii) these profiles are univariate and thus remain easily interpretable on tree structures. We show how to extend the smoothing algorithms for hidden Markov chain and tree models to compute these entropy profiles efficiently.
Georg Lausen - One of the best experts on this subject based on the ideXlab platform.
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well founded semantics for deductive object oriented database languages
International Conference on Deductive and Object-Oriented Databases, 1997Co-Authors: Wolfgang May, Bertram Ludascher, Georg LausenAbstract:We present a well-founded semantics for deductive object-oriented database (dood) languages by applying the alternating-fixpoint characterization of the well-founded model to them. In order to compute the State Sequence, States are explicitly integrated by making them first-class citizens of the underlying language. The concept is applied to Florid, an implementation of F-Logic, previously supporting only inflationary negation. Using our approach, well-founded models of F-Logic programs can be computed.
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well founded semantics for deductive object oriented database languages
Lecture Notes in Computer Science, 1997Co-Authors: Wolfgang May, Bertram Ludascher, Georg LausenAbstract:We present a well-founded semantics for deductive object-oriented database (dood) languages by applying the alternating-fixpoint characterization of the well-founded model to them. In order to compute the State Sequence, States are explicitly integrated by making them first-class citizens of the underlying language. The concept is applied to FLORID, an implementation of F-Logic, previously supporting only inflationary negation. Using our approach, well-founded models of F-Logic programs can be computed. The method is also applicable to arbitrary dood languages which provide a sufficiently flexible syntax and semantics. Given an implementation of the underlying database language, any program given in this language can be evaluated wrt. the well-founded semantics.
Ashish Khisti - One of the best experts on this subject based on the ideXlab platform.
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State-dependent Gaussian Z-channel with mismatched side-information and interference
2013 IEEE Information Theory Workshop (ITW), 2013Co-Authors: Ruchen Duan, Ashish Khisti, Yingbin Liang, Shlomo Shamai ShitzAbstract:A State-dependent Gaussian Z-interference channel model is investigated in the regime of high State power, in which transmitters 1 and 2 communicate with receivers 1 and 2, and only receiver 2 is interfered by transmitter 1's signal and a random State Sequence. The State Sequence is known noncausally only to transmitter 1, not to the corresponding transmitter 2. A layered coding scheme is designed for transmitter 1 to help interference cancelation at receiver 2 (using a cognitive dirty paper coding) and to transmit its own message to receiver 1. Inner and outer bounds are derived, and are further analyzed to characterize the boundary of the capacity region either fully or partially for all Gaussian channel parameters. Our results imply that the capacity region of such a channel with mismatched transmitter-side State cognition and receiver-side State interference is strictly less than that of the corresponding channel without State, which is in contrast to Costa type of dirty channels, for which dirty paper coding achieves the capacity of the corresponding channels without State.
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on modulo sum computation over an erasure multiple access channel
arXiv: Information Theory, 2012Co-Authors: Ashish Khisti, Brett Hern, Krishna R NarayananAbstract:We study computation of a modulo-sum of two binary source Sequences over a two-user erasure multiple access channel. The channel is modeled as a binary-input, erasure multiple access channel, which can be in one of three States - either the channel output is a modulo-sum of the two input symbols, or the channel output equals the input symbol on the first link and an erasure on the second link, or vice versa. The associated State Sequence is independent and identically distributed. We develop a new upper bound on the sum-rate by revealing only part of the State Sequence to the transmitters. Our coding scheme is based on the compute and forward and the decode and forward techniques. When a (strictly) causal feedback of the channel State is available to the encoders, we show that the modulo-sum capacity is increased. Extensions to the case of lossy reconstruction of the modulo-sum and to channels involving additional States are also treated briefly.
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secret key agreement on wiretap channels with transmitter side information
arXiv: Information Theory, 2010Co-Authors: Ashish KhistiAbstract:Secret-key agreement protocols over wiretap channels controlled by a State parameter are studied. The entire State Sequence is known (non-causally) to the sender but not to the receiver and the eavesdropper. Upper and lower bounds on the secret-key capacity are established both with and without public discussion. The proposed coding scheme involves constructing a codebook to create common reconstruction of the State Sequence at the sender and the receiver and another secret-key codebook constructed by random binning. For the special case of Gaussian channels, with no public discussion, - the secret-key generation with dirty paper problem, the gap between our bounds is at-most 1/2 bit and the bounds coincide in the high signal-to-noise ratio and high interference-to-noise ratio regimes. In the presence of public discussion our bounds coincide, yielding the capacity, when then the channels of the receiver and the eavesdropper satisfy an in- dependent noise condition.
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secret key agreement on wiretap channel with transmitter side information
European Wireless Conference, 2010Co-Authors: Ashish KhistiAbstract:The secret-key agreement problem over wiretap channels controlled by a State parameter is studied. The entire State Sequence is known (non-causally) to the sender but not to the receiver and the eavesdropper. Upper and lower bounds on the secret-key capacity are established both with and without public discussion. In absence of public-discussion, our lower bound is strictly better than the best known lower bound for transmitting an independent secret message. This illustrates that the coding schemes for secret-key agreement are substantially different than secret-message transmission. For the special case of Gaussian channels, we establish the secret-key capacity when the legitimate receiver's signal-to-noise-ratio is greater than 0 dB. When a public discussion channel is available between the sender and the receiver, upper and lower bounds on the secret-key capacity are again established. These bounds coincide, yielding the capacity, when then the channels of the receiver and the eavesdropper satisfy an independent noise condition.
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secret key agreement using asymmetry in channel State knowledge
International Symposium on Information Theory, 2009Co-Authors: Ashish Khisti, Suhas Diggavi, Gregory W WornellAbstract:We study secret-key agreement protocols over a wiretap channel controlled by a State parameter. The secret-key capacity is established when the wiretap channel is discrete and memoryless, the sender and receiver are both revealed the underlying State parameter, and no public discussion is allowed. An optimal coding scheme involves a two step approach — (i) design a wiretap codebook assuming that the State parameter is also known to the eavesdropper (ii) generate an additional secret key by exploiting the uncertainty of the State parameter at the eavesdropper. When unlimited public discussion is allowed between the legitimate terminals, we provide an upper bound on the secret-key capacity and establish its tightness when the channel outputs of the legitimate receiver and eavesdropper satisfy a conditional independence property. Numerical results for an on-off fading model suggest that the proposed coding schemes significantly outperform naive schemes that either disregard the contribution of the common State Sequence or the contribution of the underlying channel.
Shlomo Shamai Shitz - One of the best experts on this subject based on the ideXlab platform.
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on capacity of the writing onto fast fading dirt channel
IEEE Transactions on Wireless Communications, 2018Co-Authors: Stefano Rini, Shlomo Shamai ShitzAbstract:The Writing onto Fast Fading Dirt (WFFD) channel is investigated to study the effect of partial channel knowledge on the performance of interference pre-cancellation. The WFFD channel is the Gel’fand-Pinsker channel in which the channel output is the sum of the channel input, white Gaussian noise, and a fading-times-State term. The fading-times-State term is obtained as the product of the channel State Sequence, known only at the transmitter, and a fast fading process, known only at the receiver. We consider the case of Gaussian-distributed channel States and derive an approximate characterization of capacity for different classes of fading distributions, both continuous and discrete. In particular, we prove that if the fading distribution concentrates in a sufficiently small interval, then capacity is approximately equal to the AWGN capacity times the probability of such interval. We also show that there exists a class of fading distributions for which having the transmitter treat the fading-times-State term as additional noise closely approaches capacity.
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on the capacity of the dirty paper channel with fast fading and discrete channel States
arXiv: Information Theory, 2016Co-Authors: Stefano Rini, Shlomo Shamai ShitzAbstract:The "writing dirty paper" capacity result crucially dependents on the perfect channel knowledge at the transmitter as the presence of even a small uncertainty in the channel realization gravely hampers the ability of the transmitter to pre-code its transmission against the channel State. This is particularly disappointing as it implies that interference pre-coding in practical systems is effective only when the channel estimates at the users have very high precision, a condition which is generally unattainable in wireless environments. In this paper we show that substantial improvements are possible when the State Sequence is drawn from a discrete distribution, such as a constrained input constellation, for which State decoding can be approximately optimal. We consider the "writing on dirty paper" channel in which the State Sequence is multiplied by a fast fading process and derive conditions on the fading and State distributions for which State decoding closely approaches capacity. These conditions intuitively relate to the ability of the receiver to correctly identify both the input and the State realization despite of the uncertainty introduced by fading.
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State-dependent Gaussian Z-channel with mismatched side-information and interference
2013 IEEE Information Theory Workshop (ITW), 2013Co-Authors: Ruchen Duan, Ashish Khisti, Yingbin Liang, Shlomo Shamai ShitzAbstract:A State-dependent Gaussian Z-interference channel model is investigated in the regime of high State power, in which transmitters 1 and 2 communicate with receivers 1 and 2, and only receiver 2 is interfered by transmitter 1's signal and a random State Sequence. The State Sequence is known noncausally only to transmitter 1, not to the corresponding transmitter 2. A layered coding scheme is designed for transmitter 1 to help interference cancelation at receiver 2 (using a cognitive dirty paper coding) and to transmit its own message to receiver 1. Inner and outer bounds are derived, and are further analyzed to characterize the boundary of the capacity region either fully or partially for all Gaussian channel parameters. Our results imply that the capacity region of such a channel with mismatched transmitter-side State cognition and receiver-side State interference is strictly less than that of the corresponding channel without State, which is in contrast to Costa type of dirty channels, for which dirty paper coding achieves the capacity of the corresponding channels without State.
Jean-baptiste Durand - One of the best experts on this subject based on the ideXlab platform.
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Localizing the latent structure canonical uncertainty: entropy profiles for hidden Markov models
Statistics and Computing, 2016Co-Authors: Jean-baptiste Durand, Yann GuédonAbstract:This paper addresses State inference for hidden Markov models. These models rely on unobserved States, which often have a meaningful interpretation. This makes it necessary to develop diagnostic tools for quantification of State uncertainty. The entropy of the State Sequence that explains an observed Sequence for a given hidden Markov chain model can be considered as the canonical measure of State Sequence uncertainty. This canonical measure of State Sequence uncertainty is not reflected by the classic multidimensional posterior State (or smoothed) probability profiles because of the marginalization that is intrinsic in the computation of these posterior probabilities. Here, we introduce a new type of profiles that have the following properties: (i) these profiles of conditional entropies are a decomposition of the canonical measure of State Sequence uncertainty along the Sequence and makes it possible to localise this uncertainty, (ii) these profiles are unidimensional and thus remain easily interpretable on tree structures. We show how to extend the smoothing algorithms for hidden Markov chain and tree models to compute these entropy profiles efficiently. The use of entropy profiles is illustrated by Sequence and tree data examples.
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Localizing the Latent Structure Canonical Uncertainty: Entropy Profiles for Hidden Markov Models
2012Co-Authors: Jean-baptiste Durand, Yann GuédonAbstract:This report addresses State inference for hidden Markov models. These models rely on unobserved States, which often have a meaningful interpretation. This makes it necessary to develop diagnostic tools for quantification of State uncertainty. The entropy of the State Sequence that explains an observed Sequence for a given hidden Markov chain model can be considered as the canonical measure of State Sequence uncertainty. This canonical measure of State Sequence uncertainty is not reflected by the classic multivariate State profiles computed by the smoothing algorithm, which summarizes the possible State Sequences. Here, we introduce a new type of profiles which have the following properties: (i) these profiles of conditional entropies are a decomposition of the canonical measure of State Sequence uncertainty along the Sequence and makes it possible to localize this uncertainty, (ii) these profiles are univariate and thus remain easily interpretable on tree structures. We show how to extend the smoothing algorithms for hidden Markov chain and tree models to compute these entropy profiles efficiently.
