id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
|---|---|---|---|
1311.6916 | Spectral Compressive Sensing with Model Selection | cs.IT math.IT | The performance of existing approaches to the recovery of frequency-sparse
signals from compressed measurements is limited by the coherence of required
sparsity dictionaries and the discretization of frequency parameter space. In
this paper, we adopt a parametric joint recovery-estimation method based on
model selection in spectral compressive sensing. Numerical experiments show
that our approach outperforms most state-of-the-art spectral CS recovery
approaches in fidelity, tolerance to noise and computation efficiency.
|
1311.6932 | A novel framework for image forgery localization | cs.CV | Image forgery localization is a very active and open research field for the
difficulty to handle the large variety of manipulations a malicious user can
perform by means of more and more sophisticated image editing tools. Here, we
propose a localization framework based on the fusion of three very different
tools, based, respectively, on sensor noise, patch-matching, and machine
learning. The binary masks provided by these tools are finally fused based on
some suitable reliability indexes. According to preliminary experiments on the
training set, the proposed framework provides often a very good localization
accuracy and sometimes valuable clues for visual scrutiny.
|
1311.6934 | Image forgery detection based on the fusion of machine learning and
block-matching methods | cs.CV | Dense local descriptors and machine learning have been used with success in
several applications, like classification of textures, steganalysis, and
forgery detection. We develop a new image forgery detector building upon some
descriptors recently proposed in the steganalysis field suitably merging some
of such descriptors, and optimizing a SVM classifier on the available training
set. Despite the very good performance, very small forgeries are hardly ever
detected because they contribute very little to the descriptors. Therefore we
also develop a simple, but extremely specific, copy-move detector based on
region matching and fuse decisions so as to reduce the missing detection rate.
Overall results appear to be extremely encouraging.
|
1311.6976 | Dimensionality reduction for click-through rate prediction: Dense versus
sparse representation | stat.ML cs.LG stat.AP stat.ME | In online advertising, display ads are increasingly being placed based on
real-time auctions where the advertiser who wins gets to serve the ad. This is
called real-time bidding (RTB). In RTB, auctions have very tight time
constraints on the order of 100ms. Therefore mechanisms for bidding
intelligently such as clickthrough rate prediction need to be sufficiently
fast. In this work, we propose to use dimensionality reduction of the
user-website interaction graph in order to produce simplified features of users
and websites that can be used as predictors of clickthrough rate. We
demonstrate that the Infinite Relational Model (IRM) as a dimensionality
reduction offers comparable predictive performance to conventional
dimensionality reduction schemes, while achieving the most economical usage of
features and fastest computations at run-time. For applications such as
real-time bidding, where fast database I/O and few computations are key to
success, we thus recommend using IRM based features as predictors to exploit
the recommender effects from bipartite graphs.
|
1311.7038 | Group Coding with Complex Isometries | math.CO cs.IT math.IT math.RT | We investigate group coding for arbitrary finite groups acting linearly on a
vector space. These yield robust codes based on real or complex matrix groups.
We give necessary and sufficient conditions for correct subgroup decoding using
geometric notions of minimal length coset representatives. The infinite family
of complex reflection groups G(r,1,n) produces effective codes of arbitrarily
large size that can be decoded in relatively few steps.
|
1311.7045 | Phase retrieval from low-rate samples | cs.IT math.IT | The paper considers the phase retrieval problem in N-dimensional complex
vector spaces. It provides two sets of deterministic measurement vectors which
guarantee signal recovery for all signals, excluding only a specific subspace
and a union of subspaces, respectively. A stable analytic reconstruction
procedure of low complexity is given. Additionally it is proven that signal
recovery from these measurements can be solved exactly via a semidefinite
program. A practical implementation with 4 deterministic diffraction patterns
is provided and some numerical experiments with noisy measurements complement
the analytic approach.
|
1311.7071 | Sparse Linear Dynamical System with Its Application in Multivariate
Clinical Time Series | cs.AI cs.LG stat.ML | Linear Dynamical System (LDS) is an elegant mathematical framework for
modeling and learning multivariate time series. However, in general, it is
difficult to set the dimension of its hidden state space. A small number of
hidden states may not be able to model the complexities of a time series, while
a large number of hidden states can lead to overfitting. In this paper, we
study methods that impose an $\ell_1$ regularization on the transition matrix
of an LDS model to alleviate the problem of choosing the optimal number of
hidden states. We incorporate a generalized gradient descent method into the
Maximum a Posteriori (MAP) framework and use Expectation Maximization (EM) to
iteratively achieve sparsity on the transition matrix of an LDS model. We show
that our Sparse Linear Dynamical System (SLDS) improves the predictive
performance when compared to ordinary LDS on a multivariate clinical time
series dataset.
|
1311.7080 | Cross-Domain Sparse Coding | cs.CV stat.ML | Sparse coding has shown its power as an effective data representation method.
However, up to now, all the sparse coding approaches are limited within the
single domain learning problem. In this paper, we extend the sparse coding to
cross domain learning problem, which tries to learn from a source domain to a
target domain with significant different distribution. We impose the Maximum
Mean Discrepancy (MMD) criterion to reduce the cross-domain distribution
difference of sparse codes, and also regularize the sparse codes by the class
labels of the samples from both domains to increase the discriminative ability.
The encouraging experiment results of the proposed cross-domain sparse coding
algorithm on two challenging tasks --- image classification of photograph and
oil painting domains, and multiple user spam detection --- show the advantage
of the proposed method over other cross-domain data representation methods.
|
1311.7084 | Explicit rank-metric codes list-decodable with optimal redundancy | cs.IT cs.CC cs.DM math.IT | We construct an explicit family of linear rank-metric codes over any field
${\mathbb F}_h$ that enables efficient list decoding up to a fraction $\rho$ of
errors in the rank metric with a rate of $1-\rho-\epsilon$, for any desired
$\rho \in (0,1)$ and $\epsilon > 0$. Previously, a Monte Carlo construction of
such codes was known, but this is in fact the first explicit construction of
positive rate rank-metric codes for list decoding beyond the unique decoding
radius.
Our codes are subcodes of the well-known Gabidulin codes, which encode
linearized polynomials of low degree via their values at a collection of
linearly independent points. The subcode is picked by restricting the message
polynomials to an ${\mathbb F}_h$-subspace that evades the structured subspaces
over an extension field ${\mathbb F}_{h^t}$ that arise in the linear-algebraic
list decoder for Gabidulin codes due to Guruswami and Xing (STOC'13). This
subspace is obtained by combining subspace designs contructed by Guruswami and
Kopparty (FOCS'13) with subspace evasive varieties due to Dvir and Lovett
(STOC'12).
We establish a similar result for subspace codes, which are a collection of
subspaces, every pair of which have low-dimensional intersection, and which
have received much attention recently in the context of network coding. We also
give explicit subcodes of folded Reed-Solomon (RS) codes with small folding
order that are list-decodable (in the Hamming metric) with optimal redundancy,
motivated by the fact that list decoding RS codes reduces to list decoding such
folded RS codes. However, as we only list decode a subcode of these codes, the
Johnson radius continues to be the best known error fraction for list decoding
RS codes.
|
1311.7113 | Systematic Codes for Rank Modulation | cs.IT math.IT | The goal of this paper is to construct systematic error-correcting codes for
permutations and multi-permutations in the Kendall's $\tau$-metric. These codes
are important in new applications such as rank modulation for flash memories.
The construction is based on error-correcting codes for multi-permutations and
a partition of the set of permutations into error-correcting codes. For a given
large enough number of information symbols $k$, and for any integer $t$, we
present a construction for ${(k+r,k)}$ systematic $t$-error-correcting codes,
for permutations from $S_{k+r}$, with less redundancy symbols than the number
of redundancy symbols in the codes of the known constructions. In particular,
for a given $t$ and for sufficiently large $k$ we can obtain $r=t+1$. The same
construction is also applied to obtain related systematic error-correcting
codes for multi-permutations.
|
1311.7139 | Introduction to Neutrosophic Measure, Neutrosophic Integral, and
Neutrosophic Probability | cs.AI | In this paper, we introduce for the first time the notions of neutrosophic
measure and neutrosophic integral, and we develop the 1995 notion of
neutrosophic probability. We present many practical examples. It is possible to
define the neutrosophic measure and consequently the neutrosophic integral and
neutrosophic probability in many ways, because there are various types of
indeterminacies, depending on the problem we need to solve. Neutrosophics study
the indeterminacy. Indeterminacy is different from randomness. It can be caused
by physical space materials and type of construction, by items involved in the
space, etc.
|
1311.7183 | Knowledge-Aided STAP Using Low Rank and Geometry Properties | cs.IT math.IT | This paper presents knowledge-aided space-time adaptive processing (KA-STAP)
algorithms that exploit the low-rank dominant clutter and the array geometry
properties (LRGP) for airborne radar applications. The core idea is to exploit
the fact that the clutter subspace is only determined by the space-time
steering vectors,
{red}{where the Gram-Schmidt orthogonalization approach is employed to
compute the clutter subspace. Specifically, for a side-looking uniformly spaced
linear array, the} algorithm firstly selects a group of linearly independent
space-time steering vectors using LRGP that can represent the clutter subspace.
By performing the Gram-Schmidt orthogonalization procedure, the orthogonal
bases of the clutter subspace are obtained, followed by two approaches to
compute the STAP filter weights. To overcome the performance degradation caused
by the non-ideal effects, a KA-STAP algorithm that combines the covariance
matrix taper (CMT) is proposed. For practical applications, a reduced-dimension
version of the proposed KA-STAP algorithm is also developed. The simulation
results illustrate the effectiveness of our proposed algorithms, and show that
the proposed algorithms converge rapidly and provide a SINR improvement over
existing methods when using a very small number of snapshots.
|
1311.7184 | Using Multiple Samples to Learn Mixture Models | stat.ML cs.LG | In the mixture models problem it is assumed that there are $K$ distributions
$\theta_{1},\ldots,\theta_{K}$ and one gets to observe a sample from a mixture
of these distributions with unknown coefficients. The goal is to associate
instances with their generating distributions, or to identify the parameters of
the hidden distributions. In this work we make the assumption that we have
access to several samples drawn from the same $K$ underlying distributions, but
with different mixing weights. As with topic modeling, having multiple samples
is often a reasonable assumption. Instead of pooling the data into one sample,
we prove that it is possible to use the differences between the samples to
better recover the underlying structure. We present algorithms that recover the
underlying structure under milder assumptions than the current state of art
when either the dimensionality or the separation is high. The methods, when
applied to topic modeling, allow generalization to words not present in the
training data.
|
1311.7186 | A Novel Illumination-Invariant Loss for Monocular 3D Pose Estimation | cs.CV | The problem of identifying the 3D pose of a known object from a given 2D
image has important applications in Computer Vision. Our proposed method of
registering a 3D model of a known object on a given 2D photo of the object has
numerous advantages over existing methods. It does not require prior training,
knowledge of the camera parameters, explicit point correspondences or matching
features between the image and model. Unlike techniques that estimate a partial
3D pose (as in an overhead view of traffic or machine parts on a conveyor
belt), our method estimates the complete 3D pose of the object. It works on a
single static image from a given view under varying and unknown lighting
conditions. For this purpose we derive a novel illumination-invariant distance
measure between the 2D photo and projected 3D model, which is then minimised to
find the best pose parameters. Results for vehicle pose detection in real
photographs are presented.
|
1311.7194 | Real-time High Resolution Fusion of Depth Maps on GPU | cs.GR cs.CV | A system for live high quality surface reconstruction using a single moving
depth camera on a commodity hardware is presented. High accuracy and real-time
frame rate is achieved by utilizing graphics hardware computing capabilities
via OpenCL and by using sparse data structure for volumetric surface
representation. Depth sensor pose is estimated by combining serial texture
registration algorithm with iterative closest points algorithm (ICP) aligning
obtained depth map to the estimated scene model. Aligned surface is then fused
into the scene. Kalman filter is used to improve fusion quality. Truncated
signed distance function (TSDF) stored as block-based sparse buffer is used to
represent surface. Use of sparse data structure greatly increases accuracy of
scanned surfaces and maximum scanning area. Traditional GPU implementation of
volumetric rendering and fusion algorithms were modified to exploit sparsity to
achieve desired performance. Incorporation of texture registration for sensor
pose estimation and Kalman filter for measurement integration improved accuracy
and robustness of scanning process.
|
1311.7198 | ADMM Algorithm for Graphical Lasso with an $\ell_{\infty}$ Element-wise
Norm Constraint | cs.LG math.OC stat.ML | We consider the problem of Graphical lasso with an additional $\ell_{\infty}$
element-wise norm constraint on the precision matrix. This problem has
applications in high-dimensional covariance decomposition such as in
\citep{Janzamin-12}. We propose an ADMM algorithm to solve this problem. We
also use a continuation strategy on the penalty parameter to have a fast
implemenation of the algorithm.
|
1311.7200 | Searching and Establishment of S-P-O Relationships for Linked RDF Graphs
: An Adaptive Approach | cs.IR cs.DB | In the coming era of semantic web linked data analysis is a very burning
issue for efficient searching and retrieval of information. One way of
establishing this link is to implement subject predicate object relationship
through Set Theory approach which is already done in our previous work. For
analyzing inter relationship between two RDF Graphs, RDF- Schema (RDFS) should
also be taken care of. In the present paper, an adaptive combination rule based
framework has been proposed for establishment of S P O relationship and RDF
Graph searching is reported. Hence the identification of criteria for
inter-relationship of RDF Graphs opens up new road in semantic search.
|
1311.7204 | A Hybrid Web Recommendation System based on the Improved Association
Rule Mining Algorithm | cs.IR | As the growing interest of web recommendation systems those are applied to
deliver customized data for their users, we started working on this system.
Generally the recommendation systems are divided into two major categories such
as collaborative recommendation system and content based recommendation system.
In case of collaborative recommen-dation systems, these try to seek out users
who share same tastes that of given user as well as recommends the websites
according to the liking given user. Whereas the content based recommendation
systems tries to recommend web sites similar to those web sites the user has
liked. In the recent research we found that the efficient technique based on
asso-ciation rule mining algorithm is proposed in order to solve the problem of
web page recommendation. Major problem of the same is that the web pages are
given equal importance. Here the importance of pages changes according to the
fre-quency of visiting the web page as well as amount of time user spends on
that page. Also recommendation of newly added web pages or the pages those are
not yet visited by users are not included in the recommendation set. To
over-come this problem, we have used the web usage log in the adaptive
association rule based web mining where the asso-ciation rules were applied to
personalization. This algorithm was purely based on the Apriori data mining
algorithm in order to generate the association rules. However this method also
suffers from some unavoidable drawbacks. In this paper we are presenting and
investigating the new approach based on weighted Association Rule Mining
Algorithm and text mining. This is improved algorithm which adds semantic
knowledge to the results, has more efficiency and hence gives better quality
and performances as compared to existing approaches.
|
1311.7213 | Finding a Maximum Clique using Ant Colony Optimization and Particle
Swarm Optimization in Social Networks | cs.SI cs.NE | Interaction between users in online social networks plays a key role in
social network analysis. One on important types of social group is full
connected relation between some users, which known as clique structure.
Therefore finding a maximum clique is essential for some analysis. In this
paper, we proposed a new method using ant colony optimization algorithm and
particle swarm optimization algorithm. In the proposed method, in order to
attain better results, it is improved process of pheromone update by particle
swarm optimization. Simulation results on popular standard social network
benchmarks in comparison standard ant colony optimization algorithm are shown a
relative enhancement of proposed algorithm.
|
1311.7215 | Solving Minimum Vertex Cover Problem Using Learning Automata | cs.AI cs.DM | Minimum vertex cover problem is an NP-Hard problem with the aim of finding
minimum number of vertices to cover graph. In this paper, a learning automaton
based algorithm is proposed to find minimum vertex cover in graph. In the
proposed algorithm, each vertex of graph is equipped with a learning automaton
that has two actions in the candidate or non-candidate of the corresponding
vertex cover set. Due to characteristics of learning automata, this algorithm
significantly reduces the number of covering vertices of graph. The proposed
algorithm based on learning automata iteratively minimize the candidate vertex
cover through the update its action probability. As the proposed algorithm
proceeds, a candidate solution nears to optimal solution of the minimum vertex
cover problem. In order to evaluate the proposed algorithm, several experiments
conducted on DIMACS dataset which compared to conventional methods.
Experimental results show the major superiority of the proposed algorithm over
the other methods.
|
1311.7219 | Partitioning Clustering algorithms for handling numerical and
categorical data: a review | cs.DB | Clustering is widely used in different field such as biology, psychology, and
economics. Most traditional clustering algorithms are limited to handling
datasets that contain either numeric or categorical attributes. However,
datasets with mixed types of attributes are common in real life data mining
applications. In this paper, we review partitioning based algorithm such as
K-prototype, Extension of K-prototype, K-histogram, Fuzzy approaches, genetic
approaches, etc. These algorithm works on both numerical and categorical data.
The approaches has been proposed to handle mixed data are based on four
different perceptive: i) split data set into two part such that each part
contain either numerical or categorical data, then apply separate clustering
algorithm on each data set, finally combined the result of both clustering
algorithm, ii) converting categorical attribute into numerical attribute and
apply numerical attribute clustering algorithm; iii) discrimination of
numerical attribute and apply categorical based clustering algorithm; iv)
Conversion of the categorical attributes into binary ones and apply any
numerical based clustering algorithm
|
1311.7225 | Link Quality Control Mechanism for Selective and Opportunistic AF
Relaying in Cooperative ARQs: A MLSD Perspective | cs.IT math.IT | Incorporating relaying techniques into Automatic Repeat reQuest (ARQ)
mechanisms gives a general impression of diversity and throughput enhancements.
Allowing overhearing among multiple relays is also a known approach to increase
the number of participating relays in ARQs. However, when opportunistic
amplify-and-forward (AF) relaying is applied to cooperative ARQs, the system
design becomes nontrivial and even involved. Based on outage analysis, the
spatial and temporal diversities are first found sensitive to the received
signal qualities of relays, and a link quality control mechanism is then
developed to prescreen candidate relays in order to explore the diversity of
cooperative ARQs with a selective and opportunistic AF (SOAF) relaying method.
According to the analysis, the temporal and spatial diversities can be fully
exploited if proper thresholds are set for each hop along the relaying routes.
The SOAF relaying method is further examined from a packet delivery viewpoint.
By the principle of the maximum likelihood sequence detection (MLSD),
sufficient conditions on the link quality are established for the proposed
SOAF-relaying-based ARQ scheme to attain its potential diversity order in the
packet error rates (PERs) of MLSD. The conditions depend on the minimum
codeword distance and the average signal-to-noise ratio (SNR). Furthermore,
from a heuristic viewpoint, we also develop a threshold searching algorithm for
the proposed SOAF relaying and link quality method to exploit both the
diversity and the SNR gains in PER. The effectiveness of the proposed
thresholding mechanism is verified via simulations with trellis codes.
|
1311.7235 | Downscaling of global solar irradiation in R | physics.ao-ph cs.CE | A methodology for downscaling solar irradiation from satellite-derived
databases is described using R software. Different packages such as raster,
parallel, solaR, gstat, sp and rasterVis are considered in this study for
improving solar resource estimation in areas with complex topography, in which
downscaling is a very useful tool for reducing inherent deviations in
satellite-derived irradiation databases, which lack of high global spatial
resolution. A topographical analysis of horizon blocking and sky-view is
developed with a digital elevation model to determine what fraction of hourly
solar irradiation reaches the Earth's surface. Eventually, kriging with
external drift is applied for a better estimation of solar irradiation
throughout the region analyzed. This methodology has been implemented as an
example within the region of La Rioja in northern Spain, and the mean absolute
error found is a striking 25.5% lower than with the original database.
|
1311.7237 | Beamforming for MISO Interference Channels with QoS and RF Energy
Transfer | cs.IT math.IT | We consider a multiuser multiple-input single-output interference channel
where the receivers are characterized by both quality-of-service (QoS) and
radio-frequency (RF) energy harvesting (EH) constraints. We consider the power
splitting RF-EH technique where each receiver divides the received signal into
two parts a) for information decoding and b) for battery charging. The minimum
required power that supports both the QoS and the RF-EH constraints is
formulated as an optimization problem that incorporates the transmitted power
and the beamforming design at each transmitter as well as the power splitting
ratio at each receiver. We consider both the cases of fixed beamforming and
when the beamforming design is incorporated into the optimization problem. For
fixed beamforming we study three standard beamforming schemes, the zero-forcing
(ZF), the regularized zero-forcing (RZF) and the maximum ratio transmission
(MRT); a hybrid scheme, MRT-ZF, comprised of a linear combination of MRT and ZF
beamforming is also examined. The optimal solution for ZF beamforming is
derived in closed-form, while optimization algorithms based on second-order
cone programming are developed for MRT, RZF and MRT-ZF beamforming to solve the
problem. In addition, the joint-optimization of beamforming and power
allocation is studied using semidefinite programming (SDP) with the aid of rank
relaxation.
|
1311.7245 | Multiuser Broadcast Erasure Channel with Feedback and Side Information,
and Related Index Coding Results | cs.IT math.IT | We consider the N-user broadcast erasure channel with public feedback and
side information. Before the beginning of transmission, each receiver knows a
function of the messages of some of the other receivers. This situation arises
naturally in wireless and in particular cognitive networks where a node may
overhear transmitted messages destined to other nodes before transmission over
a given broadcast channel begins. We provide an upper bound to the capacity
region of this system. Furthermore, when the side information is linear, we
show that the bound is tight for the case of two-user broadcast channels. The
special case where each user knows the whole or nothing of the message of each
other node, constitutes a generalization of the index coding problem. For this
instance, and when there are no channel errors, we show that the bound reduces
to the known Maximum Weighted Acyclic Induced Subgraph bound. We also show how
to convert the capacity upper bound to transmission completion rate (broadcast
rate) lower bound and provide examples of codes for certain information graphs
for which the bound is either achieved of closely approximated
|
1311.7251 | Spatially-Adaptive Reconstruction in Computed Tomography using Neural
Networks | cs.CV cs.LG cs.NE | We propose a supervised machine learning approach for boosting existing
signal and image recovery methods and demonstrate its efficacy on example of
image reconstruction in computed tomography. Our technique is based on a local
nonlinear fusion of several image estimates, all obtained by applying a chosen
reconstruction algorithm with different values of its control parameters.
Usually such output images have different bias/variance trade-off. The fusion
of the images is performed by feed-forward neural network trained on a set of
known examples. Numerical experiments show an improvement in reconstruction
quality relatively to existing direct and iterative reconstruction methods.
|
1311.7295 | Glasgow's Stereo Image Database of Garments | cs.RO cs.CV | To provide insight into cloth perception and manipulation with an active
binocular robotic vision system, we compiled a database of 80 stereo-pair
colour images with corresponding horizontal and vertical disparity maps and
mask annotations, for 3D garment point cloud rendering has been created and
released. The stereo-image garment database is part of research conducted under
the EU-FP7 Clothes Perception and Manipulation (CloPeMa) project and belongs to
a wider database collection released through CloPeMa (www.clopema.eu). This
database is based on 16 different off-the-shelve garments. Each garment has
been imaged in five different pose configurations on the project's binocular
robot head. A full copy of the database is made available for scientific
research only at https://sites.google.com/site/ugstereodatabase/.
|
1311.7298 | List decoding - random coding exponents and expurgated exponents | cs.IT math.IT | Some new results are derived concerning random coding error exponents and
expurgated exponents for list decoding with a deterministic list size $L$. Two
asymptotic regimes are considered, the fixed list-size regime, where $L$ is
fixed independently of the block length $n$, and the exponential list-size,
where $L$ grows exponentially with $n$. We first derive a general upper bound
on the list-decoding average error probability, which is suitable for both
regimes. This bound leads to more specific bounds in the two regimes. In the
fixed list-size regime, the bound is related to known bounds and we establish
its exponential tightness. In the exponential list-size regime, we establish
the achievability of the well known sphere packing lower bound. Relations to
guessing exponents are also provided. An immediate byproduct of our analysis in
both regimes is the universality of the maximum mutual information (MMI) list
decoder in the error exponent sense. Finally, we consider expurgated bounds at
low rates, both using Gallager's approach and the Csisz\'ar-K\"orner-Marton
approach, which is, in general better (at least for $L=1$). The latter
expurgated bound, which involves the notion of {\it multi-information}, is also
modified to apply to continuous alphabet channels, and in particular, to the
Gaussian memoryless channel, where the expression of the expurgated bound
becomes quite explicit.
|
1311.7302 | Spectral and Energy Efficiency Trade-Offs in Cellular Networks | cs.IT math.IT | This paper presents a simple and effective method to study the spectral and
energy efficiency (SE-EE) trade-off in cellular networks, an issue that has
attracted significant recent interest in the wireless community. The proposed
theoretical framework is based on an optimal radio resource allocation of
transmit power and bandwidth for the downlink direction, applicable for an
orthogonal cellular network. The analysis is initially focused on a single cell
scenario, for which in addition to the solution of the main SE-EE optimization
problem, it is proved that a traffic repartition scheme can also be adopted as
a way to simplify this approach. By exploiting this interesting result along
with properties of stochastic geometry, this work is extended to a more
challenging multi-cell environment, where interference is shown to play an
essential role and for this reason several interference reduction techniques
are investigated. Special attention is also given to the case of low signal to
noise ratio (SNR) and a way to evaluate the upper bound on EE in this regime is
provided. This methodology leads to tractable analytical results under certain
common channel properties, and thus allows the study of various models without
the need for demanding system-level simulations.
|
1311.7307 | Schemas for Unordered XML on a DIME | cs.DB | We investigate schema languages for unordered XML having no relative order
among siblings. First, we propose unordered regular expressions (UREs),
essentially regular expressions with unordered concatenation instead of
standard concatenation, that define languages of unordered words to model the
allowed content of a node (i.e., collections of the labels of children).
However, unrestricted UREs are computationally too expensive as we show the
intractability of two fundamental decision problems for UREs: membership of an
unordered word to the language of a URE and containment of two UREs.
Consequently, we propose a practical and tractable restriction of UREs,
disjunctive interval multiplicity expressions (DIMEs).
Next, we employ DIMEs to define languages of unordered trees and propose two
schema languages: disjunctive interval multiplicity schema (DIMS), and its
restriction, disjunction-free interval multiplicity schema (IMS). We study the
complexity of the following static analysis problems: schema satisfiability,
membership of a tree to the language of a schema, schema containment, as well
as twig query satisfiability, implication, and containment in the presence of
schema. Finally, we study the expressive power of the proposed schema languages
and compare them with yardstick languages of unordered trees (FO, MSO, and
Presburger constraints) and DTDs under commutative closure. Our results show
that the proposed schema languages are capable of expressing many practical
languages of unordered trees and enjoy desirable computational properties.
|
1311.7327 | Unobtrusive Low Cost Pupil Size Measurements using Web cameras | cs.CV | Unobtrusive every day health monitoring can be of important use for the
elderly population. In particular, pupil size may be a valuable source of
information, since, apart from pathological cases, it can reveal the emotional
state, the fatigue and the ageing. To allow for unobtrusive monitoring to gain
acceptance, one should seek for efficient methods of monitoring using com- mon
low-cost hardware. This paper describes a method for monitoring pupil sizes
using a common web camera in real time. Our method works by first detecting the
face and the eyes area. Subsequently, optimal iris and sclera location and
radius, modelled as ellipses, are found using efficient filtering. Finally, the
pupil center and radius is estimated by optimal filtering within the area of
the iris. Experimental result show both the efficiency and the effectiveness of
our approach.
|
1311.7359 | Zak transforms and Gabor frames of totally positive functions and
exponential B-splines | cs.IT math.IT math.NA | We study totally positive (TP) functions of finite type and exponential
B-splines as window functions for Gabor frames. We establish the connection of
the Zak transform of these two classes of functions and prove that the Zak
transforms have only one zero in their fundamental domain of quasi-periodicity.
Our proof is based on the variation-diminishing property of shifts of
exponential B-splines. For the exponential B-spline B_m of order m, we
determine a large set of lattice parameters a,b>0 such that the Gabor family of
time-frequency shifts is a frame for L^2(R). By the connection of its Zak
transform to the Zak transform of TP functions of finite type, our result
provides an alternative proof that TP functions of finite type provide Gabor
frames for all lattice parameters with ab<1. For even two-sided exponentials
and the related exponential B-spline of order 2, we find lower frame-bounds A,
which show the asymptotically linear decay A (1-ab) as the density ab of the
time-frequency lattice tends to the critical density ab=1.
|
1311.7373 | Limited-Feedback-Based Channel-Aware Power Allocation for Linear
Distributed Estimation | cs.IT math.IT | This paper investigates the problem of distributed best linear unbiased
estimation (BLUE) of a random parameter at the fusion center (FC) of a wireless
sensor network (WSN). In particular, the application of limited-feedback
strategies for the optimal power allocation in distributed estimation is
studied. In order to find the BLUE estimator of the unknown parameter, the FC
combines spatially distributed, linearly processed, noisy observations of local
sensors received through orthogonal channels corrupted by fading and additive
Gaussian noise. Most optimal power-allocation schemes proposed in the
literature require the feedback of the exact instantaneous channel state
information from the FC to local sensors. This paper proposes a
limited-feedback strategy in which the FC designs an optimal codebook
containing the optimal power-allocation vectors, in an iterative offline
process, based on the generalized Lloyd algorithm with modified distortion
functions. Upon observing a realization of the channel vector, the FC finds the
closest codeword to its corresponding optimal power-allocation vector and
broadcasts the index of the codeword. Each sensor will then transmit its analog
observations using its optimal quantized amplification gain. This approach
eliminates the requirement for infinite-rate digital feedback links and is
scalable, especially in large WSNs.
|
1311.7385 | Algorithmic Identification of Probabilities | cs.LG | TThe problem is to identify a probability associated with a set of natural
numbers, given an infinite data sequence of elements from the set. If the given
sequence is drawn i.i.d. and the probability mass function involved (the
target) belongs to a computably enumerable (c.e.) or co-computably enumerable
(co-c.e.) set of computable probability mass functions, then there is an
algorithm to almost surely identify the target in the limit. The technical tool
is the strong law of large numbers. If the set is finite and the elements of
the sequence are dependent while the sequence is typical in the sense of
Martin-L\"of for at least one measure belonging to a c.e. or co-c.e. set of
computable measures, then there is an algorithm to identify in the limit a
computable measure for which the sequence is typical (there may be more than
one such measure). The technical tool is the theory of Kolmogorov complexity.
We give the algorithms and consider the associated predictions.
|
1311.7388 | Web Mining Techniques in E-Commerce Applications | cs.IR | Today web is the best medium of communication in modern business. Many
companies are redefining their business strategies to improve the business
output. Business over internet provides the opportunity to customers and
partners where their products and specific business can be found. Nowadays
online business breaks the barrier of time and space as compared to the
physical office. Big companies around the world are realizing that e-commerce
is not just buying and selling over Internet, rather it improves the efficiency
to compete with other giants in the market. For this purpose data mining
sometimes called as knowledge discovery is used. Web mining is data mining
technique that is applied to the WWW. There are vast quantities of information
available over the Internet.
|
1311.7401 | Shape from Texture using Locally Scaled Point Processes | stat.AP cs.CV | Shape from texture refers to the extraction of 3D information from 2D images
with irregular texture. This paper introduces a statistical framework to learn
shape from texture where convex texture elements in a 2D image are represented
through a point process. In a first step, the 2D image is preprocessed to
generate a probability map corresponding to an estimate of the unnormalized
intensity of the latent point process underlying the texture elements. The
latent point process is subsequently inferred from the probability map in a
non-parametric, model free manner. Finally, the 3D information is extracted
from the point pattern by applying a locally scaled point process model where
the local scaling function represents the deformation caused by the projection
of a 3D surface onto a 2D image.
|
1311.7434 | Observability, Identifiability and Sensitivity of Vision-Aided
Navigation | cs.RO | We analyze the observability of motion estimates from the fusion of visual
and inertial sensors. Because the model contains unknown parameters, such as
sensor biases, the problem is usually cast as a mixed identification/filtering,
and the resulting observability analysis provides a necessary condition for any
algorithm to converge to a unique point estimate. Unfortunately, most models
treat sensor bias rates as noise, independent of other states including biases
themselves, an assumption that is patently violated in practice. When this
assumption is lifted, the resulting model is not observable, and therefore past
analyses cannot be used to conclude that the set of states that are
indistinguishable from the measurements is a singleton. In other words, the
resulting model is not observable. We therefore re-cast the analysis as one of
sensitivity: Rather than attempting to prove that the indistinguishable set is
a singleton, which is not the case, we derive bounds on its volume, as a
function of characteristics of the input and its sufficient excitation. This
provides an explicit characterization of the indistinguishable set that can be
used for analysis and validation purposes.
|
1311.7442 | Irreducibility is Minimum Synergy Among Parts | cs.IT math.IT | For readers already familiar with Partial Information Decomposition (PID), we
show that PID's definition of synergy enables quantifying at least four
different notions of irreducibility. First, we show four common notions of
"parts" give rise to a spectrum of four distinct measures of irreducibility.
Second, we introduce a nonnegative expression based on PID for each notion of
irreducibility. Third, we delineate these four notions of irreducibility with
exemplary binary circuits. This work will become more useful once the
complexity community has converged on a palatable $\operatorname{I}_{\cap}$ or
$\operatorname{I}_{\cup}$ measure.
|
1311.7466 | Linear Network Error Correction Multicast/Broadcast/Dispersion/Generic
Codes | cs.IT math.IT | In the practical network communications, many internal nodes in the network
are required to not only transmit messages but decode source messages. For
different applications, four important classes of linear network codes in
network coding theory, i.e., linear multicast, linear broadcast, linear
dispersion, and generic network codes, have been studied extensively. More
generally, when channels of communication networks are noisy, information
transmission and error correction have to be under consideration
simultaneously, and thus these four classes of linear network codes are
generalized to linear network error correction (LNEC) coding, and we say them
LNEC multicast, broadcast, dispersion, and generic codes, respectively.
Furthermore, in order to characterize their efficiency of information
transmission and error correction, we propose the (weakly, strongly) extended
Singleton bounds for them, and define the corresponding optimal codes, i.e.,
LNEC multicast/broadcast/dispersion/generic MDS codes, which satisfy the
corresponding Singleton bounds with equality. The existences of such MDS codes
are discussed in detail by algebraic methods and the constructive algorithms
are also proposed.
|
1311.7518 | Power Penalty Due to First-order PMD in Optical OFDM/QAM and FBMC/OQAM
Transmission System | cs.IT math.IT | Polarization mode dispersion (PMD) is a challenge for high-data-rate
optical-communication systems. More researches are desirable for impairments
that is induced by PMD in high-speed optical orthogonal frequency division
multiplexing (OFDM) transmission system. In this paper, an approximately
analytical method for evaluating the power penalty due to first-order PMD in
optical OFDM with quadrature amplitude modulation (OFDM/QAM) and filter bank
based multi-carrier with offset quadrature amplitude modulation (FBMC/OQAM)
transmission system is presented. The simulation results show that, compared
with the single carrier with quadrature phase shift keying(SC-QPSK), both the
OFDM/QAM and the FBMC/OQAM can decrease the power penalty caused by PMD by
half. Furthermore, the FBMC/OQAM shows better power penalty immunity than the
OFDM/QAM under the influence of first order PMD.
|
1311.7562 | Dynamic coupling design for nonlinear output agreement and time-varying
flow control | cs.SY | This paper studies the problem of output agreement in networks of nonlinear
dynamical systems under time-varying disturbances, using dynamic diffusive
couplings. Necessary conditions are derived for general networks of nonlinear
systems, and these conditions are explicitly interpreted as conditions relating
the node dynamics and the network topology. For the class of incrementally
passive systems, necessary and sufficient conditions for output agreement are
derived. The approach proposed in the paper lends itself to solve flow control
problems in distribution networks. As a first case study, the internal model
approach is used for designing a controller that achieves an optimal routing
and inventory balancing in a dynamic transportation network with storage and
time-varying supply and demand. It is in particular shown that the time-varying
optimal routing problem can be solved by applying an internal model controller
to the dual variables of a certain convex network optimization problem. As a
second case study, we show that droop-controllers in microgrids have also an
interpretation as internal model controllers.
|
1311.7584 | On the Communication Complexity of Secure Computation | cs.CR cs.IT math.IT | Information theoretically secure multi-party computation (MPC) is a central
primitive of modern cryptography. However, relatively little is known about the
communication complexity of this primitive.
In this work, we develop powerful information theoretic tools to prove lower
bounds on the communication complexity of MPC. We restrict ourselves to a
3-party setting in order to bring out the power of these tools without
introducing too many complications. Our techniques include the use of a data
processing inequality for residual information - i.e., the gap between mutual
information and G\'acs-K\"orner common information, a new information
inequality for 3-party protocols, and the idea of distribution switching by
which lower bounds computed under certain worst-case scenarios can be shown to
apply for the general case.
Using these techniques we obtain tight bounds on communication complexity by
MPC protocols for various interesting functions. In particular, we show
concrete functions that have "communication-ideal" protocols, which achieve the
minimum communication simultaneously on all links in the network. Also, we
obtain the first explicit example of a function that incurs a higher
communication cost than the input length in the secure computation model of
Feige, Kilian and Naor (1994), who had shown that such functions exist. We also
show that our communication bounds imply tight lower bounds on the amount of
randomness required by MPC protocols for many interesting functions.
|
1311.7590 | Universal Polar Decoding with Channel Knowledge at the Encoder | cs.IT math.IT | Polar coding over a class of binary discrete memoryless channels with channel
knowledge at the encoder is studied. It is shown that polar codes achieve the
capacity of convex and one-sided classes of symmetric channels.
|
1311.7662 | The Power of Asymmetry in Binary Hashing | cs.LG cs.CV cs.IR | When approximating binary similarity using the hamming distance between short
binary hashes, we show that even if the similarity is symmetric, we can have
shorter and more accurate hashes by using two distinct code maps. I.e. by
approximating the similarity between $x$ and $x'$ as the hamming distance
between $f(x)$ and $g(x')$, for two distinct binary codes $f,g$, rather than as
the hamming distance between $f(x)$ and $f(x')$.
|
1311.7679 | Combination of Diverse Ranking Models for Personalized Expedia Hotel
Searches | cs.LG | The ICDM Challenge 2013 is to apply machine learning to the problem of hotel
ranking, aiming to maximize purchases according to given hotel characteristics,
location attractiveness of hotels, user's aggregated purchase history and
competitive online travel agency information for each potential hotel choice.
This paper describes the solution of team "binghsu & MLRush & BrickMover". We
conduct simple feature engineering work and train different models by each
individual team member. Afterwards, we use listwise ensemble method to combine
each model's output. Besides describing effective model and features, we will
discuss about the lessons we learned while using deep learning in this
competition.
|
1312.0001 | A Proposal for the Characterization of Multi-Dimensional
Inter-relationships of RDF Graphs Based on Set Theoretic Approach | cs.DB | In this paper a Set Theoretic approach has been reported for analyzing
inter-relationship between any numbers of RDF Graphs. An RDF Graph represents
triples in Resource Description Format of semantic web. So the identification
and characterization of criteria for inter-relationship of RDF Graphs shows a
new road in semantic search. Using set theoretic approach, a sound framing
criteria can be designed that examine whether two RDF Graphs are related and if
yes, how these relationships could be described with formal set theory. Along
with this, by introducing RDF Schema, the inter-relationship status is refined
into n-dimensional induced relationships.
|
1312.0022 | On products and powers of linear codes under componentwise
multiplication | cs.IT math.AG math.IT | In this text we develop the formalism of products and powers of linear codes
under componentwise multiplication. As an expanded version of the author's talk
at AGCT-14, focus is put mostly on basic properties and descriptive statements
that could otherwise probably not fit in a regular research paper. On the other
hand, more advanced results and applications are only quickly mentioned with
references to the literature. We also point out a few open problems.
Our presentation alternates between two points of view, which the theory
intertwines in an essential way: that of combinatorial coding, and that of
algebraic geometry.
In appendices that can be read independently, we investigate topics in
multilinear algebra over finite fields, notably we establish a criterion for a
symmetric multilinear map to admit a symmetric algorithm, or equivalently, for
a symmetric tensor to decompose as a sum of elementary symmetric tensors.
|
1312.0032 | Top-k Query Answering in Datalog+/- Ontologies under Subjective Reports
(Technical Report) | cs.AI cs.DB | The use of preferences in query answering, both in traditional databases and
in ontology-based data access, has recently received much attention, due to its
many real-world applications. In this paper, we tackle the problem of top-k
query answering in Datalog+/- ontologies subject to the querying user's
preferences and a collection of (subjective) reports of other users. Here, each
report consists of scores for a list of features, its author's preferences
among the features, as well as other information. Theses pieces of information
of every report are then combined, along with the querying user's preferences
and his/her trust into each report, to rank the query results. We present two
alternative such rankings, along with algorithms for top-k (atomic) query
answering under these rankings. We also show that, under suitable assumptions,
these algorithms run in polynomial time in the data complexity. We finally
present more general reports, which are associated with sets of atoms rather
than single atoms.
|
1312.0040 | Dynamic Interference Management | cs.IT math.IT | A linear interference network is considered. Long-term fluctuations (shadow
fading) in the wireless channel can lead to any link being erased with
probability p. Each receiver is interested in one unique message that can be
available at M transmitters. In a cellular downlink scenario, the case where
M=1 reflects the cell association problem, and the case where M>1 reflects the
problem of setting up the backhaul links for Coordinated Multi-Point (CoMP)
transmission. In both cases, we analyze Degrees of Freedom (DoF) optimal
schemes for the case of no erasures, and propose new schemes with better
average DoF performance at high probabilities of erasure. For M=1, we
characterize the average per user DoF, and identify the optimal assignment of
messages to transmitters at each value of p. For general values of M, we show
that there is no strategy for assigning messages to transmitters in large
networks that is optimal for all values of p.
|
1312.0042 | Boosting the Basic Counting on Distributed Streams | cs.DS cs.DB cs.DC | We revisit the classic basic counting problem in the distributed streaming
model that was studied by Gibbons and Tirthapura (GT). In the solution for
maintaining an $(\epsilon,\delta)$-estimate, as what GT's method does, we make
the following new contributions: (1) For a bit stream of size $n$, where each
bit has a probability at least $\gamma$ to be 1, we exponentially reduced the
average total processing time from GT's $\Theta(n \log(1/\delta))$ to
$O((1/(\gamma\epsilon^2))(\log^2 n) \log(1/\delta))$, thus providing the first
sublinear-time streaming algorithm for this problem. (2) In addition to an
overall much faster processing speed, our method provides a new tradeoff that a
lower accuracy demand (a larger value for $\epsilon$) promises a faster
processing speed, whereas GT's processing speed is $\Theta(n \log(1/\delta))$
in any case and for any $\epsilon$. (3) The worst-case total time cost of our
method matches GT's $\Theta(n\log(1/\delta))$, which is necessary but rarely
occurs in our method. (4) The space usage overhead in our method is a lower
order term compared with GT's space usage and occurs only $O(\log n)$ times
during the stream processing and is too negligible to be detected by the
operating system in practice. We further validate these solid theoretical
results with experiments on both real-world and synthetic data, showing that
our method is faster than GT's by a factor of several to several thousands
depending on the stream size and accuracy demands, without any detectable space
usage overhead. Our method is based on a faster sampling technique that we
design for boosting GT's method and we believe this technique can be of other
interest.
|
1312.0048 | Stochastic Optimization of Smooth Loss | cs.LG | In this paper, we first prove a high probability bound rather than an
expectation bound for stochastic optimization with smooth loss. Furthermore,
the existing analysis requires the knowledge of optimal classifier for tuning
the step size in order to achieve the desired bound. However, this information
is usually not accessible in advanced. We also propose a strategy to address
the limitation.
|
1312.0049 | One-Class Classification: Taxonomy of Study and Review of Techniques | cs.LG cs.AI | One-class classification (OCC) algorithms aim to build classification models
when the negative class is either absent, poorly sampled or not well defined.
This unique situation constrains the learning of efficient classifiers by
defining class boundary just with the knowledge of positive class. The OCC
problem has been considered and applied under many research themes, such as
outlier/novelty detection and concept learning. In this paper we present a
unified view of the general problem of OCC by presenting a taxonomy of study
for OCC problems, which is based on the availability of training data,
algorithms used and the application domains applied. We further delve into each
of the categories of the proposed taxonomy and present a comprehensive
literature review of the OCC algorithms, techniques and methodologies with a
focus on their significance, limitations and applications. We conclude our
paper by discussing some open research problems in the field of OCC and present
our vision for future research.
|
1312.0054 | Energy Harvesting Broadband Communication Systems with Processing Energy
Cost | cs.IT math.IT | Communication over a broadband fading channel powered by an energy harvesting
transmitter is studied. Assuming non-causal knowledge of energy/data arrivals
and channel gains, optimal transmission schemes are identified by taking into
account the energy cost of the processing circuitry as well as the transmission
energy. A constant processing cost for each active sub-channel is assumed.
Three different system objectives are considered: i) throughput maximization,
in which the total amount of transmitted data by a deadline is maximized for a
backlogged transmitter with a finite capacity battery; ii) energy maximization,
in which the remaining energy in an infinite capacity battery by a deadline is
maximized such that all the arriving data packets are delivered; iii)
transmission completion time minimization, in which the delivery time of all
the arriving data packets is minimized assuming infinite size battery. For each
objective, a convex optimization problem is formulated, the properties of the
optimal transmission policies are identified, and an algorithm which computes
an optimal transmission policy is proposed. Finally, based on the insights
gained from the offline optimizations, low-complexity online algorithms
performing close to the optimal dynamic programming solution for the throughput
and energy maximization problems are developed under the assumption that the
energy/data arrivals and channel states are known causally at the transmitter.
|
1312.0060 | On the Secrecy Capacity of Block Fading Channels with a Hybrid Adversary | cs.IT math.IT | We consider a block fading wiretap channel, where a transmitter attempts to
send messages securely to a receiver in the presence of a hybrid half-duplex
adversary, which arbitrarily decides to either jam or eavesdrop the
transmitter-to- receiver channel. We provide bounds to the secrecy capacity for
various possibilities on receiver feedback and show special cases where the
bounds are tight. We show that, without any feedback from the receiver, the
secrecy capacity is zero if the transmitter-to-adversary channel stochastically
dominates the effective transmitter-to-receiver channel. However, the secrecy
capacity is non-zero even when the receiver is allowed to feed back only one
bit at the end of each block. Our novel achievable strategy improves the rates
proposed in the literature for the non-hybrid adversarial model. We also
analyze the effect of multiple adversaries and delay constraints on the secrecy
capacity. We show that our novel time sharing approach leads to positive
secrecy rates even under strict delay constraints.
|
1312.0072 | Improving Texture Categorization with Biologically Inspired Filtering | cs.CV | Within the domain of texture classification, a lot of effort has been spent
on local descriptors, leading to many powerful algorithms. However,
preprocessing techniques have received much less attention despite their
important potential for improving the overall classification performance. We
address this question by proposing a novel, simple, yet very powerful
biologically-inspired filtering (BF) which simulates the performance of human
retina. In the proposed approach, given a texture image, after applying a DoG
filter to detect the "edges", we first split the filtered image into two "maps"
alongside the sides of its edges. The feature extraction step is then carried
out on the two "maps" instead of the input image. Our algorithm has several
advantages such as simplicity, robustness to illumination and noise, and
discriminative power. Experimental results on three large texture databases
show that with an extremely low computational cost, the proposed method
improves significantly the performance of many texture classification systems,
notably in noisy environments. The source codes of the proposed algorithm can
be downloaded from https://sites.google.com/site/nsonvu/code.
|
1312.0086 | A Framework for Genetic Algorithms Based on Hadoop | cs.NE cs.DC | Genetic Algorithms (GAs) are powerful metaheuristic techniques mostly used in
many real-world applications. The sequential execution of GAs requires
considerable computational power both in time and resources. Nevertheless, GAs
are naturally parallel and accessing a parallel platform such as Cloud is easy
and cheap. Apache Hadoop is one of the common services that can be used for
parallel applications. However, using Hadoop to develop a parallel version of
GAs is not simple without facing its inner workings. Even though some
sequential frameworks for GAs already exist, there is no framework supporting
the development of GA applications that can be executed in parallel. In this
paper is described a framework for parallel GAs on the Hadoop platform,
following the paradigm of MapReduce. The main purpose of this framework is to
allow the user to focus on the aspects of GA that are specific to the problem
to be addressed, being sure that this task is going to be correctly executed on
the Cloud with a good performance. The framework has been also exploited to
develop an application for Feature Subset Selection problem. A preliminary
analysis of the performance of the developed GA application has been performed
using three datasets and shown very promising performance.
|
1312.0116 | Communication Through Collisions: Opportunistic Utilization of Past
Receptions | cs.IT math.IT | When several wireless users are sharing the spectrum, packet collision is a
simple, yet widely used model for interference. Under this model, when
transmitters cause interference at any of the receivers, their collided packets
are discarded and need to be retransmitted. However, in reality, that receiver
can still store its analog received signal and utilize it for decoding the
packets in the future (for example, by successive interference cancellation
techniques). In this work, we propose a physical layer model for wireless
packet networks that allows for such flexibility at the receivers. We assume
that the transmitters will be aware of the state of the channel (i.e. when and
where collisions occur, or an unintended receiver overhears the signal) with
some delay, and propose several coding opportunities that can be utilized by
the transmitters to exploit the available signal at the receivers for
interference management (as opposed to discarding them). We analyze the
achievable throughput of our strategy in a canonical interference channel with
two transmitter-receiver pairs, and demonstrate the gain over conventional
schemes. By deriving an outer-bound, we also prove the optimality of our scheme
for the corresponding model.
|
1312.0127 | Characterizing and Extending Answer Set Semantics using Possibility
Theory | cs.AI cs.LO | Answer Set Programming (ASP) is a popular framework for modeling
combinatorial problems. However, ASP cannot easily be used for reasoning about
uncertain information. Possibilistic ASP (PASP) is an extension of ASP that
combines possibilistic logic and ASP. In PASP a weight is associated with each
rule, where this weight is interpreted as the certainty with which the
conclusion can be established when the body is known to hold. As such, it
allows us to model and reason about uncertain information in an intuitive way.
In this paper we present new semantics for PASP, in which rules are interpreted
as constraints on possibility distributions. Special models of these
constraints are then identified as possibilistic answer sets. In addition,
since ASP is a special case of PASP in which all the rules are entirely
certain, we obtain a new characterization of ASP in terms of constraints on
possibility distributions. This allows us to uncover a new form of disjunction,
called weak disjunction, that has not been previously considered in the
literature. In addition to introducing and motivating the semantics of weak
disjunction, we also pinpoint its computational complexity. In particular,
while the complexity of most reasoning tasks coincides with standard
disjunctive ASP, we find that brave reasoning for programs with weak
disjunctions is easier.
|
1312.0132 | Critical Graphs in Index Coding | cs.IT cs.DM math.IT | In this paper we define critical graphs as minimal graphs that support a
given set of rates for the index coding problem, and study them for both the
one-shot and asymptotic setups. For the case of equal rates, we find the
critical graph with minimum number of edges for both one-shot and asymptotic
cases. For the general case of possibly distinct rates, we show that for
one-shot and asymptotic linear index coding, as well as asymptotic non-linear
index coding, each critical graph is a union of disjoint strongly connected
subgraphs (USCS). On the other hand, we identify a non-USCS critical graph for
a one-shot non-linear index coding problem. Next, we identify a few graph
structures that are critical. We also generalize some of our results to the
groupcast problem. In addition, we show that the capacity region of the index
coding is additive for union of disjoint graphs.
|
1312.0144 | Knowing Whether | cs.AI | Knowing whether a proposition is true means knowing that it is true or
knowing that it is false. In this paper, we study logics with a modal operator
Kw for knowing whether but without a modal operator K for knowing that. This
logic is not a normal modal logic, because we do not have Kw (phi -> psi) ->
(Kw phi -> Kw psi). Knowing whether logic cannot define many common frame
properties, and its expressive power less than that of basic modal logic over
classes of models without reflexivity. These features make axiomatizing knowing
whether logics non-trivial. We axiomatize knowing whether logic over various
frame classes. We also present an extension of knowing whether logic with
public announcement operators and we give corresponding reduction axioms for
that. We compare our work in detail to two recent similar proposals.
|
1312.0146 | Impact of Co-Channel Interference on Performance of Multi-Hop Relaying
over Nakagami-$m$ Fading Channels | cs.IT math.IT | This paper studies the impact of co-channel interferences (CCIs) on the
system performance of multi-hop amplify-and-forward (AF) relaying, in a simple
and explicit way. For generality, the desired channels along consecutive
relaying hops and the CCIs at all nodes are subject to Nakagami-$m$ fading with
different shape factors. This study reveals that the diversity gain is
determined only by the fading shape factor of the desired channels, regardless
of the interference and the number of relaying hops. On the other hand,
although the coding gain is in general a complex function of various system
parameters, if the desired channels are subject to Rayleigh fading, the coding
gain is inversely proportional to the accumulated interference at the
destination, i.e. the product of the number of relaying hops and the average
interference-to-noise ratio, irrespective of the fading distribution of the
CCIs.
|
1312.0148 | On Replacing PID Controller with ANN Controller for DC Motor Position
Control | cs.SY | The process industry implements many techniques with certain parameters in
its operations to control the working of several actuators on field. Amongst
these actuators, DC motor is a very common machine. The angular position of DC
motor can be controlled to drive many processes such as the arm of a robot. The
most famous and well known controller for such applications is PID controller.
It uses proportional, integral and derivative functions to control the input
signal before sending it to the plant unit. In this paper, another controller
based on Artificial Neural Network (ANN) control is examined to replace the PID
controller for controlling the angular position of a DC motor to drive a robot
arm. Simulation is performed in MATLAB after training the neural network
(supervised learning) and it is shown that results are acceptable and
applicable in process industry for reference control applications. The paper
also indicates that the ANN controller can be less complicated and less costly
to implement in industrial control applications as compared to some other
proposed schemes.
|
1312.0156 | Datom: Towards modular data management | cs.DB | Recent technology breakthroughs have enabled data collection of unprecedented
scale, rate, variety and complexity that has led to an explosion in data
management requirements. Existing theories and techniques are not adequate to
fulfil these requirements. We endeavour to rethink the way data management
research is being conducted and we propose to work towards modular data
management that will allow for unification of the expression of data management
problems and systematization of their solution. The core of such an approach is
the novel notion of a datom, i.e. a data management atom, which encapsulates
generic data management provision. The datom is the foundation for comparison,
customization and re-usage of data management problems and solutions. The
proposed approach can signal a revolution in data management research and a
long anticipated evolution in data management engineering.
|
1312.0158 | An algebraic characterization of injectivity in phase retrieval | math.FA cs.IT math.AG math.IT | A complex frame is a collection of vectors that span $\mathbb{C}^M$ and
define measurements, called intensity measurements, on vectors in
$\mathbb{C}^M$. In purely mathematical terms, the problem of phase retrieval is
to recover a complex vector from its intensity measurements, namely the modulus
of its inner product with these frame vectors. We show that any vector is
uniquely determined (up to a global phase factor) from $4M-4$ generic
measurements. To prove this, we identify the set of frames defining
non-injective measurements with the projection of a real variety and bound its
dimension.
|
1312.0162 | A Typology of Collaboration Platform Users | cs.CY cs.HC cs.SI stat.ML | In this paper we present a review of the existing typologies of Internet
service users. We zoom in on social networking services including blogs and
crowdsourcing websites. Based on the results of the analysis of the considered
typologies obtained by means of FCA we developed a new user typology of a
certain class of Internet services, namely a collaboration innovation platform.
Cluster analysis of data extracted from the collaboration platform Witology was
used to divide more than 500 participants into six groups based on three
activity indicators: idea generation, commenting, and evaluation (assigning
marks) The obtained groups and their percentages appear to follow the "90 - 9 -
1" rule.
|
1312.0169 | Entropy and the Predictability of Online Life | physics.soc-ph cs.SI | Using mobile phone records and information theory measures, our daily lives
have been recently shown to follow strict statistical regularities, and our
movement patterns are to a large extent predictable. Here, we apply entropy and
predictability measures to two data sets of the behavioral actions and the
mobility of a large number of players in the virtual universe of a massive
multiplayer online game. We find that movements in virtual human lives follow
the same high levels of predictability as offline mobility, where future
movements can to some extent be predicted well if the temporal correlations of
visited places are accounted for. Time series of behavioral actions show
similar high levels of predictability, even when temporal correlations are
neglected. Entropy conditional on specific behavioral actions reveals that in
terms of predictability negative behavior has a wider variety than positive
actions. The actions which contain information to best predict an individual's
subsequent action are negative, such as attacks or enemy markings, while
positive actions of friendship marking, trade and communication contain the
least amount of predictive information. These observations show that predicting
behavioral actions requires less information than predicting the mobility
patterns of humans for which the additional knowledge of past visited locations
is crucial, and that the type and sign of a social relation has an essential
impact on the ability to determine future behavior.
|
1312.0171 | Complex networks as an emerging property of hierarchical preferential
attachment | physics.soc-ph cs.SI | Real complex systems are not rigidly structured; no clear rules or blueprints
exist for their construction. Yet, amidst their apparent randomness, complex
structural properties universally emerge. We propose that an important class of
complex systems can be modeled as an organization of many embedded levels
(potentially infinite in number), all of them following the same universal
growth principle known as preferential attachment. We give examples of such
hierarchy in real systems, for instance in the pyramid of production entities
of the film industry. More importantly, we show how real complex networks can
be interpreted as a projection of our model, from which their scale
independence, their clustering, their hierarchy, their fractality and their
navigability naturally emerge. Our results suggest that complex networks,
viewed as growing systems, can be quite simple, and that the apparent
complexity of their structure is largely a reflection of their unobserved
hierarchical nature.
|
1312.0182 | Query Segmentation for Relevance Ranking in Web Search | cs.IR | In this paper, we try to answer the question of how to improve the
state-of-the-art methods for relevance ranking in web search by query
segmentation. Here, by query segmentation it is meant to segment the input
query into segments, typically natural language phrases, so that the
performance of relevance ranking in search is increased. We propose employing
the re-ranking approach in query segmentation, which first employs a generative
model to create top $k$ candidates and then employs a discriminative model to
re-rank the candidates to obtain the final segmentation result. The method has
been widely utilized for structure prediction in natural language processing,
but has not been applied to query segmentation, as far as we know. Furthermore,
we propose a new method for using the result of query segmentation in relevance
ranking, which takes both the original query words and the segmented query
phrases as units of query representation. We investigate whether our method can
improve three relevance models, namely BM25, key n-gram model, and dependency
model. Our experimental results on three large scale web search datasets show
that our method can indeed significantly improve relevance ranking in all the
three cases.
|
1312.0189 | Empowering Evolving Social Network Users with Privacy Rights | cs.DB cs.CR cs.SI | Considerable concerns exist over privacy on social networks, and huge debates
persist about how to extend the artifacts users need to effectively protect
their rights to privacy. While many interesting ideas have been proposed, no
single approach appears to be comprehensive enough to be the front runner. In
this paper, we propose a comprehensive and novel reference conceptual model for
privacy in constantly evolving social networks and establish its novelty by
briefly contrasting it with contemporary research. We also present the contours
of a possible query language that we can develop with desirable features in
light of the reference model, and refer to a new query language, {\em PiQL},
developed on the basis of this model that aims to support user driven privacy
policy authoring and enforcement. The strength of our model is that such
extensions are now possible by developing appropriate linguistic constructs as
part of query languages such as SQL, as demonstrated in PiQL.
|
1312.0200 | A Combined Approach for Constraints over Finite Domains and Arrays | cs.LO cs.AI cs.SE | Arrays are ubiquitous in the context of software verification. However,
effective reasoning over arrays is still rare in CP, as local reasoning is
dramatically ill-conditioned for constraints over arrays. In this paper, we
propose an approach combining both global symbolic reasoning and local
consistency filtering in order to solve constraint systems involving arrays
(with accesses, updates and size constraints) and finite-domain constraints
over their elements and indexes. Our approach, named FDCC, is based on a
combination of a congruence closure algorithm for the standard theory of arrays
and a CP solver over finite domains. The tricky part of the work lies in the
bi-directional communication mechanism between both solvers. We identify the
significant information to share, and design ways to master the communication
overhead. Experiments on random instances show that FDCC solves more formulas
than any portfolio combination of the two solvers taken in isolation, while
overhead is kept reasonable.
|
1312.0202 | Sparse Time Frequency Representations and Dynamical Systems | cs.IT math.IT | In this paper, we establish a connection between the recently developed
data-driven time-frequency analysis \cite{HS11,HS13-1} and the classical second
order differential equations. The main idea of the data-driven time-frequency
analysis is to decompose a multiscale signal into a sparsest collection of
Intrinsic Mode Functions (IMFs) over the largest possible dictionary via
nonlinear optimization. These IMFs are of the form $a(t) \cos(\theta(t))$ where
the amplitude $a(t)$ is positive and slowly varying. The non-decreasing phase
function $\theta(t)$ is determined by the data and in general depends on the
signal in a nonlinear fashion. One of the main results of this paper is that we
show that each IMF can be associated with a solution of a second order ordinary
differential equation of the form $\ddot{x}+p(x,t)\dot{x}+q(x,t)=0$. Further,
we propose a localized variational formulation for this problem and develop an
effective $l^1$-based optimization method to recover $p(x,t)$ and $q(x,t)$ by
looking for a sparse representation of $p$ and $q$ in terms of the polynomial
basis. Depending on the form of nonlinearity in $p(x,t)$ and $q(x,t)$, we can
define the degree of nonlinearity for the associated IMF. %and the
corresponding coefficients for the associated highest order nonlinear terms.
This generalizes a concept recently introduced by Prof. N. E. Huang et al.
\cite{Huang11}. Numerical examples will be provided to illustrate the
robustness and stability of the proposed method for data with or without noise.
This manuscript should be considered as a proof of concept.
|
1312.0229 | Five Disruptive Technology Directions for 5G | cs.NI cs.IT math.IT | New research directions will lead to fundamental changes in the design of
future 5th generation (5G) cellular networks. This paper describes five
technologies that could lead to both architectural and component disruptive
design changes: device-centric architectures, millimeter Wave, Massive-MIMO,
smarter devices, and native support to machine-2-machine. The key ideas for
each technology are described, along with their potential impact on 5G and the
research challenges that remain.
|
1312.0232 | Stochastic continuum armed bandit problem of few linear parameters in
high dimensions | stat.ML cs.LG math.OC | We consider a stochastic continuum armed bandit problem where the arms are
indexed by the $\ell_2$ ball $B_{d}(1+\nu)$ of radius $1+\nu$ in
$\mathbb{R}^d$. The reward functions $r :B_{d}(1+\nu) \rightarrow \mathbb{R}$
are considered to intrinsically depend on $k \ll d$ unknown linear parameters
so that $r(\mathbf{x}) = g(\mathbf{A} \mathbf{x})$ where $\mathbf{A}$ is a full
rank $k \times d$ matrix. Assuming the mean reward function to be smooth we
make use of results from low-rank matrix recovery literature and derive an
efficient randomized algorithm which achieves a regret bound of $O(C(k,d)
n^{\frac{1+k}{2+k}} (\log n)^{\frac{1}{2+k}})$ with high probability. Here
$C(k,d)$ is at most polynomial in $d$ and $k$ and $n$ is the number of rounds
or the sampling budget which is assumed to be known beforehand.
|
1312.0256 | Analysis of Regularized LS Reconstruction and Random Matrix Ensembles in
Compressed Sensing | cs.IT math.IT | Performance of regularized least-squares estimation in noisy compressed
sensing is analyzed in the limit when the dimensions of the measurement matrix
grow large. The sensing matrix is considered to be from a class of random
ensembles that encloses as special cases standard Gaussian, row-orthogonal,
geometric and so-called T-orthogonal constructions. Source vectors that have
non-uniform sparsity are included in the system model. Regularization based on
l1-norm and leading to LASSO estimation, or basis pursuit denoising, is given
the main emphasis in the analysis. Extensions to l2-norm and "zero-norm"
regularization are also briefly discussed. The analysis is carried out using
the replica method in conjunction with some novel matrix integration results.
Numerical experiments for LASSO are provided to verify the accuracy of the
analytical results. The numerical experiments show that for noisy compressed
sensing, the standard Gaussian ensemble is a suboptimal choice for the
measurement matrix. Orthogonal constructions provide a superior performance in
all considered scenarios and are easier to implement in practical applications.
It is also discovered that for non-uniform sparsity patterns the T-orthogonal
matrices can further improve the mean square error behavior of the
reconstruction when the noise level is not too high. However, as the additive
noise becomes more prominent in the system, the simple row-orthogonal
measurement matrix appears to be the best choice out of the considered
ensembles.
|
1312.0264 | Competitive Fragmentation Modeling of ESI-MS/MS spectra for putative
metabolite identification | cs.CE | Electrospray tandem mass spectrometry (ESI-MS/MS) is commonly used in high
throughput metabolomics. One of the key obstacles to the effective use of this
technology is the difficulty in interpreting measured spectra to accurately and
efficiently identify metabolites. Traditional methods for automated metabolite
identification compare the target MS or MS/MS spectrum to the spectra in a
reference database, ranking candidates based on the closeness of the match.
However the limited coverage of available databases has led to an interest in
computational methods for predicting reference MS/MS spectra from chemical
structures.
This work proposes a probabilistic generative model for the MS/MS
fragmentation process, which we call Competitive Fragmentation Modeling (CFM),
and a machine learning approach for learning parameters for this model from
MS/MS data. We show that CFM can be used in both a MS/MS spectrum prediction
task (ie, predicting the mass spectrum from a chemical structure), and in a
putative metabolite identification task (ranking possible structures for a
target MS/MS spectrum).
In the MS/MS spectrum prediction task, CFM shows significantly improved
performance when compared to a full enumeration of all peaks corresponding to
substructures of the molecule. In the metabolite identification task, CFM
obtains substantially better rankings for the correct candidate than existing
methods (MetFrag and FingerID) on tripeptide and metabolite data, when querying
PubChem or KEGG for candidate structures of similar mass.
|
1312.0285 | Distributed Data Placement via Graph Partitioning | cs.DB | With the widespread use of shared-nothing clusters of servers, there has been
a proliferation of distributed object stores that offer high availability,
reliability and enhanced performance for MapReduce-style workloads. However,
relational workloads cannot always be evaluated efficiently using MapReduce
without extensive data migrations, which cause network congestion and reduced
query throughput. We study the problem of computing data placement strategies
that minimize the data communication costs incurred by typical relational query
workloads in a distributed setting.
Our main contribution is a reduction of the data placement problem to the
well-studied problem of {\sc Graph Partitioning}, which is NP-Hard but for
which efficient approximation algorithms exist. The novelty and significance of
this result lie in representing the communication cost exactly and using
standard graphs instead of hypergraphs, which were used in prior work on data
placement that optimized for different objectives (not communication cost).
We study several practical extensions of the problem: with load balancing,
with replication, with materialized views, and with complex query plans
consisting of sequences of intermediate operations that may be computed on
different servers. We provide integer linear programs (IPs) that may be used
with any IP solver to find an optimal data placement. For the no-replication
case, we use publicly available graph partitioning libraries (e.g., METIS) to
efficiently compute nearly-optimal solutions. For the versions with
replication, we introduce two heuristics that utilize the {\sc Graph
Partitioning} solution of the no-replication case. Using the TPC-DS workload,
it may take an IP solver weeks to compute an optimal data placement, whereas
our reduction produces nearly-optimal solutions in seconds.
|
1312.0286 | Efficient Learning and Planning with Compressed Predictive States | cs.LG stat.ML | Predictive state representations (PSRs) offer an expressive framework for
modelling partially observable systems. By compactly representing systems as
functions of observable quantities, the PSR learning approach avoids using
local-minima prone expectation-maximization and instead employs a globally
optimal moment-based algorithm. Moreover, since PSRs do not require a
predetermined latent state structure as an input, they offer an attractive
framework for model-based reinforcement learning when agents must plan without
a priori access to a system model. Unfortunately, the expressiveness of PSRs
comes with significant computational cost, and this cost is a major factor
inhibiting the use of PSRs in applications. In order to alleviate this
shortcoming, we introduce the notion of compressed PSRs (CPSRs). The CPSR
learning approach combines recent advancements in dimensionality reduction,
incremental matrix decomposition, and compressed sensing. We show how this
approach provides a principled avenue for learning accurate approximations of
PSRs, drastically reducing the computational costs associated with learning
while also providing effective regularization. Going further, we propose a
planning framework which exploits these learned models. And we show that this
approach facilitates model-learning and planning in large complex partially
observable domains, a task that is infeasible without the principled use of
compression.
|
1312.0288 | Preliminary Results on 3D Channel Modeling: From Theory to
Standardization | cs.IT math.IT | Three dimensional beamforming (3D) (also elevation beamforming) is now
gaining a growing interest among researchers in wireless communication. The
reason can be attributed to its potential to enable a variety of strategies
like sector or user specific elevation beamforming and cell-splitting. Since
these techniques cannot be directly supported by current LTE releases, the 3GPP
is now working on defining the required technical specifications. In
particular, a large effort is currently made to get accurate 3D channel models
that support the elevation dimension. This step is necessary as it will
evaluate the potential of 3D and FD(Full Dimensional) beamforming techniques to
benefit from the richness of real channels. This work aims at presenting the
on-going 3GPP study item "Study on 3D-channel model for Elevation Beamforming
and FD-MIMO studies for LTE", and positioning it with respect to previous
standardization works.
|
1312.0317 | Evolutionary Dynamics of Information Diffusion over Social Networks | cs.SI physics.soc-ph | Current social networks are of extremely large-scale generating tremendous
information flows at every moment. How information diffuse over social networks
has attracted much attention from both industry and academics. Most of the
existing works on information diffusion analysis are based on machine learning
methods focusing on social network structure analysis and empirical data
mining. However, the dynamics of information diffusion, which are heavily
influenced by network users' decisions, actions and their socio-economic
interactions, is generally ignored by most of existing works. In this paper, we
propose an evolutionary game theoretic framework to model the dynamic
information diffusion process in social networks. Specifically, we derive the
information diffusion dynamics in complete networks, uniform degree and
non-uniform degree networks, with the highlight of two special networks,
Erd\H{o}s-R\'enyi random network and the Barab\'asi-Albert scale-free network.
We find that the dynamics of information diffusion over these three kinds of
networks are scale-free and the same with each other when the network scale is
sufficiently large. To verify our theoretical analysis, we perform simulations
for the information diffusion over synthetic networks and real-world Facebook
networks. Moreover, we also conduct experiment on Twitter hashtags dataset,
which shows that the proposed game theoretic model can well fit and predict the
information diffusion over real social networks.
|
1312.0336 | A Unifying Framework for the Electrical Structure-Based Approach to PMU
Placement in Electric Power Systems | cs.SY | The electrical structure of the power grid is utilized to address the phasor
measurement unit (PMU) placement problem. First, we derive the connectivity
matrix of the network using the resistance distance metric and employ it in the
linear program formulation to obtain the optimal number of PMUs, for complete
network observability without zero injection measurements. This approach was
developed by the author in an earlier work, but the solution methodology to
address the location problem did not fully utilize the electrical properties of
the network, resulting in an ambiguity. In this paper, we settle this issue by
exploiting the coupling structure of the grid derived using the singular value
decomposition (SVD)-based analysis of the resistance distance matrix to solve
the location problem. Our study, which is based on recent advances in complex
networks that promote the electrical structure of the grid over its topological
structure and the SVD analysis which throws light on the electrical coupling of
the network, results in a unified framework for the electrical structure-based
PMU placement. The proposed method is tested on IEEE bus systems, and the
results uncover intriguing connections between the singular vectors and average
resistance distance between buses in the network.
|
1312.0363 | Optimal Stochastic Coordinated Beamforming for Wireless Cooperative
Networks with CSI Uncertainty | cs.IT math.IT | Transmit optimization and resource allocation for wireless cooperative
networks with channel state information (CSI) uncertainty are important but
challenging problems in terms of both the uncertainty modeling and performance
op- timization. In this paper, we establish a generic stochastic coordinated
beamforming (SCB) framework that provides flex- ibility in the channel
uncertainty modeling, while guaranteeing optimality in the transmission
strategies. We adopt a general stochastic model for the CSI uncertainty, which
is applicable for various practical scenarios. The SCB problem turns out to be
a joint chance constrained program (JCCP) and is known to be highly
intractable. In contrast to all the previous algo- rithms for JCCP that can
only find feasible but sub-optimal solutions, we propose a novel stochastic DC
(difference-of-convex) programming algorithm with optimality guarantee, which
can serve as the benchmark for evaluating heuristic and sub-optimal algorithms.
The key observation is that the highly intractable probability constraint can
be equivalently reformulated as a DC constraint. This further enables efficient
algorithms to achieve optimality. Simulation results will illustrate the
convergence, conservativeness, stability and performance gains of the proposed
algorithm.
|
1312.0372 | Polar Codes: Graph Representation and Duality | cs.IT math.IT | In this paper, we present an iterative construction of a polar code and
develop properties of the dual of a polar code. Based on this approach, belief
propagation of a polar code can be presented in the context of low-density
parity check codes.
|
1312.0403 | Asymptotic Rate Analysis of Downlink Multi-user Systems with Co-located
and Distributed Antennas | cs.IT math.IT | A great deal of efforts have been made on the performance evaluation of
distributed antenna systems (DASs). Most of them assume a regular base-station
(BS) antenna layout where the number of BS antennas is usually small. With the
growing interest in cellular systems with large antenna arrays at BSs, it
becomes increasingly important for us to study how the BS antenna layout
affects the rate performance when a massive number of BS antennas are employed.
This paper presents a comparative study of the asymptotic rate performance of
downlink multi-user systems with multiple BS antennas either co-located or
uniformly distributed within a circular cell. Two representative linear
precoding schemes, maximum ratio transmission (MRT) and zero-forcing
beamforming (ZFBF), are considered, with which the effect of BS antenna layout
on the rate performance is characterized. The analysis shows that as the number
of BS antennas $L$ and the number of users $K$ grow infinitely while
$L/K{\rightarrow}\upsilon$, the asymptotic average user rates with the
co-located antenna (CA) layout for both MRT and ZFBF are logarithmic functions
of the ratio $\upsilon$. With the distributed antenna (DA) layout, in contrast,
the scaling behavior of the average user rate closely depends on the precoding
schemes. With ZFBF, for instance, the average user rate grows unboundedly as
$L, K{\rightarrow} \infty$ and $L/K{\rightarrow}\upsilon{>}1$, which indicates
that substantial rate gains over the CA layout can be achieved when the number
of BS antennas $L$ is large. The gain, nevertheless, becomes marginal when MRT
is adopted.
|
1312.0412 | Practical Collapsed Stochastic Variational Inference for the HDP | cs.LG | Recent advances have made it feasible to apply the stochastic variational
paradigm to a collapsed representation of latent Dirichlet allocation (LDA).
While the stochastic variational paradigm has successfully been applied to an
uncollapsed representation of the hierarchical Dirichlet process (HDP), no
attempts to apply this type of inference in a collapsed setting of
non-parametric topic modeling have been put forward so far. In this paper we
explore such a collapsed stochastic variational Bayes inference for the HDP.
The proposed online algorithm is easy to implement and accounts for the
inference of hyper-parameters. First experiments show a promising improvement
in predictive performance.
|
1312.0451 | Consistency of weighted majority votes | math.PR cs.LG stat.ML | We revisit the classical decision-theoretic problem of weighted expert voting
from a statistical learning perspective. In particular, we examine the
consistency (both asymptotic and finitary) of the optimal Nitzan-Paroush
weighted majority and related rules. In the case of known expert competence
levels, we give sharp error estimates for the optimal rule. When the competence
levels are unknown, they must be empirically estimated. We provide frequentist
and Bayesian analyses for this situation. Some of our proof techniques are
non-standard and may be of independent interest. The bounds we derive are
nearly optimal, and several challenging open problems are posed. Experimental
results are provided to illustrate the theory.
|
1312.0465 | A pattern-driven approach to biomedical ontology engineering | cs.CE cs.DL | Developing ontologies can be expensive, time-consuming, as well as difficult
to develop and maintain. This is especially true for more expressive and/or
larger ontologies. Some ontologies are, however, relatively repetitive, reusing
design patterns; building these with both generic and bespoke patterns should
reduce duplication and increase regularity which in turn should impact on the
cost of development.
Here we report on the usage of patterns applied to two biomedical ontologies:
firstly a novel ontology for karyotypes which has been built ground-up using a
pattern based approach; and, secondly, our initial refactoring of the SIO
ontology to make explicit use of patterns at development time. To enable this,
we use the Tawny-OWL library which enables full-programmatic development of
ontologies. We show how this approach can generate large numbers of classes
from much simpler data structures which is highly beneficial within biomedical
ontology engineering.
|
1312.0482 | Learning Semantic Representations for the Phrase Translation Model | cs.CL | This paper presents a novel semantic-based phrase translation model. A pair
of source and target phrases are projected into continuous-valued vector
representations in a low-dimensional latent semantic space, where their
translation score is computed by the distance between the pair in this new
space. The projection is performed by a multi-layer neural network whose
weights are learned on parallel training data. The learning is aimed to
directly optimize the quality of end-to-end machine translation results.
Experimental evaluation has been performed on two Europarl translation tasks,
English-French and German-English. The results show that the new semantic-based
phrase translation model significantly improves the performance of a
state-of-the-art phrase-based statistical machine translation sys-tem, leading
to a gain of 0.7-1.0 BLEU points.
|
1312.0485 | Precise Semidefinite Programming Formulation of Atomic Norm Minimization
for Recovering d-Dimensional ($d\geq 2$) Off-the-Grid Frequencies | cs.IT math.IT math.OC stat.ML | Recent research in off-the-grid compressed sensing (CS) has demonstrated
that, under certain conditions, one can successfully recover a spectrally
sparse signal from a few time-domain samples even though the dictionary is
continuous. In particular, atomic norm minimization was proposed in
\cite{tang2012csotg} to recover $1$-dimensional spectrally sparse signal.
However, in spite of existing research efforts \cite{chi2013compressive}, it
was still an open problem how to formulate an equivalent positive semidefinite
program for atomic norm minimization in recovering signals with $d$-dimensional
($d\geq 2$) off-the-grid frequencies. In this paper, we settle this problem by
proposing equivalent semidefinite programming formulations of atomic norm
minimization to recover signals with $d$-dimensional ($d\geq 2$) off-the-grid
frequencies.
|
1312.0489 | Capacity Based Evacuation with Dynamic Exit Signs | cs.SY | Exit paths in buildings are designed to minimise evacuation time when the
building is at full capacity. We present an evacuation support system which
does this regardless of the number of evacuees. The core concept is to even-out
congestion in the building by diverting evacuees to less-congested paths in
order to make maximal usage of all accessible routes throughout the entire
evacuation process. The system issues a set of flow-optimal routes using a
capacity-constrained routing algorithm which anticipates evolutions in path
metrics using the concept of "future capacity reservation". In order to direct
evacuees in an intuitive manner whilst implementing the routing algorithm's
scheme, we use dynamic exit signs, i.e. whose pointing direction can be
controlled. To make this system practical and minimise reliance on sensors
during the evacuation, we use an evacuee mobility model and make several
assumptions on the characteristics of the evacuee flow. We validate this
concept using simulations, and show how the underpinning assumptions may limit
the system's performance, especially in low-headcount evacuations.
|
1312.0493 | Bidirectional Recursive Neural Networks for Token-Level Labeling with
Structure | cs.LG cs.CL stat.ML | Recently, deep architectures, such as recurrent and recursive neural networks
have been successfully applied to various natural language processing tasks.
Inspired by bidirectional recurrent neural networks which use representations
that summarize the past and future around an instance, we propose a novel
architecture that aims to capture the structural information around an input,
and use it to label instances. We apply our method to the task of opinion
expression extraction, where we employ the binary parse tree of a sentence as
the structure, and word vector representations as the initial representation of
a single token. We conduct preliminary experiments to investigate its
performance and compare it to the sequential approach.
|
1312.0510 | Fault Tolerance of Small-World Regular and Stochastic Interconnection
Networks | cs.SI cs.DC physics.soc-ph | Resilience of the most important properties of stochastic and regular
(deterministic) small-world interconnection networks is studied. It is shown
that in the broad range of values of the fraction of faulty nodes the networks
under consideration possess high fault tolerance, the deterministic networks
being slightly better than the stochastic ones.
|
1312.0512 | Sensing-Aware Kernel SVM | cs.LG | We propose a novel approach for designing kernels for support vector machines
(SVMs) when the class label is linked to the observation through a latent state
and the likelihood function of the observation given the state (the sensing
model) is available. We show that the Bayes-optimum decision boundary is a
hyperplane under a mapping defined by the likelihood function. Combining this
with the maximum margin principle yields kernels for SVMs that leverage
knowledge of the sensing model in an optimal way. We derive the optimum kernel
for the bag-of-words (BoWs) sensing model and demonstrate its superior
performance over other kernels in document and image classification tasks.
These results indicate that such optimum sensing-aware kernel SVMs can match
the performance of rather sophisticated state-of-the-art approaches.
|
1312.0516 | Grid Topology Identification using Electricity Prices | cs.LG cs.SY stat.AP stat.ML | The potential of recovering the topology of a grid using solely publicly
available market data is explored here. In contemporary whole-sale electricity
markets, real-time prices are typically determined by solving the
network-constrained economic dispatch problem. Under a linear DC model,
locational marginal prices (LMPs) correspond to the Lagrange multipliers of the
linear program involved. The interesting observation here is that the matrix of
spatiotemporally varying LMPs exhibits the following property: Once
premultiplied by the weighted grid Laplacian, it yields a low-rank and sparse
matrix. Leveraging this rich structure, a regularized maximum likelihood
estimator (MLE) is developed to recover the grid Laplacian from the LMPs. The
convex optimization problem formulated includes low rank- and
sparsity-promoting regularizers, and it is solved using a scalable algorithm.
Numerical tests on prices generated for the IEEE 14-bus benchmark provide
encouraging topology recovery results.
|
1312.0525 | Near Optimal Compressed Sensing of a Class of Sparse Low-Rank Matrices
via Sparse Power Factorization | cs.IT math.IT | Compressed sensing of simultaneously sparse and low-rank matrices enables
recovery of sparse signals from a few linear measurements of their bilinear
form. One important question is how many measurements are needed for a stable
reconstruction in the presence of measurement noise. Unlike conventional
compressed sensing for sparse vectors, where convex relaxation via the
$\ell_1$-norm achieves near optimal performance, for compressed sensing of
sparse low-rank matrices, it has been shown recently Oymak et al. that convex
programmings using the nuclear norm and the mixed norm are highly suboptimal
even in the noise-free scenario.
We propose an alternating minimization algorithm called sparse power
factorization (SPF) for compressed sensing of sparse rank-one matrices. For a
class of signals whose sparse representation coefficients are fast-decaying,
SPF achieves stable recovery of the rank-1 matrix formed by their outer product
and requires number of measurements within a logarithmic factor of the
information-theoretic fundamental limit. For the recovery of general sparse
low-rank matrices, we propose subspace-concatenated SPF (SCSPF), which has
analogous near optimal performance guarantees to SPF in the rank-1 case.
Numerical results show that SPF and SCSPF empirically outperform convex
programmings using the best known combinations of mixed norm and nuclear norm.
|
1312.0579 | SpeedMachines: Anytime Structured Prediction | cs.LG | Structured prediction plays a central role in machine learning applications
from computational biology to computer vision. These models require
significantly more computation than unstructured models, and, in many
applications, algorithms may need to make predictions within a computational
budget or in an anytime fashion. In this work we propose an anytime technique
for learning structured prediction that, at training time, incorporates both
structural elements and feature computation trade-offs that affect test-time
inference. We apply our technique to the challenging problem of scene
understanding in computer vision and demonstrate efficient and anytime
predictions that gradually improve towards state-of-the-art classification
performance as the allotted time increases.
|
1312.0624 | Efficient coordinate-descent for orthogonal matrices through Givens
rotations | cs.LG stat.ML | Optimizing over the set of orthogonal matrices is a central component in
problems like sparse-PCA or tensor decomposition. Unfortunately, such
optimization is hard since simple operations on orthogonal matrices easily
break orthogonality, and correcting orthogonality usually costs a large amount
of computation. Here we propose a framework for optimizing orthogonal matrices,
that is the parallel of coordinate-descent in Euclidean spaces. It is based on
{\em Givens-rotations}, a fast-to-compute operation that affects a small number
of entries in the learned matrix, and preserves orthogonality. We show two
applications of this approach: an algorithm for tensor decomposition that is
used in learning mixture models, and an algorithm for sparse-PCA. We study the
parameter regime where a Givens rotation approach converges faster and achieves
a superior model on a genome-wide brain-wide mRNA expression dataset.
|
1312.0631 | Phase Transitions in Community Detection: A Solvable Toy Model | cs.SI cond-mat.stat-mech physics.soc-ph stat.ML | Recently, it was shown that there is a phase transition in the community
detection problem. This transition was first computed using the cavity method,
and has been proved rigorously in the case of $q=2$ groups. However, analytic
calculations using the cavity method are challenging since they require us to
understand probability distributions of messages. We study analogous
transitions in so-called "zero-temperature inference" model, where this
distribution is supported only on the most-likely messages. Furthermore,
whenever several messages are equally likely, we break the tie by choosing
among them with equal probability. While the resulting analysis does not give
the correct values of the thresholds, it does reproduce some of the qualitative
features of the system. It predicts a first-order detectability transition
whenever $q > 2$, while the finite-temperature cavity method shows that this is
the case only when $q > 4$. It also has a regime analogous to the "hard but
detectable" phase, where the community structure can be partially recovered,
but only when the initial messages are sufficiently accurate. Finally, we study
a semisupervised setting where we are given the correct labels for a fraction
$\rho$ of the nodes. For $q > 2$, we find a regime where the accuracy jumps
discontinuously at a critical value of $\rho$.
|
1312.0641 | Simple Bounds for Noisy Linear Inverse Problems with Exact Side
Information | cs.IT math.IT math.OC math.ST stat.TH | This paper considers the linear inverse problem where we wish to estimate a
structured signal $x$ from its corrupted observations. When the problem is
ill-posed, it is natural to make use of a convex function $f(\cdot)$ that
exploits the structure of the signal. For example, $\ell_1$ norm can be used
for sparse signals. To carry out the estimation, we consider two well-known
convex programs: 1) Second order cone program (SOCP), and, 2) Lasso. Assuming
Gaussian measurements, we show that, if precise information about the value
$f(x)$ or the $\ell_2$-norm of the noise is available, one can do a
particularly good job at estimation. In particular, the reconstruction error
becomes proportional to the "sparsity" of the signal rather than the ambient
dimension of the noise vector. We connect our results to existing works and
provide a discussion on the relation of our results to the standard
least-squares problem. Our error bounds are non-asymptotic and sharp, they
apply to arbitrary convex functions and do not assume any distribution on the
noise.
|
1312.0649 | Dynamics of Trends and Attention in Chinese Social Media | cs.SI cs.CY physics.soc-ph | There has been a tremendous rise in the growth of online social networks all
over the world in recent years. It has facilitated users to generate a large
amount of real-time content at an incessant rate, all competing with each other
to attract enough attention and become popular trends. While Western online
social networks such as Twitter have been well studied, the popular Chinese
microblogging network Sina Weibo has had relatively lower exposure. In this
paper, we analyze in detail the temporal aspect of trends and trend-setters in
Sina Weibo, contrasting it with earlier observations in Twitter. We find that
there is a vast difference in the content shared in China when compared to a
global social network such as Twitter. In China, the trends are created almost
entirely due to the retweets of media content such as jokes, images and videos,
unlike Twitter where it has been shown that the trends tend to have more to do
with current global events and news stories. We take a detailed look at the
formation, persistence and decay of trends and examine the key topics that
trend in Sina Weibo. One of our key findings is that retweets are much more
common in Sina Weibo and contribute a lot to creating trends. When we look
closer, we observe that most trends in Sina Weibo are due to the continuous
retweets of a small percentage of fraudulent accounts. These fake accounts are
set up to artificially inflate certain posts, causing them to shoot up into
Sina Weibo's trending list, which are in turn displayed as the most popular
topics to users.
|
1312.0650 | Differential Games of Competition in Online Content Diffusion | cs.SI cs.GT | Access to online contents represents a large share of the Internet traffic.
Most such contents are multimedia items which are user-generated, i.e., posted
online by the contents' owners. In this paper we focus on how those who provide
contents can leverage online platforms in order to profit from their large base
of potential viewers.
Actually, platforms like Vimeo or YouTube provide tools to accelerate the
dissemination of contents, i.e., recommendation lists and other re-ranking
mechanisms. Hence, the popularity of a content can be increased by paying a
cost for advertisement: doing so, it will appear with some priority in the
recommendation lists and will be accessed more frequently by the platform
users.
Ultimately, such acceleration mechanism engenders a competition among online
contents to gain popularity. In this context, our focus is on the structure of
the acceleration strategies which a content provider should use in order to
optimally promote a content given a certain daily budget. Such a best response
indeed depends on the strategies adopted by competing content providers. Also,
it is a function of the potential popularity of a content and the fee paid for
the platform advertisement service.
We formulate the problem as a differential game and we solve it for the
infinite horizon case by deriving the structure of certain Nash equilibria of
the game.
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.