pwc-archive/papers-with-abstracts huggingface dataset:
Real-time cortical simulations: energy and interconnect scaling on distributed systems
Authors: Francesco Simula, Elena Pastorelli, Pier Stanislao Paolucci, Michele Martinelli, Alessandro Lonardo, Andrea Biagioni, Cristiano Capone, Fabrizio Capuani, Paolo Cretaro, Giulia De Bonis, Francesca Lo Cicero, Luca Pontisso, Piero Vicini, Roberto Ammendola
Date: 2018-12-12 00:00:00
Tasks: Distributed Computing

We profile the impact of computation and inter-processor communication on the energy consumption and on the scaling of cortical simulations approaching the real-time regime on distributed computing platforms. Also, the speed and energy consumption of processor architectures typical of standard HPC and embedded platforms are compared. We demonstrate the importance of the design of low-latency interconnect for speed and energy consumption. The cost of cortical simulations is quantified using the Joule per synaptic event metric on both architectures. Reaching efficient real-time on large scale cortical simulations is of increasing relevance for both future bio-inspired artificial intelligence applications and for understanding the cognitive functions of the brain, a scientific quest that will require to embed large scale simulations into highly complex virtual or real worlds. This work stands at the crossroads between the WaveScalES experiment in the Human Brain Project (HBP), which includes the objective of large scale thalamo-cortical simulations of brain states and their transitions, and the ExaNeSt and EuroExa projects, that investigate the design of an ARM-based, low-power High Performance Computing (HPC) architecture with a dedicated interconnect scalable to million of cores; simulation of deep sleep Slow Wave Activity (SWA) and Asynchronous aWake (AW) regimes expressed by thalamo-cortical models are among their benchmarks.
Subjective Annotations for Vision-Based Attention Level Estimation
Authors: Andrea Coifman, Péter Rohoska, Miklas S. Kristoffersen, Sven E. Shepstone, Zheng-Hua Tan
Date: 2018-12-12 00:00:00

Attention level estimation systems have a high potential in many use cases,
such as human-robot interaction, driver modeling and smart home systems, since
being able to measure a person's attention level opens the possibility to
natural interaction between humans and computers. The topic of estimating a
human's visual focus of attention has been actively addressed recently in the
field of HCI. However, most of these previous works do not consider attention
as a subjective, cognitive attentive state. New research within the field also
faces the problem of the lack of annotated datasets regarding attention level
in a certain context. The novelty of our work is two-fold: First, we introduce
a new annotation framework that tackles the subjective nature of attention
level and use it to annotate more than 100,000 images with three attention
levels and second, we introduce a novel method to estimate attention levels,
relying purely on extracted geometric features from RGB and depth images, and
evaluate it with a deep learning fusion framework. The system achieves an
overall accuracy of 80.02%. Our framework and attention level annotations are
made publicly available.
A Style-Based Generator Architecture for Generative Adversarial Networks
Authors: Tero Karras, Samuli Laine, Timo Aila
Date: 2018-12-12 00:00:00
Proceeding: CVPR 2019 6
Tasks: Disentanglement, Image Generation

We propose an alternative generator architecture for generative adversarial
networks, borrowing from style transfer literature. The new architecture leads
to an automatically learned, unsupervised separation of high-level attributes
(e.g., pose and identity when trained on human faces) and stochastic variation
in the generated images (e.g., freckles, hair), and it enables intuitive,
scale-specific control of the synthesis. The new generator improves the
state-of-the-art in terms of traditional distribution quality metrics, leads to
demonstrably better interpolation properties, and also better disentangles the
latent factors of variation. To quantify interpolation quality and
disentanglement, we propose two new, automated methods that are applicable to
any generator architecture. Finally, we introduce a new, highly varied and
high-quality dataset of human faces.
Tree-structured Kronecker Convolutional Network for Semantic Segmentation
Authors: Tianyi Wu, Sheng Tang, Rui Zhang, Juan Cao, Jintao Li
Date: 2018-12-12 00:00:00
Tasks: Semantic Segmentation

Most existing semantic segmentation methods employ atrous convolution to
enlarge the receptive field of filters, but neglect partial information. To
tackle this issue, we firstly propose a novel Kronecker convolution which
adopts Kronecker product to expand the standard convolutional kernel for taking
into account the partial feature neglected by atrous convolutions. Therefore,
it can capture partial information and enlarge the receptive field of filters
simultaneously without introducing extra parameters. Secondly, we propose
Tree-structured Feature Aggregation (TFA) module which follows a recursive rule
to expand and forms a hierarchical structure. Thus, it can naturally learn
representations of multi-scale objects and encode hierarchical contextual
information in complex scenes. Finally, we design Tree-structured Kronecker
Convolutional Networks (TKCN) which employs Kronecker convolution and TFA
module. Extensive experiments on three datasets, PASCAL VOC 2012,
PASCAL-Context and Cityscapes, verify the effectiveness of our proposed
approach. We make the code and the trained model publicly available at
https://github.com/wutianyiRosun/TKCN.
Semi-Supervised Learning for Face Sketch Synthesis in the Wild
Authors: Chaofeng Chen, Wei Liu, Xiao Tan, Kwan-Yee K. Wong
Date: 2018-12-12 00:00:00
Tasks: Face Sketch Synthesis, Patch Matching

Face sketch synthesis has made great progress in the past few years. Recent methods based on deep neural networks are able to generate high quality sketches from face photos. However, due to the lack of training data (photo-sketch pairs), none of such deep learning based methods can be applied successfully to face photos in the wild. In this paper, we propose a semi-supervised deep learning architecture which extends face sketch synthesis to handle face photos in the wild by exploiting additional face photos in training. Instead of supervising the network with ground truth sketches, we first perform patch matching in feature space between the input photo and photos in a small reference set of photo-sketch pairs. We then compose a pseudo sketch feature representation using the corresponding sketch feature patches to supervise our network. With the proposed approach, we can train our networks using a small reference set of photo-sketch pairs together with a large face photo dataset without ground truth sketches. Experiments show that our method achieve state-of-the-art performance both on public benchmarks and face photos in the wild. Codes are available at https://github.com/chaofengc/Face-Sketch-Wild.
Separation of water and fat signal in whole-body gradient echo scans using convolutional neural networks
Authors: Jonathan Andersson, Håkan Ahlström, Joel Kullberg
Date: 2018-12-12 00:00:00
Tasks: CPU, GPU

Purpose: To perform and evaluate water-fat signal separation of whole-body
gradient echo scans using convolutional neural networks.
  Methods: Whole-body gradient echo scans of 240 subjects, each consisting of 5
bipolar echoes, were used. Reference fat fraction maps were created using a
conventional method. Convolutional neural networks, more specifically 2D
U-nets, were trained using 5-fold cross-validation with 1 or several echoes as
input, using the squared difference between the output and the reference fat
fraction maps as the loss function. The outputs of the networks were assessed
by the loss function, measured liver fat fractions, and visually. Training was
performed using a graphics processing unit (GPU). Inference was performed using
the GPU as well as a central processing unit (CPU).
  Results: The loss curves indicated convergence, and the final loss of the
validation data decreased when using more echoes as input. The liver fat
fractions could be estimated using only 1 echo, but results were improved by
use of more echoes. Visual assessment found the quality of the outputs of the
networks to be similar to the reference even when using only 1 echo, with
slight improvements when using more echoes. Training a network took at most
28.6 h. Inference time of a whole-body scan took at most 3.7 s using the GPU
and 5.8 min using the CPU.
  Conclusion: It is possible to perform water-fat signal separation of
whole-body gradient echo scans using convolutional neural networks. Separation
was possible using only 1 echo, although using more echoes improved the
results.
C3: Concentrated-Comprehensive Convolution and its application to semantic segmentation
Authors: Hyojin Park, Youngjoon Yoo, Geonseok Seo, Dongyoon Han, Sangdoo Yun, Nojun Kwak
Date: 2018-12-12 00:00:00
Tasks: Semantic Segmentation

One of the practical choices for making a lightweight semantic segmentation model is to combine a depth-wise separable convolution with a dilated convolution. However, the simple combination of these two methods results in an over-simplified operation which causes severe performance degradation due to loss of information contained in the feature map. To resolve this problem, we propose a new block called Concentrated-Comprehensive Convolution (C3) which applies the asymmetric convolutions before the depth-wise separable dilated convolution to compensate for the information loss due to dilated convolution. The C3 block consists of a concentration stage and a comprehensive convolution stage. The first stage uses two depth-wise asymmetric convolutions for compressed information from the neighboring pixels to alleviate the information loss. The second stage increases the receptive field by using a depth-wise separable dilated convolution from the feature map of the first stage. We applied the C3 block to various segmentation frameworks (ESPNet, DRN, ERFNet, ENet) for proving the beneficial properties of our proposed method. Experimental results show that the proposed method preserves the original accuracies on Cityscapes dataset while reducing the complexity. Furthermore, we modified ESPNet to achieve about 2% better performance while reducing the number of parameters by half and the number of FLOPs by 35% compared with the original ESPNet. Finally, experiments on ImageNet classification task show that C3 block can successfully replace dilated convolutions.
CFUN: Combining Faster R-CNN and U-net Network for Efficient Whole Heart Segmentation
Authors: Zhanwei Xu, Ziyi Wu, Jianjiang Feng
Date: 2018-12-12 00:00:00
Tasks: Heart Segmentation, Segmentation

In this paper, we propose a novel heart segmentation pipeline Combining
Faster R-CNN and U-net Network (CFUN). Due to Faster R-CNN's precise
localization ability and U-net's powerful segmentation ability, CFUN needs only
one-step detection and segmentation inference to get the whole heart
segmentation result, obtaining good results with significantly reduced
computational cost. Besides, CFUN adopts a new loss function based on edge
information named 3D Edge-loss as an auxiliary loss to accelerate the
convergence of training and improve the segmentation results. Extensive
experiments on the public dataset show that CFUN exhibits competitive
segmentation performance in a sharply reduced inference time. Our source code
and the model are publicly available at https://github.com/Wuziyi616/CFUN.
EasiCSDeep: A deep learning model for Cervical Spondylosis Identification using surface electromyography signal
Authors: Nana Wang, Li Cui, Xi Huang, Yingcong Xiang, Jing Xiao
Date: 2018-12-12 00:00:00
Tasks: Cervical Spondylosis Identification

Cervical spondylosis (CS) is a common chronic disease that affects up to
two-thirds of the population and poses a serious burden on individuals and
society. The early identification has significant value in improving cure rate
and reducing costs. However, the pathology is complex, and the mild symptoms
increase the difficulty of the diagnosis, especially in the early stage.
Besides, the time-consuming and costliness of hospital medical service reduces
the attention to the CS identification. Thus, a convenient, low-cost
intelligent CS identification method is imperious demanded. In this paper, we
present an intelligent method based on the deep learning to identify CS, using
the surface electromyography (sEMG) signal. Faced with the complex, high
dimensionality and weak usability of the sEMG signal, we proposed and developed
a multi-channel EasiCSDeep algorithm based on the convolutional neural network,
which consists of the feature extraction, spatial relationship representation
and classification algorithm. To the best of our knowledge, this EasiCSDeep is
the first effort to employ the deep learning and the sEMG data to identify CS.
Compared with previous state-of-the-art algorithm, our algorithm achieves a
significant improvement.
On the potential for open-endedness in neural networks
Authors: Nicholas Guttenberg, Nathaniel Virgo, Alexandra Penn
Date: 2018-12-12 00:00:00
Tasks: BIG-bench Machine Learning, Diversity

Natural evolution gives the impression of leading to an open-ended process of
increasing diversity and complexity. If our goal is to produce such
open-endedness artificially, this suggests an approach driven by evolutionary
metaphor. On the other hand, techniques from machine learning and artificial
intelligence are often considered too narrow to provide the sort of exploratory
dynamics associated with evolution. In this paper, we hope to bridge that gap
by reviewing common barriers to open-endedness in the evolution-inspired
approach and how they are dealt with in the evolutionary case - collapse of
diversity, saturation of complexity, and failure to form new kinds of
individuality. We then show how these problems map onto similar issues in the
machine learning approach, and discuss how the same insights and solutions
which alleviated those barriers in evolutionary approaches can be ported over.
At the same time, the form these issues take in the machine learning
formulation suggests new ways to analyze and resolve barriers to
open-endedness. Ultimately, we hope to inspire researchers to be able to
interchangeably use evolutionary and gradient-descent-based machine learning
methods to approach the design and creation of open-ended systems.
Automatic individual pig detection and tracking in surveillance videos
Authors: Lei Zhang, Helen Gray, Xujiong Ye, Lisa Collins, Nigel Allinson
Date: 2018-12-12 00:00:00
Tasks: Multiple Object Tracking, Object Tracking, TAG

Individual pig detection and tracking is an important requirement in many
video-based pig monitoring applications. However, it still remains a
challenging task in complex scenes, due to problems of light fluctuation,
similar appearances of pigs, shape deformations and occlusions. To tackle these
problems, we propose a robust real time multiple pig detection and tracking
method which does not require manual marking or physical identification of the
pigs, and works under both daylight and infrared light conditions. Our method
couples a CNN-based detector and a correlation filter-based tracker via a novel
hierarchical data association algorithm. The detector gains the best
accuracy/speed trade-off by using the features derived from multiple layers at
different scales in a one-stage prediction network. We define a tag-box for
each pig as the tracking target, and the multiple object tracking is conducted
in a key-points tracking manner using learned correlation filters. Under
challenging conditions, the tracking failures are modelled based on the
relations between responses of detector and tracker, and the data association
algorithm allows the detection hypotheses to be refined, meanwhile the drifted
tracks can be corrected by probing the tracking failures followed by the
re-initialization of tracking. As a result, the optimal tracklets can
sequentially grow with on-line refined detections, and tracking fragments are
correctly integrated into respective tracks while keeping the original
identifications. Experiments with a dataset captured from a commercial farm
show that our method can robustly detect and track multiple pigs under
challenging conditions. The promising performance of the proposed method also
demonstrates a feasibility of long-term individual pig tracking in a complex
environment and thus promises a commercial potential.
SMT vs NMT: A Comparison over Hindi & Bengali Simple Sentences
Authors: Sainik Kumar Mahata, Soumil Mandal, Dipankar Das, Sivaji Bandyopadhyay
Date: 2018-12-12 00:00:00
Tasks: Machine Translation, NMT, Sentence, Translation

In the present article, we identified the qualitative differences between
Statistical Machine Translation (SMT) and Neural Machine Translation (NMT)
outputs. We have tried to answer two important questions: 1. Does NMT perform
equivalently well with respect to SMT and 2. Does it add extra flavor in
improving the quality of MT output by employing simple sentences as training
units. In order to obtain insights, we have developed three core models viz.,
SMT model based on Moses toolkit, followed by character and word level NMT
models. All of the systems use English-Hindi and English-Bengali language pairs
containing simple sentences as well as sentences of other complexity. In order
to preserve the translations semantics with respect to the target words of a
sentence, we have employed soft-attention into our word level NMT model. We
have further evaluated all the systems with respect to the scenarios where they
succeed and fail. Finally, the quality of translation has been validated using
BLEU and TER metrics along with manual parameters like fluency, adequacy etc.
We observed that NMT outperforms SMT in case of simple sentences whereas SMT
outperforms in case of all types of sentence.
Mixed Uncertainty Sets for Robust Combinatorial Optimization
Authors: Trivikram Dokka, Marc Goerigk, Rahul Roy
Date: 2018-12-12 00:00:00
Tasks: Combinatorial Optimization

In robust optimization, the uncertainty set is used to model all possible
outcomes of uncertain parameters. In the classic setting, one assumes that this
set is provided by the decision maker based on the data available to her. Only
recently it has been recognized that the process of building useful uncertainty
sets is in itself a challenging task that requires mathematical support.
  In this paper, we propose an approach to go beyond the classic setting, by
assuming multiple uncertainty sets to be prepared, each with a weight showing
the degree of belief that the set is a "true" model of uncertainty. We consider
theoretical aspects of this approach and show that it is as easy to model as
the classic setting. In an extensive computational study using a shortest path
problem based on real-world data, we auto-tune uncertainty sets to the
available data, and show that with regard to out-sample performance, the
combination of multiple sets can give better results than each set on its own.
A Multimodal LSTM for Predicting Listener Empathic Responses Over Time
Authors: Zhi-Xuan Tan, Arushi Goel, Thanh-Son Nguyen, Desmond C. Ong
Date: 2018-12-12 00:00:00

People naturally understand the emotions of-and often also empathize
with-those around them. In this paper, we predict the emotional valence of an
empathic listener over time as they listen to a speaker narrating a life story.
We use the dataset provided by the OMG-Empathy Prediction Challenge, a workshop
held in conjunction with IEEE FG 2019. We present a multimodal LSTM model with
feature-level fusion and local attention that predicts empathic responses from
audio, text, and visual features. Our best-performing model, which used only
the audio and text features, achieved a concordance correlation coefficient
(CCC) of 0.29 and 0.32 on the Validation set for the Generalized and
Personalized track respectively, and achieved a CCC of 0.14 and 0.14 on the
held-out Test set. We discuss the difficulties faced and the lessons learnt
tackling this challenge.
Distributed Anomaly Detection using Autoencoder Neural Networks in WSN for IoT
Authors: Tie Luo, Sai G. Nagarajan
Date: 2018-12-12 00:00:00
Tasks: Anomaly Detection

Wireless sensor networks (WSN) are fundamental to the Internet of Things
(IoT) by bridging the gap between the physical and the cyber worlds. Anomaly
detection is a critical task in this context as it is responsible for
identifying various events of interests such as equipment faults and
undiscovered phenomena. However, this task is challenging because of the
elusive nature of anomalies and the volatility of the ambient environments. In
a resource-scarce setting like WSN, this challenge is further elevated and
weakens the suitability of many existing solutions. In this paper, for the
first time, we introduce autoencoder neural networks into WSN to solve the
anomaly detection problem. We design a two-part algorithm that resides on
sensors and the IoT cloud respectively, such that (i) anomalies can be detected
at sensors in a fully distributed manner without the need for communicating
with any other sensors or the cloud, and (ii) the relatively more
computation-intensive learning task can be handled by the cloud with a much
lower (and configurable) frequency. In addition to the minimal communication
overhead, the computational load on sensors is also very low (of polynomial
complexity) and readily affordable by most COTS sensors. Using a real WSN
indoor testbed and sensor data collected over 4 consecutive months, we
demonstrate via experiments that our proposed autoencoder-based anomaly
detection mechanism achieves high detection accuracy and low false alarm rate.
It is also able to adapt to unforeseeable and new changes in a non-stationary
environment, thanks to the unsupervised learning feature of our chosen
autoencoder neural networks.
Attentional Road Safety Networks
Authors: Sonu Gupta, Deepak Srivatsav, A. V. Subramanyam, Ponnurangam Kumaraguru
Date: 2018-12-12 00:00:00
Tasks: Domain Adaptation

Road safety mapping using satellite images is a cost-effective but a
challenging problem for smart city planning. The scarcity of labeled data,
misalignment and ambiguity makes it hard for supervised deep networks to learn
efficient embeddings in order to classify between safe and dangerous road
segments. In this paper, we address the challenges using a region guided
attention network. In our model, we extract global features from a base network
and augment it with local features obtained using the region guided attention
network. In addition, we perform domain adaptation for unlabeled target data.
In order to bridge the gap between safe samples and dangerous samples from
source and target respectively, we propose a loss function based on within and
between class covariance matrices. We conduct experiments on a public dataset
of London to show that the algorithm achieves significant results with the
classification accuracy of 86.21%. We obtain an increase of 4% accuracy for NYC
using domain adaptation network. Besides, we perform a user study and
demonstrate that our proposed algorithm achieves 23.12% better accuracy
compared to subjective analysis.