pwc-archive/papers-with-abstracts huggingface dataset:
Measuring productivity dispersion: a parametric approach using the Lévy alpha-stable distribution
Authors: Jangho Yang, Torsten Heinrich, Julian Winkler, François Lafond, Pantelis Koutroumpis, J. Doyne Farmer
Date: 2019-10-11 00:00:00

It is well-known that value added per worker is extremely heterogeneous among firms, but relatively little has been done to characterize this heterogeneity more precisely. Here we show that the distribution of value-added per worker exhibits heavy tails, a very large support, and consistently features a proportion of negative values, which prevents log transformation. We propose to model the distribution of value added per worker using the four parameter L\'evy stable distribution, a natural candidate deriving from the Generalised Central Limit Theorem, and we show that it is a better fit than key alternatives. Fitting a distribution allows us to capture dispersion through the tail exponent and scale parameters separately. We show that these parametric measures of dispersion are at least as useful as interquantile ratios, through case studies on the evolution of dispersion in recent years and the correlation between dispersion and intangible capital intensity.
Improved Material Decomposition with a Two-step Regularization for spectral CT
Date: 2019-10-11 00:00:00
Tasks: Computed Tomography (CT), Image Reconstruction, Vocal Bursts Valence Prediction

One of the advantages of spectral computed tomography (CT) is it can achieve
accurate material components using the material decomposition methods. The
image-based material decomposition is a common method to obtain specific
material components, and it can be divided into two steps: image reconstruction
and post material decomposition. To obtain accurate material maps, the image
reconstruction method mainly focuses on improving image quality by
incorporating regularization priors. Very recently, the regularization priors
are introduced into the post material decomposition procedure in the iterative
image-based methods. Since the regularization priors can be incorporated into
image reconstruction and post image-domain material decomposition, the
performance of regularization by combining these two cases is still an open
problem. To realize this goal, the material accuracy from those steps are first
analyzed and compared. Then, to further improve the accuracy of decomposition
materials, a two-step regularization based method is developed by incorporating
priors into image reconstruction and post material decomposition. Both
numerical simulation and preclinical mouse experiments are performed to
demonstrate the advantages of the two-step regularization based method in
improving material accuracy.
Building HVAC Scheduling Using Reinforcement Learning via Neural Network Based Model Approximation
Authors: Chi Zhang, Sanmukh R. Kuppannagari, Rajgopal Kannan, Viktor K. Prasanna
Date: 2019-10-11 00:00:00
Tasks: Deep Reinforcement Learning, Model-based Reinforcement Learning, Model Predictive Control, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Safe Exploration, Scheduling

Buildings sector is one of the major consumers of energy in the United States. The buildings HVAC (Heating, Ventilation, and Air Conditioning) systems, whose functionality is to maintain thermal comfort and indoor air quality (IAQ), account for almost half of the energy consumed by the buildings. Thus, intelligent scheduling of the building HVAC system has the potential for tremendous energy and cost savings while ensuring that the control objectives (thermal comfort, air quality) are satisfied. Recently, several works have focused on model-free deep reinforcement learning based techniques such as Deep Q-Network (DQN). Such methods require extensive interactions with the environment. Thus, they are impractical to implement in real systems due to low sample efficiency. Safety-aware exploration is another challenge in real systems since certain actions at particular states may result in catastrophic outcomes. To address these issues and challenges, we propose a model-based reinforcement learning approach that learns the system dynamics using a neural network. Then, we adopt Model Predictive Control (MPC) using the learned system dynamics to perform control with random-sampling shooting method. To ensure safe exploration, we limit the actions within safe range and the maximum absolute change of actions according to prior knowledge. We evaluate our ideas through simulation using widely adopted EnergyPlus tool on a case study consisting of a two zone data-center. Experiments show that the average deviation of the trajectories sampled from the learned dynamics and the ground truth is below $20\%$. Compared with baseline approaches, we reduce the total energy consumption by $17.1\% \sim 21.8\%$. Compared with model-free reinforcement learning approach, we reduce the required number of training steps to converge by 10x.
Multiple Power Quality Event Detection and Classification using Modified S Transform and WOA tuned SVM Classifier
Date: 2019-10-11 00:00:00
Tasks: Event Detection

In this paper, a novel method for classification of power quality events is
illustrated. 15 types of power quality events consisting of single and
multi-stage disturbances are considered for study. A database of the synthetic
PQ events is generated in MATLAB using mathematical models. The generated
signals are passed through a novel Modified Stockwell transform consisting of
second order gaussian window which provides the ST matrix. From the ST matrix
various statistical features such as energy and standard deviation of the
magnitude and phase contour are extracted and given as input to Support Vector
Machine (SVM). Furthermore, to improve the performance of SVM, a novel
meta-heuristic technique called Whale Optimization Algorithm (WOA) is used to
tune the hyper parameters of the SVM classifier. The performance of the
proposed method is analyzed under noisy and noiseless conditions. It is
observed that WOA tuned SVM provides improved classification accuracy than
other widely used meta-heuristic optimization algorithms such as Particle Swarm
Optimization (PSO) tuned SVM and Genetic Algorithm (GA) tuned SVM. Further, two
novel circuits for generation of sag, swell and interrupt are developed and the
proposed technique is validated on real time signals obtained from the
circuits.
The collective effect of finite-sized inhomogeneities on the spatial spread of populations in two dimensions
Authors: Wolfram Möbius, Francesca Tesser, Kim M. J. Alards, Roberto Benzi, David R. Nelson, Federico Toschi
Date: 2019-10-11 00:00:00

The dynamics of a population expanding into unoccupied habitat has been primarily studied for situations in which growth and dispersal parameters are uniform in space or vary in one dimension. Here we study the influence of finite-sized individual inhomogeneities and their collective effect on front speed if randomly placed in a two-dimensional habitat. We use an individual-based model to investigate the front dynamics for a region in which dispersal or growth of individuals is reduced to zero (obstacles) or increased above the background (hotspots), respectively. In a regime where front dynamics is determined by a local front speed only, a principle of least time can be employed to predict front speed and shape. The resulting analytical solutions motivate an event-based algorithm illustrating the effects of several obstacles or hotspots. We finally apply the principle of least time to large heterogeneous environments by solving the Eikonal equation numerically. Obstacles lead to a slow-down that is dominated by the number density and width of obstacles, but not by their precise shape. Hotspots result in a speedup, which we characterise as function of hotspot strength and density. Our findings emphasise the importance of taking the dimensionality of the environment into account.
Robust Incremental State Estimation through Covariance Adaptation
Date: 2019-10-11 00:00:00
Tasks: State Estimation

Recent advances in the fields of robotics and automation have spurred
significant interest in robust state estimation. To enable robust state
estimation, several methodologies have been proposed. One such technique, which
has shown promising performance, is the concept of iteratively estimating a
Gaussian Mixture Model (GMM), based upon the state estimation residuals, to
characterize the measurement uncertainty model. Through this iterative process,
the measurement uncertainty model is more accurately characterized, which
enables robust state estimation through the appropriate de-weighting of
erroneous observations. This approach, however, has traditionally required a
batch estimation framework to enable the estimation of the measurement
uncertainty model, which is not advantageous to robotic applications. In this
paper, we propose an efficient, incremental extension to the measurement
uncertainty model estimation paradigm. The incremental covariance estimation
(ICE) approach, as detailed within this paper, is evaluated on several
collected data sets, where it is shown to provide a significant increase in
localization accuracy when compared to other state-of-the-art robust,
incremental estimation algorithms.
Risk- and Variance-Aware Electricity Pricing
Date: 2020-03-27 00:00:00

The roll-out of stochastic renewable energy sources (RES) undermines the
efficiency of power system and market operations. This paper proposes an
approach to derive electricity prices that internalize RES stochasticity. We
leverage a chance-constrained AC Optimal Power Flow (CC AC-OPF) model, which is
robust against RES uncertainty and is also aware of the resulting variability
(variance) of the system state variables. Using conic duality theory, we derive
and analyze energy and balancing reserve prices that internalize the risk of
system limit violations and the variance of system state variables. We compare
the risk- and variance-aware prices on the IEEE 118-node testbed.
Phase space reconstruction from a biological time series. A PhotoPlethysmoGraphic signal a case study
Authors: J. de Pedro-Carracedo, A. M. Ugena, A. P. Gonzalez-Marcos
Date: 2019-10-10 00:00:00
Tasks: Time Series, Time Series Analysis

In the analysis of biological time series, the state space comprises a framework for the study of systems with presumably deterministic properties. However, a physiological experiment typically captures an observable, or, in other words, a series of scalar measurements that characterize the temporal response of the physiological system under study; the dynamic variables that make up the state of the system at any time are not available. Therefore, only from the acquired observations should state vectors reconstructed to emulate the different states of the underlying system. It is what is known as the reconstruction of the state space, called phase space in real-world signals, for now only satisfactorily resolved using the method of delays. Each state vector consists of m components, extracted from successive observations delayed a time t. The morphology of the geometric structure described by the state vectors, as well as their properties, depends on the chosen parameters t and m. The real dynamics of the system under study is subject to the correct determination of the parameters t and m. Only in this way can be deduced characteristics with true physical meaning, revealing aspects that reliably identify the dynamic complexity of the physiological system. The biological signal presented in this work, as a case study, is the PhotoPlethysmoGraphic (PPG) signal. We find that m is five for all the subjects analyzed and that t depends on the time interval in which it evaluates. The H\'enon map and the Lorenz flow are used to facilitate a more intuitive understanding of applied techniques.
Flexibility from Networks of Data Centers: A Market Clearing Formulation with Virtual Links
Date: 2019-10-11 00:00:00

Data centers owned and operated by large companies have a high power
consumption and this is expected to increase in the future. However, the
ability to shift computing loads geographically and in time can provide
flexibility to the power grid. We introduce the concept of virtual links to
capture space-time load flexibility provided by geographically-distributed data
centers in market clearing procedures. We show that the virtual link
abstraction fits well into existing market clearing frameworks and can help
analyze and establish market design properties. This is demonstrated using
illustrative case studies.
Coupling the circadian rhythms of population movement and the immune system in infectious disease modeling
Date: 2019-10-12 00:00:00
Tasks: Rhythm

The dynamics of infectious diseases propagating in populations depends both
on human interaction patterns, the contagion process and the pathogenesis
within hosts. The immune system follows a circadian rhythm and, consequently,
the chance of getting infected varies with the time of day an individual is
exposed to the pathogen. The movement and interaction of people also follow
24-hour cycles, which couples these two phenomena. We use a stochastic
metapopulation model informed by hourly mobility data for two medium-sized
Chinese cities. By this setup, we investigate how the epidemic risk depends on
the difference of the clocks governing the population movement and the immune
systems. In most of the scenarios we test, we observe circadian rhythms would
constrain the pace and extent of disease emergence. The three measures
(strength, outward transmission risk and introduction risk) are highly
correlated with each other. For example of the Yushu City, outward transmission
risk and introduction risk are correlated with a Pearson's correlation
coefficient of 0.83, and the risks correlate to strength with coefficients of
$-0.85$ and $-0.75$, respectively (all have $p<0.05$), in simulations with no
circadian effect and $R_0=1.5$. The relation between the circadian rhythms of
the immune system and daily routines in human mobility can affect the pace and
extent of infectious disease spreading. Shifting commuting times could mitigate
the emergence of outbreaks.
An optimal dispatch scheme for DSO and prosumers by implementing three-phase distribution locational marginal prices
Date: 2019-10-12 00:00:00

Since distribution system operator (DSO) cannot directly control prosumers
with controllable resources,this paper proposes an optimal dispatch method of
using three-phase distribution locational marginal prices (DLMPs) as effective
economic signals to incentivize prosumers' behaviors. In the proposed
three-phase DLMP model, DLMPs for both active power and reactive power are
calculated. To alleviate the imbalance, congestions and voltage violations in
active distribution networks (ADNs),the DSO and prosumers should be
coordinated. We develop such a coordinated control scheme for the DSO and
prosumers,in which the DSO generates and broadcasts three-phase DLMPs as price
signals to induce prosumers' behaviors. We prove that given the DLMPs for
active power and reactive power as settlement prices, the optimal dispatch of
the ADN will also maximize the surplus of prosumers. Therefore, the power
output of rational prosumers will match the optimal dispatch,resulting in
better operational conditions of ADNs. Then the three-phase
imbalance,congestions and voltage violations will be well reduced. Numerical
tests demonstrate the effectiveness of the proposed approach.
A hidden integral structure endows Absolute Concentration Robust systems with resilience to dynamical concentration disturbances
Authors: Daniele Cappelletti, Ankit Gupta, Mustafa Khammash
Date: 2019-10-12 00:00:00

Biochemical systems that express certain chemical species of interest at the same level at any positive equilibrium are called "absolute concentration robust" (ACR). These species behave in a stable, predictable way, in the sense that their expression is robust with respect to sudden changes in the species concentration, regardless the new positive equilibrium reached by the system. Such a property has been proven to be fundamentally important in certain gene regulatory networks and signaling systems. In the present paper, we mathematically prove that a well-known class of ACR systems studied by Shinar and Feinberg in 2010 hides an internal integral structure. This structure confers these systems with a higher degree of robustness that what was previously unknown. In particular, disturbances much more general than sudden changes in the species concentrations can be rejected, and robust perfect adaptation is achieved. Significantly, we show that these properties are maintained when the system is interconnected with other chemical reaction networks. This key feature enables design of insulator devices that are able to buffer the loading effect from downstream systems - a crucial requirement for modular circuit design in synthetic biology.
Systematic Asset Allocation using Flexible Views for South African Markets
Authors: Ann Sebastian, Tim Gebbie
Date: 2019-10-12 00:00:00

We implement a systematic asset allocation model using the Historical Simulation with Flexible Probabilities (HS-FP) framework developed by Meucci. The HS-FP framework is a flexible non-parametric estimation approach that considers future asset class behavior to be conditional on time and market environments, and derives a forward looking distribution that is consistent with this view while remaining close as possible to the prior distribution. The framework derives the forward looking distribution by applying unequal time and state conditioned probabilities to historical observations of asset class returns. This is achieved using relative entropy to find estimates with the least distortion to the prior distribution. Here, we use the HS-FP framework on South African financial market data for asset allocation purposes; by estimating expected returns, correlations and volatilities that are better represented through the measured market cycle. We demonstrated a range of state variables that can be useful towards understanding market environments. Concretely, we compare the out-of-sample performance for a specific configuration of the HS-FP model relative to classic Mean Variance Optimization(MVO) and Equally Weighted (EW) benchmark models. The framework displays low probability of backtest overfitting and the out-of-sample net returns and Sharpe ratio point estimates of the HS-FP model outperforms the benchmark models. However, the results are inconsistent when training windows are varied, the Sharpe ratio is seen to be inflated, and the method does not demonstrate statistically significant out-performance on a gross and net basis.
Portfolio Cuts: A Graph-Theoretic Framework to Diversification
Authors: Bruno Scalzo Dees, Ljubisa Stankovic, Anthony G. Constantinides, Danilo P. Mandic
Date: 2019-10-12 00:00:00
Tasks: Physical Intuition, Portfolio Optimization

Investment returns naturally reside on irregular domains, however, standard multivariate portfolio optimization methods are agnostic to data structure. To this end, we investigate ways for domain knowledge to be conveniently incorporated into the analysis, by means of graphs. Next, to relax the assumption of the completeness of graph topology and to equip the graph model with practically relevant physical intuition, we introduce the portfolio cut paradigm. Such a graph-theoretic portfolio partitioning technique is shown to allow the investor to devise robust and tractable asset allocation schemes, by virtue of a rigorous graph framework for considering smaller, computationally feasible, and economically meaningful clusters of assets, based on graph cuts. In turn, this makes it possible to fully utilize the asset returns covariance matrix for constructing the portfolio, even without the requirement for its inversion. The advantages of the proposed framework over traditional methods are demonstrated through numerical simulations based on real-world price data.
Decoding Working Memory Load from EEG with LSTM Networks
Date: 2019-10-12 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Retrieval, Time Series, Time Series Analysis

Working memory (WM) is a mechanism that temporarily stores and manipulates
information in service of behavioral goals and is a highly dynamic process.
Previous studies have considered decoding WM load using EEG but have not
investigated the contribution of sequential information contained in the
temporal patterns of the EEG data that can differentiate different WM loads. In
our study, we develop a novel method of investigating the role of sequential
information in the manipulation and storage of verbal information at various
time scales and localize topographically the sources of the sequential
information based decodability. High density EEG (128-channel) were recorded
from twenty subjects performing a Sternberg verbal WM task with varying memory
loads. Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN) were trained
to decode memory load during encoding, retention, activity-silent, and
retrieval periods. Decoding accuracy was compared between ordered data and a
temporally shuffled version that retains pattern based information of the data
but not temporal relation to assess the contribution of sequential information
to decoding memory load. The results show that (1) decoding accuracy increases
with increase in the length of the EEG time series given to the LSTM for both
ordered and temporally shuffled cases, with the increase being faster for
ordered than temporally shuffled time series, and (2) according to the decoding
weight maps, the frontal, temporal and some parietal areas are an important
source of sequential information based decodability. This study, to our
knowledge, is the first study applying a LSTM-RNN approach to investigate
temporal dynamics in human EEG data in encoding WM load information.
What is the Largest Sparsity Pattern that Can Be Recovered by 1-Norm Minimization?
Authors: Mustafa D. Kaba, Mengnan Zhao, Rene Vidal, Daniel P. Robinson, Enrique Mallada
Date: 2019-10-12 00:00:00

Much of the existing literature in sparse recovery is concerned with the following question: given a sparsity pattern and a corresponding regularizer, derive conditions on the dictionary under which exact recovery is possible. In this paper, we study the opposite question: given a dictionary and the 1-norm regularizer, find the largest sparsity pattern that can be recovered. We show that such a pattern is described by a mathematical object called a "maximum abstract simplicial complex", and provide two different characterizations of this object: one based on extreme points and the other based on vectors of minimal support. In addition, we show how this new framework is useful in the study of sparse recovery problems when the dictionary takes the form of a graph incidence matrix or a partial discrete Fourier transform. In case of incidence matrices, we show that the largest sparsity pattern that can be recovered is determined by the set of simple cycles of the graph. As a byproduct, we show that standard sparse recovery can be certified in polynomial time, although this is known to be NP-hard for general matrices. In the case of the partial discrete Fourier transform, our characterization of the largest sparsity pattern that can be recovered requires the unknown signal to be real and its dimension to be a prime number.