pwc-archive/papers-with-abstracts huggingface dataset:
A financial market with singular drift and no arbitrage
Authors: Nacira Agram, Bernt Øksendal
Date: 2019-09-27 00:00:00

We study a financial market where the risky asset is modelled by a geometric It\^o-L\'{e}vy process, with a singular drift term. This can for example model a situation where the asset price is partially controlled by a company which intervenes when the price is reaching a certain lower barrier. See e.g. Jarrow & Protter for an explanation and discussion of this model in the Brownian motion case. As already pointed out by Karatzas & Shreve (in the continuous setting), this allows for arbitrages in the market. However, the situation in the case of jumps is not clear. Moreover, it is not clear what happens if there is a delay in the system. In this paper we consider a jump diffusion market model with a singular drift term modelled as the local time of a given process, and with a delay \theta>0 in the information flow available for the trader. We allow the stock price dynamics to depend on both a continuous process (Brownian motion) and a jump process (Poisson random measure). We believe that jumps and delays are essential in order to get more realistic financial market models. Using white noise calculus we compute explicitly the optimal consumption rate and portfolio in this case and we show that the maximal value is finite as long as the delay \theta> 0. This implies that there is no arbitrage in the market in that case. However, when \theta goes to 0, the value goes to infinity. This is in agreement with the above result that is an arbitrage when there is no delay. Our model is also relevant for high frequency trading issues. This is because high frequency trading often leads to intensive trading taking place on close to infinitesimal lengths of time, which in the limit corresponds to trading on time sets of measure 0. This may in turn lead to a singular drift in the pricing dynamics. See e.g. Lachapelle et al and the references therein.
Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions
Authors: Jau-er Chen, Chien-Hsun Huang, Jia-Jyun Tien
Date: 2019-09-27 00:00:00
Tasks: BIG-bench Machine Learning, quantile regression, regression

In this study, we investigate estimation and inference on a low-dimensional causal parameter in the presence of high-dimensional controls in an instrumental variable quantile regression. Our proposed econometric procedure builds on the Neyman-type orthogonal moment conditions of a previous study Chernozhukov, Hansen and Wuthrich (2018) and is thus relatively insensitive to the estimation of the nuisance parameters. The Monte Carlo experiments show that the estimator copes well with high-dimensional controls. We also apply the procedure to empirically reinvestigate the quantile treatment effect of 401(k) participation on accumulated wealth.
Suitability Analysis of Holographic vs Light Field and 2D Displays for Subjective Quality Assessment of Fourier Holograms
Date: 2019-09-27 00:00:00

Visual quality assessment of digital holograms is facing many challenges.
Main difficulties are related to the limited spatial resolution and angular
field of view of holographic displays in combination with the complexity of
steering and operating them for such tasks. Alternatively, non-holographic
displays - and in particular light-field displays - can be utilized to
visualize the numerically reconstructed content of a digital hologram. However,
their suitability as alternative for holographic displays has not been
validated. In this research, we have investigated this issue via a set of
comprehensive experiments. We used Fourier holographic principle to acquire a
diverse set of holograms, which were either computer-generated from point
clouds or optically recorded from real macroscopic objects. A final public data
set comprising 96 holograms was created using three compression methods which
encoded the holograms at four bit-depths. Three separate subjective-tests were
conducted using a holographic display, a light field display and a 2D display.
For these subjective experiments, a double stimulus, multi-perspective,
multi-depth subjective testing methodology was designed and implemented. The
tests show that the non-holographic displays indicate a higher sensitivity to
artifacts than the holographic display, though at the same time it is
demonstrated they are highly correlated. This indicates that the numerically
reconstructed holograms rendered on a light field or 2D display have a high
predictive value for the perceived quality on holographic display.
Allostery and cooperativity in multimeric proteins: bond-to-bond propensities in ATCase
Date: 2019-09-27 00:00:00

Aspartate carbamoyltransferase (ATCase) is a large dodecameric enzyme with
six active sites that exhibits allostery: its catalytic rate is modulated by
the binding of various substrates at distal points from the active sites. A
recently developed method, bond-to-bond propensity analysis, has proven capable
of predicting allosteric sites in a wide range of proteins using an
energy-weighted atomistic graph obtained from the protein structure and given
knowledge only of the location of the active site. Bond-to-bond propensity
establishes if energy fluctuations at given bonds have significant effects on
any other bond in the protein, by considering their propagation through the
protein graph. In this work, we use bond-to-bond propensity analysis to study
different aspects of ATCase activity using three different protein structures
and sources of fluctuations. First, we predict key residues and bonds involved
in the transition between inactive (T) and active (R) states of ATCase by
analysing allosteric substrate binding as a source of energy perturbations in
the protein graph. Our computational results also indicate that the effect of
multiple allosteric binding is non linear: a switching effect is observed after
a particular number and arrangement of substrates is bound suggesting a form of
long range communication between the distantly arranged allosteric sites.
Second, cooperativity is explored by considering a bisubstrate analogue as the
source of energy fluctuations at the active site, also leading to the
identification of highly significant residues to the T-R transition that
enhance cooperativity across active sites. Finally, the inactive (T) structure
is shown to exhibit a strong, non linear communication between the allosteric
sites and the interface between catalytic subunits, rather than the active
site.
Optimal Allocation of ESSs in Active Distribution Networks to achieve their Dispatchability
Date: 2020-04-14 00:00:00

This paper presents a method for the optimal siting and sizing of energy
storage systems (ESSs) in active distribution networks (ADNs) to achieve their
dispatchability. The problem formulation accounts for the uncertainty inherent
to the stochastic nature of distributed energy sources and loads. Thanks to the
operation of ESSs, the main optimization objective is to minimize the dispatch
error, which accounts for the mismatch between the realization and prediction
of the power profile at the ADN connecting point to the upper layer grid, while
respecting the grid voltages and ampacity constraints. The proposed formulation
relies on the so-called Augmented Relaxed Optimal Power Flow (AR-OPF) method:
it expresses a convex full AC optimal power flow, which is proven to provide a
global optimal and exact solution in the case of radial power grids. The AR-OPF
is coupled with the proposed dispatching control resulting in a two-level
optimization problem. In the first block, the site and size of the ESSs are
decided along with the level of dispatchability that the ADN can achieve. Then,
in the second block, the adequacy of the ESS allocations and the feasibility of
the grid operating points are verified over operating scenarios using the
Benders decomposition technique. Consequently, the optimal size and site of the
ESSs are adjusted. To validate the proposed method, simulations are conducted
on a real Swiss ADN hosting a large amount of stochastic Photovoltaic (PV)
generation.
Optimized Quantization in Distributed Graph Signal Filtering
Date: 2019-09-27 00:00:00
Tasks: Quantization

Distributed graph signal processing algorithms require the network nodes to
communicate by exchanging messages in order to achieve a common objective.
These messages have a finite precision in realistic networks, which may
necessitate to implement message quantization. Quantization, in turn, may
generate distortion and performance penalty in the distributed processing
tasks. This paper proposes a novel method for distributed graph filtering that
minimizes the error due to message quantization without compromising the
communication costs. It first bounds the exchanged messages and then allocates
a limited bit budget in an optimized way to the different messages and network
nodes. In particular, our novel quantization algorithm adapts to both the
network topology and the message importance in a distributed processing task.
Our results show that the proposed method is effective in minimizing the error
due to quantization and that it permits to outperform baseline distributed
algorithms when the bit budget is limited. They further show that errors
produced in nodes with high eccentricity or in the first steps of the
distributed algorithm contribute more to the global error. Also, sparse and
irregular graphs require more irregular bit distribution. Our method provides
one of the first quantization solutions for distributed graph processing, which
is able to adapt to the target task, the graph properties and the communication
constraints.
Frequency Stability Using MPC-based Inverter Power Control in Low-Inertia Power Systems
Date: 2020-07-27 00:00:00
Tasks: Model Predictive Control

The electrical grid is evolving from a network consisting of mostly
synchronous machines to a mixture of synchronous machines and inverter-based
resources such as wind, solar, and energy storage. This transformation has led
to a decrease in mechanical inertia, which necessitate a need for the new
resources to provide frequency responses through their inverter interfaces. In
this paper we proposed a new strategy based on model predictive control to
determine the optimal active-power set-point for inverters in the event of a
disturbance in the system. Our framework explicitly takes the hard constraints
in power and energy into account, and we show that it is robust to measurement
noise, limited communications and delay by using an observer to estimate the
model mismatches in real-time. We demonstrate the proposed controller
significantly outperforms an optimally tuned virtual synchronous machine on a
standard 39-bus system under a number of scenarios. In turn, this implies
optimized inverter-based resources can provide better frequency responses
compared to conventional synchronous machines.
Decision Models for Workforce and Technology Planning in Services
Date: 2019-09-27 00:00:00

Today's service companies operate in a technology-oriented and
knowledge-intensive environment while recruiting and training individuals from
an increasingly diverse population. One of the resulting challenges is ensuring
strategic alignment between their two key resources - technology and workforce
- through the resource planning and allocation processes. The traditional
hierarchical decision approach to resource planning and allocation considers
only technology planning as a strategic-level decision, with workforce
recruiting and training planning as a subsequent tactical-level decision.
However, two other decision approaches - joint and integrated - elevate
workforce planning to the same strategic level as technology planning. Thus we
investigate the impact of strategically aligning technology and workforce
decisions through the comparison of joint and integrated models to each other
and to a baseline hierarchical model in terms of the total cost. Numerical
experiments are conducted to characterize key features of solutions provided by
these approaches under conditions typically found in this type of service
company. Our results show that the integrated model is the lowest cost across
all conditions. This is because the integrated approach maintains a small but
skilled workforce that can operate new and more advanced technology with higher
capacity. However, the cost performance of the joint model is very close to the
integrated model under many conditions and is easier to implement
computationally and managerially, making it a good choice in many environments.
Managerial insights derived from this study can serve as a valuable guide for
choosing the proper decision approach for technology-oriented and
knowledge-intensive service companies.
Accurate Reduced-Order Models for Heterogeneous Coherent Generators
Date: 2020-11-16 00:00:00

We introduce a novel framework to approximate the aggregate frequency
dynamics of coherent generators. By leveraging recent results on dynamics
concentration of tightly connected networks, and frequency weighted balanced
truncation, a hierarchy of reduced-order models is developed. This hierarchy
provides increasing accuracy in the approximation of the aggregate system
response, outperforming existing aggregation techniques.
Distance Estimation Methods for a Practical Macroscale Molecular Communication System
Date: 2019-09-27 00:00:00
Tasks: parameter estimation, regression

Accurate estimation of the distance between the transmitter (TX) and the
receiver (RX) in molecular communication (MC) systems can provide faster and
more reliable communication. Existing theoretical models in the literature are
not suitable for distance estimation in a practical scenario. Furthermore,
deriving an analytical model is not easy due to effects such as boundary
conditions in the diffusion process, the initial velocity of the molecules and
unsteady flows. Therefore, five different practical methods comprising three
novel data analysis based methods and two supervised machine learning (ML)
methods, Multivariate Linear Regression (MLR) and Neural Network Regression
(NNR), are proposed for distance estimation at the RX side. In order to apply
the ML methods, a macroscale practical MC system, which consists of an electric
sprayer without a fan, alcohol molecules, an alcohol sensor and a
microcontroller, is established, and the received signals are recorded. A
feature extraction algorithm is proposed to utilize the measured signals as the
inputs in ML methods. The numerical results show that the ML methods outperform
the data analysis based methods in the root mean square error sense with the
cost of complexity. Moreover, the peak time based estimation, which is one of
the proposed data analysis based methods, yields better results with respect to
the other proposed four methods, as the distance increases. Given the
experimental data and fluid dynamics theory, a possible trajectory of the
molecules between the TX and RX is given. Our findings show that distance
estimation performance is jointly affected by unsteady flows and the
non-linearity of the sensor. According to our findings based on fluid dynamics,
it is evaluated that fluid dynamics should be taken into account for more
accurate parameter estimation in practical macroscale MC systems.
Quantum Annealing Algorithm for Expected Shortfall based Dynamic Asset Allocation
Authors: Samudra Dasgupta, Arnab Banerjee
Date: 2019-09-27 00:00:00

The 2008 mortgage crisis is an example of an extreme event. Extreme value theory tries to estimate such tail risks. Modern finance practitioners prefer Expected Shortfall based risk metrics (which capture tail risk) over traditional approaches like volatility or even Value-at-Risk. This paper provides a quantum annealing algorithm in QUBO form for a dynamic asset allocation problem using expected shortfall constraint. It was motivated by the need to refine the current quantum algorithms for Markowitz type problems which are academically interesting but not useful for practitioners. The algorithm is dynamic and the risk target emerges naturally from the market volatility. Moreover, it avoids complicated statistics like generalized pareto distribution. It translates the problem into qubit form suitable for implementation by a quantum annealer like D-Wave. Such QUBO algorithms are expected to be solved faster using quantum annealing systems than any classical algorithm using classical computer (but yet to be demonstrated at scale).
Stochastic Geometry of Network of Randomly Distributed Moving Vehicles on a Highway
Date: 2019-09-27 00:00:00

Vehicular ad-hoc networks (VANETs) have become an extensively studied topic
in contemporary research. One of the fundamental problems that has arisen in
such research is understanding the network statistical properties, such as the
cluster number distribution and the cluster size distribution. In this paper,
we analyze these characteristics in the case in which vehicles are located on a
straight road. Assuming the Rayleigh fading model and a probabilistic model of
intervehicle distance, we derive probabilistic distributions of the
aforementioned connectivity characteristics, as well as distributions of the
biggest cluster and the number of disconnected vehicles. All of the results are
confirmed by simulations carried out for the realistic values of parameters.
Evolution of Vehicle Network on a Highway
Date: 2019-09-27 00:00:00

One of the challenges related to the investigation of vehicular networks is
associated with predicting a network state regarding both short-term and
long-term network evolutionary changes. This paper analyzes a case in which
vehicles are located on a straight road, and the connectivity state between two
consecutive cars is determined by the Markov chain model with two states. The
transition probabilities of the considered model are explicitly expressed in
terms of known parameters of the network using the Wang-Moayery model. Within
the presented model, the network evolution is described in terms of
determinative parameters, such as average link duration, average cluster
lifetime, and a clusters' existence probability between two fixed moments of
time. In support of the theoretically obtained probabilistic distributions, the
results of numerical simulations are provided.
Monotonicity-Constrained Nonparametric Estimation and Inference for First-Price Auctions
Date: 2019-09-27 00:00:00

We propose a new nonparametric estimator for first-price auctions with
independent private values that imposes the monotonicity constraint on the
estimated inverse bidding strategy. We show that our estimator has a smaller
asymptotic variance than that of Guerre, Perrigne and Vuong's (2000) estimator.
In addition to establishing pointwise asymptotic normality of our estimator, we
provide a bootstrap-based approach to constructing uniform confidence bands for
the density function of latent valuations.
Information Closure Theory of Consciousness
Date: 2020-06-11 00:00:00

Information processing in neural systems can be described and analysed at
multiple spatiotemporal scales. Generally, information at lower levels is more
fine-grained and can be coarse-grained in higher levels. However, information
processed only at specific levels seems to be available for conscious
awareness. We do not have direct experience of information available at the
level of individual neurons, which is noisy and highly stochastic. Neither do
we have experience of more macro-level interactions such as interpersonal
communications. Neurophysiological evidence suggests that conscious experiences
co-vary with information encoded in coarse-grained neural states such as the
firing pattern of a population of neurons. In this article, we introduce a new
informational theory of consciousness: Information Closure Theory of
Consciousness (ICT). We hypothesise that conscious processes are processes
which form non-trivial informational closure (NTIC) with respect to the
environment at certain coarse-grained levels. This hypothesis implies that
conscious experience is confined due to informational closure from conscious
processing to other coarse-grained levels. ICT proposes new quantitative
definitions of both conscious content and conscious level. With the
parsimonious definitions and a hypothesise, ICT provides explanations and
predictions of various phenomena associated with consciousness. The
implications of ICT naturally reconciles issues in many existing theories of
consciousness and provides explanations for many of our intuitions about
consciousness. Most importantly, ICT demonstrates that information can be the
common language between consciousness and physical reality.
An Impedance Sensing Platform for Monitoring Heterogeneous Connectivity and Diagnostics in Lab-on-a-Chip Systems
Date: 2020-04-14 00:00:00

Reliable hardware connectivity is vital in heterogeneous integrated systems.
For example, in digital microfluidics lab-on-a-chip systems, there are hundreds
of physical connections required between a micro-electro-mechanical fabricated
device and the driving system that can be remotely located on a
printed-circuit-board. Unfortunately, the connection reliability cannot be
checked or monitored by vision-based detection methods that are commonly used
in the semiconductor industry. Therefore, a sensing platform that can be
seamlessly integrated into existing digital microfluidics systems and provide
real-time monitoring of multi-connectivity is highly desired. Here, we report
an impedance sensing platform that can provide fast detection of a single
physical connection in timescales of milli-seconds. Once connectivity is
established, the same set-up can be used to determine droplet location. The
sensing system can be scaled up to support multiple channels or applied to
other heterogeneously integrated systems that require real-time monitoring and
diagnostics of multi-connectivity systems.
Unified Attitude Determination Problem from Vector Observations and Hand-eye Measurements
Date: 2020-03-25 00:00:00

The hand-eye measurements have recently been proven to be very efficient for
spacecraft attitude determination relative to an ellipsoidal asteroid. However,
recent method does not guarantee full attitude observability for all
conditions. This paper refines this problem by taking the vector observations
into account so that the accuracy and robustness of the spacecraft attitude
estimation can be improved. The vector observations come from many sources
including visual perspective geometry, optical navigation and point clouds that
frequently occur in aerospace electronic systems. Completely closed-form
solutions along with their uncertainty descriptions are presented for the
proposed problem. Experiments using our simulated dataset and real-world
spacecraft measurements from NASA dawn spacecraft verify the effectiveness and
superiority of the derived solution.