pwc-archive/papers-with-abstracts huggingface dataset:
Preliminary prediction of the basic reproduction number of the Wuhan novel coronavirus 2019-nCoV
Date: 2020-01-31 00:00:00

Objectives.--To estimate the basic reproduction number of the Wuhan novel
coronavirus (2019-nCoV). Methods.--Based on the
susceptible-exposed-infected-removed (SEIR) compartment model and the
assumption that the infectious cases with symptoms occurred before January 25,
2020 are resulted from free propagation without intervention, we estimate the
basic reproduction number of 2019-nCoV according to the reported confirmed
cases and suspected cases, as well as the theoretical estimated number of
infected cases by other research teams, together with some epidemiological
determinants learned from the severe acute respiratory syndrome. Results The
basic reproduction number falls between 2.8 to 3.3 by using the real-time
reports on the number of 2019-nCoV infected cases from People's Daily in China,
and falls between 3.2 and 3.9 on the basis of the predicted number of infected
cases from colleagues. Conclusions.--The early transmission ability of
2019-nCoV is closed to or slightly higher than SARS. It is a controllable
disease with moderate-high transmissibility. Timely and effective control
measures are needed to suppress the further transmissions. Notes Added.--Using
a newly reported epidemiological determinants for early 2019-nCoV, the
estimated basic reproduction number is in the range [2.2,3.0].
Reduction in respiratory mortality not associated with influenza in Russia: effect of the introduction of pneumococcal vaccination (PCV13), or improvement in care?
Date: 2020-01-28 00:00:00
Tasks: Epidemiology

Background. Pneumococcal vaccination (PCV13) for children (as well as older
adults) in Russia was introduced in 2014, with no prior PCV7 use. While
pneumonia hospitalization rates, both in children and adults did not decrease
in the following years, respiratory mortality rates did decline with time.
Moreover, there is a strong association between antibiotic, including multidrug
resistance and PCV13 vaccine serotypes for S. pneumoniae in children in Russia,
and presence of S. pneumoniae among sepsis cases in Russia during the recent
years has been very low. Annual variability in influenza circulation may affect
some of the changes in respiratory mortality rates. Methods. We applied the
inference method from our recent study of influenza-associated mortality in
Russia to relate monthly respiratory mortality rates between 09/2010 and
08/2019 to monthly indices of influenza circulation and baseline rates of
mortality not associated with influenza, allowing for a potential trend in the
baseline rates starting 2015. Results. Baseline rates of respiratory mortality
not associated with influenza decreased with time starting from the 2014/15
season (with each season running from September to August), with the 2018/19
baseline rates of respiratory mortality per 100,000 individuals being lower by
12.41 (95% CI (10.6,14.2)) compared to the 2010-2014 period. Conclusions. While
improvement in care might have contributed to the reduction in the rates of
respiratory mortality not associated with influenza in Russia, the above
temporally consistent reduction is compatible with the gradual replacement of
vaccine serotypes in the transmission and carriage of S. pneumoniae following
the introduction of PCV13. Further work is needed to better understand the
impact of PCV13 on the epidemiology of respiratory infections and related
mortality in Russia.
Analysis, Online Estimation, and Validation of a Competing Virus Model
Date: 2020-01-28 00:00:00
Tasks: Time Series, Time Series Analysis

In this paper we introduce a discrete time competing virus model and the
assumptions necessary for the model to be well posed. We analyze the system
exploring its different equilibria. We provide necessary and sufficient
conditions for the estimation of the model parameters from time series data and
introduce an online estimation algorithm. We employ a dataset of two competing
subsidy programs from the US Department of Agriculture to validate the model by
employing the identification techniques. To the best of our knowledge, this
work is the first to study competing virus models in discrete-time, online
identification of spread parameters from time series data, and validation of
said models using real data. These new contributions are important for
applications since real data is naturally sampled.
Bridging the Gap Between Consciousness and Matter: Recurrent Out-of-Body Projection of Visual Awareness Revealed by the Law of Non-Identity
Authors: Jinsong Meng
Date: 2020-01-29 00:00:00

Consciousness is an explicit outcome of brain activity. However, the link between consciousness and the material world remains to be explored. We applied a new logic tool, the non-identity law, to the analysis of the visual dynamics related to the naturalistic observation of a night-shot still life. We show that visual awareness possesses a postponed, recurrent out-of-body projection pathway and that the out-of-body projection is superimposed onto the original, which is reciprocally verified by vision and touch. This suggests that the visual system instinctually not only represents the subjective image (brain-generated imagery) but also projects the image back onto the original or to a specific place according to the cues of the manipulated afferent messenger light signaling pathway. This finding provides a foundation for understanding the subjectivity and intentionality of consciousness from the perspective of visual awareness and the isomorphic relations between an unknowable original, private experience, and shareable expression. The result paves the way for scientific research on consciousness and facilitates the integration of humanities and natural science.
Study of dose-dependent combination immunotherapy using engineered T cells and IL-2 in cervical cancer
Authors: Heyrim Cho, Zuping Wang, Doron Levy
Date: 2020-01-28 00:00:00

Adoptive T cell based immunotherapy is gaining significant traction in cancer treatment. Despite its limited success, so far, in treating solid cancers, it is increasingly successful, demonstrating to have a broader therapeutic potential. In this paper we develop a mathematical model to study the efficacy of engineered T cell receptor (TCR) T cell therapy targeting the E7 antigen in cervical cancer cell lines. We consider a dynamical system that follows the population of cancer cells, TCR T cells, and IL-2. We demonstrate that there exists a TCR T cell dosage window for a successful cancer elimination that can be expressed in terms of the initial tumor size. We obtain the TCR T cell dose for two cervical cancer cell lines: 4050 and CaSki. Finally, a combination therapy of TCR T cell and IL-2 treatment is studied. We show that certain treatment protocols can improve therapy responses in the 4050 cell line, but not in the CaSki cell line.
Frequentist Shrinkage under Inequality Constraints
Date: 2020-01-28 00:00:00

This paper shows how to shrink extremum estimators towards inequality
constraints motivated by economic theory. We propose an Inequality Constrained
Shrinkage Estimator (ICSE) which takes the form of a weighted average between
the unconstrained and inequality constrained estimators with the data dependent
weight. The weight drives both the direction and degree of shrinkage. We use a
local asymptotic framework to derive the asymptotic distribution and risk of
the ICSE. We provide conditions under which the asymptotic risk of the ICSE is
strictly less than that of the unrestricted extremum estimator. The degree of
shrinkage cannot be consistently estimated under the local asymptotic
framework. To address this issue, we propose a feasible plug-in estimator and
investigate its finite sample behavior. We also apply our framework to gasoline
demand estimation under the Slutsky restriction.
Weighted Speech Distortion Losses for Neural-network-based Real-time Speech Enhancement
Date: 2020-02-12 00:00:00
Proceeding: IEEE International Conference on Acoustics,  Speech and Signal Processing (ICASSP) 2020 2
Tasks: Speech Enhancement

This paper investigates several aspects of training a RNN (recurrent neural
network) that impact the objective and subjective quality of enhanced speech
for real-time single-channel speech enhancement. Specifically, we focus on a
RNN that enhances short-time speech spectra on a single-frame-in,
single-frame-out basis, a framework adopted by most classical signal processing
methods. We propose two novel mean-squared-error-based learning objectives that
enable separate control over the importance of speech distortion versus noise
reduction. The proposed loss functions are evaluated by widely accepted
objective quality and intelligibility measures and compared to other
competitive online methods. In addition, we study the impact of feature
normalization and varying batch sequence lengths on the objective quality of
enhanced speech. Finally, we show subjective ratings for the proposed approach
and a state-of-the-art real-time RNN-based method.
Flexible Multiple Access Enabling Low-Latency Communications: Introducing NOMA-R
Date: 2020-01-28 00:00:00

Various verticals in 5G and beyond (B5G) networks require very stringent
latency guarantees, while at the same time envisioning massive connectivity. As
a result, choosing the optimal multiple access (MA) technique to achieve low
latency is a key enabler of B5G. In particular, this issue is more acute in
uplink transmissions due to the potentially high number of collisions. On this
premise, in the present contribution we discuss the issue of delay-sensitive
uplink connectivity using optimized MA techniques; to this end, we perform a
comparative analysis of various MA approaches with respect to the achievable
effective capacity (EC). As opposed to standard rate (PHY) or throughput (MAC)
analyses, we propose the concept of the effective capacity as a suitable metric
for characterizing jointly PHY-MAC layer delays. The palette of investigated MA
approaches includes standard orthogonal MA (OMA) and power domain non
orthogonal MA (NOMA) in uplink scenarios, both considering random pairing and
optimized pairing alternatives. It further extends to encompass a recently
proposed third alternative, referred to as NOMA-Relevant (NOMA-R), which
extends OMA and NOMA approaches by flexibly selecting the MA technique. We show
that optimizing both user pairing and MA selection increases the network EC,
especially when stringent delay constraints are in place; thus a flexible MA is
a potentially preferable strategy for future low latency applications
An Improved Quadrature Voltage-Controlled Oscillator with Through-Silicon-Via Inductor in Three-dimensional Integrated Circuits
Date: 2020-01-28 00:00:00

Low-power quadrature voltage-controlled oscillator (QVCO) design utilizing
transformer-feedback and current-reuse techniques with increased frequency
range is proposed in this paper. With increasing demand for QVCOs in on-chip
applications, the conventional spiral inductor based approaches for QVCOs has
become a major bottleneck due to their large size. To address this concern, we
propose to replace the conventional spiral inductor based approaches with
through-silicon-via (TSV) inductor based approach in three-dimensional
integrated circuits (3D ICs). In addition, the proposed QVCO circuit can
provide higher frequency range of operation compared with conventional designs.
Experimental results show by replacing conventional spiral transformers with
TSV transformers, up to 3.9x reduction in metal resource consumption. The
proposed QVCOs achieves a phase noise of -114 $dBc/Hz$@1 $MHz$ and -111.2
$dBc/Hz$@1 $MHz$ at the carrier of 2.5 $GHz$ for toroidal TSV transformed
based-QVCO and vertical spiral transformer based-QVCO respectively. The power
consumption is only 1.5 $mW$ and 1.7 $mW$ for toroidal TSV transformed
based-QVCO and vertical spiral transformer based-QVCO respectively, under the
supply voltage of 0.7 $V$.
Environment-aware Reconfigurable Noise Suppression
Date: 2020-01-29 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

The paper proposes an efficient, robust, and reconfigurable technique to
suppress various types of noises for any sampling rate. The theoretical
analyses, subjective and objective test results show that the proposed noise
suppression (NS) solution significantly enhances the speech transmission index
(STI), speech intelligibility (SI), signal-to-noise ratio (SNR), and subjective
listening experience. The STI and SI consists of 5 levels, i.e., bad, poor,
fair, good, and excellent. The most common noisy condition is of SNR ranging
from -5 to 8 dB. For the input SNR between -5 and 2.5 dB, the proposed NS
improves the STI and SI from "fair" to "good". For the input SNR between 2.5 to
8 dB, the STI and SI are improved from "good" to "excellent" by the proposed
NS. The proposed NS can be adopted in both capture and playback paths for voice
over internet protocol, voice-trigger, and automatic speech recognition
applications.
Design of Non-orthogonal and Noncoherent Massive MIMO for Scalable URLLC Beyond 5G
Date: 2020-02-10 00:00:00

This paper is to design and optimize a non-orthogonal and noncoherent massive
multiple-input multiple-output (MIMO) framework towards enabling scalable
ultra-reliable low-latency communications (sURLLC) in wireless systems beyond
5G. In this framework, the huge diversity gain associated with the large-scale
antenna array in massive MIMO systems is leveraged to ensure ultrahigh
reliability. To reduce the overhead and latency induced by the channel
estimation process, we advocate the noncoherent communication technique which
does not need the knowledge of instantaneous channel state information (CSI)
but only depends on the large-scale fading coefficients for information
decoding. To boost the scalability of the system considered, we enable the
non-orthogonal channel access of multiple users by devising a new differential
modulation scheme to assure that each transmitted signal matrix can be uniquely
determined in the noise-free case and be reliably estimated in noisy cases when
the antenna array size is scaled up. The key idea is to make the transmitted
signals from multiple users be superimposed properly over the air such that
when the sum-signal is correctly detected, the signals sent by all users can be
uniquely determined. To further improve the average error performance when the
array antenna number is large, we propose a max-min Kullback-Leibler (KL)
divergence-based design by jointly optimizing the transmitted powers of all
users and the sub-constellation assignment among them. Simulation results show
that the proposed design significantly outperforms the existing max-min
Euclidean distance-based counterpart in terms of error performance. Moreover,
our proposed approach also has a better error performance than the conventional
coherent zero-forcing (ZF) receiver with orthogonal channel training,
particularly for cell-edge users.
Linear State Estimation and Bad Data Detection for Power Systems with RTU and PMU Measurements
Date: 2020-01-29 00:00:00
Tasks: State Estimation

In this paper, a novel linear algorithm is proposed for state estimation
including bad data detection of power systems that are monitored both by
conventional and synchrophasor measurements. Both types of data are treated
simultaneously and the states are estimated in rectangular coordinates. The
proposed estimator is based on the linear weighted least square method. To
enable the derivation of linear measurement functions, the network is modelled
in terms of voltages and currents in rectangular form and pseudo-measurements
are used to represent conventional measurements. Furthermore, the largest
normalized residual test is used to detect bad data. To validate the accuracy
and robustness of the proposed algorithm, several test cases of different sizes
are solved and the results are presented and discussed.
RPCA-Based High Resolution Through-the-Wall Human Motion Detection and Classification
Date: 2020-01-29 00:00:00
Tasks: Classification, Motion Detection

Radar based assisted living has received great amount of research interest in
recent years. By employing the micro-Doppler features of indoor human motions,
accurate recognition and classification of different types of movements become
possible. Whereas, most of the existing works are focused only on free space
detection, the literature on detection and recognition of human motions in
through-the-wall scenarios is still in its infancy. As can be anticipated, the
wall media and indoor static non-human targets would cause clutters and
significantly corrupt the motion information of human subjects behind wall.
However, no relevant work is reported to effectively handle this problem. In
the present work, we aim to fill the gap and propose to use a low
center-frequency ultra-wideband (UWB) radar system to probe the behind wall
scene. Then, a Robust Principal Component Analysis (RPCA) based subspace
decomposition technique, as its first reported implementation, is employed not
only to remove the stationary clutters in raw range slow-time map but also to
mitigate the multipath effects in the time-frequency map. Onsite experiments of
detecting human motions behind a single layer of concrete wall is carried out
to investigate the performance of the technique. Lastly, a two dimensional
(2D)-PCA algorithm-based motion classification is provided to further verify
the effectiveness of the proposed technique. Classification result shows that
an enhanced recognition capability can be achieved using the proposed technique
in detection and classification of indoor human motions.
Cross Currency Valuation and Hedging in the Multiple Curve Framework
Authors: Alessandro Gnoatto, Nicole Seiffert
Date: 2020-01-29 00:00:00

We generalize the results of Bielecki and Rutkowski (2015) on funding and collateralization to a multi-currency framework and link their results with those of Piterbarg (2012), Moreni and Pallavicini (2017), and Fujii et al. (2010b). In doing this, we provide a complete study of absence of arbitrage in a multi-currency market where, in each single monetary area, multiple interest rates coexist. We first characterize absence of arbitrage in the case without collateral. After that we study collateralization schemes in a very general situation: the cash flows of the contingent claim and those associated to the collateral agreement can be specified in any currency. We study both segregation and rehypothecation and allow for cash and risky collateral in arbitrary currency specifications. Absence of arbitrage and pricing in the presence of collateral are discussed under all possible combinations of conventions. Our work provides a reference for the analysis of wealth dynamics, we also provide valuation formulas that are a useful foundation for cross-currency curve construction techniques. Our framework provides also a solid foundation for the construction of multi-currency simulation models for the generation of exposure profiles in the context of xVA calculations.
Signatures of brain criticality unveiled by maximum entropy analysis across cortical states
Date: 2020-01-29 00:00:00
Tasks: Time Series, Time Series Analysis

It has recently been reported that statistical signatures of brain
criticality, obtained from distributions of neuronal avalanches, can depend on
the cortical state. We revisit these claims with a completely different and
independent approach, employing a maximum entropy model to test whether
signatures of criticality appear in urethane-anesthetized rats. To account for
the spontaneous variation of cortical state, we parse the time series and
perform the maximum entropy analysis as a function of the variability of the
population spiking activity. To compare data sets with different number of
neurons, we define a normalized distance to criticality that takes into account
the peak and width of the specific heat curve. We found an universal collapse
of the normalized distance to criticality dependence on the cortical state on
an animal by animal basis. This indicates a universal dynamics and a critical
point at an intermediate value of spiking variability.
Time- and frequency-limited H2-optimal model order reduction of bilinear control systems
Date: 2020-01-29 00:00:00

In the time- and frequency-limited model order reduction, a reduced-order approximation of the original high-order model is sought to ensure superior accuracy in some desired time and frequency intervals. We first consider the time-limited H2-optimal model order reduction problem for bilinear control systems and derive first-order optimality conditions that a local optimum reduced-order model should satisfy. We then propose a heuristic algorithm that generates a reduced-order model, which tends to achieve these optimality conditions. The frequency-limited and the time-limited H2-pseudo-optimal model reduction problems are also considered wherein we restrict our focus on constructing a reduced-order model that satisfies a subset of the respective optimality conditions for the local optimum. Two new algorithms have been proposed that enforce two out of four optimality conditions on the reduced-order model upon convergence. The algorithms are tested on three numerical examples to validate the theoretical results presented in the paper. The numerical results confirm the efficacy of the proposed algorithms.
Logic-based switching finite-time stabilization with applications in mechanical systems
Authors: Shiqi Zheng, Shihao Wang, Xiang Chen, Yuanlong Xie
Date: 2020-01-30 00:00:00

This paper investigates the finite time stabilization problem for a class of nonlinear systems with unknown control directions and unstructured uncertainties. The unstructured uncertainties indicate that not only the parameters but also the structure of the system nonlinearities are uncertain. The contributions are mainly in the following two aspects: First, a new adaptive control method is proposed for the considered system. Logic-based switching rule is utilized to tune the controller parameters online to stabilize the system in finite time. Different from the existing adaptive controllers for structured/parametric uncertainties, a new switching barrier Lyapunov method and supervisory functions are introduced to overcome the obstacles caused by unstructured uncertainties and unknown control directions. Second, based on a time-varying backstepping scheme, an extension is made to the proposed method such that all the system states can be regulated to zero in prescribed finite time. Moreover, a new analysis tool is presented to show the boundedness of the control signals. Simulations are conducted to verify the effectiveness of the proposed methods.
Nonparametric sign prediction of high-dimensional correlation matrix coefficients
Authors: Christian Bongiorno, Damien Challet
Date: 2020-01-30 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We introduce a method to predict which correlation matrix coefficients are likely to change their signs in the future in the high-dimensional regime, i.e. when the number of features is larger than the number of samples per feature. The stability of correlation signs, two-by-two relationships, is found to depend on three-by-three relationships inspired by Heider social cohesion theory in this regime. We apply our method to US and Hong Kong equities historical data to illustrate how the structure of correlation matrices influences the stability of the sign of its coefficients.