Miqing Li, Xin Yao
Comments: 23 pages, 10 figures
Subjects: Neural and Evolutionary Computing (cs.NE); Optimization and Control (math.OC)
One of the most common approaches for multiobjective optimization is to
generate a solution set that well approximates the whole Pareto-optimal
frontier to facilitate the later decision-making process. However, how to
evaluate and compare the quality of different solution sets remains
challenging. Existing measures typically require additional problem knowledge
and information, such as a reference point or a substituted set of the
Pareto-optimal frontier. In this paper, we propose a quality measure, called
dominance move (DoM), to compare solution sets generated by multiobjective
optimizers. Given two solution sets, DoM measures the minimum sum of move
distances for one set to weakly Pareto dominate the other set. DoM can be seen
as a natural reflection of the difference between two solutions, capturing all
aspects of solution sets’ quality, being compliant with Pareto dominance, and
does not need any additional problem knowledge and parameters. We present an
exact method to calculate the DoM in the biobjective case. We show the
necessary condition of constructing the optimal partition for a solution set’s
minimum move, and accordingly propose an efficient algorithm to recursively
calculate the DoM. Finally, DoM is evaluated on several groups of artificial
and real test cases as well as by a comparison with two well-established
quality measures.
Riccardo Zucca, Xerxes D. Arsiwalla, Hoang Le, Mikail Rubinov, Paul Verschure
Comments: International Conference on Artificial Neural Networks – ICANN 2016
Journal-ref: Artificial Neural Networks and Machine Learning, Lecture Notes in
Computer Science, vol 9886, 2016
Subjects: Neurons and Cognition (q-bio.NC); Disordered Systems and Neural Networks (cond-mat.dis-nn); Neural and Evolutionary Computing (cs.NE); Data Analysis, Statistics and Probability (physics.data-an)
We investigate scaling properties of human brain functional networks in the
resting-state. Analyzing network degree distributions, we statistically test
whether their tails scale as power-law or not. Initial studies, based on
least-squares fitting, were shown to be inadequate for precise estimation of
power-law distributions. Subsequently, methods based on maximum-likelihood
estimators have been proposed and applied to address this question.
Nevertheless, no clear consensus has emerged, mainly because results have shown
substantial variability depending on the data-set used or its resolution. In
this study, we work with high-resolution data (10K nodes) from the Human
Connectome Project and take into account network weights. We test for the
power-law, exponential, log-normal and generalized Pareto distributions. Our
results show that the statistics generally do not support a power-law, but
instead these degree distributions tend towards the thin-tail limit of the
generalized Pareto model. This may have implications for the number of hubs in
human brain functional networks.
Miguel Aguilera, Manuel G. Bedia
Subjects: Adaptation and Self-Organizing Systems (nlin.AO); Disordered Systems and Neural Networks (cond-mat.dis-nn); Statistical Mechanics (cond-mat.stat-mech); Neural and Evolutionary Computing (cs.NE); Neurons and Cognition (q-bio.NC)
Many biological and cognitive systems do not operate deep into one or other
regime of activity. Instead, they exploit critical surfaces poised at
transitions in their parameter space. The pervasiveness of criticality in
natural systems suggests that there may be general principles inducing this
behaviour. However, there is a lack of conceptual models explaining how
embodied agents propel themselves towards these critical points. In this paper,
we present a learning model driving an embodied Boltzmann Machine towards
critical behaviour by maximizing the heat capacity of the network. We test and
corroborate the model implementing an embodied agent in the mountain car
benchmark, controlled by a Boltzmann Machine that adjust its weights according
to the model. We find that the neural controller reaches a point of
criticality, which coincides with a transition point of the behaviour of the
agent between two regimes of behaviour, maximizing the synergistic information
between its sensors and the hidden and motor neurons. Finally, we discuss the
potential of our learning model to study the contribution of criticality to the
behaviour of embodied living systems in scenarios not necessarily constrained
by biological restrictions of the examples of criticality we find in nature.
Wentao Huang, Xin Huang, Kechen Zhang
Comments: The 51st Annual Conference on Information Sciences and Systems (CISS), 2017
Subjects: Information Theory (cs.IT); Neural and Evolutionary Computing (cs.NE); Neurons and Cognition (q-bio.NC); Quantitative Methods (q-bio.QM)
We have developed an efficient information-maximization method for computing
the optimal shapes of tuning curves of sensory neurons by optimizing the
parameters of the underlying feedforward network model. When applied to the
problem of population coding of visual motion with multiple directions, our
method yields several types of tuning curves with both symmetric and asymmetric
shapes that resemble what have been found in the visual cortex. Our result
suggests that the diversity or heterogeneity of tuning curve shapes as observed
in neurophysiological experiment might actually constitute an optimal
population representation of visual motions with multiple components.
Eugene Vorontsov, Chiheb Trabelsi, Samuel Kadoury, Chris Pal
Subjects: Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
It is well known that it is challenging to train deep neural networks and
recurrent neural networks for tasks that exhibit long term dependencies. The
vanishing or exploding gradient problem is a well known issue associated with
these challenges. One approach to addressing vanishing and exploding gradients
is to use either soft or hard constraints on weight matrices so as to encourage
or enforce orthogonality. Orthogonal matrices preserve gradient norm during
backpropagation and can therefore be a desirable property; however, we find
that hard constraints on orthogonality can negatively affect the speed of
convergence and model performance. This paper explores the issues of
optimization convergence, speed and gradient stability using a variety of
different methods for encouraging or enforcing orthogonality. In particular we
propose a weight matrix factorization and parameterization strategy through
which we can bound matrix norms and therein control the degree of expansivity
induced during backpropagation.
Ryan Dahl, Mohammad Norouzi, Jonathon Shlens
Subjects: Computer Vision and Pattern Recognition (cs.CV); Learning (cs.LG)
We present a pixel recursive super resolution model that synthesizes
realistic details into images while enhancing their resolution. A low
resolution image may correspond to multiple plausible high resolution images,
thus modeling the super resolution process with a pixel independent conditional
model often results in averaging different details–hence blurry edges. By
contrast, our model is able to represent a multimodal conditional distribution
by properly modeling the statistical dependencies among the high resolution
image pixels, conditioned on a low resolution input. We employ a PixelCNN
architecture to define a strong prior over natural images and jointly optimize
this prior with a deep conditioning convolutional network. Human evaluations
indicate that samples from our proposed model look more photo realistic than a
strong L2 regression baseline.
D. K. Prasad, C. K. Prasath, D. Rajan, L. Rachmawati, E. Rajabally, C. Quek
Comments: 11 pages, 2 figures, MTEC 2017
Subjects: Computer Vision and Pattern Recognition (cs.CV)
This paper presents a multi-sensor architecture with an adaptive multi-sensor
management system suitable for control and navigation of autonomous maritime
vessels in hazy and poor-visibility conditions. This architecture resides in
the autonomous maritime vessels. It augments the data from on-board imaging
sensors and weather sensors with the AIS data and weather data from sensors on
other vessels and the on-shore vessel traffic surveillance system. The combined
data is analyzed using computational intelligence and data analytics to
determine suitable course of action while utilizing historically learnt
knowledge and performing live learning from the current situation. Such
framework is expected to be useful in diverse weather conditions and shall be a
useful architecture to provide autonomy to maritime vessels.
Norhidayu Abdul Hamid, Nilam Nur Amir Sjarif
Comments: 11 pages ; 22 Figures
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Handwritten recognition (HWR) is the ability of a computer to receive and
interpret intelligible handwritten input from source such as paper documents,
photographs, touch-screens and other devices. In this paper we will using three
(3) classification t o re cognize the handwritten which is SVM, KNN and Neural
Network.
Antoine Coutrot, Nathalie Guyader
Subjects: Computer Vision and Pattern Recognition (cs.CV)
To predict the most salient regions of complex natural scenes, saliency
models commonly compute several feature maps (contrast, orientation, motion…)
and linearly combine them into a master saliency map. Since feature maps have
different spatial distribution and amplitude dynamic ranges, determining their
contributions to overall saliency remains an open problem. Most
state-of-the-art models do not take time into account and give feature maps
constant weights across the stimulus duration. However, visual exploration is a
highly dynamic process shaped by many time-dependent factors. For instance,
some systematic viewing patterns such as the center bias are known to
dramatically vary across the time course of the exploration. In this paper, we
use maximum likelihood and shrinkage methods to dynamically and jointly learn
feature map and systematic viewing pattern weights directly from eye-tracking
data recorded on videos. We show that these weights systematically vary as a
function of time, and heavily depend upon the semantic visual category of the
videos being processed. Our fusion method allows taking these variations into
account, and outperforms other state-of-the-art fusion schemes using constant
weights over time. The code, videos and eye-tracking data we used for this
study are available online:
this http URL
Niannan Xue, Yannis Panagakis, Stefanos Zafeiriou
Comments: Submitted to CVPR
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Robust rank minimisation aims at recovering a low-rank subspace from grossly
corrupted high-dimensional (often visual) data and is a cornerstone in many
machine learning and computer vision applications. The most prominent method
for this task is the Robust Principal Component Analysis (PCA). It recovers a
low-rank matrix from sparse corruptions of unknown magnitude and support by
Principal Component Pursuit (PCP), which is a convex approximation to the
otherwise NP-hard rank minimisation problem. Even though PCP has been shown to
be very successful in solving many rank minimisation problems, there are cases
where degenerate or suboptimal solutions are obtained. This can be attributed
to the fact that domain-dependent prior knowledge is not taken into account by
PCP. In this paper, we address the problem of PCP when prior information is
available. To this end, we propose algorithms for solving the PCP problem with
the aid of prior information on the low-rank structure of the data. The
versatility of the proposed methods is demonstrated by applying them to four
applications, namely background substraction, facial image denoising, face and
facial expression recognition. Experimental results on synthetic and five real
world datasets indicate the robustness and effectiveness of the proposed
methods on these application domains, largely outperforming previous approaches
that incorporate side information within Robust PCA.
Xuanyang Xi, Yongkang Luo, Fengfu Li, Peng Wang, Hong Qiao
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Visual saliency detection aims at identifying the most visually distinctive
parts in an image, and serves as a pre-processing step for a variety of
computer vision and image processing tasks. To this end, the saliency detection
procedure must be as fast and compact as possible and optimally processes input
images in a real time manner. However, contemporary detection methods always
take hundreds of milliseconds to pursue feeble improvements on the detection
precession. In this paper, we tackle this problem by proposing a fast and
compact salient score regression network which employs deep convolutional
neural networks (CNN) to estimate the saliency of objects in images. It
operates (including training and testing) in an end-to-end manner
(image-to-image prediction) and also directly produces whole saliency maps from
original images without any pre-processings and post-processings. Comparing
with contemporary CNN-based saliency detection methods, the proposed method
extremely simplifies the detection procedure and further promotes the
representation ability of CNN for the saliency detection. Our method is
evaluated on six public datasets, and experimental results show that the
precision can be comparable to the published state-of-the-art methods while the
speed gets a significant improvement (35 FPS, processing in real time).
Mikhail Breslav, Tyson L. Hedrick, Stan Sclaroff, Margrit Betke
Comments: 30 pages
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Image and video analysis is often a crucial step in the study of animal
behavior and kinematics. Often these analyses require that the position of one
or more animal landmarks are annotated (marked) in numerous images. The process
of annotating landmarks can require a significant amount of time and tedious
labor, which motivates the need for algorithms that can automatically annotate
landmarks. In the community of scientists that use image and video analysis to
study the 3D flight of animals, there has been a trend of developing more
automated approaches for annotating landmarks, yet they fall short of being
generally applicable. Inspired by the success of Deep Neural Networks (DNNs) on
many problems in the field of computer vision, we investigate how suitable DNNs
are for accurate and automatic annotation of landmarks in video datasets
representative of those collected by scientists studying animals.
Our work shows, through extensive experimentation on videos of hawkmoths,
that DNNs are suitable for automatic and accurate landmark localization. In
particular, we show that one of our proposed DNNs is more accurate than the
current best algorithm for automatic localization of landmarks on hawkmoth
videos. Moreover, we demonstrate how these annotations can be used to
quantitatively analyze the 3D flight of a hawkmoth. To facilitate the use of
DNNs by scientists from many different fields, we provide a self contained
explanation of what DNNs are, how they work, and how to apply them to other
datasets using the freely available library Caffe and supplemental code that we
provide.
Satish Palaniappan, Ronojoy Adhikari
Comments: 17 pages, 10 figures, 7 supporting figures (2 pages)
Subjects: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL); Learning (cs.LG)
Standardized corpora of undeciphered scripts, a necessary starting point for
computational epigraphy, requires laborious human effort for their preparation
from raw archaeological records. Automating this process through machine
learning algorithms can be of significant aid to epigraphical research. Here,
we take the first steps in this direction and present a deep learning pipeline
that takes as input images of the undeciphered Indus script, as found in
archaeological artifacts, and returns as output a string of graphemes, suitable
for inclusion in a standard corpus. The image is first decomposed into regions
using Selective Search and these regions are classified as containing textual
and/or graphical information using a convolutional neural network. Regions
classified as potentially containing text are hierarchically merged and trimmed
to remove non-textual information. The remaining textual part of the image is
segmented using standard image processing techniques to isolate individual
graphemes. This set is finally passed to a second convolutional neural network
to classify the graphemes, based on a standard corpus. The classifier can
identify the presence or absence of the most frequent Indus grapheme, the “jar”
sign, with an accuracy of 92%. Our results demonstrate the great potential of
deep learning approaches in computational epigraphy and, more generally, in the
digital humanities.
Jen Hong Tan, U. Rajendra Acharya, Sulatha V. Bhandary, Kuang Chua Chua, Sobha Sivaprasad
Subjects: Computer Vision and Pattern Recognition (cs.CV); Learning (cs.LG)
We have developed and trained a convolutional neural network to automatically
and simultaneously segment optic disc, fovea and blood vessels. Fundus images
were normalised before segmentation was performed to enforce consistency in
background lighting and contrast. For every effective point in the fundus
image, our algorithm extracted three channels of input from the neighbourhood
of the point and forward the response across the 7 layer network. In average,
our segmentation achieved an accuracy of 92.68 percent on the testing set from
Drive database.
Soumyadip Sengupta, Hao Zhou, Walter Forkel, Ronen Basri, Tom Goldstein, David W. Jacobs
Subjects: Computer Vision and Pattern Recognition (cs.CV)
We introduce a new, integrated approach to uncalibrated photometric stereo.
We perform 3D reconstruction of Lambertian objects using multiple images
produced by unknown, directional light sources. We show how to formulate a
single optimization that includes rank and integrability constraints, allowing
also for missing data. We then solve this optimization using the Alternate
Direction Method of Multipliers (ADMM). We conduct extensive experimental
evaluation on real and synthetic data sets. Our integrated approach is
particularly valuable when performing photometric stereo using as few as 4-6
images, since the integrability constraint is capable of improving estimation
of the linear subspace of possible solutions. We show good improvements over
prior work in these cases.
Luigi Nardi, Bruno Bodin, Sajad Saeedi, Emanuele Vespa, Andrew J. Davison, Paul H. J. Kelly
Comments: 10 pages, Keywords: design space exploration, machine learning, computer vision, SLAM, embedded systems, GPU, crowd-sourcing
Subjects: Computer Vision and Pattern Recognition (cs.CV); Distributed, Parallel, and Cluster Computing (cs.DC); Learning (cs.LG); Performance (cs.PF)
In this paper we investigate an emerging application, 3D scene understanding,
likely to be significant in the mobile space in the near future. The goal of
this exploration is to reduce execution time while meeting our quality of
result objectives. In previous work we showed for the first time that it is
possible to map this application to power constrained embedded systems,
highlighting that decision choices made at the algorithmic design-level have
the most impact.
As the algorithmic design space is too large to be exhaustively evaluated, we
use a previously introduced multi-objective Random Forest Active Learning
prediction framework dubbed HyperMapper, to find good algorithmic designs. We
show that HyperMapper generalizes on a recent cutting edge 3D scene
understanding algorithm and on a modern GPU-based computer architecture.
HyperMapper is able to beat an expert human hand-tuning the algorithmic
parameters of the class of Computer Vision applications taken under
consideration in this paper automatically. In addition, we use crowd-sourcing
using a 3D scene understanding Android app to show that the Pareto front
obtained on an embedded system can be used to accelerate the same application
on all the 83 smart-phones and tablets crowd-sourced with speedups ranging from
2 to over 12.
Yi-Ling Chen, Jan Klopp, Min Sun, Shao-Yi Chien, Kwan-Liu Ma
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Photo composition is an important factor affecting the aesthetics in
photography. However, it is a highly challenging task to model the aesthetic
properties of good compositions due to the lack of globally applicable rules to
the wide variety of photographic styles. Inspired by the thinking process of
photo taking, we treat the photo composition problem as a view finding process
which successively examines pairs of views and determines the aesthetic
preference. Without devising complex hand-crafted features, the ranking model
is built upon a deep convolutional neural network through joint representation
learning from raw pixels. Exploiting rich professional photographs on the web
as data source, we devise a nearly unsupervised approach to generate unlimited
high quality image pairs for training the network. The resulting ranking model
is generic and without any heuristics. The experimental results show that the
proposed view finding network achieves state-of-the-art performance with simple
sliding window search strategy on two image cropping datasets.
Zhangjie Cao, Mingsheng Long, Jianmin Wang, Philip S. Yu
Subjects: Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
Learning to hash has been widely applied to approximate nearest neighbor
search for large-scale multimedia retrieval, due to its computation efficiency
and retrieval quality. Deep learning to hash, which improves retrieval quality
by end-to-end representation learning and hash encoding, has received
increasing attention recently. Subject to the vanishing gradient difficulty in
the optimization with binary activations, existing deep learning to hash
methods need to first learn continuous representations and then generate binary
hash codes in a separated binarization step, which suffer from substantial loss
of retrieval quality. This paper presents HashNet, a novel deep architecture
for deep learning to hash by continuation method, which learns exactly binary
hash codes from imbalanced similarity data where the number of similar pairs is
much smaller than the number of dissimilar pairs. The key idea is to attack the
vanishing gradient problem in optimizing deep networks with non-smooth binary
activations by continuation method, in which we begin from learning an easier
network with smoothed activation function and let it evolve during the
training, until it eventually goes back to being the original, difficult to
optimize, deep network with the sign activation function. Comprehensive
empirical evidence shows that HashNet can generate exactly binary hash codes
and yield state-of-the-art multimedia retrieval performance on standard
benchmarks.
Zimi Li, Andrea Cohen, Simon Parsons
Subjects: Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
Many systems of structured argumentation explicitly require that the facts
and rules that make up the argument for a conclusion be the minimal set
required to derive the conclusion. ASPIC+ does not place such a requirement on
arguments, instead requiring that every rule and fact that are part of an
argument be used in its construction. Thus ASPIC+ arguments are minimal in the
sense that removing any element of the argument would lead to a structure that
is not an argument. In this brief note we discuss these two types of minimality
and show how the first kind of minimality can, if desired, be recovered in
ASPIC+.
Adam Summerville, Sam Snodgrass, Matthew Guzdial, Christoffer Holmgård, Amy K. Hoover, Aaron Isaksen, Andy Nealen, Julian Togelius
Subjects: Artificial Intelligence (cs.AI)
This survey explores Procedural Content Generation via Machine Learning
(PCGML), defined as the generation of game content using machine learning
models trained on existing content. As the importance of PCG for game
development increases, researchers explore new avenues for generating
high-quality content with or without human involvement; this paper addresses
the relatively new paradigm of using machine learning (in contrast with
search-based, solver-based, and constructive methods). We focus on what is most
often considered functional game content such as platformer levels, game maps,
interactive fiction stories, and cards in collectible card games, as opposed to
cosmetic content such as sprites and sound effects. In addition to using PCG
for autonomous generation, co-creativity, mixed-initiative design, and
compression, PCGML is suited for repair, critique, and content analysis because
of its focus on modeling existing content. We discuss various data sources and
representations that affect the resulting generated content. Multiple PCGML
methods are covered, including neural networks, long short-term memory (LSTM)
networks, autoencoders, and deep convolutional networks; Markov models,
(n)-grams, and multi-dimensional Markov chains; clustering; and matrix
factorization. Finally, we discuss open problems in the application of PCGML,
including learning from small datasets, lack of training data, multi-layered
learning, style-transfer, parameter tuning, and PCG as a game mechanic.
Egoitz Laparra, Rodrigo Agerri, Itziar Aldabe, German Rigau
Comments: 20 pages, 7 tables, 7 figures; submitted to Knowledge Based Systems (Elsevier), January, 2017
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
In this paper we present an approach to extract ordered timelines of events,
their participants, locations and times from a set of multilingual and
cross-lingual data sources. Based on the assumption that event-related
information can be recovered from different documents written in different
languages, we extend the Cross-document Event Ordering task presented at
SemEval 2015 by specifying two new tasks for, respectively, Multilingual and
Cross-lingual Timeline Extraction. We then develop three deterministic
algorithms for timeline extraction based on two main ideas. First, we address
implicit temporal relations at document level since explicit time-anchors are
too scarce to build a wide coverage timeline extraction system. Second, we
leverage several multilingual resources to obtain a single, inter-operable,
semantic representation of events across documents and across languages. The
result is a highly competitive system that strongly outperforms the current
state-of-the-art. Nonetheless, further analysis of the results reveals that
linking the event mentions with their target entities and time-anchors remains
a difficult challenge. The systems, resources and scorers are freely available
to facilitate its use and guarantee the reproducibility of results.
Tarcisio Souza, Elena Demidova, Thomas Risse, Helge Holzmann, Gerhard Gossen, Julian Szymanski
Subjects: Information Retrieval (cs.IR)
Long-term Web archives comprise Web documents gathered over longer time
periods and can easily reach hundreds of terabytes in size. Semantic
annotations such as named entities can facilitate intelligent access to the Web
archive data. However, the annotation of the entire archive content on this
scale is often infeasible. The most efficient way to access the documents
within Web archives is provided through their URLs, which are typically stored
in dedicated index files.The URLs of the archived Web documents can contain
semantic information and can offer an efficient way to obtain initial semantic
annotations for the archived documents. In this paper, we analyse the
applicability of semantic analysis techniques such as named entity extraction
to the URLs in a Web archive. We evaluate the precision of the named entity
extraction from the URLs in the Popular German Web dataset and analyse the
proportion of the archived URLs from 1,444 popular domains in the time interval
from 2000 to 2012 to which these techniques are applicable. Our results
demonstrate that named entity recognition can be successfully applied to a
large number of URLs in our Web archive and provide a good starting point to
efficiently annotate large scale collections of Web documents.
Lorenzo Ferrone, Fabio Massimo Zanzotto
Comments: 25 pages
Subjects: Computation and Language (cs.CL)
Natural language and symbols are intimately correlated. Recent advances in
machine learning (ML) and in natural language processing (NLP) seem to
contradict the above intuition: symbols are fading away, erased by vectors or
tensors called distributed and distributional representations. However, there
is a strict link between distributed/distributional representations and
symbols, being the first an approximation of the second. A clearer
understanding of the strict link between distributed/distributional
representations and symbols will certainly lead to radically new deep learning
networks. In this paper we make a survey that aims to draw the link between
symbolic representations and distributed/distributional representations. This
is the right time to revitalize the area of interpreting how symbols are
represented inside neural networks.
Simon Gottschalk, Elena Demidova
Comments: Published in the SIGIR ’16 Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval
Subjects: Computation and Language (cs.CL)
Wikipedia articles representing an entity or a topic in different language
editions evolve independently within the scope of the language-specific user
communities. This can lead to different points of views reflected in the
articles, as well as complementary and inconsistent information. An analysis of
how the information is propagated across the Wikipedia language editions can
provide important insights in the article evolution along the temporal and
cultural dimensions and support quality control. To facilitate such analysis,
we present MultiWiki – a novel web-based user interface that provides an
overview of the similarities and differences across the article pairs
originating from different language editions on a timeline. MultiWiki enables
users to observe the changes in the interlingual article similarity over time
and to perform a detailed visual comparison of the article snapshots at a
particular time point.
Egoitz Laparra, Rodrigo Agerri, Itziar Aldabe, German Rigau
Comments: 20 pages, 7 tables, 7 figures; submitted to Knowledge Based Systems (Elsevier), January, 2017
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
In this paper we present an approach to extract ordered timelines of events,
their participants, locations and times from a set of multilingual and
cross-lingual data sources. Based on the assumption that event-related
information can be recovered from different documents written in different
languages, we extend the Cross-document Event Ordering task presented at
SemEval 2015 by specifying two new tasks for, respectively, Multilingual and
Cross-lingual Timeline Extraction. We then develop three deterministic
algorithms for timeline extraction based on two main ideas. First, we address
implicit temporal relations at document level since explicit time-anchors are
too scarce to build a wide coverage timeline extraction system. Second, we
leverage several multilingual resources to obtain a single, inter-operable,
semantic representation of events across documents and across languages. The
result is a highly competitive system that strongly outperforms the current
state-of-the-art. Nonetheless, further analysis of the results reveals that
linking the event mentions with their target entities and time-anchors remains
a difficult challenge. The systems, resources and scorers are freely available
to facilitate its use and guarantee the reproducibility of results.
Linfeng Song, Xiaochang Peng, Yue Zhang, Zhiguo Wang, Daniel Gildea
Subjects: Computation and Language (cs.CL)
This paper addresses the task of AMR-to-text generation by leveraging
synchronous node replacement grammar. During training, graph-to-string rules
are learned using a heuristic extraction algorithm. At test time, a graph
transducer is applied to collapse input AMRs and generate output sentences.
Evaluated on SemEval-2016 Task 8, our method gives a BLEU score of 25.62, which
is the best reported so far.
Shravan Vasishth, Nicolas Chopin, Robin Ryder, Bruno Nicenboim
Comments: 6 pages, 3 figures. Submitted to the conference Cognitive Science 2017, London, UK
Subjects: Applications (stat.AP); Computation and Language (cs.CL); Methodology (stat.ME); Machine Learning (stat.ML)
In sentence comprehension, it is widely assumed (Gibson 2000, Lewis &
Vasishth, 2005) that the distance between linguistic co-dependents affects the
latency of dependency resolution: the longer the distance, the longer the
retrieval time (the distance-based account). An alternative theory of
dependency resolution difficulty is the direct-access model (McElree et al.,
2003); this model assumes that retrieval times are a mixture of two
distributions: one distribution represents successful retrieval and the other
represents an initial failure to retrieve the correct dependent, followed by a
reanalysis that leads to successful retrieval. The time needed for a successful
retrieval is independent of the dependency distance (cf. the distance-based
account), but reanalyses cost extra time, and the proportion of failures
increases with increasing dependency distance. We implemented a series of
increasingly complex hierarchical Bayesian models to compare the distance-based
account and the direct-access model; the latter was implemented as a
hierarchical finite mixture model with heterogeneous variances for the two
mixture distributions. We evaluated the models using two published data-sets on
Chinese relative clauses which have been used to argue in favour of the
distance account, but this account has found little support in subsequent work
(e.g., J”ager et al., 2015). The hierarchical finite mixture model, i.e., an
implementation of direct-access, is shown to provide a superior account of the
data than the distance account.
Satish Palaniappan, Ronojoy Adhikari
Comments: 17 pages, 10 figures, 7 supporting figures (2 pages)
Subjects: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL); Learning (cs.LG)
Standardized corpora of undeciphered scripts, a necessary starting point for
computational epigraphy, requires laborious human effort for their preparation
from raw archaeological records. Automating this process through machine
learning algorithms can be of significant aid to epigraphical research. Here,
we take the first steps in this direction and present a deep learning pipeline
that takes as input images of the undeciphered Indus script, as found in
archaeological artifacts, and returns as output a string of graphemes, suitable
for inclusion in a standard corpus. The image is first decomposed into regions
using Selective Search and these regions are classified as containing textual
and/or graphical information using a convolutional neural network. Regions
classified as potentially containing text are hierarchically merged and trimmed
to remove non-textual information. The remaining textual part of the image is
segmented using standard image processing techniques to isolate individual
graphemes. This set is finally passed to a second convolutional neural network
to classify the graphemes, based on a standard corpus. The classifier can
identify the presence or absence of the most frequent Indus grapheme, the “jar”
sign, with an accuracy of 92%. Our results demonstrate the great potential of
deep learning approaches in computational epigraphy and, more generally, in the
digital humanities.
Vinci Chow
Subjects: Computation and Language (cs.CL); Learning (cs.LG); Economics (q-fin.EC); Machine Learning (stat.ML)
In Chinese societies where superstition is of paramount importance, vehicle
license plates with desirable numbers can fetch for very high prices in
auctions. Unlike auctions of other valuable items, however, license plates do
not get an estimated price before auction. In this paper, I propose that the
task of predicting plate prices can be viewed as a natural language processing
task, because the value of a plate depends on the meaning of each individual
character on the plate as well as the semantics. I construct a deep recurrent
neural network to predict the prices of vehicle license plates in Hong Kong
based on the characters on a plate. Trained with 13-years of historical auction
prices, the deep RNN outperforms previous models by significant margin.
Luigi Nardi, Bruno Bodin, Sajad Saeedi, Emanuele Vespa, Andrew J. Davison, Paul H. J. Kelly
Comments: 10 pages, Keywords: design space exploration, machine learning, computer vision, SLAM, embedded systems, GPU, crowd-sourcing
Subjects: Computer Vision and Pattern Recognition (cs.CV); Distributed, Parallel, and Cluster Computing (cs.DC); Learning (cs.LG); Performance (cs.PF)
In this paper we investigate an emerging application, 3D scene understanding,
likely to be significant in the mobile space in the near future. The goal of
this exploration is to reduce execution time while meeting our quality of
result objectives. In previous work we showed for the first time that it is
possible to map this application to power constrained embedded systems,
highlighting that decision choices made at the algorithmic design-level have
the most impact.
As the algorithmic design space is too large to be exhaustively evaluated, we
use a previously introduced multi-objective Random Forest Active Learning
prediction framework dubbed HyperMapper, to find good algorithmic designs. We
show that HyperMapper generalizes on a recent cutting edge 3D scene
understanding algorithm and on a modern GPU-based computer architecture.
HyperMapper is able to beat an expert human hand-tuning the algorithmic
parameters of the class of Computer Vision applications taken under
consideration in this paper automatically. In addition, we use crowd-sourcing
using a 3D scene understanding Android app to show that the Pareto front
obtained on an embedded system can be used to accelerate the same application
on all the 83 smart-phones and tablets crowd-sourced with speedups ranging from
2 to over 12.
Zhangjie Cao, Mingsheng Long, Jianmin Wang, Philip S. Yu
Subjects: Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
Learning to hash has been widely applied to approximate nearest neighbor
search for large-scale multimedia retrieval, due to its computation efficiency
and retrieval quality. Deep learning to hash, which improves retrieval quality
by end-to-end representation learning and hash encoding, has received
increasing attention recently. Subject to the vanishing gradient difficulty in
the optimization with binary activations, existing deep learning to hash
methods need to first learn continuous representations and then generate binary
hash codes in a separated binarization step, which suffer from substantial loss
of retrieval quality. This paper presents HashNet, a novel deep architecture
for deep learning to hash by continuation method, which learns exactly binary
hash codes from imbalanced similarity data where the number of similar pairs is
much smaller than the number of dissimilar pairs. The key idea is to attack the
vanishing gradient problem in optimizing deep networks with non-smooth binary
activations by continuation method, in which we begin from learning an easier
network with smoothed activation function and let it evolve during the
training, until it eventually goes back to being the original, difficult to
optimize, deep network with the sign activation function. Comprehensive
empirical evidence shows that HashNet can generate exactly binary hash codes
and yield state-of-the-art multimedia retrieval performance on standard
benchmarks.
Min Ye, Alexander Barg
Comments: A short version is submitted to ISIT 2017
Subjects: Learning (cs.LG); Information Theory (cs.IT)
We consider the minimax estimation problem of a discrete distribution with
support size (k) under privacy constraints. A privatization scheme is applied
to each raw sample independently, and we need to estimate the distribution of
the raw samples from the privatized samples. A positive number (epsilon)
measures the privacy level of a privatization scheme. For a given (epsilon,)
we consider the problem of constructing optimal privatization schemes with
(epsilon)-privacy level, i.e., schemes that minimize the expected estimation
loss for the worst-case distribution. Two schemes in the literature provide
order optimal performance in the high privacy regime where (epsilon) is very
close to (0,) and in the low privacy regime where (e^{epsilon}approx k,)
respectively.
In this paper, we propose a new family of schemes which substantially improve
the performance of the existing schemes in the medium privacy regime when (1ll
e^{epsilon} ll k.) More concretely, we prove that when (3.8 < epsilon
<ln(k/9) ,) our schemes reduce the expected estimation loss by (50\%) under
(ell_2^2) metric and by (30\%) under (ell_1) metric over the existing
schemes. We also prove a lower bound for the region (e^{epsilon} ll k,) which
implies that our schemes are order optimal in this regime.
Ryan Dahl, Mohammad Norouzi, Jonathon Shlens
Subjects: Computer Vision and Pattern Recognition (cs.CV); Learning (cs.LG)
We present a pixel recursive super resolution model that synthesizes
realistic details into images while enhancing their resolution. A low
resolution image may correspond to multiple plausible high resolution images,
thus modeling the super resolution process with a pixel independent conditional
model often results in averaging different details–hence blurry edges. By
contrast, our model is able to represent a multimodal conditional distribution
by properly modeling the statistical dependencies among the high resolution
image pixels, conditioned on a low resolution input. We employ a PixelCNN
architecture to define a strong prior over natural images and jointly optimize
this prior with a deep conditioning convolutional network. Human evaluations
indicate that samples from our proposed model look more photo realistic than a
strong L2 regression baseline.
Zeyuan Allen-Zhu
Subjects: Optimization and Control (math.OC); Data Structures and Algorithms (cs.DS); Learning (cs.LG); Machine Learning (stat.ML)
Given a non-convex function (f(x)) that is an average of (n) smooth
functions, we design stochastic first-order methods to find its approximate
stationary points. The performance of our new methods depend on the smallest
(negative) eigenvalue (-sigma) of the Hessian. This parameter (sigma)
captures how strongly non-convex (f(x)) is, and is analogous to the strong
convexity parameter for convex optimization.
Our methods outperform the best known results for a wide range of (sigma),
and can also be used to find approximate local minima.
In particular, we find an interesting dichotomy: there exists a threshold
(sigma_0) so that the fastest methods for (sigma>sigma_0) and for
(sigma<sigma_0) have drastically different behaviors: the former scales with
(n^{2/3}) and the latter scales with (n^{3/4}).
Aryan Mokhtari, Mark Eisen, Alejandro Ribeiro
Subjects: Optimization and Control (math.OC); Learning (cs.LG)
This paper studies the problem of minimizing a global objective function
which can be written as the average of a set of (n) smooth and strongly convex
functions. Quasi-Newton methods, which build on the idea of approximating the
Newton step using the first-order information of the objective function, are
successful in reducing the computational complexity of Newton’s method by
avoiding the Hessian and its inverse computation at each iteration, while
converging at a superlinear rate to the optimal argument. However, quasi-Newton
methods are impractical for solving the finite sum minimization problem since
they operate on the information of all (n) functions at each iteration. This
issue has been addressed by incremental quasi-Newton methods which use the
information of a subset of functions at each iteration. Although incremental
quasi-Newton methods are able to reduce the computational complexity of
traditional quasi-Newton methods significantly, they fail to converge at a
superlinear rate. In this paper, we propose the IQN method as the first
incremental quasi-Newton method with a local superlinear convergence rate. In
IQN, we compute and update the information of only a single function at each
iteration and use the gradient information to approximate the Newton direction
without a computationally expensive inversion. IQN differs from
state-of-the-art incremental quasi-Newton methods in three criteria. First, the
use of aggregated information of variables, gradients, and quasi-Newton Hessian
approximations; second, the approximation of each individual function by its
Taylor’s expansion in which the linear and quadratic terms are evaluated with
respect to the same iterate; and third, the use of a cyclic scheme to update
the functions in lieu of a random selection routine. We use these fundamental
properties of IQN to establish its local superlinear convergence rate.
Satish Palaniappan, Ronojoy Adhikari
Comments: 17 pages, 10 figures, 7 supporting figures (2 pages)
Subjects: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL); Learning (cs.LG)
Standardized corpora of undeciphered scripts, a necessary starting point for
computational epigraphy, requires laborious human effort for their preparation
from raw archaeological records. Automating this process through machine
learning algorithms can be of significant aid to epigraphical research. Here,
we take the first steps in this direction and present a deep learning pipeline
that takes as input images of the undeciphered Indus script, as found in
archaeological artifacts, and returns as output a string of graphemes, suitable
for inclusion in a standard corpus. The image is first decomposed into regions
using Selective Search and these regions are classified as containing textual
and/or graphical information using a convolutional neural network. Regions
classified as potentially containing text are hierarchically merged and trimmed
to remove non-textual information. The remaining textual part of the image is
segmented using standard image processing techniques to isolate individual
graphemes. This set is finally passed to a second convolutional neural network
to classify the graphemes, based on a standard corpus. The classifier can
identify the presence or absence of the most frequent Indus grapheme, the “jar”
sign, with an accuracy of 92%. Our results demonstrate the great potential of
deep learning approaches in computational epigraphy and, more generally, in the
digital humanities.
Shantanu Jain, Martha White, Predrag Radivojac
Comments: Full paper with supplement
Subjects: Machine Learning (stat.ML); Learning (cs.LG)
A common approach in positive-unlabeled learning is to train a classification
model between labeled and unlabeled data. This strategy is in fact known to
give an optimal classifier under mild conditions; however, it results in biased
empirical estimates of the classifier performance. In this work, we show that
the typically used performance measures such as the receiver operating
characteristic curve, or the precision-recall curve obtained on such data can
be corrected with the knowledge of class priors; i.e., the proportions of the
positive and negative examples in the unlabeled data. We extend the results to
a noisy setting where some of the examples labeled positive are in fact
negative and show that the correction also requires the knowledge of the
proportion of noisy examples in the labeled positives. Using state-of-the-art
algorithms to estimate the positive class prior and the proportion of noise, we
experimentally evaluate two correction approaches and demonstrate their
efficacy on real-life data.
Jen Hong Tan, U. Rajendra Acharya, Sulatha V. Bhandary, Kuang Chua Chua, Sobha Sivaprasad
Subjects: Computer Vision and Pattern Recognition (cs.CV); Learning (cs.LG)
We have developed and trained a convolutional neural network to automatically
and simultaneously segment optic disc, fovea and blood vessels. Fundus images
were normalised before segmentation was performed to enforce consistency in
background lighting and contrast. For every effective point in the fundus
image, our algorithm extracted three channels of input from the neighbourhood
of the point and forward the response across the 7 layer network. In average,
our segmentation achieved an accuracy of 92.68 percent on the testing set from
Drive database.
Luigi Nardi, Bruno Bodin, Sajad Saeedi, Emanuele Vespa, Andrew J. Davison, Paul H. J. Kelly
Comments: 10 pages, Keywords: design space exploration, machine learning, computer vision, SLAM, embedded systems, GPU, crowd-sourcing
Subjects: Computer Vision and Pattern Recognition (cs.CV); Distributed, Parallel, and Cluster Computing (cs.DC); Learning (cs.LG); Performance (cs.PF)
In this paper we investigate an emerging application, 3D scene understanding,
likely to be significant in the mobile space in the near future. The goal of
this exploration is to reduce execution time while meeting our quality of
result objectives. In previous work we showed for the first time that it is
possible to map this application to power constrained embedded systems,
highlighting that decision choices made at the algorithmic design-level have
the most impact.
As the algorithmic design space is too large to be exhaustively evaluated, we
use a previously introduced multi-objective Random Forest Active Learning
prediction framework dubbed HyperMapper, to find good algorithmic designs. We
show that HyperMapper generalizes on a recent cutting edge 3D scene
understanding algorithm and on a modern GPU-based computer architecture.
HyperMapper is able to beat an expert human hand-tuning the algorithmic
parameters of the class of Computer Vision applications taken under
consideration in this paper automatically. In addition, we use crowd-sourcing
using a 3D scene understanding Android app to show that the Pareto front
obtained on an embedded system can be used to accelerate the same application
on all the 83 smart-phones and tablets crowd-sourced with speedups ranging from
2 to over 12.
Qiuyi Zhang, Rina Panigrahy, Sushant Sachdeva, Ali Rahimi
Subjects: Data Structures and Algorithms (cs.DS); Learning (cs.LG); Data Analysis, Statistics and Probability (physics.data-an)
We study the efficacy of learning neural networks with neural networks by the
(stochastic) gradient descent method. While gradient descent enjoys empirical
success in a variety of applications, there is a lack of theoretical guarantees
that explains the practical utility of deep learning. We focus on two-layer
neural networks with a linear activation on the output node. We show that under
some mild assumptions and certain classes of activation functions, gradient
descent does learn the parameters of the neural network and converges to the
global minima. Using a node-wise gradient descent algorithm, we show that
learning can be done in finite, sometimes (poly(d,1/epsilon)), time and sample
complexity.
Kevin Schawinski, Ce Zhang, Hantian Zhang, Lucas Fowler, Gokula Krishnan Santhanam
Comments: Accepted for publication in MNRAS, for the full code and a virtual machine set up to run it, see this http URL
Subjects: Instrumentation and Methods for Astrophysics (astro-ph.IM); Astrophysics of Galaxies (astro-ph.GA); Learning (cs.LG); Machine Learning (stat.ML)
Observations of astrophysical objects such as galaxies are limited by various
sources of random and systematic noise from the sky background, the optical
system of the telescope and the detector used to record the data. Conventional
deconvolution techniques are limited in their ability to recover features in
imaging data by the Shannon-Nyquist sampling theorem. Here we train a
generative adversarial network (GAN) on a sample of (4,550) images of nearby
galaxies at (0.01<z<0.02) from the Sloan Digital Sky Survey and conduct
(10 imes) cross validation to evaluate the results. We present a method using
a GAN trained on galaxy images that can recover features from artificially
degraded images with worse seeing and higher noise than the original with a
performance which far exceeds simple deconvolution. The ability to better
recover detailed features such as galaxy morphology from low-signal-to-noise
and low angular resolution imaging data significantly increases our ability to
study existing data sets of astrophysical objects as well as future
observations with observatories such as the Large Synoptic Sky Telescope (LSST)
and the Hubble and James Webb space telescopes.
Xiang Xiang, Trac D. Tran
Comments: The tutorial and program associated with this paper are available at this https URL yet for non-commercial use
Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Learning (cs.LG); Machine Learning (stat.ML)
In this paper, we deal with two challenges for measuring the similarity of
the subject identities in practical video-based face recognition – the
variation of the head pose in uncontrolled environments and the computational
expense of processing videos. Since the frame-wise feature mean is unable to
characterize the pose diversity among frames, we define and preserve the
overall pose diversity and closeness in a video. Then, identity will be the
only source of variation across videos since the pose varies even within a
single video. Instead of simply using all the frames, we select those faces
whose pose point is closest to the centroid of the K-means cluster containing
that pose point. Then, we represent a video as a bag of frame-wise deep face
features while the number of features has been reduced from hundreds to K.
Since the video representation can well represent the identity, now we measure
the subject similarity between two videos as the max correlation among all
possible pairs in the two bags of features. On the official 5,000 video-pairs
of the YouTube Face dataset for face verification, our algorithm achieves a
comparable performance with VGG-face that averages over deep features of all
frames. Other vision tasks can also benefit from the generic idea of employing
geometric cues to improve the descriptiveness of deep features.
Kyle Whetzel, Matthew C. Valenti
Comments: Conference on Information Sciences and Systems (CISS) 2017, to appear
Subjects: Information Theory (cs.IT)
Centralized Radio Access Network (C-RAN) is a new paradigm for wireless
networks that centralizes the signal processing in a computing cloud, allowing
commodity computational resources to be pooled. While C-RAN improves
utilization and efficiency, the computational load occasionally exceeds the
available resources, creating a computational outage. This paper provides a
mathematical characterization of the computational outage probability for
low-density parity check (LDPC) codes, a common class of error-correcting
codes. For tractability, a binary erasures channel is assumed. Using the
concept of density evolution, the computational demand is determined for a
given ensemble of codes as a function of the erasure probability. The analysis
reveals a trade-off: aggressively signaling at a high rate stresses the
computing pool, while conservatively backing-off the rate can avoid
computational outages. Motivated by this trade-off, an effective
computationally aware scheduling algorithm is developed that balances demands
for high throughput and low outage rates.
Rajai Nasser
Comments: 30 pages. Submitted to IEEE Trans. Inform. Theory and in part to ISIT2017. arXiv admin note: substantial text overlap with arXiv:1701.04467
Subjects: Information Theory (cs.IT)
A channel (W) is said to be input-degraded from another channel (W’) if (W)
can be simulated from (W’) by randomization at the input. We provide a
necessary and sufficient condition for a channel to be input-degraded from
another one. We show that any decoder that is good for (W’) is also good for
(W). We provide two characterizations for input-degradedness, one of which is
similar to the Blackwell-Sherman-Stein theorem. We say that two channels are
input-equivalent if they are input-degraded from each other. We study the
topologies that can be constructed on the space of input-equivalent channels,
and we investigate their properties. Moreover, we study the continuity of
several channel parameters and operations under these topologies.
Bernhard C. Geiger, Tobias Koch
Comments: 13 pages
Subjects: Information Theory (cs.IT)
Jalali and Poor (“Universal compressed sensing,” arXiv:1406.7807v3, Jan.
2016) have recently proposed a generalization of R’enyi’s information
dimension to stationary stochastic processes by defining the information
dimension rate as the information dimension of (k) samples divided by (k) in
the limit as (k oinfty). This paper proposes an alternative definition of
information dimension rate as the entropy rate of the uniformly-quantized
stochastic process divided by minus the logarithm of the quantizer step size
(1/m) in the limit as (m oinfty). It is demonstrated that both definitions
are equivalent for stochastic processes that are (psi^*)-mixing, but may
differ in general. In particular, it is shown that for Gaussian processes with
essentially-bounded power spectral density (PSD), the proposed information
dimension rate equals the Lebesgue measure of the PSD’s support. This is in
stark contrast to the information dimension rate proposed by Jalali and Poor,
which is (1) if the process’s PSD is positive on any set with positive Lebesgue
measure, irrespective of its support size.
Antonio Campello
Comments: 15 pages
Subjects: Information Theory (cs.IT); Metric Geometry (math.MG)
We propose a general framework to study constructions of Euclidean lattices
from linear codes over finite fields. In particular, we prove general
conditions for an ensemble constructed using linear codes to contain dense
lattices (i.e., with packing density comparable to the Minkowski-Hlawka lower
bound). Specializing to number field lattices, we obtain a number of
interesting corollaries – for instance, the best known packing density of ideal
lattices, and an elementary coding-theoretic construction of asymptotically
dense Hurwitz lattices. All results are algorithmically effective, in the sense
that, for any dimension, a finite family containing dense lattices is
exhibited. For suitable constructions based on Craig’s lattices, this family is
significantly smaller, in terms of alphabet-size, than previous ones in the
literature.
Feng Wang, Jie Xu, Xin Wang, Shuguang Cui
Comments: Accepted by IEEE ICC 2017
Subjects: Information Theory (cs.IT)
Integrating mobile-edge computing (MEC) and wireless power transfer (WPT) is
a promising technique in the Internet of Things (IoT) era. It can provide
massive lowpower mobile devices with enhanced computation capability and
sustainable energy supply. In this paper, we consider a wireless powered
multiuser MEC system, where a multi-antenna access point (AP) (integrated with
an MEC server) broadcasts wireless power to charge multiple users and each user
node relies on the harvested energy to execute latency-sensitive computation
tasks. With MEC, these users can execute their respective tasks locally by
themselves or offload all or part of the tasks to the AP based on a time
division multiple access (TDMA) protocol. Under this setup, we pursue an
energy-efficient wireless powered MEC system design by jointly optimizing the
transmit energy beamformer at the AP, the central processing unit (CPU)
frequency and the offloaded bits at each user, as well as the time allocation
among different users. In particular, we minimize the energy consumption at the
AP over a particular time block subject to the computation latency and energy
harvesting constraints per user. By formulating this problem into a convex
framework and employing the Lagrange duality method, we obtain its optimal
solution in a semi-closed form. Numerical results demonstrate the benefit of
the proposed joint design over alternative benchmark schemes in terms of the
achieved energy efficiency.
Onel L. Alcaraz López, Hirley Alves, Richard Demo Souza, Evelio Martín García Fernández
Comments: Accepted in ICC 2017
Subjects: Information Theory (cs.IT); Applications (stat.AP)
We consider a dual-hop wireless network where an energy constrained relay
node first harvests energy through the received radio-frequency signal from the
source, and then uses the harvested energy to forward the source’s information
to the destination node. The throughput and delay metrics are investigated for
a decode-and-forward relaying mechanism at finite blocklength regime and
delay-limited transmission mode. We consider ultra-reliable communication
scenarios under discussion for the next fifth-generation of wireless systems,
with error and latency constraints. The impact on these metrics of the
blocklength, information bits, and relay position is investigated.
Seonho Kim, Namyoon Lee, Songnam Hong
Comments: to be published in IEEE WCNC 2017
Subjects: Information Theory (cs.IT)
This paper investigates an uplink multiuser massive multiple-input
multiple-output (MIMO) system with one-bit analog-to-digital converters (ADCs),
in which (K) users with a single-antenna communicate with one base station (BS)
with (n_r) antennas. In this system, we propose a novel MIMO detection
framework, which is inspired by coding theory. The key idea of the proposed
framework is to create a non-linear code (Cc) of length (n_r) and rate (K/n_r)
using the encoding function that is completely characterized by a non-linear
MIMO channel matrix. From this, a multiuser MIMO detection problem is converted
into an equivalent channel coding problem, in which a codeword of the (Cc) is
sent over (n_r) parallel binary symmetric channels, each with different
crossover probabilities. Levereging this framework, we develop a maximum
likelihood decoding method, and show that the minimum distance of the (Cc) is
strongly related to a diversity order. Furthermore, we propose a practical
implementation method of the proposed framework when the channel state
information is not known to the BS. The proposed method is to estimate the code
(Cc) at the BS using a training sequence. Then, the proposed {em weighted}
minimum distance decoding is applied. Simulations results show that the
proposed method almost achieves an ideal performance with a reasonable training
overhead.
Wentao Huang, Xin Huang, Kechen Zhang
Comments: The 51st Annual Conference on Information Sciences and Systems (CISS), 2017
Subjects: Information Theory (cs.IT); Neural and Evolutionary Computing (cs.NE); Neurons and Cognition (q-bio.NC); Quantitative Methods (q-bio.QM)
We have developed an efficient information-maximization method for computing
the optimal shapes of tuning curves of sensory neurons by optimizing the
parameters of the underlying feedforward network model. When applied to the
problem of population coding of visual motion with multiple directions, our
method yields several types of tuning curves with both symmetric and asymmetric
shapes that resemble what have been found in the visual cortex. Our result
suggests that the diversity or heterogeneity of tuning curve shapes as observed
in neurophysiological experiment might actually constitute an optimal
population representation of visual motions with multiple components.
Min Ye, Alexander Barg
Comments: A short version is submitted to ISIT 2017
Subjects: Learning (cs.LG); Information Theory (cs.IT)
We consider the minimax estimation problem of a discrete distribution with
support size (k) under privacy constraints. A privatization scheme is applied
to each raw sample independently, and we need to estimate the distribution of
the raw samples from the privatized samples. A positive number (epsilon)
measures the privacy level of a privatization scheme. For a given (epsilon,)
we consider the problem of constructing optimal privatization schemes with
(epsilon)-privacy level, i.e., schemes that minimize the expected estimation
loss for the worst-case distribution. Two schemes in the literature provide
order optimal performance in the high privacy regime where (epsilon) is very
close to (0,) and in the low privacy regime where (e^{epsilon}approx k,)
respectively.
In this paper, we propose a new family of schemes which substantially improve
the performance of the existing schemes in the medium privacy regime when (1ll
e^{epsilon} ll k.) More concretely, we prove that when (3.8 < epsilon
<ln(k/9) ,) our schemes reduce the expected estimation loss by (50\%) under
(ell_2^2) metric and by (30\%) under (ell_1) metric over the existing
schemes. We also prove a lower bound for the region (e^{epsilon} ll k,) which
implies that our schemes are order optimal in this regime.