pwc-archive/papers-with-abstracts huggingface dataset:
Automated MRI based pipeline for glioma segmentation and prediction of grade, IDH mutation and 1p19q co-deletion
Date: 2020-05-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Classification, Prognosis, Segmentation

In the WHO glioma classification guidelines grade, IDH mutation and 1p19q
co-deletion play a central role as they are important markers for prognosis and
optimal therapy planning. Therefore, we propose a fully automatic, MRI based,
3D pipeline for glioma segmentation and classification. The designed
segmentation network was a 3D U-Net achieving an average whole tumor dice score
of 90%. After segmentation, the 3D tumor ROI is extracted and fed into the
multi-task classification network. The network was trained and evaluated on a
large heterogeneous dataset of 628 patients, collected from The Cancer Imaging
Archive and BraTS 2019 databases. Additionally, the network was validated on an
independent dataset of 110 patients retrospectively acquired at the Ghent
University Hospital (GUH). Classification AUC scores are 0.93, 0.94 and 0.82 on
the TCIA test data and 0.94, 0.86 and 0.87 on the GUH data for grade, IDH and
1p19q status respectively.
Masked Pre-trained Encoder base on Joint CTC-Transformer
Date: 2020-08-13 00:00:00
Tasks: Decoder

This study (The work was accomplished during the internship in Tencent AI
lab) addresses semi-supervised acoustic modeling, i.e. attaining high-level
representations from unsupervised audio data and fine-tuning the parameters of
pre-trained model with supervised data. The proposed approach adopts a
two-stage training framework, consisting of masked pre-trained encoder (MPE)
and Joint CTC-Transformer (JCT). In the MPE framework, part of input frames are
masked and reconstructed after the encoder with massive unsupervised data. In
JCT framework, compared with original Transformer, acoustic features are
applied as input instead of plain text. CTC loss performs as the prediction
target on top of the encoder, and decoder blocks remain unchanged. This paper
presents a comparison between two-stage training method and the fully
supervised JCT. In addition, this paper investigates the our approach's
robustness against different volumns of training data. Experiments on the
two-stage training method deliver much better performance than fully supervised
model. The word error rate (WER) with two-stage training which only exploits
30\% of WSJ labeled data achieves 17\% reduction than which trained by 50\% of
WSJ in a fully supervised way. Moreover, increasing unlabeled data for MPE from
WSJ (81h) to Librispeech (960h) attains about 22\% WER reduction.
Financial option valuation by unsupervised learning with artificial neural networks
Authors: Beatriz Salvador, Cornelis W. Oosterlee, Remco van der Meer
Date: 2020-05-25 00:00:00

Artificial neural networks (ANNs) have recently also been applied to solve partial differential equations (PDEs). In this work, the classical problem of pricing European and American financial options, based on the corresponding PDE formulations, is studied. Instead of using numerical techniques based on finite element or difference methods, we address the problem using ANNs in the context of unsupervised learning. As a result, the ANN learns the option values for all possible underlying stock values at future time points, based on the minimization of a suitable loss function. For the European option, we solve the linear Black-Scholes equation, whereas for the American option, we solve the linear complementarity problem formulation. Two-asset exotic option values are also computed, since ANNs enable the accurate valuation of high-dimensional options. The resulting errors of the ANN approach are assessed by comparing to the analytic option values or to numerical reference solutions (for American options, computed by finite elements).
An Effective Entropy-assisted Mind-wandering Detection System with EEG Signals based on MM-SART Database
Authors: Yi-Ta Chen, Hsing-Hao Lee, Ching-Yen Shih, Zih-Ling Chen, Win-Ken Beh, Su-Ling Yeh, An-Yeu Wu
Date: 2020-05-25 00:00:00
Tasks: channel selection, EEG, Electroencephalogram (EEG), feature selection, Photoplethysmography (PPG)

Mind-wandering (MW), which usually defined as a lapse of attention, occurs between 20%-40% of the time, has negative effects on our daily life. Therefore, detecting when MW occurs can prevent us from those negative outcomes resulting from MW, such as failing to keep track of course during learning. In this work, we first collect a multi-modal Sustained Attention to Response Task (MM-SART) database for detecting MW. Eighty-two participants' data are collected in our experiments. For each participant, we collect measures of 32-channels electroencephalogram (EEG) signals, photoplethysmography (PPG) signals, galvanic skin response (GSR) signals, eye tracker signals, and several questionnaires for detailed analyses. Then, we propose an effective MW detection system based on the collected EEG signals. To explore the non-linear characteristics of EEG signals, we utilize the entropy-based features in time, frequency, and wavelet domains. The experimental results show that we can reach 0.712 AUC score by using the random forest (RF) classifier with the leave-one-subject-out cross-validation. Moreover, to lower the overall computational complexity of the MW detection system, we apply techniques of channel selection and feature selection. By using the only two most significant EEG channels, we can reduce the training time of the classifier by 44.16%. By performing correlation importance feature elimination (CIFE) on the feature set, we can further improve the AUC score to 0.725 but with only 14.6% of the selection time compared with the recursive feature elimination (RFE) method. By proposing the MW detection engine, current work can be applied to educational scenarios, especially in the era of remote learning nowadays.
Green production as a factor of survival for innovative startups. Evidence from Italy
Authors: Riccardo Gianluigi Serio, Maria Michela Dickson, Diego Giuliani, Giuseppe Espa
Date: 2020-05-25 00:00:00
Tasks: Management

Many studies have analyzed empirically the determinants of survival for innovative startup companies using data about the characteristics of entrepreneurs and management or focusing on firm- and industry-specific variables. However, no attempts have been made so far to assess the role of the environmental sustainability of the production process. Based on data describing the characteristics of the Italian innovative startups in the period 2009-2018, this article studies the differences in survival between green and non-green companies. We show that, while controlling for other confounding factors, startups characterized by a green production process tend to survive longer than their counterparts. In particular, we estimate that a green innovative startup is more than twice as likely to survive than a non-green one. This evidence may support the idea that environment sustainability can help economic development.
On Evaluation of Risky Investment Projects. Investment Certainty Equivalence
Authors: Andrey Leonidov, Ilya Tipunin, Ekaterina Serebryannikova
Date: 2020-05-25 00:00:00

The purpose of the study is to propose a methodology for evaluation and ranking of risky investment projects.An investment certainty equivalence approach dual to the conventional separation of riskless and risky contributions based on cash flow certainty equivalence is introduced. Proposed ranking of investment projects is based on gauging them with the Omega measure, which is defined as the ratio of chances to obtain profit/return greater than some critical (minimal acceptable) profitability over the chances to obtain the profit/return less than the critical one.Detailed consideration of alternative riskless investment is presented. Various performance measures characterizing investment projects with a special focus on the role of reinvestment are discussed. Relation between the proposed methodology and the conventional approach based on utilization of risk-adjusted discount rate (RADR) is discussed. Findings are supported with an illustrative example.The methodology proposed can be used to rank projects of different nature, scale and lifespan. In contrast to the conventional RADR approach for investment project evaluation, in the proposed method a risk profile of a specific project is explicitly analyzed in terms of appropriate performance measure distribution. No ad-hoc assumption about suitable risk-premium is made.
Cognitive State Analysis, Understanding, and Decoding from the Perspective of Brain Connectivity
Date: 2020-09-14 00:00:00

Cognitive states are involving in our daily life, which motivates us to
explore them and understand them by a vast variety of perspectives. Among these
perspectives, brain connectivity is increasingly receiving attention in recent
years. It is the right time to summarize the past achievements, serving as a
cornerstone for the upcoming progress in the field. In this chapter, the
definition of the cognitive state is first given and the cognitive states that
are frequently investigated are then outlined. The introduction of the methods
for estimating connectivity strength and graph theoretical metrics is followed.
Subsequently, each cognitive state is separately described and the progress in
cognitive state investigation is summarized, including analysis, understanding,
and decoding. We concentrate on the literature ascertaining macro-scale
representations of cognitive states from the perspective of brain connectivity
and give an overview of achievements related to cognitive states to date,
especially within the past ten years. The discussions and future prospects are
stated at the end of the chapter.
Passivity-based distributed acquisition and station-keeping control of a satellite constellation in areostationary orbit
Date: 2020-05-25 00:00:00

We present a distributed control law to assemble a cluster of satellites into
an equally-spaced, planar constellation in a desired circular orbit about a
planet. We assume each satellite only uses local information, transmitted
through communication links with neighboring satellites. The same control law
is used to maintain relative angular positions in the presence of disturbance
forces. The stability of the constellation in the desired orbit is proved using
a compositional approach. We first show the existence and uniqueness of an
equilibrium of the interconnected system. We then certify each satellite and
communication link is equilibrium-independent passive with respective storage
functions. By leveraging the skew symmetric coupling structure of the
constellation and the equilibrium-independent passivity property of each
subsystem, we show that the equilibrium of the interconnected system is stable
with a Lyapunov function composed of the individual subsystem storage
functions. We further prove that the angular velocity of each satellite
converges to the desired value necessary to maintain circular, areostationary
orbit. Finally, we present simulation results to demonstrate the efficacy of
the proposed control law in acquisition and station-keeping of an
equally-spaced satellite constellation in areostationary orbit despite the
presence of unmodeled disturbance forces.
The short-term seasonal analyses between atmospheric environment and COVID-19 in epidemic areas of Cities in Australia, South Korea, and Italy
Date: 2020-05-23 00:00:00

The impact of the outbreak of COVID-19 on health has been widely concerned.
Disease risk assessment, prediction, and early warning have become a
significant research field. Previous research suggests that there is a
relationship between air quality and the disease. This paper investigated the
impact of the atmospheric environment on the basic reproduction number (R$_0$)
in Australia, South Korea, and Italy by using atmospheric environment data,
confirmed case data, and the distributed lag non-linear model (DLNM) model
based on Quasi-Poisson regression. The results show that the air temperature
and humidity have lag and persistence on short-term R$_0$, and seasonal factors
have an apparent decorating effect on R$_0$. PM$_{10}$ is the primary pollutant
that affects the excess morbidity rate. Moreover, O$_3$, PM$_{2.5}$, and SO$_2$
as perturbation factors have an apparent cumulative effect. These results
present beneficial knowledge for correlation between environment and COVID-19,
which guiding prospective analyses of disease data.
The origin of the allometric scaling of lung ventilation in mammals
Authors: Frédérique Noël, Cyril Karamaoun, Jerome A. Dempsey, Benjamin Mauroy
Date: 2020-05-04 00:00:00

A model of optimal control of ventilation has recently been developed for humans. This model highlights the importance of the localization of the transition between a convective and a diffusive transport of respiratory gas. This localization determines how ventilation should be controlled in order to minimize its energetic cost at any metabolic regime. We generalized this model to any mammal, based on the core morphometric characteristics shared by all mammalian lungs and on their allometric scaling from the literature. Since the main energetic costs of ventilation are related to convective transport, we prove that, for all mammals, the localization of the shift from a convective transport to a diffusive transport plays a critical role on keeping this cost low while fulfilling the lung function. Our model predicts for the first time the localization of this transition in order to minimize the energetic cost of ventilation, depending on mammal mass and metabolic regime. From this optimal localization, we are able to predict allometric scaling laws for both tidal volumes and breathing rates, at any metabolic rate. We ran our model for the three common metabolic rates -- basal, field and maximal -- and showed that our predictions reproduce accurately experimental data available in the literature. Our analysis supports the hypothesis that mammals allometric scaling laws of tidal volumes and breathing rates at a given metabolic rate are driven by a few core geometrical characteristics shared by mammalian lungs and by the physical processes of respiratory gas transport.
Robust exact differentiators with predefined convergence time
Authors: Richard Seeber, Hernan Haimovich, Martin Horn, Leonid Fridman, Hernán De Battista
Date: 2020-05-25 00:00:00

The problem of exactly differentiating a signal with bounded second derivative is considered. A class of differentiators is proposed, which converge to the derivative of such a signal within a fixed, i.e., a finite and uniformly bounded convergence time. A tuning procedure is derived that allows to assign an arbitrary, predefined upper bound for this convergence time. It is furthermore shown that this bound can be made arbitrarily tight by appropriate tuning. The usefulness of the procedure is demonstrated by applying it to the well-known uniform robust exact differentiator, which is included in the considered class of differentiators as a special case.
Large-Signal Stability Criteria in DC Power Grids with Distributed-Controlled Converters and Constant Power Loads
Date: 2020-05-25 00:00:00

The increasing adoption of power electronic devices may lead to large
disturbance and destabilization of future power systems. However, stability
criteria are still an unsolved puzzle, since traditional small-signal stability
analysis is not applicable to power electronics-enabled power systems when a
large disturbance occurs, such as a fault, a pulse power load, or load
switching. To address this issue, this paper presents for the first time the
rigorous derivation of the sufficient criteria for large-signal stability in DC
microgrids with distributed-controlled DC-DC power converters. A novel type of
closed-loop converter controllers is designed and considered. Moreover, this
paper is the first to prove that the well-known and frequently cited
Brayton-Moser mixed potential theory (published in 1964) is incomplete. Case
studies are carried out to illustrate the defects of Brayton-Moser mixed
potential theory and verify the effectiveness of the proposed novel stability
criteria.
Aging-induced fragility of the immune system
Authors: Eric W. Jones, Jiming Sheng, Jean M. Carlson, Shenshen Wang
Date: 2020-05-18 00:00:00

The adaptive and innate branches of the vertebrate immune system work in close collaboration to protect organisms from harmful pathogens. As an organism ages its immune system undergoes immunosenescence, characterized by declined performance or malfunction in either immune branch, which can lead to disease and death. In this study we develop a mathematical framework of coupled innate and adaptive immune responses, namely the integrated immune branch (IIB) model. This model describes dynamics of immune components in both branches, uses a shape-space representation to encode pathogen-specific immune memory, and exhibits three steady states -- health, septic death, and chronic inflammation -- qualitatively similar to clinically-observed immune outcomes. In this model, the immune system (initialized in the health state) is subjected to a sequence of pathogen encounters, and we use the number of prior pathogen encounters as a proxy for the "age" of the immune system. We find that repeated pathogen encounters may trigger a fragility in which any encounter with a novel pathogen will cause the system to irreversibly switch from health to chronic inflammation. This transition is consistent with the onset of "inflammaging", a condition observed in aged individuals who experience chronic low-grade inflammation even in the absence of pathogens. The IIB model predicts that the onset of chronic inflammation strongly depends on the history of encountered pathogens; the timing of onset differs drastically when the same set of infections occurs in a different order. Lastly, the coupling between the innate and adaptive immune branches generates a trade-off between rapid pathogen clearance and a delayed onset of immunosenescence.
Continuous Reachability Task Transition Using Control Barrier Functions
Date: 2020-05-25 00:00:00

In this paper, a method to achieve smooth transitions between sequential
reachability tasks for a continuous time mobile robotic system is presented.
Control barrier functions provide formal guarantees of forward invariance of
safe sets and finite-time reachability and are able to enforce task execution.
Barrier functions used in quadratic programs result in implementation of
controllers with real-time performance guarantees. Existing approaches for
multi-objective task execution using control barrier functions leverage
discretely switched, sequential quadratic programs to achieve successive tasks.
However, discrete switching can lead to control input discontinuities which can
affect a robot's performance. Hence, we propose a method which ensures
continuous transitions between sequential quadratic programs. In particular, a
time varying component to the barrier function constraint is introduced which
allows for a smooth transition between objectives. Robotic implementation
results are also provided.
Constrained nonlinear output regulation using model predictive control -- extended version
Authors: Johannes Köhler, Matthias A. Müller, Frank Allgöwer
Date: 2020-05-25 00:00:00
Tasks: Model Predictive Control

We present a model predictive control (MPC) framework to solve the constrained nonlinear output regulation problem. The main feature of the proposed framework is that the application does not require the solution to classical regulator (Francis-Byrnes-Isidori) equations or any other offline design procedure. In particular, the proposed formulation simply minimizes the predicted output error, possibly with some input regularization. Instead of using terminal cost/sets or a positive definite stage cost as is standard in MPC theory, we build on the theoretical results by Grimm et al. 2005 using a detectability notion. The proposed formulation is applicable if the constrained nonlinear regulation problem is (strictly) feasible, the plant is incrementally stabilizable and incrementally input-output to state stable (i-IOSS/detectable). We show that for minimum phase systems such a design ensures exponential stability of the regulator manifold. We also provide a design procedure in case of unstable zero dynamics using an incremental input regularization and a nonresonance condition. Inherent robustness properties for the noisy error/output-feedback case are established under simplifying assumptions (e.g. no state constraints). The theoretical results are illustrated with an example involving offset free tracking with noisy error feedback. The paper also contains novel results for MPC without terminal constraints with positive semidefinite input/output stage costs that are of independent interest.
Absolute ethanol intake drives ethanol preference in Drosophila
Date: 2020-05-25 00:00:00

Factors that mediate ethanol preference in Drosophila melanogaster are not
well understood. A major confound has been the use of diverse methods to
estimate ethanol consumption. We measured fly consumptive ethanol preference on
base diets varying in nutrients, taste, and ethanol concentration. Both sexes
showed ethanol preference that was abolished on high nutrient concentration
diets. Additionally, manipulating total food intake without altering the
nutritive value of the base diet or the ethanol concentration was sufficient to
evoke or eliminate ethanol preference. Absolute ethanol intake and food volume
consumed were stronger predictors of ethanol preference than caloric intake or
the dietary caloric content. Our findings suggest that the effect of the base
diet on ethanol preference is largely mediated by total consumption associated
with the delivery medium, which ultimately determines the level of ethanol
intake. We speculate that a physiologically relevant threshold for ethanol
intake is essential for preferential ethanol consumption.
Identifying the measurements required to estimate rates of COVID-19 transmission, infection, and detection, using variational data assimilation
Date: 2020-07-31 00:00:00
Tasks: parameter estimation

We demonstrate the ability of statistical data assimilation to identify the
measurements required for accurate state and parameter estimation in an
epidemiological model for the novel coronavirus disease COVID-19. Our context
is an effort to inform policy regarding social behavior, to mitigate strain on
hospital capacity. The model unknowns are taken to be: the time-varying
transmission rate, the fraction of exposed cases that require hospitalization,
and the time-varying detection probabilities of new asymptomatic and
symptomatic cases. In simulations, we obtain accurate estimates of undetected
(that is, unmeasured) infectious populations, by measuring the detected cases
together with the recovered and dead - and without assumed knowledge of the
detection rates. Given a noiseless measurement of the recovered population,
excellent estimates of all quantities are obtained using a temporal baseline of
101 days, with the exception of the time-varying transmission rate at times
prior to the implementation of social distancing. With low noise added to the
recovered population, accurate state estimates require a lengthening of the
temporal baseline of measurements. Estimates of all parameters are sensitive to
the contamination, highlighting the need for accurate and uniform methods of
reporting. The aim of this paper is to exemplify the power of SDA to determine
what properties of measurements will yield estimates of unknown parameters to a
desired precision, in a model with the complexity required to capture important
features of the COVID-19 pandemic.