pwc-archive/papers-with-abstracts huggingface dataset:
Affine Jump-Diffusions: Stochastic Stability and Limit Theorems
Date: 2018-10-31 00:00:00
Tasks: parameter estimation

Affine jump-diffusions constitute a large class of continuous-time stochastic
models that are particularly popular in finance and economics due to their
analytical tractability. Methods for parameter estimation for such processes
require ergodicity in order establish consistency and asymptotic normality of
the associated estimators. In this paper, we develop stochastic stability
conditions for affine jump-diffusions, thereby providing the needed
large-sample theoretical support for estimating such processes. We establish
ergodicity for such models by imposing a `strong mean reversion' condition and
a mild condition on the distribution of the jumps, i.e. the finiteness of a
logarithmic moment. Exponential ergodicity holds if the jumps have a finite
moment of a positive order. In addition, we prove strong laws of large numbers
and functional central limit theorems for additive functionals for this class
of models.
Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models
Date: 2018-10-31 00:00:00

Partial mean with generated regressors arises in several econometric
problems, such as the distribution of potential outcomes with continuous
treatments and the quantile structural function in a nonseparable triangular
model. This paper proposes a nonparametric estimator for the partial mean
process, where the second step consists of a kernel regression on regressors
that are estimated in the first step. The main contribution is a uniform
expansion that characterizes in detail how the estimation error associated with
the generated regressor affects the limiting distribution of the marginal
integration estimator. The general results are illustrated with two examples:
the generalized propensity score for a continuous treatment (Hirano and Imbens,
2004) and control variables in triangular models (Newey, Powell, and Vella,
1999; Imbens and Newey, 2009). An empirical application to the Job Corps
program evaluation demonstrates the usefulness of the method.
Precise asymptotics: robust stochastic volatility models
Authors: Peter K. Friz, Paul Gassiat, Paolo Pigato
Date: 2018-11-01 00:00:00

We present a new methodology to analyze large classes of (classical and rough) stochastic volatility models, with special regard to short-time and small noise formulae for option prices. Our main tool is the theory of regularity structures, which we use in the form of [Bayer et al; A regularity structure for rough volatility, 2017]. In essence, we implement a Laplace method on the space of models (in the sense of Hairer), which generalizes classical works of Azencott and Ben Arous on path space and then Aida, Inahama--Kawabi on rough path space. When applied to rough volatility models, e.g. in the setting of [Forde-Zhang, Asymptotics for rough stochastic volatility models, 2017], one obtains precise asymptotic for European options which refine known large deviation asymptotics.
Robust risk aggregation with neural networks
Authors: Stephan Eckstein, Michael Kupper, Mathias Pohl
Date: 2018-11-01 00:00:00

We consider settings in which the distribution of a multivariate random variable is partly ambiguous. We assume the ambiguity lies on the level of the dependence structure, and that the marginal distributions are known. Furthermore, a current best guess for the distribution, called reference measure, is available. We work with the set of distributions that are both close to the given reference measure in a transportation distance (e.g. the Wasserstein distance), and additionally have the correct marginal structure. The goal is to find upper and lower bounds for integrals of interest with respect to distributions in this set. The described problem appears naturally in the context of risk aggregation. When aggregating different risks, the marginal distributions of these risks are known and the task is to quantify their joint effect on a given system. This is typically done by applying a meaningful risk measure to the sum of the individual risks. For this purpose, the stochastic interdependencies between the risks need to be specified. In practice the models of this dependence structure are however subject to relatively high model ambiguity. The contribution of this paper is twofold: Firstly, we derive a dual representation of the considered problem and prove that strong duality holds. Secondly, we propose a generally applicable and computationally feasible method, which relies on neural networks, in order to numerically solve the derived dual problem. The latter method is tested on a number of toy examples, before it is finally applied to perform robust risk aggregation in a real world instance.
Deep Learning for Tube Amplifier Emulation
Date: 2019-02-20 00:00:00
Tasks: Deep Learning

Analog audio effects and synthesizers often owe their distinct sound to
circuit nonlinearities. Faithfully modeling such significant aspect of the
original sound in virtual analog software can prove challenging. The current
work proposes a generic data-driven approach to virtual analog modeling and
applies it to the Fender Bassman 56F-A vacuum-tube amplifier. Specifically, a
feedforward variant of the WaveNet deep neural network is trained to carry out
a regression on audio waveform samples from input to output of a SPICE model of
the tube amplifier. The output signals are pre-emphasized to assist the model
at learning the high-frequency content. The results of a listening test suggest
that the proposed model accurately emulates the reference device. In
particular, the model responds to user control changes, and faithfully
restitutes the range of sonic characteristics found across the configurations
of the original device.
Pymc-learn: Practical Probabilistic Machine Learning in Python
Authors: Emaasit Daniel
Date: 2018-10-31 00:00:00
Tasks: BIG-bench Machine Learning

$\textit{Pymc-learn}$ is a Python package providing a variety of
state-of-the-art probabilistic models for supervised and unsupervised machine
learning. It is inspired by $\textit{scikit-learn}$ and focuses on bringing
probabilistic machine learning to non-specialists. It uses a general-purpose
high-level language that mimics $\textit{scikit-learn}$. Emphasis is put on
ease of use, productivity, flexibility, performance, documentation, and an API
consistent with $\textit{scikit-learn}$. It depends on $\textit{scikit-learn}$
and $\textit{pymc3}$ and is distributed under the new BSD-3 license,
encouraging its use in both academia and industry. Source code, binaries, and
documentation are available on http://github.com/pymc-learn/pymc-learn.
Modeling tropotaxis in ant colonies: recruitment and trail formation
Date: 2019-08-16 00:00:00

We propose an active walker model for the motion of individual ants
communicating via chemical signals. It is assumed that communication takes the
form of a time-dependent pheromone field that feedbacks into the motion ants
through tropotaxis: individuals can sense the gradient of the pheromone
concentration field and adjust their orientation accordingly. The individual
model takes the form of a Langevin system of equations in polar coordinates
driven by two-dimensional Gaussian fluctuations and with orientation changes in
response to two pheromone fields: one emanating from the nest, and other
actively produced by ants in their nest-bound journey after finding a food
source. We explicitly track the evolution of both fields in three dimensions.
The proposed tropotaxis model relating the pheromone field to the orientation
changes is similar to Weber's law, but depends explicitly only on the gradient
of the pheromone concentration. We identify ranges of values for the model
parameters that yield the emergence of two key foraging patterns: successful
recruitment to newly found sources, and colony-wide trail networks.
MDFS - MultiDimensional Feature Selection
Authors: Piliszek Radosław, Mnich Krzysztof, Migacz Szymon, Tabaszewski Paweł, Sułecki Andrzej, Polewko-Klim Aneta, Rudnicki Witold
Date: 2018-10-31 00:00:00
Tasks: feature selection

Identification of informative variables in an information system is often
performed using simple one-dimensional filtering procedures that discard
information about interactions between variables. Such approach may result in
removing some relevant variables from consideration. Here we present an R
package MDFS (MultiDimensional Feature Selection) that performs identification
of informative variables taking into account synergistic interactions between
multiple descriptors and the decision variable. MDFS is an implementation of an
algorithm based on information theory. Computational kernel of the package is
implemented in C++. A high-performance version implemented in CUDA C is also
available. The applications of MDFS are demonstrated using the well-known
Madelon dataset that has synergistic variables by design. The dataset comes
from the UCI Machine Learning Repository. It is shown that multidimensional
analysis is more sensitive than one-dimensional tests and returns more reliable
rankings of importance.
Understanding Deep Neural Networks Using Topological Data Analysis
Authors: Goldfarb Daniel
Date: 2018-10-31 00:00:00
Tasks: Topological Data Analysis

Deep neural networks (DNN) are black box algorithms. They are trained using a
gradient descent back propagation technique which trains weights in each layer
for the sole goal of minimizing training error. Hence, the resulting weights
cannot be directly explained. Using Topological Data Analysis (TDA) we can get
an insight on how the neural network is thinking, specifically by analyzing the
activation values of validation images as they pass through each layer.
Principled Design and Implementation of Steerable Detectors
Authors: Julien Fageot, Virginie Uhlmann, Zsuzsanna Püspöki, Benjamin Beck, Michael Unser, Adrien Depeursinge
Date: 2018-10-23 00:00:00

We provide a complete pipeline for the detection of patterns of interest in an image. In our approach, the patterns are assumed to be adequately modeled by a known template, and are located at unknown positions and orientations that we aim at retrieving. We propose a continuous-domain additive image model, where the analyzed image is the sum of the patterns to localize and a background with self-similar isotropic power-spectrum. We are then able to compute the optimal filter fulfilling the SNR criterion based on one single template and background pair: it strongly responds to the template while being optimally decoupled from the background model. In addition, we constrain our filter to be steerable, which allows for a fast template detection together with orientation estimation. In practice, the implementation requires to discretize a continuous-domain formulation on polar grids, which is performed using quadratic radial B-splines. We demonstrate the practical usefulness of our method on a variety of template approximation and pattern detection experiments. We show that the detection performance drastically improves when we exploit the statistics of the background via its power-spectrum decay, which we refer to as spectral-shaping. The proposed scheme outperforms state-of-the-art steerable methods by up to 50% of absolute detection performance.
ADEPOS: Anomaly Detection based Power Saving for Predictive Maintenance using Edge Computing
Date: 2018-10-30 00:00:00
Tasks: Anomaly Detection, Edge-computing

In industry 4.0, predictive maintenance(PM) is one of the most important
applications pertaining to the Internet of Things(IoT). Machine learning is
used to predict the possible failure of a machine before the actual event
occurs. However, the main challenges in PM are (a) lack of enough data from
failing machines, and (b) paucity of power and bandwidth to transmit sensor
data to cloud throughout the lifetime of the machine. Alternatively, edge
computing approaches reduce data transmission and consume low energy. In this
paper, we propose Anomaly Detection based Power Saving(ADEPOS) scheme using
approximate computing through the lifetime of the machine. In the beginning of
the machines life, low accuracy computations are used when the machine is
healthy. However, on the detection of anomalies, as time progresses, the system
is switched to higher accuracy modes. We show using the NASA bearing dataset
that using ADEPOS, we need 8.8X less neurons on average and based on
post-layout results, the resultant energy savings are 6.4 to 6.65X
CHIRRUP: a practical algorithm for unsourced multiple access
Date: 2019-12-12 00:00:00
Tasks: Computational Efficiency, Decoder

Unsourced multiple access abstracts grantless simultaneous communication of a
large number of devices (messages) each of which transmits (is transmitted)
infrequently. It provides a model for machine-to-machine communication in the
Internet of Things (IoT), including the special case of radio-frequency
identification (RFID), as well as neighbor discovery in ad hoc wireless
networks. This paper presents a fast algorithm for unsourced multiple access
that scales to $2^{100}$ devices (arbitrary $100$ bit messages). The primary
building block is multiuser detection of binary chirps which are simply
codewords in the second order Reed Muller code. The chirp detection algorithm
originally presented by Howard et al. is enhanced and integrated into a peeling
decoder designed for a patching and slotting framework. In terms of both energy
per bit and number of transmitted messages, the proposed algorithm is within a
factor of $2$ of state of the art approaches. A significant advantage of our
algorithm is its computational efficiency. We prove that the worst-case
complexity of the basic chirp reconstruction algorithm is
$\mathcal{O}[nK(\log_2 n + K)]$, where $n$ is the codeword length and $K$ is
the number of active users, and we report computing times for our algorithm.
Our performance and computing time results represent a benchmark against which
other practical algorithms can be measured.
Learning to decompose the modes in few-mode fibers with deep convolutional neural network
Date: 2019-04-18 00:00:00
Tasks: Deep Learning

We introduce deep learning technique to perform complete mode decomposition
for few-mode optical fiber for the first time. Our goal is to learn a fast and
accurate mapping from near-field beam profiles to the complete mode
coefficients, including both modal amplitudes and phases. We train the
convolutional neural network with simulated beam patterns, and evaluate the
network on both of the simulated beam data and the real beam data. In simulated
beam data testing, the correlation between the reconstructed and the ideal beam
profiles can achieve 0.9993 and 0.995 for 3-mode case and 5-mode case
respectively. While in the real 3-mode beam data testing, the average
correlation is 0.9912 and the mode decomposition can be potentially performed
at 33 Hz frequency on Graphic Processing Unit, indicating real-time processing
ability. The quantitative evaluations demonstrate the superiority of our deep
learning based approach.
A Hybrid Approach to Joint Estimation of Channel and Antenna impedance
Date: 2018-11-28 00:00:00

This paper considers a hybrid approach to joint estimation of channel
information and antenna impedance, for single-input, single-output channels.
Based on observation of training sequences via synchronously switched load at
the receiver, we derive joint maximum a posteriori and maximum-likelihood
(MAP/ML) estimators for channel and impedance over multiple packets. We
investigate important properties of these estimators, e.g., bias and
efficiency. We also explore the performance of these estimators through
numerical examples.
Continuity of Utility Maximization under Weak Convergence
Date: 2020-06-18 00:00:00

In this paper we find tight sufficient conditions for the continuity of the
value of the utility maximization problem from terminal wealth with respect to
the convergence in distribution of the underlying processes. We also establish
a weak convergence result for the terminal wealths of the optimal portfolios.
Finally, we apply our results to the computation of the minimal expected
shortfall (shortfall risk) in the Heston model by building an appropriate
lattice approximation.
Capture and Recovery of Connected Vehicle Data: A Compressive Sensing Approach
Date: 2018-11-15 00:00:00
Tasks: Compressive Sensing, Travel Time Estimation

Connected vehicles (CVs) can capture and transmit detailed data through
vehicle-to-vehicle and vehicle-to-infrastructure communications, which bring
new opportunities to improve the safety, mobility, and sustainability of
transportation systems. However, the potential data explosion is likely to
over-burden storage and communication systems. We design a compressive sensing
(CS) approach which allows CVs to capture and compress data in real-time and
later recover the original data accurately and efficiently. We have evaluated
our approach using two case studies. In the first case study, the CS approach
is applied to re-capture 10 million CV Basic Safety Message (BSM) speed samples
from the Safety Pilot Model Deployment program. The recovery performances of
our approach regarding several BSM variables are explored in detail. In the
second case study, a freeway traffic simulation model is built to evaluate the
impact of our approach on travel time estimation. Multiple scenarios with
various CV market penetration rates, On-board Unit (OBU) capacities,
compression ratios, arrival rate patterns, and data capture rates are
simulated. The results show the potential of saving large amounts of OBU
hardware cost. Furthermore, our approach can greatly improve the accuracy of
travel time estimation when CVs are in traffic congestion.
Accelerating System Log Processing by Semi-supervised Learning: A Technical Report
Authors: Li Guofu, Zhu Pengjia, Chen Zhiyi
Date: 2018-10-29 00:00:00

There is an increasing need for more automated system-log analysis tools for
large scale online system in a timely manner. However, conventional way to
monitor and classify the log output based on keyword list does not scale well
for complex system in which codes contributed by a large group of developers,
with diverse ways of encoding the error messages, often with misleading pre-set
labels. In this paper, we propose that the design of a large scale online log
analysis should follow the "Least Prior Knowledge Principle", in which
unsupervised or semi-supervised solution with the minimal prior knowledge of
the log should be encoded directly. Thereby, we report our experience in
designing a two-stage machine learning based method, in which the system logs
are regarded as the output of a quasi-natural language, pre-filtered by a
perplexity score threshold, and then undergo a fine-grained classification
procedure. Tests on empirical data show that our method has obvious advantage
regarding to the processing speed and classification accuracy.
Design of non-uniformly spaced phase-stepped algorithms using their frequency transfer function
Date: 2018-10-31 00:00:00

Here we show how to design phase-shifting algorithms (PSAs) for nonuniform
phase-shifted fringe patterns using their frequency transfer function (FTF).
Assuming that the nonuniform/nonlinear (NL) phase-steps are known, we introduce
the desired zeroes in the FTF to obtain the specific NL-PSA formula. The
advantage of designing NL-PSAs based on their FTF is that one can reject many
distorting harmonics of the fringes. We can also estimate the signal-to-noise
ratio (SNR) for interferograms corrupted by additive white Gaussian noise
(AWGN). Finally, for non-distorted noiseless fringes, the proposed NL-PSA
retrieves the modulating phase error-free, just as standard/linear PSAs do.
Advanced Denoising for X-ray Ptychography
Date: 2019-02-15 00:00:00
Tasks: Denoising

The success of ptychographic imaging experiments strongly depends on
achieving high signal-to-noise ratio. This is particularly important in
nanoscale imaging experiments when diffraction signals are very weak and the
experiments are accompanied by significant parasitic scattering (background),
outliers or correlated noise sources. It is also critical when rare events such
as cosmic rays, or bad frames caused by electronic glitches or shutter timing
malfunction take place.
  In this paper, we propose a novel iterative algorithm with rigorous analysis
that exploits the direct forward model for parasitic noise and sample
smoothness to achieve a thorough characterization and removal of structured and
random noise. We present a formal description of the proposed algorithm and
prove its convergence under mild conditions. Numerical experiments from
simulations and real data (both soft and hard X-ray beamlines) demonstrate that
the proposed algorithms produce better results when compared to
state-of-the-art methods.
Proofs of life: molecular-biology reasoning simulates cell behaviors from first principles
Authors: René Vestergaard, Emmanuel Pietriga
Date: 2018-10-30 00:00:00

We axiomatize the molecular-biology reasoning style, show compliance of the standard reference: Ptashne, A Genetic Switch, and present proof-theory-induced technologies to help infer phenotypes and to predict life cycles from genotypes. The key is to note that `reductionist discipline' entails constructive reasoning: any proof of a compound property can be decomposed to proofs of constituent properties. Proof theory makes explicit the inner structure of the axiomatized reasoning style and allows the permissible dynamics to be presented as a mode of computation that can be executed and analyzed. Constructivity and execution guarantee simulation when working over domain-specific languages. Here, we exhibit phenotype properties for genotype reasons: a molecular-biology argument is an open-system concurrent computation that results in compartment changes and is performed among processes of physiology change as determined from the molecular programming of given DNA. Life cycles are the possible sequentializations of the processes. A main implication of our construction is that formal correctness provides a complementary perspective on science that is as fundamental there as for pure mathematics. The bulk of the presented work has been verified formally correct by computer.
Mobile Data Science: Towards Understanding Data-Driven Intelligent Mobile Applications
Authors: Sarker Iqbal H.
Date: 2018-10-15 00:00:00

Due to the popularity of smart mobile phones and context-aware technology,
various contextual data relevant to users' diverse activities with mobile
phones is available around us. This enables the study on mobile phone data and
context-awareness in computing, for the purpose of building data-driven
intelligent mobile applications, not only on a single device but also in a
distributed environment for the benefit of end users. Based on the availability
of mobile phone data, and the usefulness of data-driven applications, in this
paper, we discuss about mobile data science that involves in collecting the
mobile phone data from various sources and building data-driven models using
machine learning techniques, in order to make dynamic decisions intelligently
in various day-to-day situations of the users. For this, we first discuss the
fundamental concepts and the potentiality of mobile data science to build
intelligent applications. We also highlight the key elements and explain
various key modules involving in the process of mobile data science. This
article is the first in the field to draw a big picture, and thinking about
mobile data science, and it's potentiality in developing various data-driven
intelligent mobile applications. We believe this study will help both the
researchers and application developers for building smart data-driven mobile
applications, to assist the end mobile phone users in their daily activities.