pwc-archive/papers-with-abstracts huggingface dataset:
End-to-end Network for Twitter Geolocation Prediction and Hashing
Authors: Jey Han Lau, Lianhua Chi, Khoi-Nguyen Tran, Trevor Cohn
Date: 2017-10-13 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Feature Engineering

We propose an end-to-end neural network to predict the geolocation of a
tweet. The network takes as input a number of raw Twitter metadata such as the
tweet message and associated user account information. Our model is language
independent, and despite minimal feature engineering, it is interpretable and
capable of learning location indicative words and timing patterns. Compared to
state-of-the-art systems, our model outperforms them by 2%-6%. Additionally, we
propose extensions to the model to compress representation learnt by the
network into binary codes. Experiments show that it produces compact codes
compared to benchmark hashing algorithms. An implementation of the model is
released publicly.
Sparse Weighted Canonical Correlation Analysis
Authors: Wenwen Min, Juan Liu, Shihua Zhang
Date: 2017-10-13 00:00:00

Given two data matrices $X$ and $Y$, sparse canonical correlation analysis
(SCCA) is to seek two sparse canonical vectors $u$ and $v$ to maximize the
correlation between $Xu$ and $Yv$. However, classical and sparse CCA models
consider the contribution of all the samples of data matrices and thus cannot
identify an underlying specific subset of samples. To this end, we propose a
novel sparse weighted canonical correlation analysis (SWCCA), where weights are
used for regularizing different samples. We solve the $L_0$-regularized SWCCA
($L_0$-SWCCA) using an alternating iterative algorithm. We apply $L_0$-SWCCA to
synthetic data and real-world data to demonstrate its effectiveness and
superiority compared to related methods. Lastly, we consider also SWCCA with
different penalties like LASSO (Least absolute shrinkage and selection
operator) and Group LASSO, and extend it for integrating more than three data
matrices.
Judging a Book By its Cover
Authors: Brian Kenji Iwana, Syed Tahseen Raza Rizvi, Sheraz Ahmed, Andreas Dengel, Seiichi Uchida
Date: 2016-10-28 00:00:00
Tasks: Genre classification

Book covers communicate information to potential readers, but can that same
information be learned by computers? We propose using a deep Convolutional
Neural Network (CNN) to predict the genre of a book based on the visual clues
provided by its cover. The purpose of this research is to investigate whether
relationships between books and their covers can be learned. However,
determining the genre of a book is a difficult task because covers can be
ambiguous and genres can be overarching. Despite this, we show that a CNN can
extract features and learn underlying design rules set by the designer to
define a genre. Using machine learning, we can bring the large amount of
resources available to the book cover design process. In addition, we present a
new challenging dataset that can be used for many pattern recognition tasks.
Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer's Disease using structural MR and FDG-PET images
Authors: Donghuan Lu, Karteek Popuri, Weiguang Ding, Rakesh Balachandar, Mirza Faisal Beg
Date: 2017-10-13 00:00:00

Alzheimer's Disease (AD) is a progressive neurodegenerative disease. Amnestic
mild cognitive impairment (MCI) is a common first symptom before the conversion
to clinical impairment where the individual becomes unable to perform
activities of daily living independently. Although there is currently no
treatment available, the earlier a conclusive diagnosis is made, the earlier
the potential for interventions to delay or perhaps even prevent progression to
full-blown AD. Neuroimaging scans acquired from MRI and metabolism images
obtained by FDG-PET provide in-vivo view into the structure and function
(glucose metabolism) of the living brain. It is hypothesized that combining
different image modalities could better characterize the change of human brain
and result in a more accuracy early diagnosis of AD. In this paper, we proposed
a novel framework to discriminate normal control(NC) subjects from subjects
with AD pathology (AD and NC, MCI subjects convert to AD in future). Our novel
approach utilizing a multimodal and multiscale deep neural network was found to
deliver a 85.68\% accuracy in the prediction of subjects within 3 years to
conversion. Cross validation experiments proved that it has better
discrimination ability compared with results in existing published literature.
Retinal Fluid Segmentation and Detection in Optical Coherence Tomography Images using Fully Convolutional Neural Network
Authors: Donghuan Lu, Morgan Heisler, Sieun Lee, Gavin Ding, Marinko V. Sarunic, Mirza Faisal Beg
Date: 2017-10-13 00:00:00
Tasks: General Classification, Segmentation

As a non-invasive imaging modality, optical coherence tomography (OCT) can
provide micrometer-resolution 3D images of retinal structures. Therefore it is
commonly used in the diagnosis of retinal diseases associated with edema in and
under the retinal layers. In this paper, a new framework is proposed for the
task of fluid segmentation and detection in retinal OCT images. Based on the
raw images and layers segmented by a graph-cut algorithm, a fully convolutional
neural network was trained to recognize and label the fluid pixels. Random
forest classification was performed on the segmented fluid regions to detect
and reject the falsely labeled fluid regions. The leave-one-out cross
validation experiments on the RETOUCH database show that our method performs
well in both segmentation (mean Dice: 0.7317) and detection (mean AUC: 0.985)
tasks.
Quantifying Facial Age by Posterior of Age Comparisons
Authors: Yunxuan Zhang, Li Liu, Cheng Li, Chen Change Loy
Date: 2017-08-31 00:00:00
Tasks: Age And Gender Classification, Age Estimation

We introduce a novel approach for annotating large quantity of in-the-wild
facial images with high-quality posterior age distribution as labels. Each
posterior provides a probability distribution of estimated ages for a face. Our
approach is motivated by observations that it is easier to distinguish who is
the older of two people than to determine the person's actual age. Given a
reference database with samples of known ages and a dataset to label, we can
transfer reliable annotations from the former to the latter via
human-in-the-loop comparisons. We show an effective way to transform such
comparisons to posterior via fully-connected and SoftMax layers, so as to
permit end-to-end training in a deep network. Thanks to the efficient and
effective annotation approach, we collect a new large-scale facial age dataset,
dubbed `MegaAge', which consists of 41,941 images. Data can be downloaded from
our project page mmlab.ie.cuhk.edu.hk/projects/MegaAge and
github.com/zyx2012/Age_estimation_BMVC2017. With the dataset, we train a
network that jointly performs ordinal hyperplane classification and posterior
distribution learning. Our approach achieves state-of-the-art results on
popular benchmarks such as MORPH2, Adience, and the newly proposed MegaAge.
HyperENTM: Evolving Scalable Neural Turing Machines through HyperNEAT
Authors: Jakob Merrild, Mikkel Angaju Rasmussen, Sebastian Risi
Date: 2017-10-12 00:00:00

Recent developments within memory-augmented neural networks have solved
sequential problems requiring long-term memory, which are intractable for
traditional neural networks. However, current approaches still struggle to
scale to large memory sizes and sequence lengths. In this paper we show how
access to memory can be encoded geometrically through a HyperNEAT-based Neural
Turing Machine (HyperENTM). We demonstrate that using the indirect HyperNEAT
encoding allows for training on small memory vectors in a bit-vector copy task
and then applying the knowledge gained from such training to speed up training
on larger size memory vectors. Additionally, we demonstrate that in some
instances, networks trained to copy bit-vectors of size 9 can be scaled to
sizes of 1,000 without further training. While the task in this paper is
simple, these results could open up the problems amendable to networks with
external memories to problems with larger memory vectors and theoretically
unbounded memory sizes.
Can the early human visual system compete with Deep Neural Networks?
Authors: Samuel Dodge, Lina Karam
Date: 2017-10-12 00:00:00
Tasks: General Classification

We study and compare the human visual system and state-of-the-art deep neural
networks on classification of distorted images. Different from previous works,
we limit the display time to 100ms to test only the early mechanisms of the
human visual system, without allowing time for any eye movements or other
higher level processes. Our findings show that the human visual system still
outperforms modern deep neural networks under blurry and noisy images. These
findings motivate future research into developing more robust deep networks.
Identifying On-time Reward Delivery Projects with Estimating Delivery Duration on Kickstarter
Authors: Thanh Tran, Kyumin Lee, Nguyen Vo, Hongkyu Choi
Date: 2017-10-12 00:00:00

In Crowdfunding platforms, people turn their prototype ideas into real
products by raising money from the crowd, or invest in someone else's projects.
In reward-based crowdfunding platforms such as Kickstarter and Indiegogo,
selecting accurate reward delivery duration becomes crucial for creators,
backers, and platform providers to keep the trust between the creators and the
backers, and the trust between the platform providers and users. According to
Kickstarter, 35% backers did not receive rewards on time. Unfortunately, little
is known about on-time and late reward delivery projects, and there is no prior
work to estimate reward delivery duration. To fill the gap, in this paper, we
(i) extract novel features that reveal latent difficulty levels of project
rewards; (ii) build predictive models to identify whether a creator will
deliver all rewards in a project on time or not; and (iii) build a regression
model to estimate accurate reward delivery duration (i.e., how long it will
take to produce and deliver all the rewards). Experimental results show that
our models achieve good performance -- 82.5% accuracy, 78.1 RMSE, and 0.108
NRMSE at the first 5% of the longest reward delivery duration.
On the Runtime-Efficacy Trade-off of Anomaly Detection Techniques for Real-Time Streaming Data
Authors: Dhruv Choudhary, Arun Kejariwal, Francois Orsini
Date: 2017-10-12 00:00:00
Tasks: Anomaly Detection, Astronomy, Econometrics, Marketing, Model Selection

Ever growing volume and velocity of data coupled with decreasing attention
span of end users underscore the critical need for real-time analytics. In this
regard, anomaly detection plays a key role as an application as well as a means
to verify data fidelity. Although the subject of anomaly detection has been
researched for over 100 years in a multitude of disciplines such as, but not
limited to, astronomy, statistics, manufacturing, econometrics, marketing, most
of the existing techniques cannot be used as is on real-time data streams.
Further, the lack of characterization of performance -- both with respect to
real-timeliness and accuracy -- on production data sets makes model selection
very challenging. To this end, we present an in-depth analysis, geared towards
real-time streaming data, of anomaly detection techniques. Given the
requirements with respect to real-timeliness and accuracy, the analysis
presented in this paper should serve as a guide for selection of the "best"
anomaly detection technique. To the best of our knowledge, this is the first
characterization of anomaly detection techniques proposed in very diverse set
of fields, using production data sets corresponding to a wide set of
application domains.
STDP Based Pruning of Connections and Weight Quantization in Spiking Neural Networks for Energy Efficient Recognition
Authors: Nitin Rathi, Priyadarshini Panda, Kaushik Roy
Date: 2017-10-12 00:00:00
Tasks: General Classification, Quantization

Spiking Neural Networks (SNNs) with a large number of weights and varied
weight distribution can be difficult to implement in emerging in-memory
computing hardware due to the limitations on crossbar size (implementing dot
product), the constrained number of conductance levels in non-CMOS devices and
the power budget. We present a sparse SNN topology where non-critical
connections are pruned to reduce the network size and the remaining critical
synapses are weight quantized to accommodate for limited conductance levels.
Pruning is based on the power law weight-dependent Spike Timing Dependent
Plasticity (STDP) model; synapses between pre- and post-neuron with high spike
correlation are retained, whereas synapses with low correlation or uncorrelated
spiking activity are pruned. The weights of the retained connections are
quantized to the available number of conductance levels. The process of pruning
non-critical connections and quantizing the weights of critical synapses is
performed at regular intervals during training. We evaluated our sparse and
quantized network on MNIST dataset and on a subset of images from Caltech-101
dataset. The compressed topology achieved a classification accuracy of 90.1%
(91.6%) on the MNIST (Caltech-101) dataset with 3.1x (2.2x) and 4x (2.6x)
improvement in energy and area, respectively. The compressed topology is energy
and area efficient while maintaining the same classification accuracy of a
2-layer fully connected SNN topology.
Opinion Polarization by Learning from Social Feedback
Authors: Sven Banisch, Eckehard Olbrich
Date: 2017-04-07 00:00:00

We explore a new mechanism to explain polarization phenomena in opinion
dynamics in which agents evaluate alternative views on the basis of the social
feedback obtained on expressing them. High support of the favored opinion in
the social environment, is treated as a positive feedback which reinforces the
value associated to this opinion. In connected networks of sufficiently high
modularity, different groups of agents can form strong convictions of competing
opinions. Linking the social feedback process to standard equilibrium concepts
we analytically characterize sufficient conditions for the stability of
bi-polarization. While previous models have emphasized the polarization effects
of deliberative argument-based communication, our model highlights an affective
experience-based route to polarization, without assumptions about negative
influence or bounded confidence.
Meta Inverse Reinforcement Learning via Maximum Reward Sharing for Human Motion Analysis
Authors: Kun Li, Joel W. Burdick
Date: 2017-10-07 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This work handles the inverse reinforcement learning (IRL) problem where only
a small number of demonstrations are available from a demonstrator for each
high-dimensional task, insufficient to estimate an accurate reward function.
Observing that each demonstrator has an inherent reward for each state and the
task-specific behaviors mainly depend on a small number of key states, we
propose a meta IRL algorithm that first models the reward function for each
task as a distribution conditioned on a baseline reward function shared by all
tasks and dependent only on the demonstrator, and then finds the most likely
reward function in the distribution that explains the task-specific behaviors.
We test the method in a simulated environment on path planning tasks with
limited demonstrations, and show that the accuracy of the learned reward
function is significantly improved. We also apply the method to analyze the
motion of a patient under rehabilitation.
Hardware design for binarization and thinning of fingerprint images
Authors: Farshad Kheiri, Shadrokh Samavi, Nader Karimi
Date: 2017-10-12 00:00:00
Tasks: Binarization

Two critical steps in fingerprint recognition are binarization and thinning
of the image. The need for real time processing motivates us to select local
adaptive thresholding approach for the binarization step. We introduce a new
hardware for this purpose based on pipeline architecture. We propose a formula
for selecting an optimal block size for the thresholding purpose. To decrease
minutiae false detection, the binarized image is dilated. We also present in
this paper a new pipeline structure for implementing the thinning algorithm
An Ensemble Deep Learning Based Approach for Red Lesion Detection in Fundus Images
Authors: José Ignacio Orlando, Elena Prokofyeva, Mariana del Fresno, Matthew B. Blaschko
Date: 2017-06-09 00:00:00
Tasks: Lesion Detection

Diabetic retinopathy is one of the leading causes of preventable blindness in
the world. Its earliest sign are red lesions, a general term that groups both
microaneurysms and hemorrhages. In daily clinical practice, these lesions are
manually detected by physicians using fundus photographs. However, this task is
tedious and time consuming, and requires an intensive effort due to the small
size of the lesions and their lack of contrast. Computer-assisted diagnosis of
DR based on red lesion detection is being actively explored due to its
improvement effects both in clinicians consistency and accuracy. Several
methods for detecting red lesions have been proposed in the literature, most of
them based on characterizing lesion candidates using hand crafted features, and
classifying them into true or false positive detections. Deep learning based
approaches, by contrast, are scarce in this domain due to the high expense of
annotating the lesions manually. In this paper we propose a novel method for
red lesion detection based on combining both deep learned and domain knowledge.
Features learned by a CNN are augmented by incorporating hand crafted features.
Such ensemble vector of descriptors is used afterwards to identify true lesion
candidates using a Random Forest classifier. We empirically observed that
combining both sources of information significantly improve results with
respect to using each approach separately. Furthermore, our method reported the
highest performance on a per-lesion basis on DIARETDB1 and e-ophtha, and for
screening and need for referral on MESSIDOR compared to a second human expert.
Results highlight the fact that integrating manually engineered approaches with
deep learned features is relevant to improve results when the networks are
trained from lesion-level annotated data. An open source implementation of our
system is publicly available online.
Robust Communication-Optimal Distributed Clustering Algorithms
Authors: Pranjal Awasthi, Ainesh Bakshi, Maria-Florina Balcan, Colin White, David Woodruff
Date: 2017-03-02 00:00:00
Tasks: Clustering

In this work, we study the $k$-median and $k$-means clustering problems when
the data is distributed across many servers and can contain outliers. While
there has been a lot of work on these problems for worst-case instances, we
focus on gaining a finer understanding through the lens of beyond worst-case
analysis. Our main motivation is the following: for many applications such as
clustering proteins by function or clustering communities in a social network,
there is some unknown target clustering, and the hope is that running a
$k$-median or $k$-means algorithm will produce clusterings which are close to
matching the target clustering. Worst-case results can guarantee constant
factor approximations to the optimal $k$-median or $k$-means objective value,
but not closeness to the target clustering.
  Our first result is a distributed algorithm which returns a near-optimal
clustering assuming a natural notion of stability, namely, approximation
stability [Balcan et. al 2013], even when a constant fraction of the data are
outliers. The communication complexity is $\tilde O(sk+z)$ where $s$ is the
number of machines, $k$ is the number of clusters, and $z$ is the number of
outliers.
  Next, we show this amount of communication cannot be improved even in the
setting when the input satisfies various non-worst-case assumptions. We give a
matching $\Omega(sk+z)$ lower bound on the communication required both for
approximating the optimal $k$-means or $k$-median cost up to any constant, and
for returning a clustering that is close to the target clustering in Hamming
distance. These lower bounds hold even when the data satisfies approximation
stability or other common notions of stability, and the cluster sizes are
balanced. Therefore, $\Omega(sk+z)$ is a communication bottleneck, even for
real-world instances.
Social Attention: Modeling Attention in Human Crowds
Authors: Anirudh Vemula, Katharina Muelling, Jean Oh
Date: 2017-10-12 00:00:00
Tasks: Navigate, Trajectory Prediction

Robots that navigate through human crowds need to be able to plan safe,
efficient, and human predictable trajectories. This is a particularly
challenging problem as it requires the robot to predict future human
trajectories within a crowd where everyone implicitly cooperates with each
other to avoid collisions. Previous approaches to human trajectory prediction
have modeled the interactions between humans as a function of proximity.
However, that is not necessarily true as some people in our immediate vicinity
moving in the same direction might not be as important as other people that are
further away, but that might collide with us in the future. In this work, we
propose Social Attention, a novel trajectory prediction model that captures the
relative importance of each person when navigating in the crowd, irrespective
of their proximity. We demonstrate the performance of our method against a
state-of-the-art approach on two publicly available crowd datasets and analyze
the trained attention model to gain a better understanding of which surrounding
agents humans attend to, when navigating in a crowd.
Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean
Authors: Koushik Nagasubramanian, Sarah Jones, Soumik Sarkar, Asheesh K. Singh, Arti Singh, Baskar Ganapathysubramanian
Date: 2017-10-12 00:00:00
Tasks: Binary Classification, General Classification, Management

Charcoal rot is a fungal disease that thrives in warm dry conditions and
affects the yield of soybeans and other important agronomic crops worldwide.
There is a need for robust, automatic and consistent early detection and
quantification of disease symptoms which are important in breeding programs for
the development of improved cultivars and in crop production for the
implementation of disease control measures for yield protection. Current
methods of plant disease phenotyping are predominantly visual and hence are
slow and prone to human error and variation. There has been increasing interest
in hyperspectral imaging applications for early detection of disease symptoms.
However, the high dimensionality of hyperspectral data makes it very important
to have an efficient analysis pipeline in place for the identification of
disease so that effective crop management decisions can be made. The focus of
this work is to determine the minimal number of most effective hyperspectral
bands that can distinguish between healthy and diseased specimens early in the
growing season. Healthy and diseased hyperspectral data cubes were captured at
3, 6, 9, 12, and 15 days after inoculation. We utilized inoculated and control
specimens from 4 different genotypes. Each hyperspectral image was captured at
240 different wavelengths in the range of 383 to 1032 nm. We used a combination
of genetic algorithm as an optimizer and support vector machines as a
classifier for identification of maximally effective band combinations. A
binary classification between healthy and infected samples using six selected
band combinations obtained a classification accuracy of 97% and a F1 score of
0.97 for the infected class. The results demonstrated that these carefully
chosen bands are more informative than RGB images, and could be used in a
multispectral camera for remote identification of charcoal rot infection in
soybean.