pwc-archive/papers-with-abstracts huggingface dataset:
A single-phase, proximal path-following framework
Authors: Quoc Tran-Dinh, Anastasios Kyrillidis, Volkan Cevher
Date: 2016-03-05 00:00:00

We propose a new proximal, path-following framework for a class of
constrained convex problems. We consider settings where the nonlinear---and
possibly non-smooth---objective part is endowed with a proximity operator, and
the constraint set is equipped with a self-concordant barrier. Our approach
relies on the following two main ideas. First, we re-parameterize the
optimality condition as an auxiliary problem, such that a good initial point is
available; by doing so, a family of alternative paths towards the optimum is
generated. Second, we combine the proximal operator with path-following ideas
to design a single-phase, proximal, path-following algorithm. Our method has
several advantages. First, it allows handling non-smooth objectives via
proximal operators; this avoids lifting the problem dimension in order to
accommodate non-smooth components in optimization. Second, it consists of only
a \emph{single phase}: While the overall convergence rate of classical
path-following schemes for self-concordant objectives does not suffer from the
initialization phase, proximal path-following schemes undergo slow convergence,
in order to obtain a good starting point \cite{TranDinh2013e}. In this work, we
show how to overcome this limitation in the proximal setting and prove that our
scheme has the same $\mathcal{O}(\sqrt{\nu}\log(1/\varepsilon))$ worst-case
iteration-complexity with standard approaches \cite{Nesterov2004,Nesterov1994}
without requiring an initial phase, where $\nu$ is the barrier parameter and
$\varepsilon$ is a desired accuracy. Finally, our framework allows errors in
the calculation of proximal-Newton directions, without sacrificing the
worst-case iteration complexity. We demonstrate the merits of our algorithm via
three numerical examples, where proximal operators play a key role.
Cross-scale predictive dictionaries
Authors: Vishwanath Saragadam, Xin Li, Aswin Sankaranarayanan
Date: 2015-11-16 00:00:00

Sparse representations using data dictionaries provide an efficient model
particularly for signals that do not enjoy alternate analytic sparsifying
transformations. However, solving inverse problems with sparsifying
dictionaries can be computationally expensive, especially when the dictionary
under consideration has a large number of atoms. In this paper, we incorporate
additional structure on to dictionary-based sparse representations for visual
signals to enable speedups when solving sparse approximation problems. The
specific structure that we endow onto sparse models is that of a multi-scale
modeling where the sparse representation at each scale is constrained by the
sparse representation at coarser scales. We show that this cross-scale
predictive model delivers significant speedups, often in the range of
10-60$\times$, with little loss in accuracy for linear inverse problems
associated with images, videos, and light fields.
Predicting the Industry of Users on Social Media
Authors: Konstantinos Pappas, Rada Mihalcea
Date: 2016-12-24 00:00:00
Tasks: Ensemble Learning, Feature Engineering, General Classification

Automatic profiling of social media users is an important task for supporting
a multitude of downstream applications. While a number of studies have used
social media content to extract and study collective social attributes, there
is a lack of substantial research that addresses the detection of a user's
industry. We frame this task as classification using both feature engineering
and ensemble learning. Our industry-detection system uses both posted content
and profile information to detect a user's industry with 64.3% accuracy,
significantly outperforming the majority baseline in a taxonomy of fourteen
industry classes. Our qualitative analysis suggests that a person's industry
not only affects the words used and their perceived meanings, but also the
number and type of emotions being expressed.
JU_KS_Group@FIRE 2016: Consumer Health Information Search
Authors: Kamal Sarkar, Debanjan Das, Indra Banerjee, Mamta Kumari, Prasenjit Biswas
Date: 2016-12-24 00:00:00
Tasks: Information Retrieval, Retrieval, Task 2

In this paper, we describe the methodology used and the results obtained by
us for completing the tasks given under the shared task on Consumer Health
Information Search (CHIS) collocated with the Forum for Information Retrieval
Evaluation (FIRE) 2016, ISI Kolkata. The shared task consists of two sub-tasks
- (1) task1: given a query and a document/set of documents associated with that
query, the task is to classify the sentences in the document as relevant to the
query or not and (2) task 2: the relevant sentences need to be further
classified as supporting the claim made in the query, or opposing the claim
made in the query. We have participated in both the sub-tasks. The percentage
accuracy obtained by our developed system for task1 was 73.39 which is third
highest among the 9 teams participated in the shared task.
KS_JU@DPIL-FIRE2016:Detecting Paraphrases in Indian Languages Using Multinomial Logistic Regression Model
Authors: Kamal Sarkar
Date: 2016-12-24 00:00:00
Tasks: Information Retrieval, regression, Retrieval

In this work, we describe a system that detects paraphrases in Indian
Languages as part of our participation in the shared Task on detecting
paraphrases in Indian Languages (DPIL) organized by Forum for Information
Retrieval Evaluation (FIRE) in 2016. Our paraphrase detection method uses a
multinomial logistic regression model trained with a variety of features which
are basically lexical and semantic level similarities between two sentences in
a pair. The performance of the system has been evaluated against the test set
released for the FIRE 2016 shared task on DPIL. Our system achieves the highest
f-measure of 0.95 on task1 in Punjabi language.The performance of our system on
task1 in Hindi language is f-measure of 0.90. Out of 11 teams participated in
the shared task, only four teams participated in all four languages, Hindi,
Punjabi, Malayalam and Tamil, but the remaining 7 teams participated in one of
the four languages. We also participated in task1 and task2 both for all four
Indian Languages. The overall average performance of our system including task1
and task2 overall four languages is F1-score of 0.81 which is the second
highest score among the four systems that participated in all four languages.
Joint denoising and distortion correction of atomic scale scanning transmission electron microscopy images
Authors: Benjamin Berkels, Benedikt Wirth
Date: 2016-12-24 00:00:00
Tasks: Denoising, distortion correction

Nowadays, modern electron microscopes deliver images at atomic scale. The
precise atomic structure encodes information about material properties. Thus,
an important ingredient in the image analysis is to locate the centers of the
atoms shown in micrographs as precisely as possible. Here, we consider scanning
transmission electron microscopy (STEM), which acquires data in a rastering
pattern, pixel by pixel. Due to this rastering combined with the magnification
to atomic scale, movements of the specimen even at the nanometer scale lead to
random image distortions that make precise atom localization difficult. Given a
series of STEM images, we derive a Bayesian method that jointly estimates the
distortion in each image and reconstructs the underlying atomic grid of the
material by fitting the atom bumps with suitable bump functions. The resulting
highly non-convex minimization problems are solved numerically with a trust
region approach. Well-posedness of the reconstruction method and the model
behavior for faster and faster rastering are investigated using variational
techniques. The performance of the method is finally evaluated on both
synthetic and real experimental data.
Unsupervised Video Segmentation via Spatio-Temporally Nonlocal Appearance Learning
Authors: Kaihua Zhang, Xuejun Li, Qingshan Liu
Date: 2016-12-24 00:00:00
Tasks: Segmentation, Semantic Segmentation, Superpixels, Video Object Segmentation, Video Segmentation, Video Semantic Segmentation

Video object segmentation is challenging due to the factors like rapidly fast
motion, cluttered backgrounds, arbitrary object appearance variation and shape
deformation. Most existing methods only explore appearance information between
two consecutive frames, which do not make full use of the usefully long-term
nonlocal information that is helpful to make the learned appearance stable, and
hence they tend to fail when the targets suffer from large viewpoint changes
and significant non-rigid deformations. In this paper, we propose a simple yet
effective approach to mine the long-term sptatio-temporally nonlocal appearance
information for unsupervised video segmentation. The motivation of our
algorithm comes from the spatio-temporal nonlocality of the region appearance
reoccurrence in a video. Specifically, we first generate a set of superpixels
to represent the foreground and background, and then update the appearance of
each superpixel with its long-term sptatio-temporally nonlocal counterparts
generated by the approximate nearest neighbor search method with the efficient
KD-tree algorithm. Then, with the updated appearances, we formulate a
spatio-temporal graphical model comprised of the superpixel label consistency
potentials. Finally, we generate the segmentation by optimizing the graphical
model via iteratively updating the appearance model and estimating the labels.
Extensive evaluations on the SegTrack and Youtube-Objects datasets demonstrate
the effectiveness of the proposed method, which performs favorably against some
state-of-art methods.
EgoReID: Cross-view Self-Identification and Human Re-identification in Egocentric and Surveillance Videos
Authors: Shervin Ardeshir, Sandesh Sharma, Ali Broji
Date: 2016-12-24 00:00:00
Tasks: Person Re-Identification, Visual Reasoning

Human identification remains to be one of the challenging tasks in computer
vision community due to drastic changes in visual features across different
viewpoints, lighting conditions, occlusion, etc. Most of the literature has
been focused on exploring human re-identification across viewpoints that are
not too drastically different in nature. Cameras usually capture oblique or
side views of humans, leaving room for a lot of geometric and visual reasoning.
Given the recent popularity of egocentric and top-view vision,
re-identification across these two drastically different views can now be
explored. Having an egocentric and a top view video, our goal is to identify
the cameraman in the content of the top-view video, and also re-identify the
people visible in the egocentric video, by matching them to the identities
present in the top-view video. We propose a CRF-based method to address the two
problems. Our experimental results demonstrates the efficiency of the proposed
approach over a variety of video recorded from two views.
A Comparative Study of Algorithms for Realtime Panoramic Video Blending
Authors: Zhe Zhu, Jiaming Lu, Minxuan Wang, Song-Hai Zhang, Ralph Martin, Hantao Liu, Shi-Min Hu
Date: 2016-06-01 00:00:00
Tasks: CPU, GPU

Unlike image blending algorithms, video blending algorithms have been little
studied. In this paper, we investigate 6 popular blending algorithms---feather
blending, multi-band blending, modified Poisson blending, mean value coordinate
blending, multi-spline blending and convolution pyramid blending. We consider
in particular realtime panoramic video blending, a key problem in various
virtual reality tasks. To evaluate the performance of the 6 algorithms on this
problem, we have created a video benchmark of several videos captured under
various conditions. We analyze the time and memory needed by the above 6
algorithms, for both CPU and GPU implementations (where readily
parallelizable). The visual quality provided by these algorithms is also
evaluated both objectively and subjectively. The video benchmark and algorithm
implementations are publicly available.
Learning Social Circles in Ego Networks based on Multi-View Social Graphs
Authors: Chao Lan, Yuhao Yang, Xiao-Li Li, Bo Luo, Jun Huan
Date: 2016-07-16 00:00:00
Tasks: Clustering

In social network analysis, automatic social circle detection in ego-networks
is becoming a fundamental and important task, with many potential applications
such as user privacy protection or interest group recommendation. So far, most
studies have focused on addressing two questions, namely, how to detect
overlapping circles and how to detect circles using a combination of network
structure and network node attributes. This paper asks an orthogonal research
question, that is, how to detect circles based on network structures that are
(usually) described by multiple views? Our investigation begins with crawling
ego-networks from Twitter and employing classic techniques to model their
structures by six views, including user relationships, user interactions and
user content. We then apply both standard and our modified multi-view spectral
clustering techniques to detect social circles in these ego-networks. Based on
extensive automatic and manual experimental evaluations, we deliver two major
findings: first, multi-view clustering techniques perform better than common
single-view clustering techniques, which only use one view or naively integrate
all views for detection, second, the standard multi-view clustering technique
is less robust than our modified technique, which selectively transfers
information across views based on an assumption that sparse network structures
are (potentially) incomplete. In particular, the second finding makes us
believe a direct application of standard clustering on potentially incomplete
networks may yield biased results. We lightly examine this issue in theory,
where we derive an upper bound for such bias by integrating theories of
spectral clustering and matrix perturbation, and discuss how it may be affected
by several network characteristics.
Predicting and visualizing psychological attributions with a deep neural network
Authors: Edward Grant, Stephan Sahm, Mariam Zabihi, Marcel van Gerven
Date: 2015-12-04 00:00:00
Tasks: Decision Making

Judgments about personality based on facial appearance are strong effectors
in social decision making, and are known to have impact on areas from
presidential elections to jury decisions. Recent work has shown that it is
possible to predict perception of memorability, trustworthiness, intelligence
and other attributes in human face images. The most successful of these
approaches require face images expertly annotated with key facial landmarks. We
demonstrate a Convolutional Neural Network (CNN) model that is able to perform
the same task without the need for landmark features, thereby greatly
increasing efficiency. The model has high accuracy, surpassing human-level
performance in some cases. Furthermore, we use a deconvolutional approach to
visualize important features for perception of 22 attributes and demonstrate a
new method for separately visualizing positive and negative features.
Robustness in sparse linear models: relative efficiency based on robust approximate message passing
Authors: Jelena Bradic
Date: 2015-07-31 00:00:00
Tasks: Model Selection

Understanding efficiency in high dimensional linear models is a longstanding
problem of interest. Classical work with smaller dimensional problems dating
back to Huber and Bickel has illustrated the benefits of efficient loss
functions. When the number of parameters $p$ is of the same order as the sample
size $n$, $p \approx n$, an efficiency pattern different from the one of Huber
was recently established. In this work, we consider the effects of model
selection on the estimation efficiency of penalized methods. In particular, we
explore whether sparsity, results in new efficiency patterns when $p > n$. In
the interest of deriving the asymptotic mean squared error for regularized
M-estimators, we use the powerful framework of approximate message passing. We
propose a novel, robust and sparse approximate message passing algorithm
(RAMP), that is adaptive to the error distribution. Our algorithm includes many
non-quadratic and non-differentiable loss functions. We derive its asymptotic
mean squared error and show its convergence, while allowing $p, n, s \to
\infty$, with $n/p \in (0,1)$ and $n/s \in (1,\infty)$. We identify new
patterns of relative efficiency regarding a number of penalized $M$ estimators,
when $p$ is much larger than $n$. We show that the classical information bound
is no longer reachable, even for light--tailed error distributions. We show
that the penalized least absolute deviation estimator dominates the penalized
least square estimator, in cases of heavy--tailed distributions. We observe
this pattern for all choices of the number of non-zero parameters $s$, both $s
\leq n$ and $s \approx n$. In non-penalized problems where $s =p \approx n$,
the opposite regime holds. Therefore, we discover that the presence of model
selection significantly changes the efficiency patterns.
Improving Human-Machine Cooperative Visual Search With Soft Highlighting
Authors: Ronald T. Kneusel, Michael C. Mozer
Date: 2016-12-24 00:00:00

Advances in machine learning have produced systems that attain human-level
performance on certain visual tasks, e.g., object identification. Nonetheless,
other tasks requiring visual expertise are unlikely to be entrusted to machines
for some time, e.g., satellite and medical imagery analysis. We describe a
human-machine cooperative approach to visual search, the aim of which is to
outperform either human or machine acting alone. The traditional route to
augmenting human performance with automatic classifiers is to draw boxes around
regions of an image deemed likely to contain a target. Human experts typically
reject this type of hard highlighting. We propose instead a soft highlighting
technique in which the saliency of regions of the visual field is modulated in
a graded fashion based on classifier confidence level. We report on experiments
with both synthetic and natural images showing that soft highlighting achieves
a performance synergy surpassing that attained by hard highlighting.
Solving Combinatorial Optimization problems with Quantum inspired Evolutionary Algorithm Tuned using a Novel Heuristic Method
Authors: Nija Mani, Gursaran, Ashish Mani
Date: 2016-12-23 00:00:00
Tasks: Combinatorial Optimization, Evolutionary Algorithms

Quantum inspired Evolutionary Algorithms were proposed more than a decade ago and have been employed for solving a wide range of difficult search and optimization problems. A number of changes have been proposed to improve performance of canonical QEA. However, canonical QEA is one of the few evolutionary algorithms, which uses a search operator with relatively large number of parameters. It is well known that performance of evolutionary algorithms is dependent on specific value of parameters for a given problem. The advantage of having large number of parameters in an operator is that the search process can be made more powerful even with a single operator without requiring a combination of other operators for exploration and exploitation. However, the tuning of operators with large number of parameters is complex and computationally expensive. This paper proposes a novel heuristic method for tuning parameters of canonical QEA. The tuned QEA outperforms canonical QEA on a class of discrete combinatorial optimization problems which, validates the design of the proposed parameter tuning framework. The proposed framework can be used for tuning other algorithms with both large and small number of tunable parameters.
On Spectral Analysis of Directed Signed Graphs
Authors: Yuemeng Li, Xintao Wu, Aidong Lu
Date: 2016-12-23 00:00:00
Tasks: Clustering

It has been shown that the adjacency eigenspace of a network contains key
information of its underlying structure. However, there has been no study on
spectral analysis of the adjacency matrices of directed signed graphs. In this
paper, we derive theoretical approximations of spectral projections from such
directed signed networks using matrix perturbation theory. We use the derived
theoretical results to study the influences of negative intra cluster and inter
cluster directed edges on node spectral projections. We then develop a spectral
clustering based graph partition algorithm, SC-DSG, and conduct evaluations on
both synthetic and real datasets. Both theoretical analysis and empirical
evaluation demonstrate the effectiveness of the proposed algorithm.
Parallelizing Word2Vec in Multi-Core and Many-Core Architectures
Authors: Shihao Ji, Nadathur Satish, Sheng Li, Pradeep Dubey
Date: 2016-11-18 00:00:00
Tasks: CPU

Word2vec is a widely used algorithm for extracting low-dimensional vector
representations of words. State-of-the-art algorithms including those by
Mikolov et al. have been parallelized for multi-core CPU architectures, but are
based on vector-vector operations with "Hogwild" updates that are
memory-bandwidth intensive and do not efficiently use computational resources.
In this paper, we propose "HogBatch" by improving reuse of various data
structures in the algorithm through the use of minibatching and negative sample
sharing, hence allowing us to express the problem using matrix multiply
operations. We also explore different techniques to distribute word2vec
computation across nodes in a compute cluster, and demonstrate good strong
scalability up to 32 nodes. The new algorithm is particularly suitable for
modern multi-core/many-core architectures, especially Intel's latest Knights
Landing processors, and allows us to scale up the computation near linearly
across cores and nodes, and process hundreds of millions of words per second,
which is the fastest word2vec implementation to the best of our knowledge.
Variational Latent Gaussian Process for Recovering Single-Trial Dynamics from Population Spike Trains
Authors: Yuan Zhao, Il Memming Park
Date: 2016-04-11 00:00:00
Tasks: Point Processes

When governed by underlying low-dimensional dynamics, the interdependence of
simultaneously recorded population of neurons can be explained by a small
number of shared factors, or a low-dimensional trajectory. Recovering these
latent trajectories, particularly from single-trial population recordings, may
help us understand the dynamics that drive neural computation. However, due to
the biophysical constraints and noise in the spike trains, inferring
trajectories from data is a challenging statistical problem in general. Here,
we propose a practical and efficient inference method, called the variational
latent Gaussian process (vLGP). The vLGP combines a generative model with a
history-dependent point process observation together with a smoothness prior on
the latent trajectories. The vLGP improves upon earlier methods for recovering
latent trajectories, which assume either observation models inappropriate for
point processes or linear dynamics. We compare and validate vLGP on both
simulated datasets and population recordings from the primary visual cortex. In
the V1 dataset, we find that vLGP achieves substantially higher performance
than previous methods for predicting omitted spike trains, as well as capturing
both the toroidal topology of visual stimuli space, and the noise-correlation.
These results show that vLGP is a robust method with a potential to reveal
hidden neural dynamics from large-scale neural recordings.