id
stringlengths 9
16
| title
stringlengths 4
278
| categories
stringlengths 5
104
| abstract
stringlengths 6
4.09k
|
|---|---|---|---|
0903.3995
|
Gradient-based adaptive interpolation in super-resolution image
restoration
|
cs.MM cs.CV
|
This paper presents a super-resolution method based on gradient-based
adaptive interpolation. In this method, in addition to considering the distance
between the interpolated pixel and the neighboring valid pixel, the
interpolation coefficients take the local gradient of the original image into
account. The smaller the local gradient of a pixel is, the more influence it
should have on the interpolated pixel. And the interpolated high resolution
image is finally deblurred by the application of wiener filter. Experimental
results show that our proposed method not only substantially improves the
subjective and objective quality of restored images, especially enhances edges,
but also is robust to the registration error and has low computational
complexity.
|
0903.4014
|
Construction of Codes for Wiretap Channel and Secret Key Agreement from
Correlated Source Outputs by Using Sparse Matrices
|
cs.IT cs.CR math.IT
|
The aim of this paper is to prove coding theorems for the wiretap channel
coding problem and secret key agreement problem based on the the notion of a
hash property for an ensemble of functions. These theorems imply that codes
using sparse matrices can achieve the optimal rate. Furthermore, fixed-rate
universal coding theorems for a wiretap channel and a secret key agreement are
also proved.
|
0903.4035
|
BLOGRANK: Ranking Weblogs Based On Connectivity And Similarity Features
|
cs.IR
|
A large part of the hidden web resides in weblog servers. New content is
produced in a daily basis and the work of traditional search engines turns to
be insufficient due to the nature of weblogs. This work summarizes the
structure of the blogosphere and highlights the special features of weblogs. In
this paper we present a method for ranking weblogs based on the link graph and
on several similarity characteristics between weblogs. First we create an
enhanced graph of connected weblogs and add new types of edges and weights
utilising many weblog features. Then, we assign a ranking to each weblog using
our algorithm, BlogRank, which is a modified version of PageRank. For the
validation of our method we run experiments on a weblog dataset, which we
process and adapt to our search engine. (http://spiderwave.aueb.gr/Blogwave).
The results suggest that the use of the enhanced graph and the BlogRank
algorithm is preferred by the users.
|
0903.4036
|
Feedback control logic synthesis for non safe Petri nets
|
cs.IT math.IT
|
This paper addresses the problem of forbidden states of non safe Petri Net
(PN) modelling discrete events systems. To prevent the forbidden states, it is
possible to use conditions or predicates associated with transitions.
Generally, there are many forbidden states, thus many complex conditions are
associated with the transitions. A new idea for computing predicates in non
safe Petri nets will be presented. Using this method, we can construct a
maximally permissive controller if it exists.
|
0903.4101
|
Polylog space compression, pushdown compression, and Lempel-Ziv are
incomparable
|
cs.CC cs.IR
|
The pressing need for efficient compression schemes for XML documents has
recently been focused on stack computation, and in particular calls for a
formulation of information-lossless stack or pushdown compressors that allows a
formal analysis of their performance and a more ambitious use of the stack in
XML compression, where so far it is mainly connected to parsing mechanisms. In
this paper we introduce the model of pushdown compressor, based on pushdown
transducers that compute a single injective function while keeping the widest
generality regarding stack computation.
We also consider online compression algorithms that use at most
polylogarithmic space (plogon). These algorithms correspond to compressors in
the data stream model.
We compare the performance of these two families of compressors with each
other and with the general purpose Lempel-Ziv algorithm. This comparison is
made without any a priori assumption on the data's source and considering the
asymptotic compression ratio for infinite sequences. We prove that in all cases
they are incomparable.
|
0903.4128
|
Rate Adaptation via Link-Layer Feedback for Goodput Maximization over a
Time-Varying Channel
|
cs.IT cs.NI math.IT math.OC
|
We consider adapting the transmission rate to maximize the goodput, i.e., the
amount of data transmitted without error, over a continuous Markov flat-fading
wireless channel. In particular, we consider schemes in which transmitter
channel state is inferred from degraded causal error-rate feedback, such as
packet-level ACK/NAKs in an automatic repeat request (ARQ) system. In such
schemes, the choice of transmission rate affects not only the subsequent
goodput but also the subsequent feedback, implying that the optimal rate
schedule is given by a partially observable Markov decision process (POMDP).
Because solution of the POMDP is computationally impractical, we consider
simple suboptimal greedy rate assignment and show that the optimal scheme would
itself be greedy if the error-rate feedback was non-degraded. Furthermore, we
show that greedy rate assignment using non-degraded feedback yields a total
goodput that upper bounds that of optimal rate assignment using degraded
feedback. We then detail the implementation of the greedy scheme and propose a
reduced-complexity greedy scheme that adapts the transmission rate only once
per block of packets. We also investigate the performance of the schemes
numerically, and show that the proposed greedy scheme achieves steady-state
goodputs that are reasonably close to the upper bound on goodput calculated
using non-degraded feedback. A similar improvement is obtained in steady-state
goodput, drop rate, and average buffer occupancy in the presence of data
buffers. We also investigate an upper bound on the performance of optimal rate
assignment for a discrete approximation of the channel and show that such
quantization leads to a significant loss in achievable goodput.
|
0903.4132
|
Switcher-random-walks: a cognitive-inspired mechanism for network
exploration
|
cs.AI cond-mat.dis-nn physics.soc-ph
|
Semantic memory is the subsystem of human memory that stores knowledge of
concepts or meanings, as opposed to life specific experiences. The organization
of concepts within semantic memory can be understood as a semantic network,
where the concepts (nodes) are associated (linked) to others depending on
perceptions, similarities, etc. Lexical access is the complementary part of
this system and allows the retrieval of such organized knowledge. While
conceptual information is stored under certain underlying organization (and
thus gives rise to a specific topology), it is crucial to have an accurate
access to any of the information units, e.g. the concepts, for efficiently
retrieving semantic information for real-time needings. An example of an
information retrieval process occurs in verbal fluency tasks, and it is known
to involve two different mechanisms: -clustering-, or generating words within a
subcategory, and, when a subcategory is exhausted, -switching- to a new
subcategory. We extended this approach to random-walking on a network
(clustering) in combination to jumping (switching) to any node with certain
probability and derived its analytical expression based on Markov chains.
Results show that this dual mechanism contributes to optimize the exploration
of different network models in terms of the mean first passage time.
Additionally, this cognitive inspired dual mechanism opens a new framework to
better understand and evaluate exploration, propagation and transport phenomena
in other complex systems where switching-like phenomena are feasible.
|
0903.4207
|
MacWilliams Identities for Codes on Graphs
|
cs.IT math.IT
|
The MacWilliams identity for linear time-invariant convolutional codes that
has recently been found by Gluesing-Luerssen and Schneider is proved concisely,
and generalized to arbitrary group codes on graphs. A similar development
yields a short, transparent proof of the dual sum-product update rule.
|
0903.4217
|
Conditional Probability Tree Estimation Analysis and Algorithms
|
cs.LG cs.AI
|
We consider the problem of estimating the conditional probability of a label
in time $O(\log n)$, where $n$ is the number of possible labels. We analyze a
natural reduction of this problem to a set of binary regression problems
organized in a tree structure, proving a regret bound that scales with the
depth of the tree. Motivated by this analysis, we propose the first online
algorithm which provably constructs a logarithmic depth tree on the set of
labels to solve this problem. We test the algorithm empirically, showing that
it works succesfully on a dataset with roughly $10^6$ labels.
|
0903.4237
|
Projection-Forcing Multisets of Weight Changes
|
math.CO cs.IT math.IT
|
Let $F$ be a finite field. A multiset $S$ of integers is projection-forcing
if for every linear function $\phi : F^n \to F^m$ whose multiset of weight
changes is $S$, $\phi$ is a coordinate projection up to permutation and scaling
of entries. The MacWilliams Extension Theorem from coding theory says that $S =
\{0, 0, ..., 0\}$ is projection-forcing. We give a (super-polynomial) algorithm
to determine whether or not a given $S$ is projection-forcing. We also give a
condition that can be checked in polynomial time that implies that $S$ is
projection-forcing. This result is a generalization of the MacWilliams
Extension Theorem and work by the first author.
|
0903.4298
|
Design of Log-Map / Max-Log-Map Decoder
|
cs.IT math.IT
|
The process of turbo-code decoding starts with the formation of a posteriori
probabilities (APPs) for each data bit, which is followed by choosing the
data-bit value that corresponds to the maximum a posteriori (MAP) probability
for that data bit. Upon reception of a corrupted code-bit sequence, the process
of decision making with APPs allows the MAP algorithm to determine the most
likely information bit to have been transmitted at each bit time.
|
0903.4305
|
Evaluation d'une requete en SQL
|
cs.DB
|
The objective of this paper is to show how the interrogation processor
responds to SQL interrogation. The interrogation processor is split into two
parts. The first, called the interrogation compiler translates an SQL query
into a plan of physical execution. The second, called evaluation query runs the
execution plan.
|
0903.4386
|
Error-and-Erasure Decoding for Block Codes with Feedback
|
cs.IT math.IT
|
Inner and outer bounds are derived on the optimal performance of fixed length
block codes on discrete memoryless channels with feedback and
errors-and-erasures decoding. First an inner bound is derived using a two phase
encoding scheme with communication and control phases together with the optimal
decoding rule for the given encoding scheme, among decoding rules that can be
represented in terms of pairwise comparisons between the messages. Then an
outer bound is derived using a generalization of the straight-line bound to
errors-and-erasures decoders and the optimal error exponent trade off of a
feedback encoder with two messages. In addition upper and lower bounds are
derived, for the optimal erasure exponent of error free block codes in terms of
the rate. Finally we present a proof of the fact that the optimal trade off
between error exponents of a two message code does not increase with feedback
on DMCs.
|
0903.4426
|
Capacity Scaling Laws for Underwater Networks
|
cs.IT math.IT
|
The underwater acoustic channel is characterized by a path loss that depends
not only on the transmission distance, but also on the signal frequency.
Signals transmitted from one user to another over a distance $l$ are subject to
a power loss of $l^{-\alpha}{a(f)}^{-l}$. Although a terrestrial radio channel
can be modeled similarly, the underwater acoustic channel has different
characteristics. The spreading factor $\alpha$, related to the geometry of
propagation, has values in the range $1 \leq \alpha \leq 2$. The absorption
coefficient $a(f)$ is a rapidly increasing function of frequency: it is three
orders of magnitude greater at 100 kHz than at a few Hz. Existing results for
capacity of wireless networks correspond to scenarios for which $a(f) = 1$, or
a constant greater than one, and $\alpha \geq 2$. These results cannot be
applied to underwater acoustic networks in which the attenuation varies over
the system bandwidth. We use a water-filling argument to assess the minimum
transmission power and optimum transmission band as functions of the link
distance and desired data rate, and study the capacity scaling laws under this
model.
|
0903.4434
|
Random Linear Network Coding for Time-Division Duplexing: Queueing
Analysis
|
cs.IT math.IT
|
We study the performance of random linear network coding for time division
duplexing channels with Poisson arrivals. We model the system as a bulk-service
queue with variable bulk size. A full characterization for random linear
network coding is provided for time division duplexing channels [1] by means of
the moment generating function. We present numerical results for the mean
number of packets in the queue and consider the effect of the range of
allowable bulk sizes. We show that there exists an optimal choice of this range
that minimizes the mean number of data packets in the queue.
|
0903.4443
|
Broadcasting in Time-Division Duplexing: A Random Linear Network Coding
Approach
|
cs.IT math.IT
|
We study random linear network coding for broadcasting in time division
duplexing channels. We assume a packet erasure channel with nodes that cannot
transmit and receive information simultaneously. The sender transmits coded
data packets back-to-back before stopping to wait for the receivers to
acknowledge the number of degrees of freedom, if any, that are required to
decode correctly the information. We study the mean time to complete the
transmission of a block of packets to all receivers. We also present a bound on
the number of stops to wait for acknowledgement in order to complete
transmission with probability at least $1-\epsilon$, for any $\epsilon>0$. We
present analysis and numerical results showing that our scheme outperforms
optimal scheduling policies for broadcast, in terms of the mean completion
time. We provide a simple heuristic to compute the number of coded packets to
be sent before stopping that achieves close to optimal performance with the
advantage of a considerable reduction in the search time.
|
0903.4513
|
Building the information kernel and the problem of recognition
|
cs.CV cs.AI
|
At this point in time there is a need for a new representation of different
information, to identify and organize descending its characteristics. Today,
science is a powerful tool for the description of reality - the numbers. Why
the most important property of numbers. Suppose we have a number 0.2351734, it
is clear that the figures are there in order of importance. If necessary, we
can round the number up to some value, eg 0.235. Arguably, the 0,235 - the most
important information of 0.2351734. Thus, we can reduce the size of numbers is
not losing much with the accuracy. Clearly, if learning to provide a graphical
or audio information kernel, we can provide the most relevant information,
discarding the rest. Introduction of various kinds of information in an
information kernel, is an important task, to solve many problems in artificial
intelligence and information theory.
|
0903.4526
|
On the Achievable Rate of the Fading Dirty Paper Channel with Imperfect
CSIT
|
cs.IT math.IT
|
The problem of dirty paper coding (DPC) over the (multi-antenna) fading dirty
paper channel (FDPC) Y = H(X + S) + Z is considered when there is imperfect
knowledge of the channel state information H at the transmitter (CSIT). The
case of FDPC with positive definite (p.d.) input covariance matrix was studied
by the authors in a recent paper, and here the more general case of positive
semi-definite (p.s.d.) input covariance is dealt with. Towards this end, the
choice of auxiliary random variable is modified. The algorithms for
determination of inflation factor proposed in the p.d. case are then
generalized to the case of p.s.d. input covariance. Subsequently, the largest
DPC-achievable high-SNR (signal-to-noise ratio) scaling factor over the no-CSIT
FDPC with p.s.d. input covariance matrix is derived. This scaling factor is
seen to be a non-trivial generalization of the one achieved for the p.d. case.
Next, in the limit of low SNR, it is proved that the choice of all-zero
inflation factor (thus treating interference as noise) is optimal in the
'ratio' sense, regardless of the covariance matrix used. Further, in the p.d.
covariance case, the inflation factor optimal at high SNR is obtained when the
number of transmit antennas is greater than the number of receive antennas,
with the other case having been already considered in the earlier paper.
Finally, the problem of joint optimization of the input covariance matrix and
the inflation factor is dealt with, and an iterative numerical algorithm is
developed.
|
0903.4527
|
Graph polynomials and approximation of partition functions with Loopy
Belief Propagation
|
cs.DM cs.LG
|
The Bethe approximation, or loopy belief propagation algorithm is a
successful method for approximating partition functions of probabilistic models
associated with a graph. Chertkov and Chernyak derived an interesting formula
called Loop Series Expansion, which is an expansion of the partition function.
The main term of the series is the Bethe approximation while other terms are
labeled by subgraphs called generalized loops. In our recent paper, we derive
the loop series expansion in form of a polynomial with coefficients positive
integers, and extend the result to the expansion of marginals. In this paper,
we give more clear derivation for the results and discuss the properties of the
polynomial which is introduced in the paper.
|
0903.4530
|
Nonnegative approximations of nonnegative tensors
|
cs.NA cs.IR
|
We study the decomposition of a nonnegative tensor into a minimal sum of
outer product of nonnegative vectors and the associated parsimonious naive
Bayes probabilistic model. We show that the corresponding approximation
problem, which is central to nonnegative PARAFAC, will always have optimal
solutions. The result holds for any choice of norms and, under a mild
assumption, even Bregman divergences.
|
0903.4545
|
Computer- and robot-assisted Medical Intervention
|
cs.RO
|
Medical robotics includes assistive devices used by the physician in order to
make his/her diagnostic or therapeutic practices easier and more efficient.
This chapter focuses on such systems. It introduces the general field of
Computer-Assisted Medical Interventions, its aims, its different components and
describes the place of robots in that context. The evolutions in terms of
general design and control paradigms in the development of medical robots are
presented and issues specific to that application domain are discussed. A view
of existing systems, on-going developments and future trends is given. A
case-study is detailed. Other types of robotic help in the medical environment
(such as for assisting a handicapped person, for rehabilitation of a patient or
for replacement of some damaged/suppressed limbs or organs) are out of the
scope of this chapter.
|
0903.4554
|
Fountain Codes and Invertible Matrices
|
cs.IT math.IT
|
This paper deals with Fountain codes, and especially with their encoding
matrices, which are required here to be invertible. A result is stated that an
encoding matrix induces a permutation. Also, a result is that encoding matrices
form a group with multiplication operation. An encoding is a transformation,
which reduces the entropy of an initially high-entropy input vector. A special
encoding matrix, with which the entropy reduction is more effective than with
matrices created by the Ideal Soliton distribution is formed. Experimental
results with entropy reduction are shown.
|
0903.4582
|
On the Achievable Diversity-Multiplexing Tradeoff in MIMO Fading
Channels with Imperfect CSIT
|
cs.IT math.IT
|
In this paper, we analyze the fundamental tradeoff of diversity and
multiplexing in multi-input multi-output (MIMO) channels with imperfect channel
state information at the transmitter (CSIT). We show that with imperfect CSIT,
a higher diversity gain as well as a more efficient diversity-multiplexing
tradeoff (DMT) can be achieved. In the case of multi-input single-output
(MISO)/single-input multi-output (SIMO) channels with K transmit/receive
antennas, one can achieve a diversity gain of d(r)=K(1-r+K\alpha) at spatial
multiplexing gain r, where \alpha is the CSIT quality defined in this paper.
For general MIMO channels with M (M>1) transmit and N (N>1) receive antennas,
we show that depending on the value of \alpha, different DMT can be derived and
the value of \alpha has a great impact on the achievable diversity, especially
at high multiplexing gains. Specifically, when \alpha is above a certain
threshold, one can achieve a diversity gain of d(r)=MN(1+MN\alpha)-(M+N-1)r;
otherwise, the achievable DMT is much lower and can be described as a
collection of discontinuous line segments depending on M, N, r and \alpha. Our
analysis reveals that imperfect CSIT significantly improves the achievable
diversity gain while enjoying high spatial multiplexing gains.
|
0903.4594
|
Dynamic Control of Tunable Sub-optimal Algorithms for Scheduling of
Time-varying Wireless Networks
|
cs.IT math.IT
|
It is well known that for ergodic channel processes the Generalized
Max-Weight Matching (GMWM) scheduling policy stabilizes the network for any
supportable arrival rate vector within the network capacity region. This
policy, however, often requires the solution of an NP-hard optimization
problem. This has motivated many researchers to develop sub-optimal algorithms
that approximate the GMWM policy in selecting schedule vectors. One implicit
assumption commonly shared in this context is that during the algorithm
runtime, the channel states remain effectively unchanged. This assumption may
not hold as the time needed to select near-optimal schedule vectors usually
increases quickly with the network size. In this paper, we incorporate channel
variations and the time-efficiency of sub-optimal algorithms into the scheduler
design, to dynamically tune the algorithm runtime considering the tradeoff
between algorithm efficiency and its robustness to changing channel states.
Specifically, we propose a Dynamic Control Policy (DCP) that operates on top of
a given sub-optimal algorithm, and dynamically but in a large time-scale
adjusts the time given to the algorithm according to queue backlog and channel
correlations. This policy does not require knowledge of the structure of the
given sub-optimal algorithm, and with low overhead can be implemented in a
distributed manner. Using a novel Lyapunov analysis, we characterize the
throughput stability region induced by DCP and show that our characterization
can be tight. We also show that the throughput stability region of DCP is at
least as large as that of any other static policy. Finally, we provide two case
studies to gain further intuition into the performance of DCP.
|
0903.4696
|
Multidimensional Online Robot Motion
|
cs.CG cs.RO
|
We consider three related problems of robot movement in arbitrary dimensions:
coverage, search, and navigation. For each problem, a spherical robot is asked
to accomplish a motion-related task in an unknown environment whose geometry is
learned by the robot during navigation. The robot is assumed to have tactile
and global positioning sensors. We view these problems from the perspective of
(non-linear) competitiveness as defined by Gabriely and Rimon. We first show
that in 3 dimensions and higher, there is no upper bound on competitiveness:
every online algorithm can do arbitrarily badly compared to the optimal. We
then modify the problems by assuming a fixed clearance parameter. We are able
to give optimally competitive algorithms under this assumption.
|
0903.4738
|
Constellation Precoded Beamforming
|
cs.IT math.IT
|
We present and analyze the performance of constellation precoded beamforming.
This multi-input multi-output transmission technique is based on the singular
value decomposition of a channel matrix. In this work, the beamformer is
precoded to improve its diversity performance. It was shown previously that
while single beamforming achieves full diversity without channel coding,
multiple beamforming results in diversity loss. In this paper, we show that a
properly designed constellation precoder makes uncoded multiple beamforming
achieve full diversity order. We also show that partially precoded multiple
beamforming gets better diversity order than multiple beamforming without
constellation precoder if the subchannels to be precoded are properly chosen.
We propose several criteria to design the constellation precoder. Simulation
results match the analysis, and show that precoded multiple beamforming
actually outperforms single beamforming without precoding at the same system
data rate while achieving full diversity order.
|
0903.4742
|
Guaranteed Minimum Rank Approximation from Linear Observations by
Nuclear Norm Minimization with an Ellipsoidal Constraint
|
cs.IT math.IT
|
The rank minimization problem is to find the lowest-rank matrix in a given
set. Nuclear norm minimization has been proposed as an convex relaxation of
rank minimization. Recht, Fazel, and Parrilo have shown that nuclear norm
minimization subject to an affine constraint is equivalent to rank minimization
under a certain condition given in terms of the rank-restricted isometry
property. However, in the presence of measurement noise, or with only
approximately low rank generative model, the appropriate constraint set is an
ellipsoid rather than an affine space. There exist polynomial-time algorithms
to solve the nuclear norm minimization with an ellipsoidal constraint, but no
performance guarantee has been shown for these algorithms. In this paper, we
derive such an explicit performance guarantee, bounding the error in the
approximate solution provided by nuclear norm minimization with an ellipsoidal
constraint.
|
0903.4817
|
An Exponential Lower Bound on the Complexity of Regularization Paths
|
cs.LG cs.CG cs.CV math.OC stat.ML
|
For a variety of regularized optimization problems in machine learning,
algorithms computing the entire solution path have been developed recently.
Most of these methods are quadratic programs that are parameterized by a single
parameter, as for example the Support Vector Machine (SVM). Solution path
algorithms do not only compute the solution for one particular value of the
regularization parameter but the entire path of solutions, making the selection
of an optimal parameter much easier.
It has been assumed that these piecewise linear solution paths have only
linear complexity, i.e. linearly many bends. We prove that for the support
vector machine this complexity can be exponential in the number of training
points in the worst case. More strongly, we construct a single instance of n
input points in d dimensions for an SVM such that at least \Theta(2^{n/2}) =
\Theta(2^d) many distinct subsets of support vectors occur as the
regularization parameter changes.
|
0903.4826
|
New Linear Codes from Matrix-Product Codes with Polynomial Units
|
cs.IT math.IT
|
A new construction of codes from old ones is considered, it is an extension
of the matrix-product construction. Several linear codes that improve the
parameters of the known ones are presented.
|
0903.4856
|
A Combinatorial Algorithm to Compute Regularization Paths
|
cs.LG cs.AI cs.CV
|
For a wide variety of regularization methods, algorithms computing the entire
solution path have been developed recently. Solution path algorithms do not
only compute the solution for one particular value of the regularization
parameter but the entire path of solutions, making the selection of an optimal
parameter much easier. Most of the currently used algorithms are not robust in
the sense that they cannot deal with general or degenerate input. Here we
present a new robust, generic method for parametric quadratic programming. Our
algorithm directly applies to nearly all machine learning applications, where
so far every application required its own different algorithm.
We illustrate the usefulness of our method by applying it to a very low rank
problem which could not be solved by existing path tracking methods, namely to
compute part-worth values in choice based conjoint analysis, a popular
technique from market research to estimate consumers preferences on a class of
parameterized options.
|
0903.4860
|
Learning Multiple Belief Propagation Fixed Points for Real Time
Inference
|
cs.LG cond-mat.dis-nn physics.data-an
|
In the context of inference with expectation constraints, we propose an
approach based on the "loopy belief propagation" algorithm LBP, as a surrogate
to an exact Markov Random Field MRF modelling. A prior information composed of
correlations among a large set of N variables, is encoded into a graphical
model; this encoding is optimized with respect to an approximate decoding
procedure LBP, which is used to infer hidden variables from an observed subset.
We focus on the situation where the underlying data have many different
statistical components, representing a variety of independent patterns.
Considering a single parameter family of models we show how LBP may be used to
encode and decode efficiently such information, without solving the NP hard
inverse problem yielding the optimal MRF. Contrary to usual practice, we work
in the non-convex Bethe free energy minimization framework, and manage to
associate a belief propagation fixed point to each component of the underlying
probabilistic mixture. The mean field limit is considered and yields an exact
connection with the Hopfield model at finite temperature and steady state, when
the number of mixture components is proportional to the number of variables. In
addition, we provide an enhanced learning procedure, based on a straightforward
multi-parameter extension of the model in conjunction with an effective
continuous optimization procedure. This is performed using the stochastic
search heuristic CMAES and yields a significant improvement with respect to the
single parameter basic model.
|
0903.4930
|
Time manipulation technique for speeding up reinforcement learning in
simulations
|
cs.AI cs.LG cs.RO
|
A technique for speeding up reinforcement learning algorithms by using time
manipulation is proposed. It is applicable to failure-avoidance control
problems running in a computer simulation. Turning the time of the simulation
backwards on failure events is shown to speed up the learning by 260% and
improve the state space exploration by 12% on the cart-pole balancing task,
compared to the conventional Q-learning and Actor-Critic algorithms.
|
0903.4939
|
A Novel Algorithm for Compressive Sensing: Iteratively Reweighed
Operator Algorithm (IROA)
|
cs.IT math.IT
|
Compressive sensing claims that the sparse signals can be reconstructed
exactly from many fewer measurements than traditionally believed necessary. One
of issues ensuring the successful compressive sensing is to deal with the
sparsity-constraint optimization. Up to now, many excellent theories,
algorithms and software have been developed, for example, the so-called greedy
algorithm ant its variants, the sparse Bayesian algorithm, the convex
optimization methods, and so on. The formulations for them consist of two
terms, in which one is and the other is (, mostly, p=1 is adopted due to good
characteristic of the convex function) (NOTE: without the loss of generality,
itself is assumed to be sparse). It is noted that all of them specify the
sparsity constraint by the second term. Different from them, the developed
formulation in this paper consists of two terms where one is with () and the
other is . For each iteration the measurement matrix (linear operator) is
reweighed by determined by which is obtained in the previous iteration, so the
proposed method is called the iteratively reweighed operator algorithm (IROA).
Moreover, in order to save the computation time, another reweighed operation
has been carried out; in particular, the columns of corresponding to small have
been excluded out. Theoretical analysis and numerical simulations have shown
that the proposed method overcomes the published algorithms.
|
0903.5045
|
Digital Restoration of Ancient Papyri
|
cs.CV
|
Image processing can be used for digital restoration of ancient papyri, that
is, for a restoration performed on their digital images. The digital
manipulation allows reducing the background signals and enhancing the
readability of texts. In the case of very old and damaged documents, this is
fundamental for identification of the patterns of letters. Some examples of
restoration, obtained with an image processing which uses edges detection and
Fourier filtering, are shown. One of them concerns 7Q5 fragment of the Dead Sea
Scrolls.
|
0903.5049
|
SQS-graphs of extended 1-perfect codes
|
math.CO cs.IT math.IT
|
A binary extended 1-perfect code $\mathcal C$ folds over its kernel via the
Steiner quadruple systems associated with its codewords. The resulting folding,
proposed as a graph invariant for $\mathcal C$, distinguishes among the 361
nonlinear codes $\mathcal C$ of kernel dimension $\kappa$ with $9\geq\kappa\geq
5$ obtained via Solov'eva-Phelps doubling construction. Each of the 361
resulting graphs has most of its nonloop edges expressible in terms of the
lexicographically disjoint quarters of the products of the components of two of
the ten 1-perfect partitions of length 8 classified by Phelps, and loops mostly
expressible in terms of the lines of the Fano plane.
|
0903.5054
|
Flow of Activity in the Ouroboros Model
|
cs.AI
|
The Ouroboros Model is a new conceptual proposal for an algorithmic structure
for efficient data processing in living beings as well as for artificial
agents. Its central feature is a general repetitive loop where one iteration
cycle sets the stage for the next. Sensory input activates data structures
(schemata) with similar constituents encountered before, thus expectations are
kindled. This corresponds to the highlighting of empty slots in the selected
schema, and these expectations are compared with the actually encountered
input. Depending on the outcome of this consumption analysis different next
steps like search for further data or a reset, i.e. a new attempt employing
another schema, are triggered. Monitoring of the whole process, and in
particular of the flow of activation directed by the consumption analysis,
yields valuable feedback for the optimum allocation of attention and resources
including the selective establishment of useful new memory entries.
|
0903.5066
|
Modified-CS: Modifying Compressive Sensing for Problems with Partially
Known Support
|
cs.IT math.IT math.ST stat.ME stat.TH
|
We study the problem of reconstructing a sparse signal from a limited number
of its linear projections when a part of its support is known, although the
known part may contain some errors. The ``known" part of the support, denoted
T, may be available from prior knowledge. Alternatively, in a problem of
recursively reconstructing time sequences of sparse spatial signals, one may
use the support estimate from the previous time instant as the ``known" part.
The idea of our proposed solution (modified-CS) is to solve a convex relaxation
of the following problem: find the signal that satisfies the data constraint
and is sparsest outside of T. We obtain sufficient conditions for exact
reconstruction using modified-CS. These are much weaker than those needed for
compressive sensing (CS) when the sizes of the unknown part of the support and
of errors in the known part are small compared to the support size. An
important extension called Regularized Modified-CS (RegModCS) is developed
which also uses prior signal estimate knowledge. Simulation comparisons for
both sparse and compressible signals are shown.
|
0903.5074
|
Analyzing Least Squares and Kalman Filtered Compressed Sensing
|
cs.IT math.IT
|
In recent work, we studied the problem of causally reconstructing time
sequences of spatially sparse signals, with unknown and slow time-varying
sparsity patterns, from a limited number of linear "incoherent" measurements.
We proposed a solution called Kalman Filtered Compressed Sensing (KF-CS). The
key idea is to run a reduced order KF only for the current signal's estimated
nonzero coefficients' set, while performing CS on the Kalman filtering error to
estimate new additions, if any, to the set. KF may be replaced by Least Squares
(LS) estimation and we call the resulting algorithm LS-CS. In this work, (a) we
bound the error in performing CS on the LS error and (b) we obtain the
conditions under which the KF-CS (or LS-CS) estimate converges to that of a
genie-aided KF (or LS), i.e. the KF (or LS) which knows the true nonzero sets.
|
0903.5108
|
Multi-mode Transmission for the MIMO Broadcast Channel with Imperfect
Channel State Information
|
cs.IT math.IT
|
This paper proposes an adaptive multi-mode transmission strategy to improve
the spectral efficiency achieved in the multiple-input multiple-output (MIMO)
broadcast channel with delayed and quantized channel state information. The
adaptive strategy adjusts the number of active users, denoted as the
transmission mode, to balance transmit array gain, spatial division
multiplexing gain, and residual inter-user interference. Accurate closed-form
approximations are derived for the achievable rates for different modes, which
help identify the active mode that maximizes the average sum throughput for
given feedback delay and channel quantization error. The proposed transmission
strategy is combined with round-robin scheduling, and is shown to provide
throughput gain over single-user MIMO at moderate signal-to-noise ratio. It
only requires feedback of instantaneous channel state information from a small
number of users. With a feedback load constraint, the proposed algorithm
provides performance close to that achieved by opportunistic scheduling with
instantaneous feedback from a large number of users.
|
0903.5122
|
A Constructive Generalization of Nash Equilibrium for Better Payoffs and
Stability
|
cs.GT cs.MA
|
In a society of completely selfish individuals where everybody is only
interested in maximizing his own payoff, does any equilibrium exist for the
society? John Nash proved more than 50 years ago that an equilibrium always
exists such that nobody would benefit from unilaterally changing his strategy.
Nash Equilibrium is a central concept in game theory, which offers a
mathematical foundation for social science and economy. However, it is
important from both a theoretical and a practical point of view to understand
game playing where individuals are less selfish. This paper offers a
constructive generalization of Nash equilibrium to study n-person games where
the selfishness of individuals can be defined at any level, including the
extreme of complete selfishness. The generalization is constructive since it
offers a protocol for individuals in a society to reach an equilibrium. Most
importantly, this paper presents experimental results and theoretical
investigation to show that the individuals in a society can reduce their
selfishness level together to reach a new equilibrium where they can have
better payoffs and the society is more stable at the same time. This study
suggests that, for the benefit of everyone in a society (including the
financial market), the pursuit of maximal payoff by each individual should be
controlled at some level either by voluntary good citizenship or by imposed
regulations.
|
0903.5168
|
Mathematical Model for Transformation of Sentences from Active Voice to
Passive Voice
|
cs.CL
|
Formal work in linguistics has both produced and used important mathematical
tools. Motivated by a survey of models for context and word meaning, syntactic
categories, phrase structure rules and trees, an attempt is being made in the
present paper to present a mathematical model for structuring of sentences from
active voice to passive voice, which is is the form of a transitive verb whose
grammatical subject serves as the patient, receiving the action of the verb.
For this purpose we have parsed all sentences of a corpus and have generated
Boolean groups for each of them. It has been observed that when we take
constituents of the sentences as subgroups, the sequences of phrases form
permutation roups. Application of isomorphism property yields permutation
mapping between the important subgroups. It has resulted in a model for
transformation of sentences from active voice to passive voice. A computer
program has been written to enable the software developers to evolve grammar
software for sentence transformations.
|
0903.5172
|
Delocalization transition for the Google matrix
|
cs.IR cond-mat.dis-nn nlin.AO
|
We study the localization properties of eigenvectors of the Google matrix,
generated both from the World Wide Web and from the Albert-Barabasi model of
networks. We establish the emergence of a delocalization phase for the PageRank
vector when network parameters are changed. In the phase of localized PageRank,
a delocalization takes place in the complex plane of eigenvalues of the matrix,
leading to delocalized relaxation modes. We argue that the efficiency of
information retrieval by Google-type search is strongly affected in the phase
of delocalized PageRank.
|
0903.5188
|
Quantum decision theory as quantum theory of measurement
|
quant-ph cs.AI
|
We present a general theory of quantum information processing devices, that
can be applied to human decision makers, to atomic multimode registers, or to
molecular high-spin registers. Our quantum decision theory is a generalization
of the quantum theory of measurement, endowed with an action ring, a prospect
lattice and a probability operator measure. The algebra of probability
operators plays the role of the algebra of local observables. Because of the
composite nature of prospects and of the entangling properties of the
probability operators, quantum interference terms appear, which make actions
noncommutative and the prospect probabilities non-additive. The theory provides
the basis for explaining a variety of paradoxes typical of the application of
classical utility theory to real human decision making. The principal advantage
of our approach is that it is formulated as a self-consistent mathematical
theory, which allows us to explain not just one effect but actually all known
paradoxes in human decision making. Being general, the approach can serve as a
tool for characterizing quantum information processing by means of atomic,
molecular, and condensed-matter systems.
|
0903.5254
|
Comparing Bibliometric Statistics Obtained from the Web of Science and
Scopus
|
cs.IR cs.DL
|
For more than 40 years, the Institute for Scientific Information (ISI, now
part of Thomson Reuters) produced the only available bibliographic databases
from which bibliometricians could compile large-scale bibliometric indicators.
ISI's citation indexes, now regrouped under the Web of Science (WoS), were the
major sources of bibliometric data until 2004, when Scopus was launched by the
publisher Reed Elsevier. For those who perform bibliometric analyses and
comparisons of countries or institutions, the existence of these two major
databases raises the important question of the comparability and stability of
statistics obtained from different data sources. This paper uses macro-level
bibliometric indicators to compare results obtained from the WoS and Scopus. It
shows that the correlations between the measures obtained with both databases
for the number of papers and the number of citations received by countries, as
well as for their ranks, are extremely high (R2 > .99). There is also a very
high correlation when countries' papers are broken down by field. The paper
thus provides evidence that indicators of scientific production and citations
at the country level are stable and largely independent of the database.
|
0903.5267
|
Equitable Partitioning Policies for Mobile Robotic Networks
|
cs.RO
|
The most widely applied strategy for workload sharing is to equalize the
workload assigned to each resource. In mobile multi-agent systems, this
principle directly leads to equitable partitioning policies in which (i) the
workspace is divided into subregions of equal measure, (ii) there is a
bijective correspondence between agents and subregions, and (iii) each agent is
responsible for service requests originating within its own subregion. In this
paper, we design provably correct, spatially-distributed and adaptive policies
that allow a team of agents to achieve a convex and equitable partition of a
convex workspace, where each subregion has the same measure. We also consider
the issue of achieving convex and equitable partitions where subregions have
shapes similar to those of regular polygons. Our approach is related to the
classic Lloyd algorithm, and exploits the unique features of power diagrams. We
discuss possible applications to routing of vehicles in stochastic and dynamic
environments. Simulation results are presented and discussed.
|
0903.5282
|
Multi-agent Q-Learning of Channel Selection in Multi-user Cognitive
Radio Systems: A Two by Two Case
|
cs.IT math.IT
|
Resource allocation is an important issue in cognitive radio systems. It can
be done by carrying out negotiation among secondary users. However, significant
overhead may be incurred by the negotiation since the negotiation needs to be
done frequently due to the rapid change of primary users' activity. In this
paper, a channel selection scheme without negotiation is considered for
multi-user and multi-channel cognitive radio systems. To avoid collision
incurred by non-coordination, each user secondary learns how to select channels
according to its experience. Multi-agent reinforcement leaning (MARL) is
applied in the framework of Q-learning by considering the opponent secondary
users as a part of the environment. The dynamics of the Q-learning are
illustrated using Metrick-Polak plot. A rigorous proof of the convergence of
Q-learning is provided via the similarity between the Q-learning and
Robinson-Monro algorithm, as well as the analysis of convergence of the
corresponding ordinary differential equation (via Lyapunov function). Examples
are illustrated and the performance of learning is evaluated by numerical
simulations.
|
0903.5289
|
Heterogeneous knowledge representation using a finite automaton and
first order logic: a case study in electromyography
|
cs.AI
|
In a certain number of situations, human cognitive functioning is difficult
to represent with classical artificial intelligence structures. Such a
difficulty arises in the polyneuropathy diagnosis which is based on the spatial
distribution, along the nerve fibres, of lesions, together with the synthesis
of several partial diagnoses. Faced with this problem while building up an
expert system (NEUROP), we developed a heterogeneous knowledge representation
associating a finite automaton with first order logic. A number of knowledge
representation problems raised by the electromyography test features are
examined in this study and the expert system architecture allowing such a
knowledge modeling are laid out.
|
0903.5328
|
A Stochastic View of Optimal Regret through Minimax Duality
|
cs.LG stat.ML
|
We study the regret of optimal strategies for online convex optimization
games. Using von Neumann's minimax theorem, we show that the optimal regret in
this adversarial setting is closely related to the behavior of the empirical
minimization algorithm in a stochastic process setting: it is equal to the
maximum, over joint distributions of the adversary's action sequence, of the
difference between a sum of minimal expected losses and the minimal empirical
loss. We show that the optimal regret has a natural geometric interpretation,
since it can be viewed as the gap in Jensen's inequality for a concave
functional--the minimizer over the player's actions of expected loss--defined
on a set of probability distributions. We use this expression to obtain upper
and lower bounds on the regret of an optimal strategy for a variety of online
learning problems. Our method provides upper bounds without the need to
construct a learning algorithm; the lower bounds provide explicit optimal
strategies for the adversary.
|
0903.5341
|
Unspecified distribution in single disorder problem
|
math.PR cs.IT math.IT math.ST stat.TH
|
We register a stochastic sequence affected by one disorder. Monitoring of the
sequence is made in the circumstances when not full information about
distributions before and after the change is available. The initial problem of
disorder detection is transformed to optimal stopping of observed sequence.
Formula for optimal decision functions is derived.
|
0903.5342
|
Exact Non-Parametric Bayesian Inference on Infinite Trees
|
math.PR cs.LG math.ST stat.TH
|
Given i.i.d. data from an unknown distribution, we consider the problem of
predicting future items. An adaptive way to estimate the probability density is
to recursively subdivide the domain to an appropriate data-dependent
granularity. A Bayesian would assign a data-independent prior probability to
"subdivide", which leads to a prior over infinite(ly many) trees. We derive an
exact, fast, and simple inference algorithm for such a prior, for the data
evidence, the predictive distribution, the effective model dimension, moments,
and other quantities. We prove asymptotic convergence and consistency results,
and illustrate the behavior of our model on some prototypical functions.
|
0903.5346
|
Cooperative Update Exchange in the Youtopia System
|
cs.DB
|
Youtopia is a platform for collaborative management and integration of
relational data. At the heart of Youtopia is an update exchange abstraction:
changes to the data propagate through the system to satisfy user-specified
mappings. We present a novel change propagation model that combines a
deterministic chase with human intervention. The process is fundamentally
cooperative and gives users significant control over how mappings are repaired.
An additional advantage of our model is that mapping cycles can be permitted
without compromising correctness.
We investigate potential harmful interference between updates in our model;
we introduce two appropriate notions of serializability that avoid such
interference if enforced. The first is very general and related to classical
final-state serializability; the second is more restrictive but highly
practical and related to conflict-serializability. We present an algorithm to
enforce the latter notion. Our algorithm is an optimistic one, and as such may
sometimes require updates to be aborted. We develop techniques for reducing the
number of aborts and we test these experimentally.
|
0903.5372
|
A game theory approach for self-coexistence analysis among IEEE 802.22
networks
|
cs.IT cs.GT math.IT
|
This paper has been withdrawn by the author due to some errors
|
0903.5399
|
Regret and Jeffreys Integrals in Exp. Families
|
cs.IT math.IT
|
The problem of whether minimax redundancy, minimax regret and Jeffreys
integrals are finite or infinite are discussed.
|
0903.5426
|
Testing Goodness-of-Fit via Rate Distortion
|
cs.IT math.IT math.ST stat.TH
|
A framework is developed using techniques from rate distortion theory in
statistical testing. The idea is first to do optimal compression according to a
certain distortion function and then use information divergence from the
compressed empirical distribution to the compressed null hypothesis as
statistic. Only very special cases have been studied in more detail, but they
indicate that the approach can be used under very general conditions.
|
0904.0016
|
Stochastic Models of User-Contributory Web Sites
|
cs.CY cs.IR
|
We describe a general stochastic processes-based approach to modeling
user-contributory web sites, where users create, rate and share content. These
models describe aggregate measures of activity and how they arise from simple
models of individual users. This approach provides a tractable method to
understand user activity on the web site and how this activity depends on web
site design choices, especially the choice of what information about other
users' behaviors is shown to each user. We illustrate this modeling approach in
the context of user-created content on the news rating site Digg.
|
0904.0019
|
On Solving Boolean Multilevel Optimization Problems
|
cs.LO cs.AI
|
Many combinatorial optimization problems entail a number of hierarchically
dependent optimization problems. An often used solution is to associate a
suitably large cost with each individual optimization problem, such that the
solution of the resulting aggregated optimization problem solves the original
set of hierarchically dependent optimization problems. This paper starts by
studying the package upgradeability problem in software distributions.
Straightforward solutions based on Maximum Satisfiability (MaxSAT) and
pseudo-Boolean (PB) optimization are shown to be ineffective, and unlikely to
scale for large problem instances. Afterwards, the package upgradeability
problem is related to multilevel optimization. The paper then develops new
algorithms for Boolean Multilevel Optimization (BMO) and highlights a large
number of potential applications. The experimental results indicate that the
proposed algorithms for BMO allow solving optimization problems that existing
MaxSAT and PB solvers would otherwise be unable to solve.
|
0904.0027
|
Faith in the Algorithm, Part 2: Computational Eudaemonics
|
cs.CY cs.AI
|
Eudaemonics is the study of the nature, causes, and conditions of human
well-being. According to the ethical theory of eudaemonia, reaping satisfaction
and fulfillment from life is not only a desirable end, but a moral
responsibility. However, in modern society, many individuals struggle to meet
this responsibility. Computational mechanisms could better enable individuals
to achieve eudaemonia by yielding practical real-world systems that embody
algorithms that promote human flourishing. This article presents eudaemonic
systems as the evolutionary goal of the present day recommender system.
|
0904.0029
|
Learning for Dynamic subsumption
|
cs.AI
|
In this paper a new dynamic subsumption technique for Boolean CNF formulae is
proposed. It exploits simple and sufficient conditions to detect during
conflict analysis, clauses from the original formula that can be reduced by
subsumption. During the learnt clause derivation, and at each step of the
resolution process, we simply check for backward subsumption between the
current resolvent and clauses from the original formula and encoded in the
implication graph. Our approach give rise to a strong and dynamic
simplification technique that exploits learning to eliminate literals from the
original clauses. Experimental results show that the integration of our dynamic
subsumption approach within the state-of-the-art SAT solvers Minisat and Rsat
achieves interesting improvements particularly on crafted instances.
|
0904.0037
|
Deterministic Capacity of MIMO Relay Networks
|
cs.IT math.IT
|
The deterministic capacity of a relay network is the capacity of a network
when relays are restricted to transmitting \emph{reliable} information, that
is, (asymptotically) deterministic function of the source message. In this
paper it is shown that the deterministic capacity of a number of MIMO relay
networks can be found in the low power regime where $\SNR\to0$. This is
accomplished through deriving single letter upper bounds and finding the limit
of these as $\SNR\to0$. The advantage of this technique is that it overcomes
the difficulty of finding optimum distributions for mutual information.
|
0904.0052
|
Stiffness Analysis of Overconstrained Parallel Manipulators
|
cs.RO
|
The paper presents a new stiffness modeling method for overconstrained
parallel manipulators with flexible links and compliant actuating joints. It is
based on a multidimensional lumped-parameter model that replaces the link
flexibility by localized 6-dof virtual springs that describe both
translational/rotational compliance and the coupling between them. In contrast
to other works, the method involves a FEA-based link stiffness evaluation and
employs a new solution strategy of the kinetostatic equations for the unloaded
manipulator configuration, which allows computing the stiffness matrix for the
overconstrained architectures, including singular manipulator postures. The
advantages of the developed technique are confirmed by application examples,
which deal with comparative stiffness analysis of two translational parallel
manipulators of 3-PUU and 3-PRPaR architectures. Accuracy of the proposed
approach was evaluated for a case study, which focuses on stiffness analysis of
Orthoglide parallel manipulator.
|
0904.0058
|
Kinematics of A 3-PRP planar parallel robot
|
cs.RO
|
Recursive modelling for the kinematics of a 3-PRP planar parallel robot is
presented in this paper. Three planar chains connecting to the moving platform
of the manipulator are located in a vertical plane. Knowing the motion of the
platform, we develop the inverse kinematics and determine the positions,
velocities and accelerations of the robot. Several matrix equations offer
iterative expressions and graphs for the displacements, velocities and
accelerations of three prismatic actuators.
|
0904.0145
|
Kinematic and Dynamic Analysis of the 2-DOF Spherical Wrist of
Orthoglide 5-axis
|
cs.RO physics.class-ph
|
This paper deals with the kinematics and dynamics of a two degree of freedom
spherical manipulator, the wrist of Orthoglide 5-axis. The latter is a parallel
kinematics machine composed of two manipulators: i) the Orthoglide 3-axis; a
three-dof translational parallel manipulator that belongs to the family of
Delta robots, and ii) the Agile eye; a two-dof parallel spherical wrist. The
geometric and inertial parameters used in the model are determined by means of
a CAD software. The performance of the spherical wrist is emphasized by means
of several test trajectories. The effects of machining and/or cutting forces
and the length of the cutting tool on the dynamic performance of the wrist are
also analyzed. Finally, a preliminary selection of the motors is proposed from
the velocities and torques required by the actuators to carry out the test
trajectories.
|
0904.0226
|
Coding Versus ARQ in Fading Channels: How reliable should the PHY be?
|
cs.IT math.IT
|
This paper studies the tradeoff between channel coding and ARQ (automatic
repeat request) in Rayleigh block-fading channels. A heavily coded system
corresponds to a low transmission rate with few ARQ re-transmissions, whereas
lighter coding corresponds to a higher transmitted rate but more
re-transmissions. The optimum error probability, where optimum refers to the
maximization of the average successful throughput, is derived and is shown to
be a decreasing function of the average signal-to-noise ratio and of the
channel diversity order. A general conclusion of the work is that the optimum
error probability is quite large (e.g., 10% or larger) for reasonable channel
parameters, and that operating at a very small error probability can lead to a
significantly reduced throughput. This conclusion holds even when a number of
practical ARQ considerations, such as delay constraints and acknowledgement
feedback errors, are taken into account.
|
0904.0228
|
Safe Reasoning Over Ontologies
|
cs.AI cs.DS
|
As ontologies proliferate and automatic reasoners become more powerful, the
problem of protecting sensitive information becomes more serious. In
particular, as facts can be inferred from other facts, it becomes increasingly
likely that information included in an ontology, while not itself deemed
sensitive, may be able to be used to infer other sensitive information.
We first consider the problem of testing an ontology for safeness defined as
its not being able to be used to derive any sensitive facts using a given
collection of inference rules. We then consider the problem of optimizing an
ontology based on the criterion of making as much useful information as
possible available without revealing any sensitive facts.
|
0904.0274
|
Interference Alignment with Asymmetric Complex Signaling - Settling the
Host-Madsen-Nosratinia Conjecture
|
cs.IT math.IT
|
It has been conjectured by Host-Madsen and Nosratinia that complex Gaussian
interference channels with constant channel coefficients have only one
degree-of-freedom regardless of the number of users. While several examples are
known of constant channels that achieve more than 1 degree of freedom, these
special cases only span a subset of measure zero. In other words, for almost
all channel coefficient values, it is not known if more than 1
degree-of-freedom is achievable. In this paper, we settle the
Host-Madsen-Nosratinia conjecture in the negative. We show that at least 1.2
degrees-of-freedom are achievable for all values of complex channel
coefficients except for a subset of measure zero. For the class of linear
beamforming and interference alignment schemes considered in this paper, it is
also shown that 1.2 is the maximum number of degrees of freedom achievable on
the complex Gaussian 3 user interference channel with constant channel
coefficients, for almost all values of channel coefficients. To establish the
achievability of 1.2 degrees of freedom we introduce the novel idea of
asymmetric complex signaling - i.e., the inputs are chosen to be complex but
not circularly symmetric. It is shown that unlike Gaussian point-to-point,
multiple-access and broadcast channels where circularly symmetric complex
Gaussian inputs are optimal, for interference channels optimal inputs are in
general asymmetric. With asymmetric complex signaling, we also show that the 2
user complex Gaussian X channel with constant channel coefficients achieves the
outer bound of 4/3 degrees-of-freedom, i.e., the assumption of
time-variations/frequency-selectivity used in prior work to establish the same
result, is not needed.
|
0904.0300
|
Design, development and implementation of a tool for construction of
declarative functional descriptions of semantic web services based on WSMO
methodology
|
cs.AI cs.LO
|
Semantic web services (SWS) are self-contained, self-describing, semantically
marked-up software resources that can be published, discovered, composed and
executed across the Web in a semi-automatic way. They are a key component of
the future Semantic Web, in which networked computer programs become providers
and users of information at the same time. This work focuses on developing a
full-life-cycle software toolset for creating and maintaining Semantic Web
Services (SWSs) based on the Web Service Modelling Ontology (WSMO) framework. A
main part of WSMO-based SWS is service capability - a declarative description
of Web service functionality. A formal syntax and semantics for such a
description is provided by Web Service Modeling Language (WSML), which is based
on different logical formalisms, namely, Description Logics, First-Order Logic
and Logic Programming. A WSML description of a Web service capability is
represented as a set of complex logical expressions (axioms). We develop a
specialized user-friendly tool for constructing and editing WSMO-based SWS
capabilities. Since the users of this tool are not specialists in first-order
logic, a graphical way for constricting and editing axioms is proposed. The
designed process for constructing logical expressions is ontology-driven, which
abstracts away as much as possible from any concrete syntax of logical
language. We propose several mechanisms to guarantees the semantic consistency
of the produced logical expressions. The tool is implemented in Java using
Eclipse for IDE and GEF (Graphical Editing Framework) for visualization.
|
0904.0308
|
Exponential decreasing rate of leaked information in universal random
privacy amplification
|
cs.IT cs.CR math.AC math.IT
|
We derive a new upper bound for Eve's information in secret key generation
from a common random number without communication. This bound improves on
Bennett et al(1995)'s bound based on the R\'enyi entropy of order 2 because the
bound obtained here uses the R\'enyi entropy of order $1+s$ for $s \in [0,1]$.
This bound is applied to a wire-tap channel. Then, we derive an exponential
upper bound for Eve's information. Our exponent is compared with
Hayashi(2006)'s exponent. For the additive case, the bound obtained here is
better. The result is applied to secret key agreement by public discussion.
|
0904.0313
|
Visual approach for data mining on medical information databases using
Fastmap algorithm
|
cs.IR cs.DB
|
The rapid development of tools for acquisition and storage of information has
lead to the formation of enormous medical databases. The large quantity of data
definitely surpasses the abilities of humans for efficient usage without
specialized tools for analysis. The situation is described as rich in data, but
poor in information. In order to fill this growing gap, different approaches
from the field of Data Mining are applied. These methods perform analysis of
large sets of observed data in order to find new dependencies or concise
representation of the data, which is more meaningful to humans. One of the
possible approaches for discovery of dependencies is the visual approach, in
which data is processed and visualized in a way suitable for analysis by a
domain expert. This work proposes a visual approach, in which data is processed
and visualized in a way suitable for analysis by a domain expert. We design and
implement a software solution for visualization of multi-dimensional,
classified medical data using the FastMap algorithm for graduate reduction of
dimensions. The implementation of the graphical user interface is described in
detail since it is the most important factor for the ease of use of these tools
by non-professionals in data mining.
|
0904.0477
|
Message Passing for Optimization and Control of Power Grid: Model of
Distribution System with Redundancy
|
cond-mat.stat-mech cs.CE cs.NI
|
We use a power grid model with $M$ generators and $N$ consumption units to
optimize the grid and its control. Each consumer demand is drawn from a
predefined finite-size-support distribution, thus simulating the instantaneous
load fluctuations. Each generator has a maximum power capability. A generator
is not overloaded if the sum of the loads of consumers connected to a generator
does not exceed its maximum production. In the standard grid each consumer is
connected only to its designated generator, while we consider a more general
organization of the grid allowing each consumer to select one generator
depending on the load from a pre-defined consumer-dependent and sufficiently
small set of generators which can all serve the load. The model grid is
interconnected in a graph with loops, drawn from an ensemble of random
bipartite graphs, while each allowed configuration of loaded links represent a
set of graph covering trees. Losses, the reactive character of the grid and the
transmission-level connections between generators (and many other details
relevant to realistic power grid) are ignored in this proof-of-principles
study. We focus on the asymptotic limit and we show that the interconnects
allow significant expansion of the parameter domains for which the probability
of a generator overload is asymptotically zero. Our construction explores the
formal relation between the problem of grid optimization and the modern theory
of sparse graphical models. We also design heuristic algorithms that achieve
the asymptotically optimal selection of loaded links. We conclude discussing
the ability of this approach to include other effects, such as a more realistic
modeling of the power grid and related optimization and control algorithms.
|
0904.0494
|
Average Case Analysis of Multichannel Sparse Recovery Using Convex
Relaxation
|
cs.IT math.IT
|
In this paper, we consider recovery of jointly sparse multichannel signals
from incomplete measurements. Several approaches have been developed to recover
the unknown sparse vectors from the given observations, including thresholding,
simultaneous orthogonal matching pursuit (SOMP), and convex relaxation based on
a mixed matrix norm. Typically, worst-case analysis is carried out in order to
analyze conditions under which the algorithms are able to recover any jointly
sparse set of vectors. However, such an approach is not able to provide
insights into why joint sparse recovery is superior to applying standard sparse
reconstruction methods to each channel individually. Previous work considered
an average case analysis of thresholding and SOMP by imposing a probability
model on the measured signals. In this paper, our main focus is on analysis of
convex relaxation techniques. In particular, we focus on the mixed l_2,1
approach to multichannel recovery. We show that under a very mild condition on
the sparsity and on the dictionary characteristics, measured for example by the
coherence, the probability of recovery failure decays exponentially in the
number of channels. This demonstrates that most of the time, multichannel
sparse recovery is indeed superior to single channel methods. Our probability
bounds are valid and meaningful even for a small number of signals. Using the
tools we develop to analyze the convex relaxation method, we also tighten the
previous bounds for thresholding and SOMP.
|
0904.0525
|
The Minimal Polynomial over F_q of Linear Recurring Sequence over
F_{q^m}
|
cs.IT cs.CR math.IT
|
Recently, motivated by the study of vectorized stream cipher systems, the
joint linear complexity and joint minimal polynomial of multisequences have
been investigated. Let S be a linear recurring sequence over finite field
F_{q^m} with minimal polynomial h(x) over F_{q^m}. Since F_{q^m} and F_{q}^m
are isomorphic vector spaces over the finite field F_q, S is identified with an
m-fold multisequence S^{(m)} over the finite field F_q. The joint minimal
polynomial and joint linear complexity of the m-fold multisequence S^{(m)} are
the minimal polynomial and linear complexity over F_q of S respectively. In
this paper, we study the minimal polynomial and linear complexity over F_q of a
linear recurring sequence S over F_{q^m} with minimal polynomial h(x) over
F_{q^m}. If the canonical factorization of h(x) in F_{q^m}[x] is known, we
determine the minimal polynomial and linear complexity over F_q of the linear
recurring sequence S over F_{q^m}.
|
0904.0544
|
Mission-Aware Medium Access Control in Random Access Networks
|
cs.NI cs.GT cs.IT math.IT
|
We study mission-critical networking in wireless communication networks,
where network users are subject to critical events such as emergencies and
crises. If a critical event occurs to a user, the user needs to send necessary
information for help as early as possible. However, most existing medium access
control (MAC) protocols are not adequate to meet the urgent need for
information transmission by users in a critical situation. In this paer, we
propose a novel class of MAC protocols that utilize available past information
as well as current information. Our proposed protocols are mission-aware since
they prescribe different transmission decision rules to users in different
situations. We show that the proposed protocols perform well not only when the
system faces a critical situation but also when there is no critical situation.
By utilizing past information, the proposed protocols coordinate transmissions
by users to achieve high throughput in the normal phase of operation and to let
a user in a critical situation make successful transmissions while it is in the
critical situation. Moreover, the proposed protocols require short memory and
no message exchanges.
|
0904.0545
|
Time Hopping technique for faster reinforcement learning in simulations
|
cs.AI cs.LG cs.RO
|
This preprint has been withdrawn by the author for revision
|
0904.0546
|
Eligibility Propagation to Speed up Time Hopping for Reinforcement
Learning
|
cs.AI cs.LG cs.RO
|
A mechanism called Eligibility Propagation is proposed to speed up the Time
Hopping technique used for faster Reinforcement Learning in simulations.
Eligibility Propagation provides for Time Hopping similar abilities to what
eligibility traces provide for conventional Reinforcement Learning. It
propagates values from one state to all of its temporal predecessors using a
state transitions graph. Experiments on a simulated biped crawling robot
confirm that Eligibility Propagation accelerates the learning process more than
3 times.
|
0904.0570
|
The Derivational Complexity Induced by the Dependency Pair Method
|
cs.LO cs.AI cs.CC cs.PL
|
We study the derivational complexity induced by the dependency pair method,
enhanced with standard refinements. We obtain upper bounds on the derivational
complexity induced by the dependency pair method in terms of the derivational
complexity of the base techniques employed. In particular we show that the
derivational complexity induced by the dependency pair method based on some
direct technique, possibly refined by argument filtering, the usable rules
criterion, or dependency graphs, is primitive recursive in the derivational
complexity induced by the direct method. This implies that the derivational
complexity induced by a standard application of the dependency pair method
based on traditional termination orders like KBO, LPO, and MPO is exactly the
same as if those orders were applied as the only termination technique.
|
0904.0585
|
Controller synthesis with very simplified linear constraints in PN model
|
cs.IT math.IT
|
This paper addresses the problem of forbidden states for safe Petri net
modeling discrete event systems. We present an efficient method to construct a
controller. A set of linear constraints allow forbidding the reachability of
specific states. The number of these so-called forbidden states and
consequently the number of constraints are large and lead to a large number of
control places. A systematic method for constructing very simplified controller
is offered. By using a method based on Petri nets partial invariants, maximal
permissive controllers are determined.
|
0904.0586
|
Optimal Supervisory Control Synthesis
|
cs.IT math.IT
|
The place invariant method is well known as an elegant way to construct a
Petri net controller. It is possible to use the constraint for preventing
forbidden states. But in general case, the number forbidden states can be very
large giving a great number of control places. In this paper is presented a
systematic method to reduce the size and the number of constraints. This method
is applicable for safe and conservative Petri nets giving a maximally
permissive controller.
|
0904.0643
|
Performing Nonlinear Blind Source Separation with Signal Invariants
|
cs.AI cs.LG
|
Given a time series of multicomponent measurements x(t), the usual objective
of nonlinear blind source separation (BSS) is to find a "source" time series
s(t), comprised of statistically independent combinations of the measured
components. In this paper, the source time series is required to have a density
function in (s,ds/dt)-space that is equal to the product of density functions
of individual components. This formulation of the BSS problem has a solution
that is unique, up to permutations and component-wise transformations.
Separability is shown to impose constraints on certain locally invariant
(scalar) functions of x, which are derived from local higher-order correlations
of the data's velocity dx/dt. The data are separable if and only if they
satisfy these constraints, and, if the constraints are satisfied, the sources
can be explicitly constructed from the data. The method is illustrated by using
it to separate two speech-like sounds recorded with a single microphone.
|
0904.0648
|
Evolvability need not imply learnability
|
cs.LG cs.CC
|
We show that Boolean functions expressible as monotone disjunctive normal
forms are PAC-evolvable under a uniform distribution on the Boolean cube if the
hypothesis size is allowed to remain fixed. We further show that this result is
insufficient to prove the PAC-learnability of monotone Boolean functions,
thereby demonstrating a counter-example to a recent claim to the contrary. We
further discuss scenarios wherein evolvability and learnability will coincide
as well as scenarios under which they differ. The implications of the latter
case on the prospects of learning in complex hypothesis spaces is briefly
examined.
|
0904.0682
|
Privacy in Search Logs
|
cs.DB cs.IR
|
Search engine companies collect the "database of intentions", the histories
of their users' search queries. These search logs are a gold mine for
researchers. Search engine companies, however, are wary of publishing search
logs in order not to disclose sensitive information. In this paper we analyze
algorithms for publishing frequent keywords, queries and clicks of a search
log. We first show how methods that achieve variants of $k$-anonymity are
vulnerable to active attacks. We then demonstrate that the stronger guarantee
ensured by $\epsilon$-differential privacy unfortunately does not provide any
utility for this problem. We then propose an algorithm ZEALOUS and show how to
set its parameters to achieve $(\epsilon,\delta)$-probabilistic privacy. We
also contrast our analysis of ZEALOUS with an analysis by Korolova et al. [17]
that achieves $(\epsilon',\delta')$-indistinguishability. Our paper concludes
with a large experimental study using real applications where we compare
ZEALOUS and previous work that achieves $k$-anonymity in search log publishing.
Our results show that ZEALOUS yields comparable utility to $k-$anonymity while
at the same time achieving much stronger privacy guarantees.
|
0904.0721
|
Optimal Tableau Decision Procedures for PDL
|
cs.LO cs.AI cs.CC
|
We reformulate Pratt's tableau decision procedure of checking satisfiability
of a set of formulas in PDL. Our formulation is simpler and more direct for
implementation. Extending the method we give the first EXPTIME (optimal)
tableau decision procedure not based on transformation for checking consistency
of an ABox w.r.t. a TBox in PDL (here, PDL is treated as a description logic).
We also prove the new result that the data complexity of the instance checking
problem in PDL is coNP-complete.
|
0904.0747
|
Bethe Free Energy Approach to LDPC Decoding on Memory Channels
|
cs.IT math.IT
|
We address the problem of the joint sequence detection in partial-response
(PR) channels and decoding of low-density parity-check (LDPC) codes. We model
the PR channel and the LDPC code as a combined inference problem. We present
for the first time the derivation of the belief propagation (BP) equations that
allow the simultaneous detection and decoding of a LDPC codeword in a PR
channel. To accomplish this we follow an approach from statistical mechanics,
in which the Bethe free energy is minimized with respect to the beliefs on the
nodes of the PR-LDPC graph. The equations obtained are explicit and are optimal
for decoding LDPC codes on PR channels with polynomial $h(D) = 1 - a D^n$ (a
real, n positive integer) in the sense that they provide the exact inference of
the marginal probabilities on the nodes in a graph free of loops. A simple
algorithmic solution to the set of BP equations is proposed and evaluated using
numerical simulations, yielding bit-error rate performances that surpass those
of turbo equalization.
|
0904.0751
|
Distributed Source Coding of Correlated Gaussian Remote Sources
|
cs.IT math.IT
|
We consider the distributed source coding system for $L$ correlated Gaussian
observations $Y_i, i=1,2, ..., L$. Let $X_i,i=1,2, ..., L$ be $L$ correlated
Gaussian random variables and $N_i,$ $i=1,2,... L$ be independent additive
Gaussian noises also independent of $X_i, i=1,2,..., L$. We consider the case
where for each $i=1,2,..., L$, $Y_i$ is a noisy observation of $X_i$, that is,
$Y_i=X_i+N_i$. On this coding system the determination problem of the rate
distortion region remains open. In this paper, we derive explicit outer and
inner bounds of the rate distortion region. We further find an explicit
sufficient condition for those two to match. We also study the sum rate part of
the rate distortion region when the correlation has some symmetrical property
and derive a new lower bound of the sum rate part. We derive a sufficient
condition for this lower bound to be tight. The derived sufficient condition
depends only on the correlation property of the sources and their observations.
|
0904.0768
|
Codes on Planar Graphs
|
cs.IT math.IT
|
Codes defined on graphs and their properties have been subjects of intense
recent research. On the practical side, constructions for capacity-approaching
codes are graphical. On the theoretical side, codes on graphs provide several
intriguing problems in the intersection of coding theory and graph theory. In
this paper, we study codes defined by planar Tanner graphs. We derive an upper
bound on minimum distance $d$ of such codes as a function of the code rate $R$
for $R \ge 5/8$. The bound is given by $$d\le \lceil \frac{7-8R}{2(2R-1)}
\rceil + 3\le 7.$$ Among the interesting conclusions of this result are the
following: (1) planar graphs do not support asymptotically good codes, and (2)
finite-length, high-rate codes on graphs with high minimum distance will
necessarily be non-planar.
|
0904.0776
|
Induction of High-level Behaviors from Problem-solving Traces using
Machine Learning Tools
|
stat.ML cs.LG
|
This paper applies machine learning techniques to student modeling. It
presents a method for discovering high-level student behaviors from a very
large set of low-level traces corresponding to problem-solving actions in a
learning environment. Basic actions are encoded into sets of domain-dependent
attribute-value patterns called cases. Then a domain-independent hierarchical
clustering identifies what we call general attitudes, yielding automatic
diagnosis expressed in natural language, addressed in principle to teachers.
The method can be applied to individual students or to entire groups, like a
class. We exhibit examples of this system applied to thousands of students'
actions in the domain of algebraic transformations.
|
0904.0811
|
The density of weights of Generalized Reed--Muller codes
|
cs.IT math.IT
|
We study the density of the weights of Generalized Reed--Muller codes. Let
$RM_p(r,m)$ denote the code of multivariate polynomials over $\F_p$ in $m$
variables of total degree at most $r$. We consider the case of fixed degree
$r$, when we let the number of variables $m$ tend to infinity. We prove that
the set of relative weights of codewords is quite sparse: for every $\alpha \in
[0,1]$ which is not rational of the form $\frac{\ell}{p^k}$, there exists an
interval around $\alpha$ in which no relative weight exists, for any value of
$m$. This line of research is to the best of our knowledge new, and complements
the traditional lines of research, which focus on the weight distribution and
the divisibility properties of the weights.
Equivalently, we study distributions taking values in a finite field, which
can be approximated by distributions coming from constant degree polynomials,
where we do not bound the number of variables. We give a complete
characterization of all such distributions.
|
0904.0813
|
Projective Space Codes for the Injection Metric
|
cs.IT math.IT
|
In the context of error control in random linear network coding, it is useful
to construct codes that comprise well-separated collections of subspaces of a
vector space over a finite field. In this paper, the metric used is the
so-called "injection distance", introduced by Silva and Kschischang. A
Gilbert-Varshamov bound for such codes is derived. Using the code-construction
framework of Etzion and Silberstein, new non-constant-dimension codes are
constructed; these codes contain more codewords than comparable codes designed
for the subspace metric.
|
0904.0814
|
Stability Analysis and Learning Bounds for Transductive Regression
Algorithms
|
cs.LG
|
This paper uses the notion of algorithmic stability to derive novel
generalization bounds for several families of transductive regression
algorithms, both by using convexity and closed-form solutions. Our analysis
helps compare the stability of these algorithms. It also shows that a number of
widely used transductive regression algorithms are in fact unstable. Finally,
it reports the results of experiments with local transductive regression
demonstrating the benefit of our stability bounds for model selection, for one
of the algorithms, in particular for determining the radius of the local
neighborhood used by the algorithm.
|
0904.0821
|
Imaging of moving targets with multi-static SAR using an overcomplete
dictionary
|
cs.IT math.IT
|
This paper presents a method for imaging of moving targets using multi-static
SAR by treating the problem as one of spatial reflectivity signal inversion
over an overcomplete dictionary of target velocities. Since SAR sensor returns
can be related to the spatial frequency domain projections of the scattering
field, we exploit insights from compressed sensing theory to show that moving
targets can be effectively imaged with transmitters and receivers randomly
dispersed in a multi-static geometry within a narrow forward cone around the
scene of interest. Existing approaches to dealing with moving targets in SAR
solve a coupled non-linear problem of target scattering and motion estimation
typically through matched filtering. In contrast, by using an overcomplete
dictionary approach we effectively linearize the forward model and solve the
moving target problem as a larger, unified regularized inversion problem
subject to sparsity constraints.
|
0904.0828
|
On approximating Gaussian relay networks by deterministic networks
|
cs.IT math.IT
|
We examine the extent to which Gaussian relay networks can be approximated by
deterministic networks, and present two results, one negative and one positive.
The gap between the capacities of a Gaussian relay network and a
corresponding linear deterministic network can be unbounded. The key reasons
are that the linear deterministic model fails to capture the phase of received
signals, and there is a loss in signal strength in the reduction to a linear
deterministic network.
On the positive side, Gaussian relay networks are indeed well approximated by
certain discrete superposition networks, where the inputs and outputs to the
channels are discrete, and channel gains are signed integers.
As a corollary, MIMO channels cannot be approximated by the linear
deterministic model but can be by the discrete superposition model.
|
0904.0879
|
On Superposition Coding for the Wyner-Ziv Problem
|
cs.IT math.IT
|
In problems of lossy source/noisy channel coding with side information, the
theoretical bounds are achieved using "good" source/channel codes that can be
partitioned into "good" channel/source codes. A scheme that achieves optimality
in channel coding with side information at the encoder using independent
channel and source codes was outlined in previous works. In practice, the
original problem is transformed into a multiple-access problem in which the
superposition of the two independent codes can be decoded using successive
interference cancellation. Inspired by this work, we analyze the superposition
approach for source coding with side information at the decoder. We present a
random coding analysis that shows achievability of the Wyner-Ziv bound. Then,
we discuss some issues related to the practical implementation of this method.
|
0904.0942
|
Boosting the Accuracy of Differentially-Private Histograms Through
Consistency
|
cs.DB cs.CR
|
We show that it is possible to significantly improve the accuracy of a
general class of histogram queries while satisfying differential privacy. Our
approach carefully chooses a set of queries to evaluate, and then exploits
consistency constraints that should hold over the noisy output. In a
post-processing phase, we compute the consistent input most likely to have
produced the noisy output. The final output is differentially-private and
consistent, but in addition, it is often much more accurate. We show, both
theoretically and experimentally, that these techniques can be used for
estimating the degree sequence of a graph very precisely, and for computing a
histogram that can support arbitrary range queries accurately.
|
0904.0962
|
Color Dipole Moments for Edge Detection
|
cs.CV
|
Dipole and higher moments are physical quantities used to describe a charge
distribution. In analogy with electromagnetism, it is possible to define the
dipole moments for a gray-scale image, according to the single aspect of a
gray-tone map. In this paper we define the color dipole moments for color
images. For color maps in fact, we have three aspects, the three primary
colors, to consider. Associating three color charges to each pixel, color
dipole moments can be easily defined and used for edge detection.
|
0904.0973
|
A statistical mechanical interpretation of algorithmic information
theory III: Composite systems and fixed points
|
cs.IT cs.CC math.IT math.PR
|
The statistical mechanical interpretation of algorithmic information theory
(AIT, for short) was introduced and developed by our former works [K. Tadaki,
Local Proceedings of CiE 2008, pp.425-434, 2008] and [K. Tadaki, Proceedings of
LFCS'09, Springer's LNCS, vol.5407, pp.422-440, 2009], where we introduced the
notion of thermodynamic quantities, such as partition function Z(T), free
energy F(T), energy E(T), and statistical mechanical entropy S(T), into AIT. We
then discovered that, in the interpretation, the temperature T equals to the
partial randomness of the values of all these thermodynamic quantities, where
the notion of partial randomness is a stronger representation of the
compression rate by means of program-size complexity. Furthermore, we showed
that this situation holds for the temperature itself as a thermodynamic
quantity, namely, for each of all the thermodynamic quantities above, the
computability of its value at temperature T gives a sufficient condition for T
in (0,1) to be a fixed point on partial randomness. In this paper, we develop
the statistical mechanical interpretation of AIT further and pursue its formal
correspondence to normal statistical mechanics. The thermodynamic quantities in
AIT are defined based on the halting set of an optimal computer, which is a
universal decoding algorithm used to define the notion of program-size
complexity. We show that there are infinitely many optimal computers which give
completely different sufficient conditions in each of the thermodynamic
quantities in AIT. We do this by introducing the notion of composition of
computers into AIT, which corresponds to the notion of composition of systems
in normal statistical mechanics.
|
0904.0981
|
Dependency Pairs and Polynomial Path Orders
|
cs.LO cs.AI cs.CC cs.SC
|
We show how polynomial path orders can be employed efficiently in conjunction
with weak innermost dependency pairs to automatically certify polynomial
runtime complexity of term rewrite systems and the polytime computability of
the functions computed. The established techniques have been implemented and we
provide ample experimental data to assess the new method.
|
0904.0986
|
Approche conceptuelle par un processus d'annotation pour la
repr\'esentation et la valorisation de contenus informationnels en
intelligence \'economique (IE)
|
cs.IR
|
In the era of the information society, the impact of the information systems
on the economy of material and immaterial is certainly perceptible. With
regards to the information resources of an organization, the annotation
involved to enrich informational content, to track the intellectual activities
on a document and to set the added value on information for the benefit of
solving a decision-making problem in the context of economic intelligence. Our
contribution is distinguished by the representation of an annotation process
and its inherent concepts to lead the decisionmaker to an anticipated decision:
the provision of relevant and annotated information. Such information in the
system is made easy by taking into account the diversity of resources and those
that are well annotated so formally and informally by the EI actors. A capital
research framework consist of integrating in the decision-making process the
annotator activity, the software agent (or the reasoning mechanisms) and the
information resources enhancement.
|
0904.0994
|
Breaking through the Thresholds: an Analysis for Iterative Reweighted
$\ell_1$ Minimization via the Grassmann Angle Framework
|
math.PR cs.IT math.IT
|
It is now well understood that $\ell_1$ minimization algorithm is able to
recover sparse signals from incomplete measurements [2], [1], [3] and sharp
recoverable sparsity thresholds have also been obtained for the $\ell_1$
minimization algorithm. However, even though iterative reweighted $\ell_1$
minimization algorithms or related algorithms have been empirically observed to
boost the recoverable sparsity thresholds for certain types of signals, no
rigorous theoretical results have been established to prove this fact. In this
paper, we try to provide a theoretical foundation for analyzing the iterative
reweighted $\ell_1$ algorithms. In particular, we show that for a nontrivial
class of signals, the iterative reweighted $\ell_1$ minimization can indeed
deliver recoverable sparsity thresholds larger than that given in [1], [3]. Our
results are based on a high-dimensional geometrical analysis (Grassmann angle
analysis) of the null-space characterization for $\ell_1$ minimization and
weighted $\ell_1$ minimization algorithms.
|
0904.1144
|
Alternative evaluation of statistical indicators in atoms: the
non-relativistic and relativistic cases
|
nlin.AO cs.IT math.IT physics.atom-ph
|
In this work, the calculation of a statistical measure of complexity and the
Fisher-Shannon information is performed for all the atoms in the periodic
table. Non-relativistic and relativistic cases are considered. We follow the
method suggested in [C.P. Panos, N.S. Nikolaidis, K. Ch. Chatzisavvas, C.C.
Tsouros, arXiv:0812.3963v1] that uses the fractional occupation probabilities
of electrons in atomic orbitals, instead of the continuous electronic wave
functions. For the order of shell filling in the relativistic case, we take
into account the effect due to electronic spin-orbit interaction. The
increasing of both magnitudes, the statistical complexity and the
Fisher-Shannon information, with the atomic number $Z$ is observed. The shell
structure and the irregular shell filling is well displayed by the
Fisher-Shannon information in the relativistic case.
|
0904.1149
|
Chaitin \Omega numbers and halting problems
|
math.LO cs.CC cs.IT math.IT
|
Chaitin [G. J. Chaitin, J. Assoc. Comput. Mach., vol.22, pp.329-340, 1975]
introduced \Omega number as a concrete example of random real. The real \Omega
is defined as the probability that an optimal computer halts, where the optimal
computer is a universal decoding algorithm used to define the notion of
program-size complexity. Chaitin showed \Omega to be random by discovering the
property that the first n bits of the base-two expansion of \Omega solve the
halting problem of the optimal computer for all binary inputs of length at most
n. In the present paper we investigate this property from various aspects. We
consider the relative computational power between the base-two expansion of
\Omega and the halting problem by imposing the restriction to finite size on
both the problems. It is known that the base-two expansion of \Omega and the
halting problem are Turing equivalent. We thus consider an elaboration of the
Turing equivalence in a certain manner.
|
0904.1150
|
Upper Bounds on the Capacities of Noncontrollable Finite-State Channels
with/without Feedback
|
cs.IT cs.SY math.IT math.OC
|
Noncontrollable finite-state channels (FSCs) are FSCs in which the channel
inputs have no influence on the channel states, i.e., the channel states evolve
freely. Since single-letter formulae for the channel capacities are rarely
available for general noncontrollable FSCs, computable bounds are usually
utilized to numerically bound the capacities. In this paper, we take the
delayed channel state as part of the channel input and then define the {\em
directed information rate} from the new channel input (including the source and
the delayed channel state) sequence to the channel output sequence. With this
technique, we derive a series of upper bounds on the capacities of
noncontrollable FSCs with/without feedback. These upper bounds can be achieved
by conditional Markov sources and computed by solving an average reward per
stage stochastic control problem (ARSCP) with a compact state space and a
compact action space. By showing that the ARSCP has a uniformly continuous
reward function, we transform the original ARSCP into a finite-state and
finite-action ARSCP that can be solved by a value iteration method. Under a
mild assumption, the value iteration algorithm is convergent and delivers a
near-optimal stationary policy and a numerical upper bound.
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.