pwc-archive/papers-with-abstracts huggingface dataset:
Strategic Insider Trading Equilibrium with a Non-fiduciary Market Maker
Authors: Knut Aase, Bernt Øksendal
Date: 2019-08-23 00:00:00

The continuous-time version of Kyle's (1985) model is studied, in which market makers are not fiduciaries. They have some market power which they utilize to set the price to their advantage, resulting in positive expected profits. This has several implications for the equilibrium, the most important being that by setting a modest fee conditional of the order flow, the market maker is able to obtain a profit of the order of magnitude, and even better than, a perfectly informed insider. Our model also indicates why speculative prices are more volatile than predicted by fundamentals.
Nonparametric estimation of causal heterogeneity under high-dimensional confounding
Date: 2019-08-23 00:00:00
Tasks: BIG-bench Machine Learning, Vocal Bursts Intensity Prediction

This paper considers the practically important case of nonparametrically
estimating heterogeneous average treatment effects that vary with a limited
number of discrete and continuous covariates in a selection-on-observables
framework where the number of possible confounders is very large. We propose a
two-step estimator for which the first step is estimated by machine learning.
We show that this estimator has desirable statistical properties like
consistency, asymptotic normality and rate double robustness. In particular, we
derive the coupled convergence conditions between the nonparametric and the
machine learning steps. We also show that estimating population average
treatment effects by averaging the estimated heterogeneous effects is
semi-parametrically efficient. The new estimator is an empirical example of the
effects of mothers' smoking during pregnancy on the resulting birth weight.
Dynamic Programming with State-Dependent Discounting
Date: 2020-10-14 00:00:00

This paper extends the core results of discrete time infinite horizon dynamic
programming to the case of state-dependent discounting. We obtain a condition
on the discount factor process under which all of the standard optimality
results can be recovered. We also show that the condition cannot be
significantly weakened. Our framework is general enough to handle complications
such as recursive preferences and unbounded rewards. Economic and financial
applications are discussed.
Frequency-Domain Modeling of OFDM Transmission with Insufficient Cyclic Prefix using Toeplitz Matrices
Date: 2019-08-23 00:00:00

A novel mathematical framework is proposed to model Intersymbol Interference
(ISI) phenomenon in wireless communication systems based on Orthogonal
Frequency Division Multiplexing (OFDM) with or without cyclic prefix. The
framework is based on a new formula to calculate the Fast Fourier Transform
(FFT) of a triangular Toeplitz matrix, which is derived and proven in this
paper. It is shown that distortion inducted by the ISI from a given subcarrier
is the most significant for the closest subcarriers and the contribution decays
as the distance between subcarriers grows. According to numerical experiments,
knowledge of ISI coefficients concentrated around the diagonal of Channel
Frequency Response (CFR) matrix improves the receiver's error floor
significantly. The potential use of the framework for real-time frequency
domain channel simulation was also investigated and demonstrated to be more
efficient than conventional time domain Tapped Delay Line (TDL) model when a
number of simulated users is high.
Power Efficient Discontinuous Reception in THz and mmWave Wireless Systems
Date: 2019-08-23 00:00:00
Tasks: Diversity, Link Prediction

Discontinuous reception (DRX), where a user equip-ment (UE) temporarily
disables its receiver, is a critical power saving feature in modern cellular
systems. DRX is likely tobe particularly aggressively used in the mmWave and
THzfrequencies due to the high front end power consumption. A keychallenge of
DRX in these frequencies is that individual links are directional and highly
susceptible to blockage. MmWave and THz UEs will therefore likely need to
monitor multiple cells in multiple directions to ensure continuous reliable
connectivity.This work proposes a novel, heuristic algorithm to dynamically
select the cells to monitor to attempt to optimally trade-off link reliability
and power consumption. The paper provides preliminary estimates of connected
mode DRX mode consumption using detailed and realistic statistical models of
blockers at both 28 and 140 GHz. It is found that although blockage dynamics
are faster at 140 GHz, reliable connectivity at low power can be maintained
with sufficient macro-diversity and link prediction
Non Cooperative Game Theoretic Approach for Residential Energy Management in Smart Grid
Date: 2019-08-23 00:00:00
Tasks: energy management, Management, Scheduling

Demand side management (DSM) is one of the main functionalities of the smart
grid as it allows the consumer to adjust its energy consumption for an
efficient energy management. Most of the existing DSM techniques aim at
minimizing the energy cost while not considering the comfort of consumers.
Therefore, maintaining a trade-off between these two conflicting objectives is
still a challenging task. This paper proposes a novel DSM approach for
residential consumers based on a non-cooperative game theoretic approach, where
each player is encouraged to reshape its electricity consumption pattern
through the dynamic pricing policy applied by the smart grid operator. The
players are guided to select the best strategy that consists of scheduling
their electric appliances in order to minimize the daily energy cost and their
discomfort level. The Nash Equilibrium of the energy management game is
achieved using Non-Sorting Genetic Algorithm NSGA-II. Simulation results show
the effectiveness of the distributed non cooperative game approach for the
residential energy management problem where an appreciable energy cost
reduction is reached while maintaining the discomfort in an acceptable level.
Dyadic Regression
Date: 2019-08-23 00:00:00
Tasks: regression

Dyadic data, where outcomes reflecting pairwise interaction among sampled
units are of primary interest, arise frequently in social science research.
Regression analyses with such data feature prominently in many research
literatures (e.g., gravity models of trade). The dependence structure
associated with dyadic data raises special estimation and, especially,
inference issues. This chapter reviews currently available methods for
(parametric) dyadic regression analysis and presents guidelines for empirical
researchers.
Learning to Demodulate from Few Pilots via Offline and Online Meta-Learning
Authors: Sangwoo Park, Hyeryung Jang, Osvaldo Simeone, Joonhyuk Kang
Date: 2019-08-23 00:00:00
Tasks: Meta-Learning

This paper considers an Internet-of-Things (IoT) scenario in which devices sporadically transmit short packets with few pilot symbols over a fading channel. Devices are characterized by unique transmission non-idealities, such as I/Q imbalance. The number of pilots is generally insufficient to obtain an accurate estimate of the end-to-end channel, which includes the effects of fading and of the transmission-side distortion. This paper proposes to tackle this problem by using meta-learning. Accordingly, pilots from previous IoT transmissions are used as meta-training data in order to train a demodulator that is able to quickly adapt to new end-to-end channel conditions from few pilots. Various state-of-the-art meta-learning schemes are adapted to the problem at hand and evaluated, including Model-Agnostic Meta-Learning (MAML), First-Order MAML (FOMAML), REPTILE, and fast Context Adaptation VIA meta-learning (CAVIA). Both offline and online solutions are developed. In the latter case, an integrated online meta-learning and adaptive pilot number selection scheme is proposed. Numerical results validate the advantages of meta-learning as compared to training schemes that either do not leverage prior transmissions or apply a standard joint learning algorithms on previously received data.
Web-enabled Intelligent System for Continuous Sensor Data Processing and Visualization
Date: 2019-08-24 00:00:00
Tasks: Decision Making

A large number of sensors deployed in recent years in various setups and
their data is readily available in dedicated databases or in the cloud. Of
particular interest is real-time data processing and 3D visualization in
web-based user interfaces that facilitate spatial information understanding and
sharing, hence helping the decision making process for all the parties
involved. In this research, we provide a prototype system for near real-time,
continuous X3D-based visualization of processed sensor data for two significant
applications: thermal monitoring for residential/commercial buildings and
nitrogen cycle monitoring in water beds for aquaponics systems. As sensors are
sparsely placed, in each application, where they collect data for large periods
(of up to one year), we employ a Finite Differences Method and a Neural
Networks model to approximate data distribution in the entire volume.
Constraint Qualifications in Partial Identification
Authors: Hiroaki Kaido, Francesca Molinari, Jörg Stoye
Date: 2019-08-24 00:00:00
Tasks: Econometrics, Relation

The literature on stochastic programming typically restricts attention to problems that fulfill constraint qualifications. The literature on estimation and inference under partial identification frequently restricts the geometry of identified sets with diverse high-level assumptions. These superficially appear to be different approaches to closely related problems. We extensively analyze their relation. Among other things, we show that for partial identification through pure moment inequalities, numerous assumptions from the literature essentially coincide with the Mangasarian-Fromowitz constraint qualification. This clarifies the relation between well-known contributions, including within econometrics, and elucidates stringency, as well as ease of verification, of some high-level assumptions in seminal papers.
Heat kernels with functional connectomes reveal atypical energy transport in peripheral subnetworks in autism
Date: 2019-09-25 00:00:00

Autism is increasing in prevalence and is a neurodevelopmental disorder
characterised by impairments in communication skills and social behaviour.
Connectomes enable a systems-level representation of the brain with recent
interests in understanding the distributed nature of higher order cognitive
function using modules or subnetworks. By dividing the connectome according to
a central component of the brain critical for its function (it's hub), we
investigate network organisation in autism from hub through to peripheral
subnetworks. We complement this analysis by extracting features of energy
transport computed from heat kernels fitted with increasing time steps. This
heat kernel framework is advantageous as it can capture the energy transported
in all direct and indirect pathways between pair-wise regions over 'time', with
features that have correspondence to small-world properties. We apply our
framework to resting-state functional MRI connectomes from a large, publically
available autism dataset, ABIDE. We show that energy propagating through the
brain over time are different between subnetworks, and that heat kernel
features significantly differ between autism and controls. Furthermore, the hub
was functionally preserved and similar to controls, however, increasing
statistical significance between groups was found in increasingly peripheral
subnetworks. Our results support the increasing opinion of non-hub regions
playing an important role in functional organisation. This work shows that
analysing autism by subnetworks with the heat kernel reflects the atypical
activations in peripheral regions as alterations in energy dispersion and may
provide useful features towards understanding the distributed impact of this
disorder on the functional connectome.
A simple stochastic model to describe the evolution over time of core genome SNP GC content in prokaryotes
Date: 2019-08-24 00:00:00

Genomes in living organisms consist of the nucleotides adenine (A), guanine
(G), cytosine (C) and thymine (T). All prokaryotes have genomes consisting of
double-stranded DNA, where the A's and G's (purines) of one strand bind
respectively to the T's and C's (pyrimidines) of the other. As such, the number
of A's on one strand nearly equals the number of T's on the other, and the same
is true of one strand's G's and the other's C's. Globally, this relationship is
formalized as Chargaff's first parity rule; its strandwise equivalent is
Chargaff's second parity rule. Therefore, the GC content of any double-stranded
DNA genome can be expressed as %GC=100%-%AT. Variation in prokaryotic GC
content can be substantial between taxa but is generally small within microbial
genomes. This variation has been found to correlate with both phylogeny and
environmental factors. Since novel single-nucleotide polymorphisms (SNPs)
within genomes are at least partially linked to the environment, SNP GC content
can be considered a compound measure of an organism's environmental influences,
lifestyle and phylogeny. We present a mathematical model that describes how SNP
GC content in microbial genomes evolves over time as a function of the AT->GC
and GC->AT mutation rates with Gaussian white noise disturbances. The model
suggests that, in non-recombining bacteria, mutations can first accumulate
unnoticeably and then abruptly fluctuate out of control. Thus, minuscule
variations in mutation rates can suddenly become unsustainable, ultimately
driving a species to extinction if not counteracted early enough. This model,
which is suited specifically to symbiotic prokaryotes, conforms to scenarios
predicted by Muller's ratchet and may suggest that this is not always a
gradual, degrading process.
An I + PI Controller Structure for Integrating Processes with Dead-Time: Application to Depth Control of an Autonomous Underwater Vehicle
Date: 2019-08-25 00:00:00

The paper presents a feedforward plus feedback controller structure with I
and PI controllers for control of an integrating process with dead time.
Guidelines for controller gain selection based on time domain specifications of
damping factor and natural frequency are provided along with simulations
indicating the selectivity of process response. The utility of proposed
controller structure is shown by simulating the depth control of a nonlinear
autonomous underwater vehicle system by the proposed controller structure.
An Age-Dependent Model for Dengue Transmission: Analysis and Comparison to Field Data from Semarang, Indonesia
Date: 2019-08-25 00:00:00

Medical statistics reveal a significant dependence of hospitalized dengue
patient on the patient's age. To incorporate an age-dependence into a
mathematical model, we extend the classical ODE system of disease dynamics to a
PDE system. The equilibrium distribution is then determined by the fixed points
of resulting integro-differential equations. In this paper we use an extension
of the concept of the basic reproductive number to characterize parameter
regimes, where either only the disease-free or an endemic equilibrium exists.
Using rather general and minimal assumptions on the population distribution and
on the age-dependent transmission rate, we prove the existence of those
equilibria. Furthermore, we are able to prove the convergence of an iteration
scheme to compute the endemic equilibrium. To validate our model, we use
existing data from the city of Semarang, Indonesia for comparison and to
identify the model parameters.
UAV-Enabled Uplink Non-Orthogonal Multiple Access System: Joint Deployment and Power Control
Date: 2019-08-25 00:00:00

In order to overcome the inherent latency in multi-user unmanned aerial
vehicle (UAV) networks with orthogonal multiple access (OMA). In this paper, we
investigate the UAV enabled uplink non-orthogonal multiple access (NOMA)
network, where a UAV is deployed to collect the messages transmitted by ground
users. In order to maximize the sum rate of all users and to meet the quality
of service (QoS) requirement, we formulate an optimization problem, in which
the UAV deployment position and the power control are jointly optimized. This
problem is non-convex and some variables are binary, and thus it is a typical
NP hard problem. In this paper, an iterative algorithm is proposed with the
assistance of successive convex approximate (SCA) technique and the penalty
function method. In order to reduce the high computational complexity of the
iterative algorithm, a low complexity approximation algorithm is then proposed,
which can achieve a similar performance compared to the iterative algorithm.
Compared with OMA scheme and conventional NOMA scheme, numerical results show
that our proposed algorithms can efficiently improve the sum rate.
Surround Inhibition Mechanism by Deep Learning
Date: 2019-08-25 00:00:00
Tasks: Deep Learning

In the sensation of tones, visions and other stimuli, the "surround
inhibition mechanism" (or "lateral inhibition mechanism") is crucial. The
mechanism enhances the signals of the strongest tone, color and other stimuli,
by reducing and inhibiting the surrounding signals, since the latter signals
are less important. This surround inhibition mechanism is well studied in the
physiology of sensor systems. The neural network with two hidden layers in
addition to input and output layers is constructed; having 60 neurons (units)
in each of the four layers. The label (correct answer) is prepared from an
input signal by applying seven times operations of the "Hartline mechanism",
that is, by sending inhibitory signals from the neighboring neurons and
amplifying all the signals afterwards. The implication obtained by the deep
learning of this neural network is compared with the standard physiological
understanding of the surround inhibition mechanism.
A Coupled Karhunen--Loève and Anisotropic Sparse Grid Interpolation Method for the Probabilistic Load Flow Problem
Authors: Brandon Johnson, Nathan L. Gibson, Eduardo Cotilla-Sanchez
Date: 2019-08-25 00:00:00
Tasks: valid

In the traditional load flow analysis, a key assumption is that the input variables, i.e., generator output and customer demand, are fixed in time and the associated response has no variability. This assumption, however, is no longer valid as the adoption of renewable energy resources add more variability and uncertainty to the modern electrical system. Addressing these concerns is the definition of the Probabilistic Load Flow (PLF) problem. The challenge of the PLF problem lies in handling high-dimensional input uncertainties and the non-linearity of the load flow equations. The most straightforward way to address these problems, but at the cost of computational time, is to perform a Monte Carlo method. This work, however, solves these problems -- accuracy, high-dimensionality, and computational time -- with a coupled Karhunen-Lo\`eve (KL) expansion and Anisotropic Sparse Grid algorithm. The proposed method is implemented and tested on the IEEE 118-bus test system and a modernized version of the IEEE Reliability Test System - 1996, the Reliability Test System - Grid Modernization Lab Consortium (RTS-GMLC). Results for the 194-dimensional case show a decrease in computational time when compared to the 10,000 sample Monte Carlo method given a bound on mean and standard deviation error.
Machine-learning enables Image Reconstruction and Classification in a "see-through" camera
Date: 2019-08-25 00:00:00
Tasks: BIG-bench Machine Learning, Classification, Image Reconstruction

We demonstrate that image reconstruction can be achieved via a convolutional
neural network for a "see-through" computational camera comprised of a
transparent window and a CMOS image sensor. Furthermore, we compared
classification results using a classifier network for the raw sensor data vs
the reconstructed images. The results suggest that similar classification
accuracy is likely possible in both cases with appropriate network
optimizations. All networks were trained and tested for the MNIST (6 classes),
EMNIST and the Kanji49 datasets.
A multi-modal neural network for learning cis and trans regulation of stress response in yeast
Date: 2019-08-26 00:00:00

Deciphering gene regulatory networks is a central problem in computational
biology. Here, we explore the use of multi-modal neural networks to learn
predictive models of gene expression that include cis and trans regulatory
components. We learn models of stress response in the budding yeast
Saccharomyces cerevisiae. Our models achieve high performance and substantially
outperform other state-of-the-art methods such as boosting algorithms that use
pre-defined cis-regulatory features. Our model learns several cis and trans
regulators including well-known master stress response regulators. We use our
models to perform in-silico TF knock-out experiments and demonstrate that
in-silico predictions of target gene changes correlate with the results of the
corresponding TF knockout microarray experiment.
Multichannel signal detection in interference and noise when signal mismatch happens
Date: 2019-08-26 00:00:00

In this paper, we consider the problem of detecting a multichannel signal in
interference and noise when signal mismatch happens. We first propose two
selective detectors, since their strong selectivity is preferred in some
situations. However, these two detectors would not be suitable candidates if a
robust detector is needed. To overcome this shortcoming, we then devise a
tunable detector, which is parametrized by a non-negative scaling factor,
referred to as the tunable parameter. By adjusting the tunable parameter, the
proposed detector can smoothly change its capability in rejecting or robustly
detecting a mismatch signal. Moreover, one selective detector and the tunable
detector with an appropriate tunable parameter can provide nearly the same
detection performance as existing detectors in the absence of signal mismatch.
We obtain analytical expressions for the probabilities of detection (PDs) and
probabilities of false alarm (PFAs) of the three proposed detectors, which are
verified by Monte Carlo simulations.
Grid Influenced Peer-to-Peer Energy Trading
Date: 2019-08-26 00:00:00
Tasks: energy trading

This paper proposes a peer-to-peer energy trading scheme that can help the
centralized power system to reduce the total electricity demand of its
customers at the peak hour. To do so, a cooperative Stackelberg game is
formulated, in which the centralized power system acts as the leader that needs
to decide on a price at the peak demand period to incentivize prosumers to not
seeking any energy from it. The prosumers, on the other hand, act as followers
and respond to the leader's decision by forming suitable coalitions with
neighboring prosumers in order to participate in P2P energy trading to meet
their energy demand. The properties of the proposed Stackelberg game are
studied. It is shown that the game has a unique and stable Stackelberg
equilibrium, as a result of the stability of prosumers' coalitions. At the
equilibrium, the leader chooses its strategy using a derived closed-form
expression, while the prosumers choose their equilibrium coalition structure.
An algorithm is proposed that enables the centralized power system and the
prosumers to reach the equilibrium solution. Numerical case studies demonstrate
the beneficial properties of the proposed scheme.
A Reference Governor for Nonlinear Systems with Disturbance Inputs Based on Logarithmic Norms and Quadratic Programming
Date: 2019-08-26 00:00:00
Tasks: Prediction

This note describes a reference governor design for a continuous-time
nonlinear system with an additive disturbance. The design is based on
predicting the response of the nonlinear system by the response of a linear
model with a set-bounded prediction error, where a state-and-input dependent
bound on the prediction error is explicitly characterized using logarithmic
norms. The online optimization is reduced to a convex quadratic program with
linear inequality constraints. Two numerical examples are reported.