pwc-archive/papers-with-abstracts huggingface dataset:
CNN-based approach for glaucoma diagnosis using transfer learning and LBP-based data augmentation
Date: 2020-02-19 00:00:00
Tasks: Data Augmentation, Transfer Learning

Glaucoma causes an irreversible damage to retinal nerve fibers which results
in vision loss, if undetected in early stage. Therefore, diagnosis of glaucoma
in its early stage may prevent further vision loss. In this paper, we propose a
convolutional neural network (CNN) based approach for automated glaucoma
diagnosis by employing retinal fundus images. This approach employs transfer
learning technique and local binary pattern (LBP) based data augmentation. In
the proposed approach, we employ Alexnet as a pre-trained CNN model which is
used for transfer learning. Initially, the proposed approach divides the fundus
image dataset into training and testing data. Further, the color fundus images
in training and testing data are separated into red (R), green (G), and blue
(B) channels. Additionally, the LBP-based data augmentation is performed on
training data. Specifically, we compute LPBs for each of the channel. Finally,
the augmented training data is used to train the CNN model via transfer
learning. In testing stage, the R, G, and B channels of test image are fed to
the trained CNN model which generates 3 decisions. We employ a decision level
fusion technique to combine the decisions obtained from the trained CNN model.
The experimental evaluation of the proposed approach on the public RIM-ONE
fundus image database, achieves state-of-the-art performance for glaucoma
diagnosis.
Geometrical Optical Illusion via Sub-Riemannian Geodesics in the Roto-Translation Group
Date: 2020-02-19 00:00:00
Tasks: Translation

We present a neuro-mathematical model for geometrical optical illusions
(GOIs), a class of illusory phenomena that consists in a mismatch of
geometrical properties of the visual stimulus and its associated percept. They
take place in the visual areas V1/V2 whose functional architecture have been
modelled in previous works by Citti and Sarti as a Lie group equipped with a
sub-Riemannian (SR) metric. Here we extend their model proposing that the
metric responsible for the cortical connectivity is modulated by the modelled
neuro-physiological response of simple cells to the visual stimulus, hence
providing a more biologically plausible model that takes into account a
presence of visual stimulus. Illusory contours in our model are described as
geodesics in the new metric. The model is confirmed by numerical simulations,
where we compute the geodesics via SR-Fast Marching.
Cointegration without Unit Roots
Authors: James A. Duffy, Jerome R. Simons
Date: 2020-02-19 00:00:00
Tasks: Unity

It has been known since Elliott (1998) that standard methods of inference on cointegrating relationships break down entirely when autoregressive roots are near but not exactly equal to unity. We consider this problem within the framework of a structural VAR, arguing this it is as much a problem of identification failure as it is of inference. We develop a characterisation of cointegration based on the impulse response function, which allows long-run equilibrium relationships to remain identified even in the absence of exact unit roots. Our approach also provides a framework in which the structural shocks driving the common persistent components continue to be identified via long-run restrictions, just as in an SVAR with exact unit roots. We show that inference on the cointegrating relationships is affected by nuisance parameters, in a manner familiar from predictive regression; indeed the two problems are asymptotically equivalent. By adapting the approach of Elliott, M\"uller and Watson (2015) to our setting, we develop tests that robustly control size while sacrificing little power (relative to tests that are efficient in the presence of exact unit roots).
FPGA-based Neural Network Accelerator for Millimeter-Wave Radio-over-Fiber Systems
Date: 2020-02-19 00:00:00
Tasks: GPU

With the rapidly-developing high-speed wireless communications, the 60 GHz
millimeter-wave frequency range and radio-over-fiber systems have been
investigated as a promising solution to deliver mm-wave signals. Neural
networks have been studied to improve the mm-wave RoF system performances at
the receiver side by suppressing linear and nonlinear impairments. However,
previous neural network studies in mm-wave RoF systems focus on the off-line
implementation with high-end GPUs , which is not practical for low
power-consumption, low-cost and limited computation platform applications. To
solve this issue, we investigate neural network hardware accelerator
implementations using the field programmable gate array (FPGA), taking
advantage of the low power consumption, parallel computation capability, and
reconfigurablity features of FPGA. Convolutional neural network (CNN) and
binary convolutional neural network (BCNN) hardware accelerators are
demonstrated. In addition, to satisfy the low-latency requirement in mm-wave
RoF systems and to enable the use of low-cost compact FPGA devices, a novel
inner parallel optimization method is proposed. Compared with the embedded
processor (ARM Cortex A9) execution latency, the CNN/BCNN FPGA-based hardware
accelerator reduces their latency by over 92%. Compared with non-optimized FPGA
implementations, the proposed optimization method reduces the processing
latency by over 44% for CNN and BCNN. Compared with the GPU implementation, the
latency of CNN implementation with the proposed optimization method is reduced
by 85.49%, while the power consumption is reduced by 86.91%. Although the
latency of BCNN implementation with the proposed optimization method is larger
compared with the GPU implementation, the power consumption is reduced by
86.14%. The FPGA-based neural network hardware accelerators provide a promising
solution for mm-wave RoF systems.
Inventory effects on the price dynamics of VSTOXX futures quantified via machine learning
Authors: Daniel Guterding
Date: 2020-02-19 00:00:00
Tasks: BIG-bench Machine Learning

The VSTOXX index tracks the expected 30-day volatility of the EURO STOXX 50 equity index. Futures on the VSTOXX index can, therefore, be used to hedge against economic uncertainty. We investigate the effect of trader inventory on the price of VSTOXX futures through a combination of stochastic processes and machine learning methods. We formulate a simple and efficient pricing methodology for VSTOXX futures, which assumes a Heston-type stochastic process for the underlying EURO STOXX 50 market. Under these dynamics, approximate analytical formulas for the implied volatility smile and the VSTOXX index have recently been derived. We use the EURO STOXX 50 option implied volatilities and the VSTOXX index value to estimate the parameters of this Heston model. Following the calibration, we calculate theoretical VSTOXX future prices and compare them to the actual market prices. While theoretical and market prices are usually in line, we also observe time periods, during which the market price does not agree with our Heston model. We collect a variety of market features that could potentially explain the price deviations and calibrate two machine learning models to the price difference: a regularized linear model and a random forest. We find that both models indicate a strong influence of accumulated trader positions on the VSTOXX futures price.
Price impact equilibrium with transaction costs and TWAP trading
Authors: Eunjung Noh, Kim Weston
Date: 2020-02-19 00:00:00

We prove the existence of an equilibrium in a model with transaction costs and price impact where two agents are incentivized to trade towards a target. The two types of frictions -- price impact and transaction costs -- lead the agents to two distinct changes in their optimal investment approach: price impact causes agents to continuously trade in smaller amounts, while transaction costs cause the agents to cease trading before the end of the trading period. As the agents lose wealth because of transaction costs, the exchange makes a profit. We prove the existence of a strictly positive optimal transaction cost from the exchange's perspective.
Exponential increase in mortality with age is a generic property of a simple model system of damage accumulation and death
Date: 2020-02-19 00:00:00

The risk of dying increases exponentially with age, in humans as well as in
many other species. This increase is often attributed to the "accumulation of
damage" known to occur in many biological structures and systems. The aim of
this paper is to describe a generic model of damage accumulation and death in
which mortality increases exponentially with age. The damage-accumulation
process is modeled by a stochastic process know as a queue, and risk of dying
is a function of the accumulated damage, i.e. length of the queue. The model
has four parameters and the main characteristics of the model are: (i) damage
occurs at random times with a constant high rate; (ii) the damage is repaired
at a limited rate, and consequently damage can accumulate; (iii) the efficiency
of the repair mechanism decays linearly with age; (iv) the risk of dying is a
function of the accumulated damage. Using standard results from the
mathematical theory of queues it is shown that there is an exponential
dependence between risk of dying and age in these models, and that this
dependency holds irrespective of how the damage-accumulation process is
modeled. Furthermore, the ways in which this exponential dependence is shaped
by the model parameters are also independent of the details of the damage
accumulation process. These generic features suggest that the model could be
useful when interpreting changes in the relation between age and mortality in
real data. To examplify, historical mortality data from Sweden are interpreted
in the light of the model. The decrease in mortality seen between cohorts born
in 1905, compared to those born in 1885, can be accounted for by higher
threshold to damage. This fits well with the many advances made in public
health during the 20th century.
Phase Imaging with Computational Specificity (PICS) for measuring dry mass changes in sub-cellular compartments
Date: 2020-03-13 00:00:00
Tasks: Specificity

Due to its specificity, fluorescence microscopy (FM) has become a
quintessential imaging tool in cell biology. However, photobleaching,
phototoxicity, and related artifacts continue to limit FM's utility. Recently,
it has been shown that artificial intelligence (AI) can transform one form of
contrast into another. We present PICS, a combination of quantitative phase
imaging and AI, which provides information about unlabeled live cells with high
specificity. Our imaging system allows for automatic training, while inference
is built into the acquisition software and runs in real-time. Applying the
computed fluorescence maps back to the QPI data, we measured the growth of both
nuclei and cytoplasm independently, over many days, without loss of viability.
Using a QPI method that suppresses multiple scattering, we measured the dry
mass content of individual cell nuclei within spheroids. In its current
implementation, PICS offers a versatile quantitative technique for continuous
simultaneous monitoring of individual cellular components in biological
applications where long-term label-free imaging is desirable.
Fragment-synthesis-based multiparty cryptographic key distribution over a public network
Date: 2020-02-19 00:00:00

A secure optical communication requires both high transmission efficiency and
high authentication performance, while existing cryptographic key distribution
protocols based on ghost imaging have many shortcomings. Here, based on
computational ghost imaging, we propose an interactive protocol that enables
multi-party cryptographic key distribution over a public network and
self-authentication by setting an intermediary that shares partial roles of the
server. This fragment-synthesis-based authentication method may facilitate the
remote distribution of cryptographic keys.
UAV Aided Search and Rescue Operation Using Reinforcement Learning
Date: 2020-02-19 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Owing to the enhanced flexibility in deployment and decreasing costs of
manufacturing, the demand for unmanned aerial vehicles (UAVs) is expected to
soar in the upcoming years. In this paper, we explore a UAV aided search and
rescue~(SAR) operation in indoor environments, where the GPS signals might not
be reliable. We consider a SAR scenario where the UAV tries to locate a victim
trapped in an indoor environment by sensing the RF signals emitted from a smart
device owned by the victim. To locate the victim as fast as possible, we
leverage tools from reinforcement learning~(RL). Received signal strength~(RSS)
at the UAV depends on the distance from the source, indoor shadowing, and
fading parameters, and antenna radiation pattern of the receiver mounted on the
UAV. To make our analysis more realistic, we model two indoor scenarios with
different dimensions using commercial ray-tracing software. Then, the
corresponding RSS values at each possible discrete UAV location are extracted
and used in a Q-learning framework. Unlike the traditional location-based
navigation approach that exploits GPS coordinates, our method uses the RSS to
define the states and rewards of the RL algorithm. We compare the performance
of the proposed method where directional and omnidirectional antennas are used.
The results reveal that the use of directional antennas provides faster
convergence rates than the omnidirectional antennas.
Modeling microbial cross-feeding at intermediate scale portrays community dynamics and species coexistence
Authors: Chen Liao, Tong Wang, Sergei Maslov, Joao B. Xavier
Date: 2020-02-19 00:00:00

Social interaction between microbes can be described at many levels of details, ranging from the biochemistry of cell-cell interactions to the ecological dynamics of populations. Choosing the best level to model microbial communities without losing generality remains a challenge. Here we propose to model cross-feeding interactions at an intermediate level between genome-scale metabolic models of individual species and consumer-resource models of ecosystems, which is suitable to empirical data. We applied our method to three published examples of multi-strain Escherichia coli communities with increasing complexity consisting of uni-, bi-, and multi-directional cross-feeding of either substitutable metabolic byproducts or essential nutrients. The intermediate-scale model accurately described empirical data and could quantify exchange rates elusive by other means, such as the byproduct secretions, even for a complex community of 14 amino acid auxotrophs. We used the three models to study each community's limits of robustness to perturbations such as variations in resource supply, antibiotic treatments and invasion by other "cheaters" species. Our analysis provides a foundation to quantify cross-feeding interactions from experimental data, and highlights the importance of metabolic exchanges in the dynamics and stability of microbial communities.
Simple post-translational circadian clock models from selective sequestration
Authors: Mark Byrne
Date: 2020-02-20 00:00:00

It is possible that there are post-translational circadian oscillators that continue functioning in the absence of negative feedback transcriptional repression in many cell types from diverse organisms. Apart from the KaiABC system from cyanobacteria, the molecular components and interactions required to create in-vitro ("test-tube") circadian oscillations in different cell types are currently unknown. Inspired by the KaiABC system, I provide "proof-of-principle" mathematical models that a protein with 2 (or more) modification sites which selectively sequesters an effector/cofactor molecule can function as a circadian time-keeper. The 2-site mechanism can be implemented using two relatively simple coupled non-linear ODEs in terms of site occupancy; the models do not require overly special fine-tuning of parameters for generating stable limit cycle oscillations.
The Fair Basis: Funding and capital in the reduced form framework
Authors: Wujiang Lou
Date: 2020-02-20 00:00:00
Tasks: Form, Position

A negative basis trade enters a long bond position and buys protection on the issuer of the bond through credit default swap (CDS), aiming at arbitrage profit due to the bond-CDS basis. To classic reduced form model theorists, the existence of the basis is an abnormality or merely liquidity noise. Such a view, however, fails to explain large basis trading losses incurred during the financial crisis. Employing a bond continuously hedged by CDS under a dynamic spread model with bond repo financing, we find that there is unhedged and unhedgeable residual jump to default risk that can't be diversified because of credit correlation. An economic capital approach has to apply and a charge on the use of capital follows. Together with the hedge funding cost, it allows us to better understand the basis's economics and to predict its fair level.
Derivatives Discounting Explained
Authors: Wujiang Lou
Date: 2020-02-20 00:00:00

Derivative pricing is about cash flow discounting at the riskfree rate. This teaching has lost its meaning post the financial crisis, due to the addition of extra value adjustments (XVA), which also made derivatives pricing and valuation a very difficult task for investors. This article recovers a properly defined discount rate that corresponds to different collateral and margin schemes. A binomial tree model is developed, enabling end-users to price in counterparty default and funding risk. Coherent XVAs, if needed, naturally result from decomposing the discount rate, and can be computed on the same tree.
Kalman Filtering With Censored Measurements
Date: 2020-02-20 00:00:00

This paper concerns Kalman filtering when the measurements of the process are
censored. The censored measurements are addressed by the Tobit model of Type I
and are one-dimensional with two censoring limits, while the (hidden) state
vectors are multidimensional. For this model, Bayesian estimates for the state
vectors are provided through a recursive algorithm of Kalman filtering type.
Experiments are presented to illustrate the effectiveness and applicability of
the algorithm. The experiments show that the proposed method outperforms other
filtering methodologies in minimizing the computational cost as well as the
overall Root Mean Square Error (RMSE) for synthetic and real data sets.
Designing Asymmetric Shift Operators for Decentralized Subspace Projection
Date: 2020-04-11 00:00:00

A large number of applications in wireless sensor networks include projecting
a vector of noisy observations onto a subspace dictated by prior information
about the field being monitored. In general, accomplishing such a task in a
centralized fashion, entails a large power consumption, congestion at certain
nodes, and suffers from robustness issues against possible node failures.
Computing such projections in a decentralized fashion is an alternative
solution that solves these issues. Recent works have shown that this task can
be done via the so-called graph filters where only local inter-node
communication is performed in a distributed manner using a graph shift
operator. Existing methods designed the graph filters for symmetric topologies.
However, in this paper, the design of the graph shift operators to perform
decentralized subspace projection for asymmetric topologies is studied.
Joint Interleaver and Modulation Design For Multi-User SWIPT-NOMA
Date: 2020-02-20 00:00:00

Radio frequency (RF) signals can be relied upon for conventional wireless
information transfer (WIT) and for challenging wireless power transfer (WPT),
which triggers the significant research interest in the topic of simultaneous
wireless information and power transfer (SWIPT). By further exploiting the
advanced non-orthogonal-multiple-access (NOMA) technique, we are capable of
improving the spectrum efficiency of the resource-limited SWIPT system. In our
SWIPT system, a hybrid access point (H-AP) superimposes the modulated symbols
destined to multiple WIT users by exploiting the power-domain NOMA, while WPT
users are capable of harvesting the energy carried by the superposition
symbols. In order to maximise the amount of energy transferred to the WPT
users, we propose a joint design of the energy interleaver and the
constellation rotation based modulator in the symbol-block level by
constructively superimposing the symbols destined to the WIT users in the power
domain. Furthermore, a transmit power allocation scheme is proposed to
guarantee the symbol-error-ratio (SER) of all the WIT users. By considering the
sensitivity of practical energy harvesters, the simulation results demonstrate
that our scheme is capable of substantially increasing the WPT performance
without any remarkable degradation of the WIT performance.
Combining Shrinkage and Sparsity in Conjugate Vector Autoregressive Models
Date: 2020-08-26 00:00:00

Conjugate priors allow for fast inference in large dimensional vector
autoregressive (VAR) models but, at the same time, introduce the restriction
that each equation features the same set of explanatory variables. This paper
proposes a straightforward means of post-processing posterior estimates of a
conjugate Bayesian VAR to effectively perform equation-specific covariate
selection. Compared to existing techniques using shrinkage alone, our approach
combines shrinkage and sparsity in both the VAR coefficients and the error
variance-covariance matrices, greatly reducing estimation uncertainty in large
dimensions while maintaining computational tractability. We illustrate our
approach by means of two applications. The first application uses synthetic
data to investigate the properties of the model across different
data-generating processes, the second application analyzes the predictive gains
from sparsification in a forecasting exercise for US data.
Optimal Resource Allocation for Dynamic Product Development Process via Convex Optimization
Date: 2020-09-19 00:00:00

Resource allocation is an essential aspect of successful Product Development
(PD). In this paper, we formulate the dynamic resource allocation of the PD
process as a convex optimization problem. Specially, we build and solve two
variants of this issue: the budget-constrained problem and the
performance-constrained problem. By using convex optimization, we propose a
framework to optimally solve large problem instances at a relatively small
computational cost. The solutions to both problems exhibit similar trends
regarding resource allocation decisions and performance evolution. Furthermore,
we show that the product architecture affects resource allocation, which in
turn affects the performance of the PD process. By introducing centrality
metrics for measuring the location of the modules and design rules within the
product architecture network, we find that resource allocation decisions
correlate to their metrics. These results provide simple, but powerful,
managerial guidelines for efficiently designing and managing the PD process.
Finally, for validating the model and its results, we introduce and solve two
design case studies for a mechanical manipulator and for an automotive
appearance design process.