pwc-archive/papers-with-abstracts huggingface dataset:
Knowledge extraction, modeling and formalization: EEG case study
Authors: Morozov Dmitry, Lezoche Mario, Panetto Hervé
Date: 2018-09-11 00:00:00
Tasks: Clustering, EEG, Electroencephalogram (EEG)

Formal Concept Analysis (FCA) is a well-established method for data analysis
which finds many applications in data mining. Its extension on complex data
representation formats brought a wave of new applications to the problems such
as gene expression mining, prediction of toxicity of chemical compounds or
clustering of sequences in process event logs. Insipired from this work our
research inherits their model and designs an experiment for mining
electroencephalographic recordings for patterns of sleep spindles. The
contribution of this paper lies in the specification of desritizition procedure
and the architecture of FCA experiment. We also provide some reflection on the
related research papers.
On recovery of signals with single point spectrum degeneracy
Date: 2018-09-25 00:00:00
Tasks: Missing Values

The paper study recovery problem for discrete time signals with a finite
number of missing values. The paper establishes recoverability of these missing
values for signals with Z-transform vanishing with a certain rate at a single
point.
  The transfer functions for the corresponding recovering kernels are presented
explicitly.
  Some robustness of the recovery with respect to data truncation or noise
contamination is established.
Risk sharing for capital requirements with multidimensional security markets
Date: 2018-09-26 00:00:00

We consider the risk sharing problem for capital requirements induced by
capital adequacy tests and security markets. The agents involved in the sharing
procedure may be heterogeneous in that they apply varying capital adequacy
tests and have access to different security markets. We discuss conditions
under which there exists a representative agent. Thereafter, we study two
frameworks of capital adequacy more closely, polyhedral constraints and
distribution based constraints. We prove existence of optimal risk allocations
and equilibria within these frameworks and elaborate on their robustness.
Smart Grids empowerment with Edge Computing: An Overview
Date: 2018-10-17 00:00:00
Tasks: Edge-computing, Management

Electric grids represent the angular stone of distribution networks. Since
their introduction, a huge evolutionary process turned them from conventional
electrical power network to advanced, real-time monitoring systems. In this
process, the Internet of Things (IoT) proved itself as a fast forwarding
paradigm: smart devices, networks and communication protocol stacks are more
and more integrated in a number of general purpose, industrial grade systems.
In this framework, the upcoming Internet of Services (IoS) is going to
orchestrate the many sensors and components in Smart Grids, simultaneously
enabling complex information management for energy suppliers, operators and
consumers. The opportunity to continuously monitor and send important data
(i.e., energy production, distribution, usage and storage) issues and
facilitates the implementation of trailblazing functionalities. Of course, the
more information are sent throughout the network, the higher the overload will
be, thus resulting in a potentially worsening of the Quality of Service (QoS)
(i.e., communication latencies). This work overviews the concept of Smart Grids
while investigating de-centralized computing and elaboration possibilities,
leveraging the IoT paradigm towards the IoS.
Multispecies fruit flower detection using a refined semantic segmentation network
Authors: Dias Philipe A., Tabb Amy, Medeiros Henry
Date: 2018-09-20 00:00:00
Tasks: Management, Semantic Segmentation

In fruit production, critical crop management decisions are guided by bloom
intensity, i.e., the number of flowers present in an orchard. Despite its
importance, bloom intensity is still typically estimated by means of human
visual inspection. Existing automated computer vision systems for flower
identification are based on hand-engineered techniques that work only under
specific conditions and with limited performance. This work proposes an
automated technique for flower identification that is robust to uncontrolled
environments and applicable to different flower species. Our method relies on
an end-to-end residual convolutional neural network (CNN) that represents the
state-of-the-art in semantic segmentation. To enhance its sensitivity to
flowers, we fine-tune this network using a single dataset of apple flower
images. Since CNNs tend to produce coarse segmentations, we employ a refinement
method to better distinguish between individual flower instances. Without any
pre-processing or dataset-specific training, experimental results on images of
apple, peach and pear flowers, acquired under different conditions demonstrate
the robustness and broad applicability of our method.
Real-time Interference Identification via Supervised Learning: Embedding Coexistence Awareness in IoT Devices
Date: 2018-12-11 00:00:00

Energy sampling-based interference detection and identification (IDI) methods
collide with the limitations of commercial off-the-shelf (COTS) IoT hardware.
Moreover, long sensing times, complexity and inability to track concurrent
interference strongly inhibit their applicability in most IoT deployments.
Motivated by the increasing need for on-device IDI for wireless coexistence, we
develop a lightweight and efficient method targeting interference
identification already at the level of single interference bursts. Our method
exploits real-time extraction of envelope and model-aided spectral features,
specifically designed considering the physical properties of signals captured
with COTS hardware. We adopt manifold supervised-learning (SL) classifiers
ensuring suitable performance and complexity trade-off for IoT platforms with
different computational capabilities. The proposed IDI method is capable of
real-time identification of IEEE 802.11b/g/n, 802.15.4, 802.15.1 and Bluetooth
Low Energy wireless standards, enabling isolation and extraction of
standard-specific traffic statistics even in the case of heavy concurrent
interference. We perform an experimental study in real environments with
heterogeneous interference scenarios, showing 90%-97% burst identification
accuracy. Meanwhile, the lightweight SL methods, running online on wireless
sensor networks-COTS hardware, ensure sub-ms identification time and limited
performance gap from machine-learning approaches.
Performances of a GNSS receiver for space-based applications
Date: 2018-09-24 00:00:00

Space Vehicle (SV) life span depends on its station keeping capability.
Station keeping is the ability of the vehicle to maintain position and
orientation. Due to external perturbations, the trajectory of the SV derives
from the ideal orbit. Actual positioning systems for satellites are mainly
based on ground equipment, which means heavy infrastructures. Autonomous
positioning and navigation systems using Global Navigation Satellite Systems
(GNSS) can then represent a great reduction in platform design and operating
costs. Studies have been carried out and the first operational systems, based
on GPS receivers, become available. But better availability of service could be
obtained considering a receiver able to process GPS and Galileo signals. Indeed
Galileo system will be compatible with the current and the modernized GPS
system in terms of signals representation and navigation data. The greater
availability obtained with such a receiver would allow significant increase of
the number of point solutions and performance enhancement. For a mid-term
perspective Thales Alenia Space finances a PhD to develop the concept of a
reconfigurable receiver able to deal with both the GPS system and the future
Galileo system. In this context, the aim of this paper is to assess the
performances of a receiver designed for Geosynchronous Earth Orbit (GEO)
applications. It is shown that high improvements are obtained with a receiver
designed to track both GPS and Galileo satellites. The performance assessments
have been used to define the specifications of the future satellite GNSS
receiver.
TDMA in Adaptive Resonant Beam Charging for IoT Devices
Date: 2018-09-24 00:00:00
Tasks: Scheduling

Resonant beam charging (RBC) can realize wireless power transfer (WPT) from a
transmitter to multiple receivers via resonant beams. The adaptive RBC (ARBC)
can effectively improve its energy utilization. In order to support multi-user
WPT in the ARBC system, we propose the time-division multiple access (TDMA)
method and design the TDMA-based WPT scheduling algorithm. Our TDMA WPT method
has the features of concurrently charging, continuous charging current,
individual user power control, constant driving power and flexible driving
power control. The simulation shows that the TDMA scheduling algorithm has high
efficiency, as the total charging time is roughly half (46.9% when charging 50
receivers) of that of the alternative scheduling algorithm. Furthermore, the
TDMA for WPT inspires the ideas of enhancing the ARBC system, such as flow
control and quality of service (QoS).
Short-term load forecasting using optimized LSTM networks based on EMD
Authors: Li Tiantian, Wang Bo, Zhou Min, Watada Junzo
Date: 2018-08-16 00:00:00
Tasks: Load Forecasting, Time Series, Time Series Analysis

Short-term load forecasting is one of the crucial sections in smart grid.
Precise forecasting enables system operators to make reliable unit commitment
and power dispatching decisions. With the advent of big data, a number of
artificial intelligence techniques such as back propagation, support vector
machine have been used to predict the load of the next day. Nevertheless, due
to the noise of raw data and the randomness of power load, forecasting errors
of existing approaches are relatively large. In this study, a short-term load
forecasting method is proposed on the basis of empirical mode decomposition and
long short-term memory networks, the parameters of which are optimized by a
particle swarm optimization algorithm. Essentially, empirical mode
decomposition can decompose the original time series of historical data into
relatively stationary components and long short-term memory network is able to
emphasize as well as model the timing of data, the joint use of which is
expected to effectively apply the characteristics of data itself, so as to
improve the predictive accuracy. The effectiveness of this research is
exemplified on a realistic data set, the experimental results of which show
that the proposed method has higher forecasting accuracy and applicability, as
compared with existing methods.
Dynamic Solar Hosting Capacity Calculations in Microgrids
Date: 2018-09-26 00:00:00

Microgrids, as small-scale islandable power systems, are considered as viable
promotors of renewable energy resources. In particular, microgrids can
efficiently integrate solar photovoltaic (PV) as the deployment of this
customer-deployed technology is growing in distribution grids. However, there
is a limit on how much PV can be hosted by a microgrid. High penetration of PVs
can potentially put the microgrid at operational risks including but not
limited to over and under voltages, excessive line losses, overloading of
transformers and feeders, and protection failure. To avoid such potential
negative impacts, the concept of hosting capacity is introduced and used. The
hosting capacity is defined as the total capacity of DERs that can be
integrated into a given feeder/microgrid without exceeding operational
restrictions and/or requiring system upgrades. Hosting capacity studies are
primarily done based on steadystate analyses. However, in case of microgrids
and when transitioning between grid-connected and islanded modes, dynamic
operation becomes more restrictive than steady-state operation and thus is
worthy of detailed investigation to provide a better understanding of the
amount of DER that the microgrid can host. This paper examines this problem and
provides extensive simulations on a practical test system to show its
importance and merits.
High Performance Zero-Memory Overhead Direct Convolutions
Authors: Zhang Jiyuan, Franchetti Franz, Low Tze Meng
Date: 2018-09-20 00:00:00
Proceeding: ICML 2018 7
Tasks: CPU, Vocal Bursts Intensity Prediction

The computation of convolution layers in deep neural networks typically rely
on high performance routines that trade space for time by using additional
memory (either for packing purposes or required as part of the algorithm) to
improve performance. The problems with such an approach are two-fold. First,
these routines incur additional memory overhead which reduces the overall size
of the network that can fit on embedded devices with limited memory capacity.
Second, these high performance routines were not optimized for performing
convolution, which means that the performance obtained is usually less than
conventionally expected. In this paper, we demonstrate that direct convolution,
when implemented correctly, eliminates all memory overhead, and yields
performance that is between 10% to 400% times better than existing high
performance implementations of convolution layers on conventional and embedded
CPU architectures. We also show that a high performance direct convolution
exhibits better scaling performance, i.e. suffers less performance drop, when
increasing the number of threads.
Monotone Sharpe ratios and related measures of investment performance
Authors: Mikhail Zhitlukhin
Date: 2018-09-26 00:00:00

We introduce a new measure of performance of investment strategies, the monotone Sharpe ratio. We study its properties, establish a connection with coherent risk measures, and obtain an efficient representation for using in applications.
Computer-Aided Diagnosis of Label-Free 3-D Optical Coherence Microscopy Images of Human Cervical Tissue
Authors: Ma Yutao, Xu Tao, Huang Xiaolei, Wang Xiaofang, Li Canyu, Jerwick Jason, Ning Yuan, Zeng Xianxu, Wang Baojin, Wang Yihong, Zhang Zhan, Zhang Xiaoan, Zhou Chao
Date: 2018-09-17 00:00:00
Tasks: Binary Classification, General Classification, Specificity

Objective: Ultrahigh-resolution optical coherence microscopy (OCM) has
recently demonstrated its potential for accurate diagnosis of human cervical
diseases. One major challenge for clinical adoption, however, is the steep
learning curve clinicians need to overcome to interpret OCM images. Developing
an intelligent technique for computer-aided diagnosis (CADx) to accurately
interpret OCM images will facilitate clinical adoption of the technology and
improve patient care. Methods: 497 high-resolution 3-D OCM volumes (600
cross-sectional images each) were collected from 159 ex vivo specimens of 92
female patients. OCM image features were extracted using a convolutional neural
network (CNN) model, concatenated with patient information (e.g., age, HPV
results), and classified using a support vector machine classifier. Ten-fold
cross-validations were utilized to test the performance of the CADx method in a
five-class classification task and a binary classification task. Results: An
88.3 plus or minus 4.9% classification accuracy was achieved for five
fine-grained classes of cervical tissue, namely normal, ectropion, low-grade
and high-grade squamous intraepithelial lesions (LSIL and HSIL), and cancer. In
the binary classification task (low-risk [normal, ectropion and LSIL] vs.
high-risk [HSIL and cancer]), the CADx method achieved an area-under-the-curve
(AUC) value of 0.959 with an 86.7 plus or minus 11.4% sensitivity and 93.5 plus
or minus 3.8% specificity. Conclusion: The proposed deep-learning based CADx
method outperformed three human experts. It was also able to identify
morphological characteristics in OCM images that were consistent with
histopathological interpretations. Significance: Label-free OCM imaging,
combined with deep-learning based CADx methods, hold a great promise to be used
in clinical settings for the effective screening and diagnosis of cervical
diseases.
DLO: Direct LiDAR Odometry for 2.5D Outdoor Environment
Authors: Sun Lu, Zhao Junqiao, He Xudong, Ye Chen
Date: 2018-09-15 00:00:00
Tasks: Autonomous Vehicles, Visual Odometry

For autonomous vehicles, high-precision real-time localization is the
guarantee of stable driving. Compared with the visual odometry (VO), the LiDAR
odometry (LO) has the advantages of higher accuracy and better stability.
However, 2D LO is only suitable for the indoor environment, and 3D LO has less
efficiency in general. Both are not suitable for the online localization of an
autonomous vehicle in an outdoor driving environment. In this paper, a direct
LO method based on the 2.5D grid map is proposed. The fast semi-dense direct
method proposed for VO is employed to register two 2.5D maps. Experiments show
that this method is superior to both the 3D-NDT and LOAM in the outdoor
environment.
Multi-Scale Fully Convolutional Network for Cardiac Left Ventricle Segmentation
Authors: Kang Han, Chen Defeng
Date: 2018-09-19 00:00:00
Tasks: Decoder, Left Ventricle Segmentation, Segmentation, Semantic Segmentation

The morphological structure of left ventricle segmented from cardiac magnetic
resonance images can be used to calculate key clinical parameters, and it is of
great significance to the accurate and efficient diagnosis of cardiovascular
diseases. Compared with traditional methods, the segmentation algorithms based
on fully convolutional neural network greatly improve the accuracy of semantic
segmentation. For the problem of left ventricular segmentation, a new fully
convolutional neural network structure named MS-FCN is proposed in this paper.
The MS-FCN network employs a multi-scale pooling module to ensure that the
network maximises the feature extraction ability and uses a dense connectivity
decoder to refine the boundaries of the object. Based on the Sunnybrook cine-MR
dataset provided by the MICCAI 2009 challenge, numerical experiments
demonstrate that our proposed model has obtained state-of-the-art segmentation
results: the Dice score of our method reaches 0.93 on the endocardium, and 0.96
on the epicardium.
Optimal scheduling of isolated microgrid with an electric vehicle battery swapping station in multi-stakeholder scenarios: a bi-level programming approach via real-time pricing
Date: 2018-09-26 00:00:00
Tasks: Scheduling

In order to coordinate the scheduling problem between an isolated microgrid
(IMG) and electric vehicle battery swapping stations (BSSs) in
multi-stakeholder scenarios, a new bi-level optimal scheduling model is
proposed for promoting the participation of BSSs in regulating the IMG economic
operation. In this model, the upper-level sub-problem is formulated to minimize
the IMG net costs, while the lower-level aims to maximize the profits of the
BSS under real-time pricing environments determined by demand responses in the
upper-level decision. To solve the model, a hybrid algorithm, called JAYA-BBA,
is put forward by combining a real/integer-coded JAYA algorithm and the branch
and bound algorithm (BBA), in which the JAYA and BBA are respectively employed
to address the upper- and lower- level sub-problems, and the bi-level model is
eventually solved through alternate iterations between the two levels. The
simulation results on a microgrid test system verify the effectiveness and
superiority of the presented approach.
Cell size regulation induces sustained oscillations in the population growth rate
Date: 2018-12-10 00:00:00

We study the effect of correlations in generation times on the dynamics of
population growth of microorganisms. We show that any non-zero correlation that
is due to cell-size regulation, no matter how small, induces long-term
oscillations in the population growth rate. The population only reaches its
steady state when we include the often-neglected variability in the growth
rates of individual cells. We discover that the relaxation time scale of the
population to its steady state is determined by the distribution of single-cell
growth rates and is surprisingly independent of details of the division process
such as the noise in the timing of division and the mechanism of cell-size
regulation. We validate the predictions of our model using existing
experimental data and propose an experimental method to measure single-cell
growth variability by observing how long it takes for the population to reach
its steady state or balanced growth.
Generic Vehicle Tracking Framework Capable of Handling Occlusions Based on Modified Mixture Particle Filter
Authors: Li Jiachen, Zhan Wei, Tomizuka Masayoshi
Date: 2018-09-20 00:00:00
Tasks: Autonomous Driving, Object, State Estimation

Accurate and robust tracking of surrounding road participants plays an
important role in autonomous driving. However, there is usually no prior
knowledge of the number of tracking targets due to object emergence, object
disappearance and false alarms. To overcome this challenge, we propose a
generic vehicle tracking framework based on modified mixture particle filter,
which can make the number of tracking targets adaptive to real-time
observations and track all the vehicles within sensor range simultaneously in a
uniform architecture without explicit data association. Each object corresponds
to a mixture component whose distribution is non-parametric and approximated by
particle hypotheses. Most tracking approaches employ vehicle kinematic models
as the prediction model. However, it is hard for these models to make proper
predictions when sensor measurements are lost or become low quality due to
partial or complete occlusions. Moreover, these models are incapable of
forecasting sudden maneuvers. To address these problems, we propose to
incorporate learning-based behavioral models instead of pure vehicle kinematic
models to realize prediction in the prior update of recursive Bayesian state
estimation. Two typical driving scenarios including lane keeping and lane
change are demonstrated to verify the effectiveness and accuracy of the
proposed framework as well as the advantages of employing learning-based
models.
C4Synth: Cross-Caption Cycle-Consistent Text-to-Image Synthesis
Authors: Joseph K J, Pal Arghya, Rajanala Sailaja, Balasubramanian Vineeth N
Date: 2018-09-20 00:00:00
Tasks: Image Generation

Generating an image from its description is a challenging task worth solving
because of its numerous practical applications ranging from image editing to
virtual reality. All existing methods use one single caption to generate a
plausible image. A single caption by itself, can be limited, and may not be
able to capture the variety of concepts and behavior that may be present in the
image. We propose two deep generative models that generate an image by making
use of multiple captions describing it. This is achieved by ensuring
'Cross-Caption Cycle Consistency' between the multiple captions and the
generated image(s). We report quantitative and qualitative results on the
standard Caltech-UCSD Birds (CUB) and Oxford-102 Flowers datasets to validate
the efficacy of the proposed approach.