id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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1404.5458 | Complex Workflow Management and Integration of Distributed Computing
Resources by Science Gateway Portal for Molecular Dynamics Simulations in
Materials Science | cs.CE cond-mat.mtrl-sci cs.DC | The "IMP Science Gateway Portal" (http://scigate.imp.kiev.ua) for complex
workflow management and integration of distributed computing resources (like
clusters, service grids, desktop grids, clouds) is presented. It is created on
the basis of WS-PGRADE and gUSE technologies, where WS-PGRADE is designed for
science workflow operation and gUSE - for smooth integration of available
resources for parallel and distributed computing in various heterogeneous
distributed computing infrastructures (DCI). The typical scientific workflow
with possible scenarios of its preparation and usage is considered. Several
typical science applications (scientific workflows) are considered for
molecular dynamics (MD) simulations of complex behavior of various
nanostructures (nanoindentation of graphene layers, defect system relaxation in
metal nanocrystals, thermal stability of boron nitride nanotubes, etc.). The
advantages and drawbacks of the solution are shortly analyzed in the context of
its practical applications for MD simulations in materials science, physics and
nanotechnologies with available heterogeneous DCIs.
|
1404.5475 | Combining pattern-based CRFs and weighted context-free grammars | cs.FL cs.DS cs.LG | We consider two models for the sequence labeling (tagging) problem. The first
one is a {\em Pattern-Based Conditional Random Field }(\PB), in which the
energy of a string (chain labeling) $x=x_1\ldots x_n\in D^n$ is a sum of terms
over intervals $[i,j]$ where each term is non-zero only if the substring
$x_i\ldots x_j$ equals a prespecified word $w\in \Lambda$. The second model is
a {\em Weighted Context-Free Grammar }(\WCFG) frequently used for natural
language processing. \PB and \WCFG encode local and non-local interactions
respectively, and thus can be viewed as complementary.
We propose a {\em Grammatical Pattern-Based CRF model }(\GPB) that combines
the two in a natural way. We argue that it has certain advantages over existing
approaches such as the {\em Hybrid model} of Bened{\'i} and Sanchez that
combines {\em $\mbox{$N$-grams}$} and \WCFGs. The focus of this paper is to
analyze the complexity of inference tasks in a \GPB such as computing MAP. We
present a polynomial-time algorithm for general \GPBs and a faster version for
a special case that we call {\em Interaction Grammars}.
|
1404.5478 | Optimal control of information epidemics modeled as Maki Thompson rumors | cs.SY cs.SI math.OC | We model the spread of information in a homogeneously mixed population using
the Maki Thompson rumor model. We formulate an optimal control problem, from
the perspective of single campaigner, to maximize the spread of information
when the campaign budget is fixed. Control signals, such as advertising in the
mass media, attempt to convert ignorants and stiflers into spreaders. We show
the existence of a solution to the optimal control problem when the campaigning
incurs non-linear costs under the isoperimetric budget constraint. The solution
employs Pontryagin's Minimum Principle and a modified version of forward
backward sweep technique for numerical computation to accommodate the
isoperimetric budget constraint. The techniques developed in this paper are
general and can be applied to similar optimal control problems in other areas.
We have allowed the spreading rate of the information epidemic to vary over
the campaign duration to model practical situations when the interest level of
the population in the subject of the campaign changes with time. The shape of
the optimal control signal is studied for different model parameters and
spreading rate profiles. We have also studied the variation of the optimal
campaigning costs with respect to various model parameters. Results indicate
that, for some model parameters, significant improvements can be achieved by
the optimal strategy compared to the static control strategy. The static
strategy respects the same budget constraint as the optimal strategy and has a
constant value throughout the campaign horizon. This work finds application in
election and social awareness campaigns, product advertising, movie promotion
and crowdfunding campaigns.
|
1404.5501 | Polar Codes for Distributed Hierarchical Source Coding | cs.IT math.IT | We show that polar codes can be used to achieve the rate-distortion functions
in the problem of hierarchical source coding also known as the successive
refinement problem. We also analyze the distributed version of this problem,
constructing a polar coding scheme that achieves the rate distortion functions
for successive refinement with side information.
|
1404.5507 | Strong Converse and Second-Order Asymptotics of Channel Resolvability | cs.IT math.IT | We study the problem of channel resolvability for fixed i.i.d. input
distributions and discrete memoryless channels (DMCs), and derive the strong
converse theorem for any DMCs that are not necessarily full rank. We also
derive the optimal second-order rate under a condition. Furthermore, under the
condition that a DMC has the unique capacity achieving input distribution, we
derive the optimal second-order rate of channel resolvability for the worst
input distribution.
|
1404.5511 | Coactive Learning for Locally Optimal Problem Solving | cs.LG | Coactive learning is an online problem solving setting where the solutions
provided by a solver are interactively improved by a domain expert, which in
turn drives learning. In this paper we extend the study of coactive learning to
problems where obtaining a globally optimal or near-optimal solution may be
intractable or where an expert can only be expected to make small, local
improvements to a candidate solution. The goal of learning in this new setting
is to minimize the cost as measured by the expert effort over time. We first
establish theoretical bounds on the average cost of the existing coactive
Perceptron algorithm. In addition, we consider new online algorithms that use
cost-sensitive and Passive-Aggressive (PA) updates, showing similar or improved
theoretical bounds. We provide an empirical evaluation of the learners in
various domains, which show that the Perceptron based algorithms are quite
effective and that unlike the case for online classification, the PA algorithms
do not yield significant performance gains.
|
1404.5520 | A Computationally Efficient Limited Memory CMA-ES for Large Scale
Optimization | cs.NE | We propose a computationally efficient limited memory Covariance Matrix
Adaptation Evolution Strategy for large scale optimization, which we call the
LM-CMA-ES. The LM-CMA-ES is a stochastic, derivative-free algorithm for
numerical optimization of non-linear, non-convex optimization problems in
continuous domain. Inspired by the limited memory BFGS method of Liu and
Nocedal (1989), the LM-CMA-ES samples candidate solutions according to a
covariance matrix reproduced from $m$ direction vectors selected during the
optimization process. The decomposition of the covariance matrix into Cholesky
factors allows to reduce the time and memory complexity of the sampling to
$O(mn)$, where $n$ is the number of decision variables. When $n$ is large
(e.g., $n$ > 1000), even relatively small values of $m$ (e.g., $m=20,30$) are
sufficient to efficiently solve fully non-separable problems and to reduce the
overall run-time.
|
1404.5521 | Together we stand, Together we fall, Together we win: Dynamic Team
Formation in Massive Open Online Courses | cs.SI cs.CY cs.LG cs.MA | Massive Open Online Courses (MOOCs) offer a new scalable paradigm for
e-learning by providing students with global exposure and opportunities for
connecting and interacting with millions of people all around the world. Very
often, students work as teams to effectively accomplish course related tasks.
However, due to lack of face to face interaction, it becomes difficult for MOOC
students to collaborate. Additionally, the instructor also faces challenges in
manually organizing students into teams because students flock to these MOOCs
in huge numbers. Thus, the proposed research is aimed at developing a robust
methodology for dynamic team formation in MOOCs, the theoretical framework for
which is grounded at the confluence of organizational team theory, social
network analysis and machine learning. A prerequisite for such an undertaking
is that we understand the fact that, each and every informal tie established
among students offers the opportunities to influence and be influenced.
Therefore, we aim to extract value from the inherent connectedness of students
in the MOOC. These connections carry with them radical implications for the way
students understand each other in the networked learning community. Our
approach will enable course instructors to automatically group students in
teams that have fairly balanced social connections with their peers, well
defined in terms of appropriately selected qualitative and quantitative network
metrics.
|
1404.5528 | Hybrid Genetic Algorithm for Cloud Computing Applications | cs.DC cs.AI | In this paper with the aid of genetic algorithm and fuzzy theory, we present
a hybrid job scheduling approach, which considers the load balancing of the
system and reduces total execution time and execution cost. We try to modify
the standard Genetic algorithm and to reduce the iteration of creating
population with the aid of fuzzy theory. The main goal of this research is to
assign the jobs to the resources with considering the VM MIPS and length of
jobs. The new algorithm assigns the jobs to the resources with considering the
job length and resources capacities. We evaluate the performance of our
approach with some famous cloud scheduling models. The results of the
experiments show the efficiency of the proposed approach in term of execution
time, execution cost and average Degree of Imbalance (DI).
|
1404.5538 | Analysis and Design of Two-Hop Diffusion-Based Molecular Communication
Networks | cs.IT math.IT | In this paper, we consider a two-hop molecular communication network
consisting of one nanotransmitter, one nanoreceiver, and one nanotransceiver
acting as a relay. We consider two different schemes for relaying to improve
the range of diffusion-based molecular communication. In the first scheme, two
different types of messenger molecules are utilized at the relay node for
transmission and detection. In the second scheme, we assume that there is only
one type of molecule available to be used as an information carrier. We
identify self-interference as the performance-limiting effect for the second
relaying scheme. Self-interference occurs when the relay must detect the same
type of molecule that it also emits. Furthermore, we consider two relaying
modes analogous to those used in wireless communication systems, i.e.,
full-duplex and half-duplex. In particular, while our main focus is on
full-duplex relaying, half-duplex relaying is employed as a means to mitigate
self-interference. In addition, we propose the adaptation of the decision
threshold as an effective mechanism to mitigate self-interference at the relay
for full-duplex transmission. We derive closed-form expressions for the
expected error probability of the network for both considered relaying schemes.
|
1404.5557 | The Degrees of Freedom of Partly Smooth Regularizers | math.ST cs.IT math.IT stat.TH | In this paper, we are concerned with regularized regression problems where
the prior regularizer is a proper lower semicontinuous and convex function
which is also partly smooth relative to a Riemannian submanifold. This
encompasses as special cases several known penalties such as the Lasso
($\ell^1$-norm), the group Lasso ($\ell^1-\ell^2$-norm), the
$\ell^\infty$-norm, and the nuclear norm. This also includes so-called
analysis-type priors, i.e. composition of the previously mentioned penalties
with linear operators, typical examples being the total variation or fused
Lasso penalties.We study the sensitivity of any regularized minimizer to
perturbations of the observations and provide its precise local
parameterization.Our main sensitivity analysis result shows that the predictor
moves locally stably along the same active submanifold as the observations
undergo small perturbations. This local stability is a consequence of the
smoothness of the regularizer when restricted to the active submanifold, which
in turn plays a pivotal role to get a closed form expression for the variations
of the predictor w.r.t. observations. We also show that, for a variety of
regularizers, including polyhedral ones or the group Lasso and its analysis
counterpart, this divergence formula holds Lebesgue almost everywhere.When the
perturbation is random (with an appropriate continuous distribution), this
allows us to derive an unbiased estimator of the degrees of freedom and of the
risk of the estimator prediction.Our results hold true without requiring the
design matrix to be full column rank.They generalize those already known in the
literature such as the Lasso problem, the general Lasso problem (analysis
$\ell^1$-penalty), or the group Lasso where existing results for the latter
assume that the design is full column rank.
|
1404.5562 | Characterizing Information Spreading in Online Social Networks | cs.SI physics.soc-ph | Online social networks (OSNs) are changing the way in which the information
spreads throughout the Internet. A deep understanding of the information
spreading in OSNs leads to both social and commercial benefits. In this paper,
we characterize the dynamic of information spreading (e.g., how fast and widely
the information spreads against time) in OSNs by developing a general and
accurate model based on the Interactive Markov Chains (IMCs) and mean-field
theory. This model explicitly reveals the impacts of the network topology on
information spreading in OSNs. Further, we extend our model to feature the
time-varying user behaviors and the ever-changing information popularity. The
complicated dynamic patterns of information spreading are captured by our model
using six key parameters. Extensive tests based on Renren's dataset validate
the accuracy of our model, which demonstrate that it can characterize the
dynamic patterns of video sharing in Renren precisely and predict future
spreading tendency successfully.
|
1404.5585 | A Structural Query System for Han Characters | cs.CL cs.DB | The IDSgrep structural query system for Han character dictionaries is
presented. This system includes a data model and syntax for describing the
spatial structure of Han characters using Extended Ideographic Description
Sequences (EIDSes) based on the Unicode IDS syntax; a language for querying
EIDS databases, designed to suit the needs of font developers and foreign
language learners; a bit vector index inspired by Bloom filters for faster
query operations; a freely available implementation; and format translation
from popular third-party IDS and XML character databases. Experimental results
are included, with a comparison to other software used for similar
applications.
|
1404.5588 | Large Margin Image Set Representation and Classification | cs.CV | In this paper, we propose a novel image set representation and classification
method by maximizing the margin of image sets. The margin of an image set is
defined as the difference of the distance to its nearest image set from
different classes and the distance to its nearest image set of the same class.
By modeling the image sets by using both their image samples and their affine
hull models, and maximizing the margins of the images sets, the image set
representation parameter learning problem is formulated as an minimization
problem, which is further optimized by an expectation -maximization (EM)
strategy with accelerated proximal gradient (APG) optimization in an iterative
algorithm. To classify a given test image set, we assign it to the class which
could provide the largest margin. Experiments on two applications of
video-sequence-based face recognition demonstrate that the proposed method
significantly outperforms state-of-the-art image set classification methods in
terms of both effectiveness and efficiency.
|
1404.5605 | SCMA for Downlink Multiple Access of 5G Wireless Networks | cs.IT math.IT | Sparse code multiple access (SCMA) is a new frequency domain non-orthogonal
multiple-access technique which can improve spectral efficiency of wireless
radio access. With SCMA, different incoming data streams are directly mapped to
codewords of different multi-dimensional cookbooks, where each codeword
represents a spread transmission layer. Multiple SCMA layers share the same
time-frequency resources of OFDMA. The sparsity of codewords makes the
near-optimal detection feasible through iterative message passing algorithm
(MPA). Such low complexity of multi-layer detection allows excessive codeword
overloading in which the dimension of multiplexed layers exceeds the dimension
of codewords. Optimization of overloading factor along with modulation-coding
levels of layers provides a more flexible and efficient link-adaptation
mechanism. On the other hand, the signal spreading feature of SCMA can improve
link-adaptation as a result of less colored interference. In this paper a
technique is developed to enable multi-user SCMA (MU-SCMA) for downlink
wireless access. User pairing, power sharing, rate adjustment, and scheduling
algorithms are designed to improve the downlink throughput of a heavily loaded
network. The advantage of SCMA spreading for lightly loaded networks is also
evaluated.
|
1404.5611 | IMP Science Gateway: from the Portal to the Hub of Virtual Experimental
Labs in Materials Science | cs.CE cond-mat.mtrl-sci cs.DC | "Science gateway" (SG) ideology means a user-friendly intuitive interface
between scientists (or scientific communities) and different software
components + various distributed computing infrastructures (DCIs) (like grids,
clouds, clusters), where researchers can focus on their scientific goals and
less on peculiarities of software/DCI. "IMP Science Gateway Portal"
(http://scigate.imp.kiev.ua) for complex workflow management and integration of
distributed computing resources (like clusters, service grids, desktop grids,
clouds) is presented. It is created on the basis of WS-PGRADE and gUSE
technologies, where WS-PGRADE is designed for science workflow operation and
gUSE - for smooth integration of available resources for parallel and
distributed computing in various heterogeneous distributed computing
infrastructures (DCI). The typical scientific workflows with possible scenarios
of its preparation and usage are presented. Several typical use cases for these
science applications (scientific workflows) are considered for molecular
dynamics (MD) simulations of complex behavior of various nanostructures
(nanoindentation of graphene layers, defect system relaxation in metal
nanocrystals, thermal stability of boron nitride nanotubes, etc.). The user
experience is analyzed in the context of its practical applications for MD
simulations in materials science, physics and nanotechnologies with available
heterogeneous DCIs. In conclusion, the "science gateway" approach - workflow
manager (like WS-PGRADE) + DCI resources manager (like gUSE)- gives opportunity
to use the SG portal (like "IMP Science Gateway Portal") in a very promising
way, namely, as a hub of various virtual experimental labs (different software
components + various requirements to resources) in the context of its practical
MD applications in materials science, physics, chemistry, biology, and
nanotechnologies.
|
1404.5643 | A Formal Analysis of Required Cooperation in Multi-agent Planning | cs.AI cs.MA | Research on multi-agent planning has been popular in recent years. While
previous research has been motivated by the understanding that, through
cooperation, multi-agent systems can achieve tasks that are unachievable by
single-agent systems, there are no formal characterizations of situations where
cooperation is required to achieve a goal, thus warranting the application of
multi-agent systems. In this paper, we provide such a formal discussion from
the planning aspect. We first show that determining whether there is required
cooperation (RC) is intractable is general. Then, by dividing the problems that
require cooperation (referred to as RC problems) into two classes -- problems
with heterogeneous and homogeneous agents, we aim to identify all the
conditions that can cause RC in these two classes. We establish that when none
of these identified conditions hold, the problem is single-agent solvable.
Furthermore, with a few assumptions, we provide an upper bound on the minimum
number of agents required for RC problems with homogeneous agents. This study
not only provides new insights into multi-agent planning, but also has many
applications. For example, in human-robot teaming, when a robot cannot achieve
a task, it may be due to RC. In such cases, the human teammate should be
informed and, consequently, coordinate with other available robots for a
solution.
|
1404.5651 | On Scaling Limits of Power Law Shot-noise Fields | math.PR cs.IT math.IT | This article studies the scaling limit of a class of shot-noise fields
defined on an independently marked stationary Poisson point process and with a
power law response function. Under appropriate conditions, it is shown that the
shot-noise field can be scaled suitably to have a $\alpha$-stable limit,
intensity of the underlying point process goes to infinity. It is also shown
that the finite dimensional distributions of the limiting random field have
i.i.d. stable random components. We hence propose to call this limte the
$\alpha$- stable white noise field. Analogous results are also obtained for the
extremal shot-noise field which converges to a Fr\'{e}chet white noise field.
Finally, these results are applied to the analysis of wireless networks.
|
1404.5653 | Compressive sampling for energy spectrum estimation of turbulent flows | physics.flu-dyn cs.IT math.IT | Recent results from compressive sampling (CS) have demonstrated that accurate
reconstruction of sparse signals often requires far fewer samples than
suggested by the classical Nyquist--Shannon sampling theorem. Typically, signal
reconstruction errors are measured in the $\ell^2$ norm and the signal is
assumed to be sparse, compressible or having a prior distribution. Our spectrum
estimation by sparse optimization (SpESO) method uses prior information about
isotropic homogeneous turbulent flows with power law energy spectra and applies
the methods of CS to 1-D and 2-D turbulence signals to estimate their energy
spectra with small logarithmic errors. SpESO is distinct from existing energy
spectrum estimation methods which are based on sparse support of the signal in
Fourier space. SpESO approximates energy spectra with an order of magnitude
fewer samples than needed with Shannon sampling. Our results demonstrate that
SpESO performs much better than lumped orthogonal matching pursuit (LOMP), and
as well or better than wavelet-based best M-term or M/2-term methods, even
though these methods require complete sampling of the signal before
compression.
|
1404.5666 | An Importance Sampling Algorithm for the Ising Model with Strong
Couplings | stat.CO cs.IT math.IT physics.comp-ph | We consider the problem of estimating the partition function of the
ferromagnetic Ising model in a consistent external magnetic field. The
estimation is done via importance sampling in the dual of the Forney factor
graph representing the model. Emphasis is on models at low temperature
(corresponding to models with strong couplings) and on models with a mixture of
strong and weak coupling parameters.
|
1404.5668 | An Adversarial Interpretation of Information-Theoretic Bounded
Rationality | cs.AI | Recently, there has been a growing interest in modeling planning with
information constraints. Accordingly, an agent maximizes a regularized expected
utility known as the free energy, where the regularizer is given by the
information divergence from a prior to a posterior policy. While this approach
can be justified in various ways, including from statistical mechanics and
information theory, it is still unclear how it relates to decision-making
against adversarial environments. This connection has previously been suggested
in work relating the free energy to risk-sensitive control and to extensive
form games. Here, we show that a single-agent free energy optimization is
equivalent to a game between the agent and an imaginary adversary. The
adversary can, by paying an exponential penalty, generate costs that diminish
the decision maker's payoffs. It turns out that the optimal strategy of the
adversary consists in choosing costs so as to render the decision maker
indifferent among its choices, which is a definining property of a Nash
equilibrium, thus tightening the connection between free energy optimization
and game theory.
|
1404.5683 | The Likelihood Encoder for Lossy Source Compression | cs.IT math.IT | In this work, a likelihood encoder is studied in the context of lossy source
compression. The analysis of the likelihood encoder is based on a soft-covering
lemma. It is demonstrated that the use of a likelihood encoder together with
the soft-covering lemma gives alternative achievability proofs for classical
source coding problems. The case of the rate-distortion function with side
information at the decoder (i.e. the Wyner-Ziv problem) is carefully examined
and an application of the likelihood encoder to the multi-terminal source
coding inner bound (i.e. the Berger-Tung region) is outlined.
|
1404.5686 | DGFIndex for Smart Grid: Enhancing Hive with a Cost-Effective
Multidimensional Range Index | cs.DB cs.DC | In Smart Grid applications, as the number of deployed electric smart meters
increases, massive amounts of valuable meter data is generated and collected
every day. To enable reliable data collection and make business decisions fast,
high throughput storage and high-performance analysis of massive meter data
become crucial for grid companies. Considering the advantage of high
efficiency, fault tolerance, and price-performance of Hadoop and Hive systems,
they are frequently deployed as underlying platform for big data processing.
However, in real business use cases, these data analysis applications typically
involve multidimensional range queries (MDRQ) as well as batch reading and
statistics on the meter data. While Hive is high-performance at complex data
batch reading and analysis, it lacks efficient indexing techniques for MDRQ.
In this paper, we propose DGFIndex, an index structure for Hive that
efficiently supports MDRQ for massive meter data. DGFIndex divides the data
space into cubes using the grid file technique. Unlike the existing indexes in
Hive, which stores all combinations of multiple dimensions, DGFIndex only
stores the information of cubes. This leads to smaller index size and faster
query processing. Furthermore, with pre-computing user-defined aggregations of
each cube, DGFIndex only needs to access the boundary region for aggregation
query. Our comprehensive experiments show that DGFIndex can save significant
disk space in comparison with the existing indexes in Hive and the query
performance with DGFIndex is 2-50 times faster than existing indexes in Hive
and HadoopDB for aggregation query, 2-5 times faster than both for
non-aggregation query, 2-75 times faster than scanning the whole table in
different query selectivity.
|
1404.5692 | Forward - Backward Greedy Algorithms for Atomic Norm Regularization | cs.DS cs.LG math.OC stat.ML | In many signal processing applications, the aim is to reconstruct a signal
that has a simple representation with respect to a certain basis or frame.
Fundamental elements of the basis known as "atoms" allow us to define "atomic
norms" that can be used to formulate convex regularizations for the
reconstruction problem. Efficient algorithms are available to solve these
formulations in certain special cases, but an approach that works well for
general atomic norms, both in terms of speed and reconstruction accuracy,
remains to be found. This paper describes an optimization algorithm called
CoGEnT that produces solutions with succinct atomic representations for
reconstruction problems, generally formulated with atomic-norm constraints.
CoGEnT combines a greedy selection scheme based on the conditional gradient
approach with a backward (or "truncation") step that exploits the quadratic
nature of the objective to reduce the basis size. We establish convergence
properties and validate the algorithm via extensive numerical experiments on a
suite of signal processing applications. Our algorithm and analysis also allow
for inexact forward steps and for occasional enhancements of the current
representation to be performed. CoGEnT can outperform the basic conditional
gradient method, and indeed many methods that are tailored to specific
applications, when the enhancement and truncation steps are defined
appropriately. We also introduce several novel applications that are enabled by
the atomic-norm framework, including tensor completion, moment problems in
signal processing, and graph deconvolution.
|
1404.5699 | Quantum Trajectories for a Class of Continuous Matrix Product Input
States | quant-ph cs.SY math-ph math.MP math.OC | We introduce a new class of continuous matrix product (CMP) states and
establish the stochastic master equations (quantum filters) for an arbitrary
quantum system probed by a bosonic input field in this class of states. We show
that this class of CMP states arise naturally as outputs of a Markovian model,
and that input fields in these states lead to master and filtering (quantum
trajectory) equations which are matrix-valued. Furthermore, it is shown that
this class of continuous matrix product states include the (continuous-mode)
single photon and time-ordered multi-photon states.
|
1404.5701 | Achieving Shannon Capacity in a Wiretap Channel via Previous Messages | cs.IT cs.CR math.IT | In this paper we consider a wiretap channel with a secret key buffer. We use
the coding scheme of [1] to enhance the secrecy rate to the capacity of the
main channel, while storing each securely transmitted message in the secret key
buffer. We use the oldest secret bits from the buffer to be used as a secret
key to transmit a message in a slot and then remove those bits. With this
scheme we are able to prove stronger results than those in [1]. i.e., not only
the message which is being transmitted currently, but all the messages
transmitted in last $N_1$ slots are secure with respect to all the information
that the eavesdropper possesses, where $N_1$ can be chosen arbitrarily large.
|
1404.5708 | Converging Work-Talk Patterns in Online Task-Oriented Communities | cs.SE cs.HC cs.SI physics.data-an | Much of what we do is accomplished by working collaboratively with others,
and a large portion of our lives are spent working and talking; the patterns
embodied in the alternation of working and talking can provide much useful
insight into task-oriented social behaviors. The available electronic traces of
the different kinds of human activities in online communities are an empirical
goldmine that can enable the holistic study and understanding of these social
systems. Open Source Software projects are prototypical examples of
collaborative, task-oriented communities, depending on volunteers for
high-quality work. Here, we use sequence analysis methods to identify the
work-talk patterns of software developers in these online communities.
We find that software developers prefer to persist in same kinds of
activities, i.e., a string of work activities followed by a string of talk
activities and so forth, rather than switch them frequently; this tendency
strengthens with time, suggesting that developers become more efficient, and
can work longer with fewer interruptions. This process is accompanied by the
formation of community culture: developers' patterns in the same communities
get closer with time while different communities get relatively more different.
The emergence of community culture is apparently driven by both "talk" and
"work". Finally, we also find that workers with good balance between "work" and
"talk" tend to produce just as much work as those that focus strongly on
"work"; however, the former appear to be more likely to continue to be active
contributors in the communities.
|
1404.5711 | Modeling multi-stage decision optimization problems | math.OC cs.AI | Multi-stage optimization under uncertainty techniques can be used to solve
long-term management problems. Although many optimization modeling language
extensions as well as computational environments have been proposed, the
acceptance of this technique is generally low, due to the inherent complexity
of the modeling and solution process. In this paper a simplification to
annotate multi-stage decision problems under uncertainty is presented - this
simplification contrasts with the common approach to create an extension on top
of an existing optimization modeling language. This leads to the definition of
meta models, which can be instanced in various programming languages. An
example using the statistical computing language R is shown.
|
1404.5715 | Converses for Secret Key Agreement and Secure Computing | cs.IT cs.CR math.IT | We consider information theoretic secret key agreement and secure function
computation by multiple parties observing correlated data, with access to an
interactive public communication channel. Our main result is an upper bound on
the secret key length, which is derived using a reduction of binary hypothesis
testing to multiparty secret key agreement. Building on this basic result, we
derive new converses for multiparty secret key agreement. Furthermore, we
derive converse results for the oblivious transfer problem and the bit
commitment problem by relating them to secret key agreement. Finally, we derive
a necessary condition for the feasibility of secure computation by trusted
parties that seek to compute a function of their collective data, using an
interactive public communication that by itself does not give away the value of
the function. In many cases, we strengthen and improve upon previously known
converse bounds. Our results are single-shot and use only the given joint
distribution of the correlated observations. For the case when the correlated
observations consist of independent and identically distributed (in time)
sequences, we derive strong versions of previously known converses.
|
1404.5716 | List-Decoding Gabidulin Codes via Interpolation and the Euclidean
Algorithm | cs.IT math.IT | We show how Gabidulin codes can be list decoded by using a parametrization
approach. For this we consider a certain module in the ring of linearized
polynomials and find a minimal basis for this module using the Euclidean
algorithm with respect to composition of polynomials. For a given received
word, our decoding algorithm computes a list of all codewords that are closest
to the received word with respect to the rank metric.
|
1404.5719 | A Scaling Law to Predict the Finite-Length Performance of
Spatially-Coupled LDPC Codes | cs.IT math.IT | Spatially-coupled LDPC codes are known to have excellent asymptotic
properties. Much less is known regarding their finite-length performance. We
propose a scaling law to predict the error probability of finite-length
spatially-coupled ensembles when transmission takes place over the binary
erasure channel. We discuss how the parameters of the scaling law are connected
to fundamental quantities appearing in the asymptotic analysis of these
ensembles and we verify that the predictions of the scaling law fit well to the
data derived from simulations over a wide range of parameters. The ultimate
goal of this line of research is to develop analytic tools for the design of
spatially-coupled LDPC codes under practical constraints.
|
1404.5756 | A Revised Scheme to Compute Horizontal Covariances in an Oceanographic
3D-VAR Assimilation System | cs.NA cs.CE cs.DC math.NA | We propose an improvement of an oceanographic three dimensional variational
assimilation scheme (3D-VAR), named OceanVar, by introducing a recursive filter
(RF) with the third order of accuracy (3rd-RF), instead of a RF with first
order of accuracy (1st-RF), to approximate horizontal Gaussian covariances. An
advantage of the proposed scheme is that the CPU's time can be substantially
reduced with benefits on the large scale applications. Experiments estimating
the impact of 3rd-RF are performed by assimilating oceanographic data in two
realistic oceanographic applications. The results evince benefits in terms of
assimilation process computational time, accuracy of the Gaussian correlation
modeling, and show that the 3rd-RF is a suitable tool for operational data
assimilation.
|
1404.5764 | From Quantity to Quality: Massive Molecular Dynamics Simulation of
Nanostructures under Plastic Deformation in Desktop and Service Grid
Distributed Computing Infrastructure | cs.CE cond-mat.mtrl-sci cs.DC | The distributed computing infrastructure (DCI) on the basis of BOINC and
EDGeS-bridge technologies for high-performance distributed computing is used
for porting the sequential molecular dynamics (MD) application to its parallel
version for DCI with Desktop Grids (DGs) and Service Grids (SGs). The actual
metrics of the working DG-SG DCI were measured, and the normal distribution of
host performances, and signs of log-normal distributions of other
characteristics (CPUs, RAM, and HDD per host) were found. The practical
feasibility and high efficiency of the MD simulations on the basis of DG-SG DCI
were demonstrated during the experiment with the massive MD simulations for the
large quantity of aluminum nanocrystals ($\sim10^2$-$10^3$). Statistical
analysis (Kolmogorov-Smirnov test, moment analysis, and bootstrapping analysis)
of the defect density distribution over the ensemble of nanocrystals had shown
that change of plastic deformation mode is followed by the qualitative change
of defect density distribution type over ensemble of nanocrystals. Some
limitations (fluctuating performance, unpredictable availability of resources,
etc.) of the typical DG-SG DCI were outlined, and some advantages (high
efficiency, high speedup, and low cost) were demonstrated. Deploying on DG DCI
allows to get new scientific $\it{quality}$ from the simulated $\it{quantity}$
of numerous configurations by harnessing sufficient computational power to
undertake MD simulations in a wider range of physical parameters
(configurations) in a much shorter timeframe.
|
1404.5765 | Find my mug: Efficient object search with a mobile robot using semantic
segmentation | cs.CV cs.RO | In this paper, we propose an efficient semantic segmentation framework for
indoor scenes, tailored to the application on a mobile robot. Semantic
segmentation can help robots to gain a reasonable understanding of their
environment, but to reach this goal, the algorithms not only need to be
accurate, but also fast and robust. Therefore, we developed an optimized 3D
point cloud processing framework based on a Randomized Decision Forest,
achieving competitive results at sufficiently high frame rates. We evaluate the
capabilities of our method on the popular NYU depth dataset and our own data
and demonstrate its feasibility by deploying it on a mobile service robot, for
which we could optimize an object search procedure using our results.
|
1404.5767 | Codynamic Fitness Landscapes of Coevolutionary Minimal Substrates | cs.NE | Coevolutionary minimal substrates are simple and abstract models that allow
studying the relationships and codynamics between objective and subjective
fitness. Using these models an approach is presented for defining and analyzing
fitness landscapes of coevolutionary problems. We devise similarity measures of
codynamic fitness landscapes and experimentally study minimal substrates of
test--based and compositional problems for both cooperative and competitive
interaction.
|
1404.5772 | Sequential Click Prediction for Sponsored Search with Recurrent Neural
Networks | cs.IR cs.LG cs.NE | Click prediction is one of the fundamental problems in sponsored search. Most
of existing studies took advantage of machine learning approaches to predict ad
click for each event of ad view independently. However, as observed in the
real-world sponsored search system, user's behaviors on ads yield high
dependency on how the user behaved along with the past time, especially in
terms of what queries she submitted, what ads she clicked or ignored, and how
long she spent on the landing pages of clicked ads, etc. Inspired by these
observations, we introduce a novel framework based on Recurrent Neural Networks
(RNN). Compared to traditional methods, this framework directly models the
dependency on user's sequential behaviors into the click prediction process
through the recurrent structure in RNN. Large scale evaluations on the
click-through logs from a commercial search engine demonstrate that our
approach can significantly improve the click prediction accuracy, compared to
sequence-independent approaches.
|
1404.5828 | Motion planning and Collision Avoidance using Non-Gradient Vector
Fields. Technical Report | cs.RO | This paper presents a novel feedback method on the motion planning for
unicycle robots in environments with static obstacles, along with an extension
to the distributed planning and coordination in multi-robot systems. The method
employs a family of 2-dimensional analytic vector fields, whose integral curves
exhibit various patterns depending on the value of a parameter lambda. More
specifically, for an a priori known value of lambda, the vector field has a
unique singular point of dipole type and can be used to steer the unicycle to a
goal configuration. Furthermore, for the unique value of lambda that the vector
field has a continuum of singular points, the integral curves are used to
define flows around obstacles. An almost global feedback motion plan can then
be constructed by suitably blending attractive and repulsive vector fields in a
static obstacle environment. The method does not suffer from the appearance of
sinks (stable nodes) away from goal point. Compared to other similar methods
which are free of local minima, the proposed approach does not require any
parameter tuning to render the desired convergence properties. The paper also
addresses the extension of the method to the distributed coordination and
control of multiple robots, where each robot needs to navigate to a goal
configuration while avoiding collisions with the remaining robots, and while
using local information only. More specifically, based on the results which
apply to the single-robot case, a motion coordination protocol is presented
which guarantees the safety of the multi-robot system and the almost global
convergence of the robots to their goal configurations. The efficacy of the
proposed methodology is demonstrated via simulation results in static and
dynamic environments.
|
1404.5859 | Distributed Channel Assignment in Cognitive Radio Networks: Stable
Matching and Walrasian Equilibrium | cs.IT math.IT | We consider a set of secondary transmitter-receiver pairs in a cognitive
radio setting. Based on channel sensing and access performances, we consider
the problem of assigning channels orthogonally to secondary users through
distributed coordination and cooperation algorithms. Two economic models are
applied for this purpose: matching markets and competitive markets. In the
matching market model, secondary users and channels build two agent sets. We
implement a stable matching algorithm in which each secondary user, based on
his achievable rate, proposes to the coordinator to be matched with desirable
channels. The coordinator accepts or rejects the proposals based on the channel
preferences which depend on interference from the secondary user. The
coordination algorithm is of low complexity and can adapt to network dynamics.
In the competitive market model, channels are associated with prices and
secondary users are endowed with monetary budget. Each secondary user, based on
his utility function and current channel prices, demands a set of channels. A
Walrasian equilibrium maximizes the sum utility and equates the channel demand
to their supply. We prove the existence of Walrasian equilibrium and propose a
cooperative mechanism to reach it. The performance and complexity of the
proposed solutions are illustrated by numerical simulations.
|
1404.5874 | Using Triangles to Improve Community Detection in Directed Networks | cs.SI physics.soc-ph | In a graph, a community may be loosely defined as a group of nodes that are
more closely connected to one another than to the rest of the graph. While
there are a variety of metrics that can be used to specify the quality of a
given community, one common theme is that flows tend to stay within
communities. Hence, we expect cycles to play an important role in community
detection. For undirected graphs, the importance of triangles -- an undirected
3-cycle -- has been known for a long time and can be used to improve community
detection. In directed graphs, the situation is more nuanced. The smallest
cycle is simply two nodes with a reciprocal connection, and using information
about reciprocation has proven to improve community detection. Our new idea is
based on the four types of directed triangles that contain cycles. To identify
communities in directed networks, then, we propose an undirected edge-weighting
scheme based on the type of the directed triangles in which edges are involved.
We also propose a new metric on quality of the communities that is based on the
number of 3-cycles that are split across communities. To demonstrate the impact
of our new weighting, we use the standard METIS graph partitioning tool to
determine communities and show experimentally that the resulting communities
result in fewer 3-cycles being cut. The magnitude of the effect varies between
a 10 and 50% reduction, and we also find evidence that this weighting scheme
improves a task where plausible ground-truth communities are known.
|
1404.5889 | Coherence Optimization and Best Complex Antipodal Spherical Codes | cs.IT math.IT | Vector sets with optimal coherence according to the Welch bound cannot exist
for all pairs of dimension and cardinality. If such an optimal vector set
exists, it is an equiangular tight frame and represents the solution to a
Grassmannian line packing problem. Best Complex Antipodal Spherical Codes
(BCASCs) are the best vector sets with respect to the coherence. By extending
methods used to find best spherical codes in the real-valued Euclidean space,
the proposed approach aims to find BCASCs, and thereby, a complex-valued vector
set with minimal coherence. There are many applications demanding vector sets
with low coherence. Examples are not limited to several techniques in wireless
communication or to the field of compressed sensing. Within this contribution,
existing analytical and numerical approaches for coherence optimization of
complex-valued vector spaces are summarized and compared to the proposed
approach. The numerically obtained coherence values improve previously reported
results. The drawback of increased computational effort is addressed and a
faster approximation is proposed which may be an alternative for time critical
cases.
|
1404.5899 | A Comparison of Clustering and Missing Data Methods for Health Sciences | math.NA cs.LG | In this paper, we compare and analyze clustering methods with missing data in
health behavior research. In particular, we propose and analyze the use of
compressive sensing's matrix completion along with spectral clustering to
cluster health related data. The empirical tests and real data results show
that these methods can outperform standard methods like LPA and FIML, in terms
of lower misclassification rates in clustering and better matrix completion
performance in missing data problems. According to our examination, a possible
explanation of these improvements is that spectral clustering takes advantage
of high data dimension and compressive sensing methods utilize the
near-to-low-rank property of health data.
|
1404.5901 | Linearization of Time-Varying Nonlinear Systems Using A Modified Linear
Iterative Method | cs.SY | The linearization of nonlinear systems is an important digital enhancement
technique. In this paper, a real-time capable post- and pre-linearization
method for the widely applicable time-varying discrete-time Volterra series is
presented. To this end, an alternative view on the Volterra series is
established, which enables the utilization of certain modified linear iterative
methods for linearization. For one particular linear iterative method, the
Richardson iteration, the corresponding post- and pre-linearizers are discussed
in detail. It is motivated that the resulting algorithm can be regarded as a
generalization of some existing methods. Furthermore, a simply verifiable
condition for convergence is presented, which allows the straightforward
evaluation of applicability. The proposed method is demonstrated by means of
the linearization of a time-varying nonlinear amplifier, which highlights its
capability of linearizing significantly distorted signals, illustrates the
advantageous convergence behavior, and depicts its robustness against modeling
errors.
|
1404.5903 | Most Correlated Arms Identification | stat.ML cs.LG | We study the problem of finding the most mutually correlated arms among many
arms. We show that adaptive arms sampling strategies can have significant
advantages over the non-adaptive uniform sampling strategy. Our proposed
algorithms rely on a novel correlation estimator. The use of this accurate
estimator allows us to get improved results for a wide range of problem
instances.
|
1404.5905 | STFU NOOB! Predicting Crowdsourced Decisions on Toxic Behavior in Online
Games | cs.SI cs.CY physics.soc-ph | One problem facing players of competitive games is negative, or toxic,
behavior. League of Legends, the largest eSport game, uses a crowdsourcing
platform called the Tribunal to judge whether a reported toxic player should be
punished or not. The Tribunal is a two stage system requiring reports from
those players that directly observe toxic behavior, and human experts that
review aggregated reports. While this system has successfully dealt with the
vague nature of toxic behavior by majority rules based on many votes, it
naturally requires tremendous cost, time, and human efforts.
In this paper, we propose a supervised learning approach for predicting
crowdsourced decisions on toxic behavior with large-scale labeled data
collections; over 10 million user reports involved in 1.46 million toxic
players and corresponding crowdsourced decisions. Our result shows good
performance in detecting overwhelmingly majority cases and predicting
crowdsourced decisions on them. We demonstrate good portability of our
classifier across regions. Finally, we estimate the practical implications of
our approach, potential cost savings and victim protection.
|
1404.5927 | Secure MIMO Communications under Quantized Channel Feedback in the
presence of Jamming | cs.IT math.IT | We consider the problem of secure communications in a MIMO setting in the
presence of an adversarial jammer equipped with $n_j$ transmit antennas and an
eavesdropper equipped with $n_e$ receive antennas. A multiantenna transmitter,
equipped with $n_t$ antennas, desires to secretly communicate a message to a
multiantenna receiver equipped with $n_r$ antennas. We propose a transmission
method based on artificial noise and linear precoding and a two-stage receiver
method employing beamforming. Under this strategy, we first characterize the
achievable secrecy rates of communication and prove that the achievable secure
degrees-of-freedom (SDoF) is given by $d_s = n_r - n_j$ in the perfect channel
state information (CSI) case. Second, we consider quantized CSI feedback using
Grassmannian quantization of a function of the direct channel matrix and derive
sufficient conditions for the quantization bit rate scaling as a function of
transmit power for maintaining the achievable SDoF $d_s$ with perfect CSI and
for having asymptotically zero secrecy rate loss due to quantization. Numerical
simulations are also provided to support the theory.
|
1404.5940 | A strong converse for the quantum state merging protocol | quant-ph cs.IT math.IT | The Polyanskiy-Verd\'{u} paradigm provides an elegant way of using
generalized-divergences to obtain strong converses and thus far has remained
confined to protocols involving channels (classical or quantum). In this paper,
drawing inspirations from it, we provide strong converses for protocols
involving LOCC (local operations and classical communication). The key quantity
that we work with is the R\'{e}nyi relative entropy of entanglement. We provide
a strong converse for the quantum state merging protocol that gives an
exponential decay of the fidelity of the protocol for rates below the optimum
with the number of copies of the state and are provided both for entanglement
rate with LOCC as well as for classical communication with one-way LOCC. As an
aside, the developments also yield short strong converses for the
entanglement-concentration of pure states and the Schumacher compression.
|
1404.5945 | Previous Messages Provide the Key to Achieve Shannon Capacity in a
Wiretap Channel | cs.IT cs.CR math.IT | We consider a wiretap channel and use previously transmitted messages to
generate a secret key which increases the secrecy capacity. This can be
bootstrapped to increase the secrecy capacity to the Shannon capacity without
using any feedback or extra channel while retaining the strong secrecy of the
wiretap channel.
|
1404.5997 | One weird trick for parallelizing convolutional neural networks | cs.NE cs.DC cs.LG | I present a new way to parallelize the training of convolutional neural
networks across multiple GPUs. The method scales significantly better than all
alternatives when applied to modern convolutional neural networks.
|
1404.6000 | Robust and computationally feasible community detection in the presence
of arbitrary outlier nodes | math.ST cs.IT math.IT math.OC stat.ML stat.TH | Community detection, which aims to cluster $N$ nodes in a given graph into
$r$ distinct groups based on the observed undirected edges, is an important
problem in network data analysis. In this paper, the popular stochastic block
model (SBM) is extended to the generalized stochastic block model (GSBM) that
allows for adversarial outlier nodes, which are connected with the other nodes
in the graph in an arbitrary way. Under this model, we introduce a procedure
using convex optimization followed by $k$-means algorithm with $k=r$. Both
theoretical and numerical properties of the method are analyzed. A theoretical
guarantee is given for the procedure to accurately detect the communities with
small misclassification rate under the setting where the number of clusters can
grow with $N$. This theoretical result admits to the best-known result in the
literature of computationally feasible community detection in SBM without
outliers. Numerical results show that our method is both computationally fast
and robust to different kinds of outliers, while some popular computationally
fast community detection algorithms, such as spectral clustering applied to
adjacency matrices or graph Laplacians, may fail to retrieve the major clusters
due to a small portion of outliers. We apply a slight modification of our
method to a political blogs data set, showing that our method is competent in
practice and comparable to existing computationally feasible methods in the
literature. To the best of the authors' knowledge, our result is the first in
the literature in terms of clustering communities with fast growing numbers
under the GSBM where a portion of arbitrary outlier nodes exist.
|
1404.6012 | Degrees of Freedom of Uplink-Downlink Multiantenna Cellular Networks | cs.IT math.IT | An uplink-downlink two-cell cellular network is studied in which the first
base station (BS) with $M_1$ antennas receives independent messages from its
$N_1$ serving users, while the second BS with $M_2$ antennas transmits
independent messages to its $N_2$ serving users. That is, the first and second
cells operate as uplink and downlink, respectively. Each user is assumed to
have a single antenna. Under this uplink-downlink setting, the sum degrees of
freedom (DoF) is completely characterized as the minimum of
$(N_1N_2+\min(M_1,N_1)(N_1-N_2)^++\min(M_2,N_2)(N_2-N_1)^+)/\max(N_1,N_2)$,
$M_1+N_2,M_2+N_1$, $\max(M_1,M_2)$, and $\max(N_1,N_2)$, where $a^+$ denotes
$\max(0,a)$. The result demonstrates that, for a broad class of network
configurations, operating one of the two cells as uplink and the other cell as
downlink can strictly improve the sum DoF compared to the conventional uplink
or downlink operation, in which both cells operate as either uplink or
downlink. The DoF gain from such uplink-downlink operation is further shown to
be achievable for heterogeneous cellular networks having hotspots and with
delayed channel state information.
|
1404.6020 | A Fast Multiple Attractor Cellular Automata with Modified Clonal
Classifier for Splicing Site Prediction in Human Genome | cs.CE | Bioinformatics encompass storing, analyzing and interpreting the biological
data. Most of the challenges for Machine Learning methods like Cellular
Automata is to furnish the functional information with the corresponding
biological sequences. In eukaryotes DNA is divided into introns and exons. The
introns will be removed to make the coding region by a process called splicing.
By indentifying a splice site we can easily specify the DNA sequence category
(Donor/Accepter/Neither).Splicing sites play an important role in understanding
the genes. A class of CA which can handle fuzzy logic is employed with modified
clonal algorithm is proposed to identify the splicing site. This classifier is
tested with Irvine Primate Splice Junction Database. It is compared with
NNspIICE, GENIO, HSPL and SPIICE VIEW. The reported accuracy and efficiency of
prediction is quite promising.
|
1404.6026 | Proximal linearized iteratively reweighted least squares for a class of
nonconvex and nonsmooth problems | math.OC cs.IT math.IT | For solving a wide class of nonconvex and nonsmooth problems, we propose a
proximal linearized iteratively reweighted least squares (PL-IRLS) algorithm.
We first approximate the original problem by smoothing methods, and second
write the approximated problem into an auxiliary problem by introducing new
variables. PL-IRLS is then built on solving the auxiliary problem by utilizing
the proximal linearization technique and the iteratively reweighted least
squares (IRLS) method, and has remarkable computation advantages. We show that
PL-IRLS can be extended to solve more general nonconvex and nonsmooth problems
via adjusting generalized parameters, and also to solve nonconvex and nonsmooth
problems with two or more blocks of variables. Theoretically, with the help of
the Kurdyka- Lojasiewicz property, we prove that each bounded sequence
generated by PL-IRLS globally converges to a critical point of the approximated
problem. To the best of our knowledge, this is the first global convergence
result of applying IRLS idea to solve nonconvex and nonsmooth problems. At
last, we apply PL-IRLS to solve three representative nonconvex and nonsmooth
problems in sparse signal recovery and low-rank matrix recovery and obtain new
globally convergent algorithms.
|
1404.6029 | Optimization and design of a laser-cutting machine using delta robot | cs.RO | Industrial high speed laser operations the use of delta parallel robots
potentially offers many benefits due to their structural stiffness and limited
moving masses. This paper deals with a particular Delta, developed for high
speed laser cutting. Parallel delta robot has numerous advantages in comparison
with serial robots Higher stiffness and connected with that a lower mass of
links the possibility of transporting heavier loads, and higher accuracy. The
main drawback is however a smaller workspace. Hence there exists an interest
for the research concerning the workspace of robots.In industrial cutting tool
maximum do not have more prescribe measurement to cut so that in This paper is
oriented to parallel kinematic robots definition description of their specific
application of laser cutting comparison of robots made by different producers
and determination of velocity and acceleration parameters kinematic analysis
inverse and forward kinematic. It brings information about development of Delta
robot. The production of laser cutting machines began thirty years ago. The
progress was very fast and at present time every year over 3000 laser cutting
machines is installed in the world. Laser cutting is one of the largest
applications of lasers in metal working industry.
|
1404.6031 | Maximum Margin Vector Correlation Filter | cs.CV | Correlation Filters (CFs) are a class of classifiers which are designed for
accurate pattern localization. Traditionally CFs have been used with scalar
features only, which limits their ability to be used with vector feature
representations like Gabor filter banks, SIFT, HOG, etc. In this paper we
present a new CF named Maximum Margin Vector Correlation Filter (MMVCF) which
extends the traditional CF designs to vector features. MMVCF further combines
the generalization capability of large margin based classifiers like Support
Vector Machines (SVMs) and the localization properties of CFs for better
robustness to outliers. We demonstrate the efficacy of MMVCF for object
detection and landmark localization on a variety of databases and demonstrate
that MMVCF consistently shows improved pattern localization capability in
comparison to SVMs.
|
1404.6036 | Gradual Classical Logic for Attributed Objects | cs.AI cs.LO | There is knowledge. There is belief. And there is tacit agreement.' 'We may
talk about objects. We may talk about attributes of the objects. Or we may talk
both about objects and their attributes.' This work inspects tacit agreements
on assumptions about the relation between objects and their attributes, and
studies a way of expressing them, presenting as the result what we term gradual
logic in which the sense of truth gradually shifts. It extends classical logic
instances with a new logical connective capturing the object-attribute
relation. A formal semantics is presented. Decidability is proved. Para-
consistent/epistemic/conditional/intensional/description/combined logics are
compared.
|
1404.6039 | The fshape framework for the variability analysis of functional shapes | cs.CG cs.CV math.DG | This article introduces a full mathematical and numerical framework for
treating functional shapes (or fshapes) following the landmarks of shape spaces
and shape analysis. Functional shapes can be described as signal functions
supported on varying geometrical supports. Analysing variability of fshapes'
ensembles require the modelling and quantification of joint variations in
geometry and signal, which have been treated separately in previous approaches.
Instead, building on the ideas of shape spaces for purely geometrical objects,
we propose the extended concept of fshape bundles and define Riemannian metrics
for fshape metamorphoses to model geometrico-functional transformations within
these bundles. We also generalize previous works on data attachment terms based
on the notion of varifolds and demonstrate the utility of these distances.
Based on these, we propose variational formulations of the atlas estimation
problem on populations of fshapes and prove existence of solutions for the
different models. The second part of the article examines the numerical
implementation of the models by detailing discrete expressions for the metrics
and gradients and proposing an optimization scheme for the atlas estimation
problem. We present a few results of the methodology on a synthetic dataset as
well as on a population of retinal membranes with thickness maps.
|
1404.6044 | Harnessing Bursty Interference in Multicarrier Systems with Feedback | cs.IT math.IT | We study parallel symmetric 2-user interference channels when the
interference is bursty and feedback is available from the respective receivers.
Presence of interference in each subcarrier is modeled as a memoryless
Bernoulli random state. The states across subcarriers are drawn from an
arbitrary joint distribution with the same marginal probability for each
subcarrier and instantiated i.i.d. over time. For the linear deterministic
setup, we give a complete characterization of the capacity region. For the
setup with Gaussian noise, we give outer bounds and a tight generalized degrees
of freedom characterization. We propose a novel helping mechanism which enables
subcarriers in very strong interference regime to help in recovering interfered
signals for subcarriers in strong and weak interference regimes. Depending on
the interference and burstiness regime, the inner bounds either employ the
proposed helping mechanism to code across subcarriers or treat the subcarriers
separately. The outer bounds demonstrate a connection to a subset entropy
inequality by Madiman and Tetali.
|
1404.6048 | List and Unique Error-Erasure Decoding of Interleaved Gabidulin Codes
with Interpolation Techniques | cs.IT math.IT | A new interpolation-based decoding principle for interleaved Gabidulin codes
is presented. The approach consists of two steps: First, a multi-variate
linearized polynomial is constructed which interpolates the coefficients of the
received word and second, the roots of this polynomial have to be found. Due to
the specific structure of the interpolation polynomial, both steps
(interpolation and root-finding) can be accomplished by solving a linear system
of equations. This decoding principle can be applied as a list decoding
algorithm (where the list size is not necessarily bounded polynomially) as well
as an efficient probabilistic unique decoding algorithm. For the unique
decoder, we show a connection to known unique decoding approaches and give an
upper bound on the failure probability. Finally, we generalize our approach to
incorporate not only errors, but also row and column erasures.
|
1404.6055 | A General Homogeneous Matrix Formulation to 3D Rotation Geometric
Transformations | cs.CV | We present algebraic projective geometry definitions of 3D rotations so as to
bridge a small gap between the applications and the definitions of 3D rotations
in homogeneous matrix form. A general homogeneous matrix formulation to 3D
rotation geometric transformations is proposed which suits for the cases when
the rotation axis is unnecessarily through the coordinate system origin given
their rotation axes and rotation angles.
General three-dimensional rotation formula~\eqref{eqn:3D homogeneous roation}
and~\eqref{eqn:3D rotation matrix vector Euclidean} similar to the
Euler-Rodrigues formula were presented. The matrix-vector form of 3D rotation
in Euclidean space is especially suited for numerical applications where gimbal
lock is a concern.}
|
1404.6059 | A Comparative study Between Fuzzy Clustering Algorithm and Hard
Clustering Algorithm | cs.AI | Data clustering is an important area of data mining. This is an unsupervised
study where data of similar types are put into one cluster while data of
another types are put into different cluster. Fuzzy C means is a very important
clustering technique based on fuzzy logic. Also we have some hard clustering
techniques available like K-means among the popular ones. In this paper a
comparative study is done between Fuzzy clustering algorithm and hard
clustering algorithm
|
1404.6071 | Rough Clustering Based Unsupervised Image Change Detection | cs.CV cs.AI | This paper introduces an unsupervised technique to detect the changed region
of multitemporal images on a same reference plane with the help of rough
clustering. The proposed technique is a soft-computing approach, based on the
concept of rough set with rough clustering and Pawlak's accuracy. It is less
noisy and avoids pre-deterministic knowledge about the distribution of the
changed and unchanged regions. To show the effectiveness, the proposed
technique is compared with some other approaches.
|
1404.6074 | Classifying pairs with trees for supervised biological network inference | cs.LG stat.ML | Networks are ubiquitous in biology and computational approaches have been
largely investigated for their inference. In particular, supervised machine
learning methods can be used to complete a partially known network by
integrating various measurements. Two main supervised frameworks have been
proposed: the local approach, which trains a separate model for each network
node, and the global approach, which trains a single model over pairs of nodes.
Here, we systematically investigate, theoretically and empirically, the
exploitation of tree-based ensemble methods in the context of these two
approaches for biological network inference. We first formalize the problem of
network inference as classification of pairs, unifying in the process
homogeneous and bipartite graphs and discussing two main sampling schemes. We
then present the global and the local approaches, extending the later for the
prediction of interactions between two unseen network nodes, and discuss their
specializations to tree-based ensemble methods, highlighting their
interpretability and drawing links with clustering techniques. Extensive
computational experiments are carried out with these methods on various
biological networks that clearly highlight that these methods are competitive
with existing methods.
|
1404.6075 | Unsupervised Text Extraction from G-Maps | cs.CV cs.AI | This paper represents an text extraction method from Google maps, GIS
maps/images. Due to an unsupervised approach there is no requirement of any
prior knowledge or training set about the textual and non-textual parts. Fuzzy
CMeans clustering technique is used for image segmentation and Prewitt method
is used to detect the edges. Connected component analysis and gridding
technique enhance the correctness of the results. The proposed method reaches
98.5% accuracy level on the basis of experimental data sets.
|
1404.6097 | Degree Variance and Emotional Strategies Catalyze Cooperation in Dynamic
Signed Networks | physics.soc-ph cs.SI | We study the problem of the emergence of cooperation in dynamic signed
networks where agent strategies coevolve with relational signs and network
topology. Running simulations based on an agent-based model, we compare results
obtained in a regular lattice initialization with those obtained on a
comparable random network initialization. We show that the increased degree
heterogeneity at the outset enlarges the parametric conditions in which
cooperation survives in the long run. Furthermore, we show how the presence of
sign-dependent emotional strategies catalyze the evolution of cooperation with
both network topology initializations.
|
1404.6116 | Development of an open source software module for enhanced visualization
during MR-guided interstitial gynecologic brachytherapy | cs.SY cs.SE | In 2010, gynecologic malignancies were the 4th leading cause of death in U.S.
women and for patients with extensive primary or recurrent disease, treatment
with interstitial brachytherapy may be an option. However, brachytherapy
requires precise insertion of hollow catheters with introducers into the tumor
in order to eradicate the cancer. In this study, a software solution to assist
interstitial gynecologic brachytherapy has been investigated and the software
has been realized as an own module under (3D) Slicer, which is a free open
source software platform for (translational) biomedical research. The developed
research module allows on-time processing of intra-operative magnetic resonance
imaging (iMRI) data over a direct DICOM connection to a MR scanner. Afterwards
follows a multi-stage registration of CAD models of the medical brachytherapy
devices (template, obturator) to the patient's MR images, enabling the virtual
placement of interstitial needles to assist the physician during the
intervention.
|
1404.6150 | Compressed Sensing Based Direct Conversion Receiver With Interference
Reducing Sampling | cs.IT math.IT | This paper describes a direct conversion receiver applying compressed sensing
with the objective to relax the analog filtering requirements seen in the
traditional architecture. The analog filter is cumbersome in an \gls{IC} design
and relaxing its requirements is an advantage in terms of die area, performance
and robustness of the receiver. The objective is met by a selection of sampling
pattern matched to the prior knowledge of the frequency placement of the
desired and interfering signals. A simple numerical example demonstrates the
principle. The work is part of an ongoing research effort and the different
project phases are explained.
|
1404.6151 | SimpleTrack:Adaptive Trajectory Compression with Deterministic
Projection Matrix for Mobile Sensor Networks | cs.IT cs.NI math.IT | Some mobile sensor network applications require the sensor nodes to transfer
their trajectories to a data sink. This paper proposes an adaptive trajectory
(lossy) compression algorithm based on compressive sensing. The algorithm has
two innovative elements. First, we propose a method to compute a deterministic
projection matrix from a learnt dictionary. Second, we propose a method for the
mobile nodes to adaptively predict the number of projections needed based on
the speed of the mobile nodes. Extensive evaluation of the proposed algorithm
using 6 datasets shows that our proposed algorithm can achieve sub-metre
accuracy. In addition, our method of computing projection matrices outperforms
two existing methods. Finally, comparison of our algorithm against a
state-of-the-art trajectory compression algorithm show that our algorithm can
reduce the error by 10-60 cm for the same compression ratio.
|
1404.6163 | Overlapping Trace Norms in Multi-View Learning | cs.LG | Multi-view learning leverages correlations between different sources of data
to make predictions in one view based on observations in another view. A
popular approach is to assume that, both, the correlations between the views
and the view-specific covariances have a low-rank structure, leading to
inter-battery factor analysis, a model closely related to canonical correlation
analysis. We propose a convex relaxation of this model using structured norm
regularization. Further, we extend the convex formulation to a robust version
by adding an l1-penalized matrix to our estimator, similarly to convex robust
PCA. We develop and compare scalable algorithms for several convex multi-view
models. We show experimentally that the view-specific correlations are
improving data imputation performances, as well as labeling accuracy in
real-world multi-label prediction tasks.
|
1404.6216 | CoRE Kernels | stat.ML cs.DS cs.LG stat.ME | The term "CoRE kernel" stands for correlation-resemblance kernel. In many
applications (e.g., vision), the data are often high-dimensional, sparse, and
non-binary. We propose two types of (nonlinear) CoRE kernels for non-binary
sparse data and demonstrate the effectiveness of the new kernels through a
classification experiment. CoRE kernels are simple with no tuning parameters.
However, training nonlinear kernel SVM can be (very) costly in time and memory
and may not be suitable for truly large-scale industrial applications (e.g.
search). In order to make the proposed CoRE kernels more practical, we develop
basic probabilistic hashing algorithms which transform nonlinear kernels into
linear kernels.
|
1404.6230 | Ensemble estimation of multivariate f-divergence | cs.IT math.IT | f-divergence estimation is an important problem in the fields of information
theory, machine learning, and statistics. While several divergence estimators
exist, relatively few of their convergence rates are known. We derive the MSE
convergence rate for a density plug-in estimator of f-divergence. Then by
applying the theory of optimally weighted ensemble estimation, we derive a
divergence estimator with a convergence rate of O(1/T) that is simple to
implement and performs well in high dimensions. We validate our theoretical
results with experiments.
|
1404.6247 | Career on the Move: Geography, Stratification, and Scientific Impact | physics.soc-ph cs.SI physics.data-an | Changing institutions is an integral part of an academic life. Yet little is
known about the mobility patterns of scientists at an institutional level and
how these career choices affect scientific outcomes. Here, we examine over
420,000 papers, to track the affiliation information of individual scientists,
allowing us to reconstruct their career trajectories over decades. We find that
career movements are not only temporally and spatially localized, but also
characterized by a high degree of stratification in institutional ranking. When
cross-group movement occurs, we find that while going from elite to lower-rank
institutions on average associates with modest decrease in scientific
performance, transitioning into elite institutions does not result in
subsequent performance gain. These results offer empirical evidence on
institutional level career choices and movements and have potential
implications for science policy.
|
1404.6265 | Determination of the functional state of the fruits by parameters of the
electric impedance | cs.SY | Introduction. To assess the freshness of various products are often used
measuring impedance module. But due to the structure of plant foods diagnostic
value should have exactly a complex component of impedance. Article tasked with
developing criteria for assessing the functional state of the products subject
to a comprehensive component of the impedance. Research methodology. To
determine the functional status of the fruit were measured module and phase of
impedance at the three frequencies of 20, 100 and 500 kHz. Criteria for
recognition of functional status determined by the dynamics of changes in the
parameters of the complex impedance due to destructive processes caused by
dehydration and putrefaction processes. Data processing and analysis. On the
basis of experimental data obtained at three frequencies modeled frequency and
phase response and their changes during losing of freshness and appearance of
destructive processes. Discussion and conclusions. In fresh and stale fruit
modulus and phase of the impedance at low and high frequencies have
characteristic differences. But this is especially evident on the
phase-frequency characteristic, which can be seen that the value of the phase
with the loss of freshness at low frequency decreases and increases at high
more than twice during one week. Therefore, to assess the functional state of
fresh and stale products we suggest use phase portraits of phase response.
|
1404.6272 | Scalable Similarity Learning using Large Margin Neighborhood Embedding | cs.CV cs.LG | Classifying large-scale image data into object categories is an important
problem that has received increasing research attention. Given the huge amount
of data, non-parametric approaches such as nearest neighbor classifiers have
shown promising results, especially when they are underpinned by a learned
distance or similarity measurement. Although metric learning has been well
studied in the past decades, most existing algorithms are impractical to handle
large-scale data sets. In this paper, we present an image similarity learning
method that can scale well in both the number of images and the dimensionality
of image descriptors. To this end, similarity comparison is restricted to each
sample's local neighbors and a discriminative similarity measure is induced
from large margin neighborhood embedding. We also exploit the ensemble of
projections so that high-dimensional features can be processed in a set of
lower-dimensional subspaces in parallel without much performance compromise.
The similarity function is learned online using a stochastic gradient descent
algorithm in which the triplet sampling strategy is customized for quick
convergence of classification performance. The effectiveness of our proposed
model is validated on several data sets with scales varying from tens of
thousands to one million images. Recognition accuracies competitive with the
state-of-the-art performance are achieved with much higher efficiency and
scalability.
|
1404.6281 | Explicit factorization of $x^n-1\in \mathbb F_q[x]$ | cs.IT math.IT math.NT | Let $\mathbb F_q$ be a finite field and $n$ a positive integer. In this
article, we prove that, under some conditions on $q$ and $n$, the polynomial
$x^n-1$ can be split into irreducible binomials $x^t-a$ and an explicit
factorization into irreducible factors is given.
Finally, weakening one of our hypothesis, we also obtain factors of the form
$x^{2t}-ax^t+b$ and explicit splitting of $x^n-1$ into irreducible factors is
given.
|
1404.6304 | Non-Reconstructability in the Stochastic Block Model | math.PR cs.SI | We consider the problem of clustering (or reconstruction) in the stochastic
block model, in the regime where the average degree is constant. For the case
of two clusters with equal sizes, recent results by Mossel, Neeman and Sly, and
by Massoulie, show that reconstructability undergoes a phase transition at the
Kesten-Stigum bound of $\lambda_2^2 d = 1$, where $\lambda_2$ is the second
largest eigenvalue of a related stochastic matrix and $d$ is the average
degree. In this paper, we address the general case of more than two clusters
and/or unbalanced cluster sizes. Our main result is a sufficient condition for
clustering to be impossible, which matches the existing result for two clusters
of equal sizes. A key ingredient in our result is a new connection between
non-reconstructability and non-distinguishability of the block model from an
Erd\H{o}s-R\'enyi model with the same average degree. We also show that it is
some times possible to reconstruct even when $\lambda_2^2 d < 1$. Our results
provide evidence supporting a series of conjectures made by Decelle, Krzkala,
Moore and Zdeborov\'a regarding reconstructability and distinguishability of
stochastic block models (but do not settle them).
|
1404.6312 | Reconstructing Native Language Typology from Foreign Language Usage | cs.CL | Linguists and psychologists have long been studying cross-linguistic
transfer, the influence of native language properties on linguistic performance
in a foreign language. In this work we provide empirical evidence for this
process in the form of a strong correlation between language similarities
derived from structural features in English as Second Language (ESL) texts and
equivalent similarities obtained from the typological features of the native
languages. We leverage this finding to recover native language typological
similarity structure directly from ESL text, and perform prediction of
typological features in an unsupervised fashion with respect to the target
languages. Our method achieves 72.2% accuracy on the typology prediction task,
a result that is highly competitive with equivalent methods that rely on
typological resources.
|
1404.6320 | Demystifying the Scaling Laws of Dense Wireless Networks: No Linear
Scaling in Practice | cs.IT math.IT | We optimize the hierarchical cooperation protocol of Ozgur, Leveque and Tse,
which is supposed to yield almost linear scaling of the capacity of a dense
wireless network with the number of users $n$. Exploiting recent results on the
optimality of "treating interference as noise" in Gaussian interference
channels, we are able to optimize the achievable average per-link rate and not
just its scaling law. Our optimized hierarchical cooperation protocol
significantly outperforms the originally proposed scheme. On the negative side,
we show that even for very large $n$, the rate scaling is far from linear, and
the optimal number of stages $t$ is less than 4, instead of $t \rightarrow
\infty$ as required for almost linear scaling. Combining our results and the
fact that, beyond a certain user density, the network capacity is fundamentally
limited by Maxwell laws, as shown by Francheschetti, Migliore and Minero, we
argue that there is indeed no intermediate regime of linear scaling for dense
networks in practice.
|
1404.6325 | Global and Local Information in Clustering Labeled Block Models | math.PR cs.SI | The stochastic block model is a classical cluster-exhibiting random graph
model that has been widely studied in statistics, physics and computer science.
In its simplest form, the model is a random graph with two equal-sized
clusters, with intra-cluster edge probability p, and inter-cluster edge
probability q. We focus on the sparse case, i.e., p, q = O(1/n), which is
practically more relevant and also mathematically more challenging. A
conjecture of Decelle, Krzakala, Moore and Zdeborova, based on ideas from
statistical physics, predicted a specific threshold for clustering. The
negative direction of the conjecture was proved by Mossel, Neeman and Sly
(2012), and more recently the positive direction was proven independently by
Massoulie and Mossel, Neeman, and Sly.
In many real network clustering problems, nodes contain information as well.
We study the interplay between node and network information in clustering by
studying a labeled block model, where in addition to the edge information, the
true cluster labels of a small fraction of the nodes are revealed. In the case
of two clusters, we show that below the threshold, a small amount of node
information does not affect recovery. On the other hand, we show that for any
small amount of information efficient local clustering is achievable as long as
the number of clusters is sufficiently large (as a function of the amount of
revealed information).
|
1404.6331 | Active Adversaries from an Information-Theoretic Perspective: Data
Modification Attacks | cs.IT cs.CR math.IT | We investigate the problem of reliable communication in the presence of
active adversaries that can tamper with the transmitted data. We consider a
legitimate transmitter-receiver pair connected over multiple communication
paths (routes). We propose two new models of adversary, a "memoryless" and a
"foreseer" adversary. For both models, the adversaries are placing themselves
arbitrarily on the routes, keeping their placement fixed throughout the
transmission block. This placement may or may not be known to the transmitter.
The adversaries can choose their best modification strategy to increase the
error at the legitimate receiver, subject to a maximum distortion constraint.
We investigate the communication rates that can be achieved in the presence of
the two types of adversaries and the channel (benign) stochastic behavior. For
memoryless adversaries, the capacity is derived. Our method is to use the
typical set of the anticipated received signal for all possible adversarial
strategies (including their best one) in a compound channel that also captures
adversarial placement. For the foreseer adversaries, which have enhanced
observation capabilities compared to the memoryless ones, we propose a new
coding scheme to guarantee resilience, i.e., recovery of the codeword
independently of the adversarial (best) choice. We derive an achievable rate
and we propose an upper bound on the capacity. We evaluate our general results
for specific cases (e.g., binary symbol replacement or erasing attacks), to
gain insights.
|
1404.6334 | Input anticipating critical reservoirs show power law forgetting of
unexpected input events | cs.NE | Usually, reservoir computing shows an exponential memory decay. This paper
investigates under which circumstances echo state networks can show a power law
forgetting. That means traces of earlier events can be found in the reservoir
for very long time spans. Such a setting requires critical connectivity exactly
at the limit of what is permissible according the echo state condition.
However, for general matrices the limit cannot be determined exactly from
theory. In addition, the behavior of the network is strongly influenced by the
input flow. Results are presented that use certain types of restricted
recurrent connectivity and anticipation learning with regard to the input,
where indeed power law forgetting can be achieved.
|
1404.6348 | A DoF-Optimal Scheme for the two-user X-channel with Synergistic
Alternating CSIT | cs.IT math.IT | In this paper, the degrees of freedom (DoF) of the two-user single input
single output (SISO) X-channel are investigated. Three cases are considered for
the availability of channel state information at the transmitters (CSIT);
perfect, delayed, and no-CSIT. A new achievable scheme is proposed to elucidate
the potency of interference creation-resurrection (IRC) when the available CSIT
alternates between these three cases. For some patterns of alternating CSIT,
the proposed scheme achieves $4/3$ DoF, and hence, coincides with the
information theoretic upper bound on the DoF of the two-user X-channel with
perfect and instantaneous CSIT. The CSIT alternation patterns are investigated
where the patterns that provide extraordinary synergistic gain and dissociative
ones are identified.
|
1404.6351 | Improving weather radar by fusion and classification | cs.CV | In air traffic management (ATM) all necessary operations (tactical planing,
sector configuration, required staffing, runway configuration, routing of
approaching aircrafts) rely on accurate measurements and predictions of the
current weather situation. An essential basis of information is delivered by
weather radar images (WXR), which, unfortunately, exhibit a vast amount of
disturbances. Thus, the improvement of these datasets is the key factor for
more accurate predictions of weather phenomena and weather conditions. Image
processing methods based on texture analysis and geometric operators allow to
identify regions including artefacts as well as zones of missing information.
Correction of these zones is implemented by exploiting multi-spectral satellite
data (Meteosat Second Generation). Results prove that the proposed system for
artefact detection and data correction significantly improves the quality of
WXR data and, thus, enables more reliable weather now- and forecast leading to
increased ATM safety.
|
1404.6360 | Networks maximizing the consensus time of voter models | cond-mat.dis-nn cs.SI physics.soc-ph | We explore the networks that yield the largest mean consensus time of voter
models under different update rules. By analytical and numerical means, we show
that the so-called lollipop graph, barbell graph, and double star graph
maximise the mean consensus time under the update rules called the link
dynamics, voter model, and invasion process, respectively. For each update
rule, the largest mean consensus time scales as O(N^3), where N is the number
of nodes in the network.
|
1404.6369 | Applying machine learning to the problem of choosing a heuristic to
select the variable ordering for cylindrical algebraic decomposition | cs.SC cs.LG | Cylindrical algebraic decomposition(CAD) is a key tool in computational
algebraic geometry, particularly for quantifier elimination over real-closed
fields. When using CAD, there is often a choice for the ordering placed on the
variables. This can be important, with some problems infeasible with one
variable ordering but easy with another. Machine learning is the process of
fitting a computer model to a complex function based on properties learned from
measured data. In this paper we use machine learning (specifically a support
vector machine) to select between heuristics for choosing a variable ordering,
outperforming each of the separate heuristics.
|
1404.6384 | CATOS: Computer Aided Training/Observing System | cs.CE cs.HC | In animal behavioral biology, there are several cases in which an autonomous
observing/training system would be useful. 1) Observation of certain species
continuously, or for documenting specific events, which happen irregularly; 2)
Longterm intensive training of animals in preparation for behavioral
experiments; and 3) Training and testing of animals without human interference,
to eliminate potential cues and biases induced by humans. The primary goal of
this study is to build a system named CATOS (Computer Aided Training/Observing
System) that could be used in the above situations. As a proof of concept, the
system was built and tested in a pilot experiment, in which cats were trained
to press three buttons differently in response to three different sounds (human
speech) to receive food rewards. The system was built in use for about 6
months, successfully training two cats. One cat learned to press a particular
button, out of three buttons, to obtain the food reward with over 70 percent
correctness.
|
1404.6385 | High-Content Digital Microscopy with Python | cs.CE cs.PL | High-Content Digital Microscopy enhances user comfort, data storage and
analysis throughput, paving the way to new researches and medical diagnostics.
A digital microscopy platform aims at capturing an image of a cover slip, at
storing information on a file server and a database, at visualising the image
and analysing its content. We will discuss how the Python ecosystem can provide
such software framework efficiently. Moreover this paper will give an
illustration of the data chunking approach to manage the huge amount of data.
|
1404.6389 | Computing an Optimal Control Policy for an Energy Storage | cs.SY | We introduce StoDynProg, a small library created to solve Optimal Control
problems arising in the management of Renewable Power Sources, in particular
when coupled with an Energy Storage System. The library implements generic
Stochastic Dynamic Programming (SDP) numerical methods which can solve a large
class of Dynamic Optimization problems. We demonstrate the library capabilities
with a prototype problem: smoothing the power of an Ocean Wave Energy
Converter. First we use time series analysis to derive a stochastic Markovian
model of this system since it is required by Dynamic Programming. Then, we
briefly describe the "policy iteration" algorithm we have implemented and the
numerical tools being used. We show how the API design of the library is
generic enough to address Dynamic Optimization problems outside the field of
Energy Management. Finally, we solve the power smoothing problem and compare
the optimal control with a simpler heuristic control.
|
1404.6391 | SfePy - Write Your Own FE Application | cs.CE cs.PL | SfePy (Simple Finite Elements in Python) is a framework for solving various
kinds of problems (mechanics, physics, biology, ...) described by partial
differential equations in two or three space dimensions by the finite element
method. The paper illustrates its use in an interactive environment or as a
framework for building custom finite-element based solvers.
|
1404.6413 | Indoor Activity Detection and Recognition for Sport Games Analysis | cs.CV | Activity recognition in sport is an attractive field for computer vision
research. Game, player and team analysis are of great interest and research
topics within this field emerge with the goal of automated analysis. The very
specific underlying rules of sports can be used as prior knowledge for the
recognition task and present a constrained environment for evaluation. This
paper describes recognition of single player activities in sport with special
emphasis on volleyball. Starting from a per-frame player-centered activity
recognition, we incorporate geometry and contextual information via an activity
context descriptor that collects information about all player's activities over
a certain timespan relative to the investigated player. The benefit of this
context information on single player activity recognition is evaluated on our
new real-life dataset presenting a total amount of almost 36k annotated frames
containing 7 activity classes within 6 videos of professional volleyball games.
Our incorporation of the contextual information improves the average
player-centered classification performance of 77.56% by up to 18.35% on
specific classes, proving that spatio-temporal context is an important clue for
activity recognition.
|
1404.6441 | A note on the minimum distance of quantum LDPC codes | quant-ph cs.IT math.CO math.IT | We provide a new lower bound on the minimum distance of a family of quantum
LDPC codes based on Cayley graphs proposed by MacKay, Mitchison and
Shokrollahi. Our bound is exponential, improving on the quadratic bound of
Couvreur, Delfosse and Z\'emor. This result is obtained by examining a family
of subsets of the hypercube which locally satisfy some parity conditions.
|
1404.6445 | Belief merging within fragments of propositional logic | cs.AI cs.LO | Recently, belief change within the framework of fragments of propositional
logic has gained increasing attention. Previous works focused on belief
contraction and belief revision on the Horn fragment. However, the problem of
belief merging within fragments of propositional logic has been neglected so
far. This paper presents a general approach to define new merging operators
derived from existing ones such that the result of merging remains in the
fragment under consideration. Our approach is not limited to the case of Horn
fragment but applicable to any fragment of propositional logic characterized by
a closure property on the sets of models of its formulae. We study the logical
properties of the proposed operators in terms of satisfaction of merging
postulates, considering in particular distance-based merging operators for Horn
and Krom fragments.
|
1404.6471 | The Capacity Region of the Source-Type Model for Secret Key and Private
Key Generation | cs.IT math.IT | The problem of simultaneously generating a secret key (SK) and private key
(PK) pair among three terminals via public discussion is investigated, in which
each terminal observes a component of correlated sources. All three terminals
are required to generate a common secret key concealed from an eavesdropper
that has access to public discussion, while two designated terminals are
required to generate an extra private key concealed from both the eavesdropper
and the remaining terminal. An outer bound on the SK-PK capacity region was
established in [1], and was shown to be achievable for one case. In this paper,
achievable schemes are designed to achieve the outer bound for the remaining
two cases, and hence the SK-PK capacity region is established in general. The
main technique lies in the novel design of a random binning-joint decoding
scheme that achieves the existing outer bound.
|
1404.6472 | Parallel Gaussian Networks with a Common State-Cognitive Helper | cs.IT math.IT | A class of state-dependent parallel networks with a common state-cognitive
helper, in which $K$ transmitters wish to send $K$ messages to their
corresponding receivers over $K$ state-corrupted parallel channels, and a
helper who knows the state information noncausally wishes to assist these
receivers to cancel state interference. Furthermore, the helper also has its
own message to be sent simultaneously to its corresponding receiver. Since the
state information is known only to the helper, but not to the corresponding
transmitters $1,\dots,K$, transmitter-side state cognition and receiver-side
state interference are mismatched. Our focus is on the high state power regime,
i.e., the state power goes to infinity. Three (sub)models are studied. Model I
serves as a basic model, which consists of only one transmitter-receiver (with
state corruption) pair in addition to a helper that assists the receiver to
cancel state in addition to transmitting its own message. Model II consists of
two transmitter-receiver pairs in addition to a helper, and only one receiver
is interfered by a state sequence. Model III generalizes model I include
multiple transmitter-receiver pairs with each receiver corrupted by independent
state. For all models, inner and outer bounds on the capacity region are
derived, and comparison of the two bounds leads to characterization of either
full or partial boundary of the capacity region under various channel
parameters.
|
1404.6474 | An Information Theoretic Approach to Secret Sharing | cs.IT math.IT | A novel information theoretic approach is proposed to solve the secret
sharing problem, in which a dealer distributes one or multiple secrets among a
set of participants that for each secret only qualified sets of users can
recover it by pooling their shares together while non-qualified sets of users
obtain no information about the secret even if they pool their shares together.
While existing secret sharing systems (implicitly) assume that communications
between the dealer and participants are noiseless, this paper takes a more
practical assumption that the dealer delivers shares to the participants via a
noisy broadcast channel. An information theoretic approach is proposed, which
exploits the channel as additional resources to achieve secret sharing
requirements. In this way, secret sharing problems can be reformulated as
equivalent secure communication problems via wiretap channels, and can be
solved by employing powerful information theoretic security techniques. This
approach is first developed for the classic secret sharing problem, in which
only one secret is to be shared. This classic problem is shown to be equivalent
to a communication problem over a compound wiretap channel. The lower and upper
bounds on the secrecy capacity of the compound channel provide the
corresponding bounds on the secret sharing rate. The power of the approach is
further demonstrated by a more general layered multi-secret sharing problem,
which is shown to be equivalent to the degraded broadcast multiple-input
multiple-output (MIMO) channel with layered decoding and secrecy constraints.
The secrecy capacity region for the degraded MIMO broadcast channel is
characterized, which provides the secret sharing capacity region. Furthermore,
these secure encoding schemes that achieve the secrecy capacity region provide
an information theoretic scheme for sharing the secrets.
|
1404.6491 | An Account of Opinion Implicatures | cs.CL cs.IR | While previous sentiment analysis research has concentrated on the
interpretation of explicitly stated opinions and attitudes, this work initiates
the computational study of a type of opinion implicature (i.e.,
opinion-oriented inference) in text. This paper described a rule-based
framework for representing and analyzing opinion implicatures which we hope
will contribute to deeper automatic interpretation of subjective language. In
the course of understanding implicatures, the system recognizes implicit
sentiments (and beliefs) toward various events and entities in the sentence,
often attributed to different sources (holders) and of mixed polarities; thus,
it produces a richer interpretation than is typical in opinion analysis.
|
1404.6512 | Cellular Interference Alignment: Omni-Directional Antennas and
Asymmetric Configurations | cs.IT math.IT | Although interference alignment (IA) can theoretically achieve the optimal
degrees of freedom (DoFs) in the $K$-user Gaussian interference channel, its
direct application comes at the prohibitive cost of precoding over
exponentially-many signaling dimensions. On the other hand, it is known that
practical "one-shot" IA precoding (i.e., linear schemes without symbol
expansion) provides a vanishing DoFs gain in large fully-connected networks
with generic channel coefficients. In our previous work, we introduced the
concept of "Cellular IA" for a network topology induced by hexagonal cells with
sectors and nearest-neighbor interference. Assuming that neighboring sectors
can exchange decoded messages (and not received signal samples) in the uplink,
we showed that linear one-shot IA precoding over $M$ transmit/receive antennas
can achieve the optimal $M/2$ DoFs per user. In this paper we extend this
framework to networks with omni-directional (non-sectorized) cells and consider
the practical scenario where users have $2$ antennas, and base-stations have
$2$, $3$ or $4$ antennas. In particular, we provide linear one-shot IA schemes
for the $2\times 2$, $2\times3$ and $2\times 4$ cases, and show the
achievability of $3/4$, $1$ and $7/6$ DoFs per user, respectively. DoFs
converses for one-shot schemes require the solution of a discrete optimization
problem over a number of variables that grows with the network size. We develop
a new approach to transform such challenging optimization problem into a
tractable linear program (LP) with significantly fewer variables. This approach
is used to show that the achievable $3/4$ DoFs per user are indeed optimal for
a large (extended) cellular network with $2\times 2$ links.
|
1404.6535 | Quadratization of Symmetric Pseudo-Boolean Functions | math.OC cs.CC cs.CV math.CO | A pseudo-Boolean function is a real-valued function
$f(x)=f(x_1,x_2,\ldots,x_n)$ of $n$ binary variables; that is, a mapping from
$\{0,1\}^n$ to $\mathbb{R}$. For a pseudo-Boolean function $f(x)$ on
$\{0,1\}^n$, we say that $g(x,y)$ is a quadratization of $f$ if $g(x,y)$ is a
quadratic polynomial depending on $x$ and on $m$ auxiliary binary variables
$y_1,y_2,\ldots,y_m$ such that $f(x)= \min \{g(x,y) : y \in \{0,1\}^m \}$ for
all $x \in \{0,1\}^n$. By means of quadratizations, minimization of $f$ is
reduced to minimization (over its extended set of variables) of the quadratic
function $g(x,y)$. This is of some practical interest because minimization of
quadratic functions has been thoroughly studied for the last few decades, and
much progress has been made in solving such problems exactly or heuristically.
A related paper \cite{ABCG} initiated a systematic study of the minimum number
of auxiliary $y$-variables required in a quadratization of an arbitrary
function $f$ (a natural question, since the complexity of minimizing the
quadratic function $g(x,y)$ depends, among other factors, on the number of
binary variables). In this paper, we determine more precisely the number of
auxiliary variables required by quadratizations of symmetric pseudo-Boolean
functions $f(x)$, those functions whose value depends only on the Hamming
weight of the input $x$ (the number of variables equal to $1$).
|
1404.6538 | On Quadratization of Pseudo-Boolean Functions | math.OC cs.CV math.CO | We survey current term-wise techniques for quadratizing high-degree
pseudo-Boolean functions and introduce a new one, which allows multiple splits
of terms. We also introduce the first aggregative approach, which splits a
collection of terms based on their common parts.
|
1404.6544 | Interference Mitigating Satellite Broadcast Receiver using Reduced
Complexity List-Based Detection in Correlated Noise | cs.IT math.IT | The recent commercial trends towards using smaller dish antennas for
satellite receivers, and the growing density of broadcasting satellites,
necessitate the application of robust adjacent satellite interference (ASI)
cancellation schemes. This orbital density growth along with the wider
beamwidth of a smaller dish have imposed an overloaded scenario at the
satellite receiver, where the number of transmitting satellites exceeds the
number of receiving elements at the dish antenna. To ensure successful
operation in this practical scenario, we propose a satellite receiver that
enhances signal detection from the desired satellite by mitigating the
interference from neighboring satellites. Towards this objective, we propose a
reduced complexity list-based group-wise search detection (RC-LGSD) receiver
under the assumption of spatially correlated additive noise. To further enhance
detection performance, the proposed satellite receiver utilizes a newly
designed whitening filter to remove the spatial correlation amongst the noise
parameters, while also applying a preprocessor that maximizes the
signal-to-interference-plus-noise ratio (SINR). Extensive simulations under
practical scenarios show that the proposed receiver enhances the performance of
satellite broadcast systems in the presence of ASI compared to existing
methods.
|
1404.6556 | Asymptotic Deployment Gain: A Simple Approach to Characterize the SINR
Distribution in General Cellular Networks | cs.IT cs.NI math.IT math.PR | In cellular network models, the base stations are usually assumed to form a
lattice or a Poisson point process (PPP). In reality, however, they are
deployed neither fully regularly nor completely randomly. Accordingly, in this
paper, we consider the very general class of motion-invariant models and
analyze the behavior of the outage probability (the probability that the
signal-to-interference-plus-noise-ratio (SINR) is smaller than a threshold) as
the threshold goes to zero. We show that, remarkably, the slope of the outage
probability (in dB) as a function of the threshold (also in dB) is the same for
essentially all motion-invariant point processes. The slope merely depends on
the fading statistics. Using this result, we introduce the notion of the
asymptotic deployment gain (ADG), which characterizes the horizontal gap
between the success probabilities of the PPP and another point process in the
high-reliability regime (where the success probability is near 1). To
demonstrate the usefulness of the ADG for the characterization of the SINR
distribution, we investigate the outage probabilities and the ADGs for
different point processes and fading statistics by simulations.
|
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