pwc-archive/papers-with-abstracts huggingface dataset:
Optimal Schemes for Discrete Distribution Estimation under Locally Differential Privacy
Authors: Min Ye, Alexander Barg
Date: 2017-02-02 00:00:00

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 $1\ll
e^{\epsilon} \ll k.$ 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.
Automating Image Analysis by Annotating Landmarks with Deep Neural Networks
Authors: Mikhail Breslav, Tyson L. Hedrick, Stan Sclaroff, Margrit Betke
Date: 2017-02-02 00:00:00

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.
Multi-Objective Software Suite of Two-Dimensional Shape Descriptors for Object-Based Image Analysis
Authors: Andrea Baraldi, João V. B. Soares
Date: 2017-01-08 00:00:00
Tasks: Earth Observation, feature selection, Informativeness

In recent years two sets of planar (2D) shape attributes, provided with an
intuitive physical meaning, were proposed to the remote sensing community by,
respectively, Nagao & Matsuyama and Shackelford & Davis in their seminal works
on the increasingly popular geographic object based image analysis (GEOBIA)
paradigm. These two published sets of intuitive geometric features were
selected as initial conditions by the present R&D software project, whose
multi-objective goal was to accomplish: (i) a minimally dependent and maximally
informative design (knowledge/information representation) of a general purpose,
user and application independent dictionary of 2D shape terms provided with a
physical meaning intuitive to understand by human end users and (ii) an
effective (accurate, scale invariant, easy to use) and efficient implementation
of 2D shape descriptors. To comply with the Quality Assurance Framework for
Earth Observation guidelines, the proposed suite of geometric functions is
validated by means of a novel quantitative quality assurance policy, centered
on inter feature dependence (causality) assessment. This innovative
multivariate feature validation strategy is alternative to traditional feature
selection procedures based on either inductive data learning classification
accuracy estimation, which is inherently case specific, or cross correlation
estimation, because statistical cross correlation does not imply causation. The
project deliverable is an original general purpose software suite of seven
validated off the shelf 2D shape descriptors intuitive to use. Alternative to
existing commercial or open source software libraries of tens of planar shape
functions whose informativeness remains unknown, it is eligible for use in
(GE)OBIA systems in operating mode, expected to mimic human reasoning based on
a convergence of evidence approach.
SMPOST: Parts of Speech Tagger for Code-Mixed Indic Social Media Text
Authors: Deepak Gupta, Shubham Tripathi, Asif Ekbal, Pushpak Bhattacharyya
Date: 2017-02-01 00:00:00
Tasks: Part-Of-Speech Tagging, POS, POS Tagging, Sentence, TAG

Use of social media has grown dramatically during the last few years. Users
follow informal languages in communicating through social media. The language
of communication is often mixed in nature, where people transcribe their
regional language with English and this technique is found to be extremely
popular. Natural language processing (NLP) aims to infer the information from
these text where Part-of-Speech (PoS) tagging plays an important role in
getting the prosody of the written text. For the task of PoS tagging on
Code-Mixed Indian Social Media Text, we develop a supervised system based on
Conditional Random Field classifier. In order to tackle the problem
effectively, we have focused on extracting rich linguistic features. We
participate in three different language pairs, ie. English-Hindi,
English-Bengali and English-Telugu on three different social media platforms,
Twitter, Facebook & WhatsApp. The proposed system is able to successfully
assign coarse as well as fine-grained PoS tag labels for a given a code-mixed
sentence. Experiments show that our system is quite generic that shows
encouraging performance levels on all the three language pairs in all the
domains.
A correlation coefficient of belief functions
Authors: Wen Jiang
Date: 2016-12-16 00:00:00

How to manage conflict is still an open issue in Dempster-Shafer evidence
theory. The correlation coefficient can be used to measure the similarity of
evidence in Dempster-Shafer evidence theory. However, existing correlation
coefficients of belief functions have some shortcomings. In this paper, a new
correlation coefficient is proposed with many desirable properties. One of its
applications is to measure the conflict degree among belief functions. Some
numerical examples and comparisons demonstrate the effectiveness of the
correlation coefficient.
Deep Learning the Indus Script
Authors: Satish Palaniappan, Ronojoy Adhikari
Date: 2017-02-02 00:00:00
Tasks: Deep Learning

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.
Recovering True Classifier Performance in Positive-Unlabeled Learning
Authors: Shantanu Jain, Martha White, Predrag Radivojac
Date: 2017-02-02 00:00:00

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.
Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network
Authors: Jen Hong Tan, U. Rajendra Acharya, Sulatha V. Bhandary, Kuang Chua Chua, Sobha Sivaprasad
Date: 2017-02-02 00:00:00
Tasks: Segmentation

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.
Solving Uncalibrated Photometric Stereo Using Fewer Images by Jointly Optimizing Low-rank Matrix Completion and Integrability
Authors: Soumyadip Sengupta, Hao Zhou, Walter Forkel, Ronen Basri, Tom Goldstein, David W. Jacobs
Date: 2017-02-02 00:00:00
Tasks: 3D Reconstruction, Low-Rank Matrix Completion, Matrix Completion

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.
Question Generation from a Knowledge Base with Web Exploration
Authors: Linfeng Song, Lin Zhao
Date: 2016-10-12 00:00:00
Tasks: Natural Questions, Question Generation, Question-Generation

Question generation from a knowledge base (KB) is the task of generating
questions related to the domain of the input KB. We propose a system for
generating fluent and natural questions from a KB, which significantly reduces
the human effort by leveraging massive web resources. In more detail, a seed
question set is first generated by applying a small number of hand-crafted
templates on the input KB, then more questions are retrieved by iteratively
forming already obtained questions as search queries into a standard search
engine, before finally questions are selected by estimating their fluency and
domain relevance. Evaluated by human graders on 500 random-selected triples
from Freebase, questions generated by our system are judged to be more fluent
than those of \newcite{serban-EtAl:2016:P16-1} by human graders.
Stable and Controllable Neural Texture Synthesis and Style Transfer Using Histogram Losses
Authors: Eric Risser, Pierre Wilmot, Connelly Barnes
Date: 2017-01-31 00:00:00
Tasks: Style Transfer, Texture Synthesis

Recently, methods have been proposed that perform texture synthesis and style
transfer by using convolutional neural networks (e.g. Gatys et al.
[2015,2016]). These methods are exciting because they can in some cases create
results with state-of-the-art quality. However, in this paper, we show these
methods also have limitations in texture quality, stability, requisite
parameter tuning, and lack of user controls. This paper presents a multiscale
synthesis pipeline based on convolutional neural networks that ameliorates
these issues. We first give a mathematical explanation of the source of
instabilities in many previous approaches. We then improve these instabilities
by using histogram losses to synthesize textures that better statistically
match the exemplar. We also show how to integrate localized style losses in our
multiscale framework. These losses can improve the quality of large features,
improve the separation of content and style, and offer artistic controls such
as paint by numbers. We demonstrate that our approach offers improved quality,
convergence in fewer iterations, and more stability over the optimization.
Information-theoretic interpretation of tuning curves for multiple motion directions
Authors: Wentao Huang, Xin Huang, Kechen Zhang
Date: 2017-02-01 00:00:00
Tasks: Diversity

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.
Sub-Gaussian estimators of the mean of a random vector
Authors: Gábor Lugosi, Shahar Mendelson
Date: 2017-02-01 00:00:00

We study the problem of estimating the mean of a random vector $X$ given a
sample of $N$ independent, identically distributed points. We introduce a new
estimator that achieves a purely sub-Gaussian performance under the only
condition that the second moment of $X$ exists. The estimator is based on a
novel concept of a multivariate median.
A Simplified and Improved Free-Variable Framework for Hilbert's epsilon as an Operator of Indefinite Committed Choice
Authors: Claus-Peter Wirth
Date: 2011-04-13 00:00:00

Free variables occur frequently in mathematics and computer science with ad
hoc and altering semantics. We present the most recent version of our
free-variable framework for two-valued logics with properly improved
functionality, but only two kinds of free variables left (instead of three):
implicitly universally and implicitly existentially quantified ones, now simply
called "free atoms" and "free variables", respectively. The quantificational
expressiveness and the problem-solving facilities of our framework exceed
standard first-order and even higher-order modal logics, and directly support
Fermat's descente infinie. With the improved version of our framework, we can
now model also Henkin quantification, neither using quantifiers (binders) nor
raising (Skolemization). We propose a new semantics for Hilbert's epsilon as a
choice operator with the following features: We avoid overspecification (such
as right-uniqueness), but admit indefinite choice, committed choice, and
classical logics. Moreover, our semantics for the epsilon supports reductive
proof search optimally.
Dominance Move: A Measure of Comparing Solution Sets in Multiobjective Optimization
Authors: Miqing Li, Xin Yao
Date: 2017-02-01 00:00:00
Tasks: Decision Making, Multiobjective Optimization

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.
Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limit
Authors: Kevin Schawinski, Ce Zhang, Hantian Zhang, Lucas Fowler, Gokula Krishnan Santhanam
Date: 2017-02-01 00:00:00
Tasks: Generative Adversarial Network

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
$10\times$ 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.
Product Graph-based Higher Order Contextual Similarities for Inexact Subgraph Matching
Authors: Anjan Dutta, Josep Lladós, Horst Bunke, Umapada Pal
Date: 2017-02-01 00:00:00
Tasks: Graph Matching

Many algorithms formulate graph matching as an optimization of an objective
function of pairwise quantification of nodes and edges of two graphs to be
matched. Pairwise measurements usually consider local attributes but disregard
contextual information involved in graph structures. We address this issue by
proposing contextual similarities between pairs of nodes. This is done by
considering the tensor product graph (TPG) of two graphs to be matched, where
each node is an ordered pair of nodes of the operand graphs. Contextual
similarities between a pair of nodes are computed by accumulating weighted
walks (normalized pairwise similarities) terminating at the corresponding
paired node in TPG. Once the contextual similarities are obtained, we formulate
subgraph matching as a node and edge selection problem in TPG. We use
contextual similarities to construct an objective function and optimize it with
a linear programming approach. Since random walk formulation through TPG takes
into account higher order information, it is not a surprise that we obtain more
reliable similarities and better discrimination among the nodes and edges.
Experimental results shown on synthetic as well as real benchmarks illustrate
that higher order contextual similarities add discriminating power and allow
one to find approximate solutions to the subgraph matching problem.
Handwritten Recognition Using SVM, KNN and Neural Network
Authors: Norhidayu Abdul Hamid, Nilam Nur Amir Sjarif
Date: 2017-02-01 00:00:00
Tasks: General Classification

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.
Understanding trained CNNs by indexing neuron selectivity
Authors: Ivet Rafegas, Maria Vanrell, Luis A. Alexandre, Guillem Arias
Date: 2017-02-01 00:00:00

The impressive performance of Convolutional Neural Networks (CNNs) when solving different vision problems is shadowed by their black-box nature and our consequent lack of understanding of the representations they build and how these representations are organized. To help understanding these issues, we propose to describe the activity of individual neurons by their Neuron Feature visualization and quantify their inherent selectivity with two specific properties. We explore selectivity indexes for: an image feature (color); and an image label (class membership). Our contribution is a framework to seek or classify neurons by indexing on these selectivity properties. It helps to find color selective neurons, such as a red-mushroom neuron in layer Conv4 or class selective neurons such as dog-face neurons in layer Conv5 in VGG-M, and establishes a methodology to derive other selectivity properties. Indexing on neuron selectivity can statistically draw how features and classes are represented through layers in a moment when the size of trained nets is growing and automatic tools to index neurons can be helpful.