pwc-archive/papers-with-abstracts huggingface dataset:
CAR T cell therapy in B-cell acute lymphoblastic leukaemia: Insights from mathematical models
Authors: Odelaisy Leon-Triana, Soukaina Sabir, Gabriel F. Calvo, Juan Belmonte-Beitia, Salvador Chulian, Alvaro Martinez-Rubio, Maria Rosa, Antonio Perez-Martinez, Manuel Ramirez Orellana, Victor M. Perez-Garcia
Date: 2020-03-18 00:00:00

Immunotherapies use components of the patient immune system to selectively target cancer cells. The use of CAR T cells to treat B-cell malignancies --leukaemias and lymphomas-- is one of the most successful examples, with many patients experiencing long-lasting complete responses to this therapy. This treatment works by extracting the patient's T cells and adding them the CAR group, which enables them to recognize and target cells carrying the antigen CD19+, that is expressed in these haematological tumors. Here we put forward a mathematical model describing the time response of leukaemias to the injection of CAR T-cells. The model accounts for mature and progenitor B-cells, tumor cells, CAR T cells and side effects by incorporating the main biological processes involved. The model explains the early post-injection dynamics of the different compartments and the fact that the number of CAR T cells injected does not critically affect the treatment outcome. An explicit formula is found that provides the maximum CAR T cell expansion in-vivo and the severity of side effects. Our mathematical model captures other known features of the response to this immunotherapy. It also predicts that CD19+ tumor relapses could be the result of the competition between tumor and CAR T cells analogous to predator-prey dynamics. We discuss this fact on the light of available evidences and the possibility of controlling relapses by early re-challenging of the tumor with stored CAR T cells.
A Gridless Compressive Sensing Based Channel Estimation for Millimeter Wave Massive MIMO Systems from 1-Bit Measurements
Date: 2020-03-18 00:00:00
Tasks: Compressive Sensing

This paper considers the problem of estimating the sparse millimeter wave
(mmWave) massive multiple-input multiple-output (MIMO) OFDM channel from 1-bit
quantized measurements. Unlike previous quantized on-grid approaches to
compressive sensing, we introduce an off-grid convex scheme which uses binary
atomic norm minimization (BiANM) to estimate sparse channel form 1-bit
measurements. Moreover, in this paper, we propose a decoupled angle-delay
version of BiANM called decoupled binary atomic norm minimization (DeBiANM) in
order to reduce complexity. Further, for improving the accuracy of the
estimation, we develop reweighted version of BiANM named reweighted binary
atomic norm minimization (ReBiANM) that is a trade-off between atomic $\ell_0$
norm and atomic $\ell_1$ norm. Also, for improvement the performance of the
DeBiANM, we introduce the reweighted version of DeBiANM, termed as reweighted
decoupled binary atomic minimization (ReDeBiANM). Finally, through the
simulation results, the performance of the proposed methods are studied.
A nearly-neutral biallelic Moran model with biased mutation and linear and quadratic selection
Authors: Claus Vogl, Lynette Caitlin Mikula
Date: 2020-03-23 00:00:00

In this article, a biallelic reversible mutation model with linear and quadratic selection is analyzed. The approach reconnects to one proposed by Kimura ( Possibility of extensive neutral evolution under stabilizing selection with special reference to nonrandom use of codons (PNAS,1981)), who starts from a diffusion model and derives its equilibrium distribution up to a constant. We use a boundary-mutation Moran model, which approximates a general mutation model for small effective mutation rates, and derive its equilibrium distribution for polymorphic and monomorphic variants in small to moderately sized populations. Using this model, we show that biased mutation rates and linear selection alone can cause patterns of polymorphism rates within and substitution rates between populations that are usually ascribed to balancing or overdominant selection. We illustrate this using a data set of short introns and fourfold degenerate sites from Drosophila simulans and Drosophila melanogaster.
BVI-CC: A Dataset for Research on Video Compression and Quality Assessment
Authors: Angeliki V. Katsenou, Fan Zhang, Mariana Afonso, Goce Dimitrov, David R. Bull
Date: 2020-03-23 00:00:00
Tasks: Video Compression

The video technology scenery has been very vivid over the past years, with novel video coding technologies introduced that promise improved compression performance over state-of-the-art technologies. Despite the fact that a lot of video datasets are available, representative content of the wide parameter space along with subjective evaluations of variations of encoded content from an unpartial end is required. In response to this requirement, this paper features a dataset, the BVI-CC. Three video codecs were deployed to create the variations of the encoded sequences: High Efficiency Video Coding (HEVC) Test Model (HM), AOMedia Video 1 (AV1), and Versatile Video Coding (VVC) Test Model (VTM). Nine source video sequences were carefully selected to offer both diversity and representativeness in the spatio-temporal domain. Different spatial resolution versions of the sequences were created and encoded by all three codecs at pre-defined target bit rates. The compression efficiency of the codecs was evaluated with commonly used objective quality metrics, and the subjective quality of their reconstructed content was also evaluated through psychophysical experiments. Additionally, an adaptive bit rate (convex hull rate-distortion optimization across spatial resolutions) test case was assessed using both objective and subjective evaluations. Finally, the computational complexities of the tested codecs were examined. All data have been made publicly available as part of the dataset, which can be used for coding performance evaluation and video quality metric development.
Stochastic Event-based Sensor Schedules for Remote State Estimation in Cognitive Radio Sensor Networks
Date: 2020-07-25 00:00:00
Tasks: Scheduling, State Estimation

We consider the problem of communication allocation for remote state
estimation in a cognitive radio sensor network~(CRSN). A sensor collects
measurements of a physical plant, and transmits the data to a remote estimator
as a secondary user (SU) in the shared network. The existence of the primal
users (PUs) brings exogenous uncertainties into the transmission scheduling
process, and how to design an event-based scheduling scheme considering these
uncertainties has not been addressed in the literature. In this work, we start
from the formulation of a discrete-time remote estimation process in the CRSN,
and then analyze the hidden information contained in the absence of data
transmission. In order to achieve a better tradeoff between estimation
performance and communication consumption, we propose both open-loop and
closed-loop schedules using the hidden information under a Bayesian setting.
The open-loop schedule does not rely on any feedback signal but only works for
stable plants. For unstable plants, a closed-loop schedule is designed based on
feedback signals. The parameter design problems in both schedules are
efficiently solved by convex programming. Numerical simulations are included to
illustrate the theoretical results.
Distributed Set-Based Observers Using Diffusion Strategies
Authors: Amr Alanwar, Jagat Jyoti Rath, Hazem Said, Karl Henrik Johansson, Matthias Althoff
Date: 2020-03-23 00:00:00

We propose two distributed set-based observers using strip-based and set-propagation approaches for linear discrete-time dynamical systems with bounded modeling and measurement uncertainties. Both algorithms utilize a set-based diffusion step, which decreases the estimation errors and the size of estimated sets, and can be seen as a lightweight approach to achieve partial consensus between the distributed estimated sets. Every node shares its measurement with its neighbor in the measurement update step. In the diffusion step, the neighbors intersect their estimated sets using our novel lightweight zonotope intersection technique. A localization example demonstrates the applicability of our algorithms.
Effects of MiFID II on stock price formation
Authors: Mike Derksen, Bas Kleijn, Robin de Vilder
Date: 2020-03-23 00:00:00

This paper examines effects of MiFID II on European stock markets. We study the effects of the new tick size regime, both intraday and in the closing auction. An increase (decrease) in tick size is associated with a decrease (increase) in intraday liquidity, but a more (less) stable market. In the closing auction an increase in tick size has a positive effect on liquidity. Moreover, we report a positive relationship between tick size and transacted volume, in particular in the closing auction. Finally, closing auction volumes increased heavily since MiFID II and price formation in closing auctions became more efficient.
Moment State Dynamical Systems for Nonlinear Chance-Constrained Motion Planning
Authors: Allen Wang, Ashkan Jasour, Brian Williams
Date: 2020-03-23 00:00:00
Tasks: Motion Planning

Chance-constrained motion planning requires uncertainty in dynamics to be propagated into uncertainty in state. When nonlinear models are used, Gaussian assumptions on the state distribution do not necessarily apply since almost all random variables propagated through nonlinear dynamics results in non-Gaussian state distributions. To address this, recent works have developed moment-based approaches for enforcing chance-constraints on non-Gaussian state distributions. However, there still lacks fast and accurate moment propagation methods to determine the necessary statistical moments of these state distributions. To address this gap, we present a framework that, given a stochastic dynamical system, can algorithmically search for a new dynamical system in terms of moment state that can be used to propagate moments of disturbance random variables into moments of the state distribution. The key algorithm, TreeRing, can be applied to a large class of nonlinear systems which we refer to as trigonometric polynomial systems. As an example application, we present a distributionally robust RRT (DR-RRT) algorithm that propagates uncertainty through the nonlinear Dubin's car model without linearization.
Spatial Modulation for Joint Radar-Communications Systems: Design, Analysis, and Hardware Prototype
Authors: Dingyou Ma, Nir Shlezinger, Tianyao Huang, Yariv Shavit, Moshe Namer, Yimin Liu, Yonina C. Eldar
Date: 2020-03-23 00:00:00

Dual-function radar-communications (DFRC) systems implement radar and communication functionalities on a single platform. Jointly designing these subsystems can lead to substantial gains in performance as well as size, cost, and power consumption. In this paper, we propose a DFRC system, which utilizes generalized spatial modulation (GSM) to realize coexisting radar and communications waveforms. Our proposed GSM-based scheme, referred to as spatial modulation based communication-radar (SpaCoR) system, allocates antenna elements among the subsystems based on the transmitted message, thus achieving increased communication rates by embedding additional data bits in the antenna selection. We formulate the resulting signal models, and present a dedicated radar processing scheme. To evaluate the radar performance, we characterize the statistical properties of the transmit beam pattern. Then, we present a hardware prototype of the proposed DFRC system, demonstrating the feasibility of the scheme. Our results show that the proposed GSM system achieves improved communication performance compared to techniques utilizing fixed allocations operating at the same data rate. For the radar subsystem, our experiments show that the spatial agility induced by the GSM transmission improves the angular resolution and reduces the sidelobe level in the transmit beam pattern compared to using fixed antenna allocations.
Equity Factors: To Short Or Not To Short, That Is The Question
Authors: Florent Benaych-Georges, Jean-Philippe Bouchaud, Stefano Ciliberti
Date: 2020-03-23 00:00:00
Tasks: Management

What is the best market-neutral implementation of classical Equity Factors? Should one use the specific predictability of the short-leg to build a zero beta Long-Short portfolio, in spite of the specific costs associated to shorting, or is it preferable to ban the shorts and hedge the long-leg with -- say -- an index future? We revisit this question by focusing on the relative predictability of the two legs, the issue of diversification, and various sources of costs. Our conclusion is that, using the same Factors, a Long-Short implementation leads to superior risk-adjusted returns than its Hedged Long-Only counterpart, at least when Assets Under Management are not too large.
Rock Classification in Petrographic Thin Section Images Based on Concatenated Convolutional Neural Networks
Date: 2020-03-23 00:00:00
Tasks: Classification

Rock classification plays an important role in rock mechanics, petrology,
mining engineering, magmatic processes, and numerous other fields pertaining to
geosciences. This study proposes a concatenated convolutional neural network
(Con-CNN) method for classifying the geologic rock type based on petrographic
thin sections. Herein, plane polarized light (PPL) and crossed polarized light
(XPL) were used to acquire thin section images as the fundamental data. After
conducting the necessary pre-processing analyses, the PPL and XPL images as
well as their comprehensive image (CI) were incorporated in three convolutional
neural networks (CNNs) comprising the same structure for achieving a
preliminary classification; these images were developed by employing the fused
principal component analysis (PCA). Subsequently, the results of the CNNs were
concatenated by using the maximum likelihood detection to obtain a
comprehensive classification result. Finally, a statistical revision was
applied to fix the misclassification due to the proportion difference of
minerals that were similar in appearance. In this study, 13 types of 92 rock
samples, 196 petrographic thin sections, 588 images, and 63504 image patches
were fabricated for the training and validation of the Con-CNN. The five-folds
cross validation shows that the method proposed provides an overall accuracy of
89.97%, which facilitates the automation of rock classification in petrographic
thin sections.
Exploring the nuances of R0: Eight estimates and application to 2009 pandemic influenza
Date: 2020-03-23 00:00:00

For nearly a century, the initial reproduction number (R0) has been used as a
one number summary to compare outbreaks of infectious disease, yet there is no
`standard' estimator for R0. Difficulties in estimating R0 arise both from how
a disease transmits through a population as well as from differences in
statistical estimation method. We describe eight methods used to estimate R0
and provide a thorough simulation study of how these estimates change in the
presence of different disease parameters. As motivation, we analyze the 2009
outbreak of the H1N1 pandemic influenza in the USA and compare the results from
our eight methods to a previous study. We discuss the most important aspects
from our results which effect the estimation of R0, which include the
population size, time period used, and the initial percent of infectious
individuals. Additionally, we discuss how pre-processing incidence counts may
effect estimates of R0. Finally, we provide guidelines for estimating point
estimates and confidence intervals to create reliable, comparable estimates of
R0.
Non-asymptotic convergence rates for the plug-in estimation of risk measures
Authors: Daniel Bartl, Ludovic Tangpi
Date: 2020-03-23 00:00:00

Let $\rho$ be a general law--invariant convex risk measure, for instance the average value at risk, and let $X$ be a financial loss, that is, a real random variable. In practice, either the true distribution $\mu$ of $X$ is unknown, or the numerical computation of $\rho(\mu)$ is not possible. In both cases, either relying on historical data or using a Monte-Carlo approach, one can resort to an i.i.d.\ sample of $\mu$ to approximate $\rho(\mu)$ by the finite sample estimator $\rho(\mu_N)$ (where $\mu_N$ denotes the empirical measure of $\mu$). In this article we investigate convergence rates of $\rho(\mu_N)$ to $\rho(\mu)$. We provide non-asymptotic convergence rates for both the deviation probability and the expectation of the estimation error. The sharpness of these convergence rates is analyzed. Our framework further allows for hedging, and the convergence rates we obtain depend neither on the dimension of the underlying assets, nor on the number of options available for trading.
A closed-form solution for optimal mean-reverting trading strategies
Authors: Alexander Lipton, Marcos Lopez de Prado
Date: 2020-03-23 00:00:00
Tasks: Form, Position

When prices reflect all available information, they oscillate around an equilibrium level. This oscillation is the result of the temporary market impact caused by waves of buyers and sellers. This price behavior can be approximated through an Ornstein-Uhlenbeck (O-U) process. Market makers provide liquidity in an attempt to monetize this oscillation. They enter a long position when a security is priced below its estimated equilibrium level, and they enter a short position when a security is priced above its estimated equilibrium level. They hold that position until one of three outcomes occur: (1) they achieve the targeted profit; (2) they experience a maximum tolerated loss; (3) the position is held beyond a maximum tolerated horizon. All market makers are confronted with the problem of defining profit-taking and stop-out levels. More generally, all execution traders acting on behalf of a client must determine at what levels an order must be fulfilled. Those optimal levels can be determined by maximizing the trader's Sharpe ratio in the context of O-U processes via Monte Carlo experiments. This paper develops an analytical framework and derives those optimal levels by using the method of heat potentials.
Understanding the fate of corona virus transmission using a simple model
Date: 2020-03-17 00:00:00

We propose a simple model for understanding the kinetics of corona virus
transmission. Our model assume spreading of corona virus can happen from one to
another only, if someone without enough protection comes close contact to a
person carrying the corona virus. Therefore this virus spreads on a large scale
within a short time through chains of such events. Using our model we provide
an estimation of the number of people affected by this virus within reasonable
duration of time. We choose values of different parameters of our model by
non-linear least square fit of the real time data and we predict fate of this
corona virus transmission using our model.
Analytical Derivation and Comparison of Alarm Similarity Measures
Authors: Amir Hossein Kargaran, Amir Neshastegaran, Iman Izadi, Ehsan Yazdian
Date: 2020-03-24 00:00:00

An industrial process includes many devices, variables, and sub-processes that are physically or electronically interconnected. These interconnections imply some level of correlation between different process variables. Since most of the alarms in a process plant are defined on process variables, alarms are also correlated. However, this can be a nuisance to operators, for one fault might trigger a, sometimes large, number of alarms. So, it is essential to find and correct correlated alarms. In this paper, we study different methods and techniques proposed to measure correlation or similarity between alarms. The similarity indices are first analytically calculated and then studied and compared. The results are also validated using Monte-Carlo simulation.
Adaptive Cooperative Tracking and Parameter Estimation of an Uncertain Leader over General Directed Graphs
Authors: Shimin Wang, Hongwei Zhang, Zhiyong Chen
Date: 2020-03-24 00:00:00
Tasks: parameter estimation

This paper studies cooperative tracking problem of heterogeneous Euler-Lagrange systems with an uncertain leader. Different from most existing works, system dynamic knowledge of the leader node is unaccessible to any follower node in our paper. Distributed adaptive observers are designed for all follower nodes, simultaneously estimate the state and parameters of the leader node. The observer design does not rely on the frequency knowledge of the leader node, and the estimation errors are shown to converge to zero exponentially. Moreover, the results are applied to general directed graphs, where the symmetry of Laplacian matrix does not hold. This is due to two newly developed Lyapunov equations, which solely depend on communication network topologies. Interestingly, using these Lyapunov equations, many results of multi-agent systems over undirected graphs can be extended to general directed graphs. Finally, this paper also advances the knowledge base of adaptive control systems by providing a main tool in the analysis of parameter convergence for adaptive observers.
Fixed-Order H-infinity Optimization of Time-Delay Systems
Date: 2020-03-24 00:00:00

H-infinity controllers are frequently used in control theory due to their
robust performance and stabilization. Classical H-infinity controller synthesis
methods for finite dimensional LTI MIMO plants result in high-order controllers
for high-order plants whereas low-order controllers are desired in practice. We
design fixed-order H-infinity controllers for a class of time-delay systems
based on a non-smooth, non-convex optimization method and a recently developed
numerical method for H-infinity norm computations.
Personalized workflow to identify optimal T-cell epitopes for peptide-based vaccines against COVID-19
Date: 2020-03-24 00:00:00

Traditional vaccines against viruses are designed to target their surface
proteins, i.e., antigens, which can trigger the immune system to produce
specific antibodies to capture and neutralize the viruses. However, viruses
often evolve quickly, and their antigens are prone to mutations to avoid
recognition by the antibodies (antigenic drift). This limitation of the
antibody-mediated immunity could be addressed by the T-cell mediated immunity,
which is able to recognize conserved viral HLA peptides presented on
virus-infected cells. Thus, by targeting conserved regions on the genome of a
virus, T-cell epitope-based vaccines are less subjected to mutations and may
work effectively on different strains of the virus. Here we propose a
personalized workflow to identify an optimal set of T-cell epitopes based on
the HLA alleles and the immunopeptidome of an individual person. Specifically,
our workflow trains a machine learning model on the immunopeptidome and then
predicts HLA peptides from conserved regions of a virus that are most likely to
trigger responses from the person T cells. We applied the workflow to identify
T-cell epitopes for the SARS-COV-2 virus, which has caused the recent COVID-19
pandemic in more than 100 countries across the globe.
Comments on "Low-Complexity SIC Detection Algorithms for Multiple-Input Multiple-Output Systems"
Date: 2020-03-24 00:00:00

In the above paper, the optimal-ordered successive interference cancellation
(SIC) detector proposed for multiple input multiple output (MIMO) systems was
claimed to require a lower computational complexity than the optimal-ordered
SIC detector proposed in the paper "An Improved Square-Root Algorithm for
V-BLAST Based on Efficient Inverse Cholesky Factorization" (IEEE Trans.
Wireless Commun., vol. 10, no. 1, Jan. 2011), since several incorrect
complexities were quoted or claimed. In this comment, we revise the incorrect
complexities, to draw the conclusion that the above-mentioned two detectors
actually require the same dominant complexity.