pwc-archive/papers-with-abstracts huggingface dataset:
Synthetic ablations in the C. elegans nervous system
Date: 2019-07-25 00:00:00

Synthetic lethality, the finding that the simultaneous knockout of two or
more individually non-essential genes leads to cell or organism death, has
offered a systematic framework to explore cellular function, and also offered
therapeutic applications. Yet, the concept lacks its parallel in neuroscience -
a systematic knowledge base on the role of double or higher order ablations in
the functioning of a neural system. Here, we use the framework of network
control to systematically predict the ablation of neuron pairs and triplets. We
find that surprisingly small sets of 58 pairs and 46 triplets can reduce muscle
controllability, and that these sets are localised in the nervous system in
distinct groups. Further, they lead to highly specific experimentally testable
predictions about mechanisms of loss of control, and which muscle cells are
expected to experience this loss.
An Open Petri Net Implementation of Gene Regulatory Networks
Date: 2019-07-25 00:00:00

Gene regulatory network (GRN) plays a central role in system biology and
genomics. It provides a promising way to model and study complex biological
processes. Several computational methods have been developed for the
construction and analysis of GRN. In particular, Petri net and its variants
were introduced for GRN years ago. On the other hand, Petri net theory itself
has been rapidly advanced recently. Especially noteworthy is the combination or
treatment of Petri net with the mathematical framework of category theory
(categorization), which endows Petri net with the power of abstraction and
composability. Open Petri net is a state-of-art implementation of such
"categorized" Petri nets. Applying open Petri net to GRN may potentially
facilitate the modeling of large scale GRNs. In this manuscript, we took a
shallow step towards that direction.
Time-inconsistency with rough volatility
Authors: Bingyan Han, Hoi Ying Wong
Date: 2019-07-26 00:00:00

In this paper, we consider equilibrium strategies under Volterra processes and time-inconsistent preferences embracing mean-variance portfolio selection (MVP). Using a functional It\^o calculus approach, we overcome the non-Markovian and non-semimartingale difficulty in Volterra processes. The equilibrium strategy is then characterized by an extended path-dependent Hamilton-Jacobi-Bellman equation system under a game-theoretic framework. A verification theorem is provided. We derive explicit solutions to three problems, including MVP with constant risk aversion, MVP for log returns, and a mean-variance objective with a linear controlled Volterra process. We also thoroughly examine the effect of volatility roughness on equilibrium strategies. Numerical experiments demonstrate that trading rules with rough volatility outperform the classic counterparts.
A Color Compensation Method Using Inverse Camera Response Function for Multi-exposure Image Fusion
Date: 2019-07-26 00:00:00
Tasks: Multi-Exposure Image Fusion

Multi-exposure image fusion is a method for producing an image with a wide
dynamic range by fusing multiple images taken under various exposure values. In
this paper, we discuss color distortion included in fused images, and propose a
novel color compensation method for multi-exposure image fusion. In the
proposed method, an inverse camera response function (CRF) is estimated by
using multi-exposure images, and then a high dynamic range (HDR) radiance map
is recovered. The color information of the radiance map is applied to images
fused by conventional multi-exposure imaging to correct the color distortion.
The proposed method can be applied to any existing fusion approaches for
improving the quality of the fused images.
Channel Extrapolation for FDD Massive MIMO: Procedure and Experimental Results
Date: 2019-07-26 00:00:00

Application of massive multiple-input multiple-output (MIMO) systems to
frequency division duplex (FDD) is challenging mainly due to the considerable
overhead required for downlink training and feedback. Channel extrapolation,
i.e., estimating the channel response at the downlink frequency band based on
measurements in the disjoint uplink band, is a promising solution to overcome
this bottleneck. This paper presents measurement campaigns obtained by using a
wideband (350 MHz) channel sounder at 3.5 GHz composed of a calibrated 64
element antenna array, in both an anechoic chamber and outdoor environment. The
Space Alternating Generalized Expectation-Maximization (SAGE) algorithm was
used to extract the parameters (amplitude, delay, and angular information) of
the multipath components from the attained channel data within the training
(uplink) band. The channel in the downlink band is then reconstructed based on
these path parameters. The performance of the extrapolated channel is evaluated
in terms of mean squared error (MSE) and reduction of beamforming gain (RBG) in
comparison to the ground truth, i.e., the measured channel at the downlink
frequency. We find strong sensitivity to calibration errors and model mismatch,
and also find that performance depends on propagation conditions: LOS performs
significantly better than NLOS.
Development of test materials for assessment broadcasting video path
Date: 2019-07-26 00:00:00

The analysis of metrological agents for the estimation of the quality of
telecommunication path treatments is carried out. Analysis of subjective and
objective estimation methods is presented. The justification for choosing an
objective method of measuring is presented. Are reported a list of existing
goals and objectives, which face the current progress of the implementation of
promising systems for broadcasting and improvement of existing ones. The latter
include the absence of normative documents and recommendations for optical
specimens of testing tables, etc. Researches related to the choice of optical
samples from existing sets of colors from a number of international documents
and recommendations are presented. Deficiency of these sets is substantiated,
as some of the colors necessary for evaluation of telecommunication paths
cannot be provided with no color of sets. Provides recommendations and
refinements about which colors from existing sets should be used and what
constraints exist when used and how to solve. It is shown, that saturated
colors of green and red colors are not provided with spectral from the existing
sets, therefore, it is proposed to expand the traditional understanding of
metrological means by using atlases of colors. Satin colors In contrast to
existing ones have a number of advantages and can be used for evaluation in
advanced conditions, rather than in the studio that is rigidly defined by the
regulations. It is noted that for different brightness values there are a
different number of testing points. The number of testing points directly
depends on the evaluation criteria, is driven by several possible sets of
colors for evaluation. Recommendations for other evaluation conditions are
provided. Use satin colors to construct testing tables
Piecewise Robust Barrier Tubes for Nonlinear Hybrid Systems with Uncertainty
Date: 2019-07-25 00:00:00

Piecewise Barrier Tubes (PBT) is a new technique for flowpipe
overapproximation for nonlinear systems with polynomial dynamics, which
leverages a combination of barrier certificates. PBT has advantages over
traditional time-step based methods in dealing with those nonlinear dynamical
systems in which there is a large difference in speed between trajectories,
producing an overapproximation that is time independent. However, the existing
approach for PBT is not efficient due to the application of interval methods
for enclosure-box computation, and it can only deal with continuous dynamical
systems without uncertainty. In this paper, we extend the approach with the
ability to handle both continuous and hybrid dynamical systems with uncertainty
that can reside in parameters and/or noise. We also improve the efficiency of
the method significantly, by avoiding the use of interval-based methods for the
enclosure-box computation without loosing soundness. We have developed a C++
prototype implementing the proposed approach and we evaluate it on several
benchmarks. The experiments show that our approach is more efficient and
precise than other methods in the literature.
Decoding the circuitry of consciousness: from local microcircuits to brain-scale networks
Date: 2019-07-26 00:00:00

Identifying the physiological processes underlying the emergence and
maintenance of consciousness is one of the most fundamental problems of
neuroscience, with implications ranging from fundamental neuroscience to the
treatment of patients with disorders of consciousness (DOC). One major
challenge is to understand how cortical circuits at drastically different
spatial scales, from local networks to brain-scale networks, operate in concert
to enable consciousness, and how those processes are impaired in DOC patients.
In this review, we attempt to relate available neurophysiological and clinical
data with existing theoretical models of consciousness, while linking the
micro- and macro-circuit levels. First, we address the relationships between
awareness and wakefulness on the one hand, and cortico-cortical, and
thalamo-cortical connectivity on the other hand. Second, we discuss the role of
three main types of GABAergic interneurons in specific circuits responsible for
the dynamical re-organization of functional networks. Third, we explore
advances in the functional role of nested oscillations for neural
synchronization and communication, emphasizing the importance of the balance
between local (high-frequency) and distant (low-frequency) activity for
efficient information processing. The clinical implications of these
theoretical considerations are presented. We propose that such cellular-scale
mechanisms could extend current theories of consciousness.
Correlation transfer by layer 5 cortical neurons under recreated synaptic inputs in vitro
Date: 2019-07-26 00:00:00

Correlated electrical activity in neurons is a prominent characteristic of
cortical microcircuits. Despite a growing amount of evidence concerning both
spike-count and subthreshold membrane potential pairwise correlations, little
is known about how different types of cortical neurons convert correlated
inputs into correlated outputs. We studied pyramidal neurons and two classes of
GABAergic interneurons of layer 5 in neocortical brain slices obtained from
rats of both sexes, and we stimulated them with biophysically realistic
correlated inputs, generated using dynamic clamp. We found that the
physiological differences between cell types manifested unique features in
their capacity to transfer correlated inputs. We used linear response theory
and computational modeling to gain clear insights into how cellular properties
determine both the gain and timescale of correlation transfer, thus tying
single-cell features with network interactions. Our results provide further
ground for the functionally distinct roles played by various types of neuronal
cells in the cortical microcircuit.
Analysis of RF Energy Harvesting in Uplink-NOMA IoT-based Network
Date: 2019-07-25 00:00:00

Internet of Things (IoT) systems in general consist of a lot of devices with
massive connectivity. Those devices are usually constrained with limited energy
supply and can only operate at low power and low rate. In this paper, we
investigate a cellular-based IoT system combined with energy harvesting and
NOMA. We consider all base stations (BS) and IoT devices follow the Poisson
Point Process (PPP) distribution in a given area. The unit time slot is divided
into two phases, energy harvesting phase in downlink (DL) and data transmission
phase in uplink (uplink). That is, IoT devices will first harvest energy from
all BS transmissions and then use the harvested energy to do the NOMA
information transmission. We define an energy harvesting circle within which
all IoT devices can harvest enough energy for NOMA transmission. The design
objective is to maximize the total throughput in uplink within the circle by
varying the duration T of energy harvesting phase. In our work, we also
consider the inter-cell interference in the throughput calculation. The
analysis of Probability Mass Function (PMF) for IoT devices in the energy
harvesting circle is also compared with simulation results. It is shown that
the BS density needs to be carefully set so that the IoT devices in the energy
harvesting circle receive relatively smaller interference and energy circles
overlap only with a small probability. Our simulations show that there exists
an optimal T to achieve the maximum throughput. When the BSs are densely
deployed consequently the total throughput will decrease because of the
interference.
Real-time Electrical Power Prediction in a Combined Cycle Power Plant
Date: 2019-07-22 00:00:00

The prediction of electrical power in combined cycle power plants is a key
challenge in the electrical power and energy systems field. This power output
can vary depending on environmental variables, such as temperature, pressure,
and humidity. Thus, the business problem is how to predict the power output as
a function of these environmental conditions in order to maximize the profit.
The research community has solved this problem by applying machine learning
techniques and has managed to reduce the computational and time costs in
comparison with the traditional thermodynamical analysis. Until now, this
challenge has been tackled from a batch learning perspective in which data is
assumed to be at rest, and where models do not continuously integrate new
information into already constructed models. We present an approach closer to
the Big Data and Internet of Things paradigms in which data is arriving
continuously and where models learn incrementally, achieving significant
enhancements in terms of data processing (time, memory and computational
costs), and obtaining competitive performances. This work compares and examines
the hourly electrical power prediction of several streaming regressors, and
discusses about the best technique in terms of time processing and performance
to be applied on this streaming scenario.
Market and Long Term Accounting Operational Performance
Date: 2019-07-26 00:00:00

Following the value relevance literature, this study verifies whether the
marketplace differentiates companies of high, medium, and low long-term
operational performance, measured by accounting information on profitability,
sales variation and indebtedness. The data comprises the Corporate Financial
Statements disclosed during the period from 1996 to 2009 and stock prices of
companies listed on the Sao Paulo Stock Exchange and Commodities and Futures
Exchange - BM&FBOVESPA. The final sample is composed of 142 non-financial
companies. Five year mobile windows were used, which resulted in ten five-year
periods. After checking each company indices, the accounting variables were
unified in an Index Performance Summary to synthesize the final performance for
each five-year period, which allowed segregation in operational performance
levels. Multiple regressions were performed using panel data techniques, fixed
effects model and dummies variables, and then hypothesis tests were made.
Regarding the explanatory power of each individual variable, the results show
that not all behaviors are according to the research hypothesis and that the
Brazilian stock market differentiates companies of high and low long-term
operational performance. This distinction is not fully perceived between
companies of high and medium operational performance.
Relevance analysis of MRI sequences for automatic liver tumor segmentation
Date: 2019-07-26 00:00:00
Tasks: Segmentation, Semantic Segmentation, Tumor Segmentation

Explainability of decisions made by deep neural networks is of high value as
it allows for validation and improvement of models. This work proposes an
approach to explain semantic segmentation networks by means of layer-wise
relevance propagation. As an exemplary application, we investigate which MRI
sequences are most relevant for liver tumor segmentation.
A New Recurrent Plug-and-Play Prior Based on the Multiple Self-Similarity Network
Date: 2019-07-26 00:00:00
Tasks: Denoising, Image Reconstruction

Recent work has shown the effectiveness of the plug-and-play priors (PnP)
framework for regularized image reconstruction. However, the performance of PnP
depends on the quality of the denoisers used as priors. In this letter, we
design a novel PnP denoising prior, called multiple self-similarity net (MSSN),
based on the recurrent neural network (RNN) with self-similarity matching using
multi-head attention mechanism. Unlike traditional neural net denoisers, MSSN
exploits different types of relationships among non-local and repeating
features to remove the noise in the input image. We numerically evaluate the
performance of MSSN as a module within PnP for solving magnetic resonance (MR)
image reconstruction. Experimental results show the stable convergence and
excellent performance of MSSN for reconstructing images from highly compressive
Fourier measurements.
Recurrent Neural Networks with Long Term Temporal Dependencies in Machine Tool Wear Diagnosis and Prognosis
Date: 2019-07-27 00:00:00
Tasks: Prognosis

Data-driven approaches to automated machine condition monitoring are gaining
popularity due to advancements made in sensing technologies and computing
algorithms. This paper proposes the use of a deep learning model, based on Long
Short-Term Memory (LSTM) architecture for a recurrent neural network (RNN)
which captures long term dependencies for modeling sequential data. In the
context of estimating cutting tool wear amounts, this LSTM based RNN approach
utilizes a system transition and system observation function based on a
minimally intrusive vibration sensor signal located near the workpiece
fixtures. By applying an LSTM based RNN, the method helps to avoid building an
analytic model for specific tool wear machine degradation, overcoming the
assumptions made by Hidden Markov Models, Kalman filter, and Particle filter
based approaches. The proposed approach is tested using experiments performed
on a milling machine. We have demonstrated one-step and two-step look ahead
cutting tool state prediction using online indirect measurements obtained from
vibration signals. Additionally, the study also estimates remaining useful life
(RUL) of a machine cutting tool insert through generative RNN. The experimental
results show that our approach, applying the LSTM to model system observation
and transition function is able to outperform the functions modeled with a
simple RNN.
Generalization of Spectrum Differential based Direct Waveform Modification for Voice Conversion
Date: 2019-07-27 00:00:00
Tasks: Voice Conversion

We present a modification to the spectrum differential based direct waveform
modification for voice conversion (DIFFVC) so that it can be directly applied
as a waveform generation module to voice conversion models. The recently
proposed DIFFVC avoids the use of a vocoder, meanwhile preserves rich spectral
details hence capable of generating high quality converted voice. To apply the
DIFFVC framework, a model that can estimate the spectral differential from the
F0 transformed input speech needs to be trained beforehand. This requirement
imposes several constraints, including a limitation on the estimation model to
parallel training and the need of extra training on each conversion pair, which
make DIFFVC inflexible. Based on the above motivations, we propose a new DIFFVC
framework based on an F0 transformation in the residual domain. By performing
inverse filtering on the input signal followed by synthesis filtering on the F0
transformed residual signal using the converted spectral features directly, the
spectral conversion model does not need to be retrained or capable of
predicting the spectral differential. We describe several details that need to
be taken care of under this modification, and by applying our proposed method
to a non-parallel, variational autoencoder (VAE)-based spectral conversion
model, we demonstrate that this framework can be generalized to any spectral
conversion model, and experimental evaluations show that it can outperform a
baseline framework whose waveform generation process is carried out by a
vocoder.
Amplitude Retrieval for Channel Estimation of MIMO Systems with One-Bit ADCs
Date: 2019-07-27 00:00:00
Tasks: Compressive Sensing, Retrieval

This letter revisits the channel estimation problem for MIMO systems with
one-bit analog-to-digital converters (ADCs) through a novel
algorithm--Amplitude Retrieval (AR). Unlike the state-of-the-art methods such
as those based on one-bit compressive sensing, AR takes a different approach.
It accounts for the lost amplitudes of the one-bit quantized measurements, and
performs channel estimation and amplitude completion jointly. This way, the
direction information of the propagation paths can be estimated via accurate
direction finding algorithms in array processing, e.g., maximum likelihood. The
upsot is that AR is able to handle off-grid angles and provide more accurate
channel estimates. Simulation results are included to showcase the advantages
of AR.
Automated Mammogram Analysis with a Deep Learning Pipeline
Date: 2019-07-27 00:00:00
Tasks: Binary Classification, Classification, Deep Learning, Generative Adversarial Network, Lesion Detection, Segmentation

Current deep learning based detection models tackle detection and
segmentation tasks by casting them to pixel or patch-wise classification. To
automate the initial mass lesion detection and segmentation on the whole
mammographic images and avoid the computational redundancy of patch-based and
sliding window approaches, the conditional generative adversarial network
(cGAN) was used in this study. Subsequently, feeding the detected regions to
the trained densely connected network (DenseNet), the binary classification of
benign versus malignant was predicted. We used a combination of publicly
available mammographic data repositories to train the pipeline, while
evaluating the model's robustness toward our clinically collected repository,
which was unseen to the pipeline.
Sub-optimal Tracking in Switched Systems with Controlled Subsystems and Fixed-mode Sequence using Approximate Dynamic Programming
Date: 2019-07-27 00:00:00

Optimal tracking in switched systems with controlled subsystem and
Discrete-time (DT) dynamics is investigated. A feedback control policy is
generated such that a) the system tracks the desired reference signal, and b)
the optimal switching time instants are sought. For finding the optimal
solution, approximate dynamic programming is used. Simulation results are
provided to illustrate the effectiveness of the solution.
Marked Hawkes process modeling of price dynamics and volatility estimation
Authors: Kyungsub Lee, Byoung Ki Seo
Date: 2019-07-28 00:00:00
Tasks: Clustering

A simple Hawkes model have been developed for the price tick structure dynamics incorporating market microstructure noise and trade clustering. In this paper, the model is extended with random mark to deal with more realistic price tick structures of equities. We examine the impact of jump in price dynamics to the future movements and dependency between the jump sizes and ground intensities. We also derive the volatility formula based on stochastic and statistical methods and compare with realized volatility in simulation and empirical studies. The marked Hawkes model is useful to estimate the intraday volatility similarly in the case of simple Hawkes model.