pwc-archive/papers-with-abstracts huggingface dataset:
Rotationally Invariant Image Representation for Viewing Direction Classification in Cryo-EM
Authors: Zhizhen Zhao, Amit Singer
Date: 2013-09-29 00:00:00
Tasks: Classification, Clustering, General Classification

We introduce a new rotationally invariant viewing angle classification method
for identifying, among a large number of Cryo-EM projection images, similar
views without prior knowledge of the molecule. Our rotationally invariant
features are based on the bispectrum. Each image is denoised and compressed
using steerable principal component analysis (PCA) such that rotating an image
is equivalent to phase shifting the expansion coefficients. Thus we are able to
extend the theory of bispectrum of 1D periodic signals to 2D images. The
randomized PCA algorithm is then used to efficiently reduce the dimensionality
of the bispectrum coefficients, enabling fast computation of the similarity
between any pair of images. The nearest neighbors provide an initial
classification of similar viewing angles. In this way, rotational alignment is
only performed for images with their nearest neighbors. The initial nearest
neighbor classification and alignment are further improved by a new
classification method called vector diffusion maps. Our pipeline for viewing
angle classification and alignment is experimentally shown to be faster and
more accurate than reference-free alignment with rotationally invariant K-means
clustering, MSA/MRA 2D classification, and their modern approximations.
Automatic Image Registration in Infrared-Visible Videos using Polygon Vertices
Authors: Tanushri Chakravorty, Guillaume-Alexandre Bilodeau, Eric Granger
Date: 2014-03-17 00:00:00
Tasks: Image Registration

In this paper, an automatic method is proposed to perform image registration
in visible and infrared pair of video sequences for multiple targets. In
multimodal image analysis like image fusion systems, color and IR sensors are
placed close to each other and capture a same scene simultaneously, but the
videos are not properly aligned by default because of different fields of view,
image capturing information, working principle and other camera specifications.
Because the scenes are usually not planar, alignment needs to be performed
continuously by extracting relevant common information. In this paper, we
approximate the shape of the targets by polygons and use affine transformation
for aligning the two video sequences. After background subtraction, keypoints
on the contour of the foreground blobs are detected using DCE (Discrete Curve
Evolution)technique. These keypoints are then described by the local shape at
each point of the obtained polygon. The keypoints are matched based on the
convexity of polygon's vertices and Euclidean distance between them. Only good
matches for each local shape polygon in a frame, are kept. To achieve a global
affine transformation that maximises the overlapping of infrared and visible
foreground pixels, the matched keypoints of each local shape polygon are stored
temporally in a buffer for a few number of frames. The matrix is evaluated at
each frame using the temporal buffer and the best matrix is selected, based on
an overlapping ratio criterion. Our experimental results demonstrate that this
method can provide highly accurate registered images and that we outperform a
previous related method.
A reversible infinite HMM using normalised random measures
Authors: Konstantina Palla, David A. Knowles, Zoubin Ghahramani
Date: 2014-03-17 00:00:00

We present a nonparametric prior over reversible Markov chains. We use
completely random measures, specifically gamma processes, to construct a
countably infinite graph with weighted edges. By enforcing symmetry to make the
edges undirected we define a prior over random walks on graphs that results in
a reversible Markov chain. The resulting prior over infinite transition
matrices is closely related to the hierarchical Dirichlet process but enforces
reversibility. A reinforcement scheme has recently been proposed with similar
properties, but the de Finetti measure is not well characterised. We take the
alternative approach of explicitly constructing the mixing measure, which
allows more straightforward and efficient inference at the cost of no longer
having a closed form predictive distribution. We use our process to construct a
reversible infinite HMM which we apply to two real datasets, one from
epigenomics and one ion channel recording.
Computer Vision Accelerators for Mobile Systems based on OpenCL GPGPU Co-Processing
Authors: Guohui Wang, Yingen Xiong, Jay Yun, Joseph R. Cavallaro
Date: 2014-03-17 00:00:00
Tasks: CPU, GPU, Image Inpainting

In this paper, we present an OpenCL-based heterogeneous implementation of a
computer vision algorithm -- image inpainting-based object removal algorithm --
on mobile devices. To take advantage of the computation power of the mobile
processor, the algorithm workflow is partitioned between the CPU and the GPU
based on the profiling results on mobile devices, so that the
computationally-intensive kernels are accelerated by the mobile GPGPU
(general-purpose computing using graphics processing units). By exploring the
implementation trade-offs and utilizing the proposed optimization strategies at
different levels including algorithm optimization, parallelism optimization,
and memory access optimization, we significantly speed up the algorithm with
the CPU-GPU heterogeneous implementation, while preserving the quality of the
output images. Experimental results show that heterogeneous computing based on
GPGPU co-processing can significantly speed up the computer vision algorithms
and makes them practical on real-world mobile devices.
A-infinity Persistence
Authors: Francisco Belchí Guillamón, Aniceto Murillo Mas
Date: 2014-03-10 00:00:00
Tasks: General Classification

We introduce and study A-infinity persistence of a given homology filtration
of topological spaces. This is a family, one for each n > 0, of homological
invariants which provide information not readily available by the (persistent)
Betti numbers of the given filtration. This may help to detect noise, not just
in the simplicial structure of the filtration but in further geometrical
properties in which the higher codiagonals of the A-infinity structure are
translated. Based in the classification of zigzag modules, a characterization
of the A-infinity persistence in terms of its associated barcode is given.
Multi-task Feature Selection based Anomaly Detection
Authors: Longqi Yang, Yibing Wang, Zhisong Pan, Guyu Hu
Date: 2014-03-17 00:00:00
Tasks: Anomaly Detection, feature selection, Multi-Task Learning

Network anomaly detection is still a vibrant research area. As the fast
growth of network bandwidth and the tremendous traffic on the network, there
arises an extremely challengeable question: How to efficiently and accurately
detect the anomaly on multiple traffic? In multi-task learning, the traffic
consisting of flows at different time periods is considered as a task. Multiple
tasks at different time periods performed simultaneously to detect anomalies.
In this paper, we apply the multi-task feature selection in network anomaly
detection area which provides a powerful method to gather information from
multiple traffic and detect anomalies on it simultaneously. In particular, the
multi-task feature selection includes the well-known l1-norm based feature
selection as a special case given only one task. Moreover, we show that the
multi-task feature selection is more accurate by utilizing more information
simultaneously than the l1-norm based method. At the evaluation stage, we
preprocess the raw data trace from trans-Pacific backbone link between Japan
and the United States, label with anomaly communities, and generate a
248-feature dataset. We show empirically that the multi-task feature selection
outperforms independent l1-norm based feature selection on real traffic
dataset.
Image processing using miniKanren
Authors: Hirotaka Niitsuma
Date: 2014-03-16 00:00:00

An integral image is one of the most efficient optimization technique for
image processing. However an integral image is only a special case of delayed
stream or memoization. This research discusses generalizing concept of integral
image optimization technique, and how to generate an integral image optimized
program code automatically from abstracted image processing algorithm. In oder
to abstruct algorithms, we forces to miniKanren.
A General Framework for Interacting Bayes-Optimally with Self-Interested Agents using Arbitrary Parametric Model and Model Prior
Authors: Trong Nghia Hoang, Kian Hsiang Low
Date: 2013-04-07 00:00:00
Tasks: model, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent advances in Bayesian reinforcement learning (BRL) have shown that
Bayes-optimality is theoretically achievable by modeling the environment's
latent dynamics using Flat-Dirichlet-Multinomial (FDM) prior. In
self-interested multi-agent environments, the transition dynamics are mainly
controlled by the other agent's stochastic behavior for which FDM's
independence and modeling assumptions do not hold. As a result, FDM does not
allow the other agent's behavior to be generalized across different states nor
specified using prior domain knowledge. To overcome these practical limitations
of FDM, we propose a generalization of BRL to integrate the general class of
parametric models and model priors, thus allowing practitioners' domain
knowledge to be exploited to produce a fine-grained and compact representation
of the other agent's behavior. Empirical evaluation shows that our approach
outperforms existing multi-agent reinforcement learning algorithms.
Adaptivity of averaged stochastic gradient descent to local strong convexity for logistic regression
Authors: Francis Bach
Date: 2013-03-25 00:00:00
Tasks: regression

In this paper, we consider supervised learning problems such as logistic
regression and study the stochastic gradient method with averaging, in the
usual stochastic approximation setting where observations are used only once.
We show that after $N$ iterations, with a constant step-size proportional to
$1/R^2 \sqrt{N}$ where $N$ is the number of observations and $R$ is the maximum
norm of the observations, the convergence rate is always of order
$O(1/\sqrt{N})$, and improves to $O(R^2 / \mu N)$ where $\mu$ is the lowest
eigenvalue of the Hessian at the global optimum (when this eigenvalue is
greater than $R^2/\sqrt{N}$). Since $\mu$ does not need to be known in advance,
this shows that averaged stochastic gradient is adaptive to \emph{unknown
local} strong convexity of the objective function. Our proof relies on the
generalized self-concordance properties of the logistic loss and thus extends
to all generalized linear models with uniformly bounded features.
Knapsack Constrained Contextual Submodular List Prediction with Application to Multi-document Summarization
Authors: Jiaji Zhou, Stephane Ross, Yisong Yue, Debadeepta Dey, J. Andrew Bagnell
Date: 2013-08-16 00:00:00
Tasks: Diversity, Document Summarization, Multi-Document Summarization

We study the problem of predicting a set or list of options under knapsack
constraint. The quality of such lists are evaluated by a submodular reward
function that measures both quality and diversity. Similar to DAgger (Ross et
al., 2010), by a reduction to online learning, we show how to adapt two
sequence prediction models to imitate greedy maximization under knapsack
constraint problems: CONSEQOPT (Dey et al., 2012) and SCP (Ross et al., 2013).
Experiments on extractive multi-document summarization show that our approach
outperforms existing state-of-the-art methods.
Geometric VLAD for Large Scale Image Search
Authors: Zixuan Wang, Wei Di, Anurag Bhardwaj, Vignesh Jagadeesh, Robinson Piramuthu
Date: 2014-03-15 00:00:00
Tasks: Clustering, Image Retrieval, Retrieval

We present a novel compact image descriptor for large scale image search. Our
proposed descriptor - Geometric VLAD (gVLAD) is an extension of VLAD (Vector of
Locally Aggregated Descriptors) that incorporates weak geometry information
into the VLAD framework. The proposed geometry cues are derived as a membership
function over keypoint angles which contain evident and informative information
but yet often discarded. A principled technique for learning the membership
function by clustering angles is also presented. Further, to address the
overhead of iterative codebook training over real-time datasets, a novel
codebook adaptation strategy is outlined. Finally, we demonstrate the efficacy
of proposed gVLAD based retrieval framework where we achieve more than 15%
improvement in mAP over existing benchmarks.
Automatic Classification of Human Epithelial Type 2 Cell Indirect Immunofluorescence Images using Cell Pyramid Matching
Authors: Arnold Wiliem, Conrad Sanderson, Yongkang Wong, Peter Hobson, Rodney F. Minchin, Brian C. Lovell
Date: 2014-03-15 00:00:00
Tasks: Diagnostic, General Classification, image-classification, Image Classification

This paper describes a novel system for automatic classification of images
obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial
type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The
IIF protocol on HEp-2 cells has been the hallmark method to identify the
presence of ANAs, due to its high sensitivity and the large range of antigens
that can be detected. However, it suffers from numerous shortcomings, such as
being subjective as well as time and labour intensive. Computer Aided
Diagnostic (CAD) systems have been developed to address these problems, which
automatically classify a HEp-2 cell image into one of its known patterns (eg.
speckled, homogeneous). Most of the existing CAD systems use handpicked
features to represent a HEp-2 cell image, which may only work in limited
scenarios. We propose a novel automatic cell image classification method termed
Cell Pyramid Matching (CPM), which is comprised of regional histograms of
visual words coupled with the Multiple Kernel Learning framework. We present a
study of several variations of generating histograms and show the efficacy of
the system on two publicly available datasets: the ICPR HEp-2 cell
classification contest dataset and the SNPHEp-2 dataset.
A Geometric Algorithm for Scalable Multiple Kernel Learning
Authors: John Moeller, Parasaran Raman, Avishek Saha, Suresh Venkatasubramanian
Date: 2012-06-25 00:00:00

We present a geometric formulation of the Multiple Kernel Learning (MKL)
problem. To do so, we reinterpret the problem of learning kernel weights as
searching for a kernel that maximizes the minimum (kernel) distance between two
convex polytopes. This interpretation combined with novel structural insights
from our geometric formulation allows us to reduce the MKL problem to a simple
optimization routine that yields provable convergence as well as quality
guarantees. As a result our method scales efficiently to much larger data sets
than most prior methods can handle. Empirical evaluation on eleven datasets
shows that we are significantly faster and even compare favorably with a
uniform unweighted combination of kernels.
Learning the Latent State Space of Time-Varying Graphs
Authors: Nesreen K. Ahmed, Christopher Cole, Jennifer Neville
Date: 2014-03-14 00:00:00

From social networks to Internet applications, a wide variety of electronic
communication tools are producing streams of graph data; where the nodes
represent users and the edges represent the contacts between them over time.
This has led to an increased interest in mechanisms to model the dynamic
structure of time-varying graphs. In this work, we develop a framework for
learning the latent state space of a time-varying email graph. We show how the
framework can be used to find subsequences that correspond to global real-time
events in the Email graph (e.g. vacations, breaks, ...etc.). These events
impact the underlying graph process to make its characteristics non-stationary.
Within the framework, we compare two different representations of the temporal
relationships; discrete vs. probabilistic. We use the two representations as
inputs to a mixture model to learn the latent state transitions that correspond
to important changes in the Email graph structure over time.
Removal and Contraction Operations in $n$D Generalized Maps for Efficient Homology Computation
Authors: Guillaume Damiand, Rocio Gonzalez-Diaz, Samuel Peltier
Date: 2014-03-14 00:00:00

In this paper, we show that contraction operations preserve the homology of
$n$D generalized maps, under some conditions. Removal and contraction
operations are used to propose an efficient algorithm that compute homology
generators of $n$D generalized maps. Its principle consists in simplifying a
generalized map as much as possible by using removal and contraction
operations. We obtain a generalized map having the same homology than the
initial one, while the number of cells decreased significantly.
  Keywords: $n$D Generalized Maps; Cellular Homology; Homology Generators;
Contraction and Removal Operations.
Test Set Selection using Active Information Acquisition for Predictive Models
Authors: Sneha Chaudhari, Pankaj Dayama, Vinayaka Pandit, Indrajit Bhattacharya
Date: 2013-12-03 00:00:00
Tasks: Medical Diagnosis

In this paper, we consider active information acquisition when the prediction
model is meant to be applied on a targeted subset of the population. The goal
is to label a pre-specified fraction of customers in the target or test set by
iteratively querying for information from the non-target or training set. The
number of queries is limited by an overall budget. Arising in the context of
two rather disparate applications- banking and medical diagnosis, we pose the
active information acquisition problem as a constrained optimization problem.
We propose two greedy iterative algorithms for solving the above problem. We
conduct experiments with synthetic data and compare results of our proposed
algorithms with few other baseline approaches. The experimental results show
that our proposed approaches perform better than the baseline schemes.
Mixed-norm Regularization for Brain Decoding
Authors: Rémi Flamary, Nisrine Jrad, Ronald Phlypo, Marco Congedo, Alain Rakotomamonjy
Date: 2014-03-14 00:00:00
Tasks: Brain Decoding, ERP, General Classification, Multi-Task Learning

This work investigates the use of mixed-norm regularization for sensor
selection in Event-Related Potential (ERP) based Brain-Computer Interfaces
(BCI). The classification problem is cast as a discriminative optimization
framework where sensor selection is induced through the use of mixed-norms.
This framework is extended to the multi-task learning situation where several
similar classification tasks related to different subjects are learned
simultaneously. In this case, multi-task learning helps in leveraging data
scarcity issue yielding to more robust classifiers. For this purpose, we have
introduced a regularizer that induces both sensor selection and classifier
similarities. The different regularization approaches are compared on three ERP
datasets showing the interest of mixed-norm regularization in terms of sensor
selection. The multi-task approaches are evaluated when a small number of
learning examples are available yielding to significant performance
improvements especially for subjects performing poorly.
Patch-based Probabilistic Image Quality Assessment for Face Selection and Improved Video-based Face Recognition
Authors: Yongkang Wong, Shaokang Chen, Sandra Mau, Conrad Sanderson, Brian C. Lovell
Date: 2013-04-03 00:00:00
Tasks: Face Image Quality, Face Image Quality Assessment, Face Model, Face Recognition, Face Selection, Image Quality Assessment

In video based face recognition, face images are typically captured over multiple frames in uncontrolled conditions, where head pose, illumination, shadowing, motion blur and focus change over the sequence. Additionally, inaccuracies in face localisation can also introduce scale and alignment variations. Using all face images, including images of poor quality, can actually degrade face recognition performance. While one solution it to use only the "best" subset of images, current face selection techniques are incapable of simultaneously handling all of the abovementioned issues. We propose an efficient patch-based face image quality assessment algorithm which quantifies the similarity of a face image to a probabilistic face model, representing an "ideal" face. Image characteristics that affect recognition are taken into account, including variations in geometric alignment (shift, rotation and scale), sharpness, head pose and cast shadows. Experiments on FERET and PIE datasets show that the proposed algorithm is able to identify images which are simultaneously the most frontal, aligned, sharp and well illuminated. Further experiments on a new video surveillance dataset (termed ChokePoint) show that the proposed method provides better face subsets than existing face selection techniques, leading to significant improvements in recognition accuracy.
Spontaneous expression classification in the encrypted domain
Authors: Segun Aina, Yogachandran Rahulamathavan, Raphael C. -W. Phan, Jonathon A. Chambers
Date: 2014-03-14 00:00:00
Tasks: Classification, General Classification

To date, most facial expression analysis have been based on posed image
databases and is carried out without being able to protect the identity of the
subjects whose expressions are being recognised. In this paper, we propose and
implement a system for classifying facial expressions of images in the
encrypted domain based on a Paillier cryptosystem implementation of Fisher
Linear Discriminant Analysis and k-nearest neighbour (FLDA + kNN). We present
results of experiments carried out on a recently developed natural visible and
infrared facial expression (NVIE) database of spontaneous images. To the best
of our knowledge, this is the first system that will allow the recog-nition of
encrypted spontaneous facial expressions by a remote server on behalf of a
client.
Engaging with Massive Online Courses
Authors: Ashton Anderson, Daniel Huttenlocher, Jon Kleinberg, Jure Leskovec
Date: 2014-03-12 00:00:00

The Web has enabled one of the most visible recent developments in
education---the deployment of massive open online courses. With their global
reach and often staggering enrollments, MOOCs have the potential to become a
major new mechanism for learning. Despite this early promise, however, MOOCs
are still relatively unexplored and poorly understood.
  In a MOOC, each student's complete interaction with the course materials
takes place on the Web, thus providing a record of learner activity of
unprecedented scale and resolution. In this work, we use such trace data to
develop a conceptual framework for understanding how users currently engage
with MOOCs. We develop a taxonomy of individual behavior, examine the different
behavioral patterns of high- and low-achieving students, and investigate how
forum participation relates to other parts of the course.
  We also report on a large-scale deployment of badges as incentives for
engagement in a MOOC, including randomized experiments in which the
presentation of badges was varied across sub-populations. We find that making
badges more salient produced increases in forum engagement.