pwc-archive/papers-with-abstracts huggingface dataset:
High-SNR snapshot multiplex spectrometer with sub-Hadamard-S matrix coding
Date: 2019-05-18 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We present a robust high signal-to-noise ratio (SNR) snapshot multiplex
spectrometer with sub-Hadamard-S matrix coding. We demonstrated for the first
time that the sub-Hadamard-S matrix coding could provide comparable SNR
improvement with Hadamard-S matrix in Hadamard transform spectrometer (HTS).
Normally, HTS should change the coding mask to obtain a reasonable spectrum
result, causing unexpected time-consuming. An extra imaging path to collect the
light intensity of the aperture is added in this paper. Both light intensity of
the aperture and overlapped spectra are captured within one shot, turning
Hadamard-S matrix coding into sub-Hadamard-S matrix coding. Simulations and
experiments show that the proposed method could obtain comparable SNR
improvement with the traditional HTS, maintaining snapshot.
Secure Analysis Over Generalized-K Channels
Date: 2019-05-18 00:00:00

In this letter, we adopt the SOP definition in [4] and the simplified model
of [8], and derive a closed-form expression for the proposed SOP over GK fading
channels. To simplify this expression and obtain additional insights, we also
perform an asymptotic analysis of the main link in the high SNR region.
Spectral risk measures and uncertainty
Date: 2019-05-19 00:00:00

Risk assessment under different possible scenarios is a source of uncertainty
that may lead to concerning financial losses. We address this issue, first, by
adapting a robust framework to the class of spectral risk measures. Second, we
propose a Deviation-based approach to quantify uncertainty. Furthermore, the
theory is illustrated with a practical case study from NASDAQ index.
Iterative Estimation of Nonparametric Regressions with Continuous Endogenous Variables and Discrete Instruments
Authors: Samuele Centorrino, Frédérique Fève, Jean-Pierre Florens
Date: 2019-05-19 00:00:00
Tasks: regression

We consider a nonparametric regression model with continuous endogenous independent variables when only discrete instruments are available that are independent of the error term. Although this framework is very relevant for applied research, its implementation is challenging, as the regression function becomes the solution to a nonlinear integral equation. We propose a simple iterative procedure to estimate such models and showcase some of its asymptotic properties. In a simulation experiment, we detail its implementation in the case when the instrumental variable is binary. We conclude with an empirical application to returns to education.
Time Series Analysis and Forecasting of the US Housing Starts using Econometric and Machine Learning Model
Date: 2019-05-20 00:00:00
Tasks: regression, Time Series, Time Series Analysis

In this research paper, I have performed time series analysis and forecasted
the monthly value of housing starts for the year 2019 using several econometric
methods - ARIMA(X), VARX, (G)ARCH and machine learning algorithms - artificial
neural networks, ridge regression, K-Nearest Neighbors, and support vector
regression, and created an ensemble model. The ensemble model stacks the
predictions from various individual models, and gives a weighted average of all
predictions. The analyses suggest that the ensemble model has performed the
best among all the models as the prediction errors are the lowest, while the
econometric models have higher error rates.
A novel Algorithm for Optimal Placement of Multiple Inertial Sensors to Improve the Sensing Accuracy
Date: 2019-05-20 00:00:00

This paper proposes a novel algorithm to determine the optimal placement of
redundant inertial sensors such as accelerometers and gyroscopes (gyros) for
increasing the sensing accuracy. In this paper, we have proposed a novel
iterative algorithm to find the optimal sensor configuration. The proposed
algorithm utilizes the majorization-minimization (MM) algorithm and the duality
principle to find the optimal configuration. Unlike the state-of-the-art which
are mainly geometrical in nature and restricted to certain noise statistics,
the proposed algorithm gives the exact positions of the sensors, and moreover,
the proposed algorithm is independent of the nature of the noise at different
sensors. The proposed alogrithm has been implemented and tested via numerical
simulation in the MATLAB. The simulation results show that the algorithm
converges to the optimal configurations and show the effectiveness of the
proposed algorithm.
Risk-Sensitive Credit Portfolio Optimization under Partial Information and Contagion Risk
Authors: Lijun Bo, Huafu Liao, Xiang Yu
Date: 2019-05-20 00:00:00
Tasks: Portfolio Optimization

This paper investigates the finite horizon risk-sensitive portfolio optimization in a regime-switching credit market with physical and information-induced default contagion. It is assumed that the underlying regime-switching process has countable states and is unobservable. The stochastic control problem is formulated under partial observations of asset prices and sequential default events. By establishing a martingale representation theorem based on incomplete and phasing out filtration, we connect the control problem to a quadratic BSDE with jumps, in which the driver term is non-standard and carries the conditional filter as an infinite-dimensional parameter. By proposing some truncation techniques and proving a uniform a priori estimates, we obtain the existence of a solution to the BSDE using the convergence of solutions associated to some truncated BSDEs. The verification theorem can be concluded with the aid of our BSDE results, which in turn yields the uniqueness of the solution to the BSDE.
Testing Sharpe ratio: luck or skill?
Date: 2019-05-21 00:00:00
Tasks: Asset Management, Management

Sharpe ratio (sometimes also referred to as information ratio) is widely used
in asset management to compare and benchmark funds and asset managers. It
computes the ratio of the (excess) net return over the strategy standard
deviation. However, the elements to compute the Sharpe ratio, namely, the
expected returns and the volatilities are unknown numbers and need to be
estimated statistically. This means that the Sharpe ratio used by funds is
likely to be error prone because of statistical estimation errors. In this
paper, we provide various tests to measure the quality of the Sharpe ratios. By
quality, we are aiming at measuring whether a manager was indeed lucky of
skillful. The test assesses this through the statistical significance of the
Sharpe ratio. We not only look at the traditional Sharpe ratio but also compute
a modified Sharpe insensitive to used Capital. We provide various statistical
tests that can be used to precisely quantify the fact that the Sharpe is
statistically significant. We illustrate in particular the number of trades for
a given Sharpe level that provides statistical significance as well as the
impact of auto-correlation by providing reference tables that provides the
minimum required Sharpe ratio for a given time period and correlation. We also
provide for a Sharpe ratio of 0.5, 1.0, 1.5 and 2.0 the skill percentage given
the auto-correlation level.
The self-organised, non-equilibrium dynamics of spontaneous cancerous buds
Authors: Abramo Agosti, Stefano Marchesi, Giorgio Scita, Pasquale Ciarletta
Date: 2019-05-20 00:00:00

Tissue self-organization into defined and well-controlled three-dimensional structures is essential during development for the generation of organs. A similar, but highly deranged process might also occur during the aberrant growth of cancers, which frequently display a loss of the orderly structures of the tissue of origin, but retain a multicellular organization in the form of spheroids, strands, and buds. The latter structures are often seen when tumors masses switch to an invasive behavior into surrounding tissues. However, the general physical principles governing the self-organized architectures of tumor cell populations remain by and large unclear. In this work, we perform in-vitro experiments to characterize the growth properties of glioblastoma budding emerging from monolayers. Using a theoretical model and numerical tools here we find that such a topological transition is a self-organised, non-equilibrium phenomenon driven by the trade--off of mechanical forces and physical interactions exerted at cell-cell and cell-substrate adhesions. Notably, the unstable disorder states of uncontrolled cellular proliferation macroscopically emerge as complex spatio--temporal patterns that evolve statistically correlated by a universal law.
Deep learning to generate in silico chemical property libraries and candidate molecules for small molecule identification in complex samples
Date: 2019-05-21 00:00:00
Tasks: Drug Discovery, Transfer Learning

Comprehensive and unambiguous identification of small molecules in complex
samples will revolutionize our understanding of the role of metabolites in
biological systems. Existing and emerging technologies have enabled measurement
of chemical properties of molecules in complex mixtures and, in concert, are
sensitive enough to resolve even stereoisomers. Despite these experimental
advances, small molecule identification is inhibited by (i) chemical reference
libraries representing <1% of known molecules, limiting the number of possible
identifications, and (ii) the lack of a method to generate candidate matches
directly from experimental features (i.e. without a library). To this end, we
developed a variational autoencoder (VAE) to learn a continuous numerical, or
latent, representation of molecular structure to expand reference libraries for
small molecule identification. We extended the VAE to include a chemical
property decoder, trained as a multitask network, in order to shape the latent
representation such that it assembles according to desired chemical properties.
The approach is unique in its application to small molecule identification,
with its focus on m/z and CCS, paired with its training paradigm, which
involved a cascade of transfer learning iterations. This allows the network to
learn as much as possible at each stage, enabling success with progressively
smaller datasets without overfitting. Once trained, the network can rapidly
predict chemical properties directly from structure, as well as generate
candidate structures with desired chemical properties. Additionally, the
ability to generate novel molecules along manifolds, defined by chemical
property analogues, positions DarkChem as highly useful in a number of
application areas, including metabolomics and small molecule identification,
drug discovery and design, chemical forensics, and beyond.
Predicting and Forecasting the Price of Constituents and Index of Cryptocurrency Using Machine Learning
Date: 2019-05-21 00:00:00
Tasks: BIG-bench Machine Learning, Management

At present, cryptocurrencies have become a global phenomenon in financial
sectors as it is one of the most traded financial instruments worldwide.
Cryptocurrency is not only one of the most complicated and abstruse fields
among financial instruments, but it is also deemed as a perplexing problem in
finance due to its high volatility. This paper makes an attempt to apply
machine learning techniques on the index and constituents of cryptocurrency
with a goal to predict and forecast prices thereof. In particular, the purpose
of this paper is to predict and forecast the close (closing) price of the
cryptocurrency index 30 and nine constituents of cryptocurrencies using machine
learning algorithms and models so that, it becomes easier for people to trade
these currencies. We have used several machine learning techniques and
algorithms and compared the models with each other to get the best output. We
believe that our work will help reduce the challenges and difficulties faced by
people, who invest in cryptocurrencies. Moreover, the obtained results can play
a major role in cryptocurrency portfolio management and in observing the
fluctuations in the prices of constituents of cryptocurrency market. We have
also compared our approach with similar state of the art works from the
literature, where machine learning approaches are considered for predicting and
forecasting the prices of these currencies. In the sequel, we have found that
our best approach presents better and competitive results than the best works
from the literature thereby advancing the state of the art. Using such
prediction and forecasting methods, people can easily understand the trend and
it would be even easier for them to trade in a difficult and challenging
financial instrument like cryptocurrency.
DNN-Based Speech Presence Probability Estimation for Multi-Frame Single-Microphone Speech Enhancement
Authors: Marvin Tammen, Dörte Fischer, Bernd T. Meyer, Simon Doclo
Date: 2019-05-21 00:00:00
Tasks: Speech Enhancement

Multi-frame approaches for single-microphone speech enhancement, e.g., the multi-frame minimum-power-distortionless-response (MFMPDR) filter, are able to exploit speech correlations across neighboring time frames. In contrast to single-frame approaches such as the Wiener gain, it has been shown that multi-frame approaches achieve a substantial noise reduction with hardly any speech distortion, provided that an accurate estimate of the correlation matrices and especially the speech interframe correlation (IFC) vector is available. Typical estimation procedures of the IFC vector require an estimate of the speech presence probability (SPP) in each time-frequency (TF) bin. In this paper, we propose to use a bi-directional long short-term memory deep neural network (DNN) to estimate the SPP for each TF bin. Aiming at achieving a robust performance, the DNN is trained for various noise types and within a large signal-to-noise-ratio range. Experimental results show that the MFMPDR in combination with the proposed data-driven SPP estimator yields an increased speech quality compared to a state-of-the-art model-based SPP estimator. Furthermore, it is confirmed that exploiting interframe correlations in the MFMPDR is beneficial when compared to the Wiener gain especially in adverse scenarios.
Source Localization and Tracking for Dynamic Radio Cartography using Directional Antennas
Date: 2019-05-21 00:00:00
Tasks: Compressive Sensing

Utilization of directional antennas is a promising solution for efficient
spectrum sensing and accurate source localization and tracking. Spectrum
sensors equipped with directional antennas should constantly scan the space in
order to track emitting sources and discover new activities in the area of
interest. In this paper, we propose a new formulation that unifies
received-signal-strength (RSS) and direction of arrival (DoA) in a compressive
sensing (CS) framework. The underlying CS measurement matrix is a function of
beamforming vectors of sensors and is referred to as the propagation matrix.
Comparing to the omni-directional antenna case, our employed propagation matrix
provides more incoherent projections, an essential factor in the compressive
sensing theory. Based on the new formulation, we optimize the antenna beams,
enhance spectrum sensing efficiency, track active primary users accurately and
monitor spectrum activities in an area of interest. In many practical scenarios
there is no fusion center to integrate received data from spectrum sensors. We
propose the distributed version of our algorithm for such cases. Experimental
results show a significant improvement in source localization accuracy,
compared with the scenario when sensors are equipped with omni-directional
antennas. Applicability of the proposed framework for dynamic radio cartography
is shown. Moreover, comparing the estimated dynamic RF map over time with the
ground truth demonstrates the effectiveness of our proposed method for accurate
signal estimation and recovery.
A Reconfigurable Dual-Mode Tracking SAR ADC without Analog Subtraction
Date: 2019-05-28 00:00:00
Tasks: Quantization

In this contribution, it is proposes to limit the quantization search space
of a successive approximation analog-to-digital converter through an analytic
derivation of maximum possible sample-to-sample variation. The presented
example design of the proposed ADC is an 8-bit 1MS/s ADC with SAR logic
customized to incorporate this priori information while no modification has
been required to the analog circuitry. In comparison to conventional SAR
conversion, the proposed tracking approach yields significant reduction in
total power consumption in oversampling mode. The power savings are due to the
reduced number of SAR cycles, and voltage variation minimization across DAC
capacitors. The design is reconfigurable both to conventional SAR sampling and
the proposed tracking scheme. The approach is attractive for SAR ADCs embedded
in very low power micro-controllers.
Estimation of black bear abundance by management zone in Wisconsin
Date: 2019-05-22 00:00:00
Tasks: Management, State Space Models

Estimates of population abundance are fundamental to wildlife management and
conservation, but are difficult to obtain across large geographic scales and
for cryptic species. We used a state-space model with age-at-harvest data in a
Bayesian framework to model American black bear (Ursus americanus) abundance
and demographic parameters in four management zones in Wisconsin from
2011-2017. We had limited demographic data available from the population, and
relied upon a) the model, b) age-at-harvest data, and c) informative prior
distributions from a literature review. The estimated posterior means and
distributions for abundance and demographic parameters from our models were
reasonable for each management zone, and indicated a decreasing trend in zones
A and B, and a generally stable trend in zones C and D. The age-at-harvest data
updated the posterior distribution and means for initial population size,
harvest season survival, and non-harvest season survival, with a notable
increase in precision for the survival values. A strength of the model for
managers is the formalized process for providing biologically supported
information as prior distributions to transparently accommodate expert opinion
with measures of confidence when estimating wildlife populations, which can
then be updated by the age-at-harvest data and model structure. The integration
of prior information and age-at-harvest state-space models in a Bayesian
framework efficiently leverage all available information for making
zone-specific abundance estimates for the management of harvested species. This
may create more informative data for decision-makers when setting harvest
quotas, and could lead to more effective monitoring, conservation, and
management of cryptic carnivore species.
Selection of a Minimal Number of Significant Porcine SNPs by an Information Gain and Genetic Algorithm Hybrid Model
Date: 2019-05-22 00:00:00
Tasks: Classification, feature selection

A panel of large number of common Single Nucleotide Polymorphisms (SNPs)
distributed across an entire porcine genome has been widely used to represent
genetic variability of pig. With the advent of SNP-array technology, a
genome-wide genetic profile of a specimen can be easily observed. Among the
large number of such variations, there exist a much smaller subset of the SNP
panel that could equally be used to correctly identify the corresponding breed.
This work presents a SNP selection heuristic that can still be used effectively
in the breed classification process. The proposed feature selection was done by
the approach of combining a filter method and a wrapper method--information
gain method and genetic algorithm--plus a feature frequency selection step,
while classification was done by support vector machine. The approach was able
to reduce the number of significant SNPs to 0.86 % of the total number of SNPs
in a swine dataset and provided a high classification accuracy of 94.80 %.
A Trust Management and Misbehaviour Detection Mechanism for Multi-Agent Systems and its Application to Intelligent Transportation Systems
Date: 2019-05-22 00:00:00
Tasks: Decision Making, Fault Detection, Management

Cooperative information shared among a multi-agent system (MAS) can be useful
to agents to efficiently fulfill their missions. Relying on wrong information,
however, can have severe consequences. While classical approaches only consider
measurement uncertainty, reliability information on the incoming data can be
useful for decision making. In this work, a subjective logic based mechanism is
proposed that amends reliability information to the data shared among the MAS.
  If multiple agents report the same event, their information is fused. In
order to maintain high reliability, the mechanism detects and isolates
misbehaving agents. Therefore, an attacker model is specified that includes
faulty as well as malicious agents. The mechanism is applied to Intelligent
Transportation Systems (ITS) and it is shown in simulation that the approach
scales well with the size of the MAS and that it is able to efficiently
detected and isolated misbehaving agents.
  Keywords: Multi-agent systems, Fault Detection, Sensor/data fusion, Control
Applications
A Prerequisite for Life
Authors: Søren Toxvaerd
Date: 2019-05-22 00:00:00

The complex physicochemical structures and chemical reactions in living organism have some common features: (1) The life processes take place in the cytosol in the cells, which, from a physicochemical point of view is an emulsion of biomolecules in a dilute aqueous suspension. (2) All living systems are homochiral with respect to the units of amino acids and carbohydrates, but (some) proteins are chiral unstable in the cytosol. (3) And living organism are mortal. These three common features together give a prerequisite for the prebiotic self-assembly at the start of the Abiogenesis. Here we argue , that it all together indicates, that the prebiotic self-assembly of structures and reactions took place in a more saline environment, whereby the homochirality of proteins not only could be obtained, but also preserved. A more saline environment for the prebiotic self-assembly of organic molecules and establishment of biochemical reactions could have been the hydrothermal vents.
High Throughput Polar Decoding Using Two-Staged Adaptive Successive Cancellation List Decoding
Date: 2019-05-22 00:00:00

Polar codes are the first class of capacity-achieving forward error
correction (FEC) codes. They have been selected as one of the coding schemes
for the 5G communication systems due to their excellent error correction
performance when successive cancellation list (SCL) decoding with cyclic
redundancy check (CRC) is used. A large list size is necessary for SCL decoding
to achieve a low error rate. However, it impedes SCL decoding from achieving a
high throughput as the computational complexity is very high when a large list
size is used. In this paper, we propose a two-staged adaptive SCL (TA-SCL)
decoding scheme and the corresponding hardware architecture to accelerate SCL
decoding with a large list size. Constant system latency and data rate are
supported by TA-SCL decoding. To analyse the decoding performance of TA-SCL, an
accurate mathematical model based on Markov Chain is derived, which can be used
to determine the parameters for practical designs. A VLSI architecture
implementing TA-SCL decoding is then proposed. The proposed architecture is
implemented using UMC 90nm technology. Experimental results show that TA-SCL
can achieve throughputs of 3.00 and 2.35 Gbps when the list sizes are 8 and 32,
respectively, which are nearly 3 times as that of the state-ofthe-art SCL
decoding architectures, with negligible performance degradation on a wide
signal-to-noise ratio (SNR) range and small hardware overhead.