pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Representational Learning for Offline Signature Verification using Multi-Loss Snapshot Ensemble of CNNs
Authors: Saeed Masoudnia, Omid Mersa, Babak N. Araabi, Abdol-Hossein Vahabie, Mohammad Amin Sadeghi, Majid Nili Ahmadabadi
Date: 2019-03-11 00:00:00

Offline Signature Verification (OSV) is a challenging pattern recognition
task, especially in presence of skilled forgeries that are not available during
training. This study aims to tackle its challenges and meet the substantial
need for generalization for OSV by examining different loss functions for
Convolutional Neural Network (CNN). We adopt our new approach to OSV by asking
two questions: 1. which classification loss provides more generalization for
feature learning in OSV? , and 2. How integration of different losses into a
unified multi-loss function lead to an improved learning framework? These
questions are studied based on analysis of three loss functions, including
cross entropy, Cauchy-Schwarz divergence, and hinge loss. According to
complementary features of these losses, we combine them into a dynamic
multi-loss function and propose a novel ensemble framework for simultaneous use
of them in CNN. Our proposed Multi-Loss Snapshot Ensemble (MLSE) consists of
several sequential trials. In each trial, a dominant loss function is selected
from the multi-loss set, and the remaining losses act as a regularizer.
Different trials learn diverse representations for each input based on
signature identification task. This multi-representation set is then employed
for the verification task. An ensemble of SVMs is trained on these
representations, and their decisions are finally combined according to the
selection of most generalizable SVM for each user. We conducted two sets of
experiments based on two different protocols of OSV, i.e., writer-dependent and
writer-independent on three signature datasets: GPDS-Synthetic, MCYT, and
UT-SIG. Based on the writer-dependent OSV protocol, we achieved substantial
improvements over the best EERs in the literature. The results of the second
set of experiments also confirmed the robustness to the arrival of new users
enrolled in the OSV system.
Alignment Based Matching Networks for One-Shot Classification and Open-Set Recognition
Authors: Paresh Malalur, Tommi Jaakkola
Date: 2019-03-11 00:00:00
Tasks: Classification, General Classification, Open Set Learning

Deep learning for object classification relies heavily on convolutional
models. While effective, CNNs are rarely interpretable after the fact. An
attention mechanism can be used to highlight the area of the image that the
model focuses on thus offering a narrow view into the mechanism of
classification. We expand on this idea by forcing the method to explicitly
align images to be classified to reference images representing the classes. The
mechanism of alignment is learned and therefore does not require that the
reference objects are anything like those being classified. Beyond explanation,
our exemplar based cross-alignment method enables classification with only a
single example per category (one-shot). Our model cuts the 5-way, 1-shot error
rate in Omniglot from 2.1% to 1.4% and in MiniImageNet from 53.5% to 46.5%
while simultaneously providing point-wise alignment information providing some
understanding on what the network is capturing. This method of alignment also
enables the recognition of an unsupported class (open-set) in the one-shot
setting while maintaining an F1-score of above 0.5 for Omniglot even with 19
other distracting classes while baselines completely fail to separate the
open-set class in the one-shot setting.
Age prediction using a large chest X-ray dataset
Authors: Alexandros Karargyris, Satyananda Kashyap, Joy T. Wu, Arjun Sharma, Mehdi Moradi, Tanveer Syeda-Mahmood
Date: 2019-03-09 00:00:00

Age prediction based on appearances of different anatomies in medical images
has been clinically explored for many decades. In this paper, we used deep
learning to predict a persons age on Chest X-Rays. Specifically, we trained a
CNN in regression fashion on a large publicly available dataset. Moreover, for
interpretability, we explored activation maps to identify which areas of a CXR
image are important for the machine (i.e. CNN) to predict a patients age,
offering insight. Overall, amongst correctly predicted CXRs, we see areas near
the clavicles, shoulders, spine, and mediastinum being most activated for age
prediction, as one would expect biologically. Amongst incorrectly predicted
CXRs, we have qualitatively identified disease patterns that could possibly
make the anatomies appear older or younger than expected. A further technical
and clinical evaluation would improve this work. As CXR is the most commonly
requested imaging exam, a potential use case for estimating age may be found in
the preventative counseling of patient health status compared to their
age-expected average, particularly when there is a large discrepancy between
predicted age and the real patient age.
Efficient Memory Management for GPU-based Deep Learning Systems
Authors: Junzhe Zhang, Sai Ho Yeung, Yao Shu, Bingsheng He, Wei Wang
Date: 2019-02-19 00:00:00
Tasks: CPU, Deep Learning, GPU, Management, Model Compression

GPU (graphics processing unit) has been used for many data-intensive
applications. Among them, deep learning systems are one of the most important
consumer systems for GPU nowadays. As deep learning applications impose deeper
and larger models in order to achieve higher accuracy, memory management
becomes an important research topic for deep learning systems, given that GPU
has limited memory size. Many approaches have been proposed towards this issue,
e.g., model compression and memory swapping. However, they either degrade the
model accuracy or require a lot of manual intervention. In this paper, we
propose two orthogonal approaches to reduce the memory cost from the system
perspective. Our approaches are transparent to the models, and thus do not
affect the model accuracy. They are achieved by exploiting the iterative nature
of the training algorithm of deep learning to derive the lifetime and
read/write order of all variables. With the lifetime semantics, we are able to
implement a memory pool with minimal fragments. However, the optimization
problem is NP-complete. We propose a heuristic algorithm that reduces up to
13.3% of memory compared with Nvidia's default memory pool with equal time
complexity. With the read/write semantics, the variables that are not in use
can be swapped out from GPU to CPU to reduce the memory footprint. We propose
multiple swapping strategies to automatically decide which variable to swap and
when to swap out (in), which reduces the memory cost by up to 34.2% without
communication overhead.
TrojDRL: Trojan Attacks on Deep Reinforcement Learning Agents
Authors: Panagiota Kiourti, Kacper Wardega, Susmit Jha, Wenchao Li
Date: 2019-03-01 00:00:00
Tasks: Data Poisoning, Deep Reinforcement Learning, General Classification, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent work has identified that classification models implemented as neural
networks are vulnerable to data-poisoning and Trojan attacks at training time.
In this work, we show that these training-time vulnerabilities extend to deep
reinforcement learning (DRL) agents and can be exploited by an adversary with
access to the training process. In particular, we focus on Trojan attacks that
augment the function of reinforcement learning policies with hidden behaviors.
We demonstrate that such attacks can be implemented through minuscule data
poisoning (as little as 0.025% of the training data) and in-band reward
modification that does not affect the reward on normal inputs. The policies
learned with our proposed attack approach perform imperceptibly similar to
benign policies but deteriorate drastically when the Trojan is triggered in
both targeted and untargeted settings. Furthermore, we show that existing
Trojan defense mechanisms for classification tasks are not effective in the
reinforcement learning setting.
Estimating Dynamic Conditional Spread Densities to Optimise Daily Storage Trading of Electricity
Authors: Ekaterina Abramova, Derek Bunn
Date: 2019-03-09 00:00:00

This paper formulates dynamic density functions, based upon skewed-t and
similar representations, to model and forecast electricity price spreads
between different hours of the day. This supports an optimal day ahead storage
and discharge schedule, and thereby facilitates a bidding strategy for a
merchant arbitrage facility into the day-ahead auctions for wholesale
electricity. The four latent moments of the density functions are dynamic and
conditional upon exogenous drivers, thereby permitting the mean, variance,
skewness and kurtosis of the densities to respond hourly to such factors as
weather and demand forecasts. The best specification for each spread is
selected based on the Pinball Loss function, following the closed form
analytical solutions of the cumulative density functions. Those analytical
properties also allow the calculation of risk associated with the spread
arbitrages. From these spread densities, the optimal daily operation of a
battery storage facility is determined.
Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations
Authors: Lily Xu, Shahrzad Gholami, Sara Mc Carthy, Bistra Dilkina, Andrew Plumptre, Milind Tambe, Rohit Singh, Mustapha Nsubuga, Joshua Mabonga, Margaret Driciru, Fred Wanyama, Aggrey Rwetsiba, Tom Okello, Eric Enyel
Date: 2019-03-08 00:00:00
Tasks: Gaussian Processes

Illegal wildlife poaching threatens ecosystems and drives endangered species toward extinction. However, efforts for wildlife protection are constrained by the limited resources of law enforcement agencies. To help combat poaching, the Protection Assistant for Wildlife Security (PAWS) is a machine learning pipeline that has been developed as a data-driven approach to identify areas at high risk of poaching throughout protected areas and compute optimal patrol routes. In this paper, we take an end-to-end approach to the data-to-deployment pipeline for anti-poaching. In doing so, we address challenges including extreme class imbalance (up to 1:200), bias, and uncertainty in wildlife poaching data to enhance PAWS, and we apply our methodology to three national parks with diverse characteristics. (i) We use Gaussian processes to quantify predictive uncertainty, which we exploit to improve robustness of our prescribed patrols and increase detection of snares by an average of 30%. We evaluate our approach on real-world historical poaching data from Murchison Falls and Queen Elizabeth National Parks in Uganda and, for the first time, Srepok Wildlife Sanctuary in Cambodia. (ii) We present the results of large-scale field tests conducted in Murchison Falls and Srepok Wildlife Sanctuary which confirm that the predictive power of PAWS extends promisingly to multiple parks. This paper is part of an effort to expand PAWS to 800 parks around the world through integration with SMART conservation software.
Development details and computational benchmarking of DEPAM
Authors: Paul Nguyen Hong Duc, Dorian Cazau
Date: 2019-03-03 00:00:00
Tasks: Benchmarking, Distributed Computing

In the big data era of observational oceanography, passive acoustics datasets are becoming too high volume to be processed on local computers due to their processor and memory limitations. As a result there is a current need for our community to turn to cloud-based distributed computing. We present a scalable computing system for FFT (Fast Fourier Transform)-based features (e.g., Power Spectral Density) based on the Apache distributed frameworks Hadoop and Spark. These features are at the core of many different types of acoustic analysis where the need of processing data at scale with speed is evident, e.g. serving as long-term averaged learning representations of soundscapes to identify periods of acoustic interest. In addition to provide a complete description of our system implementation, we also performed a computational benchmark comparing our system to three other Scala-only, Matlab and Python based systems in standalone executions, and evaluated its scalability using the speed up metric. Our current results are very promising in terms of computational performance, as we show that our proposed Hadoop/Spark system performs reasonably well on a single node setup comparatively to state-of-the-art processing tools used by the PAM community, and that it could also fully leverage more intensive cluster resources with a almost-linear scalability behaviour above a certain dataset volume.
Scaling Distributed Machine Learning with In-Network Aggregation
Authors: Amedeo Sapio, Marco Canini, Chen-Yu Ho, Jacob Nelson, Panos Kalnis, Changhoon Kim, Arvind Krishnamurthy, Masoud Moshref, Dan R. K. Ports, Peter Richtárik
Date: 2019-02-22 00:00:00
Tasks: BIG-bench Machine Learning

Training machine learning models in parallel is an increasingly important workload. We accelerate distributed parallel training by designing a communication primitive that uses a programmable switch dataplane to execute a key step of the training process. Our approach, SwitchML, reduces the volume of exchanged data by aggregating the model updates from multiple workers in the network. We co-design the switch processing with the end-host protocols and ML frameworks to provide an efficient solution that speeds up training by up to 5.5$\times$ for a number of real-world benchmark models.
Markov Chain-based Cost-Optimal Control Charts for Healthcare Data
Authors: Balázs Dobi, András Zempléni
Date: 2019-02-14 00:00:00

Control charts have traditionally been used in industrial statistics, but are
constantly seeing new areas of application, especially in the age of Industry
4.0. This paper introduces a new method, which is suitable for applications in
the healthcare sector, especially for monitoring a health-characteristic of a
patient. We adapt a Markov chain-based approach and develop a method in which
not only the shift size (i.e. the degradation of the patient's health) can be
random, but the effect of the repair (i.e. treatment) and time between
samplings (i.e. visits) too. This means that we do not use many often-present
assumptions which are usually not applicable for medical treatments. The
average cost of the protocol, which is determined by the time between samplings
and the control limit, can be estimated using the stationary distribution of
the Markov chain.
  Furthermore, we incorporate the standard deviation of the cost into the
optimisation procedure, which is often very important from a process control
viewpoint. The sensitivity of the optimal parameters and the resulting average
cost and cost standard deviation on different parameter values is investigated.
We demonstrate the usefulness of the approach for real-life data of patients
treated in Hungary: namely the monitoring of cholesterol level of patients with
cardiovascular event risk. The results showed that the optimal parameters from
our approach can be somewhat different from the original medical parameters.
SuperTML: Two-Dimensional Word Embedding for the Precognition on Structured Tabular Data
Authors: Baohua Sun, Lin Yang, Wenhan Zhang, Michael Lin, Patrick Dong, Charles Young, Jason Dong
Date: 2019-02-26 00:00:00
Tasks: General Classification, Text Classification, Transfer Learning, Word Embeddings

Tabular data is the most commonly used form of data in industry. Gradient Boosting Trees, Support Vector Machine, Random Forest, and Logistic Regression are typically used for classification tasks on tabular data. DNN models using categorical embeddings are also applied in this task, but all attempts thus far have used one-dimensional embeddings. The recent work of Super Characters method using two-dimensional word embeddings achieved the state of art result in text classification tasks, showcasing the promise of this new approach. In this paper, we propose the SuperTML method, which borrows the idea of Super Characters method and two-dimensional embeddings to address the problem of classification on tabular data. For each input of tabular data, the features are first projected into two-dimensional embeddings like an image, and then this image is fed into fine-tuned two-dimensional CNN models for classification. Experimental results have shown that the proposed SuperTML method had achieved state-of-the-art results on both large and small datasets.
Object tracking in video signals using Compressive Sensing
Authors: Marijana Kracunov, Milica Bastica, Jovana Tesovic
Date: 2019-02-08 00:00:00
Tasks: Compressive Sensing, Object, Object Tracking

Reducing the number of pixels in video signals while maintaining quality
needed for recovering the trace of an object using Compressive Sensing is main
subject of this work. Quality of frames, from video that contains moving
object, are gradually reduced by keeping different number of pixels in each
iteration, going from 45% all the way to 1%. Using algorithm for tracing
object, results were satisfactory and showed mere changes in trajectory graphs,
obtained from original and reconstructed videos.
Demonstration of Vector Flow Imaging using Convolutional Neural Networks
Authors: Thomas Robins, Antonio Stanziola, Kai Reimer, Peter Weinberg, Meng-Xing Tang
Date: 2019-03-11 00:00:00
Tasks: Optical Flow Estimation, Video Recognition

Synthetic Aperture Vector Flow Imaging (SA-VFI) can visualize complex cardiac
and vascular blood flow patterns at high temporal resolution with a large field
of view. Convolutional neural networks (CNNs) are commonly used in image and
video recognition and classification. However, most recently presented CNNs
also allow for making per-pixel predictions as needed in optical flow
velocimetry. To our knowledge we demonstrate here for the first time a CNN
architecture to produce 2D full flow field predictions from high frame rate SA
ultrasound images using supervised learning. The CNN was initially trained
using CFD-generated and augmented noiseless SA ultrasound data of a realistic
vessel geometry. Subsequently, a mix of noisy simulated and real \textit{in
vivo} acquisitions were added to increase the network's robustness. The
resulting flow field of the CNN resembled the ground truth accurately with an
endpoint-error percentage between 6.5\% to 14.5\%. Furthermore, when confronted
with an unknown geometry of an arterial bifurcation, the CNN was able to
predict an accurate flow field indicating its ability for generalization.
Remarkably, the CNN also performed well for rotational flows, which usually
requires advanced, computationally intensive VFI methods. We have demonstrated
that convolutional neural networks can be used to estimate complex
multidirectional flow.
Unpaired image denoising using a generative adversarial network in X-ray CT
Authors: Hyoung Suk Park, Jineon Baek, Sun Kyoung You, Jae Kyu Choi, Jin Keun Seo
Date: 2019-03-04 00:00:00
Tasks: Denoising, Generative Adversarial Network, Image Denoising

This paper proposes a deep learning-based denoising method for noisy low-dose computerized tomography (CT) images in the absence of paired training data. The proposed method uses a fidelity-embedded generative adversarial network (GAN) to learn a denoising function from unpaired training data of low-dose CT (LDCT) and standard-dose CT (SDCT) images, where the denoising function is the optimal generator in the GAN framework. This paper analyzes the f-GAN objective to derive a suitable generator that is optimized by minimizing a weighted sum of two losses: the Kullback-Leibler divergence between an SDCT data distribution and a generated distribution, and the $\ell_2$ loss between the LDCT image and the corresponding generated images (or denoised image). The computed generator reflects the prior belief about SDCT data distribution through training. We observed that the proposed method allows the preservation of fine anomalous features while eliminating noise. The experimental results show that the proposed deep-learning method with unpaired datasets performs comparably to a method using paired datasets. A clinical experiment was also performed to show the validity of the proposed method for noise arising in the low-dose X-ray CT.
Graph Hierarchical Convolutional Recurrent Neural Network (GHCRNN) for Vehicle Condition Prediction
Authors: Mingming Lu, Kunfang Zhang, Haiying Liu, Naixue Xiong
Date: 2019-03-12 00:00:00
Tasks: Decision Making, Time Series Analysis

The prediction of urban vehicle flow and speed can greatly facilitate
people's travel, and also can provide reasonable advice for the decision-making
of relevant government departments. However, due to the spatial, temporal and
hierarchy of vehicle flow and many influencing factors such as weather, it is
difficult to prediction. Most of the existing research methods are to extract
spatial structure information on the road network and extract time series
information from the historical data. However, when extracting spatial
features, these methods have higher time and space complexity, and incorporate
a lot of noise. It is difficult to apply on large graphs, and only considers
the influence of surrounding connected road nodes on the central node, ignoring
a very important hierarchical relationship, namely, similar information of
similar node features and road network structures. In response to these
problems, this paper proposes the Graph Hierarchical Convolutional Recurrent
Neural Network (GHCRNN) model. The model uses GCN (Graph Convolutional
Networks) to extract spatial feature, GRU (Gated Recurrent Units) to extract
temporal feature, and uses the learnable Pooling to extract hierarchical
information, eliminate redundant information and reduce complexity. Applying
this model to the vehicle flow and speed data of Shenzhen and Los Angeles has
been well verified, and the time and memory consumption are effectively reduced
under the compared precision.
A Grid-based Method for Removing Overlaps of Dimensionality Reduction Scatterplot Layouts
Authors: Gladys M. Hilasaca, Wilson E. Marcílio-Jr, Danilo M. Eler, Rafael M. Martins, Fernando V. Paulovich
Date: 2019-03-08 00:00:00
Tasks: Dimensionality Reduction

Dimensionality Reduction (DR) scatterplot layouts have become a ubiquitous visualization tool for analyzing multidimensional datasets. Despite their popularity, such scatterplots suffer from occlusion, especially when informative glyphs are used to represent data instances, potentially obfuscating critical information for the analysis under execution. Different strategies have been devised to address this issue, either producing overlap-free layouts that lack the powerful capabilities of contemporary DR techniques in uncovering interesting data patterns or eliminating overlaps as a post-processing strategy. Despite the good results of post-processing techniques, most of the best methods typically expand or distort the scatterplot area, thus reducing glyphs' size (sometimes) to unreadable dimensions, defeating the purpose of removing overlaps. This paper presents Distance Grid (DGrid), a novel post-processing strategy to remove overlaps from DR layouts that faithfully preserves the original layout's characteristics and bounds the minimum glyph sizes. We show that DGrid surpasses the state-of-the-art in overlap removal (through an extensive comparative evaluation considering multiple different metrics) while also being one of the fastest techniques, especially for large datasets. A user study with 51 participants also shows that DGrid is consistently ranked among the top techniques for preserving the original scatterplots' visual characteristics and the aesthetics of the final results.