pwc-archive/papers-with-abstracts huggingface dataset:
Improving inference for nonlinear state-space models of animal population dynamics given biased sequential life stage data
Date: 2019-09-19 00:00:00
Tasks: State Space Models

State-space models (SSMs) are a popular tool for modeling animal abundances.
Inference difficulties for simple linear SSMs are well known, particularly in
relation to simultaneous estimation of process and observation variances.
Several remedies to overcome estimation problems have been studied for
relatively simple SSMs, but whether these challenges and proposed remedies
apply for nonlinear stage-structured SSMs, an important class of ecological
models, is less well understood. Here we identify improvements for inference
about nonlinear stage-structured SSMs fit with biased sequential life stage
data. Theoretical analyses indicate parameter identifiability requires
covariates in the state processes. Simulation studies show that plugging in
externally estimated observation variances, as opposed to jointly estimating
them with other parameters, reduces bias and standard error of estimates. In
contrast to previous results for simple linear SSMs, strong confounding between
jointly estimated process and observation variance parameters was not found in
the models explored here. However, when observation variance was also estimated
in the motivating case study, the resulting process variance estimates were
implausibly low (near-zero). As SSMs are used in increasingly complex ways,
understanding when inference can be expected to be successful, and what aids
it, becomes more important. Our study illustrates (i) the need for relevant
process covariates and (ii) the benefits of using externally estimated
observation variances for inference for nonlinear stage-structured SSMs.
Deep Retinal Image Segmentation with Regularization Under Geometric Priors
Date: 2019-09-19 00:00:00
Tasks: Diagnostic, Image Segmentation, Segmentation, Semantic Segmentation

Vessel segmentation of retinal images is a key diagnostic capability in
ophthalmology. This problem faces several challenges including low contrast,
variable vessel size and thickness, and presence of interfering pathology such
as micro-aneurysms and hemorrhages. Early approaches addressing this problem
employed hand-crafted filters to capture vessel structures, accompanied by
morphological post-processing. More recently, deep learning techniques have
been employed with significantly enhanced segmentation accuracy. We propose a
novel domain enriched deep network that consists of two components: 1) a
representation network that learns geometric features specific to retinal
images, and 2) a custom designed computationally efficient residual task
network that utilizes the features obtained from the representation layer to
perform pixel-level segmentation. The representation and task networks are {\em
jointly learned} for any given training set. To obtain physically meaningful
and practically effective representation filters, we propose two new
constraints that are inspired by expected prior structure on these filters: 1)
orientation constraint that promotes geometric diversity of curvilinear
features, and 2) a data adaptive noise regularizer that penalizes false
positives. Multi-scale extensions are developed to enable accurate detection of
thin vessels. Experiments performed on three challenging benchmark databases
under a variety of training scenarios show that the proposed prior guided deep
network outperforms state of the art alternatives as measured by common
evaluation metrics, while being more economical in network size and inference
time.
Hybrid Inexact BCD for Coupled Structured Matrix Factorization in Hyperspectral Super-Resolution
Date: 2020-02-20 00:00:00
Tasks: Computational Efficiency, Super-Resolution

This paper develops a first-order optimization method for coupled structured
matrix factorization (CoSMF) problems that arise in the context of
hyperspectral super-resolution (HSR) in remote sensing. To best leverage the
problem structures for computational efficiency, we introduce a hybrid inexact
block coordinate descent (HiBCD) scheme wherein one coordinate is updated via
the fast proximal gradient (FPG) method, while another via the Frank-Wolfe (FW)
method. The FPG-type methods are known to take less number of iterations to
converge, by numerical experience, while the FW-type methods can offer lower
per-iteration complexity in certain cases; and we wish to take the best of
both. We show that the limit points of this HiBCD scheme are stationary. Our
proof treats HiBCD as an optimization framework for a class of multi-block
structured optimization problems, and our stationarity claim is applicable not
only to CoSMF but also to many other problems. Previous optimization research
showed the same stationarity result for inexact block coordinate descent with
either FPG or FW updates only. Numerical results indicate that the proposed
HiBCD scheme is computationally much more efficient than the state-of-the-art
CoSMF schemes in HSR.
Perovskite PV-powered RFID: enabling low-cost self-powered IoT sensors
Date: 2019-09-19 00:00:00

Photovoltaic (PV) cells have the potential to serve as on-board power sources
for low-power IoT devices. Here, we explore the use of perovskite solar cells
to power Radio Frequency (RF) backscatter-based IoT devices with a few {\mu}W
power demand. Perovskites are suitable for low-cost, high-performance,
low-temperature processing, and flexible light energy harvesting that hold the
possibility to significantly extend the range and lifetime of current
backscatter techniques such as Radio Frequency Identification (RFID). For these
reasons, perovskite solar cells are prominent candidates for future low-power
wireless applications. We report on realizing a functional perovskite-powered
wireless temperature sensor with 4 m communication range. We use a 10.1%
efficient perovskite PV module generating an output voltage of 4.3 V with an
active area of 1.06 cm2 under 1 sun illumination, with AM 1.5G spectrum, to
power a commercial off-the-shelf RFID IC, requiring 10 - 45 {\mu}W of power.
Having an on-board energy harvester provides extra-energy to boost the range of
the sensor (5x) in addition to providing energy to carry out high-volume sensor
measurements (hundreds of measurements per min). Our evaluation of the
prototype suggests that perovskite photovoltaic cells are able to meet the
energy needs to enable fully autonomous low-power RF backscatter applications
of the future. We conclude with an outlook into a range of applications that we
envision to leverage the synergies offered by combining perovskite
photovoltaics and RFID.
How to design a derivatives market?
Authors: Bastien Baldacci, Paul Jusselin, Mathieu Rosenbaum
Date: 2019-09-19 00:00:00
Tasks: Quantization

We consider the problem of designing a derivatives exchange aiming at addressing clients needs in terms of listed options and providing suitable liquidity. We proceed into two steps. First we use a quantization method to select the options that should be displayed by the exchange. Then, using a principal-agent approach, we design a make take fees contract between the exchange and the market maker. The role of this contract is to provide incentives to the market maker so that he offers small spreads for the whole range of listed options, hence attracting transactions and meeting the commercial requirements of the exchange.
Uncertainty Quantification in Stochastic Economic Dispatch using Gaussian Process Emulation
Date: 2019-09-20 00:00:00
Tasks: Dimensionality Reduction, Uncertainty Quantification

The increasing penetration of renewable energy resources in power systems,
represented as random processes, converts the traditional deterministic
economic dispatch problem into a stochastic one. To solve this stochastic
economic dispatch, the conventional Monte Carlo method is prohibitively time
consuming for medium- and large-scale power systems. To overcome this problem,
we propose in this paper a novel Gaussian-process-emulator-based approach to
quantify the uncertainty in the stochastic economic dispatch considering wind
power penetration. Based on the dimension-reduction results obtained by the
Karhunen-Lo\`eve expansion, a Gaussian-process emulator is constructed. This
surrogate allows us to evaluate the economic dispatch solver at sampled values
with a negligible computational cost while maintaining a desirable accuracy.
Simulation results conducted on the IEEE 118-bus system reveal that the
proposed method has an excellent performance as compared to the traditional
Monte Carlo method.
Secure Interference Exploitation Precoding in MISO Wiretap Channel: Destructive Region Redefinition with Efficient Solutions
Date: 2019-09-20 00:00:00

In this paper, we focus on the physical layer security for a K-user
multiple-input-single-output (MISO) wiretap channel in the presence of a
malicious eavesdropper, where we propose several interference exploitation (IE)
precoding schemes for different types of the eavesdropper. Specifically, in the
case where a common eavesdropper decodes the signal directly and Eve's full
channel state information (CSI) is available at the transmitter, we show that
the required transmit power can be further reduced by re-designing the
`destructive region' of the constellations for symbol-level precoding and
re-formulating the power minimization problem. We further study the SINR
balancing problems with the derived `complete destructive region' with full,
statistical and no Eve's CSI, respectively, and show that the SINR balancing
problem becomes non-convex with statistical or no Eve's CSI. On the other hand,
in the presence of a smart eavesdropper using maximal likelihood (ML)
detection, the security cannot be guaranteed with all the existing approaches.
To this end, we further propose a random jamming scheme (RJS) and a random
precoding scheme (RPS), respectively. To solve the introduced convex/non-convex
problems in an efficient manner, we propose an iterative algorithm for the
convex ones based on the Karush-Kuhn-Tucker (KKT) conditions, and deal with the
non-convex ones by resorting to Taylor expansions. Simulation results show that
all proposed schemes outperform the existing works in secrecy performance, and
that the proposed algorithm improves the computation efficiency significantly.
Doubly Robust Identification for Causal Panel Data Models
Authors: Dmitry Arkhangelsky, Guido W. Imbens
Date: 2019-09-20 00:00:00

We study identification and estimation of causal effects in settings with panel data. Traditionally researchers follow model-based identification strategies relying on assumptions governing the relation between the potential outcomes and the observed and unobserved confounders. We focus on a different, complementary approach to identification where assumptions are made about the connection between the treatment assignment and the unobserved confounders. Such strategies are common in cross-section settings but rarely used with panel data. We introduce different sets of assumptions that follow the two paths to identification and develop a doubly robust approach. We propose estimation methods that build on these identification strategies.
An Optimal Channel Estimation Scheme for Intelligent Reflecting Surfaces based on a Minimum Variance Unbiased Estimator
Date: 2019-11-22 00:00:00

In a wireless system with Intelligent Reflective Surfaces (IRS) containing
many passive elements, we consider the problem of channel estimation. All the
links from the transmitter to the receiver via each IRS elements (or groups)
are estimated. As the estimation performance are dependent on the setting of
the IRS, we design an optimal channel estimation scheme where the IRS elements
follow an optimal series of activation patterns. The optimal design is guided
by results for the minimum variance unbiased estimation. The IRS setting during
the channel estimation period mimics a series of discrete Fourier transforms.
We show theoretically and with simulations that the estimation variance is one
order smaller compared to existing on/off methods proposed in the literature.
Radar Interference Mitigation for Automated Driving
Date: 2019-09-20 00:00:00
Tasks: Autonomous Driving

Autonomous driving relies on a variety of sensors, especially on radars,
which have unique robustness under heavy rain/fog/snow and poor light
conditions. With the rapid increase of the amount of radars used on modern
vehicles, where most radars operate in the same frequency band, the risk of
radar interference becomes a compelling issue. This article analyses automotive
radar interference and proposes several new approaches, which combine
industrial and academic expertise, toward the path of interference-free
autonomous driving.
Bayesian data assimilation to support informed decision-making in individualised chemotherapy
Date: 2019-09-20 00:00:00
Tasks: Decision Making, Uncertainty Quantification

An essential component of therapeutic drug/biomarker monitoring (TDM) is to
combine patient data with prior knowledge for model-based predictions of
therapy outcomes. Current Bayesian forecasting tools typically rely only on the
most probable model parameters (maximum a-posteriori (MAP) estimate). This
MAP-based approach, however, does neither necessarily predict the most probable
outcome nor does it quantify the risks of treatment inefficacy or toxicity.
Bayesian data assimilation (DA) methods overcome these limitations by providing
a comprehensive uncertainty quantification. We compare DA methods with
MAP-based approaches and show how probabilistic statements about key markers
related to chemotherapy-induced neutropenia can be leveraged for more
informative decision support in individualised chemotherapy. Sequential
Bayesian DA proved to be most computational efficient for handling
interoccasion variability and integrating TDM data. For new digital monitoring
devices enabling more frequent data collection, these features will be of
critical importance to improve patient care decisions in various therapeutic
areas.
Modelling Protein Target-Search in Human Chromosomes
Authors: Markus Nyberg, Tobias Ambjörnsson, Per Stenberg, and Ludvig Lizana
Date: 2019-09-19 00:00:00

Several processes in the cell, such as gene regulation, start when key proteins recognise and bind to short DNA sequences. However, as these sequences can be hundreds of million times shorter than the genome, they are hard to find by simple diffusion: diffusion-limited association rates may underestimate $in~vitro$ measurements up to several orders of magnitude. Moreover, the rates increase if the DNA is coiled rather than straight. Here we model how this works $in~vivo$ in mammalian cells. We use chromatin-chromatin contact data from state-of-the-art Hi-C experiments to map the protein target-search onto a network problem. The nodes represent a DNA segment and the weight of the links is proportional to measured contact probabilities. We then put forward a master equation for the density of searching protein that allows us to calculate the association rates across the genome analytically. For segments where the rates are high, we find that they are enriched with active genes and have high RNA expression levels. This paper suggests that the DNA's 3D conformation is important for protein search times $in~vivo$ and offers a method to interpret protein-binding profiles in eukaryotes that cannot be explained by the DNA sequence itself.
CME Iceberg Order Detection and Prediction
Authors: Dmitry Zotikov, Anton Antonov
Date: 2019-09-20 00:00:00
Tasks: Prediction

We propose a method for detection and prediction of native and synthetic iceberg orders on Chicago Mercantile Exchange. Native (managed by the exchange) icebergs are detected using discrepancies between the resting volume of an order and the actual trade size as indicated by trade summary messages, as well as by tracking order modifications that follow trade events. Synthetic (managed by market participants) icebergs are detected by observing limit orders arriving within a short time frame after a trade. The obtained icebergs are then used to train a model based on the Kaplan--Meier estimator, accounting for orders that were cancelled after a partial execution. The model is utilized to predict the total size of newly detected icebergs. Out of sample validation is performed on the full order depth data, performance metrics and quantitative estimates of hidden volume are presented.
Optimal Dividend Strategy for an Insurance Group with Contagious Default Risk
Authors: Zhuo Jin, Huafu Liao, Yue Yang, Xiang Yu
Date: 2019-09-20 00:00:00

This paper studies the optimal dividend for a multi-line insurance group, in which each subsidiary runs a product line and is exposed to some external credit risk. The default contagion is considered such that one default event may increase the default probabilities of all surviving subsidiaries. The total dividend problem for the insurance group is investigated and we find that the optimal dividend strategy is still of the barrier type. Furthermore, we show that the optimal barrier of each subsidiary is modulated by the default state. That is, how many and which subsidiaries have defaulted will determine the dividend threshold of each surviving subsidiary. These conclusions are based on the analysis of the associated recursive system of Hamilton-Jacobi-Bellman variational inequalities (HJBVIs). The existence of the classical solution is established and the verification theorem is proved. In the case of two subsidiaries, the value function and optimal barriers are given in analytical forms, allowing us to conclude that the optimal barrier of one subsidiary decreases if the other subsidiary defaults.
Exact power spectrum in a minimal hybrid model of stochastic gene expression oscillations
Authors: Chen Jia, Hong Qian, Michael Q. Zhang
Date: 2019-09-21 00:00:00

Stochastic oscillations in individual cells are usually characterized by a non-monotonic power spectrum with an oscillatory autocorrelation function. Here we develop an analytical approach of stochastic oscillations in a minimal hybrid model of stochastic gene expression including promoter state switching, protein synthesis and degradation, as well as a genetic feedback loop. The oscillations observed in our model are noise-induced since the deterministic theory predicts stable fixed points. The autocorrelated function, power spectrum, and steady-state distribution of protein concentration fluctuations are computed in closed form without making any approximations. Using the exactly solvable model, we illustrate sustained oscillations as a circular motion along a stochastic hysteresis loop induced by gene state switching. A triphasic stochastic bifurcation upon the increasing strength of negative feedback is observed, which reveals how stochastic bursts evolve into stochastic oscillations. In our model, oscillations tend to occur when the protein is relatively stable and when gene switching is relatively slow. Translational bursting is found to enhance the robustness and broaden the region of stochastic oscillations. These results provide deeper insights into R. Thomas' two conjectures for single-cell gene expression kinetics.
Desperate times call for desperate measures: government spending multipliers in hard times
Date: 2020-05-28 00:00:00
Tasks: Unity

We investigate state-dependent effects of fiscal multipliers and allow for
endogenous sample splitting to determine whether the US economy is in a slack
state. When the endogenized slack state is estimated as the period of the
unemployment rate higher than about 12 percent, the estimated cumulative
multipliers are significantly larger during slack periods than non-slack
periods and are above unity. We also examine the possibility of time-varying
regimes of slackness and find that our empirical results are robust under a
more flexible framework. Our estimation results point out the importance of the
heterogenous effects of fiscal policy and shed light on the prospect of fiscal
policy in response to economic shocks from the current COVID-19 pandemic.
Topographic Deep Artificial Neural Networks (TDANNs) predict face selectivity topography in primate inferior temporal (IT) cortex
Date: 2019-09-21 00:00:00

Deep convolutional neural networks are biologically driven models that
resemble the hierarchical structure of primate visual cortex and are the
current best predictors of the neural responses measured along the ventral
stream. However, the networks lack topographic properties that are present in
the visual cortex, such as orientation maps in primary visual cortex and
category-selective maps in inferior temporal (IT) cortex. In this work, the
minimum wiring cost constraint was approximated as an additional learning rule
in order to generate topographic maps of the networks. We found that our
topographic deep artificial neural networks (ANNs) can reproduce the category
selectivity maps of the primate IT cortex.
Subspace Clustering for Panel Data with Interactive Effects
Authors: Jiangtao Duan, Wei Gao, Hao Qu, Hon Keung Tony
Date: 2019-09-22 00:00:00
Tasks: Clustering

In this paper, a statistical model for panel data with unobservable grouped factor structures which are correlated with the regressors and the group membership can be unknown. The factor loadings are assumed to be in different subspaces and the subspace clustering for factor loadings are considered. A method called least squares subspace clustering estimate (LSSC) is proposed to estimate the model parameters by minimizing the least-square criterion and to perform the subspace clustering simultaneously. The consistency of the proposed subspace clustering is proved and the asymptotic properties of the estimation procedure are studied under certain conditions. A Monte Carlo simulation study is used to illustrate the advantages of the proposed method. Further considerations for the situations that the number of subspaces for factors, the dimension of factors and the dimension of subspaces are unknown are also discussed. For illustrative purposes, the proposed method is applied to study the linkage between income and democracy across countries while subspace patterns of unobserved factors and factor loadings are allowed.
Distributed Dual Gradient Tracking for Resource Allocation in Unbalanced Networks
Authors: JiaQi Zhang, Keyou You, Kai Cai
Date: 2019-09-22 00:00:00

This paper proposes a distributed dual gradient tracking algorithm (DDGT) to solve resource allocation problems over an unbalanced network, where each node in the network holds a private cost function and computes the optimal resource by interacting only with its neighboring nodes. Our key idea is the novel use of the distributed push-pull gradient algorithm (PPG) to solve the dual problem of the resource allocation problem. To study the convergence of the DDGT, we first establish the sublinear convergence rate of PPG for non-convex objective functions, which advances the existing results on PPG as they require the strong-convexity of objective functions. Then we show that the DDGT converges linearly for strongly convex and Lipschitz smooth cost functions, and sublinearly without the Lipschitz smoothness. Finally, experimental results suggest that DDGT outperforms existing algorithms.