pwc-archive/papers-with-abstracts huggingface dataset:
TILES-2018, a longitudinal physiologic and behavioral data set of hospital workers
Authors: Karel Mundnich, Brandon M. Booth, Michelle L'Hommedieu, Tiantian Feng, Benjamin Girault, Justin L'Hommedieu, Mackenzie Wildman, Sophia Skaaden, Amrutha Nadarajan, Jennifer L. Villatte, Tiago H. Falk, Kristina Lerman, Emilio Ferrara, Shrikanth Narayanan
Date: 2020-03-18 00:00:00
Tasks: Privacy Preserving

We present a novel longitudinal multimodal corpus of physiological and behavioral data collected from direct clinical providers in a hospital workplace. We designed the study to investigate the use of off-the-shelf wearable and environmental sensors to understand individual-specific constructs such as job performance, interpersonal interaction, and well-being of hospital workers over time in their natural day-to-day job settings. We collected behavioral and physiological data from $n = 212$ participants through Internet-of-Things Bluetooth data hubs, wearable sensors (including a wristband, a biometrics-tracking garment, a smartphone, and an audio-feature recorder), together with a battery of surveys to assess personality traits, behavioral states, job performance, and well-being over time. Besides the default use of the data set, we envision several novel research opportunities and potential applications, including multi-modal and multi-task behavioral modeling, authentication through biometrics, and privacy-aware and privacy-preserving machine learning.
Bi-stability of SUDR+K model of epidemics and test kits applied to COVID-19
Date: 2020-04-24 00:00:00

Motivated with various responses of world governments to COVID-19, here we
develop a toy model of the dependence epidemics spreading on the availability
of tests for disease. Our model, that we call SUDR+K, is based on usual SIR
model, but it splits the total fraction of infected individuals into two
components: those that are undetected and those that are detected through
tests. Moreover, we assume that available tests increase at a constant rate
from the beginning of epidemics but are consumed to detect infected
individuals. Strikingly we find a bi-stable behavior between a phase with a
giant fraction of infected and a phase with a very small fraction. We show that
the separation between these two regimes is governed by a match between the
rate of testing and a rate of infection spread at given time. We also show that
the existence of two phases does not depend on the mathematical choice of the
form of the term describing the rate at which undetected individuals are tested
and detected. Presented research implies that a vigorous early testing
activity, before the epidemics enters into its giant phase, can potentially
keep epidemics under control, and that even a very small change in rate of
testing can increase or decrease the size of the whole epidemics of various
orders of magnitude. For the real application of realistic model to ongoing
epidemics, we would gladly collaborate with field epidemiologists in order to
develop quantitative models of testing process.
Coupled Control Systems: Periodic Orbit Generation with Application to Quadrupedal Locomotion
Date: 2020-03-18 00:00:00
Tasks: Dimensionality Reduction

A robotic system can be viewed as a collection of lower-dimensional systems
that are coupled via reaction forces (Lagrange multipliers) enforcing holonomic
constraints. Inspired by this viewpoint, this paper presents a novel
formulation for nonlinear control systems that are subject to coupling
constraints via virtual "coupling" inputs that abstractly play the role of
Lagrange multipliers. The main contribution of this paper is a
process---mirroring solving for Lagrange multipliers in robotic
systems---wherein we isolate subsystems free of coupling constraints that
provably encode the full-order dynamics of the coupled control system from
which it was derived. This dimension reduction is leveraged in the formulation
of a nonlinear optimization problem for the isolated subsystem that yields
periodic orbits for the full-order coupled system. We consider the application
of these ideas to robotic systems, which can be decomposed into subsystems.
Specifically, we view a quadruped as a coupled control system consisting of two
bipedal robots, wherein applying the framework developed allows for gaits
(periodic orbits) to be generated for the individual biped yielding a gait for
the full-order quadruped. This is demonstrated through walking experiments of a
quadrupedal robot in simulation and on rough terrains.
Virtual Control Contraction Metrics: Convex Nonlinear Feedback Design via Behavioral Embedding
Authors: Ruigang Wang, Roland Tóth, Patrick J. W. Koelwijn, Ian R. Manchester
Date: 2020-03-18 00:00:00

This paper presents a systematic approach to nonlinear state-feedback control design that has three main advantages: (i) it ensures exponential stability and $ \mathcal{L}_2 $-gain performance with respect to a user-defined set of reference trajectories, and (ii) it provides constructive conditions based on convex optimization and a path-integral-based control realization, and (iii) it is less restrictive than previous similar approaches. In the proposed approach, first a virtual representation of the nonlinear dynamics is constructed for which a behavioral (parameter-varying) embedding is generated. Then, by introducing a virtual control contraction metric, a convex control synthesis formulation is derived. Finally, a control realization with a virtual reference generator is computed, which is guaranteed to achieve exponential stability and $ \mathcal{L}_2 $-gain performance for all trajectories of the targeted reference behavior. We show that the proposed methodology is a unified generalization of the two distinct categories of linear-parameter-varying (LPV) state-feedback control approaches: global and local methods. Moreover, it provides rigorous stability and performance guarantees as a method for nonlinear tracking control, while such properties are not guaranteed for tracking control using standard LPV approaches.
A framework to decipher the genetic architecture of combinations of complex diseases: applications in cardiovascular medicine
Authors: Liangying Yin, Carlos Kwan-long Chau, Yu-Ping Lin, Pak-Chung Sham, Hon-Cheong So
Date: 2020-03-19 00:00:00

Genome-wide association studies(GWAS) have proven to be highly useful in revealing the genetic basis of complex diseases. At present, most GWAS are studies of a particular single disease diagnosis against controls. However, in practice, an individual is often affected by more than one condition/disorder. For example, patients with coronary artery disease(CAD) are often comorbid with diabetes mellitus(DM). Along a similar line, it is often clinically meaningful to study patients with one disease but without a comorbidity. For example, obese DM may have different pathophysiology from non-obese DM. Here we developed a statistical framework to uncover susceptibility variants for comorbid disorders (or a disorder without comorbidity), using GWAS summary statistics only. In essence, we mimicked a case-control GWAS in which the cases are affected with comorbidities or a disease without a relevant comorbid condition (in either case, we may consider the cases as those affected by a specific subtype of disease, as characterized by the presence or absence of comorbid conditions). We extended our methodology to deal with continuous traits with clinically meaningful categories (e.g. lipids). In addition, we illustrated how the analytic framework may be extended to more than two traits. We verified the feasibility and validity of our method by applying it to simulated scenarios and four cardiometabolic (CM) traits. We also analyzed the genes, pathways, cell-types/tissues involved in CM disease subtypes. LD-score regression analysis revealed some subtypes may indeed be biologically distinct with low genetic correlations. Further Mendelian randomization analysis found differential causal effects of different subtypes to relevant complications. We believe the findings are of both scientific and clinical value, and the proposed method may open a new avenue to analyzing GWAS data.
Stochastic epigenetic dynamics of gene switching
Authors: Bhaswati Bhattacharyya, Jin Wang, Masaki Sasai
Date: 2020-03-19 00:00:00
Tasks: Decision Making

Epigenetic modifications of histones crucially affect the eukaryotic gene activity, while the epigenetic histone state is largely determined by the binding of specific factors such as the transcription factors (TFs) to DNA. Here, the way how the TFs and the histone state are dynamically correlated is not obvious when the TF synthesis is regulated by the histone state. This type of feedback regulatory relations are ubiquitous in gene networks to determine cell fate in differentiation and other cell transformations. To gain insights into such dynamical feedback regulations, we theoretically analyze a model of epigenetic gene switching by extending the Doi-Peliti operator formalism of reaction kinetics to the problem of coupled molecular processes. The spin-1 and spin-1/2 coherent state representations are introduced to describe stochastic reactions of histones and binding/unbinding of TF in a unified way, which provides a concise view of the effects of timescale difference among these molecular processes; even in the case that binding/unbinding of TF to/from DNA are adiabatically fast, the slow nonadiabatic histone dynamics give rise to a distinct circular flow of the probability flux around basins in the landscape of the gene state distribution, which leads to hysteresis in gene switching. In contrast to the general belief that the change in the amount of TF precedes the histone state change, the flux drives histones to be modified prior to the change in the amount of TF in the self-regulating circuits. The flux-landscape analyses shed light on the nonlinear nonadiabatic mechanism of epigenetic cell fate decision making.
Multi-user Capacity of Cyclic Prefix Direct Sequence Spread Spectrum with Linear Detection and Precoding
Date: 2020-03-19 00:00:00

Cyclic Prefix Direct Sequence Spread Spectrum (CP-DSSS) is a promising
solution for futuristic 6G ultra-reliable low latency communications (URLLC)
and massive machine type communication (mMTC) applications. We propose that in
such applications, the CP-DSSS waveform would operate as a secondary network at
the same frequencies as the primary network but at much lower SNR. In this
paper, we evaluate per-user capacity of CP-DSSS when simple matched filtering
(MF) is performed on the uplink (UL) and time-reversal (TR) precoding is used
on the downlink (DL). In this setting when operating in the low SNR regime,
CP-DSSS achieves a per-user capacity that is near the optimum single-user
capacity. TR precoding converges to the optimal capacity as the number of
antennas at the hub/gateway increases. Using the estimated channel impulse
response for MF and TR introduces little to no capacity loss. Given the
near-optimal performance of MF detection and TR precoding for each of the
users, CP-DSSS can be implemented with simple device transceiver structures,
reducing per-unit cost for massively deployed 6G networks.
DsMCL: Dual-Level Stochastic Multiple Choice Learning for Multi-Modal Trajectory Prediction
Authors: Zehan Wang, Sihong Zhou, Yuyao Huang, Wei Tian
Date: 2020-03-19 00:00:00
Tasks: Multiple-choice, Prediction, Trajectory Prediction

For both driving safety and efficiency, automated vehicles should be able to predict the behavior of surrounding traffic participants in a complex dynamic environment. To accomplish such a task, trajectory prediction is the key. Although many researchers have been engaged in this topic, it is still challenging. One of the important and inherent factors is the multi-modality of vehicle motion. Because of the disparate driving behaviors under the same condition, the prediction of vehicle trajectory should also be multi-modal. At present, related researches have more or less shortcomings for multi-modal trajectory prediction, such as requiring explicit modal labels or multiple forward propagation caused by sampling. In this work, we focus on overcoming these issues by pointing out the dual-levels of multi-modal characteristics in vehicle motion and proposing the dual-level stochastic multiple choice learning method (named as DsMCL, for short). This method does not require modal labels and can implement a comprehensive probabilistic multi-modal trajectory prediction by a single forward propagation. By experiments on the NGSIM and HighD datasets, our method has proven significant improvement on several trajectory prediction frameworks and achieves state-of-the-art performance.
Low-Loss Reconfigurable Phase Shifter in Gap-Waveguide Technology for mm-Wave Applications
Date: 2020-03-19 00:00:00

In this brief, we present a low-loss mechanically reconfigurable phase shifter implemented in gap-waveguide technology for mm-wave frequencies. The proposed design gives a practical implementation of tuning elements inside the waveguide providing alternatives to the use of the E-plane split waveguide at high frequencies in order to avoid leakage losses. The depicted phase shifter design is based on a H-plane split waveguide. The phase shift is controlled by means of a tuning screw, which exerts pressure on a flexible metallic strip inserted inside the waveguide. The flexible strip bends with different curvature radii and determines the phase shift at the output port. Cost-effective manufacturing and simple implementation of the flexible metallic strip are achieved by means of the gap-waveguide design. A prototype has been manufactured for validation purposes. Good impedance matching is achieved from 64 GHz to 75 GHz providing a 15.8% impedance bandwidth. The results show a maximum phase shift of 250$^{o}$ with a maximum and mean insertion loss (IL) of 3 dB and 1.7 dB, respectively.
Structural-constrained Methods for the Identification of Unobservable False Data Injection Attacks in Power Systems
Authors: Gal Morgenstern, Tirza Routtenberg
Date: 2020-03-19 00:00:00
Tasks: Model Selection, State Estimation

Power system functionality is determined on the basis of the power system state estimation (PSSE). Thus, corruption of the PSSE may lead to severe consequences, such as financial losses, maintenance damage, and disruptions in electricity distribution. Classical bad data detection (BDD) methods, developed to ensure PSSE reliability, are unable to detect well-designed attacks, named unobservable false data injection (FDI) attacks. In this paper, we develop novel structural-constrained methods for the detection of unobservable FDI attacks, the identification of the attacked buses' locations, and PSSE under the presence of such attacks. The proposed methods are based on formulating structural, sparse constraints on both the attack and the system loads. First, we exploit these constraints in order to compose an appropriate model selection problem. Then, we develop the associated generalized information criterion (GIC) for this problem. However, for large networks, the GIC method's computational complexity grows exponentially with the network size. Thus, based on the proposed structural and sparse constraints, we develop two novel low-complexity methods for unobservable FDI attack identification: 1) a modification of the state-of-the-art orthogonal matching pursuit (OMP); and 2) a method that utilizes the graph Markovian property in power systems, i.e. the second-neighbor relationship between the power data at the system's buses. The methods' performance is evaluated on a IEEE-30 bus test case system.
Detecting long-range interactions between migrating cells
Authors: Claus Metzner, Franziska Hörsch, Christoph Mark, Tina Czerwinski, Alexander Winterl, Caroline Voskens, Ben Fabry
Date: 2020-03-19 00:00:00

Chemotaxis enables cells to systematically approach distant targets that emit a diffusible guiding substance. However, the visual observation of an encounter between a cell and a target does not necessarily indicate the presence of a chemotactic approach mechanism, as even a blindly migrating cell can come across a target by chance. To distinguish between the chemotactic approach and blind migration, we present an objective method that is based on the analysis of time-lapse recorded cell migration trajectories. First, we validate our method with simulated data, demonstrating that it reliably detects the presence or absence of remote cell-cell interactions. In a second step, we apply the method to data from three-dimensional collagen gels, interspersed with highly migratory natural killer (NK) cells that were derived from two different human donors. We find for one of the donors an attractive interaction between the NK cells, pointing to a cooperative behavior of these immune cells. When adding nearly stationary K562 tumor cells to the system, we find a repulsive interaction between K562 and NK cells for one of the donors. By contrast, we find attractive interactions between NK cells and an IL-15-secreting variant of K562 tumor cells. We therefore speculate that NK cells find wild-type tumor cells only by chance, but are programmed to leave a target quickly after a close encounter. We provide a freely available Python implementation of our p-value method that can serve as a general tool for detecting long-range interactions in collective systems of self-driven agents.
Locating line and node disturbances in networks of diffusively coupled dynamical agents
Authors: Robin Delabays, Laurent Pagnier, Melvyn Tyloo
Date: 2020-03-17 00:00:00
Tasks: Time Series, Time Series Analysis

A wide variety of natural and human-made systems consist of a large set of dynamical units coupled into a complex structure. Breakdown of such systems can have a dramatic impact, as in the case of neurons in the brain or lines in an electric grid, to name but a few. Preventing such catastrophic events requires in particular to be able to detect and locate the source of disturbances as fast as possible. We propose a simple method to identify and locate disturbances in networks of coupled dynamical agents, relying only on time series measurements and on the knowledge of the (possibly Kron-reduced) network structure. The strength and the appeal of the present approach lies in its simplicity paired with the ability to precisely locate disturbances and even to differentiate between line and node disturbances. If we have access to measurement at only a subset of nodes, our method is still able to identify the location of the disturbance if the disturbed nodes are measured. If not, we manage to identify the region of the network where the disturbance occurs.
Relative Stability Analysis of Closed-Loop SISO Dead-Time Systems: non-imaginary axis case
Date: 2020-03-19 00:00:00

We present a numerical method to analyze the relative stability of
closed-loop single-input-single-output (SISO) dead-time systems on a given left
complex half-plane for all positive delays. The well-known boundary crossing
method for the imaginary axis is extended to a given vertical line stability
boundary in the complex plane for these type of systems. The method allows to
compute the characteristic roots crossing the relative stability boundary and
their corresponding delays up to a maximum predefined delay. Based on this
method, we analyze the relative stability of the closed-loop system for all
positive delays. Both numerical methods are effective for high-order SISO
dead-time systems.
Semi-closed form solutions for barrier and American options written on a time-dependent Ornstein Uhlenbeck process
Authors: Peter Carr, Andrey Itkin
Date: 2020-03-19 00:00:00
Tasks: Form

In this paper we develop a semi-closed form solutions for the barrier (perhaps, time-dependent) and American options written on the underlying stock which follows a time-dependent OU process with a log-normal drift. This model is equivalent to the familiar Hull-White model in FI, or a time dependent OU model in FX. Semi-closed form means that given the time-dependent interest rate, continuous dividend and volatility functions, one need to solve numerically a linear (for the barrier option) or nonlinear (for the American option) Fredholm equation of the first kind. After that the option prices in all cases are presented as one-dimensional integrals of combination of the above solutions and Jacobi theta functions. We also demonstrate that computationally our method is more efficient than the backward finite difference method used for solving these problems, and can also be as efficient as the forward finite difference solver while providing better accuracy and stability.
Integrated Power and Thermal Management of Connected HEVs via Multi-Horizon MPC
Date: 2020-03-19 00:00:00
Tasks: Management

In this paper, a multi-horizon model predictive controller (MH-MPC) is
developed for integrated power and thermal management (iPTM) of a power-split
hybrid electric vehicle (HEV). The proposed MH-MPC leverages an accurate
short-horizon vehicle speed preview and an approximate forecast over a longer
shrinking horizon till the end of the driving cycle. This multiple-horizon
scheme is developed to cope with fast and slow dynamics associated with power
and thermal responses. The main objective of the proposed MH-MPC is to minimize
fuel consumption and enforce the power and thermal constraints on the battery
state-of-charge and engine coolant temperature, while meeting the driving
(traction) and cabin air conditioning (heating) demands. The proposed MH-MPC
allows for exploiting the engine coolant as thermal energy storage, providing
more flexibility for the HEV energy flow optimization. The simulation results
show that the proposed MH-MPC provides near-optimal results in reference to the
Dynamic Programming (DP) solution with an affordable computational cost.
Moreover, compared with a more conventional MPC strategy, the MH-MPC can
leverage the speed previews with different resolutions effectively to achieve
the desired performance with satisfactory robustness.
Adaptive motion control of parallel robots with kinematic and dynamic uncertainties
Date: 2020-03-19 00:00:00
Tasks: Form

One of the most challenging issues in adaptive control of robot manipulators
with kinematic uncertainties is requirement of the inverse of Jacobian matrix
in regressor form. This requirement is inevitable in the case of the control of
parallel robots, whose dynamic equations are written directly in the task
space. In this paper, an adaptive controller is designed for parallel robots
based on representation of Jacobian matrix in regressor form, such that
asymptotic trajectory tracking is ensured. The main idea is separation of
determinant and adjugate of Jacobian matrix and then organize new regressor
forms. Simulation and experimental results on a 2--DOF R\underline{P}R and
3--DOF redundant cable driven robot, verify promising performance of the
proposed methods.