pwc-archive/papers-with-abstracts huggingface dataset:
Dependence-Robust Inference Using Resampled Statistics
Authors: Michael P. Leung
Date: 2020-02-06 00:00:00

We develop inference procedures robust to general forms of weak dependence. The procedures utilize test statistics constructed by resampling in a manner that does not depend on the unknown correlation structure of the data. We prove that the statistics are asymptotically normal under the weak requirement that the target parameter can be consistently estimated at the parametric rate. This holds for regular estimators under many well-known forms of weak dependence and justifies the claim of dependence-robustness. We consider applications to settings with unknown or complicated forms of dependence, with various forms of network dependence as leading examples. We develop tests for both moment equalities and inequalities.
A Multi-Spatial, Multi-Temporal, Semi-Analytical Model for Bathymetry, Water Turbidity and Bottom Composition using Multispectral Imagery
Authors: Sam Blake
Date: 2020-01-24 00:00:00
Tasks: Survey

In this paper we introduce a semi-analytical model for bathymetry, water turbidity and bottom composition; which is primarily based on the physics-based model, HOPE, of Lee et al. Unlike the model of Lee, which was originally designed to use hyperspectral imagery, our model is specifically designed to use multispectral satellite imagery. In particular, we adapt to the greatly decreased spectral resolution by introducing temporal and spatial assumptions on the depth and water turbidity. We validate the extensions to the Lee et al model with a 260 km2 case study in the area of the Murion Islands off Western Australia, where we compare the atmospherically-corrected LANDSAT-8 derived bathymetry against a 2011 single-beam sonar survey by Transport Western Australia. The model validates well against the single-beam sonar survey, with R^2 = 0.85, a mean absolute error of 1.17 m and a mean relative error of 7.52%. This indicates the model could be widely applicable to LANDSAT-8 imagery.
Different approaches to unveil biomolecule configurations and their mutual interactions
Date: 2020-01-13 00:00:00

A novel technique was demonstrated that overcome important drawbacks to
crosslink cells by irradiation with ultrashort UV laser pulses
(L-crosslinking). To use this technique coupled to Chromatin
ImmunoPrecipitation (ChIP) in a high throughput context, a pre-screening fast
method needs to be implemented to set up suitable irradiation conditions of the
cell sample for efficient L-crosslinking with no final and long ChIP analysis.
Here a fast method is reported where living human cells have been first
transfected with a vector coding for Estrogen Receptor {\alpha} (ER{\alpha}),
linked to Green Florescent protein (ER{\alpha}-GFP), so that the well-known
interaction between the Estrogen Receptor Elements (ERE) region of the cell DNA
and the ER{\alpha} protein can be detected by studying the fluorometric
response of the irradiated cells. The damage induced to cells by UV irradiation
is characterized by looking at DNA integrity, proteins stability and cellular
viability. A second novel approach is presented to analyze or re-visit DNA and
RNA sequences and their molecular configurations. This approach is based on
methods derived from Chern-Simons super-gravity adapted to describe mutations
in DNA/RNA strings, as well as interactions between nucleic acids. As a
preliminary case we analyze the KRAS human gene sequence and some of its
mutations. Interestingly, our model shows how the Chern-Simons current are
capable to characterize the mutations within a sequence, in particular giving a
quantitative indication of the mutation likelihood.
Boundary solution based on rescaling method: recoup the first and second-order statistics of neuron network dynamics
Date: 2020-01-31 00:00:00

There is a strong nexus between the network size and the computational
resources available, which may impede a neuroscience study. In the meantime,
rescaling the network while maintaining its behavior is not a trivial mission.
Additionally, modeling patterns of connections under topographic organization
presents an extra challenge: to solve the network boundaries or mingled with an
unwished behavior. This behavior, for example, could be an inset oscillation
due to the torus solution; or a blend with/of unbalanced neurons due to a lack
(or overdose) of connections. We detail the network rescaling method able to
sustain behavior statistical utilized in Romaro et al. (2018) and present a
boundary solution method based on the previous statistics recoup idea.
FPGA Acceleration of Sequence Alignment: A Survey
Authors: Sahand Salamat, Tajana Rosing
Date: 2020-02-05 00:00:00
Tasks: CPU, Survey

Genomics is changing our understanding of humans, evolution, diseases, and medicines to name but a few. As sequencing technology is developed collecting DNA sequences takes less time thereby generating more genetic data every day. Today the rate of generating genetic data is outpacing the rate of computation power growth. Current sequencing machines can sequence 50 humans genome per day; however, aligning the read sequences against a reference genome and assembling the genome will take 1300 CPU hours. The main step in constructing the genome is aligning the reads against a reference genome. Numerous accelerators have been proposed to accelerate the DNA alignment process. Providing massive parallelism, FPGA-based accelerators have shown great performance in accelerating DNA alignment algorithms. Additionally, FPGA-based accelerators provide better energy efficiency than general-purpose processors. In this survey, we introduce three main DNA alignment algorithms and FPGA-based implementation of these algorithms to accelerate the DNA alignment. We also, compare these three alignment categories and show how accelerators are developing during the time.
Model of Algal Growth Depending on Nutrients and Inorganic Carbon in a Poorly Mixed Water Column
Date: 2020-01-31 00:00:00

In this paper, we establish a reaction-diffusion-advection partial
differential equation model to describe the growth of algae depending on both
nutrients and inorganic carbon in a poorly mixed water column. Nutrients from
the water bottom and inorganic carbon from the water surface form an asymmetric
resource supply mechanism on the algal growth. The existence and stability of
semi-trivial steady state and coexistence steady state of the model are proved,
and a threshold condition for the regime shift from extinction to survival of
algae is established. The influence of environmental parameters on the vertical
distribution of algae is investigated in the water column. It is shown that the
vertical distribution of algae can exhibit many different profiles under the
joint limitation of nutrients and inorganic carbon.
Neurogenetics of the Human Adenosine Receptor Genes: Genetic Structures and Involvement in Brain Diseases
Date: 2020-01-31 00:00:00

Adenosine receptors are G-protein-coupled receptors involved in a wide range
of physiological and pathological phenomena in most mammalian systems. All four
receptors are widely expressed in the central nervous system, where they
modulate neurotransmitter release and neuronal plasticity. A large number of
gene association studies have shown that common genetic variants of the
adenosine receptors (encoded by the ADORA1, ADORA2A, ADORA2B and ADORA3 genes)
have a neuroprotective or neurodegenerative role in neurologic/psychiatric
diseases. New genetic studies of rare variants and few novel associations with
depression or epilepsy subtypes have recently been reported. Here, we review
the literature on the genetics of adenosine receptors in neurologic and/or
psychiatric diseases in humans, and discuss perspectives for further genetic
research. We also provide an update on the genetic structures of the four human
adenosine receptor genes and their regulation - a topic that has not been
extensively addressed. Our review emphasizes the importance of (i) better
characterizing the genetics of adenosine receptor genes and (ii) understanding
how these genes are regulated.
Phase Retrieval for Partially Coherent Observations
Authors: Jonas Kornprobst, Alexander Paulus, Josef Knapp, Thomas F. Eibert
Date: 2020-02-07 00:00:00
Tasks: Retrieval

Phase retrieval is in general a non-convex and non-linear task and the corresponding algorithms struggle with the issue of local minima. We consider the case where the measurement samples within typically very small and disconnected subsets are coherently linked to each other - which is a reasonable assumption for our objective of antenna measurements. Two classes of measurement setups are discussed which can provide this kind of extra information: multi-probe systems and holographic measurements with multiple reference signals. We propose several formulations of the corresponding phase retrieval problem. The simplest of these formulations poses a linear system of equations similar to an eigenvalue problem where a unique non-trivial null-space vector needs to be found. Accurate phase reconstruction for partially coherent observations is, thus, possible by a reliable solution process and with judgment of the solution quality. Under ideal, noise-free conditions, the required sampling density is less than two times the number of unknowns. Noise and other observation errors increase this value slightly. Simulations for Gaussian random matrices and for antenna measurement scenarios demonstrate that reliable phase reconstruction is possible with the presented approach.
Design and Implementation of Butterworth, Chebyshev-I and Elliptic Filter for Speech Signal Analysis
Authors: Prajoy Podder, Md. Mehedi Hasan, Md. Rafiqul Islam, Mursalin Sayeed
Date: 2020-02-08 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

In the field of digital signal processing, the function of a filter is to remove unwanted parts of the signal such as random noise that is also undesirable. To remove noise from the speech signal transmission or to extract useful parts of the signal such as the components lying within a certain frequency range, filters are necessary. Filters are broadly used in signal processing and communication systems in applications such as channel equalization, noise reduction, radar, audio processing, speech signal processing, video processing, biomedical signal processing that is noisy ECG, EEG, EMG signal filtering, electrical circuit analysis and analysis of economic and financial data. In this paper, three types of infinite impulse response filter i.e. Butterworth, Chebyshev type I and Elliptical filter have been discussed theoretically and experimentally. Butterworth, Chebyshev type I and elliptic low pass, high pass, band pass and band stop filter have been designed in this paper using MATLAB Software. The impulse responses, magnitude responses, phase responses of Butterworth, Chebyshev type I and Elliptical filter for filtering the speech signal have been observed in this paper. Analyzing the Speech signal, its sampling rate and spectrum response have also been found.
Recurrent Neural Network Control of a Hybrid Dynamic Transfemoral Prosthesis with EdgeDRNN Accelerator
Authors: Chang Gao, Rachel Gehlhar, Aaron D. Ames, Shih-Chii Liu, Tobi Delbruck
Date: 2020-02-08 00:00:00

Lower leg prostheses could improve the life quality of amputees by increasing comfort and reducing energy to locomote, but currently control methods are limited in modulating behaviors based upon the human's experience. This paper describes the first steps toward learning complex controllers for dynamical robotic assistive devices. We provide the first example of behavioral cloning to control a powered transfemoral prostheses using a Gated Recurrent Unit (GRU) based recurrent neural network (RNN) running on a custom hardware accelerator that exploits temporal sparsity. The RNN is trained on data collected from the original prosthesis controller. The RNN inference is realized by a novel EdgeDRNN accelerator in real-time. Experimental results show that the RNN can replace the nominal PD controller to realize end-to-end control of the AMPRO3 prosthetic leg walking on flat ground and unforeseen slopes with comparable tracking accuracy. EdgeDRNN computes the RNN about 240 times faster than real time, opening the possibility of running larger networks for more complex tasks in the future. Implementing an RNN on this real-time dynamical system with impacts sets the ground work to incorporate other learned elements of the human-prosthesis system into prosthesis control.
Stability and asymptotic analysis of the Föllmer-Schweizer decomposition on a finite probability space
Authors: Sarah Boese, Tracy Cui, Samuel Johnston, Gianmarco Molino, Oleksii Mostovyi
Date: 2020-02-09 00:00:00
Tasks: regression

First, we consider the problem of hedging in complete binomial models. Using the discrete-time F\"ollmer-Schweizer decomposition, we demonstrate the equivalence of the backward induction and sequential regression approaches. Second, in incomplete trinomial models, we examine the extension of the sequential regression approach for approximation of contingent claims. Then, on a finite probability space, we investigate stability of the discrete-time F\"ollmer-Schweizer decomposition with respect to perturbations of the stock price dynamics and, finally, perform its asymptotic analysis under simultaneous perturbations of the drift and volatility of the underlying discounted stock price process, where we prove stability and obtain explicit formulas for the leading order correction terms.
Optimal liquidation trajectories for the Almgren-Chriss model with Levy processes
Authors: Arne Lokka, Junwei Xu
Date: 2020-02-09 00:00:00

We consider an optimal liquidation problem with infinite horizon in the Almgren-Chriss framework, where the unaffected asset price follows a Levy process. The temporary price impact is described by a general function which satisfies some reasonable conditions. We consider an investor with constant absolute risk aversion, who wants to maximise the expected utility of the cash received from the sale of his assets, and show that this problem can be reduced to a deterministic optimisation problem which we are able to solve explicitly. In order to compare our results with exponential Levy models, which provides a very good statistical fit with observed asset price data for short time horizons, we derive the (linear) Levy process approximation of such models. In particular we derive expressions for the Levy process approximation of the exponential Variance-Gamma Levy process, and study properties of the corresponding optimal liquidation strategy. We then provide a comparison of the liquidation trajectories for reasonable parameters between the Levy process model and the classical Almgren-Chriss model. In particular, we obtain an explicit expression for the connection between the temporary impact function for the Levy model and the temporary impact function for the Brownian motion model (the classical Almgren-Chriss model), for which the optimal liquidation trajectories for the two models coincide.
Optimal liquidation for a risk averse investor in a one-sided limit order book driven by a Levy process
Authors: Arne Lokka, Junwei Xu
Date: 2020-02-09 00:00:00
Tasks: Position

In a one-sided limit order book, satisfying some realistic assumptions, where the unaffected price process follows a Levy process, we consider a market agent that wants to liquidate a large position of shares. We assume that the agent has constant absolute risk aversion and aims at maximising the expected utility of the cash position at the end of time. The agent is then faced with the problem of balancing the market risk and the cost of a rapid execution. In particular we are interested in how the agent should go about optimally submitting orders. Since liquidation normally takes place within a short period of time, modelling the risk as a Levy process should provide a realistic model with good statistical fit to observed market data, and thus the model should provide a realistic reflection of the agent's market risk. We reduce the optimisation problem to a deterministic two-dimensional singular problem, to which we are able to derive an explicit solution in terms of the model data. In particular we find an expression for the optimal intervention boundary, which completely characterise the optimal liquidation strategy.
Tuning of multivariable model predictive controllersthrough expert bandit feedback
Date: 2020-02-10 00:00:00

For certain industrial control applications an explicit function capturing
the nontrivial trade-off between competing objectives in closed loop
performance is not available. In such scenarios it is common practice to use
the human innate ability to implicitly learn such a relationship and manually
tune the corresponding controller to achieve the desirable closed loop
performance. This approach has its deficiencies because of individual
variations due to experience levels and preferences in the absence of an
explicit calibration metric. Moreover, as the complexity of the underlying
system and/or the controller increase, in the effort to achieve better
performance, so does the tuning time and the associated tuning cost. To reduce
the overall tuning cost, a tuning framework is proposed herein, whereby a
supervised machine learning is used to extract the human-learned cost function
and an optimization algorithm that can efficiently deal with a large number of
variables, is used for optimizing the extracted cost function. Given the
interest in the implementation across many industrial domains and the
associated high degree of freedom present in the corresponding tuning process,
a Model Predictive Controller applied to air path control in a diesel engine is
tuned for the purpose of demonstrating the potential of the framework.
Asymmetric Cell Transmission Model-Based, Ramp-Connected Robust Traffic Density Estimation under Bounded Disturbances
Date: 2020-02-10 00:00:00
Tasks: Density Estimation

In modern transportation systems, traffic congestion is inevitable. To
minimize the loss caused by congestion, various control strategies have been
developed most of which rely on observing real-time traffic conditions. As
vintage traffic sensors are limited, traffic density estimation is very helpful
for gaining network-wide observability. This paper deals with this problem by
first, presenting a traffic model for stretched highway having multiple ramps
built based on asymmetric cell transmission model (ACTM). Second, based on the
assumption that the encompassed nonlinearity of the ACTM is Lipschitz, a robust
dynamic observer framework for performing traffic density estimation is
proposed. Numerical test results show that the observer yields a sufficient
performance in estimating traffic densities having noisy measurements, while
being computationally faster the Unscented Kalman Filter in performing
real-time estimation.
Human-Machine Collaboration for Automated Vehicles via an Intelligent Two-Phase Haptic Interface
Date: 2020-02-28 00:00:00
Tasks: Autonomous Driving

Prior to realizing fully autonomous driving, human intervention will be
required periodically to guarantee vehicle safety. This fact poses a new
challenge in human-machine interaction, particularly during control authority
transition from the automated functionality to a human driver. This paper
addresses this challenge by proposing an intelligent haptic interface based on
a newly developed two-phase human-machine interaction model. The intelligent
haptic torque is applied on the steering wheel and switches its functionality
between predictive guidance and haptic assistance according to the varying
state and control ability of human drivers, helping drivers gradually resume
manual control during takeover. The developed approach is validated by
conducting vehicle experiments with 26 human participants. The results suggest
that the proposed method can effectively enhance the driving state recovery and
control performance of human drivers during takeover compared with an existing
approach, further improving the safety and smoothness of the human-machine
interaction in automated vehicles.
Markov Switching
Date: 2020-02-10 00:00:00

Markov switching models are a popular family of models that introduces
time-variation in the parameters in the form of their state- or regime-specific
values. Importantly, this time-variation is governed by a discrete-valued
latent stochastic process with limited memory. More specifically, the current
value of the state indicator is determined only by the value of the state
indicator from the previous period, thus the Markov property, and the
transition matrix. The latter characterizes the properties of the Markov
process by determining with what probability each of the states can be visited
next period, given the state in the current period. This setup decides on the
two main advantages of the Markov switching models. Namely, the estimation of
the probability of state occurrences in each of the sample periods by using
filtering and smoothing methods and the estimation of the state-specific
parameters. These two features open the possibility for improved
interpretations of the parameters associated with specific regimes combined
with the corresponding regime probabilities, as well as for improved
forecasting performance based on persistent regimes and parameters
characterizing them.
microRNA-mediated noise processing in cells: a fight or a game?
Authors: Elsi Ferro, Chiara Enrico Bena, Silvia Grigolon, Carla Bosia
Date: 2020-02-10 00:00:00

In the past decades microRNAs (miRNA) have much attracted the attention of researchers at the interface between life and theoretical sciences for their involvement in post-transcriptional regulation and related diseases. Thanks to the always more sophisticated experimental techniques, the role of miRNAs as "noise processing units" has been further elucidated and two main ways of miRNA noise-control have emerged by combinations of theoretical and experimental studies. While on one side miRNA were thought to buffer gene expression noise, it has recently been suggested that miRNA could also increase the cell-to-cell variability of their targets. In this Mini Review, we focus on the miRNA role in noise processing and on the inference of the parameters defined by the related theoretical modelling.
A QFT approach to robust dual-rate control systems
Date: 2020-02-10 00:00:00

A dual-rate control system is a hybrid system composed of continuous-time and
discrete-time elements with two sampling frequencies. In this work, a new
frequency domain analysis and design approach, based on the Quantitative
Feedback Theory (QFT) is developed, to cope with robust stability and tracking
specifications. Tracking specifications are considered not only in the
discrete-time but also in continuous-time, that allow a precise description of
the intersample behavior (ripples), and characterization of frequencies below
and beyond the Nyquist frequency. Several illustrative examples and a case
study has been developed.