pwc-archive/papers-with-abstracts huggingface dataset:
Signal Demodulation with Machine Learning Methods for Physical Layer Visible Light Communications: Prototype Platform, Open Dataset and Algorithms
Authors: Shuai Ma, Jiahui Dai, Songtao Lu, Hang Li, Han Zhang, Chun Du, Shiyin Li
Date: 2019-03-13 00:00:00
Tasks: image-classification, Image Classification

In this paper, we investigate the design and implementation of machine
learning (ML) based demodulation methods in the physical layer of visible light
communication (VLC) systems. We build a flexible hardware prototype of an
end-to-end VLC system, from which the received signals are collected as the
real data. The dataset is available online, which contains eight types of
modulated signals. Then, we propose three ML demodulators based on
convolutional neural network (CNN), deep belief network (DBN), and adaptive
boosting (AdaBoost), respectively. Specifically, the CNN based demodulator
converts the modulated signals to images and recognizes the signals by the
image classification. The proposed DBN based demodulator contains three
restricted Boltzmann machines (RBMs) to extract the modulation features. The
AdaBoost method includes a strong classifier that is constructed by the weak
classifiers with the k-nearest neighbor (KNN) algorithm. These three
demodulators are trained and tested by our online open dataset. Experimental
results show that the demodulation accuracy of the three data-driven
demodulators drops as the transmission distance increases. A higher modulation
order negatively influences the accuracy for a given transmission distance.
Among the three ML methods, the AdaBoost modulator achieves the best
performance.
Transform Learning for Magnetic Resonance Image Reconstruction: From Model-based Learning to Building Neural Networks
Authors: Bihan Wen, Saiprasad Ravishankar, Luke Pfister, Yoram Bresler
Date: 2019-03-25 00:00:00
Tasks: Clustering, compressed sensing, Image Reconstruction, MRI Reconstruction

Magnetic resonance imaging (MRI) is widely used in clinical practice, but it has been traditionally limited by its slow data acquisition. Recent advances in compressed sensing (CS) techniques for MRI reduce acquisition time while maintaining high image quality. Whereas classical CS assumes the images are sparse in known analytical dictionaries or transform domains, methods using learned image models for reconstruction have become popular. The model could be pre-learned from datasets, or learned simultaneously with the reconstruction, i.e., blind CS (BCS). Besides the well-known synthesis dictionary model, recent advances in transform learning (TL) provide an efficient alternative framework for sparse modeling in MRI. TL-based methods enjoy numerous advantages including exact sparse coding, transform update, and clustering solutions, cheap computation, and convergence guarantees, and provide high-quality results in MRI compared to popular competing methods. This paper provides a review of some recent works in MRI reconstruction from limited data, with focus on the recent TL-based methods. A unified framework for incorporating various TL-based models is presented. We discuss the connections between transform learning and convolutional or filter bank models and corresponding multi-layer extensions, with connections to deep learning. Finally, we discuss recent trends in MRI, open problems, and future directions for the field.
Transfer Learning for Image-Based Malware Classification
Authors: Niket Bhodia, Pratikkumar Prajapati, Fabio Di Troia, Mark Stamp
Date: 2019-01-21 00:00:00
Tasks: Classification, General Classification, Malware Classification, Malware Detection, Transfer Learning

In this paper, we consider the problem of malware detection and
classification based on image analysis. We convert executable files to images
and apply image recognition using deep learning (DL) models. To train these
models, we employ transfer learning based on existing DL models that have been
pre-trained on massive image datasets. We carry out various experiments with
this technique and compare its performance to that of an extremely simple
machine learning technique, namely, k-nearest neighbors (\kNN). For our k-NN
experiments, we use features extracted directly from executables, rather than
image analysis. While our image-based DL technique performs well in the
experiments, surprisingly, it is outperformed by k-NN. We show that DL models
are better able to generalize the data, in the sense that they outperform k-NN
in simulated zero-day experiments.
Motion Deblurring with an Adaptive Network
Authors: Kuldeep Purohit, A. N. Rajagopalan
Date: 2019-03-25 00:00:00
Tasks: Deblurring, Image Deblurring, Video Deblurring

In this paper, we address the problem of dynamic scene deblurring in the presence of motion blur. Restoration of images affected by severe blur necessitates a network design with a large receptive field, which existing networks attempt to achieve through simple increment in the number of generic convolution layers, kernel-size, or the scales at which the image is processed. However, increasing the network capacity in this manner comes at the expense of increase in model size and inference speed, and ignoring the non-uniform nature of blur. We present a new architecture composed of spatially adaptive residual learning modules that implicitly discover the spatially varying shifts responsible for non-uniform blur in the input image and learn to modulate the filters. This capability is complemented by a self-attentive module which captures non-local relationships among the intermediate features and enhances the receptive field. We then incorporate a spatiotemporal recurrent module in the design to also facilitate efficient video deblurring. Our networks can implicitly model the spatially-varying deblurring process, while dispensing with multi-scale processing and large filters entirely. Extensive qualitative and quantitative comparisons with prior art on benchmark dynamic scene deblurring datasets clearly demonstrate the superiority of the proposed networks via reduction in model-size and significant improvements in accuracy and speed, enabling almost real-time deblurring.
Using machine learning to create high-efficiency freeform illumination design tools
Authors: Caleb Gannon, Rongguang Liang
Date: 2018-12-12 00:00:00
Tasks: BIG-bench Machine Learning, Vocal Bursts Intensity Prediction

We present a method for improving the efficiency and user experience of
freeform illumination design with machine learning. By utilizing orthogonal
polynomials to interface with artificial neural networks, we are able to
generalize relationships between freeform surface shapes and design parameters.
Then, by training the network to generalize the relationship between high-level
design goals and final performance, we were able to transform what is
traditionally a difficult and computationally intensive problem into a compact,
user friendly form. The potential of the proposed method is demonstrated
through the design of uniform square patterns from off-axis positions and
rectangular patterns of tuneable aspect ratios and distances from the target.
Augmented Ultrasonic Data for Machine Learning
Authors: Iikka Virkkunen, Tuomas Koskinen, Oskari Jessen-Juhler, Jari Rinta-Aho
Date: 2019-03-26 00:00:00
Tasks: BIG-bench Machine Learning, Data Augmentation

Flaw detection in non-destructive testing, especially in complex signals like
ultrasonic data, has thus far relied heavily on the expertise and judgement of
trained human inspectors. While automated systems have been used for a long
time, these have mostly been limited to using simple decision automation, such
as signal amplitude threshold. The recent advances in various machine learning
algorithms have solved many similarly difficult classification problems, that
have previously been considered intractable. For non-destructive testing,
encouraging results have already been reported in the open literature, but the
use of machine learning is still very limited in NDT applications in the field.
Key issue hindering their use, is the limited availability of representative
flawed data-sets to be used for training. In the present paper, we develop
modern, very deep convolutional network to detect flaws from phased-array
ultrasonic data. We make extensive use of data augmentation to enhance the
initially limited raw data and to aid learning. The data augmentation utilizes
virtual flaws - a technique, that has successfully been used in training human
inspectors and is soon to be used in nuclear inspection qualification. The
results from the machine learning classifier are compared to human performance.
We show, that using sophisticated data augmentation, modern deep learning
networks can be trained to achieve superhuman performance by significant
margin.
Essential guidelines for computational method benchmarking
Authors: Lukas M. Weber, Wouter Saelens, Robrecht Cannoodt, Charlotte Soneson, Alexander Hapfelmeier, Paul Gardner, Anne-Laure Boulesteix, Yvan Saeys, Mark D. Robinson
Date: 2018-12-03 00:00:00
Tasks: Benchmarking

In computational biology and other sciences, researchers are frequently faced
with a choice between several computational methods for performing data
analyses. Benchmarking studies aim to rigorously compare the performance of
different methods using well-characterized benchmark datasets, to determine the
strengths of each method or to provide recommendations regarding the best
choice of method for an analysis. However, benchmarking studies must be
carefully designed and implemented to provide accurate and unbiased results.
Here, we summarize key practical guidelines and recommendations for performing
high-quality benchmarking analyses, based on our own experiences in
computational biology.
TensorMask: A Foundation for Dense Object Segmentation
Authors: Xinlei Chen, Ross Girshick, Kaiming He, Piotr Dollár
Date: 2019-03-28 00:00:00
Proceeding: ICCV 2019 10
Tasks: Instance Segmentation, Object, object-detection, Object Detection, Prediction, Segmentation, Semantic Segmentation

Sliding-window object detectors that generate bounding-box object predictions over a dense, regular grid have advanced rapidly and proven popular. In contrast, modern instance segmentation approaches are dominated by methods that first detect object bounding boxes, and then crop and segment these regions, as popularized by Mask R-CNN. In this work, we investigate the paradigm of dense sliding-window instance segmentation, which is surprisingly under-explored. Our core observation is that this task is fundamentally different than other dense prediction tasks such as semantic segmentation or bounding-box object detection, as the output at every spatial location is itself a geometric structure with its own spatial dimensions. To formalize this, we treat dense instance segmentation as a prediction task over 4D tensors and present a general framework called TensorMask that explicitly captures this geometry and enables novel operators on 4D tensors. We demonstrate that the tensor view leads to large gains over baselines that ignore this structure, and leads to results comparable to Mask R-CNN. These promising results suggest that TensorMask can serve as a foundation for novel advances in dense mask prediction and a more complete understanding of the task. Code will be made available.
Painting with baryons: augmenting N-body simulations with gas using deep generative models
Authors: Tilman Tröster, Cameron Ferguson, Joachim Harnois-Déraps, Ian G. McCarthy
Date: 2019-03-28 00:00:00
Tasks: Generative Adversarial Network

Running hydrodynamical simulations to produce mock data of large-scale structure and baryonic probes, such as the thermal Sunyaev-Zeldovich (tSZ) effect, at cosmological scales is computationally challenging. We propose to leverage the expressive power of deep generative models to find an effective description of the large-scale gas distribution and temperature. We train two deep generative models, a variational auto-encoder and a generative adversarial network, on pairs of matter density and pressure slices from the BAHAMAS hydrodynamical simulation. The trained models are able to successfully map matter density to the corresponding gas pressure. We then apply the trained models on 100 lines-of-sight from SLICS, a suite of N-body simulations optimised for weak lensing covariance estimation, to generate maps of the tSZ effect. The generated tSZ maps are found to be statistically consistent with those from BAHAMAS. We conclude by considering a specific observable, the angular cross-power spectrum between the weak lensing convergence and the tSZ effect and its variance, where we find excellent agreement between the predictions from BAHAMAS and SLICS, thus enabling the use of SLICS for tSZ covariance estimation.
Fast video object segmentation with Spatio-Temporal GANs
Authors: Sergi Caelles, Albert Pumarola, Francesc Moreno-Noguer, Alberto Sanfeliu, Luc van Gool
Date: 2019-03-28 00:00:00
Tasks: Descriptive, Object, One-shot visual object segmentation, Optical Flow Estimation, Semantic Segmentation, Semi-Supervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation

Learning descriptive spatio-temporal object models from data is paramount for
the task of semi-supervised video object segmentation. Most existing approaches
mainly rely on models that estimate the segmentation mask based on a reference
mask at the first frame (aided sometimes by optical flow or the previous mask).
These models, however, are prone to fail under rapid appearance changes or
occlusions due to their limitations in modelling the temporal component. On the
other hand, very recently, other approaches learned long-term features using a
convolutional LSTM to leverage the information from all previous video frames.
Even though these models achieve better temporal representations, they still
have to be fine-tuned for every new video sequence. In this paper, we present
an intermediate solution and devise a novel GAN architecture, FaSTGAN, to learn
spatio-temporal object models over finite temporal windows. To achieve this, we
concentrate all the heavy computational load to the training phase with two
critics that enforce spatial and temporal mask consistency over the last K
frames. Then at test time, we only use a relatively light regressor, which
reduces the inference time considerably. As a result, our approach combines a
high resiliency to sudden geometric and photometric object changes with
efficiency at test time (no need for fine-tuning nor post-processing). We
demonstrate that the accuracy of our method is on par with state-of-the-art
techniques on the challenging YouTube-VOS and DAVIS datasets, while running at
32 fps, about 4x faster than the closest competitor.
Resilient Combination of Complementary CNN and RNN Features for Text Classification through Attention and Ensembling
Authors: Athanasios Giannakopoulos, Maxime Coriou, Andreea Hossmann, Michael Baeriswyl, Claudiu Musat
Date: 2019-03-28 00:00:00
Tasks: Classification, General Classification, text-classification, Text Classification

State-of-the-art methods for text classification include several distinct
steps of pre-processing, feature extraction and post-processing. In this work,
we focus on end-to-end neural architectures and show that the best performance
in text classification is obtained by combining information from different
neural modules. Concretely, we combine convolution, recurrent and attention
modules with ensemble methods and show that they are complementary. We
introduce ECGA, an end-to-end go-to architecture for novel text classification
tasks. We prove that it is efficient and robust, as it attains or surpasses the
state-of-the-art on varied datasets, including both low and high data regimes.
3D Whole Brain Segmentation using Spatially Localized Atlas Network Tiles
Authors: Yuankai Huo, Zhoubing Xu, Yunxi Xiong, Katherine Aboud, Prasanna Parvathaneni, Shunxing Bao, Camilo Bermudez, Susan M. Resnick, Laurie E. Cutting, Bennett A. Landman
Date: 2019-03-28 00:00:00
Tasks: Brain Segmentation, GPU, Segmentation

Detailed whole brain segmentation is an essential quantitative technique,
which provides a non-invasive way of measuring brain regions from a structural
magnetic resonance imaging (MRI). Recently, deep convolution neural network
(CNN) has been applied to whole brain segmentation. However, restricted by
current GPU memory, 2D based methods, downsampling based 3D CNN methods, and
patch-based high-resolution 3D CNN methods have been the de facto standard
solutions. 3D patch-based high resolution methods typically yield superior
performance among CNN approaches on detailed whole brain segmentation (>100
labels), however, whose performance are still commonly inferior compared with
multi-atlas segmentation methods (MAS) due to the following challenges: (1) a
single network is typically used to learn both spatial and contextual
information for the patches, (2) limited manually traced whole brain volumes
are available (typically less than 50) for training a network. In this work, we
propose the spatially localized atlas network tiles (SLANT) method to
distribute multiple independent 3D fully convolutional networks (FCN) for
high-resolution whole brain segmentation. To address the first challenge,
multiple spatially distributed networks were used in the SLANT method, in which
each network learned contextual information for a fixed spatial location. To
address the second challenge, auxiliary labels on 5111 initially unlabeled
scans were created by multi-atlas segmentation for training. Since the method
integrated multiple traditional medical image processing methods with deep
learning, we developed a containerized pipeline to deploy the end-to-end
solution. From the results, the proposed method achieved superior performance
compared with multi-atlas segmentation methods, while reducing the
computational time from >30 hours to 15 minutes
(https://github.com/MASILab/SLANTbrainSeg).
IMAE for Noise-Robust Learning: Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude's Variance Matters
Authors: Xinshao Wang, Yang Hua, Elyor Kodirov, Neil M. Robertson
Date: 2019-03-28 00:00:00
Tasks: image-classification, Image Classification, Video Retrieval

In this work, we study robust deep learning against abnormal training data from the perspective of example weighting built in empirical loss functions, i.e., gradient magnitude with respect to logits, an angle that is not thoroughly studied so far. Consequently, we have two key findings: (1) Mean Absolute Error (MAE) Does Not Treat Examples Equally. We present new observations and insightful analysis about MAE, which is theoretically proved to be noise-robust. First, we reveal its underfitting problem in practice. Second, we analyse that MAE's noise-robustness is from emphasising on uncertain examples instead of treating training samples equally, as claimed in prior work. (2) The Variance of Gradient Magnitude Matters. We propose an effective and simple solution to enhance MAE's fitting ability while preserving its noise-robustness. Without changing MAE's overall weighting scheme, i.e., what examples get higher weights, we simply change its weighting variance non-linearly so that the impact ratio between two examples are adjusted. Our solution is termed Improved MAE (IMAE). We prove IMAE's effectiveness using extensive experiments: image classification under clean labels, synthetic label noise, and real-world unknown noise. We conclude IMAE is superior to CCE, the most popular loss for training DNNs.
Automatic Defect Segmentation on Leather with Deep Learning
Authors: Sze-Teng Liong, Y. S. Gan, Yen-Chang Huang, Chang-Ann Yuan, Hsiu-Chi Chang
Date: 2019-03-28 00:00:00
Tasks: Deep Learning, Defect Detection, Specificity

Leather is a natural and durable material created through a process of
tanning of hides and skins of animals. The price of the leather is subjective
as it is highly sensitive to its quality and surface defects condition. In the
literature, there are very few works investigating on the defects detection for
leather using automatic image processing techniques. The manual defect
inspection process is essential in an leather production industry to control
the quality of the finished products. However, it is tedious, as it is labour
intensive, time consuming, causes eye fatigue and often prone to human error.
In this paper, a fully automatic defect detection and marking system on a calf
leather is proposed. The proposed system consists of a piece of leather, LED
light, high resolution camera and a robot arm. Succinctly, a machine vision
method is presented to identify the position of the defects on the leather
using a deep learning architecture. Then, a series of processes are conducted
to predict the defect instances, including elicitation of the leather images
with a robot arm, train and test the images using a deep learning architecture
and determination of the boundary of the defects using mathematical derivation
of the geometry. Note that, all the processes do not involve human
intervention, except for the defect ground truths construction stage. The
proposed algorithm is capable to exhibit 91.5% segmentation accuracy on the
train data and 70.35% on the test data. We also report confusion matrix,
F1-score, precision and specificity, sensitivity performance metrics to further
verify the effectiveness of the proposed approach.
Distilling Task-Specific Knowledge from BERT into Simple Neural Networks
Authors: Raphael Tang, Yao Lu, Linqing Liu, Lili Mou, Olga Vechtomova, Jimmy Lin
Date: 2019-03-28 00:00:00
Tasks: Natural Language Inference, Sentence, Sentiment Analysis, Sentiment Classification

In the natural language processing literature, neural networks are becoming
increasingly deeper and complex. The recent poster child of this trend is the
deep language representation model, which includes BERT, ELMo, and GPT. These
developments have led to the conviction that previous-generation, shallower
neural networks for language understanding are obsolete. In this paper,
however, we demonstrate that rudimentary, lightweight neural networks can still
be made competitive without architecture changes, external training data, or
additional input features. We propose to distill knowledge from BERT, a
state-of-the-art language representation model, into a single-layer BiLSTM, as
well as its siamese counterpart for sentence-pair tasks. Across multiple
datasets in paraphrasing, natural language inference, and sentiment
classification, we achieve comparable results with ELMo, while using roughly
100 times fewer parameters and 15 times less inference time.
Using Gaussian process regression for efficient parameter reconstruction
Authors: Philipp-Immanuel Schneider, Martin Hammerschmidt, Lin Zschiedrich, Sven Burger
Date: 2019-03-28 00:00:00
Tasks: Bayesian Optimization, regression

Optical scatterometry is a method to measure the size and shape of periodic
micro- or nanostructures on surfaces. For this purpose the geometry parameters
of the structures are obtained by reproducing experimental measurement results
through numerical simulations. We compare the performance of Bayesian
optimization to different local minimization algorithms for this numerical
optimization problem. Bayesian optimization uses Gaussian-process regression to
find promising parameter values. We examine how pre-computed simulation results
can be used to train the Gaussian process and to accelerate the optimization.