pwc-archive/papers-with-abstracts huggingface dataset:
Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks
Authors: Zhi-Qin John Xu, Yaoyu Zhang, Tao Luo, Yanyang Xiao, Zheng Ma
Date: 2019-01-19 00:00:00

We study the training process of Deep Neural Networks (DNNs) from the Fourier analysis perspective. We demonstrate a very universal Frequency Principle (F-Principle) -- DNNs often fit target functions from low to high frequencies -- on high-dimensional benchmark datasets such as MNIST/CIFAR10 and deep neural networks such as VGG16. This F-Principle of DNNs is opposite to the behavior of most conventional iterative numerical schemes (e.g., Jacobi method), which exhibit faster convergence for higher frequencies for various scientific computing problems. With a simple theory, we illustrate that this F-Principle results from the regularity of the commonly used activation functions. The F-Principle implies an implicit bias that DNNs tend to fit training data by a low-frequency function. This understanding provides an explanation of good generalization of DNNs on most real datasets and bad generalization of DNNs on parity function or randomized dataset.
The RobotriX: An eXtremely Photorealistic and Very-Large-Scale Indoor Dataset of Sequences with Robot Trajectories and Interactions
Authors: Alberto Garcia-Garcia, Pablo Martinez-Gonzalez, Sergiu Oprea, John Alejandro Castro-Vargas, Sergio Orts-Escolano, Jose Garcia-Rodriguez, Alvaro Jover-Alvarez
Date: 2019-01-19 00:00:00
Tasks: Robotic Grasping

Enter the RobotriX, an extremely photorealistic indoor dataset designed to
enable the application of deep learning techniques to a wide variety of robotic
vision problems. The RobotriX consists of hyperrealistic indoor scenes which
are explored by robot agents which also interact with objects in a visually
realistic manner in that simulated world. Photorealistic scenes and robots are
rendered by Unreal Engine into a virtual reality headset which captures gaze so
that a human operator can move the robot and use controllers for the robotic
hands; scene information is dumped on a per-frame basis so that it can be
reproduced offline to generate raw data and ground truth labels. By taking this
approach, we were able to generate a dataset of 38 semantic classes totaling 8M
stills recorded at +60 frames per second with full HD resolution. For each
frame, RGB-D and 3D information is provided with full annotations in both
spaces. Thanks to the high quality and quantity of both raw information and
annotations, the RobotriX will serve as a new milestone for investigating 2D
and 3D robotic vision tasks with large-scale data-driven techniques.
Writer Independent Offline Signature Recognition Using Ensemble Learning
Authors: Sourya Dipta Das, Himanshu Ladia, Vaibhav Kumar, Shivansh Mishra
Date: 2019-01-19 00:00:00
Tasks: Ensemble Learning

The area of Handwritten Signature Verification has been broadly researched in
the last decades, but remains an open research problem. In offline (static)
signature verification, the dynamic information of the signature writing
process is lost, and it is difficult to design good feature extractors that can
distinguish genuine signatures and skilled forgeries. This verification task is
even harder in writer independent scenarios which is undeniably fiscal for
realistic cases. In this paper, we have proposed an Ensemble model for offline
writer, independent signature verification task with Deep learning. We have
used two CNNs for feature extraction, after that RGBT for classification &
Stacking to generate final prediction vector. We have done extensive
experiments on various datasets from various sources to maintain a variance in
the dataset. We have achieved the state of the art performance on various
datasets.
Endoscopic vs. volumetric OCT imaging of mastoid bone structure for pose estimation in minimally invasive cochlear implant surgery
Authors: Max-Heinrich Laves, Sarah Latus, Jan Bergmeier, Tobias Ortmaier, Lüder A. Kahrs, Alexander Schlaefer
Date: 2019-01-19 00:00:00
Tasks: Pose Estimation

Purpose: The facial recess is a delicate structure that must be protected in
minimally invasive cochlear implant surgery. Current research estimates the
drill trajectory by using endoscopy of the unique mastoid patterns. However,
missing depth information limits available features for a registration to
preoperative CT data. Therefore, this paper evaluates OCT for enhanced imaging
of drill holes in mastoid bone and compares OCT data to original endoscopic
images.
  Methods: A catheter-based OCT probe is inserted into a drill trajectory of a
mastoid phantom in a translation-rotation manner to acquire the inner surface
state. The images are undistorted and stitched to create volumentric data of
the drill hole. The mastoid cell pattern is segmented automatically and
compared to ground truth.
  Results: The mastoid pattern segmented on images acquired with OCT show a
similarity of J = 73.6 % to ground truth based on endoscopic images and
measured with the Jaccard metric. Leveraged by additional depth information,
automated segmentation tends to be more robust and fail-safe compared to
endoscopic images.
  Conclusion: The feasibility of using a clinically approved OCT probe for
imaging the drill hole in cochlear implantation is shown. The resulting
volumentric images provide additional information on the shape of caveties in
the bone structure, which will be useful for image-to-patient registration and
to estimate the drill trajectory. This will be another step towards safe
minimally invasive cochlear implantation.
Towards Universal End-to-End Affect Recognition from Multilingual Speech by ConvNets
Authors: Dario Bertero, Onno Kampman, Pascale Fung
Date: 2019-01-19 00:00:00

We propose an end-to-end affect recognition approach using a Convolutional
Neural Network (CNN) that handles multiple languages, with applications to
emotion and personality recognition from speech. We lay the foundation of a
universal model that is trained on multiple languages at once. As affect is
shared across all languages, we are able to leverage shared information between
languages and improve the overall performance for each one. We obtained an
average improvement of 12.8% on emotion and 10.1% on personality when compared
with the same model trained on each language only. It is end-to-end because we
directly take narrow-band raw waveforms as input. This allows us to accept as
input audio recorded from any source and to avoid the overhead and information
loss of feature extraction. It outperforms a similar CNN using spectrograms as
input by 12.8% for emotion and 6.3% for personality, based on F-scores.
Analysis of the network parameters and layers activation shows that the network
learns and extracts significant features in the first layer, in particular
pitch, energy and contour variations. Subsequent convolutional layers instead
capture language-specific representations through the analysis of
supra-segmental features. Our model represents an important step for the
development of a fully universal affect recognizer, able to recognize
additional descriptors, such as stress, and for the future implementation into
affective interactive systems.
Single MR Image Super-Resolution via Channel Splitting and Serial Fusion Network
Authors: Zhao Xiaole, Huali Zhang, Hangfei Liu, Yun Qin, Tao Zhang, Xueming Zou
Date: 2019-01-19 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Spatial resolution is a critical imaging parameter in magnetic resonance
imaging (MRI). Acquiring high resolution MRI data usually takes long scanning
time and would subject to motion artifacts due to hardware, physical, and
physiological limitations. Single image super-resolution (SISR), especially
that based on deep learning techniques, is an effective and promising
alternative technique to improve the current spatial resolution of magnetic
resonance (MR) images. However, the deeper network is more difficult to be
effectively trained because the information is gradually weakened as the
network deepens. This problem becomes more serious for medical images due to
the degradation of training examples. In this paper, we present a novel channel
splitting and serial fusion network (CSSFN) for single MR image
super-resolution. Specifically, the proposed CSSFN network splits the
hierarchical features into a series of subfeatures, which are then integrated
together in a serial manner. Thus, the network becomes deeper and can deal with
the subfeatures on different channels discriminatively. Besides, a dense global
feature fusion (DGFF) is adopted to integrate the intermediate features, which
further promotes the information flow in the network. Extensive experiments on
several typical MR images show the superiority of our CSSFN model over other
advanced SISR methods.
A Conjoint Application of Data Mining Techniques for Analysis of Global Terrorist Attacks -- Prevention and Prediction for Combating Terrorism
Authors: Vivek Kumar, Manuel Mazzara, Maj. Gen., Angelo Messina, Jooyoung Lee
Date: 2019-01-19 00:00:00

Terrorism has become one of the most tedious problems to deal with and a
prominent threat to mankind. To enhance counter-terrorism, several research
works are developing efficient and precise systems, data mining is not an
exception. Immense data is floating in our lives, though the scarce
availability of authentic terrorist attack data in the public domain makes it
complicated to fight terrorism. This manuscript focuses on data mining
classification techniques and discusses the role of United Nations in
counter-terrorism. It analyzes the performance of classifiers such as Lazy
Tree, Multilayer Perceptron, Multiclass and Na\"ive Bayes classifiers for
observing the trends for terrorist attacks around the world. The database for
experiment purpose is created from different public and open access sources for
years 1970-2015 comprising of 156,772 reported attacks causing massive losses
of lives and property. This work enumerates the losses occurred, trends in
attack frequency and places more prone to it, by considering the attack
responsibilities taken as evaluation class.
Deep Representation Learning Characterized by Inter-class Separation for Image Clustering
Authors: Dipanjan Das, Ratul Ghosh, Brojeshwar Bhowmick
Date: 2019-01-19 00:00:00
Tasks: Clustering, Image Clustering, Representation Learning

Despite significant advances in clustering methods in recent years, the
outcome of clustering of a natural image dataset is still unsatisfactory due to
two important drawbacks. Firstly, clustering of images needs a good feature
representation of an image and secondly, we need a robust method which can
discriminate these features for making them belonging to different clusters
such that intra-class variance is less and inter-class variance is high. Often
these two aspects are dealt with independently and thus the features are not
sufficient enough to partition the data meaningfully. In this paper, we propose
a method where we discover these features required for the separation of the
images using deep autoencoder. Our method learns the image representation
features automatically for the purpose of clustering and also select a coherent
image and an incoherent image simultaneously for a given image so that the
feature representation learning can learn better discriminative features for
grouping the similar images in a cluster and at the same time separating the
dissimilar images across clusters. Experiment results show that our method
produces significantly better result than the state-of-the-art methods and we
also show that our method is more generalized across different dataset without
using any pre-trained model like other existing methods.
Deep Time-Frequency Representation and Progressive Decision Fusion for ECG Classification
Authors: Jing Zhang, Jing Tian, Yang Cao, Yuxiang Yang, Xiaobin Xu
Date: 2019-01-19 00:00:00
Tasks: ECG Classification, General Classification

Early recognition of abnormal rhythms in ECG signals is crucial for monitoring and diagnosing patients' cardiac conditions, increasing the success rate of the treatment. Classifying abnormal rhythms into exact categories is very challenging due to the broad taxonomy of rhythms, noises and lack of large-scale real-world annotated data. Different from previous methods that utilize hand-crafted features or learn features from the original signal domain, we propose a novel ECG classification method by learning deep time-frequency representation and progressive decision fusion at different temporal scales in an end-to-end manner. First, the ECG wave signal is transformed into the time-frequency domain by using the Short-Time Fourier Transform. Next, several scale-specific deep convolutional neural networks are trained on ECG samples of a specific length. Finally, a progressive online decision fusion method is proposed to fuse decisions from the scale-specific models into a more accurate and stable one. Extensive experiments on both synthetic and real-world ECG datasets demonstrate the effectiveness and efficiency of the proposed method.
Cross-referencing Social Media and Public Surveillance Camera Data for Disaster Response
Authors: Chittayong Surakitbanharn, Calvin Yau, Guizhen Wang, Aniesh Chawla, Yinuo Pan, Zhaoya Sun, Sam Yellin, David Ebert, Yung-Hsiang Lu, George K. Thiruvathukal
Date: 2019-01-19 00:00:00
Tasks: Disaster Response

Physical media (like surveillance cameras) and social media (like Instagram
and Twitter) may both be useful in attaining on-the-ground information during
an emergency or disaster situation. However, the intersection and reliability
of both surveillance cameras and social media during a natural disaster are not
fully understood. To address this gap, we tested whether social media is of
utility when physical surveillance cameras went off-line during Hurricane Irma
in 2017. Specifically, we collected and compared geo-tagged Instagram and
Twitter posts in the state of Florida during times and in areas where public
surveillance cameras went off-line. We report social media content and
frequency and content to determine the utility for emergency managers or first
responders during a natural disaster.
Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems
Authors: Boyi Liu, Lujia Wang, Ming Liu
Date: 2019-01-19 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Robot Navigation, Transfer Learning

This paper was motivated by the problem of how to make robots fuse and transfer their experience so that they can effectively use prior knowledge and quickly adapt to new environments. To address the problem, we present a learning architecture for navigation in cloud robotic systems: Lifelong Federated Reinforcement Learning (LFRL). In the work, We propose a knowledge fusion algorithm for upgrading a shared model deployed on the cloud. Then, effective transfer learning methods in LFRL are introduced. LFRL is consistent with human cognitive science and fits well in cloud robotic systems. Experiments show that LFRL greatly improves the efficiency of reinforcement learning for robot navigation. The cloud robotic system deployment also shows that LFRL is capable of fusing prior knowledge. In addition, we release a cloud robotic navigation-learning website based on LFRL.
Learning single-image 3D reconstruction by generative modelling of shape, pose and shading
Authors: Paul Henderson, Vittorio Ferrari
Date: 2019-01-19 00:00:00
Tasks: 3D Reconstruction

We present a unified framework tackling two problems: class-specific 3D reconstruction from a single image, and generation of new 3D shape samples. These tasks have received considerable attention recently; however, most existing approaches rely on 3D supervision, annotation of 2D images with keypoints or poses, and/or training with multiple views of each object instance. Our framework is very general: it can be trained in similar settings to existing approaches, while also supporting weaker supervision. Importantly, it can be trained purely from 2D images, without pose annotations, and with only a single view per instance. We employ meshes as an output representation, instead of voxels used in most prior work. This allows us to reason over lighting parameters and exploit shading information during training, which previous 2D-supervised methods cannot. Thus, our method can learn to generate and reconstruct concave object classes. We evaluate our approach in various settings, showing that: (i) it learns to disentangle shape from pose and lighting; (ii) using shading in the loss improves performance compared to just silhouettes; (iii) when using a standard single white light, our model outperforms state-of-the-art 2D-supervised methods, both with and without pose supervision, thanks to exploiting shading cues; (iv) performance improves further when using multiple coloured lights, even approaching that of state-of-the-art 3D-supervised methods; (v) shapes produced by our model capture smooth surfaces and fine details better than voxel-based approaches; and (vi) our approach supports concave classes such as bathtubs and sofas, which methods based on silhouettes cannot learn.
Genetic Algorithm-based Polar Code Construction for the AWGN Channel
Authors: Ahmed Elkelesh, Moustafa Ebada, Sebastian Cammerer, Stephan ten Brink
Date: 2019-01-19 00:00:00
Tasks: Playing the Game of 2048

We propose a new polar code construction framework (i.e., selecting the
frozen bit positions) for the additive white Gaussian noise (AWGN) channel,
tailored to a given decoding algorithm, rather than based on the (not
necessarily optimal) assumption of successive cancellation (SC) decoding. The
proposed framework is based on the Genetic Algorithm (GenAlg), where
populations (i.e., collections) of information sets evolve successively via
evolutionary transformations based on their individual error-rate performance.
These populations converge towards an information set that fits the decoding
behavior. Using our proposed algorithm, we construct a polar code of length
2048 with code rate 0.5, without the CRC-aid, tailored to plain successive
cancellation list (SCL) decoding, achieving the same error-rate performance as
the CRC-aided SCL decoding, and leading to a coding gain of 1 dB at BER of
$10^{-6}$. Further, a belief propagation (BP)-tailored polar code approaches
the SCL error-rate performance without any modifications in the decoding
algorithm itself.
Combating Fake News: A Survey on Identification and Mitigation Techniques
Authors: Karishma Sharma, Feng Qian, He Jiang, Natali Ruchansky, Ming Zhang, Yan Liu
Date: 2019-01-18 00:00:00
Tasks: Misinformation, Survey

The proliferation of fake news on social media has opened up new directions
of research for timely identification and containment of fake news, and
mitigation of its widespread impact on public opinion. While much of the
earlier research was focused on identification of fake news based on its
contents or by exploiting users' engagements with the news on social media,
there has been a rising interest in proactive intervention strategies to
counter the spread of misinformation and its impact on society. In this survey,
we describe the modern-day problem of fake news and, in particular, highlight
the technical challenges associated with it. We discuss existing methods and
techniques applicable to both identification and mitigation, with a focus on
the significant advances in each method and their advantages and limitations.
In addition, research has often been limited by the quality of existing
datasets and their specific application contexts. To alleviate this problem, we
comprehensively compile and summarize characteristic features of available
datasets. Furthermore, we outline new directions of research to facilitate
future development of effective and interdisciplinary solutions.
Modeling Latent Sentence Structure in Neural Machine Translation
Authors: Jasmijn Bastings, Wilker Aziz, Ivan Titov, Khalil Sima'an
Date: 2019-01-18 00:00:00
Tasks: Decoder, Machine Translation, NMT, Sentence, Translation

Recently it was shown that linguistic structure predicted by a supervised parser can be beneficial for neural machine translation (NMT). In this work we investigate a more challenging setup: we incorporate sentence structure as a latent variable in a standard NMT encoder-decoder and induce it in such a way as to benefit the translation task. We consider German-English and Japanese-English translation benchmarks and observe that when using RNN encoders the model makes no or very limited use of the structure induction apparatus. In contrast, CNN and word-embedding-based encoders rely on latent graphs and force them to encode useful, potentially long-distance, dependencies.
Machine Learning with Clos Networks
Authors: Timothy Whithing, Thiam Khean Hah
Date: 2019-01-18 00:00:00
Tasks: BIG-bench Machine Learning

We present a new methodology for improving the accuracy of small neural
networks by applying the concept of a clos network to achieve maximum
expression in a smaller network. We explore the design space to show that more
layers is beneficial, given the same number of parameters. We also present
findings on how the relu nonlinearity ffects accuracy in separable networks. We
present results on early work with Cifar-10 dataset.
A bi-partite generative model framework for analyzing and simulating large scale multiple discrete-continuous travel behaviour data
Authors: Melvin Wong, Bilal Farooq
Date: 2019-01-18 00:00:00
Tasks: Bayesian Inference, BIG-bench Machine Learning, Clustering, Dimensionality Reduction, Inference Optimization

The emergence of data-driven demand analysis has led to the increased use of generative modelling to learn the probabilistic dependencies between random variables. Although their apparent use has mostly been limited to image recognition and classification in recent years, generative machine learning algorithms can be a powerful tool for travel behaviour research by replicating travel behaviour by the underlying properties of data structures. In this paper, we examine the use of generative machine learning approach for analyzing multiple discrete-continuous (MDC) travel behaviour data. We provide a plausible perspective of how we can exploit the use of machine learning techniques to interpret the underlying heterogeneities in the data. We show that generative models are conceptually similar to the choice selection behaviour process through information entropy and variational Bayesian inference. Without loss of generality, we consider a restricted Boltzmann machine (RBM) based algorithm with multiple discrete-continuous layers, formulated as a variational Bayesian inference optimization problem. We systematically describe the proposed machine learning algorithm and develop a process of analyzing travel behaviour data from a generative learning perspective. We show parameter stability from model analysis and simulation tests on an open dataset with multiple discrete-continuous dimensions from a data size of 293,330 observations. For interpretability, we derive the conditional probabilities, elasticities and perform statistical analysis on the latent variables. We show that our model can generate statistically similar data distributions for travel forecasting and prediction and performs better than purely discriminative methods in validation. Our results indicate that latent constructs in generative models can accurately represent the joint distribution consistently on MDC data.