pwc-archive/papers-with-abstracts huggingface dataset:
A Novel Compensation Algorithm for Thickness Measurement Immune to Lift-Off Variations Using Eddy Current Method
Date: 2019-05-28 00:00:00

Lift-off variation causes errors in the eddy current thickness measurements
of metallic plates. In this paper, we have developed an algorithm that can
compensate for this variation and produce an index that is linked to the
thickness, but is virtually independent of lift-off. This index, termed as the
compensated peak frequency, can be obtained from the measured multifrequency
inductance spectral data using the algorithm we developed in this paper. This
method has been derived through mathematical manipulation and verified by both
the simulation and experimental data. Accuracy in the thickness measurements at
different lift-offs proved to be within 2%.
Picosecond-precision optical time transfer in free space using flexible binary offset carrier modulation
Date: 2020-02-20 00:00:00

Free-space optical time transfer that features high precision and flexibility
will act a crucial role in near-future ground-to-satellite/inter-satellite
clock networks and outdoor timing services. Here we propose a free-space
optical flexible-binary-offset-carrier-modulated (FlexBOC-modulated) time
transfer method. The utilized FlexBOC modulation could yield a comparative
precision, although its occupied bandwidth is tremendously reduced by at least
97.5% compared to optical binary phase modulation. Meanwhile, the adoption of
optical techniques eliminates the multi-path effect that is major limit in the
current microwave satellite time transfer system. What's more, the time
interval measurement avoids a continuous link that may be routinely broken by
physical obstructions. For verification, a time transfer experiment with our
home-built system between two sites separated by a 30-m free-space path outside
the laboratory was conducted. Over a 15 h period, the time deviation is 2.3 ps
in a 1-s averaging time, and averages down to 1.0 ps until ~60 s. The
fractional frequency instability exhibits 4.0E-12 at a gate time of 1 s, and
approaches to 2.6E10-15 at 10000 s.
Characterizing Shadow Price via Lagrangian Multiplier for Nonsmooth Problem
Authors: Yan Gao
Date: 2019-05-31 00:00:00
Tasks: Relation

In this paper, a relation between shadow price and the Lagrangian multiplier for nonsmooth problem is explored. It is shown that the Lagrangian Multiplier is the upper bound of shadow price for convex optimization and a class of Lipschtzian optimizations. This work can be used in shadow pricing for nonsmooth situation. The several nonsmooth functions involved in this class of Lipschtzian optimizations is listed. Finally, an application to electricity pricing is discussed.
Resolving New Keynesian Anomalies with Wealth in the Utility Function
Date: 2019-12-11 00:00:00

At the zero lower bound, the New Keynesian model predicts that output and
inflation collapse to implausibly low levels, and that government spending and
forward guidance have implausibly large effects. To resolve these anomalies, we
introduce wealth into the utility function; the justification is that wealth is
a marker of social status, and people value status. Since people partly save to
accrue social status, the Euler equation is modified. As a result, when the
marginal utility of wealth is sufficiently large, the dynamical system
representing the zero-lower-bound equilibrium transforms from a saddle to a
source---which resolves all the anomalies.
On Policy Evaluation with Aggregate Time-Series Shocks
Authors: Dmitry Arkhangelsky, Vasily Korovkin
Date: 2019-05-31 00:00:00
Tasks: Time Series, Time Series Analysis, valid

We develop an estimator for applications where the variable of interest is endogenous and researchers have access to aggregate instruments. Our method addresses the critical identification challenge -- unobserved confounding, which renders conventional estimators invalid. Our proposal relies on a new data-driven aggregation scheme that eliminates the unobserved confounders. We illustrate the advantages of our algorithm using data from Nakamura and Steinsson (2014) study of local fiscal multipliers. We introduce a finite population model with aggregate uncertainty to analyze our estimator. We establish conditions for consistency and asymptotic normality and show how to use our estimator to conduct valid inference.
Tensor Completion for Radio Map Reconstruction using Low Rank and Smoothness
Date: 2019-05-31 00:00:00

Radio maps are important enablers for many applications in wireless networks,
ranging from network planning and optimization to fingerprint based
localization. Sampling the complete map is prohibitively expensive in practice,
so methods for reconstructing the complete map from a subset of measurements
are increasingly gaining attention in the literature. In this paper, we propose
two algorithms for this purpose, which build on existing approaches that aim at
minimizing the tensor rank while additionally enforcing smoothness of the radio
map. Experimental results with synthetic measurements derived via ray tracing
show that our algorithms outperform state of the art techniques.
Sentiment-Driven Stochastic Volatility Model: A High-Frequency Textual Tool for Economists
Authors: Jozef Barunik, Cathy Yi-Hsuan Chen, Jan Vecer
Date: 2019-05-31 00:00:00

We propose how to quantify high-frequency market sentiment using high-frequency news from NASDAQ news platform and support vector machine classifiers. News arrive at markets randomly and the resulting news sentiment behaves like a stochastic process. To characterize the joint evolution of sentiment, price, and volatility, we introduce a unified continuous-time sentiment-driven stochastic volatility model. We provide closed-form formulas for moments of the volatility and news sentiment processes and study the news impact. Further, we implement a simulation-based method to calibrate the parameters. Empirically, we document that news sentiment raises the threshold of volatility reversion, sustaining high market volatility.
Coalitions in Repeated Games
Authors: S. Nageeb Ali, Ce Liu
Date: 2019-06-01 00:00:00
Tasks: Blocking

This paper proposes a framework and solution concept for repeated coalitional behavior. We model history-dependent schemes that deter coalitions from blocking using continuation promises and punishments. We evaluate the effectiveness of these schemes across a range of settings, and apply our results to repeated matching and negotiations.
At What Level Should One Cluster Standard Errors in Paired and Small-Strata Experiments?
Authors: Clément de Chaisemartin, Jaime Ramirez-Cuellar
Date: 2019-06-01 00:00:00
Tasks: Clustering, regression

In clustered and paired experiments, to estimate treatment effects, researchers often regress their outcome on the treatment and pair fixed effects, clustering standard errors at the unit-of-randomization level. We show that even if the treatment has no effect, a 5%-level t-test based on this regression will wrongly conclude that the treatment has an effect up to 16.5% of the time, an error rate much larger than the researcher's 5% target. To achieve their targeted error rate, researchers should instead cluster standard errors at the pair level. Using simulations, we show that similar results apply to clustered experiments with small strata.
Cloud Storage for Multi-Service Battery Operation (Extended Version)
Authors: Mohammad Rasouli, Tao Sun, Camille Pache, Patrick Panciatici, Jean Maeght, Ramesh Johari, Ram Rajagopal
Date: 2019-05-17 00:00:00
Tasks: Blocking, RTE, Stochastic Optimization

We study a cloud storage operator who provides shared storage service for electricity end-users using the residual part of a multi-service grid-scale battery primarily used for high priority grid services. We design an optimal product offering, pricing and customer portfolio. A framework and solution approach for assessing and operating such multi-service battery operations with stochastic services and different priority levels is an open problem is proposed. The methodology consists in modelling the problem as a two-stage stochastic optimization between high priority stochastic grid services and low priority cloud storage for stochastic end users. We also propose the operational metrics of multiplexing gain and probability of blocking to assess the operation of multi-service multi-user battery. To address the computational challenge of solving the stochastic optimization with a large number of end-users, we propose effective capacity as a convex approximation that allows an analytical solution. We then provide an empirical analysis based on real grid congestion data from RTE France, and a large dataset of end-users' electricity consumption in California. Our empirical analysis shows (i) our proposed effective capacity is a close approximation, (ii) battery operation and profit are sensitive to the cost of external resources, number of end-users, and RTE's leasing price of the battery, and (iii) with only a slight discount of the leasing price, the profit of the third party from a stochastic residual battery can be the same as that of a deterministic one. Cloud storage as a low priority service can profitably exist alongside other high priority battery services, making integration of more storage in the grid economically viable, and allowing larger intermittent renewables, a key path towards reduced carbon emissions.
Data-driven Vector-measurement-sensor Selection based on Greedy Algorithm
Authors: Yuji Saito, Taku Nonomura, Koki Nankai, Keigo Yamada, Keisuke Asai, Yasuo Sasaki, Daisuke Tsubakino
Date: 2019-05-30 00:00:00

A vector-measurement-sensor problem for the least squares estimation is considered, by extending a previous novel approach in this paper. An extension of the vector-measurement-sensor selection of the greedy algorithm is proposed and is applied to particle-image-velocimetry data to reconstruct the full state based on the information given by sparse vector-measurement sensors.
Deterministic and stochastic damage detection via dynamic response analysis
Date: 2019-12-23 00:00:00

The paper proposes a method of damage detection in elastic materials, which
is based on analyzing the time-dependent (dynamic) response of the material
excited by an acoustic signal. A case study is presented consisting of
experimental measurements and their mathematical analysis. The decisive
parameters (wave speed and damping coefficient) of a mathematical model of the
acoustic wave are calibrated by comparing the measurement data with the
numerically evaluated exact solution predicted by the mathematical model. The
calibration is done both deterministically by minimizing the square error over
time and stochastically by a Bayesian approach, implemented through the
Metropolis-Hastings algorithm. The resulting posterior distribution of the
parameters can be used to construct a Bayesian test for damage.
Fitts' Law for speed-accuracy trade-off describes a diversity-enabled sweet spot in sensorimotor control
Date: 2019-09-18 00:00:00
Tasks: Diversity

Human sensorimotor control exhibits remarkable speed and accuracy, and the
tradeoff between them is encapsulated in Fitts' Law for reaching and pointing.
While Fitts related this to Shannon's channel capacity theorem, despite
widespread study of Fitts' Law, a theory that connects implementation of
sensorimotor control at the system and hardware level has not emerged. Here we
describe a theory that connects hardware (neurons and muscles with inherent
severe speed-accuracy tradeoffs) with system level control to explain Fitts'
Law for reaching and related laws. The results supporting the theory show that
diversity between hardware components is exploited to achieve both fast and
accurate control performance despite slow or inaccurate hardware. Such
"diversity-enabled sweet spots" (DESSs) are ubiquitous in biology and
technology, and explain why large heterogeneities exist in biological and
technical components and how both engineers and natural selection routinely
evolve fast and accurate systems using imperfect hardware.
Optimising portfolio diversification and dimensionality
Authors: Mathias Barkhagen, Brian Fleming, Sergio Garcia Quiles, Jacek Gondzio, Joerg Kalcsics, Jens Kroeske, Sotirios Sabanis, Arne Staal
Date: 2019-06-03 00:00:00
Tasks: global-optimization

A new framework for portfolio diversification is introduced which goes beyond the classical mean-variance approach and portfolio allocation strategies such as risk parity. It is based on a novel concept called portfolio dimensionality that connects diversification to the non-Gaussianity of portfolio returns and can typically be defined in terms of the ratio of risk measures which are homogenous functions of equal degree. The latter arises naturally due to our requirement that diversification measures should be leverage invariant. We introduce this new framework and argue the benefits relative to existing measures of diversification in the literature, before addressing the question of optimizing diversification or, equivalently, dimensionality. Maximising portfolio dimensionality leads to highly non-trivial optimization problems with objective functions which are typically non-convex and potentially have multiple local optima. Two complementary global optimization algorithms are thus presented. For problems of moderate size and more akin to asset allocation problems, a deterministic Branch and Bound algorithm is developed, whereas for problems of larger size a stochastic global optimization algorithm based on Gradient Langevin Dynamics is given. We demonstrate analytically and through numerical experiments that the framework reflects the desired properties often discussed in the literature.
For Whom the Bell (Curve) Tolls: A to F, Trade Your Grade Based on the Net Present Value of Friendships with Financial Incentives
Date: 2019-09-17 00:00:00

We discuss a possible solution to an unintended consequence of having grades,
certificates, rankings and other diversions in the act of transferring
knowledge; and zoom in specifically to the topic of having grades, on a curve.
We conduct a thought experiment, taking a chapter (and some more?) from the
financial markets, (where we trade pollution and what not?), to create a
marketplace, where we can trade our grade, similar in structure to the interest
rate swap. We connect this to broader problems that are creeping up,
unintentionally, due to artificial labels we are attaching, to ourselves. The
policy and philosophical implications of our arguments are to suggest that all
trophies that we collect (including certificates, grades, medals etc.) should
be viewed as personal equity or private equity (borrowing another widely used
term in finance) and we should not use them to determine the outcomes in any
selection criteria except have a cutoff point: either for jobs, higher studies,
or, financial scholarships, other than for entertainment or spectator sports.
We suggest alternate methods for grading and performance assessment and put
forth tests for teaching and learning similar to the Turing Test for
intelligence.
Using your ADC and Backend Capacitors to Authenticate for Free: (Virtually) Free from Database, Enrollment, and Excessive Area/Power Overhead
Date: 2019-06-23 00:00:00

Detection of counterfeit chips has emerged as a crucial concern.
Physically-unclonable-function (PUF)-based techniques are widely used for
authentication, however, require dedicated hardware and large signature
database. In this work, we show intrinsic & database-free authentication using
back-end capacitors. The discussed technique simplifies authentication setup
and reduces the test cost. We show that an analog-to-digital converter (ADC)
can be modified for back-end capacitor-based authentication in addition to its
regular functionality; hence, a dedicated authentication module is not
necessary. Moreover, since back-end capacitors are quite insensitive to
temperature and aging-induced variations than transistors, the discussed
technique result in a more reliable authentication than transistor PUF-based
authentication. The modifications to conventional ADC incur 3.2% power overhead
and 75% active-area overhead; however, arguably, the advantages of the
discussed intrinsic & database-free authentication outweigh the overheads. Full
version of this article is published at IEEE TVLSI.
Graph Sampling for Matrix Completion Using Recurrent Gershgorin Disc Shift
Date: 2019-10-16 00:00:00
Tasks: Graph Sampling, Matrix Completion

Matrix completion algorithms fill missing entries in a large matrix given a
subset of observed samples. However, how to best pre-select informative matrix
entries given a sampling budget is largely unaddressed. In this paper, we
propose a fast sample selection strategy for matrix completion from a graph
signal processing perspective. Specifically, we first regularize the matrix
reconstruction objective using a dual graph signal smoothness prior, resulting
in a system of linear equations for solution. We then select appropriate
samples to maximize the smallest eigenvalue $\lambda_{\min}$ of the coefficient
matrix, thus maximizing the stability of the linear system. To efficiently
solve this combinatorial problem, we derive a greedy sampling strategy,
leveraging on Gershgorin circle theorem, that iteratively selects one sample
(equivalent to shifting one Gershgorin disc) at a time corresponding to the
largest magnitude entry in the first eigenvector of a modified graph Laplacian
matrix. Our algorithm benefits computationally from warm start as the first
eigenvectors of incremented Laplacian matrices are computed recurrently for
more samples. To achieve computation scalability when sampling large matrices,
we further rewrite the coefficient matrix as a sum of two separate components,
each of which exhibits block-diagonal structure that we exploit for alternating
block-wise sampling. Extensive experiments on both synthetic and real-world
datasets show that our graph sampling algorithm substantially outperforms
existing sampling schemes for matrix completion and reduces the completion
error, when combined with a range of modern matrix completion algorithms.
Optimal Stopping under Model Ambiguity: a Time-Consistent Equilibrium Approach
Authors: Yu-Jui Huang, Xiang Yu
Date: 2019-06-04 00:00:00
Tasks: Decision Making

An unconventional approach for optimal stopping under model ambiguity is introduced. Besides ambiguity itself, we take into account how ambiguity-averse an agent is. This inclusion of ambiguity attitude, via an $\alpha$-maxmin nonlinear expectation, renders the stopping problem time-inconsistent. We look for subgame perfect equilibrium stopping policies, formulated as fixed points of an operator. For a one-dimensional diffusion with drift and volatility uncertainty, we show that every equilibrium can be obtained through a fixed-point iteration. This allows us to capture much more diverse behavior, depending on an agent's ambiguity attitude, beyond the standard worst-case (or best-case) analysis. In a concrete example of real options valuation under volatility uncertainty, all equilibrium stopping policies, as well as the best one among them, are fully characterized. It demonstrates explicitly the effect of ambiguity attitude on decision making: the more ambiguity-averse, the more eager to stop -- so as to withdraw from the uncertain environment. The main result hinges on a delicate analysis of continuous sample paths in the canonical space and the capacity theory. To resolve measurability issues, a generalized measurable projection theorem, new to the literature, is also established.
Randomized Channel Sparsifying Hybrid Precoding for FDD Massive MIMO Systems
Date: 2019-12-12 00:00:00

We propose a novel randomized channel sparsifying hybrid precoding (RCSHP)
design to reduce the signaling overhead of channel estimation and the hardware
cost and power consumption at the base station (BS), in order to fully harvest
benefits of frequency division duplex (FDD) massive multiple-input
multiple-output (MIMO) systems. RCSHP allows time-sharing among multiple analog
precoders, each serving a compatible user group. The analog precoder is adapted
to the channel statistics to properly sparsify the channel for the associated
user group, such that the resulting effective channel (product of channel and
analog precoder) not only has enough spatial degrees of freedom (DoF) to serve
this group of users, but also can be accurately estimated under the limited
pilot budget. The digital precoder is adapted to the effective channel based on
the duality theory to facilitate the power allocation and exploit the spatial
multiplexing gain. We formulate the joint optimization of the time-sharing
factors and the associated sets of analog precoders and power allocations as a
general utility optimization problem, which considers the impact of effective
channel estimation error on the system performance. Then we propose an
efficient stochastic successive convex approximation algorithm to provably
obtain Karush-Kuhn-Tucker (KKT) points of this problem.
A Novel Bi-hemispheric Discrepancy Model for EEG Emotion Recognition
Date: 2019-05-11 00:00:00
Proceeding: arXiv:1906.01704 Search... Help | Advanced Search 2019 5
Tasks: EEG, EEG Emotion Recognition, Electroencephalogram (EEG), Emotion Recognition

The neuroscience study has revealed the discrepancy of emotion expression
between left and right hemispheres of human brain. Inspired by this study, in
this paper, we propose a novel bi-hemispheric discrepancy model (BiHDM) to
learn the asymmetric differences between two hemispheres for
electroencephalograph (EEG) emotion recognition. Concretely, we first employ
four directed recurrent neural networks (RNNs) based on two spatial
orientations to traverse electrode signals on two separate brain regions, which
enables the model to obtain the deep representations of all the EEG electrodes'
signals while keeping the intrinsic spatial dependence. Then we design a
pairwise subnetwork to capture the discrepancy information between two
hemispheres and extract higher-level features for final classification.
Besides, in order to reduce the domain shift between training and testing data,
we use a domain discriminator that adversarially induces the overall feature
learning module to generate emotion-related but domain-invariant feature, which
can further promote EEG emotion recognition. We conduct experiments on three
public EEG emotional datasets, and the experiments show that the new
state-of-the-art results can be achieved.
Optimal auction duration: A price formation viewpoint
Authors: Paul Jusselin, Thibaut Mastrolia, Mathieu Rosenbaum
Date: 2019-06-04 00:00:00

We consider an auction market in which market makers fill the order book during a given time period while some other investors send market orders. We define the clearing price of the auction as the price maximizing the exchanged volume at the clearing time according to the supply and demand of each market participants. Then we derive in a semi-explicit form the error made between this clearing price and the efficient price as a function of the auction duration. We study the impact of the behavior of market takers on this error. To do so we consider the case of naive market takers and that of rational market takers playing a Nash equilibrium to minimize their transaction costs. We compute the optimal duration of the auctions for 77 stocks traded on Euronext and compare the quality of price formation process under this optimal value to the case of a continuous limit order book. Continuous limit order books are found to be usually sub-optimal. However, in term of our metric, they only moderately impair the quality of price formation process. Order of magnitude of optimal auction durations is from 2 to 10 minutes.