pwc-archive/papers-with-abstracts huggingface dataset:
The Power of Complementary Regularizers: Image Recovery via Transform Learning and Low-Rank Modeling
Authors: Bihan Wen, Yanjun Li, Yoram Bresler
Date: 2018-08-03 00:00:00
Tasks: compressed sensing, Computational Efficiency, Denoising, Dictionary Learning, Image Denoising, Image Reconstruction, Image Restoration

Recent works on adaptive sparse and on low-rank signal modeling have
demonstrated their usefulness in various image / video processing applications.
Patch-based methods exploit local patch sparsity, whereas other works apply
low-rankness of grouped patches to exploit image non-local structures. However,
using either approach alone usually limits performance in image reconstruction
or recovery applications. In this work, we propose a simultaneous sparsity and
low-rank model, dubbed STROLLR, to better represent natural images. In order to
fully utilize both the local and non-local image properties, we develop an
image restoration framework using a transform learning scheme with joint
low-rank regularization. The approach owes some of its computational efficiency
and good performance to the use of transform learning for adaptive sparse
representation rather than the popular synthesis dictionary learning
algorithms, which involve approximation of NP-hard sparse coding and expensive
learning steps. We demonstrate the proposed framework in various applications
to image denoising, inpainting, and compressed sensing based magnetic resonance
imaging. Results show promising performance compared to state-of-the-art
competing methods.
A novel topology design approach using an integrated deep learning network architecture
Authors: Sharad Rawat, M. -H. Herman Shen
Date: 2018-08-03 00:00:00

Topology design optimization offers tremendous opportunity in design and
manufacturing freedoms by designing and producing a part from the ground-up
without a meaningful initial design as required by conventional shape design
optimization approaches. Ideally, with adequate problem statements, to
formulate and solve the topology design problem using a standard topology
optimization process, such as SIMP (Simplified Isotropic Material with
Penalization) is possible. In reality, an estimated over thousands of design
iterations is often required for just a few design variables, the conventional
optimization approach is in general impractical or computationally unachievable
for real world applications significantly diluting the development of the
topology optimization technology. There is, therefore, a need for a different
approach that will be able to optimize the initial design topology effectively
and rapidly. Therefore, this work presents a new topology design procedure to
generate optimal structures using an integrated Generative Adversarial Networks
(GANs) and convolutional neural network architecture.
Did you take the pill? - Detecting Personal Intake of Medicine from Twitter
Authors: Debanjan Mahata, Jasper Friedrichs, Rajiv Ratn Shah, Jing Jiang
Date: 2018-08-03 00:00:00
Tasks: Articles, Pharmacovigilance

Mining social media messages such as tweets, articles, and Facebook posts for
health and drug related information has received significant interest in
pharmacovigilance research. Social media sites (e.g., Twitter), have been used
for monitoring drug abuse, adverse reactions of drug usage and analyzing
expression of sentiments related to drugs. Most of these studies are based on
aggregated results from a large population rather than specific sets of
individuals. In order to conduct studies at an individual level or specific
cohorts, identifying posts mentioning intake of medicine by the user is
necessary. Towards this objective we develop a classifier for identifying
mentions of personal intake of medicine in tweets. We train a stacked ensemble
of shallow convolutional neural network (CNN) models on an annotated dataset.
We use random search for tuning the hyper-parameters of the CNN models and
present an ensemble of best models for the prediction task. Our system produces
state-of-the-art result, with a micro-averaged F-score of 0.693. We believe
that the developed classifier has direct uses in the areas of psychology,
health informatics, pharmacovigilance and affective computing for tracking
moods, emotions and sentiments of patients expressing intake of medicine in
social media.
Geared Rotationally Identical and Invariant Convolutional Neural Network Systems
Authors: ShihChung B. Lo, Matthew T. Freedman, M. D., Seong K. Mun, Ph. D., Heang-Ping Chan, Ph. D
Date: 2018-08-03 00:00:00

Theorems and techniques to form different types of transformationally
invariant processing and to produce the same output quantitatively based on
either transformationally invariant operators or symmetric operations have
recently been introduced by the authors. In this study, we further propose to
compose a geared rotationally identical CNN system (GRI-CNN) with a small step
angle by connecting networks of participated processes at the first flatten
layer. Using an ordinary CNN structure as a base, requirements for constructing
a GRI-CNN include the use of either symmetric input vector or kernels with an
angle increment that can form a complete cycle as a "gearwheel". Four basic
GRI-CNN structures were studied. Each of them can produce quantitatively
identical output results when a rotation angle of the input vector is evenly
divisible by the step angle of the gear. Our study showed when an input vector
rotated with an angle does not match to a step angle, the GRI-CNN can also
produce a highly consistent result. With a design of using an ultra-fine
gear-tooth step angle (e.g., 1 degree or 0.1 degree), all four GRI-CNN systems
can be constructed virtually isotropically.
Where-and-When to Look: Deep Siamese Attention Networks for Video-based Person Re-identification
Authors: Lin Wu, Yang Wang, Junbin Gao, Xue Li
Date: 2018-08-03 00:00:00
Tasks: Metric Learning, Person Re-Identification, Video-Based Person Re-Identification

Video-based person re-identification (re-id) is a central application in
surveillance systems with significant concern in security. Matching persons
across disjoint camera views in their video fragments is inherently challenging
due to the large visual variations and uncontrolled frame rates. There are two
steps crucial to person re-id, namely discriminative feature learning and
metric learning. However, existing approaches consider the two steps
independently, and they do not make full use of the temporal and spatial
information in videos. In this paper, we propose a Siamese attention
architecture that jointly learns spatiotemporal video representations and their
similarity metrics. The network extracts local convolutional features from
regions of each frame, and enhance their discriminative capability by focusing
on distinct regions when measuring the similarity with another pedestrian
video. The attention mechanism is embedded into spatial gated recurrent units
to selectively propagate relevant features and memorize their spatial
dependencies through the network. The model essentially learns which parts
(\emph{where}) from which frames (\emph{when}) are relevant and distinctive for
matching persons and attaches higher importance therein. The proposed Siamese
model is end-to-end trainable to jointly learn comparable hidden
representations for paired pedestrian videos and their similarity value.
Extensive experiments on three benchmark datasets show the effectiveness of
each component of the proposed deep network while outperforming
state-of-the-art methods.
Deep Neural Network for Analysis of DNA Methylation Data
Authors: Hong Yu, Zhanyu Ma
Date: 2018-08-02 00:00:00

Many researches demonstrated that the DNA methylation, which occurs in the context of a CpG, has strong correlation with diseases, including cancer. There is a strong interest in analyzing the DNA methylation data to find how to distinguish different subtypes of the tumor. However, the conventional statistical methods are not suitable for analyzing the highly dimensional DNA methylation data with bounded support. In order to explicitly capture the properties of the data, we design a deep neural network, which composes of several stacked binary restricted Boltzmann machines, to learn the low dimensional deep features of the DNA methylation data. Experiments show these features perform best in breast cancer DNA methylation data cluster analysis, comparing with some state-of-the-art methods.
Attributes' Importance for Zero-Shot Pose-Classification Based on Wearable Sensors
Authors: Hiroki Ohashi, Mohammad Al-Naser, Sheraz Ahmed, Katsuyuki Nakamura, Takuto Sato, Andreas Dengel
Date: 2018-08-02 00:00:00
Tasks: Action Recognition, Attribute, General Classification, Temporal Action Localization, Zero-Shot Learning

This paper presents a simple yet effective method for improving the
performance of zero-shot learning (ZSL). ZSL classifies instances of unseen
classes, from which no training data is available, by utilizing the attributes
of the classes. Conventional ZSL methods have equally dealt with all the
available attributes, but this sometimes causes misclassification. This is
because an attribute that is effective for classifying instances of one class
is not always effective for another class. In this case, a metric of
classifying the latter class can be undesirably influenced by the irrelevant
attribute. This paper solves this problem by taking the importance of each
attribute for each class into account when calculating the metric. In addition
to the proposal of this new method, this paper also contributes by providing a
dataset for pose classification based on wearable sensors, named HDPoseDS. It
contains 22 classes of poses performed by 10 subjects with 31 IMU sensors
across full body. To the best of our knowledge, it is the richest
wearable-sensor dataset especially in terms of sensor density, and thus it is
suitable for studying zero-shot pose/action recognition. The presented method
was evaluated on HDPoseDS and outperformed relative improvement of 5.9% in
comparison to the best baseline method.
Acoustic Scene Classification: A Competition Review
Authors: Shayan Gharib, Honain Derrar, Daisuke Niizumi, Tuukka Senttula, Janne Tommola, Toni Heittola, Tuomas Virtanen, Heikki Huttunen
Date: 2018-08-02 00:00:00
Tasks: Acoustic Scene Classification, Classification, General Classification, Scene Classification

In this paper we study the problem of acoustic scene classification, i.e.,
categorization of audio sequences into mutually exclusive classes based on
their spectral content. We describe the methods and results discovered during a
competition organized in the context of a graduate machine learning course;
both by the students and external participants. We identify the most suitable
methods and study the impact of each by performing an ablation study of the
mixture of approaches. We also compare the results with a neural network
baseline, and show the improvement over that. Finally, we discuss the impact of
using a competition as a part of a university course, and justify its
importance in the curriculum based on student feedback.
Prosodic-Enhanced Siamese Convolutional Neural Networks for Cross-Device Text-Independent Speaker Verification
Authors: Sobhan Soleymani, Ali Dabouei, Seyed Mehdi Iranmanesh, Hadi Kazemi, Jeremy Dawson, Nasser M. Nasrabadi
Date: 2018-07-31 00:00:00
Tasks: Speaker Verification, Text-Independent Speaker Verification

In this paper a novel cross-device text-independent speaker verification
architecture is proposed. Majority of the state-of-the-art deep architectures
that are used for speaker verification tasks consider Mel-frequency cepstral
coefficients. In contrast, our proposed Siamese convolutional neural network
architecture uses Mel-frequency spectrogram coefficients to benefit from the
dependency of the adjacent spectro-temporal features. Moreover, although
spectro-temporal features have proved to be highly reliable in speaker
verification models, they only represent some aspects of short-term acoustic
level traits of the speaker's voice. However, the human voice consists of
several linguistic levels such as acoustic, lexicon, prosody, and phonetics,
that can be utilized in speaker verification models. To compensate for these
inherited shortcomings in spectro-temporal features, we propose to enhance the
proposed Siamese convolutional neural network architecture by deploying a
multilayer perceptron network to incorporate the prosodic, jitter, and shimmer
features. The proposed end-to-end verification architecture performs feature
extraction and verification simultaneously. This proposed architecture displays
significant improvement over classical signal processing approaches and deep
algorithms for forensic cross-device speaker verification.
Deep Learning-Based Multiple Object Visual Tracking on Embedded System for IoT and Mobile Edge Computing Applications
Authors: Beatriz Blanco-Filgueira, Daniel García-Lesta, Mauro Fernández-Sanjurjo, Víctor M. Brea, Paula López
Date: 2018-07-31 00:00:00
Tasks: Benchmarking, Deep Learning, Edge-computing, Visual Tracking

Compute and memory demands of state-of-the-art deep learning methods are
still a shortcoming that must be addressed to make them useful at IoT
end-nodes. In particular, recent results depict a hopeful prospect for image
processing using Convolutional Neural Netwoks, CNNs, but the gap between
software and hardware implementations is already considerable for IoT and
mobile edge computing applications due to their high power consumption. This
proposal performs low-power and real time deep learning-based multiple object
visual tracking implemented on an NVIDIA Jetson TX2 development kit. It
includes a camera and wireless connection capability and it is battery powered
for mobile and outdoor applications. A collection of representative sequences
captured with the on-board camera, dETRUSC video dataset, is used to exemplify
the performance of the proposed algorithm and to facilitate benchmarking. The
results in terms of power consumption and frame rate demonstrate the
feasibility of deep learning algorithms on embedded platforms although more
effort to joint algorithm and hardware design of CNNs is needed.
A recurrent multi-scale approach to RBG-D Object Recognition
Authors: Mirco Planamente, Mohammad Reza Loghmani, Barbara Caputo
Date: 2018-07-31 00:00:00
Tasks: Object, Object Recognition

Technological development aims to produce generations of increasingly
efficient robots able to perform complex tasks. This requires considerable
efforts, from the scientific community, to find new algorithms that solve
computer vision problems, such as object recognition. The diffusion of RGB-D
cameras directed the study towards the research of new architectures able to
exploit the RGB and Depth information. The project that is developed in this
thesis concerns the realization of a new end-to-end architecture for the
recognition of RGB-D objects called RCFusion. Our method generates compact and
highly discriminative multi-modal features by combining complementary RGB and
depth information representing different levels of abstraction. We evaluate our
method on standard object recognition datasets, RGB-D Object Dataset and
JHUIT-50. The experiments performed show that our method outperforms the
existing approaches and establishes new state-of-the-art results for both
datasets.
Robust Log-Optimal Strategy with Reinforcement Learning
Authors: Yifeng Guo, Xingyu Fu, Yuyan Shi, Mingwen Liu
Date: 2018-05-01 00:00:00
Tasks: Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We proposed a new Portfolio Management method termed as Robust Log-Optimal
Strategy (RLOS), which ameliorates the General Log-Optimal Strategy (GLOS) by
approximating the traditional objective function with quadratic Taylor
expansion. It avoids GLOS's complex CDF estimation process,hence resists the
"Butterfly Effect" caused by estimation error. Besides,RLOS retains GLOS's
profitability and the optimization problem involved in RLOS is computationally
far more practical compared to GLOS. Further, we combine RLOS with
Reinforcement Learning (RL) and propose the so-called Robust Log-Optimal
Strategy with Reinforcement Learning (RLOSRL), where the RL agent receives the
analyzed results from RLOS and observes the trading environment to make
comprehensive investment decisions. The RLOSRL's performance is compared to
some traditional strategies on several back tests, where we randomly choose a
selection of constituent stocks of the CSI300 index as assets under management
and the test results validate its profitability and stability.
Learning Term Weights for Ad-hoc Retrieval
Authors: B. Piwowarski
Date: 2016-06-14 00:00:00
Tasks: Information Retrieval, Learning-To-Rank, Retrieval

Most Information Retrieval models compute the relevance score of a document
for a given query by summing term weights specific to a document or a query.
Heuristic approaches, like TF-IDF, or probabilistic models, like BM25, are used
to specify how a term weight is computed. In this paper, we propose to leverage
learning-to-rank principles to learn how to compute a term weight for a given
document based on the term occurrence pattern.
Visualizing Convolutional Networks for MRI-based Diagnosis of Alzheimer's Disease
Authors: Johannes Rieke, Fabian Eitel, Martin Weygandt, John-Dylan Haynes, Kerstin Ritter
Date: 2018-08-08 00:00:00
Tasks: Decision Making

Visualizing and interpreting convolutional neural networks (CNNs) is an
important task to increase trust in automatic medical decision making systems.
In this study, we train a 3D CNN to detect Alzheimer's disease based on
structural MRI scans of the brain. Then, we apply four different gradient-based
and occlusion-based visualization methods that explain the network's
classification decisions by highlighting relevant areas in the input image. We
compare the methods qualitatively and quantitatively. We find that all four
methods focus on brain regions known to be involved in Alzheimer's disease,
such as inferior and middle temporal gyrus. While the occlusion-based methods
focus more on specific regions, the gradient-based methods pick up distributed
relevance patterns. Additionally, we find that the distribution of relevance
varies across patients, with some having a stronger focus on the temporal lobe,
whereas for others more cortical areas are relevant. In summary, we show that
applying different visualization methods is important to understand the
decisions of a CNN, a step that is crucial to increase clinical impact and
trust in computer-based decision support systems.
Random directions stochastic approximation with deterministic perturbations
Authors: Prashanth L. A, Shalabh Bhatnagar, Nirav Bhavsar, Michael Fu, Steven I. Marcus
Date: 2018-08-08 00:00:00

We introduce deterministic perturbation schemes for the recently proposed
random directions stochastic approximation (RDSA) [17], and propose new
first-order and second-order algorithms. In the latter case, these are the
first second-order algorithms to incorporate deterministic perturbations. We
show that the gradient and/or Hessian estimates in the resulting algorithms
with deterministic perturbations are asymptotically unbiased, so that the
algorithms are provably convergent. Furthermore, we derive convergence rates to
establish the superiority of the first-order and second-order algorithms, for
the special case of a convex and quadratic optimization problem, respectively.
Numerical experiments are used to validate the theoretical results.
Parkinson's Disease Assessment from a Wrist-Worn Wearable Sensor in Free-Living Conditions: Deep Ensemble Learning and Visualization
Authors: Terry Taewoong Um, Franz Michael Josef Pfister, Daniel Christian Pichler, Satoshi Endo, Muriel Lang, Sandra Hirche, Urban Fietzek, Dana Kulić
Date: 2018-08-08 00:00:00
Tasks: Ensemble Learning

Parkinson's Disease (PD) is characterized by disorders in motor function such
as freezing of gait, rest tremor, rigidity, and slowed and hyposcaled
movements. Medication with dopaminergic medication may alleviate those motor
symptoms, however, side-effects may include uncontrolled movements, known as
dyskinesia. In this paper, an automatic PD motor-state assessment in
free-living conditions is proposed using an accelerometer in a wrist-worn
wearable sensor. In particular, an ensemble of convolutional neural networks
(CNNs) is applied to capture the large variability of daily-living activities
and overcome the dissimilarity between training and test patients due to the
inter-patient variability. In addition, class activation map (CAM), a
visualization technique for CNNs, is applied for providing an interpretation of
the results.
Additional Representations for Improving Synthetic Aperture Sonar Classification Using Convolutional Neural Networks
Authors: Isaac Gerg, David Williams
Date: 2018-08-08 00:00:00
Tasks: Classification, General Classification

Object classification in synthetic aperture sonar (SAS) imagery is usually a
data starved and class imbalanced problem. There are few objects of interest
present among much benign seafloor. Despite these problems, current
classification techniques discard a large portion of the collected SAS
information. In particular, a beamformed SAS image, which we call a single-look
complex (SLC) image, contains complex pixels composed of real and imaginary
parts. For human consumption, the SLC is converted to a magnitude-phase
representation and the phase information is discarded. Even more problematic,
the magnitude information usually exhibits a large dynamic range (>80dB) and
must be dynamic range compressed for human display. Often it is this dynamic
range compressed representation, originally designed for human consumption,
which is fed into a classifier. Consequently, the classification process is
completely void of the phase information. In this work, we show improvements in
classification performance using the phase information from the SLC as well as
information from an alternate source: photographs. We perform statistical
testing to demonstrate the validity of our results.
Choose Your Neuron: Incorporating Domain Knowledge through Neuron-Importance
Authors: Ramprasaath R. Selvaraju, Prithvijit Chattopadhyay, Mohamed Elhoseiny, Tilak Sharma, Dhruv Batra, Devi Parikh, Stefan Lee
Date: 2018-08-08 00:00:00
Proceeding: ECCV 2018 9
Tasks: Generalized Zero-Shot Learning, Zero-Shot Learning

Individual neurons in convolutional neural networks supervised for
image-level classification tasks have been shown to implicitly learn
semantically meaningful concepts ranging from simple textures and shapes to
whole or partial objects - forming a "dictionary" of concepts acquired through
the learning process. In this work we introduce a simple, efficient zero-shot
learning approach based on this observation. Our approach, which we call Neuron
Importance-AwareWeight Transfer (NIWT), learns to map domain knowledge about
novel "unseen" classes onto this dictionary of learned concepts and then
optimizes for network parameters that can effectively combine these concepts -
essentially learning classifiers by discovering and composing learned semantic
concepts in deep networks. Our approach shows improvements over previous
approaches on the CUBirds and AWA2 generalized zero-shot learning benchmarks.
We demonstrate our approach on a diverse set of semantic inputs as external
domain knowledge including attributes and natural language captions. Moreover
by learning inverse mappings, NIWT can provide visual and textual explanations
for the predictions made by the newly learned classifiers and provide neuron
names. Our code is available at
https://github.com/ramprs/neuron-importance-zsl.