Morteza Babaie, H.R. Tizhoosh, Shujin Zhu, M.E. Shiri
Comments: Accepted for publication in ICPRAM 2017: The International Conference on Pattern Recognition Applications and Methods, Porto, Portugal, 2017
Subjects: Computer Vision and Pattern Recognition (cs.CV)
The idea of Radon barcodes (RBC) has been introduced recently. In this paper,
we propose a content-based image retrieval approach for big datasets based on
Radon barcodes. Our method (Single Projection Radon Barcode, or SP-RBC) uses
only a few Radon single projections for each image as global features that can
serve as a basis for weak learners. This is our most important contribution in
this work, which improves the results of the RBC considerably. As a matter of
fact, only one projection of an image, as short as a single SURF feature
vector, can already achieve acceptable results. Nevertheless, using multiple
projections in a long vector will not deliver anticipated improvements. To
exploit the information inherent in each projection, our method uses the
outcome of each projection separately and then applies more precise local
search on the small subset of retrieved images. We have tested our method using
IRMA 2009 dataset a with 14,400 x-ray images as part of imageCLEF initiative.
Our approach leads to a substantial decrease in the error rate in comparison
with other non-learning methods.
V S R Veeravasarapu, Constantin Rothkopf, Ramesh Visvanathan
Comments: 9 pages
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Generalization performance of trained computer vision systems that use
computer graphics (CG) generated data is not yet effective due to the concept
of ‘domain-shift’ between virtual and real data. Although simulated data
augmented with a few real world samples has been shown to mitigate domain shift
and improve transferability of trained models, guiding or bootstrapping the
virtual data generation with the distributions learnt from target real world
domain is desired, especially in the fields where annotating even few real
images is laborious (such as semantic labeling, and intrinsic images etc.). In
order to address this problem in an unsupervised manner, our work combines
recent advances in CG (which aims to generate stochastic scene layouts coupled
with large collections of 3D object models) and generative adversarial training
(which aims train generative models by measuring discrepancy between generated
and real data in terms of their separability in the space of a deep
discriminatively-trained classifier). Our method uses iterative estimation of
the posterior density of prior distributions for a generative graphical model.
This is done within a rejection sampling framework. Initially, we assume
uniform distributions as priors on the parameters of a scene described by a
generative graphical model. As iterations proceed the prior distributions get
updated to distributions that are closer to the (unknown) distributions of
target data. We demonstrate the utility of adversarially tuned scene generation
on two real-world benchmark datasets (CityScapes and CamVid) for traffic scene
semantic labeling with a deep convolutional net (DeepLab). We realized
performance improvements by 2.28 and 3.14 points (using the IoU metric) between
the DeepLab models trained on simulated sets prepared from the scene generation
models before and after tuning to CityScapes and CamVid respectively.
Seunghoon Hong, Donghun Yeo, Suha Kwak, Honglak Lee, Bohyung Han
Subjects: Computer Vision and Pattern Recognition (cs.CV)
We propose a novel algorithm for weakly supervised semantic segmentation
based on image-level class labels only. In weakly supervised setting, it is
commonly observed that trained model overly focuses on discriminative parts
rather than the entire object area. Our goal is to overcome this limitation
with no additional human intervention by retrieving videos relevant to target
class labels from web repository, and generating segmentation labels from the
retrieved videos to simulate strong supervision for semantic segmentation.
During this process, we take advantage of image classification with
discriminative localization technique to reject false alarms in retrieved
videos and identify relevant spatio-temporal volumes within retrieved videos.
Although the entire procedure does not require any additional supervision, the
segmentation annotations obtained from videos are sufficiently strong to learn
a model for semantic segmentation. The proposed algorithm substantially
outperforms existing methods based on the same level of supervision and is even
as competitive as the approaches relying on extra annotations.
Carlos Oscar S. Sorzano, Jose Maria Carazo
Subjects: Computer Vision and Pattern Recognition (cs.CV); Quantitative Methods (q-bio.QM)
Since the introduction of Direct Electron Detectors (DEDs), the resolution
and range of macromolecules amenable to this technique has significantly
widened, generating a broad interest that explains the well over a dozen
reviews in top journal in the last two years. Similarly, the number of job
offers to lead EM groups and/or coordinate EM facilities has exploded, and FEI
(the main microscope manufacturer for Life Sciences) has received more than 100
orders of high-end electron microscopes by summer 2016. Strategic corporate
movements are also happening, with very big players entering the market through
key acquisitions (Thermo Fisher has recently bought FEI for \(4.2B), partly
attracted by new Pharma interest in the field, now perceived to be in a
position to impact structure-based drug design. The scientific perspectives are
indeed extremely positive but, in these moments of well-founded generalized
optimists, we want to make a reflection on some of the hurdles ahead us, since
they certainly exist and they indeed limit the informational content of cryoEM
projects. Here we focus on image processing aspects, particularly in the
so-called area of Single Particle Analysis, discussing some of the current
resolution and high-throughput limiting factors.
Denis Tome, Chris Russell, Lourdes Agapito
Subjects: Computer Vision and Pattern Recognition (cs.CV)
We propose a unified formulation for the problem of 3D human pose estimation
from a single raw RGB image that reasons jointly about 2D joint estimation and
3D pose reconstruction to improve both tasks. We take an integrated approach
that fuses probabilistic knowledge of 3D human pose with a multi-stage CNN
architecture and uses the knowledge of plausible 3D landmark locations to
refine the search for better 2D locations. The entire process is trained
end-to-end, is extremely efficient and obtains state- of-the-art results on
Human3.6M outperforming previous approaches both on 2D and 3D errors.
José Naranjo-Torres, Juliana Gambini, Alejandro C. Frery
Comments: Accepted for publication in the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (J-STARS), 1 January 2017
Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
The )mathcal{G}_I^0( distribution is able to characterize different regions
in monopolarized SAR imagery. It is indexed by three parameters: the number of
looks (which can be estimated in the whole image), a scale parameter and a
texture parameter. This paper presents a new proposal for feature extraction
and region discrimination in SAR imagery, using the geodesic distance as a
measure of dissimilarity between )mathcal{G}_I^0( models. We derive geodesic
distances between models that describe several practical situations, assuming
the number of looks known, for same and different texture and for same and
different scale. We then apply this new tool to the problems of (i)~identifying
edges between regions with different texture, and (ii)~quantify the
dissimilarity between pairs of samples in actual SAR data. We analyze the
advantages of using the geodesic distance when compared to stochastic
distances.
Hamid Hamraz, Marco A. Contreras, Jun Zhang
Journal-ref: International Journal of Applied Earth Observation and
Geoinformation 52 (pp. 532-541): Elsevier (2016)
Subjects: Computer Vision and Pattern Recognition (cs.CV); Computational Engineering, Finance, and Science (cs.CE); Computational Geometry (cs.CG)
This paper presents a non-parametric approach for segmenting trees from
airborne LiDAR data in deciduous forests. Based on the LiDAR point cloud, the
approach collects crown information such as steepness and height on-the-fly to
delineate crown boundaries, and most importantly, does not require a priori
assumptions of crown shape and size. The approach segments trees iteratively
starting from the tallest within a given area to the smallest until all trees
have been segmented. To evaluate its performance, the approach was applied to
the University of Kentucky Robinson Forest, a deciduous closed-canopy forest
with complex terrain and vegetation conditions. The approach identified 94% of
dominant and co-dominant trees with a false detection rate of 13%. About 62% of
intermediate, overtopped, and dead trees were also detected with a false
detection rate of 15%. The overall segmentation accuracy was 77%. Correlations
of the segmentation scores of the proposed approach with local terrain and
stand metrics was not significant, which is likely an indication of the
robustness of the approach as results are not sensitive to the differences in
terrain and stand structures.
Hao Liu, Zequn Jie, Karlekar Jayashree, Meibin Qi, Jianguo Jiang, Shuicheng Yan, Jiashi Feng
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Video based person re-identification plays a central role in realistic
security and video surveillance. In this paper we propose a novel Accumulative
Motion Context (AMOC) network for addressing this important problem, which
effectively exploits the long-range motion context for robustly identifying the
same person under challenging conditions. Given a video sequence of the same or
different persons, the proposed AMOC network jointly learns appearance
representation and motion context from a collection of adjacent frames using a
two-stream convolutional architecture. Then AMOC accumulates clues from motion
context by recurrent aggregation, allowing effective information flow among
adjacent frames and capturing dynamic gist of the persons. The architecture of
AMOC is end-to-end trainable and thus motion context can be adapted to
complement appearance clues under unfavorable conditions ( extit{e.g.},
occlusions). Extensive experiments are conduced on two public benchmark
datasets, extit{i.e.}, the iLIDS-VID and PRID-2011 datasets, to investigate
the performance of AMOC. The experimental results demonstrate that the proposed
AMOC network outperforms state-of-the-arts for video-based re-identification
significantly and confirm the advantage of exploiting long-range motion context
for video based person re-identification, validating our motivation evidently.
Hamid Hamraz, Marco A. Contreras, Jun Zhang
Comments: 28 double-spaced pages including 6 figures, 7 tables, and 50 references. The manuscript is currently under review
Subjects: Computer Vision and Pattern Recognition (cs.CV); Computational Engineering, Finance, and Science (cs.CE); Computational Geometry (cs.CG)
Airborne LiDAR point cloud of a forest contains three dimensional data, from
which vertical stand structure (including information about under-story trees)
can be derived. This paper presents a segmentation approach for multi-story
stands that strips the point cloud to its canopy layers, identifies individual
tree segments within each layer using a DSM-based tree identification method as
a building block, and combines the segments of immediate layers in order to fix
potential over-segmentation of tree crowns across the layers. We introduce
local layering that analyzes the vertical distributions of LiDAR points within
their local neighborhoods in order to locally determine the height thresholds
for layering the canopy. Unlike the previous work that stripped stiff layers
within constrained areas, the local layering method strips flexible (in
thickness and elevation) and narrower canopy layers within unconstrained areas.
Statistical analyses showed that layering in general strongly improves
identifying (specifically under-story) trees for the cost of moderately
increasing over-segmentation rate of the identified trees. Combining tree
segments across the immediate layers did not seem to improve tree
identification accuracy remarkably, suggesting that layers separated canopy
layers rather precisely.
Amit Shaked, Lior Wolf
Subjects: Computer Vision and Pattern Recognition (cs.CV)
We present an improved three-step pipeline for the stereo matching problem
and introduce multiple novelties at each stage. We propose a new highway
network architecture for computing the matching cost at each possible
disparity, based on multilevel weighted residual shortcuts, trained with a
hybrid loss that supports multilevel comparison of image patches. A novel
post-processing step is then introduced, which employs a second deep
convolutional neural network for pooling global information from multiple
disparities. This network outputs both the image disparity map, which replaces
the conventional “winner takes all” strategy, and a confidence in the
prediction. The confidence score is achieved by training the network with a new
technique that we call the reflective loss. Lastly, the learned confidence is
employed in order to better detect outliers in the refinement step. The
proposed pipeline achieves state of the art accuracy on the largest and most
competitive stereo benchmarks, and the learned confidence is shown to
outperform all existing alternatives.
Helge Rhodin, Christian Richardt, Dan Casas, Eldar Insafutdinov, Mohammad Shafiei, Hans-Peter Seidel, Bernt Schiele, Christian Theobalt
Comments: Short version of a SIGGRAPH Asia 2016 paper arXiv:1609.07306, presented at EPIC@ECCV16
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Marker-based and marker-less optical skeletal motion-capture methods use an
outside-in arrangement of cameras placed around a scene, with viewpoints
converging on the center. They often create discomfort by possibly needed
marker suits, and their recording volume is severely restricted and often
constrained to indoor scenes with controlled backgrounds. We therefore propose
a new method for real-time, marker-less and egocentric motion capture which
estimates the full-body skeleton pose from a lightweight stereo pair of fisheye
cameras that are attached to a helmet or virtual-reality headset. It combines
the strength of a new generative pose estimation framework for fisheye views
with a ConvNet-based body-part detector trained on a new automatically
annotated and augmented dataset. Our inside-in method captures full-body motion
in general indoor and outdoor scenes, and also crowded scenes.
E.N. Osegi
Comments: Working Paper
Subjects: Computer Vision and Pattern Recognition (cs.CV)
With the rise in militant activity and rogue behaviour in oil and gas regions
around the world, oil pipeline disturbances is on the increase leading to huge
losses to multinational operators and the countries where such facilities
exist. However, this situation can be averted if adequate predictive monitoring
schemes are put in place. We propose in the first part of this paper, an
artificial intelligence predictive monitoring system capable of predictive
classification and pattern recognition of pipeline datasets. The predictive
system is based on a highly sparse predictive Deviant Learning Algorithm
(p-DLA) designed to synthesize a sequence of memory predictive clusters for
eventual monitoring, control and decision making. The DLA (p-DLA) is compared
with a popular machine learning algorithm, the Long Short-Term Memory (LSTM)
which is based on a temporal version of the standard feed-forward
back-propagation trained artificial neural networks (ANNs). The results of
simulations study show impressive results and validates the sparse memory
predictive approach which favours the sub-synthesis of a highly compressed and
low dimensional knowledge discovery and information prediction scheme. It also
shows that the proposed new approach is competitive with a well-known and
proven AI approach such as the LSTM.
R. A. Borsoi, J. C. C. Aya, G. H. Costa, J. C. M. Bermudez
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Electrical Impedance Tomography (EIT) systems are becoming popular because
they present several advantages over competing systems. However, EIT leads to
images with very low resolution. Moreover, the nonuniform sampling
characteristic of EIT precludes the straightforward application of traditional
image ruper-resolution techniques. In this work, we propose a resampling based
Super-Resolution method for EIT image quality improvement. Preliminary results
show that the proposed technique can lead to substantial improvements in EIT
image resolution, making it more competitive with other technologies.
Guillermo Cabrera-Vives, Ignacio Reyes, Francisco Förster, Pablo A. Estévez, Juan-Carlos Maureira
Journal-ref: The Astrophysical Journal, 2017
Subjects: Instrumentation and Methods for Astrophysics (astro-ph.IM); Computer Vision and Pattern Recognition (cs.CV)
We introduce Deep-HiTS, a rotation invariant convolutional neural network
(CNN) model for classifying images of transients candidates into artifacts or
real sources for the High cadence Transient Survey (HiTS). CNNs have the
advantage of learning the features automatically from the data while achieving
high performance. We compare our CNN model against a feature engineering
approach using random forests (RF). We show that our CNN significantly
outperforms the RF model reducing the error by almost half. Furthermore, for a
fixed number of approximately 2,000 allowed false transient candidates per
night we are able to reduce the miss-classified real transients by
approximately 1/5. To the best of our knowledge, this is the first time CNNs
have been used to detect astronomical transient events. Our approach will be
very useful when processing images from next generation instruments such as the
Large Synoptic Survey Telescope (LSST). We have made all our code and data
available to the community for the sake of allowing further developments and
comparisons at this https URL
Stefan Engblom, Carl Nettelblad, Jing Liu
Comments: 21 pages
Subjects: Methodology (stat.ME); Computer Vision and Pattern Recognition (cs.CV); Data Analysis, Statistics and Probability (physics.data-an)
Modern technology for producing extremely bright and coherent X-ray laser
pulses provides the possibility to acquire a large number of diffraction
patterns from individual biological nanoparticles, including proteins, viruses,
and DNA. These two-dimensional diffraction patterns can be practically
reconstructed and retrieved down to a resolution of a few angstrom. In
principle, a sufficiently large collection of diffraction patterns will contain
the required information for a full three-dimensional reconstruction of the
biomolecule. The computational methodology for this reconstruction task is
still under development and highly resolved reconstructions have not yet been
produced.
We analyze the Expansion-Maximization-Compression scheme, the current state
of the art approach for this very challenging application, by isolating
different sources of uncertainty. Through numerical experiments on synthetic
data we evaluate their respective impact. We reach conclusions of relevance for
handling actual experimental data, as well as pointing out certain improvements
to the underlying estimation algorithm.
We also introduce a practically applicable computational methodology in the
form of bootstrap procedures for assessing reconstruction uncertainty in the
real data case. We evaluate the sharpness of this approach and argue that this
type of procedure will be critical in the near future when handling the
increasing amount of data.
Antonio Lieto, Antonio Chella, Marcello Frixione
Comments: 31 pages, 3 figures in Biologically Inspired Cognitive Architectures, 2017
Subjects: Artificial Intelligence (cs.AI)
During the last decades, many cognitive architectures (CAs) have been
realized adopting different assumptions about the organization and the
representation of their knowledge level. Some of them (e.g. SOAR [Laird
(2012)]) adopt a classical symbolic approach, some (e.g. LEABRA [O’Reilly and
Munakata (2000)]) are based on a purely connectionist model, while others (e.g.
CLARION [Sun (2006)] adopt a hybrid approach combining connectionist and
symbolic representational levels. Additionally, some attempts (e.g. biSOAR)
trying to extend the representational capacities of CAs by integrating
diagrammatical representations and reasoning are also available [Kurup and
Chandrasekaran (2007)]. In this paper we propose a reflection on the role that
Conceptual Spaces, a framework developed by Peter G”ardenfors [G”ardenfors
(2000)] more than fifteen years ago, can play in the current development of the
Knowledge Level in Cognitive Systems and Architectures. In particular, we claim
that Conceptual Spaces offer a lingua franca that allows to unify and
generalize many aspects of the symbolic, sub-symbolic and diagrammatic
approaches (by overcoming some of their typical problems) and to integrate them
on a common ground. In doing so we extend and detail some of the arguments
explored by G”ardenfors [G”ardenfors (1997)] for defending the need of a
conceptual, intermediate, representation level between the symbolic and the
sub-symbolic one.
Rohitash Chandra
Comments: under review
Subjects: Artificial Intelligence (cs.AI)
In the past, several models of consciousness have become popular and have led
to the development of models for machine consciousness with varying degrees of
success and challenges for simulation and implementations. Moreover, affective
computing attributes that involve emotions, behavior and personality have not
been the focus of models of consciousness as they lacked motivation for
deployment in software applications and robots. The affective attributes are
important factors for the future of machine consciousness with the rise of
technologies that can assist humans. Personality and affection hence can give
an additional flavor for the computational model of consciousness in humanoid
robotics. Recent advances in areas of machine learning with a focus on deep
learning can further help in developing aspects of machine consciousness in
areas that can better replicate human sensory perceptions such as speech
recognition and vision. With such advancements, one encounters further
challenges in developing models that can synchronize different aspects of
affective computing. In this paper, we review some existing models of
consciousnesses and present an affective computational model that would enable
the human touch and feel for robotic systems.
Caelan Reed Garrett, Tomás Lozano-Pérez, Leslie Pack Kaelbling
Comments: 10 pages
Subjects: Artificial Intelligence (cs.AI)
Many practical planning applications involve continuous quantities with
non-linear constraints, which cannot be modeled using modern planners that
construct a propositional representation. We introduce STRIPStream: an
extension of the STRIPS language which supports infinite streams of objects and
static predicates and provide two algorithms, which reduce the original problem
to a sequence of finite-domain planning problems. The representation and
algorithms are entirely domain independent. We demonstrate them on simple
illustrative domains, and then on a high-dimensional, continuous robotic task
and motion planning problem.
Jan-Peter Calliess
Subjects: Optimization and Control (math.OC); Artificial Intelligence (cs.AI); Learning (cs.LG); Systems and Control (cs.SY); Machine Learning (stat.ML)
Techniques known as Nonlinear Set Membership prediction, Lipschitz
Interpolation or Kinky Inference are approaches to machine learning that
utilise presupposed Lipschitz properties to compute inferences over unobserved
function values. Provided a bound on the true best Lipschitz constant of the
target function is known a priori they offer convergence guarantees as well as
bounds around the predictions. Considering a more general setting that builds
on Hoelder continuity relative to pseudo-metrics, we propose an online method
for estimating the Hoelder constant online from function value observations
that possibly are corrupted by bounded observational errors. Utilising this to
compute adaptive parameters within a kinky inference rule gives rise to a
nonparametric machine learning method, for which we establish strong universal
approximation guarantees. That is, we show that our prediction rule can learn
any continuous function in the limit of increasingly dense data to within a
worst-case error bound that depends on the level of observational uncertainty.
We apply our method in the context of nonparametric model-reference adaptive
control (MRAC). Across a range of simulated aircraft roll-dynamics and
performance metrics our approach outperforms recently proposed alternatives
that were based on Gaussian processes and RBF-neural networks. For
discrete-time systems, we provide stability guarantees for our learning-based
controllers both for the batch and the online learning setting.
Jun Suzuki, Masaaki Nagata
Comments: 10 pages
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
This paper tackles the reduction of redundant repeating generation that is
often observed in RNN-based encoder-decoder models. Our basic idea is to
jointly estimate the upper-bound frequency of each target vocabulary in the
encoder and control the output words based on the estimation in the decoder.
Our method shows significant improvement over a strong RNN-based
encoder-decoder baseline and achieved its best results on an abstractive
summarization benchmark.
Pietro Hiram Guzzi, Giuseppe Agapito, Marianna Milano, Mario Cannataro
Subjects: Quantitative Methods (q-bio.QM); Artificial Intelligence (cs.AI)
The Human Phenotype Ontology (HPO) is a structured repository of concepts
(HPO Terms) that are associated to one or more diseases. The process of
association is referred to as annotation. The relevance and the specificity of
both HPO terms and annotations are evaluated by a measure defined as
Information Content (IC). The analysis of annotated data is thus an important
challenge for bioinformatics. There exist different approaches of analysis.
From those, the use of Association Rules (AR) may provide useful knowledge, and
it has been used in some applications, e.g. improving the quality of
annotations. Nevertheless classical association rules algorithms do not take
into account the source of annotation nor the importance yielding to the
generation of candidate rules with low IC. This paper presents HPO-Miner (Human
Phenotype Ontology-based Weighted Association Rules) a methodology for
extracting Weighted Association Rules. HPO-Miner can extract relevant rules
from a biological point of view. A case study on using of HPO-Miner on publicly
available HPO annotation datasets is used to demonstrate the effectiveness of
our methodology.
Connor Sell, Jeremy Kepner
Comments: 4 pages, 3 figures, to appear in the proceedings of the 2015 IEEE MIT Undergraduate Research Conference
Subjects: Numerical Analysis (math.NA); Artificial Intelligence (cs.AI); Numerical Analysis (cs.NA)
Non-negative matrix factorization (NMF) is a prob- lem with many
applications, ranging from facial recognition to document clustering. However,
due to the variety of algorithms that solve NMF, the randomness involved in
these algorithms, and the somewhat subjective nature of the problem, there is
no clear “correct answer” to any particular NMF problem, and as a result, it
can be hard to test new algorithms. This paper suggests some test cases for NMF
algorithms derived from matrices with enumerable exact non-negative
factorizations and perturbations of these matrices. Three algorithms using
widely divergent approaches to NMF all give similar solutions over these test
cases, suggesting that these test cases could be used as test cases for
implementations of these existing NMF algorithms as well as potentially new NMF
algorithms. This paper also describes how the proposed test cases could be used
in practice.
Massimiliano Dal Mas
Comments: 6 pages; for details see: this this http URL
Subjects: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
In a Web Advertising Traffic Operation it’s necessary to manage the
day-to-day trafficking, pacing and optimization of digital and paid social
campaigns. The data analyst on Traffic Operation can not only quickly provide
answers but also speaks the language of the Process Manager and visually
displays the discovered process problems. In order to solve a growing number of
complaints in the customer service process, the weaknesses in the process
itself must be identified and communicated to the department. With the help of
Process Mining for the CRM data it is possible to identify unwanted loops and
delays in the process. With this paper we propose a process discovery based on
Machine Learning technique to automatically discover variations and detect at
first glance what the problem is, and undertake corrective measures.
Walid Shalaby, Wlodek Zadrozny
Subjects: Information Retrieval (cs.IR)
With the ever increasing number of filed patent applications every year, the
need for effective and efficient systems for managing such tremendous amounts
of data becomes inevitably important. Patent Retrieval (PR) is considered is
the pillar of almost all patent analysis tasks. PR is a subfield of Information
Retrieval (IR) which is concerned with developing techniques and methods that
effectively and efficiently retrieve relevant patent documents in response to a
given search request. In this paper we present a comprehensive review on PR
methods and approaches. It is clear that, recent successes and maturity in IR
applications such as Web search can not be transferred directly to PR without
deliberate domain adaptation and customization. Furthermore, state-of-the-art
performance in automatic PR is still around average. These observations
motivates the need for interactive search tools which provide cognitive
assistance to patent professionals with minimal effort. These tools must also
be developed in hand with patent professionals considering their practices and
expectations. We additionally touch on related tasks to PR such as patent
valuation, litigation, licensing, and highlight potential opportunities and
open directions for computational scientists in these domains.
Kodzo Wegba, Aidong Lu, Yuemeng Li, Wencheng Wang
Comments: 10 pages
Subjects: Information Retrieval (cs.IR); Social and Information Networks (cs.SI)
Recommendation has become one of the most important components of online
services for improving sale records, however visualization work for online
recommendation is still very limited. This paper presents an interactive
recommendation approach with the following two components. First, rating
records are the most widely used data for online recommendation, but they are
often processed in high-dimensional spaces that can not be easily understood or
interacted with. We propose a Latent Semantic Model (LSM) that captures the
statistical features of semantic concepts on 2D domains and abstracts user
preferences for personal recommendation. Second, we propose an interactive
recommendation approach through a storytelling mechanism for promoting the
communication between the user and the recommendation system. Our approach
emphasizes interactivity, explicit user input, and semantic information convey;
thus it can be used by general users without any knowledge of recommendation or
visualization algorithms. We validate our model with data statistics and
demonstrate our approach with case studies from the MovieLens100K dataset. Our
approaches of latent semantic analysis and interactive recommendation can also
be extended to other network-based visualization applications, including
various online recommendation systems.
Jiaming Xu, Bo Xu, Peng Wang, Suncong Zheng, Guanhua Tian, Jun Zhao, Bo Xu
Comments: 33 pages, accepted for publication in Neural Networks
Subjects: Information Retrieval (cs.IR); Computation and Language (cs.CL)
Short text clustering is a challenging problem due to its sparseness of text
representation. Here we propose a flexible Self-Taught Convolutional neural
network framework for Short Text Clustering (dubbed STC^2), which can flexibly
and successfully incorporate more useful semantic features and learn non-biased
deep text representation in an unsupervised manner. In our framework, the
original raw text features are firstly embedded into compact binary codes by
using one existing unsupervised dimensionality reduction methods. Then, word
embeddings are explored and fed into convolutional neural networks to learn
deep feature representations, meanwhile the output units are used to fit the
pre-trained binary codes in the training process. Finally, we get the optimal
clusters by employing K-means to cluster the learned representations. Extensive
experimental results demonstrate that the proposed framework is effective,
flexible and outperform several popular clustering methods when tested on three
public short text datasets.
David J.P. O'Sullivan, Guillermo Garduño-Hernández, James P. Gleeson, Mariano Beguerisse-Díaz
Comments: 16 pages, 12 figures
Subjects: Social and Information Networks (cs.SI); Computation and Language (cs.CL); Information Retrieval (cs.IR); Physics and Society (physics.soc-ph)
We investigate the relationship between social structure and sentiment
through the analysis of half a million tweets about the Irish Marriage
Referendum of 2015. We obtain the sentiment of every tweet with the hashtags
#marref and #marriageref posted in the days leading to the referendum, and
construct networks to aggregate sentiment and study the interactions among
users. The sentiment of the mention tweets that a user sends is correlated with
the sentiment of the mentions received, and there are significantly more
connections between users with similar sentiment scores than among users with
opposite scores. We combine the community structure of the follower and mention
networks, the activity level of the users, and sentiment scores to find groups
of users who support voting ‘yes’ or ‘no’ on the referendum. We find that many
conversations between users on opposing sides of the debate occurred in the
absence of follower connections, suggesting that there were efforts by some
users to establish dialogue and debate across ideological divisions. These
results show that social structures can be successfully integrated with
sentiment to analyse and understand the disposition of social media users. We
discuss the implications of our work for the integration of data and meta-data,
opinion dynamics, public opinion modelling and polling.
Yuan Zhang, Regina Barzilay, Tommi Jaakkola
Comments: TACL
Subjects: Computation and Language (cs.CL)
We introduce a neural method for transfer learning between two (source and
target) classification tasks or aspects over the same domain. Instead of target
labels, we assume a few keywords pertaining to source and target aspects
indicating sentence relevance rather than document class labels. Documents are
encoded by learning to embed and softly select relevant sentences in an
aspect-dependent manner. A shared classifier is trained on the source encoded
documents and labels, and applied to target encoded documents. We ensure
transfer through aspect-adversarial training so that encoded documents are, as
sets, aspect-invariant. Experimental results demonstrate that our approach
outperforms different baselines and model variants on two datasets, yielding an
improvement of 24% on a pathology dataset and 5% on a review dataset.
Jan Šnajder
Subjects: Computation and Language (cs.CL)
Argumentation mining from social media content has attracted increasing
attention. The task is both challenging and rewarding. The informal nature of
user-generated content makes the task dauntingly difficult. On the other hand,
the insights that could be gained by a large-scale analysis of social media
argumentation make it a very worthwhile task. In this position paper I discuss
the motivation for social media argumentation mining, as well as the tasks and
challenges involved.
Silvio Amir, Rámon Astudillo, Wang Ling, Paula C. Carvalho, Mário J. Silva
Subjects: Computation and Language (cs.CL)
Recent approaches for sentiment lexicon induction have capitalized on
pre-trained word embeddings that capture latent semantic properties. However,
embeddings obtained by optimizing performance of a given task (e.g. predicting
contextual words) are sub-optimal for other applications. In this paper, we
address this problem by exploiting task-specific representations, induced via
embedding sub-space projection. This allows us to expand lexicons describing
multiple semantic properties. For each property, our model jointly learns
suitable representations and the concomitant predictor. Experiments conducted
over multiple subjective lexicons, show that our model outperforms previous
work and other baselines; even in low training data regimes. Furthermore,
lexicon-based sentiment classifiers built on top of our lexicons outperform
similar resources and yield performances comparable to those of supervised
models.
Jun Suzuki, Masaaki Nagata
Comments: 10 pages
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
This paper tackles the reduction of redundant repeating generation that is
often observed in RNN-based encoder-decoder models. Our basic idea is to
jointly estimate the upper-bound frequency of each target vocabulary in the
encoder and control the output words based on the estimation in the decoder.
Our method shows significant improvement over a strong RNN-based
encoder-decoder baseline and achieved its best results on an abstractive
summarization benchmark.
Sree Harsha Ramesh, Raveena R Kumar
Comments: 4 Pages, 13th International Conference on Natural Language Processing, Varanasi, India
Subjects: Computation and Language (cs.CL)
Building Part-of-Speech (POS) taggers for code-mixed Indian languages is a
particularly challenging problem in computational linguistics due to a dearth
of accurately annotated training corpora. ICON, as part of its NLP tools
contest has organized this challenge as a shared task for the second
consecutive year to improve the state-of-the-art. This paper describes the POS
tagger built at Surukam to predict the coarse-grained and fine-grained POS tags
for three language pairs – Bengali-English, Telugu-English and Hindi-English,
with the text spanning three popular social media platforms – Facebook,
WhatsApp and Twitter. We employed Conditional Random Fields as the sequence
tagging algorithm and used a library called sklearn-crfsuite – a thin wrapper
around CRFsuite for training our model. Among the features we used include –
character n-grams, language information and patterns for emoji, number,
punctuation and web-address. Our submissions in the constrained
environment,i.e., without making any use of monolingual POS taggers or the
like, obtained an overall average F1-score of 76.45%, which is comparable to
the 2015 winning score of 76.79%.
David J.P. O'Sullivan, Guillermo Garduño-Hernández, James P. Gleeson, Mariano Beguerisse-Díaz
Comments: 16 pages, 12 figures
Subjects: Social and Information Networks (cs.SI); Computation and Language (cs.CL); Information Retrieval (cs.IR); Physics and Society (physics.soc-ph)
We investigate the relationship between social structure and sentiment
through the analysis of half a million tweets about the Irish Marriage
Referendum of 2015. We obtain the sentiment of every tweet with the hashtags
#marref and #marriageref posted in the days leading to the referendum, and
construct networks to aggregate sentiment and study the interactions among
users. The sentiment of the mention tweets that a user sends is correlated with
the sentiment of the mentions received, and there are significantly more
connections between users with similar sentiment scores than among users with
opposite scores. We combine the community structure of the follower and mention
networks, the activity level of the users, and sentiment scores to find groups
of users who support voting ‘yes’ or ‘no’ on the referendum. We find that many
conversations between users on opposing sides of the debate occurred in the
absence of follower connections, suggesting that there were efforts by some
users to establish dialogue and debate across ideological divisions. These
results show that social structures can be successfully integrated with
sentiment to analyse and understand the disposition of social media users. We
discuss the implications of our work for the integration of data and meta-data,
opinion dynamics, public opinion modelling and polling.
Jiaming Xu, Bo Xu, Peng Wang, Suncong Zheng, Guanhua Tian, Jun Zhao, Bo Xu
Comments: 33 pages, accepted for publication in Neural Networks
Subjects: Information Retrieval (cs.IR); Computation and Language (cs.CL)
Short text clustering is a challenging problem due to its sparseness of text
representation. Here we propose a flexible Self-Taught Convolutional neural
network framework for Short Text Clustering (dubbed STC^2), which can flexibly
and successfully incorporate more useful semantic features and learn non-biased
deep text representation in an unsupervised manner. In our framework, the
original raw text features are firstly embedded into compact binary codes by
using one existing unsupervised dimensionality reduction methods. Then, word
embeddings are explored and fed into convolutional neural networks to learn
deep feature representations, meanwhile the output units are used to fit the
pre-trained binary codes in the training process. Finally, we get the optimal
clusters by employing K-means to cluster the learned representations. Extensive
experimental results demonstrate that the proposed framework is effective,
flexible and outperform several popular clustering methods when tested on three
public short text datasets.
Chathura Sarathchandra Magurawalage, Kun Yang, Ritoa Patrik, Michael Georgiades, Kezhi Wang
Subjects: Distributed, Parallel, and Cluster Computing (cs.DC)
Cloud radio access network (C-RAN) and Mobile Cloud Computing (MCC) have
emerged as promising candidates for the next generation access network
techniques. MCC offers resource limited mobile devices to offload
computationally intensive tasks to the cloud, while C-RAN offers a technology
that addresses increasing mobile traffic. In this paper, we propose a protocol
that allows task offloading and managing resources in both C-RAN and mobile
cloud together using a centralised controller. Experiments on resource
management using cloud auto-scaling shows that resource (CPU, RAM, Storage)
scaling times vary.
Adnan Ashraf, Ivan Porres
Comments: The manuscript has been accepted for publication in the International Journal of Parallel, Emergent and Distributed Systems
Subjects: Distributed, Parallel, and Cluster Computing (cs.DC)
In this paper, we present a novel multi-objective ant colony system algorithm
for virtual machine (VM) consolidation in cloud data centers. The proposed
algorithm builds VM migration plans, which are then used to minimize
over-provisioning of physical machines (PMs) by consolidating VMs on
under-utilized PMs. It optimizes two objectives that are ordered by their
importance. The first and foremost objective in the proposed algorithm is to
maximize the number of released PMs. Moreover, since VM migration is a
resource-intensive operation, it also tries to minimize the number of VM
migrations. The proposed algorithm is empirically evaluated in a series of
experiments. The experimental results show that the proposed algorithm provides
an efficient solution for VM consolidation in cloud data centers. Moreover, it
outperforms two existing ant colony optimization based VM consolidation
algorithms in terms of number of released PMs and number of VM migrations.
Wen Sun, Véronique Simon, Sébastien Monnet, Philippe Robert, Pierre Sens
Subjects: Distributed, Parallel, and Cluster Computing (cs.DC)
Distributed storage systems such as Hadoop File System or Google File System
(GFS) ensure data availability and durability using replication. This paper is
focused on the analysis of the efficiency of replication mechanism that
determines the location of the copies of a given file at some server. The
variability of the loads of the nodes of the network is investigated for
several policies. Three replication mechanisms are tested against simulations
in the context of a real implementation of a such a system: Random, Least
Loaded and Power of Choice.
The simulations show that some of these policies may lead to quite unbalanced
situations: if )eta( is the average number of copies per node it turns out
that, at equilibrium, the load of the nodes may exhibit a high variability. It
is shown in this paper that a simple variant of a power of choice type
algorithm has a striking effect on the loads of the nodes: at equilibrium, the
load of a node has a bounded support, almost all nodes have a load less than
)2eta(.
Mathematical models are introduced and investigated to explain this unusual,
quite surprising, phenomenon. Our study relies on probabilistic methods,
mean-field analysis, to analyze the asymptotic behavior of an arbitrary node of
the network when the total number of nodes gets large. An additional ingredient
is the use of stochastic calculus with marked Poisson point processes to
establish some of our results.
Lakshmi Anantharamu, Bogdan S. Chlebus, Dariusz R. Kowalski, Mariusz A. Rokicki
Subjects: Distributed, Parallel, and Cluster Computing (cs.DC)
We study broadcasting on multiple access channels with dynamic packet
arrivals and jamming. The communication environments is represented by
adversarial models which specify constraints on packet arrivals and jamming. We
consider deterministic distributed broadcast algorithms and give upper bounds
on the worst-case packet latency and the number of queued packets in relation
to the parameters defining adversaries. Packet arrivals are determined by the
rate of injections and number of packets that can arrive in one round. Jamming
is constrained by the rate with which the adversary can jam rounds and by the
number of consecutive rounds that can be jammed.
Hamid Hamraz, Marco A. Contreras, Jun Zhang
Comments: Highlights: – A scalable distributed approach for tree segmentation was developed and analyzed. – ~2 million trees in a 7440 ha forest was segmented in 2.5 hours using 192 cores. – 2% false positive trees were identified as a result of the distributed run. – The approach can be used to scale up processing other big spatial data
Subjects: Distributed, Parallel, and Cluster Computing (cs.DC); Computational Engineering, Finance, and Science (cs.CE)
This paper presents a distributed approach that scales up to segment tree
crowns within a LiDAR point cloud representing an arbitrarily large forested
area. The approach uses a single-processor tree segmentation algorithm as a
building block in order to process the data delivered in the shape of tiles in
parallel. The distributed processing is performed in a master-slave manner, in
which the master maintains the global map of the tiles and coordinates the
slaves that segment tree crowns within and across the boundaries of the tiles.
A minimal bias was introduced to the number of detected trees because of trees
lying across the tile boundaries, which was quantified and adjusted for.
Theoretical and experimental analyses of the runtime of the approach revealed a
near linear speedup. The estimated number of trees categorized by crown class
and the associated error margins as well as the height distribution of the
detected trees aligned well with field estimations, verifying that the
distributed approach works correctly. The approach enables providing
information of individual tree locations and point cloud segments for a
forest-level area in a timely manner, which can be used to create detailed
remotely sensed forest inventories. Although the approach was presented for
tree segmentation within LiDAR point clouds, the idea can also be generalized
to scale up processing other big spatial datasets.
Lanlan Liu, Jia Deng
Comments: CVPR 2017 Submission
Subjects: Learning (cs.LG); Machine Learning (stat.ML)
We introduce Dynamic Deep Neural Networks (D2NN), a new type of feed-forward
deep neural network that allow selective execution. Given an input, only a
subset of D2NN neurons are executed, and the particular subset is determined by
the D2NN itself. By pruning unnecessary computation depending on input, D2NNs
provide a way to improve computational efficiency. To achieve dynamic selective
execution, a D2NN augments a regular feed-forward deep neural network (directed
acyclic graph of differentiable modules) with one or more controller modules.
Each controller module is a sub-network whose output is a decision that
controls whether other modules can execute. A D2NN is trained end to end. Both
regular modules and controller modules in a D2NN are learnable and are jointly
trained to optimize both accuracy and efficiency. Such training is achieved by
integrating backpropagation with reinforcement learning. With extensive
experiments of various D2NN architectures on image classification tasks, we
demonstrate that D2NNs are general and flexible, and can effectively optimize
accuracy-efficiency trade-offs.
Jiashi Feng, Huan Xu, Shie Mannor
Subjects: Learning (cs.LG); Machine Learning (stat.ML)
We consider the problem of learning from noisy data in practical settings
where the size of data is too large to store on a single machine. More
challenging, the data coming from the wild may contain malicious outliers. To
address the scalability and robustness issues, we present an online robust
learning (ORL) approach. ORL is simple to implement and has provable robustness
guarantee — in stark contrast to existing online learning approaches that are
generally fragile to outliers. We specialize the ORL approach for two concrete
cases: online robust principal component analysis and online linear regression.
We demonstrate the efficiency and robustness advantages of ORL through
comprehensive simulations and predicting image tags on a large-scale data set.
We also discuss extension of the ORL to distributed learning and provide
experimental evaluations.
Andrey Finkelstein, Ron Biton, Rami Puzis, Asaf Shabtai
Subjects: Learning (cs.LG); Cryptography and Security (cs.CR)
Today, smartphone devices are owned by a large portion of the population and
have become a very popular platform for accessing the Internet. Smartphones
provide the user with immediate access to information and services. However,
they can easily expose the user to many privacy risks. Applications that are
installed on the device and entities with access to the device’s Internet
traffic can reveal private information about the smartphone user and steal
sensitive content stored on the device or transmitted by the device over the
Internet. In this paper, we present a method to reveal various demographics and
technical computer skills of smartphone users by their Internet traffic
records, using machine learning classification models. We implement and
evaluate the method on real life data of smartphone users and show that
smartphone users can be classified by their gender, smoking habits, software
programming experience, and other characteristics.
Ian Goodfellow
Subjects: Learning (cs.LG)
This report summarizes the tutorial presented by the author at NIPS 2016 on
generative adversarial networks (GANs). The tutorial describes: (1) Why
generative modeling is a topic worth studying, (2) how generative models work,
and how GANs compare to other generative models, (3) the details of how GANs
work, (4) research frontiers in GANs, and (5) state-of-the-art image models
that combine GANs with other methods. Finally, the tutorial contains three
exercises for readers to complete, and the solutions to these exercises.
Jan-Peter Calliess
Subjects: Optimization and Control (math.OC); Artificial Intelligence (cs.AI); Learning (cs.LG); Systems and Control (cs.SY); Machine Learning (stat.ML)
Techniques known as Nonlinear Set Membership prediction, Lipschitz
Interpolation or Kinky Inference are approaches to machine learning that
utilise presupposed Lipschitz properties to compute inferences over unobserved
function values. Provided a bound on the true best Lipschitz constant of the
target function is known a priori they offer convergence guarantees as well as
bounds around the predictions. Considering a more general setting that builds
on Hoelder continuity relative to pseudo-metrics, we propose an online method
for estimating the Hoelder constant online from function value observations
that possibly are corrupted by bounded observational errors. Utilising this to
compute adaptive parameters within a kinky inference rule gives rise to a
nonparametric machine learning method, for which we establish strong universal
approximation guarantees. That is, we show that our prediction rule can learn
any continuous function in the limit of increasingly dense data to within a
worst-case error bound that depends on the level of observational uncertainty.
We apply our method in the context of nonparametric model-reference adaptive
control (MRAC). Across a range of simulated aircraft roll-dynamics and
performance metrics our approach outperforms recently proposed alternatives
that were based on Gaussian processes and RBF-neural networks. For
discrete-time systems, we provide stability guarantees for our learning-based
controllers both for the batch and the online learning setting.
David Picard
Subjects: Machine Learning (stat.ML); Learning (cs.LG)
In this paper we propose a fast online Kernel SVM algorithm under tight
budget constraints. We propose to split the input space using LVQ and train a
Kernel SVM in each cluster. To allow for online training, we propose to limit
the size of the support vector set of each cluster using different strategies.
We show in the experiment that our algorithm is able to achieve high accuracy
while having a very high number of samples processed per second both in
training and in the evaluation.
Daniel George, E. A. Huerta
Comments: 20 pages, 15 figures
Subjects: Instrumentation and Methods for Astrophysics (astro-ph.IM); Astrophysics of Galaxies (astro-ph.GA); High Energy Astrophysical Phenomena (astro-ph.HE); Learning (cs.LG); General Relativity and Quantum Cosmology (gr-qc)
We introduce a new methodology for time-domain signal processing, based on
deep learning neural networks, which has the potential to revolutionize data
analysis in science. To illustrate how this enables real-time multimessenger
astrophysics, we designed two deep convolutional neural networks that can
analyze time-series data from observatories including advanced LIGO. The first
neural network recognizes the presence of gravitational waves from binary black
hole mergers, and the second one estimates the mass of each black hole, given
weak signals hidden in extremely noisy time-series inputs. We highlight the
advantages offered by this novel method, which outperforms matched-filtering or
conventional machine learning techniques, and propose strategies to extend our
implementation for simultaneously targeting different classes of gravitational
wave sources while ignoring anomalous noise transients. Our results strongly
indicate that deep neural networks are highly efficient and versatile tools for
directly processing raw noisy data streams. Furthermore, we pioneer a new
paradigm to accelerate scientific discovery by combining high-performance
simulations on traditional supercomputers and artificial intelligence
algorithms that exploit innovative hardware architectures such as
deep-learning-optimized GPUs. This unique approach immediately provides a
natural framework to unify multi-spectrum observations in real-time, thus
enabling coincident detection campaigns of gravitational waves sources and
their electromagnetic counterparts.
Sudarshan Guruacharya, Hina Tabassum, Ekram Hossain
Comments: arXiv admin note: substantial text overlap with arXiv:1607.06887
Subjects: Information Theory (cs.IT)
Signal-to-noise-plus-interference ratio (SINR) outage probability is among
one of the key performance metrics of a wireless cellular network. In this
paper, we propose a semi-analytical method based on saddle point approximation
(SPA) technique to calculate the SINR outage of a wireless system whose SINR
can be modeled in the form )frac{sum_{i=1}^M X_i}{sum_{i=1}^N Y_i +1}( where
)X_i( denotes the useful signal power, )Y_i( denotes the power of the
interference signal, and )sum_{i=1}^M X_i(, )sum_{i=1}^N Y_i( are independent
random variables. Both )M( and )N( can also be random variables. The proposed
approach is based on the saddle point approximation to cumulative distribution
function (CDF) as given by it{Wood-Booth-Butler formula}. The approach is
applicable whenever the cumulant generating function (CGF) of the received
signal and interference exists, and it allows us to tackle distributions with
large skewness and kurtosis with higher accuracy. In this regard, we exploit a
four parameter it{normal-inverse Gaussian} (NIG) distribution as a base
distribution. Given that the skewness and kurtosis satisfy a specific
condition, NIG-based SPA works reliably. When this condition is violated, we
recommend SPA based on normal or symmetric NIG distribution, both special cases
of NIG distribution, at the expense of reduced accuracy. For the purpose of
demonstration, we apply SPA for the SINR outage evaluation of a typical user
experiencing a downlink coordinated multi-point transmission (CoMP) from the
base stations (BSs) that are modeled by homogeneous Poisson point process. We
characterize the outage of the typical user in scenarios such as (a)~when the
number and locations of interferers are random, and (b)~when the fading
channels and number of interferers are random. Numerical results are presented
to illustrate the accuracy of the proposed set of approximations.
Zhinan Xu, Markus Hofer, Thomas Zemen
Subjects: Information Theory (cs.IT)
In interference channels, channel state information (CSI) can be exploited to
reduce the interference signal dimensions and thus achieve the optimal capacity
scaling, i.e. degrees of freedom, promised by the interference alignment
technique. However, imperfect CSI, due to channel estimation error, imperfect
CSI feedback and time selectivity of the channel, lead to a performance loss.
In this work, we propose a novel limited feedback algorithm for single-input
single-output interference alignment in time-variant channels. The feedback
algorithm encodes the channel evolution in a small number of subspace
coefficients, which allow for reduced-rank channel prediction to compensate for
the channel estimation error due to time selectivity of the fading process and
feedback delay. An upper bound for the rate loss caused by feedback
quantization and channel prediction is derived. Based on this bound, we develop
a dimension switching algorithm for the reduced-rank predictor to find the best
tradeoff between quantization- and prediction-error. Besides, we characterize
the scaling of the required number of feedback bits in order to decouple the
rate loss due to channel quantization from the transmit power. Simulation
results show that a rate gain over the traditional non-predictive feedback
strategy can be secured and a 60% higher rate is achieved at 20 dB
signal-to-noise ratio with moderate mobility.
Matthew Kokshoorn, He Chen, Yonghui Li, Branka Vucetic
Comments: Submitted for journal publication. arXiv admin note: substantial text overlap with arXiv:1612.02113
Subjects: Information Theory (cs.IT)
This paper is concerned with the channel estimation problem in multi-user
millimeter wave (mmWave) wireless systems with large antenna arrays. We develop
a novel simultaneous-estimation with iterative fountain training (SWIFT)
framework, in which multiple users estimate their channels at the same time and
the required number of channel measurements is adapted to various channel
conditions of different users. To achieve this, we represent the beam direction
estimation process by a graph, referred to as the beam-on-graph, and associate
the channel estimation process with a code-on-graph decoding problem.
Specifically, the base station (BS) and each user measure the channel with a
series of random combinations of transmit/receive beamforming vectors until the
channel estimate converges. As the proposed SWIFT does not adapt the BS’s beams
to any single user, we are able to estimate all user channels simultaneously.
Simulation results show that SWIFT can significantly outperform the existing
random beamforming-based approaches, which use a predetermined number of
measurements, over a wide range of signal-to-noise ratios and channel coherence
time. Furthermore, by utilizing the users’ order in terms of completing their
channel estimation, our SWIFT framework can infer the sequence of users’
channel quality and perform effective user scheduling to achieve superior
performance.
Mustafa A. Kishk, Harpreet S. Dhillon
Subjects: Information Theory (cs.IT)
This letter presents a performance comparison of two popular secrecy
enhancement techniques in wireless networks: (i) creating guard zones by
restricting transmissions of legitimate transmitters whenever any eavesdropper
is detected in their vicinity, and (ii) adding artificial noise to the
confidential messages to make it difficult for the eavesdroppers to decode
them. Using tools from stochastic geometry, we first derive the secrecy outage
probability at the eavesdroppers as well as the coverage probability at the
legitimate users for both these techniques. Using these results, we derive a
threshold on the density of the eavesdroppers below which no secrecy enhancing
technique is required to ensure a target secrecy outage probability. For
eavesdropper densities above this threshold, we concretely characterize the
regimes in which each technique outperforms the other. Our results demonstrate
that guard zone technique is better when the distances between the transmitters
and their legitimate receivers are higher than a certain threshold.
Arman Shojaeifard, Kai-Kit Wong, Marco Di Renzo, Gan Zheng, Khairi Ashour Hamdi, Jie Tang
Subjects: Information Theory (cs.IT)
We consider a multi-user multiple-input multiple-output (MIMO) setup where
full-duplex (FD) multi-antenna nodes apply linear beamformers to simultaneously
transmit and receive multiple streams over Rician fading channels. The exact
first and second positive moments of the residual self-interference (SI),
involving the squared norm of a sum of non-identically distributed random
variables, are derived in closed-form. The method of moments is hence invoked
to provide a Gamma approximation for the residual SI distribution. The proposed
theorem holds under arbitrary linear precoder/decoder design, number of
antennas and streams, and SI cancellation capability.
Hongwei Liu, Maouche Youcef
Subjects: Information Theory (cs.IT)
Codes over the Galois rings have been studied by many researchers, negacyclic
codes over )GR(2^a,m)( of length )2^s( have been characterized by the fact that
the ring ){cal R}_2(a,m,-1)= frac{GR(2^a,m)[x]}{langle x^{2^s}+1
angle}( is
a chain ring, furthermore, these results have been generalized to
)lambda(-constacyclic codes for any unit )lambda( of the form )4z-1(, )zin
GR(2^a, m)(. In this paper, we give more general cases and investigate all
cases where ){cal R}_2(a,m,gamma)= frac{GR(2^a,m)[x]}{langle x^{2^s}-gamma
angle}( is a chain ring, moreover, we give necessary and sufficient
conditions for the ring ){cal R}_2(a,m,gamma)( to be a chain ring.
Furthermore, we generalize these results to all odd prime number, by giving
necessary and sufficient conditions for the ring ){cal
R}_p(a,m,gamma)=frac{GR(p^a,m)[x]}{langle x^{p^s}-gamma
angle}( to be a
chain ring, using this structure we investigate all )gamma(-constacyclic codes
over )GR(p^a,m)(, where ){cal R}_p(a,m,gamma)( is a chain ring. The dual
codes and necessary and sufficient conditions for the existence of
self-orthogonal and self-dual )gamma(-constacyclic codes are provided. Among
others, for any prime )p(, the structure of ){cal
R}_p(a,m,gamma)=frac{GR(p^a,m)[x]}{langle x^{p^s}-gamma
angle}( is used to
establish the Hamming and homogeneous distance.
Tingwu Wang, Jian Wang, Chunxiao Jiang, Jingjing Wang, Yong Ren
Subjects: Information Theory (cs.IT)
The recently announced Super Wi-Fi Network proposal in United States is
aiming to enable Internet access in a nation-wide area. As traditional
cable-connected power supply system becomes impractical or costly for a wide
range wireless network, new infrastructure deployment for Super Wi-Fi is
required. The fast developing Energy Harvesting (EH) techniques receive global
attentions for their potential of solving the above power supply problem. It is
a critical issue, from the user’s perspective, how to make efficient network
selection and access strategies. Unlike traditional wireless networks, the
battery charge state and tendency in EH based networks have to be taken into
account when making network selection and access, which has not been well
investigated. In this paper, we propose a practical and efficient framework for
multiple base stations access strategy in an EH powered Super Wi-Fi network. We
consider the access strategy from the user’s perspective, who exploits downlink
transmission opportunities from one base station. To formulate the problem, we
used Partially Observable Markov Decision Process (POMDP) to model users’
observations on the base stations’ battery situation and decisions on the base
station selection and access. Simulation results show that our methods are
efficacious and significantly outperform the traditional widely used CSMA
method.
Hassan Khodaiemehr, Dariush Kiani
Comments: 56 pages, 9 figures. arXiv admin note: text overlap with arXiv:cs/0611112 by other authors
Subjects: Information Theory (cs.IT)
Quasi-cyclic (QC) low-density parity-check (LDPC) codes which are known as
QC-LDPC codes, have many applications due to their simple encoding
implementation by means of cyclic shift registers. In this paper, we construct
QC-LDPC codes from group rings. A group ring is a free module (at the same time
a ring) constructed in a natural way from any given ring and any given group.
We present a structure based on the elements of a group ring for constructing
QC-LDPC codes. Some of the previously addressed methods for constructing
QC-LDPC codes based on finite fields are special cases of the proposed
construction method. The constructed QC-LDPC codes perform very well over the
additive white Gaussian noise (AWGN) channel with iterative decoding in terms
of bit-error probability and block-error probability. Simulation results
demonstrate that the proposed codes have competitive performance in comparison
with the similar existing LDPC codes. Finally, we propose a new encoding method
for the proposed group ring based QC-LDPC codes that can be implemented faster
than the current encoding methods. The encoding complexity of the proposed
method is analyzed mathematically, and indicates a significate reduction in the
required number of operations, even when compared to the available efficient
encoding methods that have linear time and space complexities.
Tao Han, Guoqiang Mao, Qiang Li, Lijun Wang, Jing Zhang
Comments: 7 pages, 3 figures
Journal-ref: Mobile Networks and Applications, vol. 20, no. 6, pp. 756-762,
2015
Subjects: Information Theory (cs.IT); Networking and Internet Architecture (cs.NI)
In this paper, we focus on one of the representative 5G network scenarios,
namely multi-tier heterogeneous cellular networks. User association is
investigated in order to reduce the down-link co-channel interference. Firstly,
in order to analyze the multi-tier heterogeneous cellular networks where the
base stations in different tiers usually adopt different transmission powers,
we propose a Transmission Power Normalization Model (TPNM), which is able to
convert a multi-tier cellular network into a single-tier network, such that all
base stations have the same normalized transmission power. Then using TPNM, the
signal and interference received at any point in the complex multi-tier
environment can be analyzed by considering the same point in the equivalent
single-tier cellular network model, thus significantly simplifying the
analysis. On this basis, we propose a new user association scheme in
heterogeneous cellular networks, where the base station that leads to the
smallest interference to other co-channel mobile stations is chosen from a set
of candidate base stations that satisfy the quality-of-service (QoS) constraint
for an intended mobile station. Numerical results show that the proposed user
association scheme is able to significantly reduce the down-link interference
compared with existing schemes while maintaining a reasonably good QoS.
Jingjing Wang, Chunxiao Jiang, Longxiang Gao, Shui Yu, Zhu Han, Yong Ren
Subjects: Networking and Internet Architecture (cs.NI); Information Theory (cs.IT)
How to enhance the communication efficiency and quality on vehicular networks
is one critical important issue. While with the larger and larger scale of
vehicular networks in dense cities, the real-world datasets show that the
vehicular networks essentially belong to the complex network model. Meanwhile,
the extensive research on complex networks has shown that the complex network
theory can both provide an accurate network illustration model and further make
great contributions to the network design, optimization and management. In this
paper, we start with analyzing characteristics of a taxi GPS dataset and then
establishing the vehicular-to-infrastructure, vehicle-to-vehicle and the hybrid
communication model, respectively. Moreover, we propose a clustering algorithm
for station selection, a traffic allocation optimization model and an
information source selection model based on the communication performances and
complex network theory.
Stefano Cavallari
Comments: PhD thesis (integrate-and-fire neurons, recurrent neural networks, current based neurons, conductance based neurons, LFP, EEG, information encoding, GLM, Wiener filtering, PV-pos interneuron) 170 pages, 57 figures, 8 tables
Subjects: Neurons and Cognition (q-bio.NC); Information Theory (cs.IT); Quantitative Methods (q-bio.QM)
Functions of brain areas in complex animals are believed to rely on the
dynamics of networks of neurons rather than on single neurons. On the other
hand, the network dynamics reflect and arise from the integration and
coordination of the activity of populations of single neurons. Understanding
how single-neurons and neural-circuits dynamics complement each other to
produce brain functions is thus of paramount importance. LFPs and EEGs are good
indicators of the dynamics of mesoscopic and macroscopic populations of
neurons, while microscopic-level activities can be documented by measuring the
membrane potential, the synaptic currents or the spiking activity of individual
neurons. In this thesis we develop mathematical modelling and mathematical
analysis tools that can help the interpretation of joint measures of neural
activity at microscopic and mesoscopic or macroscopic scales. In particular, we
develop network models of recurrent cortical circuits that can clarify the
impact of several aspects of single-neuron (i.e., microscopic-level) dynamics
on the activity of the whole neural population (as measured by LFP). We then
develop statistical tools to characterize the relationship between the action
potential firing of single neurons and mass signals. We apply these latter
analysis techniques to joint recordings of the firing activity of individual
cell-type identified neurons and mesoscopic (i.e., LFP) and macroscopic (i.e.,
EEG) signals in the mouse neocortex. We identified several general aspects of
the relationship between cell-specific neural firing and mass circuit activity,
providing for example general and robust mathematical rules which infer
single-neuron firing activity from mass measures such as the LFP and the EEG.
Soumyakanti Bose
Comments: 6 pages, 3 figures
Subjects: Quantum Physics (quant-ph); Information Theory (cs.IT); Optics (physics.optics)
Nonclassical states of quantized light, except for the Gaussian states, also
possess non-Gaussian phase-space distributions. Despite several attempts, a
unified description of nonclassicality (NC) and non-Gaussianity (NG) of quantum
states of light has not been developed as-of-yet. Here, we propose an
experimentally verifiable scheme for quantification of NC, in terms of Wehrl
entropy, that further leads to the simultaneous quantification of NC and NG.
While requiring no optimization, present work recovers earlier results
qualitatively as well as explores several new possibilities on the conjugation
of nonclassical and non-Gaussian character of quantum states. Moreover, current
formalism, due to its possible extension to the finite-dimensional systems,
bridges the gap between discrete and continuous variable systems. Our work,
thus, becomes crucial in describing NC of quantum processes including open
quantum systems as well as understanding the role of NC and NG as resources in
several information theoretic tasks processing such as entanglement
distillation, quantum network, quantum computation etc.
Jean-Gabriel Young, Patrick Desrosiers, Laurent Hébert-Dufresne, Edward Laurence, Louis J. Dubé
Comments: Main text: 16 pages, 4 figures. Supplemental Information: 11 pages, 2 figures
Subjects: Physics and Society (physics.soc-ph); Information Theory (cs.IT)
It has been shown in recent years that the stochastic block model is
undetectable in the sparse limit, i.e., that no algorithm can identify a
partition correlated with the partition used to generate an instance, if the
instance is sparse and infinitely large. Real networks are however finite
objects, and one cannot expect all results derived in the infinite limit to
hold for finite instances. In this contribution, we treat the finite case
explicitly. We give a necessary condition for finite size detectability in the
general SBM, using arguments drawn from information theory and statistics. We
then distinguish the concept of average detectability from the concept of
instance-by-instance detectability, and give explicit formulas for both
definitions. Using these formulas, we prove that there exist large equivalence
classes of parameters, where widely different network ensembles are equally
detectable with respect to our definitions of detectability. In an extensive
case study, we investigate the finite size detectability of a simplified
variant of the SBM, which encompasses a number of important models as special
cases. These models include the symmetric SBM, the planted coloring model, and
more exotic SBMs not previously studied. We obtain a number of explicit
expressions for this variant, and also show that the well-known Kesten-Stigum
bound does not capture the phenomenon of finite size detectability—even at
the qualitative level. We conclude with two Appendices, where we study the
interplay of noise and detectability, and establish a connection between our
information-theoretic approach and Random Matrix Theory.
Eftychios A. Pnevmatikakis
Comments: To appear in the 42nd IEEE International Conference on Acoustics, Speech and Signal Processing ICASSP2017
Subjects: Statistics Theory (math.ST); Information Theory (cs.IT)
We consider the problems of compressed sensing and optimal denoising for
signals )mathbf{x_0}inmathbb{R}^N( that are monotone, i.e.,
)mathbf{x_0}(i+1) geq mathbf{x_0}(i)(, and sparsely varying, i.e.,
)mathbf{x_0}(i+1) > mathbf{x_0}(i)( only for a small number )k( of indices
)i(. We approach the compressed sensing problem by minimizing the total
variation norm restricted to the class of monotone signals subject to equality
constraints obtained from a number of measurements )Amathbf{x_0}(. For random
Gaussian sensing matrices )Ainmathbb{R}^{m imes N}( we derive a closed form
expression for the number of measurements )m( required for successful
reconstruction with high probability. We show that the probability undergoes a
phase transition as )m( varies, and depends not only on the number of change
points, but also on their location. For denoising we regularize with the same
norm and derive a formula for the optimal regularizer weight that depends only
mildly on )mathbf{x_0}(. We obtain our results using the statistical dimension
tool.