pwc-archive/papers-with-abstracts huggingface dataset:
Two-Step Estimation of a Strategic Network Formation Model with Clustering
Authors: Geert Ridder, Shuyang Sheng
Date: 2020-01-12 00:00:00
Tasks: Clustering, Vocal Bursts Valence Prediction

This paper explores strategic network formation under incomplete information using data from a single large network. We allow the utility function to be nonseparable in an individual's link choices to capture the spillover effects from friends in common. In a network with n individuals, an individual with a nonseparable utility function chooses between 2^{n-1} overlapping portfolios of links. We develop a novel approach that applies the Legendre transform to the utility function so that the optimal link choices can be represented as a sequence of correlated binary choices. The link dependence that results from the preference for friends in common is captured by an auxiliary variable introduced by the Legendre transform. We propose a two-step estimator that is consistent and asymptotically normal. We also derive a limiting approximation of the game as n grows large that simplifies the computation in large networks. We apply these methods to favor exchange networks in rural India and find that the direction of support from a mutual link matters in facilitating favor provision.
Cytoskeleton polarity is essential in determining orientational order in basal bodies of multi-ciliated cells
Authors: Toshinori Namba, Shuji Ishihara
Date: 2020-01-12 00:00:00

Synchronous and directed ciliary beating in trachea allows transport and ejection of virus and dust from the body. This ciliary function depends on the coordinated configuration of basal bodies (root of cilia) in apical cell membrane. However, the mechanism for their formation remains unknown. In this study, we show that the polarity in apical microtubule bundles plays a significant role in the organization of basal bodies. A mathematical model incorporating polarity has been formulated which provides a coherent explanation and is able to reproduce experimental observations. We have clarified both necessity ('why polarity is required for pattern formation') and sufficiency ('how polarity works for pattern formation') of cytoskeleton polarity for correct pattering of basal bodies with verification by experimental data. This model further leads us to a possible mechanism for cellular chirality.
Supply Network Formation and Fragility
Authors: Matthew Elliott, Benjamin Golub, Matthew V. Leduc
Date: 2020-01-12 00:00:00

We model the production of complex goods in a large supply network. Each firm sources several essential inputs through relationships with other firms. Individual supply relationships are at risk of idiosyncratic failure, which threatens to disrupt production. To protect against this, firms multisource inputs and strategically invest to make relationships stronger, trading off the cost of investment against the benefits of increased robustness. A supply network is called fragile if aggregate output is very sensitive to small aggregate shocks. We show that supply networks of intermediate productivity are fragile in equilibrium, even though this is always inefficient. The endogenous configuration of supply networks provides a new channel for the powerful amplification of shocks.
Self-Driving like a Human driver instead of a Robocar: Personalized comfortable driving experience for autonomous vehicles
Authors: Il Bae, Jaeyoung Moon, Junekyo Jhung, Ho Suk, Taewoo Kim, Hyungbin Park, Jaekwang Cha, Jinhyuk Kim, Dohyun Kim, Shiho Kim
Date: 2020-01-12 00:00:00
Tasks: Autonomous Vehicles

This paper issues an integrated control system of self-driving autonomous vehicles based on the personal driving preference to provide personalized comfortable driving experience to autonomous vehicle users. We propose an Occupant's Preference Metric (OPM) which is defining a preferred lateral and longitudinal acceleration region with maximum allowable jerk for users. Moreover, we propose a vehicle controller based on control parameters enabling integrated lateral and longitudinal control via preference-aware maneuvering of autonomous vehicles. The proposed system not only provides the criteria for the occupant's driving preference, but also provides a personalized autonomous self-driving style like a human driver instead of a Robocar. The simulation and experimental results demonstrated that the proposed system can maneuver the self-driving vehicle like a human driver by tracking the specified criterion of admissible acceleration and jerk.
A multi-country dynamic factor model with stochastic volatility for euro area business cycle analysis
Date: 2020-01-12 00:00:00

This paper develops a dynamic factor model that uses euro area (EA)
country-specific information on output and inflation to estimate an area-wide
measure of the output gap. Our model assumes that output and inflation can be
decomposed into country-specific stochastic trends and a common cyclical
component. Comovement in the trends is introduced by imposing a factor
structure on the shocks to the latent states. We moreover introduce flexible
stochastic volatility specifications to control for heteroscedasticity in the
measurement errors and innovations to the latent states. Carefully specified
shrinkage priors allow for pushing the model towards a homoscedastic
specification, if supported by the data. Our measure of the output gap closely
tracks other commonly adopted measures, with small differences in magnitudes
and timing. To assess whether the model-based output gap helps in forecasting
inflation, we perform an out-of-sample forecasting exercise. The findings
indicate that our approach yields superior inflation forecasts, both in terms
of point and density predictions.
Towards Highly Efficient State Estimation with Nonlinear Measurements in Distribution Systems
Date: 2020-01-12 00:00:00
Tasks: State Estimation

This letter proposes a novel and highly efficient distribution system state
estimation (DSSE) algorithm with nonlinear measurements from supervisory
control and data acquisition (SCADA) systems. Conventional DSSE, i.e., a
weighted least square (WLS)-based method, requires multiple Monte Carlo
simulations for overall accuracy evaluation and has high calculation cost due
to the nonlinear iterative process. The proposed method uses the Taylor series
of voltages for constructing a linear DSSE model in the interval form and then
solve this model by interval arithmetic. Compared with the nonlinear WLS-based
method, the proposed method obtains accurate and robust estimates via a single
random sampling of measurements and is computationally efficient. The
comparative analysis of the IEEE 34-bus distribution system points to improved
estimation results relative to the WLS-based method.
Sensitivity analysis methods in the biomedical sciences
Date: 2020-01-12 00:00:00
Tasks: Sensitivity

Sensitivity analysis is an important part of a mathematical modeller's
toolbox for model analysis. In this review paper, we describe the most
frequently used sensitivity techniques, discussing their advantages and
limitations, before applying each method to a simple model. Also included is a
summary of current software packages, as well as a modeller's guide for
carrying out sensitivity analyses. Finally, we apply the popular Morris and
Sobol methods to two models with biomedical applications, with the intention of
providing a deeper understanding behind both the principles of these methods
and the presentation of their results.
Pricing Exchange Options under Stochastic Correlation
Authors: Enrique Villamor, Pablo Olivares
Date: 2020-01-12 00:00:00

In this paper we study the pricing of exchange options when underlying assets have stochastic volatility and stochastic correlation. An approximation using a closed-form approximation based on a Taylor expansion of the conditional price is proposed. Numerical results are illustrated for exchanges between WTI and Brent type oil prices.
Food Supply Chain and Business Model Innovation
Date: 2020-01-12 00:00:00
Tasks: model, Systematic Literature Review

This paper investigates the contribution of business model innovations in
improvement of food supply chains. Through a systematic literature review, the
notable business model innovations in the food industry are identified,
surveyed, and evaluated. Findings reveal that the innovations in value
proposition, value creation processes, and value delivery processes of business
models are the successful strategies proposed in food industry. It is further
disclosed that rural female entrepreneurs, social movements, and also urban
conditions are the most important driving forces inducing the farmers to
reconsider their business models. In addition, the new technologies and
environmental factors are the secondary contributors in business model
innovation for the food processors. It is concluded that digitalization has
disruptively changed the food distributors models. E-commerce models and
internet of things are reported as the essential factors imposing the retailers
to innovate their business models. Furthermore, the consumption demand and the
product quality are two main factors affecting the business models of all the
firms operating in the food supply chain regardless of their positions in the
chain. The findings of the current study provide an insight into the food
industry to design a sustainable business model to bridge the gap between food
supply and food demand.
Noncooperative Precoding for Massive MIMO HetNets: SILNR Maximization Precoding
Authors: Deokhwan Han, Namyoon Lee
Date: 2020-01-13 00:00:00

Massive multi-input multiple-out (MIMO) is a key ingredient in improving the spectral efficiencies for next-generation cellular systems. Thanks to the channel reciprocity, in time-division-duplexing mode, each base station (BS) can acquire local channel state information at the transmitter (CSIT) for a set of users possibly located in adjacent cells. When the small cell BSs equipped with not-so-many antennas are densely deployed with marcrocells, a simple noncooperative MIMO precoding technique using local CSIT fails to achieve high spectral efficiency because of strong inter-cell-interference (ICI). In this paper, we present a novel noncooperative massive MIMO precoding technique called signal-to-interference-plus-leakage-plus-noise-ratio (SILNR) maximization precoding. The key idea of the proposed precoding is to jointly find a scheduled user set per cell, the beamforming vectors for the users, and the allocated power by simultaneously mitigating both inter-user-interference (IUI) and ICI leakage power using local CSIT. To accomplish this, we present a low-complexity algorithm that finds a local-optimal solution of the maximization problem for a lower bound of the sum spectral efficiency, i.e., a non-convex optimization problem. By system-level-simulations, we show that the proposed precoding method considerably outperforms the existing noncooperative precoding techniques in terms of the ergodic spectral efficiencies and rate distributions per user.
Reconstruction of Interbank Network using Ridge Entropy Maximization Model
Authors: Yuichi Ikeda, Hidetoshi Takeda
Date: 2020-01-13 00:00:00

We develop a network reconstruction model based on entropy maximization considering the sparsity of networks. We reconstruct the interbank network in Japan from financial data in individual banks' balance sheets using the developed reconstruction model from 2000 to 2016. The observed sparsity of the interbank network is successfully reproduced. We examine the characteristics of the reconstructed interbank network by calculating important network attributes. We obtain the following characteristics, which are consistent with the previously known stylized facts. Although we do not introduce the mechanism to generate the core and peripheral structure, we impose the constraints to consider the sparsity that is no transactions within the same bank category except for major commercial banks, the core and peripheral structure has spontaneously emerged. We identify major nodes in each community using the value of PageRank and degree to examine the changing role of each bank category. The observed changing role of banks is considered a result of the quantitative and qualitative monetary easing policy started by the Bank of Japan in April 2013.
Multicoding in neural information transfer suggested by mathematical analysis of the frequency-dependent synaptic plasticity in vivo
Date: 2020-01-13 00:00:00

Two elements of neural information processing have primarily been proposed:
firing rate and spike timing of neurons. In the case of synaptic plasticity,
although spike-timing-dependent plasticity (STDP) depending on presynaptic and
postsynaptic spike times had been considered the most common rule, recent
studies have shown the inhibitory nature of the brain in vivo for precise spike
timing, which is key to the STDP. Thus, the importance of the firing frequency
in synaptic plasticity in vivo has been recognized again. However, little is
understood about how the frequency-dependent synaptic plasticity (FDP) is
regulated in vivo. Here, we focused on the presynaptic input pattern, the
intracellular calcium decay time constants, and the background synaptic
activity, which vary depending on neuron types and the anatomical and
physiological environment in the brain. By analyzing a calcium-based model, we
found that the synaptic weight differs depending on these factors
characteristic in vivo, even if neurons receive the same input rate. This
finding suggests the involvement of multifaceted factors other than input
frequency in FDP and even neural coding in vivo.
Zooming In on Equity Factor Crowding
Authors: Valerio Volpati, Michael Benzaquen, Zoltan Eisler, Iacopo Mastromatteo, Bence Toth, Jean-Philippe Bouchaud
Date: 2020-01-13 00:00:00

Crowding is most likely an important factor in the deterioration of strategy performance, the increase of trading costs and the development of systemic risk. We study the imprints of \emph{crowding} on both anonymous market data and a large database of metaorders from institutional investors in the U.S. equity market. We propose direct metrics of crowding that capture the presence of investors contemporaneously trading the same stock in the same direction by looking at fluctuations of the imbalances of trades executed on the market. We identify significant signs of crowding in well known equity signals, such as Fama-French factors and especially Momentum. We show that the rebalancing of a Momentum portfolio can explain between 1-2\% of order flow, and that this percentage has been significantly increasing in recent years.
Evaluation of convolutional neural networks using a large multi-subject P300 dataset
Date: 2020-01-10 00:00:00
Tasks: Classification, ERP

Deep neural networks (DNN) have been studied in various machine learning
areas. For example, event-related potential (ERP) signal classification is a
highly complex task potentially suitable for DNN as signal-to-noise ratio is
low, and underlying spatial and temporal patterns display a large intra- and
intersubject variability. Convolutional neural networks (CNN) have been
compared with baseline traditional models, i.e. linear discriminant analysis
(LDA) and support vector machines (SVM) for single trial classification using a
large multi-subject publicly available P300 dataset of school-age children (138
males and 112 females). For single trial classification, classification
accuracy stayed between 62% and 64% for all tested classification models. When
applying the trained classification models to averaged trials, accuracy
increased to 76-79% without significant differences among classification
models. CNN did not prove superior to baseline for the tested dataset.
Comparison with related literature, limitations and future directions are
discussed.
Synthetic aperture imaging and motion estimation using tensor methods
Date: 2020-01-13 00:00:00
Tasks: Motion Estimation, Tensor Decomposition

We consider a synthetic aperture imaging configuration, such as synthetic
aperture radar (SAR), where we want to first separate reflections from moving
targets from those coming from a stationary background, and then to image
separately the moving and the stationary reflectors. For this purpose, we
introduce a representation of the data as a third order tensor formed from data
coming from partially overlapping sub-apertures. We then apply a tensor robust
principal component analysis (TRPCA) to the tensor data which separates them
into the parts coming from the stationary and moving reflectors. Images are
formed with the separated data sets. Our analysis shows a distinctly improved
performance of TRPCA, compared to the usual matrix case. In particular, the
tensor decomposition can identify motion features that are undetectable when
using the conventional motion estimation methods, including matrix RPCA. We
illustrate the performance of the method with numerical simulations in the
X-band radar regime.
Hybrid Precoding in Cooperative Millimeter Wave Networks
Authors: Chao Fang, Behrooz Makki, Jingya Li, Tommy Svensson
Date: 2020-01-13 00:00:00

In this paper, we study the performance of cooperative millimeter wave (mmWave) networks with hybrid precoding architectures. Considering joint transmissions and BS silence strategy, we propose hybrid precoding algorithms which minimize the sum power consumption of the base stations (BSs), for both fully- and partially-connected hybrid precoding (FHP and PHP, respectively) schemes, for single-carrier and orthogonal frequency-division multiplexing systems. We reformulate the analog precoding part as an equal-gain transmission problem, which only depends on the channel information, and the digital precoding part as a relaxed convex semidefinite program subject to per-user quality-of-service constraints that gives the optimal sum power consumption in terms of the BS silence strategy. In order to reduce the complexity of the hybrid precoding algorithm with optimal BS silence strategy, we propose a sub-optimal hybrid precoding algorithm that iteratively put BSs with small power into the silent mode. The simulation results show that, depending on the parameter settings, the power consumption of the PHP may be dominated by the RF transmit power and it may result in a larger power consumption than the FHP. For the cases with 2 BSs and 4 users, implementation of the FHP and the PHP in cooperative networks reduces the required RF transmit power, compared to the case in a non-cooperative network, by 71% and 56%, respectively.
Recovering the Structural Observability of Composite Networks via Cartesian Product
Date: 2020-01-14 00:00:00

Observability is a fundamental concept in system inference and estimation.
This paper is focused on structural observability analysis of Cartesian product
networks. Cartesian product networks emerge in variety of applications
including in parallel and distributed systems. We provide a structural approach
to extend the structural observability of the constituent networks (referred as
the factor networks) to that of the Cartesian product network. The structural
approach is based on graph theory and is generic. We introduce certain
structures which are tightly related to structural observability of networks,
namely parent Strongly-Connected-Component (parent SCC), parent node, and
contractions. The results show that for particular type of networks (e.g. the
networks containing contractions) the structural observability of the factor
network can be recovered via Cartesian product. In other words, if one of the
factor networks is structurally rank-deficient, using the other factor network
containing a spanning cycle family, then the Cartesian product of the two
nwtworks is structurally full-rank. We define certain network structures for
structural observability recovery. On the other hand, we derive the number of
observer nodes--the node whose state is measured by an output-- in the
Cartesian product network based on the number of observer nodes in the factor
networks. An example illustrates the graph-theoretic analysis in the paper.
Image inpainting using directional wavelet packets originating from polynomial splines
Authors: Amir Averbuch, Pekka Neittaanmaki, Valery Zheludev, Moshe Salhov, Jonathan Hauser
Date: 2020-01-12 00:00:00
Tasks: Diversity, Image Inpainting

The paper presents a new algorithm for the image inpainting problem. The algorithm is using a recently designed versatile library of quasi-analytic complex-valued wavelet packets (qWPs) which originate from polynomial splines of arbitrary orders. Tensor products of 1D qWPs provide a diversity of 2D qWPs oriented in multiple directions. For example, a set of the fourth-level qWPs comprises 62 different directions. The properties of the presented qWPs such as refined frequency resolution, directionality of waveforms with unlimited number of orientations, (anti-)symmetry of waveforms and windowed oscillating structure of waveforms with a variety of frequencies, make them efficient in image processing applications, in particular, in dealing with the inpainting problem addressed in the paper. The obtained results for this problem are quite competitive with the best state-of-the-art algorithms. The inpainting is implemented by an iterative scheme, which, in essence, is the Split Bregman Iteration (SBI) procedure supplied with an adaptive variable soft thresholding based on the Bivariate Shrinkage algorithm. In the inpainting experiments, performance comparison between the qWP-based methods and the state-of-the-art algorithms is presented.
Computational analysis of a 9D model for a small DRG neuron
Date: 2020-01-14 00:00:00

Small dorsal root ganglion (DRG) neurons are primary nociceptors which are
responsible for sensing pain. Elucidation of their dynamics is essential for
understanding and controlling pain. To this end, we present a numerical
bifurcation analysis of a small DRG neuron model in this paper. The model is of
Hodgkin-Huxley type and has 9 state variables. It consists of a
Na$\mathrm{_v}$1.7 and a Na$\mathrm{_v}$1.8 sodium channel, a leak channel, a
delayed rectifier potassium and an A-type transient potassium channel. The
dynamics of this model strongly depends on the maximal conductances of the
voltage-gated ion channels and the external current, which can be adjusted
experimentally. We show that the neuron dynamics are most sensitive to the
Na$\mathrm{_v}$1.8 channel maximal conductance ($\bar{g}_{1.8}$). Numerical
bifurcation analysis shows that depending on $\bar{g}_{1.8}$ and the external
current, different parameter regions can be identified with stable steady
states, periodic firing of action potentials, mixed-mode oscillations (MMOs),
and bistability between stable steady states and stable periodic firing of
action potentials. We illustrate and discuss the transitions between these
different regimes. We further analyze the behavior of MMOs. Within this region,
bifurcation analysis shows a sequence of isolated periodic solution branches
with one large action potential and a number of small amplitude peaks per
period. A closer inspection reveals more complex concatenated MMOs in between
these periodic MMOs branches, forming Farey sequences. Lastly, we also find
small solution windows with aperiodic oscillations, which seem to be chaotic.
The dynamical patterns found here as a function of different parameters contain
information of translational importance as their relation to pain sensation and
its intensity is a potential source of insight into controlling pain.