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cs/0505003
|
A New Kind of Hopfield Networks for Finding Global Optimum
|
cs.NE
|
The Hopfield network has been applied to solve optimization problems over
decades. However, it still has many limitations in accomplishing this task.
Most of them are inherited from the optimization algorithms it implements. The
computation of a Hopfield network, defined by a set of difference equations,
can easily be trapped into one local optimum or another, sensitive to initial
conditions, perturbations, and neuron update orders. It doesn't know how long
it will take to converge, as well as if the final solution is a global optimum,
or not. In this paper, we present a Hopfield network with a new set of
difference equations to fix those problems. The difference equations directly
implement a new powerful optimization algorithm.
|
cs/0505006
|
Searching for image information content, its discovery, extraction, and
representation
|
cs.CV
|
Image information content is known to be a complicated and controvercial
problem. This paper posits a new image information content definition.
Following the theory of Solomonoff-Kolmogorov-Chaitin's complexity, we define
image information content as a set of descriptions of imafe data structures.
Three levels of such description can be generally distinguished: 1)the global
level, where the coarse structure of the entire scene is initially outlined; 2)
the intermediate level, where structures of separate, non-overlapping image
regions usually associated with individual scene objects are deliniated; and 3)
the low-level description, where local image structures observed in a limited
and restricted field of view are resolved. A technique for creating such image
information content descriptors is developed. Its algorithm is presented and
elucidated with some examples, which demonstrate the effectiveness of the
proposed approach.
|
cs/0505008
|
Data Mining on Crash Simulation Data
|
cs.IR cs.CE
|
The work presented in this paper is part of the cooperative research project
AUTO-OPT carried out by twelve partners from the automotive industries. One
major work package concerns the application of data mining methods in the area
of automotive design. Suitable methods for data preparation and data analysis
are developed. The objective of the work is the re-use of data stored in the
crash-simulation department at BMW in order to gain deeper insight into the
interrelations between the geometric variations of the car during its design
and its performance in crash testing. In this paper a method for data analysis
of finite element models and results from crash simulation is proposed and
application to recent data from the industrial partner BMW is demonstrated. All
necessary steps from data pre-processing to re-integration into the working
environment of the engineer are covered.
|
cs/0505010
|
On the Wyner-Ziv problem for individual sequences
|
cs.IT math.IT
|
We consider a variation of the Wyner-Ziv problem pertaining to lossy
compression of individual sequences using finite-state encoders and decoders.
There are two main results in this paper. The first characterizes the
relationship between the performance of the best $M$-state encoder-decoder pair
to that of the best block code of size $\ell$ for every input sequence, and
shows that the loss of the latter relative to the former (in terms of both rate
and distortion) never exceeds the order of $(\log M)/\ell$, independently of
the input sequence. Thus, in the limit of large $M$, the best rate-distortion
performance of every infinite source sequence can be approached universally by
a sequence of block codes (which are also implementable by finite-state
machines). While this result assumes an asymptotic regime where the number of
states is fixed, and only the length $n$ of the input sequence grows without
bound, we then consider the case where the number of states $M=M_n$ is allowed
to grow concurrently with $n$. Our second result is then about the critical
growth rate of $M_n$ such that the rate-distortion performance of $M_n$-state
encoder-decoder pairs can still be matched by a universal code. We show that
this critical growth rate of $M_n$ is linear in $n$.
|
cs/0505012
|
On the Shannon cipher system with a capacity-limited key-distribution
channel
|
cs.IT math.IT
|
We consider the Shannon cipher system in a setting where the secret key is
delivered to the legitimate receiver via a channel with limited capacity. For
this setting, we characterize the achievable region in the space of three
figures of merit: the security (measured in terms of the equivocation), the
compressibility of the cryptogram, and the distortion associated with the
reconstruction of the plaintext source. Although lossy reconstruction of the
plaintext does not rule out the option that the (noisy) decryption key would
differ, to a certain extent, from the encryption key, we show, nevertheless,
that the best strategy is to strive for perfect match between the two keys, by
applying reliable channel coding to the key bits, and to control the distortion
solely via rate-distortion coding of the plaintext source before the
encryption. In this sense, our result has a flavor similar to that of the
classical source-channel separation theorem. Some variations and extensions of
this model are discussed as well.
|
cs/0505016
|
Visual Character Recognition using Artificial Neural Networks
|
cs.NE
|
The recognition of optical characters is known to be one of the earliest
applications of Artificial Neural Networks, which partially emulate human
thinking in the domain of artificial intelligence. In this paper, a simplified
neural approach to recognition of optical or visual characters is portrayed and
discussed. The document is expected to serve as a resource for learners and
amateur investigators in pattern recognition, neural networking and related
disciplines.
|
cs/0505018
|
Temporal and Spatial Data Mining with Second-Order Hidden Models
|
cs.AI
|
In the frame of designing a knowledge discovery system, we have developed
stochastic models based on high-order hidden Markov models. These models are
capable to map sequences of data into a Markov chain in which the transitions
between the states depend on the \texttt{n} previous states according to the
order of the model. We study the process of achieving information extraction
fromspatial and temporal data by means of an unsupervised classification. We
use therefore a French national database related to the land use of a region,
named Teruti, which describes the land use both in the spatial and temporal
domain. Land-use categories (wheat, corn, forest, ...) are logged every year on
each site regularly spaced in the region. They constitute a temporal sequence
of images in which we look for spatial and temporal dependencies. The temporal
segmentation of the data is done by means of a second-order Hidden Markov Model
(\hmmd) that appears to have very good capabilities to locate stationary
segments, as shown in our previous work in speech recognition. Thespatial
classification is performed by defining a fractal scanning ofthe images with
the help of a Hilbert-Peano curve that introduces atotal order on the sites,
preserving the relation ofneighborhood between the sites. We show that the
\hmmd performs aclassification that is meaningful for the agronomists.Spatial
and temporal classification may be achieved simultaneously by means of a 2
levels \hmmd that measures the \aposteriori probability to map a temporal
sequence of images onto a set of hidden classes.
|
cs/0505019
|
Artificial Neural Networks and their Applications
|
cs.NE
|
The Artificial Neural network is a functional imitation of simplified model
of the biological neurons and their goal is to construct useful computers for
real world problems. The ANN applications have increased dramatically in the
last few years fired by both theoretical and practical applications in a wide
variety of applications. A brief theory of ANN is presented and potential areas
are identified and future trends are discussed.
|
cs/0505020
|
Asymptotic Capacity Results for Non-Stationary Time-Variant Channels
Using Subspace Projections
|
cs.IT math.IT
|
In this paper we deal with a single-antenna discrete-time flat-fading
channel. The fading process is assumed to be stationary for the duration of a
single data block. From block to block the fading process is allowed to be
non-stationary. The number of scatterers bounds the rank of the channels
covariance matrix. The signal-to-noise ratio (SNR), the user velocity, and the
data block-length define the usable rank of the time-variant channel subspace.
The usable channel subspace grows with the SNR. This growth in dimensionality
must be taken into account for asymptotic capacity results in the high-SNR
regime. Using results from the theory of time-concentrated and band-limited
sequences we are able to define an SNR threshold below which the capacity grows
logarithmically. Above this threshold the capacity grows
double-logarithmically.
|
cs/0505021
|
Distant generalization by feedforward neural networks
|
cs.NE
|
This paper discusses the notion of generalization of training samples over
long distances in the input space of a feedforward neural network. Such a
generalization might occur in various ways, that differ in how great the
contribution of different training features should be.
The structure of a neuron in a feedforward neural network is analyzed and it
is concluded, that the actual performance of the discussed generalization in
such neural networks may be problematic -- while such neural networks might be
capable for such a distant generalization, a random and spurious generalization
may occur as well.
To illustrate the differences in generalizing of the same function by
different learning machines, results given by the support vector machines are
also presented.
|
cs/0505022
|
Collaborative Beamforming for Distributed Wireless Ad Hoc Sensor
Networks
|
cs.IT cs.NI math.IT
|
The performance of collaborative beamforming is analyzed using the theory of
random arrays. The statistical average and distribution of the beampattern of
randomly generated phased arrays is derived in the framework of wireless ad hoc
sensor networks. Each sensor node is assumed to have a single isotropic antenna
and nodes in the cluster collaboratively transmit the signal such that the
signal in the target direction is coherently added in the far- eld region. It
is shown that with N sensor nodes uniformly distributed over a disk, the
directivity can approach N, provided that the nodes are located sparsely
enough. The distribution of the maximum sidelobe peak is also studied. With the
application to ad hoc networks in mind, two scenarios, closed-loop and
open-loop, are considered. Associated with these scenarios, the effects of
phase jitter and location estimation errors on the average beampattern are also
analyzed.
|
cs/0505028
|
A linear memory algorithm for Baum-Welch training
|
cs.LG cs.DS q-bio.QM
|
Background: Baum-Welch training is an expectation-maximisation algorithm for
training the emission and transition probabilities of hidden Markov models in a
fully automated way.
Methods and results: We introduce a linear space algorithm for Baum-Welch
training. For a hidden Markov model with M states, T free transition and E free
emission parameters, and an input sequence of length L, our new algorithm
requires O(M) memory and O(L M T_max (T + E)) time for one Baum-Welch
iteration, where T_max is the maximum number of states that any state is
connected to. The most memory efficient algorithm until now was the
checkpointing algorithm with O(log(L) M) memory and O(log(L) L M T_max) time
requirement. Our novel algorithm thus renders the memory requirement completely
independent of the length of the training sequences. More generally, for an
n-hidden Markov model and n input sequences of length L, the memory requirement
of O(log(L) L^(n-1) M) is reduced to O(L^(n-1) M) memory while the running time
is changed from O(log(L) L^n M T_max + L^n (T + E)) to O(L^n M T_max (T + E)).
Conclusions: For the large class of hidden Markov models used for example in
gene prediction, whose number of states does not scale with the length of the
input sequence, our novel algorithm can thus be both faster and more
memory-efficient than any of the existing algorithms.
|
cs/0505032
|
Broadcast Channels with Cooperating Decoders
|
cs.IT math.IT
|
We consider the problem of communicating over the general discrete memoryless
broadcast channel (BC) with partially cooperating receivers. In our setup,
receivers are able to exchange messages over noiseless conference links of
finite capacities, prior to decoding the messages sent from the transmitter. In
this paper we formulate the general problem of broadcast with cooperation. We
first find the capacity region for the case where the BC is physically
degraded. Then, we give achievability results for the general broadcast
channel, for both the two independent messages case and the single common
message case.
|
cs/0505035
|
Beyond Hypertree Width: Decomposition Methods Without Decompositions
|
cs.CC cs.AI
|
The general intractability of the constraint satisfaction problem has
motivated the study of restrictions on this problem that permit polynomial-time
solvability. One major line of work has focused on structural restrictions,
which arise from restricting the interaction among constraint scopes. In this
paper, we engage in a mathematical investigation of generalized hypertree
width, a structural measure that has up to recently eluded study. We obtain a
number of computational results, including a simple proof of the tractability
of CSP instances having bounded generalized hypertree width.
|
cs/0505038
|
Efficient Management of Short-Lived Data
|
cs.DB
|
Motivated by the increasing prominence of loosely-coupled systems, such as
mobile and sensor networks, which are characterised by intermittent
connectivity and volatile data, we study the tagging of data with so-called
expiration times. More specifically, when data are inserted into a database,
they may be tagged with time values indicating when they expire, i.e., when
they are regarded as stale or invalid and thus are no longer considered part of
the database. In a number of applications, expiration times are known and can
be assigned at insertion time. We present data structures and algorithms for
online management of data tagged with expiration times. The algorithms are
based on fully functional, persistent treaps, which are a combination of binary
search trees with respect to a primary attribute and heaps with respect to a
secondary attribute. The primary attribute implements primary keys, and the
secondary attribute stores expiration times in a minimum heap, thus keeping a
priority queue of tuples to expire. A detailed and comprehensive experimental
study demonstrates the well-behavedness and scalability of the approach as well
as its efficiency with respect to a number of competitors.
|
cs/0505039
|
Methods for comparing rankings of search engine results
|
cs.IR
|
In this paper we present a number of measures that compare rankings of search
engine results. We apply these measures to five queries that were monitored
daily for two periods of about 21 days each. Rankings of the different search
engines (Google, Yahoo and Teoma for text searches and Google, Yahoo and
Picsearch for image searches) are compared on a daily basis, in addition to
longitudinal comparisons of the same engine for the same query over time. The
results and rankings of the two periods are compared as well.
|
cs/0505041
|
Relational reasoning in the region connection calculus
|
cs.AI cs.LO
|
This paper is mainly concerned with the relation-algebraical aspects of the
well-known Region Connection Calculus (RCC). We show that the contact relation
algebra (CRA) of certain RCC model is not atomic complete and hence infinite.
So in general an extensional composition table for the RCC cannot be obtained
by simply refining the RCC8 relations. After having shown that each RCC model
is a consistent model of the RCC11 CT, we give an exhaustive investigation
about extensional interpretation of the RCC11 CT. More important, we show the
complemented closed disk algebra is a representation for the relation algebra
determined by the RCC11 table. The domain of this algebra contains two classes
of regions, the closed disks and closures of their complements in the real
plane.
|
cs/0505042
|
Iterative MILP Methods for Vehicle Control Problems
|
cs.RO
|
Mixed integer linear programming (MILP) is a powerful tool for planning and
control problems because of its modeling capability and the availability of
good solvers. However, for large models, MILP methods suffer computationally.
In this paper, we present iterative MILP algorithms that address this issue. We
consider trajectory generation problems with obstacle avoidance requirements
and minimum time trajectory generation problems. The algorithms use fewer
binary variables than standard MILP methods and require less computational
effort.
|
cs/0505044
|
Separating a Real-Life Nonlinear Image Mixture
|
cs.NE cs.AI cs.IT math.IT
|
When acquiring an image of a paper document, the image printed on the back
page sometimes shows through. The mixture of the front- and back-page images
thus obtained is markedly nonlinear, and thus constitutes a good real-life test
case for nonlinear blind source separation.
This paper addresses a difficult version of this problem, corresponding to
the use of "onion skin" paper, which results in a relatively strong
nonlinearity of the mixture, which becomes close to singular in the lighter
regions of the images. The separation is achieved through the MISEP technique,
which is an extension of the well known INFOMAX method. The separation results
are assessed with objective quality measures. They show an improvement over the
results obtained with linear separation, but have room for further improvement.
|
cs/0505045
|
A T Step Ahead Optimal Target Detection Algorithm for a Multi Sensor
Surveillance System
|
cs.MA cs.RO
|
This paper presents a methodology for optimal target detection in a multi
sensor surveillance system. The system consists of mobile sensors that guard a
rectangular surveillance zone crisscrossed by moving targets. Targets percolate
the surveillance zone in a poisson fashion with uniform velocities. Under these
statistics this paper computes a motion strategy for a sensor that maximizes
target detections for the next T time steps. A coordination mechanism between
sensors ensures that overlapping areas between sensors is reduced. This
coordination mechanism is interleaved with the motion strategy computation to
reduce detections of the same target by more than one sensor. To avoid an
exhaustive search in the joint space of all sensors the coordination mechanism
constraints the search by assigning priorities to the sensors. A comparison of
this methodology with other multi target tracking schemes verifies its efficacy
in maximizing detections. A tabulation of these comparisons is reported in
results section of the paper
|
cs/0505046
|
Optimum Signal Linear Detector in the Discrete Wavelet Transform-Domain
|
cs.IT cs.IR math.IT
|
The problem of known signal detection in Additive White Gaussian Noise is
considered. In this paper a new detection algorithm based on Discrete Wavelet
Transform pre-processing and threshold comparison is introduced. Current
approaches described in [7] use the maximum value obtained in the wavelet
domain for decision. Here, we use all available information in the wavelet
domain with excellent results. Detector performance is presented in Probability
of detection curves for a fixed probability of false alarm.
|
cs/0505049
|
Fading-Resilient Super-Orthogonal Space-Time Signal Sets: Can Good
Constellations Survive in Fading?
|
cs.IT math.IT
|
In this correspondence, first-tier indirect (direct) discernible
constellation expansions are defined for generalized orthogonal designs. The
expanded signal constellation, leading to so-called super-orthogonal codes,
allows the achievement of coding gains in addition to diversity gains enabled
by orthogonal designs. Conditions that allow the shape of an expanded
multidimensional constellation to be preserved at the channel output, on an
instantaneous basis, are derived. It is further shown that, for such
constellations, the channel alters neither the relative distances nor the
angles between signal points in the expanded signal constellation.
|
cs/0505051
|
Sub-Optimum Signal Linear Detector Using Wavelets and Support Vector
Machines
|
cs.IR cs.NE
|
The problem of known signal detection in Additive White Gaussian Noise is
considered. In previous work, a new detection scheme was introduced by the
authors, and it was demonstrated that optimum performance cannot be reached in
a real implementation. In this paper we analyse Support Vector Machines (SVM)
as an alternative, evaluating the results in terms of Probability of detection
curves for a fixed Probability of false alarm.
|
cs/0505052
|
Upgrading Pulse Detection with Time Shift Properties Using Wavelets and
Support Vector Machines
|
cs.IR cs.NE
|
Current approaches in pulse detection use domain transformations so as to
concentrate frequency related information that can be distinguishable from
noise. In real cases we do not know when the pulse will begin, so we need a
time search process in which time windows are scheduled and analysed. Each
window can contain the pulsed signal (either complete or incomplete) and / or
noise. In this paper a simple search process will be introduced, allowing the
algorithm to process more information, upgrading the capabilities in terms of
probability of detection (Pd) and probability of false alarm (Pfa).
|
cs/0505053
|
Wavelet Time Shift Properties Integration with Support Vector Machines
|
cs.IR cs.NE
|
This paper presents a short evaluation about the integration of information
derived from wavelet non-linear-time-invariant (non-LTI) projection properties
using Support Vector Machines (SVM). These properties may give additional
information for a classifier trying to detect known patterns hidden by noise.
In the experiments we present a simple electromagnetic pulsed signal
recognition scheme, where some improvement is achieved with respect to previous
work. SVMs are used as a tool for information integration, exploiting some
unique properties not easily found in neural networks.
|
cs/0505054
|
The Partition Weight Enumerator of MDS Codes and its Applications
|
cs.IT math.IT
|
A closed form formula of the partition weight enumerator of maximum distance
separable (MDS) codes is derived for an arbitrary number of partitions. Using
this result, some properties of MDS codes are discussed. The results are
extended for the average binary image of MDS codes in finite fields of
characteristic two. As an application, we study the multiuser error probability
of Reed Solomon codes.
|
cs/0505056
|
Text Compression and Superfast Searching
|
cs.IR cs.IT math.IT
|
In this paper, a new compression scheme for text is presented. The same is
efficient in giving high compression ratios and enables super fast searching
within the compressed text. Typical compression ratios of 70-80% and reducing
the search time by 80-85% are the features of this paper. Till now, a trade-off
between high ratios and searchability within compressed text has been seen. In
this paper, we show that greater the compression, faster the search. This finds
applicability in so many places where data as natural language text is present.
|
cs/0505057
|
Improved Bounds on the Parity-Check Density and Achievable Rates of
Binary Linear Block Codes with Applications to LDPC Codes
|
cs.IT math.IT
|
We derive bounds on the asymptotic density of parity-check matrices and the
achievable rates of binary linear block codes transmitted over memoryless
binary-input output-symmetric (MBIOS) channels. The lower bounds on the density
of arbitrary parity-check matrices are expressed in terms of the gap between
the rate of these codes for which reliable communication is achievable and the
channel capacity, and the bounds are valid for every sequence of binary linear
block codes. These bounds address the question, previously considered by Sason
and Urbanke, of how sparse can parity-check matrices of binary linear block
codes be as a function of the gap to capacity. Similarly to a previously
reported bound by Sason and Urbanke, the new lower bounds on the parity-check
density scale like the log of the inverse of the gap to capacity, but their
tightness is improved (except for a binary symmetric/erasure channel, where
they coincide with the previous bound). The new upper bounds on the achievable
rates of binary linear block codes tighten previously reported bounds by
Burshtein et al., and therefore enable to obtain tighter upper bounds on the
thresholds of sequences of binary linear block codes under ML decoding. The
bounds are applied to low-density parity-check (LDPC) codes, and the
improvement in their tightness is exemplified numerically. The upper bounds on
the achievable rates enable to assess the inherent loss in performance of
various iterative decoding algorithms as compared to optimal ML decoding. The
lower bounds on the asymptotic parity-check density are helpful in assessing
the inherent tradeoff between the asymptotic performance of LDPC codes and
their decoding complexity (per iteration) under message-passing decoding.
|
cs/0505058
|
The Cyborg Astrobiologist: Scouting Red Beds for Uncommon Features with
Geological Significance
|
cs.CV astro-ph cs.AI cs.CE cs.HC cs.RO cs.SE physics.ins-det q-bio.NC
|
The `Cyborg Astrobiologist' (CA) has undergone a second geological field
trial, at a red sandstone site in northern Guadalajara, Spain, near Riba de
Santiuste. The Cyborg Astrobiologist is a wearable computer and video camera
system that has demonstrated a capability to find uncommon interest points in
geological imagery in real-time in the field. The first (of three) geological
structures that we studied was an outcrop of nearly homogeneous sandstone,
which exhibits oxidized-iron impurities in red and and an absence of these iron
impurities in white. The white areas in these ``red beds'' have turned white
because the iron has been removed by chemical reduction, perhaps by a
biological agent. The computer vision system found in one instance several
(iron-free) white spots to be uncommon and therefore interesting, as well as
several small and dark nodules. The second geological structure contained
white, textured mineral deposits on the surface of the sandstone, which were
found by the CA to be interesting. The third geological structure was a 50 cm
thick paleosol layer, with fossilized root structures of some plants, which
were found by the CA to be interesting. A quasi-blind comparison of the Cyborg
Astrobiologist's interest points for these images with the interest points
determined afterwards by a human geologist shows that the Cyborg Astrobiologist
concurred with the human geologist 68% of the time (true positive rate), with a
32% false positive rate and a 32% false negative rate.
(abstract has been abridged).
|
cs/0505059
|
Consistent query answers on numerical databases under aggregate
constraints
|
cs.DB
|
The problem of extracting consistent information from relational databases
violating integrity constraints on numerical data is addressed. In particular,
aggregate constraints defined as linear inequalities on aggregate-sum queries
on input data are considered. The notion of repair as consistent set of updates
at attribute-value level is exploited, and the characterization of several
complexity issues related to repairing data and computing consistent query
answers is provided.
|
cs/0505060
|
A Unified Subspace Outlier Ensemble Framework for Outlier Detection in
High Dimensional Spaces
|
cs.DB cs.AI
|
The task of outlier detection is to find small groups of data objects that
are exceptional when compared with rest large amount of data. Detection of such
outliers is important for many applications such as fraud detection and
customer migration. Most such applications are high dimensional domains in
which the data may contain hundreds of dimensions. However, the outlier
detection problem itself is not well defined and none of the existing
definitions are widely accepted, especially in high dimensional space. In this
paper, our first contribution is to propose a unified framework for outlier
detection in high dimensional spaces from an ensemble-learning viewpoint. In
our new framework, the outlying-ness of each data object is measured by fusing
outlier factors in different subspaces using a combination function.
Accordingly, we show that all existing researches on outlier detection can be
regarded as special cases in the unified framework with respect to the set of
subspaces considered and the type of combination function used. In addition, to
demonstrate the usefulness of the ensemble-learning based outlier detection
framework, we developed a very simple and fast algorithm, namely SOE1 (Subspace
Outlier Ensemble using 1-dimensional Subspaces) in which only subspaces with
one dimension is used for mining outliers from large categorical datasets. The
SOE1 algorithm needs only two scans over the dataset and hence is very
appealing in real data mining applications. Experimental results on real
datasets and large synthetic datasets show that: (1) SOE1 has comparable
performance with respect to those state-of-art outlier detection algorithms on
identifying true outliers and (2) SOE1 can be an order of magnitude faster than
one of the fastest outlier detection algorithms known so far.
|
cs/0505064
|
Multi-Modal Human-Machine Communication for Instructing Robot Grasping
Tasks
|
cs.HC cs.AI cs.CV cs.LG cs.RO
|
A major challenge for the realization of intelligent robots is to supply them
with cognitive abilities in order to allow ordinary users to program them
easily and intuitively. One way of such programming is teaching work tasks by
interactive demonstration. To make this effective and convenient for the user,
the machine must be capable to establish a common focus of attention and be
able to use and integrate spoken instructions, visual perceptions, and
non-verbal clues like gestural commands. We report progress in building a
hybrid architecture that combines statistical methods, neural networks, and
finite state machines into an integrated system for instructing grasping tasks
by man-machine interaction. The system combines the GRAVIS-robot for visual
attention and gestural instruction with an intelligent interface for speech
recognition and linguistic interpretation, and an modality fusion module to
allow multi-modal task-oriented man-machine communication with respect to
dextrous robot manipulation of objects.
|
cs/0505065
|
A dissipative particle swarm optimization
|
cs.NE
|
A dissipative particle swarm optimization is developed according to the
self-organization of dissipative structure. The negative entropy is introduced
to construct an opening dissipative system that is far-from-equilibrium so as
to driving the irreversible evolution process with better fitness. The testing
of two multimodal functions indicates it improves the performance effectively
|
cs/0505067
|
Optimizing semiconductor devices by self-organizing particle swarm
|
cs.NE
|
A self-organizing particle swarm is presented. It works in dissipative state
by employing the small inertia weight, according to experimental analysis on a
simplified model, which with fast convergence. Then by recognizing and
replacing inactive particles according to the process deviation information of
device parameters, the fluctuation is introduced so as to driving the
irreversible evolution process with better fitness. The testing on benchmark
functions and an application example for device optimization with designed
fitness function indicates it improves the performance effectively.
|
cs/0505068
|
Handling equality constraints by adaptive relaxing rule for swarm
algorithms
|
cs.NE
|
The adaptive constraints relaxing rule for swarm algorithms to handle with
the problems with equality constraints is presented. The feasible space of such
problems may be similiar to ridge function class, which is hard for applying
swarm algorithms. To enter the solution space more easily, the relaxed quasi
feasible space is introduced and shrinked adaptively. The experimental results
on benchmark functions are compared with the performance of other algorithms,
which show its efficiency.
|
cs/0505069
|
Handling boundary constraints for numerical optimization by particle
swarm flying in periodic search space
|
cs.NE
|
The periodic mode is analyzed together with two conventional boundary
handling modes for particle swarm. By providing an infinite space that
comprises periodic copies of original search space, it avoids possible
disorganizing of particle swarm that is induced by the undesired mutations at
the boundary. The results on benchmark functions show that particle swarm with
periodic mode is capable of improving the search performance significantly, by
compared with that of conventional modes and other algorithms.
|
cs/0505070
|
SWAF: Swarm Algorithm Framework for Numerical Optimization
|
cs.NE
|
A swarm algorithm framework (SWAF), realized by agent-based modeling, is
presented to solve numerical optimization problems. Each agent is a bare bones
cognitive architecture, which learns knowledge by appropriately deploying a set
of simple rules in fast and frugal heuristics. Two essential categories of
rules, the generate-and-test and the problem-formulation rules, are
implemented, and both of the macro rules by simple combination and subsymbolic
deploying of multiple rules among them are also studied. Experimental results
on benchmark problems are presented, and performance comparison between SWAF
and other existing algorithms indicates that it is efficiently.
|
cs/0505071
|
Summarization Techniques for Pattern Collections in Data Mining
|
cs.DB cs.AI cs.DS
|
Discovering patterns from data is an important task in data mining. There
exist techniques to find large collections of many kinds of patterns from data
very efficiently. A collection of patterns can be regarded as a summary of the
data. A major difficulty with patterns is that pattern collections summarizing
the data well are often very large.
In this dissertation we describe methods for summarizing pattern collections
in order to make them also more understandable. More specifically, we focus on
the following themes: 1) Quality value simplifications. 2) Pattern orderings.
3) Pattern chains and antichains. 4) Change profiles. 5) Inverse pattern
discovery.
|
cs/0505074
|
Instance-Independent View Serializability for Semistructured Databases
|
cs.DB
|
Semistructured databases require tailor-made concurrency control mechanisms
since traditional solutions for the relational model have been shown to be
inadequate. Such mechanisms need to take full advantage of the hierarchical
structure of semistructured data, for instance allowing concurrent updates of
subtrees of, or even individual elements in, XML documents. We present an
approach for concurrency control which is document-independent in the sense
that two schedules of semistructured transactions are considered equivalent if
they are equivalent on all possible documents. We prove that it is decidable in
polynomial time whether two given schedules in this framework are equivalent.
This also solves the view serializability for semistructured schedules
polynomially in the size of the schedule and exponentially in the number of
transactions.
|
cs/0505078
|
On the Parity-Check Density and Achievable Rates of LDPC Codes
|
cs.IT math.IT
|
The paper introduces new bounds on the asymptotic density of parity-check
matrices and the achievable rates under ML decoding of binary linear block
codes transmitted over memoryless binary-input output-symmetric channels. The
lower bounds on the parity-check density are expressed in terms of the gap
between the channel capacity and the rate of the codes for which reliable
communication is achievable, and are valid for every sequence of binary linear
block codes. The bounds address the question, previously considered by Sason
and Urbanke, of how sparse can parity-check matrices of binary linear block
codes be as a function of the gap to capacity. The new upper bounds on the
achievable rates of binary linear block codes tighten previously reported
bounds by Burshtein et al., and therefore enable to obtain tighter upper bounds
on the thresholds of sequences of binary linear block codes under ML decoding.
The bounds are applied to low-density parity-check (LDPC) codes, and the
improvement in their tightness is exemplified numerically.
|
cs/0505080
|
Dominance Based Crossover Operator for Evolutionary Multi-objective
Algorithms
|
cs.AI cs.NA
|
In spite of the recent quick growth of the Evolutionary Multi-objective
Optimization (EMO) research field, there has been few trials to adapt the
general variation operators to the particular context of the quest for the
Pareto-optimal set. The only exceptions are some mating restrictions that take
in account the distance between the potential mates - but contradictory
conclusions have been reported. This paper introduces a particular mating
restriction for Evolutionary Multi-objective Algorithms, based on the Pareto
dominance relation: the partner of a non-dominated individual will be
preferably chosen among the individuals of the population that it dominates.
Coupled with the BLX crossover operator, two different ways of generating
offspring are proposed. This recombination scheme is validated within the
well-known NSGA-II framework on three bi-objective benchmark problems and one
real-world bi-objective constrained optimization problem. An acceleration of
the progress of the population toward the Pareto set is observed on all
problems.
|
cs/0505081
|
An ontological approach to the construction of problem-solving models
|
cs.AI
|
Our ongoing work aims at defining an ontology-centered approach for building
expertise models for the CommonKADS methodology. This approach (which we have
named "OntoKADS") is founded on a core problem-solving ontology which
distinguishes between two conceptualization levels: at an object level, a set
of concepts enable us to define classes of problem-solving situations, and at a
meta level, a set of meta-concepts represent modeling primitives. In this
article, our presentation of OntoKADS will focus on the core ontology and, in
particular, on roles - the primitive situated at the interface between domain
knowledge and reasoning, and whose ontological status is still much debated. We
first propose a coherent, global, ontological framework which enables us to
account for this primitive. We then show how this novel characterization of the
primitive allows definition of new rules for the construction of expertise
models.
|
cs/0505083
|
Defensive forecasting
|
cs.LG cs.AI
|
We consider how to make probability forecasts of binary labels. Our main
mathematical result is that for any continuous gambling strategy used for
detecting disagreement between the forecasts and the actual labels, there
exists a forecasting strategy whose forecasts are ideal as far as this gambling
strategy is concerned. A forecasting strategy obtained in this way from a
gambling strategy demonstrating a strong law of large numbers is simplified and
studied empirically.
|
cs/0505084
|
An explicit formula for the number of tunnels in digital objects
|
cs.DM cs.CG cs.CV
|
An important concept in digital geometry for computer imagery is that of
tunnel. In this paper we obtain a formula for the number of tunnels as a
function of the number of the object vertices, pixels, holes, connected
components, and 2x2 grid squares. It can be used to test for tunnel-freedom a
digital object, in particular a digital curve.
|
cs/0506002
|
HepToX: Heterogeneous Peer to Peer XML Databases
|
cs.DB
|
We study a collection of heterogeneous XML databases maintaining similar and
related information, exchanging data via a peer to peer overlay network. In
this setting, a mediated global schema is unrealistic. Yet, users/applications
wish to query the databases via one peer using its schema. We have recently
developed HepToX, a P2P Heterogeneous XML database system. A key idea is that
whenever a peer enters the system, it establishes an acquaintance with a small
number of peer databases, possibly with different schema. The peer
administrator provides correspondences between the local schema and the
acquaintance schema using an informal and intuitive notation of arrows and
boxes. We develop a novel algorithm that infers a set of precise mapping rules
between the schemas from these visual annotations. We pin down a semantics of
query translation given such mapping rules, and present a novel query
translation algorithm for a simple but expressive fragment of XQuery, that
employs the mapping rules in either direction. We show the translation
algorithm is correct. Finally, we demonstrate the utility and scalability of
our ideas and algorithms with a detailed set of experiments on top of the
Emulab, a large scale P2P network emulation testbed.
|
cs/0506004
|
Non-asymptotic calibration and resolution
|
cs.LG
|
We analyze a new algorithm for probability forecasting of binary observations
on the basis of the available data, without making any assumptions about the
way the observations are generated. The algorithm is shown to be well
calibrated and to have good resolution for long enough sequences of
observations and for a suitable choice of its parameter, a kernel on the
Cartesian product of the forecast space $[0,1]$ and the data space. Our main
results are non-asymptotic: we establish explicit inequalities, shown to be
tight, for the performance of the algorithm.
|
cs/0506007
|
Defensive forecasting for linear protocols
|
cs.LG
|
We consider a general class of forecasting protocols, called "linear
protocols", and discuss several important special cases, including multi-class
forecasting. Forecasting is formalized as a game between three players:
Reality, whose role is to generate observations; Forecaster, whose goal is to
predict the observations; and Skeptic, who tries to make money on any lack of
agreement between Forecaster's predictions and the actual observations. Our
main mathematical result is that for any continuous strategy for Skeptic in a
linear protocol there exists a strategy for Forecaster that does not allow
Skeptic's capital to grow. This result is a meta-theorem that allows one to
transform any continuous law of probability in a linear protocol into a
forecasting strategy whose predictions are guaranteed to satisfy this law. We
apply this meta-theorem to a weak law of large numbers in Hilbert spaces to
obtain a version of the K29 prediction algorithm for linear protocols and show
that this version also satisfies the attractive properties of proper
calibration and resolution under a suitable choice of its kernel parameter,
with no assumptions about the way the data is generated.
|
cs/0506009
|
Approximate MAP Decoding on Tail-Biting Trellises
|
cs.IT math.IT
|
We propose two approximate algorithms for MAP decoding on tail-biting
trellises. The algorithms work on a subset of nodes of the tail-biting trellis,
judiciously selected. We report the results of simulations on an AWGN channel
using the approximate algorithms on tail-biting trellises for the $(24,12)$
Extended Golay Code and a rate 1/2 convolutional code with memory 6.
|
cs/0506011
|
On the dimensions of certain LDPC codes based on q-regular bipartite
graphs
|
cs.IT cs.DM math.IT
|
An explicit construction of a family of binary LDPC codes called LU(3,q),
where q is a power of a prime, was recently given. A conjecture was made for
the dimensions of these codes when q is odd. The conjecture is proved in this
note. The proof involves the geometry of a 4-dimensional symplectic vector
space and the action of the symplectic group and its subgroups.
|
cs/0506012
|
A Non-Cooperative Power Control Game in Delay-Constrained
Multiple-Access Networks
|
cs.IT math.IT
|
A game-theoretic approach for studying power control in multiple-access
networks with transmission delay constraints is proposed. A non-cooperative
power control game is considered in which each user seeks to choose a transmit
power that maximizes its own utility while satisfying the user's delay
requirements. The utility function measures the number of reliable bits
transmitted per joule of energy and the user's delay constraint is modeled as
an upper bound on the delay outage probability. The Nash equilibrium for the
proposed game is derived, and its existence and uniqueness are proved. Using a
large-system analysis, explicit expressions for the utilities achieved at
equilibrium are obtained for the matched filter, decorrelating and minimum mean
square error multiuser detectors. The effects of delay constraints on the
users' utilities (in bits/Joule) and network capacity (i.e., the maximum number
of users that can be supported) are quantified.
|
cs/0506013
|
On the existence and characterization of the maxent distribution under
general moment inequality constraints
|
cs.IT math.IT
|
A broad set of sufficient conditions that guarantees the existence of the
maximum entropy (maxent) distribution consistent with specified bounds on
certain generalized moments is derived. Most results in the literature are
either focused on the minimum cross-entropy distribution or apply only to
distributions with a bounded-volume support or address only equality
constraints. The results of this work hold for general moment inequality
constraints for probability distributions with possibly unbounded support, and
the technical conditions are explicitly on the underlying generalized moment
functions. An analytical characterization of the maxent distribution is also
derived using results from the theory of constrained optimization in
infinite-dimensional normed linear spaces. Several auxiliary results of
independent interest pertaining to certain properties of convex coercive
functions are also presented.
|
cs/0506016
|
Compressing Probability Distributions
|
cs.IT math.IT
|
We show how to store good approximations of probability distributions in
small space.
|
cs/0506017
|
Treillis de concepts et ontologies pour l'interrogation d'un annuaire de
sources de donn\'{e}es biologiques (BioRegistry)
|
cs.DB cs.IR
|
Bioinformatic data sources available on the web are multiple and
heterogenous. The lack of documentation and the difficulty of interaction with
these data sources require users competence in both informatics and biological
fields for an optimal use of sources contents that remain rather under
exploited. In this paper we present an approach based on formal concept
analysis to classify and search relevant bioinformatic data sources for a given
query. It consists in building the concept lattice from the binary relation
between bioinformatic data sources and their associated metadata. The concept
built from a given query is then merged into the concept lattice. The result is
given by the extraction of the set of sources belonging to the extents of the
query concept subsumers in the resulting concept lattice. The sources ranking
is given by the concept specificity order in the concept lattice. An
improvement of the approach consists in automatic query refinement thanks to
domain ontologies. Two forms of refinement are possible by generalisation and
by specialisation.
-----
Les sources de donn\'{e}es biologiques disponibles sur le web sont multiples
et h\'{e}t\'{e}rog\`{e}nes. L'utilisation optimale de ces ressources
n\'{e}cessite aujourd'hui de la part des utilisateurs des comp\'{e}tences \`{a}
la fois en informatique et en biologie, du fait du manque de documentation et
des difficult\'{e}s d'interaction avec les sources de donn\'{e}es. De fait, les
contenus de ces ressources restent souvent sous-exploit\'{e}s. Nous
pr\'{e}sentons ici une approche bas\'{e}e sur l'analyse de concepts formels,
pour organiser et rechercher des sources de donn\'{e}es biologiques pertinentes
pour une requ\^{e}te donn\'{e}e. Le travail consiste \`{a} construire un
treillis de concepts \`{a} partir des m\'{e}ta-donn\'{e}es associ\'{e}es aux
sources. Le concept construit \`{a} partir d'une requ\^{e}te donn\'{e}e est
alors int\'{e}gr\'{e} au treillis. La r\'{e}ponse \`{a} la requ\^{e}te est
ensuite fournie par l'extraction des sources de donn\'{e}es appartenant aux
extensions des concepts subsumant le concept requ\^{e}te dans le treillis. Les
sources ainsi retourn\'{e}es peuvent \^{e}tre tri\'{e}es selon l'ordre de
sp\'{e}cificit\'{e} des concepts dans le treillis. Une proc\'{e}dure de
raffinement de requ\^{e}te, bas\'{e}e sur des ontologies du domaine, permet
d'am\'{e}liorer le rappel par g\'{e}n\'{e}ralisation ou par sp\'{e}cialisation
|
cs/0506018
|
On the Achievable Diversity-Multiplexing Tradeoffs in Half-Duplex
Cooperative Channels
|
cs.IT math.IT
|
In this paper, we propose novel cooperative transmission protocols for delay
limited coherent fading channels consisting of N (half-duplex and
single-antenna) partners and one cell site. In our work, we differentiate
between the relay, cooperative broadcast (down-link), and cooperative
multiple-access (up-link) channels. For the relay channel, we investigate two
classes of cooperation schemes; namely, Amplify and Forward (AF) protocols and
Decode and Forward (DF) protocols. For the first class, we establish an upper
bound on the achievable diversity-multiplexing tradeoff with a single relay. We
then construct a new AF protocol that achieves this upper bound. The proposed
algorithm is then extended to the general case with N-1 relays where it is
shown to outperform the space-time coded protocol of Laneman and Worenell
without requiring decoding/encoding at the relays. For the class of DF
protocols, we develop a dynamic decode and forward (DDF) protocol that achieves
the optimal tradeoff for multiplexing gains 0 < r < 1/N. Furthermore, with a
single relay, the DDF protocol is shown to dominate the class of AF protocols
for all multiplexing gains. The superiority of the DDF protocol is shown to be
more significant in the cooperative broadcast channel. The situation is
reversed in the cooperative multiple-access channel where we propose a new AF
protocol that achieves the optimal tradeoff for all multiplexing gains. A
distinguishing feature of the proposed protocols in the three scenarios is that
they do not rely on orthogonal subspaces, allowing for a more efficient use of
resources. In fact, using our results one can argue that the sub-optimality of
previously proposed protocols stems from their use of orthogonal subspaces
rather than the half-duplex constraint.
|
cs/0506019
|
An Efficient Approximation Algorithm for Point Pattern Matching Under
Noise
|
cs.CV cs.CG
|
Point pattern matching problems are of fundamental importance in various
areas including computer vision and structural bioinformatics. In this paper,
we study one of the more general problems, known as LCP (largest common point
set problem): Let $\PP$ and $\QQ$ be two point sets in $\mathbb{R}^3$, and let
$\epsilon \geq 0$ be a tolerance parameter, the problem is to find a rigid
motion $\mu$ that maximizes the cardinality of subset $\II$ of $Q$, such that
the Hausdorff distance $\distance(\PP,\mu(\II)) \leq \epsilon$. We denote the
size of the optimal solution to the above problem by $\LCP(P,Q)$. The problem
is called exact-LCP for $\epsilon=0$, and \tolerant-LCP when $\epsilon>0$ and
the minimum interpoint distance is greater than $2\epsilon$. A
$\beta$-distance-approximation algorithm for tolerant-LCP finds a subset $I
\subseteq \QQ$ such that $|I|\geq \LCP(P,Q)$ and $\distance(\PP,\mu(\II)) \leq
\beta \epsilon$ for some $\beta \ge 1$.
This paper has three main contributions. (1) We introduce a new algorithm,
called {\DA}, which gives the fastest known deterministic
4-distance-approximation algorithm for \tolerant-LCP. (2) For the exact-LCP,
when the matched set is required to be large, we give a simple sampling
strategy that improves the running times of all known deterministic algorithms,
yielding the fastest known deterministic algorithm for this problem. (3) We use
expander graphs to speed-up the \DA algorithm for \tolerant-LCP when the size
of the matched set is required to be large, at the expense of approximation in
the matched set size. Our algorithms also work when the transformation $\mu$ is
allowed to be scaling transformation.
|
cs/0506020
|
On the Throughput-Delay Tradeoff in Cellular Multicast
|
cs.IT math.IT
|
In this paper, we adopt a cross layer design approach for analyzing the
throughput-delay tradeoff of the multicast channel in a single cell system. To
illustrate the main ideas, we start with the single group case, i.e., pure
multicast, where a common information stream is requested by all the users. We
consider three classes of scheduling algorithms with progressively increasing
complexity. The first class strives for minimum complexity by resorting to a
static scheduling strategy along with memoryless decoding. Our analysis for
this class of scheduling algorithms reveals the existence of a static
scheduling policy that achieves the optimal scaling law of the throughput at
the expense of a delay that increases exponentially with the number of users.
The second scheduling policy resorts to a higher complexity incremental
redundancy encoding/decoding strategy to achieve a superior throughput-delay
tradeoff. The third, and most complex, scheduling strategy benefits from the
cooperation between the different users to minimize the delay while achieving
the optimal scaling law of the throughput. In particular, the proposed
cooperative multicast strategy is shown to simultaneously achieve the optimal
scaling laws of both throughput and delay. Then, we generalize our scheduling
algorithms to exploit the multi-group diversity available when different
information streams are requested by different subsets of the user population.
Finally, we discuss the effect of the potential gains of equipping the base
station with multi-transmit antennas and present simulation results that
validate our theoretical claims.
|
cs/0506022
|
Asymptotics of Discrete MDL for Online Prediction
|
cs.IT cs.LG math.IT math.ST stat.TH
|
Minimum Description Length (MDL) is an important principle for induction and
prediction, with strong relations to optimal Bayesian learning. This paper
deals with learning non-i.i.d. processes by means of two-part MDL, where the
underlying model class is countable. We consider the online learning framework,
i.e. observations come in one by one, and the predictor is allowed to update
his state of mind after each time step. We identify two ways of predicting by
MDL for this setup, namely a static} and a dynamic one. (A third variant,
hybrid MDL, will turn out inferior.) We will prove that under the only
assumption that the data is generated by a distribution contained in the model
class, the MDL predictions converge to the true values almost surely. This is
accomplished by proving finite bounds on the quadratic, the Hellinger, and the
Kullback-Leibler loss of the MDL learner, which are however exponentially worse
than for Bayesian prediction. We demonstrate that these bounds are sharp, even
for model classes containing only Bernoulli distributions. We show how these
bounds imply regret bounds for arbitrary loss functions. Our results apply to a
wide range of setups, namely sequence prediction, pattern classification,
regression, and universal induction in the sense of Algorithmic Information
Theory among others.
|
cs/0506023
|
Sparse Covariance Selection via Robust Maximum Likelihood Estimation
|
cs.CE cs.AI
|
We address a problem of covariance selection, where we seek a trade-off
between a high likelihood against the number of non-zero elements in the
inverse covariance matrix. We solve a maximum likelihood problem with a penalty
term given by the sum of absolute values of the elements of the inverse
covariance matrix, and allow for imposing bounds on the condition number of the
solution. The problem is directly amenable to now standard interior-point
algorithms for convex optimization, but remains challenging due to its size. We
first give some results on the theoretical computational complexity of the
problem, by showing that a recent methodology for non-smooth convex
optimization due to Nesterov can be applied to this problem, to greatly improve
on the complexity estimate given by interior-point algorithms. We then examine
two practical algorithms aimed at solving large-scale, noisy (hence dense)
instances: one is based on a block-coordinate descent approach, where columns
and rows are updated sequentially, another applies a dual version of Nesterov's
method.
|
cs/0506024
|
The Hyper-Cortex of Human Collective-Intelligence Systems
|
cs.CY cs.AI cs.DL cs.NE
|
Individual-intelligence research, from a neurological perspective, discusses
the hierarchical layers of the cortex as a structure that performs conceptual
abstraction and specification. This theory has been used to explain how
motor-cortex regions responsible for different behavioral modalities such as
writing and speaking can be utilized to express the same general concept
represented higher in the cortical hierarchy. For example, the concept of a
dog, represented across a region of high-level cortical-neurons, can either be
written or spoken about depending on the individual's context. The higher-layer
cortical areas project down the hierarchy, sending abstract information to
specific regions of the motor-cortex for contextual implementation. In this
paper, this idea is expanded to incorporate collective-intelligence within a
hyper-cortical construct. This hyper-cortex is a multi-layered network used to
represent abstract collective concepts. These ideas play an important role in
understanding how collective-intelligence systems can be engineered to handle
problem abstraction and solution specification. Finally, a collection of common
problems in the scientific community are solved using an artificial
hyper-cortex generated from digital-library metadata.
|
cs/0506025
|
Dynamic Asymmetric Communication
|
cs.IT math.IT
|
We show how any dynamic instantaneous compression algorithm can be converted
to an asymmetric communication protocol, with which a server with high
bandwidth can help clients with low bandwidth send it messages. Unlike previous
authors, we do not assume the server knows the messages' distribution, and our
protocols are the first to use only one round of communication for each
message.
|
cs/0506026
|
Database Reformulation with Integrity Constraints (extended abstract)
|
cs.DB
|
In this paper we study the problem of reducing the evaluation costs of
queries on finite databases in presence of integrity constraints, by designing
and materializing views. Given a database schema, a set of queries defined on
the schema, a set of integrity constraints, and a storage limit, to find a
solution to this problem means to find a set of views that satisfies the
storage limit, provides equivalent rewritings of the queries under the
constraints (this requirement is weaker than equivalence in the absence of
constraints), and reduces the total costs of evaluating the queries. This
problem, database reformulation, is important for many applications, including
data warehousing and query optimization. We give complexity results and
algorithms for database reformulation in presence of constraints, for
conjunctive queries, views, and rewritings and for several types of
constraints, including functional and inclusion dependencies. To obtain better
complexity results, we introduce an unchase technique, which reduces the
problem of query equivalence under constraints to equivalence in the absence of
constraints without increasing query size.
|
cs/0506028
|
Neyman-Pearson Detection of Gauss-Markov Signals in Noise: Closed-Form
Error Exponent and Properties
|
cs.IT math.IT
|
The performance of Neyman-Pearson detection of correlated stochastic signals
using noisy observations is investigated via the error exponent for the miss
probability with a fixed level. Using the state-space structure of the signal
and observation model, a closed-form expression for the error exponent is
derived, and the connection between the asymptotic behavior of the optimal
detector and that of the Kalman filter is established. The properties of the
error exponent are investigated for the scalar case. It is shown that the error
exponent has distinct characteristics with respect to correlation strength: for
signal-to-noise ratio (SNR) >1 the error exponent decreases monotonically as
the correlation becomes stronger, whereas for SNR <1 there is an optimal
correlation that maximizes the error exponent for a given SNR.
|
cs/0506029
|
A Unified Framework for Tree Search Decoding : Rediscovering the
Sequential Decoder
|
cs.IT math.IT
|
We consider receiver design for coded transmission over linear Gaussian
channels. We restrict ourselves to the class of lattice codes and formulate the
joint detection and decoding problem as a closest lattice point search (CLPS).
Here, a tree search framework for solving the CLPS is adopted. In our
framework, the CLPS algorithm decomposes into the preprocessing and tree search
stages. The role of the preprocessing stage is to expose the tree structure in
a form {\em matched} to the search stage. We argue that the minimum mean square
error decision feedback (MMSE-DFE) frontend is instrumental for solving the
joint detection and decoding problem in a single search stage. It is further
shown that MMSE-DFE filtering allows for using lattice reduction methods to
reduce complexity, at the expense of a marginal performance loss, and solving
under-determined linear systems. For the search stage, we present a generic
method, based on the branch and bound (BB) algorithm, and show that it
encompasses all existing sphere decoders as special cases. The proposed generic
algorithm further allows for an interesting classification of tree search
decoders, sheds more light on the structural properties of all known sphere
decoders, and inspires the design of more efficient decoders. In particular, an
efficient decoding algorithm that resembles the well known Fano sequential
decoder is identified. The excellent performance-complexity tradeoff achieved
by the proposed MMSE-Fano decoder is established via simulation results and
analytical arguments in several MIMO and ISI scenarios.
|
cs/0506030
|
Preferential and Preferential-discriminative Consequence relations
|
cs.AI cs.LO
|
The present paper investigates consequence relations that are both
non-monotonic and paraconsistent. More precisely, we put the focus on
preferential consequence relations, i.e. those relations that can be defined by
a binary preference relation on states labelled by valuations. We worked with a
general notion of valuation that covers e.g. the classical valuations as well
as certain kinds of many-valued valuations. In the many-valued cases,
preferential consequence relations are paraconsistant (in addition to be
non-monotonic), i.e. they are capable of drawing reasonable conclusions which
contain contradictions. The first purpose of this paper is to provide in our
general framework syntactic characterizations of several families of
preferential relations. The second and main purpose is to provide, again in our
general framework, characterizations of several families of preferential
discriminative consequence relations. They are defined exactly as the plain
version, but any conclusion such that its negation is also a conclusion is
rejected (these relations bring something new essentially in the many-valued
cases).
|
cs/0506031
|
A Constrained Object Model for Configuration Based Workflow Composition
|
cs.AI
|
Automatic or assisted workflow composition is a field of intense research for
applications to the world wide web or to business process modeling. Workflow
composition is traditionally addressed in various ways, generally via theorem
proving techniques. Recent research observed that building a composite workflow
bears strong relationships with finite model search, and that some workflow
languages can be defined as constrained object metamodels . This lead to
consider the viability of applying configuration techniques to this problem,
which was proven feasible. Constrained based configuration expects a
constrained object model as input. The purpose of this document is to formally
specify the constrained object model involved in ongoing experiments and
research using the Z specification language.
|
cs/0506032
|
Framework for Hopfield Network based Adaptive routing - A design level
approach for adaptive routing phenomena with Artificial Neural Network
|
cs.NE
|
Routing, as a basic phenomena, by itself, has got umpteen scopes to analyse,
discuss and arrive at an optimal solution for the technocrats over years.
Routing is analysed based on many factors; few key constraints that decide the
factors are communication medium, time dependency, information source nature.
Parametric routing has become the requirement of the day, with some kind of
adaptation to the underlying network environment. Satellite constellations,
particularly LEO satellite constellations have become a reality in operational
to have a non-breaking voice/data communication around the world.Routing in
these constellations has to be treated in a non conventional way, taking their
network geometry into consideration. One of the efficient methods of
optimization is putting Neural Networks to use. Few Artificial Neural Network
models are very much suitable for the adaptive control mechanism, by their
nature of network arrangement. One such efficient model is Hopfield Network
model.
This paper is an attempt to design a framework for the Hopfield Network based
adaptive routing phenomena in satellite constellations.
|
cs/0506033
|
An Event-driven Operator Model for Dynamic Simulation of Construction
Machinery
|
cs.CE
|
Prediction and optimisation of a wheel loader's dynamic behaviour is a
challenge due to tightly coupled, non-linear subsystems of different technical
domains. Furthermore, a simulation regarding performance, efficiency, and
operability cannot be limited to the machine itself, but has to include
operator, environment, and work task. This paper presents some results of our
approach to an event-driven simulation model of a human operator. Describing
the task and the operator model independently of the machine's technical
parameters, gives the possibility to change whole sub-system characteristics
without compromising the relevance and validity of the simulation.
|
cs/0506034
|
A Taxonomy of Data Grids for Distributed Data Sharing, Management and
Processing
|
cs.DC cs.CE
|
Data Grids have been adopted as the platform for scientific communities that
need to share, access, transport, process and manage large data collections
distributed worldwide. They combine high-end computing technologies with
high-performance networking and wide-area storage management techniques. In
this paper, we discuss the key concepts behind Data Grids and compare them with
other data sharing and distribution paradigms such as content delivery
networks, peer-to-peer networks and distributed databases. We then provide
comprehensive taxonomies that cover various aspects of architecture, data
transportation, data replication and resource allocation and scheduling.
Finally, we map the proposed taxonomy to various Data Grid systems not only to
validate the taxonomy but also to identify areas for future exploration.
Through this taxonomy, we aim to categorise existing systems to better
understand their goals and their methodology. This would help evaluate their
applicability for solving similar problems. This taxonomy also provides a "gap
analysis" of this area through which researchers can potentially identify new
issues for investigation. Finally, we hope that the proposed taxonomy and
mapping also helps to provide an easy way for new practitioners to understand
this complex area of research.
|
cs/0506036
|
Non prefix-free codes for constrained sequences
|
cs.IT math.IT
|
In this paper we consider the use of variable length non prefix-free codes
for coding constrained sequences of symbols. We suppose to have a Markov source
where some state transitions are impossible, i.e. the stochastic matrix
associated with the Markov chain has some null entries. We show that classic
Kraft inequality is not a necessary condition, in general, for unique
decodability under the above hypothesis and we propose a relaxed necessary
inequality condition. This allows, in some cases, the use of non prefix-free
codes that can give very good performance, both in terms of compression and
computational efficiency. Some considerations are made on the relation between
the proposed approach and other existing coding paradigms.
|
cs/0506037
|
Tradeoff Between Source and Channel Coding for Erasure Channels
|
cs.IT math.IT
|
In this paper, we investigate the optimal tradeoff between source and channel
coding for channels with bit or packet erasure. Upper and Lower bounds on the
optimal channel coding rate are computed to achieve minimal end-to-end
distortion. The bounds are calculated based on a combination of sphere packing,
straight line and expurgated error exponents and also high rate vector
quantization theory. By modeling a packet erasure channel in terms of an
equivalent bit erasure channel, we obtain bounds on the packet size for a
specified limit on the distortion.
|
cs/0506039
|
Antenna array geometry and coding performance
|
cs.IT math.IT
|
This paper provides details about experiments in realistic, urban, and
frequency flat channels with space-time coding that specifically examines the
impact of the number of receive antennas and the design criteria for code
selection on the performance. Also the performance characteristics are examined
of the coded modulations in the presence of finite size array geometries. This
paper gives some insight into which of the theories are most useful in
realistic deployments.
|
cs/0506040
|
A Fixed-Length Coding Algorithm for DNA Sequence Compression
|
cs.IT math.IT
|
While achieving a compression ratio of 2.0 bits/base, the new algorithm codes
non-N bases in fixed length. It dramatically reduces the time of coding and
decoding than previous DNA compression algorithms and some universal
compression programs.
|
cs/0506041
|
Competitive on-line learning with a convex loss function
|
cs.LG cs.AI
|
We consider the problem of sequential decision making under uncertainty in
which the loss caused by a decision depends on the following binary
observation. In competitive on-line learning, the goal is to design decision
algorithms that are almost as good as the best decision rules in a wide
benchmark class, without making any assumptions about the way the observations
are generated. However, standard algorithms in this area can only deal with
finite-dimensional (often countable) benchmark classes. In this paper we give
similar results for decision rules ranging over an arbitrary reproducing kernel
Hilbert space. For example, it is shown that for a wide class of loss functions
(including the standard square, absolute, and log loss functions) the average
loss of the master algorithm, over the first $N$ observations, does not exceed
the average loss of the best decision rule with a bounded norm plus
$O(N^{-1/2})$. Our proof technique is very different from the standard ones and
is based on recent results about defensive forecasting. Given the probabilities
produced by a defensive forecasting algorithm, which are known to be well
calibrated and to have good resolution in the long run, we use the expected
loss minimization principle to find a suitable decision.
|
cs/0506042
|
Tree-Based Construction of LDPC Codes
|
cs.IT math.IT
|
We present a construction of LDPC codes that have minimum pseudocodeword
weight equal to the minimum distance, and perform well with iterative decoding.
The construction involves enumerating a d-regular tree for a fixed number of
layers and employing a connection algorithm based on mutually orthogonal Latin
squares to close the tree. Methods are presented for degrees d=p^s and d =
p^s+1, for p a prime, -- one of which includes the well-known
finite-geometry-based LDPC codes.
|
cs/0506043
|
A Decision Feedback Based Scheme for Slepian-Wolf Coding of sources with
Hidden Markov Correlation
|
cs.IT math.IT
|
We consider the problem of compression of two memoryless binary sources, the
correlation between which is defined by a Hidden Markov Model (HMM). We propose
a Decision Feedback (DF) based scheme which when used with low density parity
check codes results in compression close to the Slepian Wolf limits.
|
cs/0506044
|
Minimal Network Coding for Multicast
|
cs.IT math.IT
|
We give an information flow interpretation for multicasting using network
coding. This generalizes the fluid model used to represent flows to a single
receiver. Using the generalized model, we present a decentralized algorithm to
minimize the number of packets that undergo network coding. We also propose a
decentralized algorithm to construct capacity achieving multicast codes when
the processing at some nodes is restricted to routing. The proposed algorithms
can be coupled with existing decentralized schemes to achieve minimum cost
muticast.
|
cs/0506045
|
Decision Feedback Based Scheme for Slepian-Wolf Coding of sources with
Hidden Markov Correlation
|
cs.IT math.IT
|
We consider the problem of compression of two memoryless binary sources, the
correlation between which is defined by a Hidden Markov Model (HMM). We propose
a Decision Feedback (DF) based scheme which when used with low density parity
check codes results in compression close to the Slepian Wolf limits.
|
cs/0506047
|
Analyse et expansion des textes en question-r\'{e}ponse
|
cs.IR
|
This paper presents an original methodology to consider question answering.
We noticed that query expansion is often incorrect because of a bad
understanding of the question. But the automatic good understanding of an
utterance is linked to the context length, and the question are often short.
This methodology proposes to analyse the documents and to construct an
informative structure from the results of the analysis and from a semantic text
expansion. The linguistic analysis identifies words (tokenization and
morphological analysis), links between words (syntactic analysis) and word
sense (semantic disambiguation). The text expansion adds to each word the
synonyms matching its sense and replaces the words in the utterances by
derivatives, modifying the syntactic schema if necessary. In this way, whatever
enrichment may be, the text keeps the same meaning, but each piece of
information matches many realisations. The questioning method consists in
constructing a local informative structure without enrichment, and matches it
with the documentary structure. If a sentence in the informative structure
matches the question structure, this sentence is the answer to the question.
|
cs/0506048
|
Enriching a Text by Semantic Disambiguation for Information Extraction
|
cs.IR
|
External linguistic resources have been used for a very long time in
information extraction. These methods enrich a document with data that are
semantically equivalent, in order to improve recall. For instance, some of
these methods use synonym dictionaries. These dictionaries enrich a sentence
with words that have a similar meaning. However, these methods present some
serious drawbacks, since words are usually synonyms only in restricted
contexts. The method we propose here consists of using word sense
disambiguation rules (WSD) to restrict the selection of synonyms to only these
that match a specific syntactico-semantic context. We show how WSD rules are
built and how information extraction techniques can benefit from the
application of these rules.
|
cs/0506051
|
Comparison of two different implementations of a
finite-difference-method for first-order pde in mathematica and matlab
|
cs.CE cs.DM
|
In this article two implementations of a symmetric finite difference
algorithm for a first-order partial differential equation are discussed. The
considered partial differential equation discribes the time evolution of the
crack length distribution of microcracks in brittle materia.
|
cs/0506052
|
Comments on `Bit Interleaved Coded Modulation'
|
cs.IT math.IT
|
Caire, Taricco and Biglieri presented a detailed analysis of bit interleaved
coded modulation, a simple and popular technique used to improve system
performance, especially in the context of fading channels. They derived an
upper bound to the probability of error, called the expurgated bound. In this
correspondence, the proof of the expurgated bound is shown to be flawed. A new
upper bound is also derived. It is not known whether the original expurgated
bound is valid for the important special case of square QAM with Gray labeling,
but the new bound is very close to, and slightly tighter than, the original
bound for a numerical example.
|
cs/0506053
|
Analysis on Transmit Antenna Selection for Spatial Multiplexing Systems:
A Geometrical Approach
|
cs.IT math.IT
|
Recently, the remarkable potential of a multiple-input multiple-output (MIMO)
wireless communication system was unveiled for its ability to provide spatial
diversity or multiplexing gains. For MIMO diversity schemes, it is already
known that. by the optimal antenna selection maximizing the post-processing
signal-to-noise ratio, the diversity order of the full system can be
maintained. On the other hand, the diversity order achieved by antenna
selection in spatial multiplexing systems, especially those exploiting
practical coding and decoding schemes, has not been rigorously analyzed thus
far. In this paper, from a geometric standpoint, we propose a new framework for
theoretically analyzing the diversity order achieved by transmit antenna
selection for separately encoded spatial multiplexing systems with linear and
decision-feedback receivers. We rigorously show that a diversity order of
(Nt-1)(Nr-1) can be achieved for an Nr by Nt SM system when L=2 antennas are
selected from the transmit side; while for L>2 scenarios, we give bounds for
the achievable diversity order and show that the optimal diversity order is at
least (Nt-L+1)(Nr-L+1) . Furthermore, the same geometrical approach can be used
to evaluate the diversity-multiplexing tradeoff curves for the considered
spatial multiplexing systems with transmit antenna selection.
|
cs/0506056
|
Large Alphabets and Incompressibility
|
cs.IT math.IT
|
We briefly survey some concepts related to empirical entropy -- normal
numbers, de Bruijn sequences and Markov processes -- and investigate how well
it approximates Kolmogorov complexity. Our results suggest $\ell$th-order
empirical entropy stops being a reasonable complexity metric for almost all
strings of length $m$ over alphabets of size $n$ about when $n^\ell$ surpasses
$m$.
|
cs/0506057
|
About one 3-parameter Model of Testing
|
cs.LG
|
This article offers a 3-parameter model of testing, with 1) the difference
between the ability level of the examinee and item difficulty; 2) the examinee
discrimination and 3) the item discrimination as model parameters.
|
cs/0506058
|
An MSE Based Ttransfer Chart to Analyze Iterative Decoding Schemes
|
cs.IT math.IT
|
An alternative to extrinsic information transfer (EXIT) charts called mean
squared error (MSE) charts that use a measure related to the MSE instead of
mutual information is proposed. Using the relationship between mutual
information and minimum mean squared error (MMSE), a relationship between the
rate of any code and the area under a plot of MSE versus signal to noise ratio
(SNR) is obtained, when the log likelihood ratios (LLR) can be assumed to be
from a Gaussian channel. Using this result, a theoretical justification is
provided for designing concatenated codes by matching the EXIT charts of the
inner and outer decoders, when the LLRs are Gaussian which is typically assumed
for code design using EXIT charts. Finally, for the special case of AWGN
channel it is shown that any capacity achieving code has an EXIT curve that is
flat. This extends Ashikhmin et als results for erasure channels to the
Gaussian channel.
|
cs/0506062
|
A CDMA multiuser detection algorithm based on survey propagation
|
cs.IT math.IT
|
A computationally tractable CDMA multiuser detection algorithm is developed
based on survey propagation.
|
cs/0506063
|
Priority-Based Conflict Resolution in Inconsistent Relational Databases
|
cs.DB
|
We study here the impact of priorities on conflict resolution in inconsistent
relational databases. We extend the framework of repairs and consistent query
answers. We propose a set of postulates that an extended framework should
satisfy and consider two instantiations of the framework: (locally preferred)
l-repairs and (globally preferred) g-repairs. We study the relationships
between them and the impact each notion of repair has on the computational
complexity of repair checking and consistent query answers.
|
cs/0506064
|
Optimal multiple assignments based on integer programming in secret
sharing schemes with general access structures
|
cs.CR cs.IT math.IT
|
It is known that for any general access structure, a secret sharing scheme
(SSS) can be constructed from an (m,m)-threshold scheme by using the so-called
cumulative map or from a (t,m)-threshold SSS by a modified cumulative map.
However, such constructed SSSs are not efficient generally. In this paper, we
propose a new method to construct a SSS from a $(t,m)$-threshold scheme for any
given general access structure. In the proposed method, integer programming is
used to distribute optimally the shares of (t,m)-threshold scheme to each
participant of the general access structure. From the optimality, it can always
attain lower coding rate than the cumulative maps except the cases that they
give the optimal distribution. The same method is also applied to construct
SSSs for incomplete access structures and/or ramp access structures.
|
cs/0506065
|
Strongly secure ramp secret sharing schemes for general access
structures
|
cs.CR cs.IT math.IT
|
Ramp secret sharing (SS) schemes can be classified into strong ramp SS
schemes and weak ramp SS schemes. The strong ramp SS schemes do not leak out
any part of a secret explicitly even in the case where some information about
the secret leaks from a non-qualified set of shares, and hence, they are more
desirable than weak ramp SS schemes. However, it is not known how to construct
the strong ramp SS schemes in the case of general access structures. In this
paper, it is shown that a strong ramp SS scheme can always be constructed from
a SS scheme with plural secrets for any feasible general access structure. As a
byproduct, it is pointed out that threshold ramp SS schemes based on Shamir's
polynomial interpolation method are {\em not} always strong.
|
cs/0506072
|
Performance Analysis of Algebraic Soft Decoding of Reed-Solomon Codes
over Binary Symmetric and Erasure Channels
|
cs.IT math.IT
|
In this paper, we characterize the decoding region of algebraic soft decoding
(ASD) of Reed-Solomon (RS) codes over erasure channels and binary symmetric
channel (BSC). Optimal multiplicity assignment strategies (MAS) are
investigated and tight bounds are derived to show the ASD can significantly
outperform conventional Berlekamp Massey (BM) decoding over these channels for
a wide code rate range. The analysis technique can also be extended to other
channel models, e.g., RS coded modulation over erasure channels.
|
cs/0506073
|
Iterative Soft Input Soft Output Decoding of Reed-Solomon Codes by
Adapting the Parity Check Matrix
|
cs.IT math.IT
|
An iterative algorithm is presented for soft-input-soft-output (SISO)
decoding of Reed-Solomon (RS) codes. The proposed iterative algorithm uses the
sum product algorithm (SPA) in conjunction with a binary parity check matrix of
the RS code. The novelty is in reducing a submatrix of the binary parity check
matrix that corresponds to less reliable bits to a sparse nature before the SPA
is applied at each iteration. The proposed algorithm can be geometrically
interpreted as a two-stage gradient descent with an adaptive potential
function. This adaptive procedure is crucial to the convergence behavior of the
gradient descent algorithm and, therefore, significantly improves the
performance. Simulation results show that the proposed decoding algorithm and
its variations provide significant gain over hard decision decoding (HDD) and
compare favorably with other popular soft decision decoding methods.
|
cs/0506074
|
Redundancy in Logic II: 2CNF and Horn Propositional Formulae
|
cs.AI cs.LO
|
We report complexity results about redundancy of formulae in 2CNF form. We
first consider the problem of checking redundancy and show some algorithms that
are slightly better than the trivial one. We then analyze problems related to
finding irredundant equivalent subsets (I.E.S.) of a given set. The concept of
cyclicity proved to be relevant to the complexity of these problems. Some
results about Horn formulae are also shown.
|
cs/0506075
|
Seeing stars: Exploiting class relationships for sentiment
categorization with respect to rating scales
|
cs.CL cs.LG
|
We address the rating-inference problem, wherein rather than simply decide
whether a review is "thumbs up" or "thumbs down", as in previous sentiment
analysis work, one must determine an author's evaluation with respect to a
multi-point scale (e.g., one to five "stars"). This task represents an
interesting twist on standard multi-class text categorization because there are
several different degrees of similarity between class labels; for example,
"three stars" is intuitively closer to "four stars" than to "one star". We
first evaluate human performance at the task. Then, we apply a meta-algorithm,
based on a metric labeling formulation of the problem, that alters a given
n-ary classifier's output in an explicit attempt to ensure that similar items
receive similar labels. We show that the meta-algorithm can provide significant
improvements over both multi-class and regression versions of SVMs when we
employ a novel similarity measure appropriate to the problem.
|
cs/0506077
|
Stability of Scheduled Multi-access Communication over Quasi-static Flat
Fading Channels with Random Coding and Independent Decoding
|
cs.IT math.IT
|
The stability of scheduled multiaccess communication with random coding and
independent decoding of messages is investigated. The number of messages that
may be scheduled for simultaneous transmission is limited to a given maximum
value, and the channels from transmitters to receiver are quasi-static, flat,
and have independent fades. Requests for message transmissions are assumed to
arrive according to an i.i.d. arrival process. Then, we show the following: (1)
in the limit of large message alphabet size, the stability region has an
interference limited information-theoretic capacity interpretation, (2)
state-independent scheduling policies achieve this asymptotic stability region,
and (3) in the asymptotic limit corresponding to immediate access, the
stability region for non-idling scheduling policies is shown to be identical
irrespective of received signal powers.
|
cs/0506078
|
Dynamical Neural Network: Information and Topology
|
cs.IR cs.NE
|
A neural network works as an associative memory device if it has large
storage capacity and the quality of the retrieval is good enough. The learning
and attractor abilities of the network both can be measured by the mutual
information (MI), between patterns and retrieval states. This paper deals with
a search for an optimal topology, of a Hebb network, in the sense of the
maximal MI. We use small-world topology. The connectivity $\gamma$ ranges from
an extremely diluted to the fully connected network; the randomness $\omega$
ranges from purely local to completely random neighbors. It is found that,
while stability implies an optimal $MI(\gamma,\omega)$ at
$\gamma_{opt}(\omega)\to 0$, for the dynamics, the optimal topology holds at
certain $\gamma_{opt}>0$ whenever $0\leq\omega<0.3$.
|
cs/0506083
|
Maxwell Construction: The Hidden Bridge between Iterative and Maximum a
Posteriori Decoding
|
cs.IT cond-mat.dis-nn math.IT
|
There is a fundamental relationship between belief propagation and maximum a
posteriori decoding. A decoding algorithm, which we call the Maxwell decoder,
is introduced and provides a constructive description of this relationship.
Both, the algorithm itself and the analysis of the new decoder are reminiscent
of the Maxwell construction in thermodynamics. This paper investigates in
detail the case of transmission over the binary erasure channel, while the
extension to general binary memoryless channels is discussed in a companion
paper.
|
cs/0506085
|
On the Job Training
|
cs.LG
|
We propose a new framework for building and evaluating machine learning
algorithms. We argue that many real-world problems require an agent which must
quickly learn to respond to demands, yet can continue to perform and respond to
new training throughout its useful life. We give a framework for how such
agents can be built, describe several metrics for evaluating them, and show
that subtle changes in system construction can significantly affect agent
performance.
|
cs/0506086
|
Large System Decentralized Detection Performance Under Communication
Constraints
|
cs.IT math.IT
|
The problem of decentralized detection in a sensor network subjected to a
total average power constraint and all nodes sharing a common bandwidth is
investigated. The bandwidth constraint is taken into account by assuming
non-orthogonal communication between sensors and the data fusion center via
direct-sequence code-division multiple-access (DS-CDMA). In the case of large
sensor systems and random spreading, the asymptotic decentralized detection
performance is derived assuming independent and identically distributed (iid)
sensor observations via random matrix theory. The results show that, even under
both power and bandwidth constraints, it is better to combine many not-so-good
local decisions rather than relying on one (or a few) very-good local
decisions.
|
cs/0506087
|
Primal-dual distance bounds of linear codes with application to
cryptography
|
cs.IT cs.CR math.IT
|
Let $N(d,d^\perp)$ denote the minimum length $n$ of a linear code $C$ with
$d$ and $d^{\bot}$, where $d$ is the minimum Hamming distance of $C$ and
$d^{\bot}$ is the minimum Hamming distance of $C^{\bot}$. In this paper, we
show a lower bound and an upper bound on $N(d,d^\perp)$. Further, for small
values of $d$ and $d^\perp$, we determine $N(d,d^\perp)$ and give a generator
matrix of the optimum linear code. This problem is directly related to the
design method of cryptographic Boolean functions suggested by Kurosawa et al.
|
cs/0506088
|
An Alternative to Huffman's Algorithm for Constructing Variable-Length
Codes
|
cs.IT math.IT
|
This paper has been withdrawn by the author.
|
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