pwc-archive/papers-with-abstracts huggingface dataset:
Blockwise SURE Shrinkage for Non-Local Means
Authors: Yue Wu, Brian Tracey, Premkumar Natarajan, Joseph P. Noonan
Date: 2013-05-18 00:00:00
Tasks: Denoising, Image Denoising, SSIM

In this letter, we investigate the shrinkage problem for the non-local means
(NLM) image denoising. In particular, we derive the closed-form of the optimal
blockwise shrinkage for NLM that minimizes the Stein's unbiased risk estimator
(SURE). We also propose a constant complexity algorithm allowing fast blockwise
shrinkage. Simulation results show that the proposed blockwise shrinkage method
improves NLM performance in attaining higher peak signal noise ratio (PSNR) and
structural similarity index (SSIM), and makes NLM more robust against parameter
changes. Similar ideas can be applicable to other patchwise image denoising
techniques.
The state-of-the-art in web-scale semantic information processing for cloud computing
Authors: Wei Yu, Junpeng Chen
Date: 2013-05-18 00:00:00
Tasks: Cloud Computing, Natural Language Understanding

Based on integrated infrastructure of resource sharing and computing in
distributed environment, cloud computing involves the provision of dynamically
scalable and provides virtualized resources as services over the Internet.
These applications also bring a large scale heterogeneous and distributed
information which pose a great challenge in terms of the semantic ambiguity. It
is critical for application services in cloud computing environment to provide
users intelligent service and precise information. Semantic information
processing can help users deal with semantic ambiguity and information overload
efficiently through appropriate semantic models and semantic information
processing technology. The semantic information processing have been
successfully employed in many fields such as the knowledge representation,
natural language understanding, intelligent web search, etc. The purpose of
this report is to give an overview of existing technologies for semantic
information processing in cloud computing environment, to propose a research
direction for addressing distributed semantic reasoning and parallel semantic
computing by exploiting semantic information newly available in cloud computing
environment.
Machine learning on images using a string-distance
Authors: Uzi Chester, Joel Ratsaby
Date: 2013-05-17 00:00:00
Tasks: BIG-bench Machine Learning

We present a new method for image feature-extraction which is based on
representing an image by a finite-dimensional vector of distances that measure
how different the image is from a set of image prototypes. We use the recently
introduced Universal Image Distance (UID) \cite{RatsabyChesterIEEE2012} to
compare the similarity between an image and a prototype image. The advantage in
using the UID is the fact that no domain knowledge nor any image analysis need
to be done. Each image is represented by a finite dimensional feature vector
whose components are the UID values between the image and a finite set of image
prototypes from each of the feature categories. The method is automatic since
once the user selects the prototype images, the feature vectors are
automatically calculated without the need to do any image analysis. The
prototype images can be of different size, in particular, different than the
image size. Based on a collection of such cases any supervised or unsupervised
learning algorithm can be used to train and produce an image classifier or
image cluster analysis. In this paper we present the image feature-extraction
method and use it on several supervised and unsupervised learning experiments
for satellite image data.
A Random Walk Based Model Incorporating Social Information for Recommendations
Authors: Shang Shang, Sanjeev R. Kulkarni, Paul W. Cuff, Pan Hui
Date: 2012-08-03 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Collaborative filtering (CF) is one of the most popular approaches to build a
recommendation system. In this paper, we propose a hybrid collaborative
filtering model based on a Makovian random walk to address the data sparsity
and cold start problems in recommendation systems. More precisely, we construct
a directed graph whose nodes consist of items and users, together with item
content, user profile and social network information. We incorporate user's
ratings into edge settings in the graph model. The model provides personalized
recommendations and predictions to individuals and groups. The proposed
algorithms are evaluated on MovieLens and Epinions datasets. Experimental
results show that the proposed methods perform well compared with other
graph-based methods, especially in the cold start case.
Wisdom of the Crowd: Incorporating Social Influence in Recommendation Models
Authors: Shang Shang, Pan Hui, Sanjeev R. Kulkarni, Paul W. Cuff
Date: 2012-08-03 00:00:00
Tasks: Collaborative Filtering, Decision Making, Recommendation Systems

Recommendation systems have received considerable attention recently.
However, most research has been focused on improving the performance of
collaborative filtering (CF) techniques. Social networks, indispensably,
provide us extra information on people's preferences, and should be considered
and deployed to improve the quality of recommendations. In this paper, we
propose two recommendation models, for individuals and for groups respectively,
based on social contagion and social influence network theory. In the
recommendation model for individuals, we improve the result of collaborative
filtering prediction with social contagion outcome, which simulates the result
of information cascade in the decision-making process. In the recommendation
model for groups, we apply social influence network theory to take
interpersonal influence into account to form a settled pattern of disagreement,
and then aggregate opinions of group members. By introducing the concept of
susceptibility and interpersonal influence, the settled rating results are
flexible, and inclined to members whose ratings are "essential".
Flying Triangulation - towards the 3D movie camera
Authors: Florian Willomitzer, Svenja Ettl, Christian Faber, Gerd Häusler
Date: 2013-05-17 00:00:00

Flying Triangulation sensors enable a free-hand and motion-robust 3D data
acquisition of complex shaped objects. The measurement principle is based on a
multi-line light-sectioning approach and uses sophisticated algorithms for
real-time registration (S. Ettl et al., Appl. Opt. 51 (2012) 281-289). As
"single-shot principle", light sectioning enables the option to get surface
data from one single camera exposure. But there is a drawback: A pixel-dense
measurement is not possible because of fundamental information-theoretical
reasons. By "pixel-dense" we understand that each pixel displays individually
measured distance information, neither interpolated from its neighbour pixels
nor using lateral context information. Hence, for monomodal single-shot
principles, the 3D data generated from one 2D raw image display a significantly
lower space-bandwidth than the camera permits. This is the price one must pay
for motion robustness. Currently, our sensors project about 10 lines (each with
1000 pixels), reaching an considerable lower data efficiency than theoretically
possible for a single-shot sensor. Our aim is to push Flying Triangulation to
its information-theoretical limits. Therefore, the line density as well as the
measurement depth needs to be significantly increased. This causes serious
indexing ambiguities. On the road to a single-shot 3D movie camera, we are
working on solutions to overcome the problem of false line indexing by
utilizing yet unexploited information. We will present several approaches and
will discuss profound information-theoretical questions about the information
efficiency of 3D sensors.
Factored expectation propagation for input-output FHMM models in systems biology
Authors: Botond Cseke, Guido Sanguinetti
Date: 2013-05-17 00:00:00
Tasks: Variational Inference

We consider the problem of joint modelling of metabolic signals and gene
expression in systems biology applications. We propose an approach based on
input-output factorial hidden Markov models and propose a structured
variational inference approach to infer the structure and states of the model.
We start from the classical free form structured variational mean field
approach and use a expectation propagation to approximate the expectations
needed in the variational loop. We show that this corresponds to a factored
expectation constrained approximate inference. We validate our model through
extensive simulations and demonstrate its applicability on a real world
bacterial data set.
Belief Propagation for Linear Programming
Authors: Andrew Gelfand, Jinwoo Shin, Michael Chertkov
Date: 2013-05-17 00:00:00

Belief Propagation (BP) is a popular, distributed heuristic for performing
MAP computations in Graphical Models. BP can be interpreted, from a variational
perspective, as minimizing the Bethe Free Energy (BFE). BP can also be used to
solve a special class of Linear Programming (LP) problems. For this class of
problems, MAP inference can be stated as an integer LP with an LP relaxation
that coincides with minimization of the BFE at ``zero temperature". We
generalize these prior results and establish a tight characterization of the LP
problems that can be formulated as an equivalent LP relaxation of MAP
inference. Moreover, we suggest an efficient, iterative annealing BP algorithm
for solving this broader class of LP problems. We demonstrate the algorithm's
performance on a set of weighted matching problems by using it as a cutting
plane method to solve a sequence of LPs tightened by adding ``blossom''
inequalities.
Rule-Based Semantic Tagging. An Application Undergoing Dictionary Glosses
Authors: Daniel Christen
Date: 2013-05-16 00:00:00
Tasks: Information Retrieval, Question Answering, Retrieval, Translation, Word Sense Disambiguation

The project presented in this article aims to formalize criteria and
procedures in order to extract semantic information from parsed dictionary
glosses. The actual purpose of the project is the generation of a semantic
network (nearly an ontology) issued from a monolingual Italian dictionary,
through unsupervised procedures. Since the project involves rule-based Parsing,
Semantic Tagging and Word Sense Disambiguation techniques, its outcomes may
find an interest also beyond this immediate intent. The cooperation of both
syntactic and semantic features in meaning construction are investigated, and
procedures which allows a translation of syntactic dependencies in semantic
relations are discussed. The procedures that rise from this project can be
applied also to other text types than dictionary glosses, as they convert the
output of a parsing process into a semantic representation. In addition some
mechanism are sketched that may lead to a kind of procedural semantics, through
which multiple paraphrases of an given expression can be generated. Which means
that these techniques may find an application also in 'query expansion'
strategies, interesting Information Retrieval, Search Engines and Question
Answering Systems.
Sémantique des déterminants dans un cadre richement typé
Authors: Christian Retoré
Date: 2013-02-06 00:00:00

The variation of word meaning according to the context leads us to enrich the
type system of our syntactical and semantic analyser of French based on
categorial grammars and Montague semantics (or lambda-DRT). The main advantage
of a deep semantic analyse is too represent meaning by logical formulae that
can be easily used e.g. for inferences. Determiners and quantifiers play a
fundamental role in the construction of those formulae. But in our rich type
system the usual semantic terms do not work. We propose a solution ins- pired
by the tau and epsilon operators of Hilbert, kinds of generic elements and
choice functions. This approach unifies the treatment of the different determi-
ners and quantifiers as well as the dynamic binding of pronouns. Above all,
this fully computational view fits in well within the wide coverage parser
Grail, both from a theoretical and a practical viewpoint.
Conditions for Convergence in Regularized Machine Learning Objectives
Authors: Patrick Hop, Xinghao Pan
Date: 2013-05-17 00:00:00
Tasks: BIG-bench Machine Learning, Distributed Computing

Analysis of the convergence rates of modern convex optimization algorithms
can be achived through binary means: analysis of emperical convergence, or
analysis of theoretical convergence. These two pathways of capturing
information diverge in efficacy when moving to the world of distributed
computing, due to the introduction of non-intuitive, non-linear slowdowns
associated with broadcasting, and in some cases, gathering operations. Despite
these nuances in the rates of convergence, we can still show the existence of
convergence, and lower bounds for the rates. This paper will serve as a helpful
cheat-sheet for machine learning practitioners encountering this problem class
in the field.
Font Acknowledgment and Character Extraction of Digital and Scanned Images
Authors: Syed Muhammad Arsalan Bashir
Date: 2013-05-17 00:00:00
Tasks: Font Recognition

The font recognition and character extraction is of immense importance as
these are many scenarios where data are in such a form, which cannot be
processed like in image form or as a hard copy. So the procedure developed in
this paper is basically related to identifying the font (Times New Roman, Arial
and Comic Sans MS) and afterwards recovering the text using simple correlation
based method where the binary templates are correlated to the input image text
characters. All of this extraction is done in the presence of a little noise as
images may have noisy patterns due to photocopying. The significance of this
method exists in extraction of data from various monitoring (Surveillance)
camera footages or even more. The method is developed on Matlab\c{opyright}
which takes input image and recovers text and font information from it in a
text file.
Binary Tree based Chinese Word Segmentation
Authors: Kaixu Zhang, Can Wang, Maosong Sun
Date: 2013-05-17 00:00:00
Tasks: Chinese Word Segmentation, Segmentation

Chinese word segmentation is a fundamental task for Chinese language
processing. The granularity mismatch problem is the main cause of the errors.
This paper showed that the binary tree representation can store outputs with
different granularity. A binary tree based framework is also designed to
overcome the granularity mismatch problem. There are two steps in this
framework, namely tree building and tree pruning. The tree pruning step is
specially designed to focus on the granularity problem. Previous work for
Chinese word segmentation such as the sequence tagging can be easily employed
in this framework. This framework can also provide quantitative error analysis
methods. The experiments showed that after using a more sophisticated tree
pruning function for a state-of-the-art conditional random field based
baseline, the error reduction can be up to 20%.
Sparse Norm Filtering
Authors: Chengxi Ye, DaCheng Tao, Mingli Song, David W. Jacobs, Min Wu
Date: 2013-05-17 00:00:00
Tasks: Colorization, Deblurring, Image Segmentation, Semantic Segmentation

Optimization-based filtering smoothes an image by minimizing a fidelity
function and simultaneously preserves edges by exploiting a sparse norm penalty
over gradients. It has obtained promising performance in practical problems,
such as detail manipulation, HDR compression and deblurring, and thus has
received increasing attentions in fields of graphics, computer vision and image
processing. This paper derives a new type of image filter called sparse norm
filter (SNF) from optimization-based filtering. SNF has a very simple form,
introduces a general class of filtering techniques, and explains several
classic filters as special implementations of SNF, e.g. the averaging filter
and the median filter. It has advantages of being halo free, easy to implement,
and low time and memory costs (comparable to those of the bilateral filter).
Thus, it is more generic than a smoothing operator and can better adapt to
different tasks. We validate the proposed SNF by a wide variety of applications
including edge-preserving smoothing, outlier tolerant filtering, detail
manipulation, HDR compression, non-blind deconvolution, image segmentation, and
colorization.
Analysis Of Interest Points Of Curvelet Coefficients Contributions Of Microscopic Images And Improvement Of Edges
Authors: A. Djimeli, D. Tchiotsop, R. Tchinda
Date: 2013-05-16 00:00:00

This paper focuses on improved edge model based on Curvelet coefficients
analysis. Curvelet transform is a powerful tool for multiresolution
representation of object with anisotropic edge. Curvelet coefficients
contributions have been analyzed using Scale Invariant Feature Transform
(SIFT), commonly used to study local structure in images. The permutation of
Curvelet coefficients from original image and edges image obtained from
gradient operator is used to improve original edges. Experimental results show
that this method brings out details on edges when the decomposition scale
increases.
Geometric primitive feature extraction - concepts, algorithms, and applications
Authors: Dilip K. Prasad
Date: 2013-05-16 00:00:00

This thesis presents important insights and concepts related to the topic of
the extraction of geometric primitives from the edge contours of digital
images. Three specific problems related to this topic have been studied, viz.,
polygonal approximation of digital curves, tangent estimation of digital
curves, and ellipse fitting anddetection from digital curves. For the problem
of polygonal approximation, two fundamental problems have been addressed.
First, the nature of the performance evaluation metrics in relation to the
local and global fitting characteristics has been studied. Second, an explicit
error bound of the error introduced by digitizing a continuous line segment has
been derived and used to propose a generic non-heuristic parameter independent
framework which can be used in several dominant point detection methods. For
the problem of tangent estimation for digital curves, a simple method of
tangent estimation has been proposed. It is shown that the method has a
definite upper bound of the error for conic digital curves. It has been shown
that the method performs better than almost all (seventy two) existing tangent
estimation methods for conic as well as several non-conic digital curves. For
the problem of fitting ellipses on digital curves, a geometric distance
minimization model has been considered. An unconstrained, linear,
non-iterative, and numerically stable ellipse fitting method has been proposed
and it has been shown that the proposed method has better selectivity for
elliptic digital curves (high true positive and low false positive) as compared
to several other ellipse fitting methods. For the problem of detecting ellipses
in a set of digital curves, several innovative and fast pre-processing,
grouping, and hypotheses evaluation concepts applicable for digital curves have
been proposed and combined to form an ellipse detection method.
One-Pass AUC Optimization
Authors: Wei Gao, Rong Jin, Shenghuo Zhu, Zhi-Hua Zhou
Date: 2013-05-07 00:00:00

AUC is an important performance measure and many algorithms have been devoted
to AUC optimization, mostly by minimizing a surrogate convex loss on a training
data set. In this work, we focus on one-pass AUC optimization that requires
only going through the training data once without storing the entire training
dataset, where conventional online learning algorithms cannot be applied
directly because AUC is measured by a sum of losses defined over pairs of
instances from different classes. We develop a regression-based algorithm which
only needs to maintain the first and second order statistics of training data
in memory, resulting a storage requirement independent from the size of
training data. To efficiently handle high dimensional data, we develop a
randomized algorithm that approximates the covariance matrices by low rank
matrices. We verify, both theoretically and empirically, the effectiveness of
the proposed algorithm.
Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data
Authors: Jan-Willem van de Meent, Jonathan E. Bronson, Frank Wood, Ruben L. Gonzalez Jr., Chris H. Wiggins
Date: 2013-05-15 00:00:00
Tasks: Time Series, Time Series Analysis

We address the problem of analyzing sets of noisy time-varying signals that
all report on the same process but confound straightforward analyses due to
complex inter-signal heterogeneities and measurement artifacts. In particular
we consider single-molecule experiments which indirectly measure the distinct
steps in a biomolecular process via observations of noisy time-dependent
signals such as a fluorescence intensity or bead position. Straightforward
hidden Markov model (HMM) analyses attempt to characterize such processes in
terms of a set of conformational states, the transitions that can occur between
these states, and the associated rates at which those transitions occur; but
require ad-hoc post-processing steps to combine multiple signals. Here we
develop a hierarchically coupled HMM that allows experimentalists to deal with
inter-signal variability in a principled and automatic way. Our approach is a
generalized expectation maximization hyperparameter point estimation procedure
with variational Bayes at the level of individual time series that learns an
single interpretable representation of the overall data generating process.
Modeling Information Propagation with Survival Theory
Authors: Manuel Gomez Rodriguez, Jure Leskovec, Bernhard Schoelkopf
Date: 2013-05-15 00:00:00

Networks provide a skeleton for the spread of contagions, like, information,
ideas, behaviors and diseases. Many times networks over which contagions
diffuse are unobserved and need to be inferred. Here we apply survival theory
to develop general additive and multiplicative risk models under which the
network inference problems can be solved efficiently by exploiting their
convexity. Our additive risk model generalizes several existing network
inference models. We show all these models are particular cases of our more
general model. Our multiplicative model allows for modeling scenarios in which
a node can either increase or decrease the risk of activation of another node,
in contrast with previous approaches, which consider only positive risk
increments. We evaluate the performance of our network inference algorithms on
large synthetic and real cascade datasets, and show that our models are able to
predict the length and duration of cascades in real data.
Hubiness, length, crossings and their relationships in dependency trees
Authors: Ramon Ferrer-i-Cancho
Date: 2013-04-15 00:00:00
Tasks: Sentence

Here tree dependency structures are studied from three different
perspectives: their degree variance (hubiness), the mean dependency length and
the number of dependency crossings. Bounds that reveal pairwise dependencies
among these three metrics are derived. Hubiness (the variance of degrees) plays
a central role: the mean dependency length is bounded below by hubiness while
the number of crossings is bounded above by hubiness. Our findings suggest that
the online memory cost of a sentence might be determined not just by the
ordering of words but also by the hubiness of the underlying structure. The 2nd
moment of degree plays a crucial role that is reminiscent of its role in large
complex networks.