pwc-archive/papers-with-abstracts huggingface dataset:
Proteins: the physics of amorphous evolving matter
Date: 2019-07-31 00:00:00

Proteins are a matter of dual nature. As a physical object, a protein
molecule is a folded chain of amino acids with multifarious biochemistry. But
it is also an instantiation along an evolutionary trajectory determined by the
function performed by the protein within a hierarchy of interwoven interaction
networks of the cell, the organism and the population. A physical theory of
proteins therefore needs to unify both aspects, the biophysical and the
evolutionary. Specifically, it should provide a model of how the DNA gene is
mapped into the functional phenotype of the protein.
  We review several physical approaches to the protein problem, focusing on a
mechanical framework which treats proteins as evolvable condensed matter:
Mutations introduce localized perturbations in the gene, which are translated
to localized perturbations in the protein matter. A natural tool to examine how
mutations shape the phenotype are Green's functions. They map the evolutionary
linkage among mutations in the gene (termed epistasis) to cooperative physical
interactions among the amino acids in the protein. We discuss how the
mechanistic view can be applied to examine basic questions of protein evolution
and design.
Hierarchical Fine-Tuning for joint Liver Lesion Segmentation and Lesion Classification in CT
Date: 2019-07-31 00:00:00
Tasks: Classification, Lesion Classification, Lesion Segmentation, Segmentation, Tumor Segmentation

We present an automatic method for joint liver lesion segmentation and
classification using a hierarchical fine-tuning framework. Our dataset is
small, containing 332 2-D CT examinations with lesion annotated into 3 lesion
types: cysts, hemangiomas, and metastases. Using a cascaded U-net that performs
segmentation and classification simultaneously, we trained a strong lesion
segmentation model on the dataset of MICCAI 2017 Liver Tumor Segmentation
(LiTS) Challenge. We used the trained weights to fine-tune a slightly modified
model to obtain improved lesion segmentation and classification, on the smaller
dataset. Since pre-training was done with similar data on a related task, we
were able to learn more representative features (especially higher-level
features in the U-Net's encoder), and improve pixel-wise classification
results. We show an improvement of over 10\% in Dice score and classification
accuracy, compared to a baseline model. We further improve the classification
performance by hierarchically freezing the encoder part of the network and
achieve an improvement of over 15\% in Dice score and classification accuracy.
We compare our results with an existing method and show an improvement of 14\%
in the success rate and 12\% in the classification accuracy.
Development of a Fragment-Based Machine Learning Algorithm for Designing Hybrid Drugs Optimized for Permeating Gram-Negative Bacteria
Date: 2019-07-29 00:00:00
Tasks: Drug Design

Gram-negative bacteria are a serious health concern due to the strong
multidrug resistance that they display, partly due to the presence of a
permeability barrier comprising two membranes with active efflux. New
approaches are urgently needed to design antibiotics effective against these
pathogens. In this work, we present a novel topological fragment-based approach
("Hunting Fragments Of X" or "Hunting FOX") to rationally "hunt for" chemical
fragments that promote compound ability to permeate the outer membrane. Our
approach generalizes to other drug design applications. We measure minimum
inhibitory concentrations of compounds in two strains of Pseudomonas aeruginosa
with variable permeability barriers and use them as an input to the Hunting FOX
algorithm to identify molecular fragments responsible for enhanced outer
membrane permeation properties and candidate molecules from an external library
that demonstrate good permeation ability. Overall, we present proof of concept
for a novel method that is expected to be valuable for rational design of
hybrid drugs.
Is type 1 diabetes a chaotic phenomenon?
Authors: Jean-Marc Ginoux, Heikki Ruskeepää, Matjaž Perc, Roomila Naeck, Véronique Di Costanzo, Moez Bouchouicha, Farhat Fnaiech, Mounir Sayadi, Takoua Hamdi
Date: 2019-07-29 00:00:00
Tasks: Time Series, Time Series Analysis, Vocal Bursts Type Prediction

A database of ten type 1 diabetes patients wearing a continuous glucose monitoring device has enabled to record their blood glucose continuous variations every minute all day long during fourteen consecutive days. These recordings represent, for each patient, a time series consisting of 1 value of glycaemia per minute during 24 hours and 14 days, i.e., 20,160 data point. Thus, while using numerical methods, these time series have been anonymously analyzed. Nevertheless, because of the stochastic inputs induced by daily activities of any human being, it has not been possible to discriminate chaos from noise. So, we have decided to keep only the 14 nights of these ten patients. Then, the determination of the time delay and embedding dimension according to the delay coordinate embedding method has allowed us to estimate for each patient the correlation dimension and the maximal Lyapunov exponent. This has led us to show that type 1 diabetes could indeed be a chaotic phenomenon. Once this result has been confirmed by the determinism test, we have computed the Lyapunov time and found that the limit of predictability of this phenomenon is nearly equal to half the 90-minutes sleep-dream cycle. We hope that our results will prove to be useful to characterize and predict blood glucose variations.
A novel theorem on motion stability
Date: 2020-04-25 00:00:00

Determination of stability and instability of singular points in nonlinear
dynamical systems is an important issue that has attracted considerable
attention in different fields of engineering and science. So far, different
well-defined theories have been presented to study the stability of singular
points among which the Lyapunov theory is well-known. However, the instability
problem of singular points has been neglected to some extent in spite of its
application in oscillator design. Besides, it is often difficult to achieve a
proper Lyapunov function for a given complex system. This work presents a novel
theorem based on defining two distinct functionals and some straightforward
criteria to study motion stability that significantly facilitate the
determination of equilibrium status at singular points without the requirement
to analytical solution. Indeed, this method is applicable to both stability and
instability problems of linear and nonlinear dynamical systems. In addition,
the presented theorem is further extended to achieve a new linearization
approach of dynamical systems based on averaging technique, which is superior
to the Jacobian approach. Lastly, the proposed linearization method is
generalized to study the stability/instability of higher-order liner/nonlinear
systems. The obtained results clearly show the effectiveness of the proposed
theorem to assess the motion stability/instability.
An L0-Norm Constrained Non-Negative Matrix Factorization Algorithm for the Simultaneous Disaggregation of Fixed and Shiftable Loads
Date: 2019-07-31 00:00:00
Tasks: Time Series, Time Series Analysis

Energy disaggregation refers to the decomposition of energy use time series
data into its constituent loads. This paper decomposes daily use data of a
household unit into fixed loads and one or more classes of shiftable loads. The
latter is characterized by ON OFF duty cycles. A novel algorithm based on
nonnegative matrix factorization NMF for energy disaggregation is proposed,
where fixed loads are represented in terms of real-valued basis vectors,
whereas shiftable loads are divided into binary signals. This binary
decomposition approach directly applies L0 norm constraints on individual
shiftable loads. The new approach obviates the need for more computationally
intensive methods e.g. spectral decomposition or mean field annealing that have
been used in earlier research for these constraints. A probabilistic framework
for the proposed approach has been addressed. The proposed approach s
effectiveness has been demonstrated with real consumer energy data.
Massive MIMO Channel Estimation with an Untrained Deep Neural Network
Date: 2019-07-31 00:00:00

This paper proposes a deep learning-based channel estimation method for
multi-cell interference-limited massive MIMO systems, in which base stations
equipped with a large number of antennas serve multiple single-antenna users.
The proposed estimator employs a specially designed deep neural network (DNN)
to first denoise the received signal, followed by a conventional least-squares
(LS) estimation. We analytically prove that our LS-type deep channel estimator
can approach minimum mean square error (MMSE) estimator performance for
high-dimensional signals, while avoiding MMSE's requirement for complex channel
inversions and knowledge of the channel covariance matrix. This analytical
result, while asymptotic, is observed in simulations to be operational for just
64 antennas and 64 subcarriers per OFDM symbol. The proposed method also does
not require any training and utilizes several orders of magnitude fewer
parameters than conventional DNNs. The proposed deep channel estimator is also
robust to pilot contamination and can even completely eliminate it under
certain conditions.
Quantifying horizon dependence of asset prices: a cluster entropy approach
Authors: L. Ponta, A. Carbone
Date: 2019-08-01 00:00:00
Tasks: Avg

Market dynamic is quantified in terms of the entropy $S(\tau,n)$ of the clusters formed by the intersections between the series of the prices $p_t$ and the moving average $\widetilde{p}_{t,n}$. The entropy $S(\tau,n)$ is defined according to Shannon as $\sum P(\tau,n)\log P(\tau,n),$ with $P(\tau,n)$ the probability for the cluster to occur with duration $\tau$. \par The investigation is performed on high-frequency data of the Nasdaq Composite, Dow Jones Industrial Avg and Standard \& Poor 500 indexes downloaded from the Bloomberg terminal. The cluster entropy $S(\tau,n)$ is analysed in raw and sampled data over a broad range of temporal horizons $M$ varying from one to twelve months over the year 2018. The cluster entropy $S(\tau,n)$ is integrated over the cluster duration $\tau$ to yield the Market Dynamic Index $I(M,n)$, a synthetic figure of price dynamics. A systematic dependence of the cluster entropy $S(\tau,n)$ and the Market Dynamic Index $I(M,n)$ on the temporal horizon $M$ is evidenced. \par Finally, the Market Horizon Dependence}, defined as $H(M,n)=I(M,n)-I(1,n)$, is compared with the horizon dependence of the pricing kernel with different representative agents obtained via a Kullback-Leibler entropy approach. The Market Horizon Dependence $H(M,n)$ of the three assets is compared against the values obtained by implementing the cluster entropy $S(\tau,n)$ approach on artificially generated series (Fractional Brownian Motion).
Kernel-Based Inverse Optimization: Application to the Power Forecasting and Bidding of a Fleet of Electric Vehicles
Date: 2020-02-14 00:00:00

This paper considers an aggregator of Electric Vehicles (EVs) who aims to
learn the aggregate power of his/her fleet while also participating in the
electricity market. The proposed approach is based on a data-driven inverse
optimization (IO) method, which is highly nonlinear. To overcome such a caveat,
we use a two-step estimation procedure which requires solving two convex
programs. Both programs depend on penalty parameters that can be adjusted by
using grid search. In addition, we propose the use of kernel regression to
account for the nonlinear relationship between the behaviour of the pool of EVs
and the explanatory variables, i.e., the past electricity prices and EV fleet's
driving patterns. Unlike any other forecasting method, the proposed IO
framework also allows the aggregator to derive a bid/offer curve, i.e. the
tuple of price-quantity to be submitted to the electricity market, according to
the market rules. We show the benefits of the proposed method against the
machine-learning techniques that are reported to exhibit the best forecasting
performance for this application in the technical literature.
Directional Measurements in Urban Street Canyons from Macro Rooftop Sites at 28 GHz for 90% Outdoor Coverage
Authors: Jinfeng Du, Dmitry Chizhik, Reinaldo A. Valenzuela, Rodolfo Feick, Guillermo Castro, Mauricio Rodriguez, Tingjun Chen, Manav Kohli, Gil Zussman
Date: 2019-08-01 00:00:00

Path gain and effective directional gain in azimuth in urban canyons from actual rooftop base station sites are characterized based on a massive data set of 3000 links on 12 streets in two cities, with over 21 million individual continuous wave power measurements at 28 GHz using vertically polarized antennas. Large street-to-street path gain variation is found, with median street path gain varying over 30 dB at similar distances. Coverage in the street directly illuminated by a roof edge antenna is found to suffer an average excess loss of 11 dB relative to free space at 200 m, with empirical slope-intercept fit model representing the data with 7.1 dB standard deviation. Offsetting the base antenna 5 m away from roof edge, as is common in macro cellular deployments, introduces an additional average loss of 15 dB at 100 m, but this additional loss reduces with distance. Around the corner loss is well modeled by a diffraction formula with an empirically obtained diffraction coefficient. Effective azimuthal gain degradation due to scatter is limited to 2 dB for 90% of data, supporting effective use of high gain antennas in urban street canyons.
Early Findings from Field Trials of Heavy-Duty Truck Connected Eco-Driving System
Date: 2019-07-31 00:00:00

In recent years, the development of connected and automated vehicle (CAV)
technology has inspired numerous advanced applications targeted at improving
existing transportation systems. As one of the widely studied applications of
CAV technology, connected eco-driving takes advantage of Signal Phase and
Timing (SPaT) information from traffic signals to enable CAVs to approach and
depart from signalized intersections in an energy-efficient manner. However the
majority of the connected eco-driving studies have been numerical or
microscopic traffic simulations. Only few studies have implemented the
application on real vehicles, and even fewer have been focused on heavy-duty
trucks. In this study, we developed a connected eco-driving system and equipped
it on a heavy-duty diesel truck using cellular-based wireless communications.
Field trials were conducted in the City ofCarson, California, along two
corridors with six connected signalized intersections capable of communicating
their SPaT information. Early results showed the benefits of the system in
smoothing the speed profiles of the equipped truck when approaching the
connected signalized intersections.
Day-ahead Operation of an Aggregator of Electric Vehicles via Optimization under Uncertainty
Date: 2020-09-25 00:00:00

We pose the aggregator's problem as a bilevel model, where the upper level
minimizes the total operation costs of the fleet of EVs, while each lower level
minimizes the energy available to each vehicle for transportation given a
certain charging plan. Thanks to the totally unimodular character of the
constraint matrix in the lower-level problems, the model can be mathematically
recast as a computationally efficient mixed-integer program that delivers
charging schedules that are robust against the uncertain availability of the
EVs. Finally, we use synthetic data from the National Household Travel Survey
2017 to analyze the behavior of the EV aggregator from both economic and
technical viewpoints and compare it with the results from a deterministic
approach.
Accurate Model of a Supercapacitor Bank for Power System Dynamics Studies
Date: 2020-03-23 00:00:00

In this paper, an accurate and simple dynamic model of a supercapacitor bank
system for power system dynamics studies is presented. It is shown through
comprehensive simulations in MATLAB-Simulink that an ideal capacitor
representation is not always adequate. The proposed model is derived from a
detailed RC circuit representation. Furthermore, a complete control system of
the supercapacitor bank is also presented. The proposed model is easy to
integrate in any power system simulation software and consists of maximally 4
easy-to-obtain parameters. The performance of the proposed model in grid
frequency control and low-voltage ride through was tested in an IEEE 14-bus
test system in DIgSILENT PowerFactory.
Statistical Modeling of Networked Solar Resources for Assessing and Mitigating Risk of Interdependent Inverter Tripping Events in Distribution Grids
Date: 2020-01-27 00:00:00

It is speculated that higher penetration of inverter-based distributed
photo-voltaic (PV) power generators can increase the risk of tripping events
due to voltage fluctuations. To quantify this risk utilities need to solve the
interactive equations of tripping events for networked PVs in real-time.
However, these equations are non-differentiable, nonlinear, and exponentially
complex, and thus, cannot be used as a tractable basis for solar curtailment
prediction and mitigation. Furthermore, load/PV power values might not be
available in real-time due to limited grid observability, which further
complicates tripping event prediction. To address these challenges, we have
employed Chebyshev's inequality to obtain an alternative probabilistic model
for quantifying the risk of tripping for networked PVs. The proposed model
enables operators to estimate the probability of interdependent inverter
tripping events using only PV/load statistics and in a scalable manner.
Furthermore, by integrating this probabilistic model into an optimization
framework, countermeasures are designed to mitigate massive interdependent
tripping events. Since the proposed model is parameterized using only the
statistical characteristics of nodal active/reactive powers, it is especially
beneficial in practical systems, which have limited real-time observability.
Numerical experiments have been performed employing real data and feeder models
to verify the performance of the proposed technique.
Relative growth optimal strategies in an asset market game
Date: 2020-07-23 00:00:00

We consider a game-theoretic model of a market where investors compete for
payoffs yielded by several assets. The main result consists in a proof of the
existence and uniqueness of a strategy, called relative growth optimal, such
that the logarithm of the share of its wealth in the total wealth of the market
is a submartingale for any strategies of the other investors. It is also shown
that this strategy is asymptotically optimal in the sense that it achieves the
maximal capital growth rate when compared to competing strategies. Based on the
results obtained, we study the asymptotic structure of the market when all the
investors use the relative growth optimal strategy.
Creation of knowledge through exchanges of knowledge: Evidence from Japanese patent data
Date: 2020-08-28 00:00:00

This study shows evidence for collaborative knowledge creation among
individual researchers through direct exchanges of their mutual differentiated
knowledge. Using patent application data from Japan, the collaborative output
is evaluated according to the quality and novelty of the developed patents,
which are measured in terms of forward citations and the order of application
within their primary technological category, respectively. Knowledge exchange
is shown to raise collaborative productivity more through the extensive margin
(i.e., the number of patents developed) in the quality dimension, whereas it
does so more through the intensive margin in the novelty dimension (i.e.,
novelty of each patent).
Blind SAR Image Despeckling Using Self-Supervised Dense Dilated Convolutional Neural Network
Date: 2019-08-13 00:00:00
Tasks: Sar Image Despeckling

Despeckling is a key and indispensable step in SAR image preprocessing,
existing deep learning-based methods achieve SAR despeckling by learning some
mappings between speckled (different looks) and clean images. However, there
exist no clean SAR image in the real world. To this end, in this paper, we
propose a self-supervised dense dilated convolutional neural network (BDSS) for
blind SAR image despeckling. Proposed BDSS can still learn to suppress speckle
noise without clean ground truth by optimized for L2 loss. Besides, three
enhanced dense blocks with dilated convolution are employed to improve network
performance. The synthetic and real-data experiments demonstrate that proposed
BDSS can achieve despeckling effectively while maintaining well features such
as edges, point targets, and radiometric. At last, we demonstrate that our
proposed BDSS can achieve blind despeckling excellently, i.e., do not need to
care about the number of looks.
Understanding the transition from paroxysmal to persistent atrial fibrillation from micro-anatomical re-entry in a simple model
Authors: Alberto Ciacci, Max Falkenberg, Kishan A. Manani, Tim S. Evans, Nicholas S. Peters, Kim Christensen
Date: 2019-08-05 00:00:00

Atrial fibrillation (AF) is the most common cardiac arrhytmia, characterised by the chaotic motion of electrical wavefronts in the atria. In clinical practice, AF is classified under two primary categories: paroxysmal AF, short intermittent episodes separated by periods of normal electrical activity, and persistent AF, longer uninterrupted episodes of chaotic electrical activity. However, the precise reasons why AF in a given patient is paroxysmal or persistent is poorly understood. Recently, we have introduced the percolation based Christensen-Manani-Peters (CMP) model of AF which naturally exhibits both paroxysmal and persistent AF, but precisely how these differences emerge in the model is unclear. In this paper, we dissect the CMP model to identify the cause of these different AF classifications. Starting from a mean-field model where we describe AF as a simple birth-death process, we add layers of complexity to the model and show that persistent AF arises from re-entrant circuits which exhibit an asymmetry in their probability of activation relative to deactivation. As a result, different simulations generated at identical model parameters can exhibit fibrillatory episodes spanning several orders of magnitude from a few seconds to months. These findings demonstrate that diverse, complex fibrillatory dynamics can emerge from very simple dynamics in models of AF.
A Merge/Split Algorithm for Multitarget Tracking Using Generalized Labeled Multi-Bernoulli Filters
Authors: Lingji Chen
Date: 2019-08-05 00:00:00

The class of Labeled Random Finite Set filters known as the delta-Generalized Labeled Multi-Bernoulli (dGLMB) filter represents the filtering density as a set of weighted hypotheses, with each hypothesis consisting of a set of labeled tracks, which are in turn pairs of a track label and a track kinematic density. Upon update with a batch of measurements, each hypothesis gives rise to many child hypotheses, and therefore truncation has to be performed for any practical application. Finite compute budget can lead to degeneracy that drops tracks. To mitigate, we adopt a factored filtering density through the use of a novel Merge/Split algorithm. Merging has long been established in the literature; our splitting algorithm is enabled by an efficient and effective marginalization scheme, through indexing a kinematic density by the measurement IDs (in a moving window) that have been used in its update. This allows us to determine when independence can be considered to hold approximately for a given tolerance, so that the "resolution" of tracking is adaptively chosen, from a single factor (dGLMB), to all-singleton factors (Labeled Multi-Bernoulli, LMB), and anywhere in between.