pwc-archive/papers-with-abstracts huggingface dataset:
Complex market dynamics in the light of random matrix theory
Date: 2018-09-24 00:00:00
Tasks: Time Series, Time Series Analysis

We present a brief overview of random matrix theory (RMT) with the objectives
of highlighting the computational results and applications in financial markets
as complex systems. An oft-encountered problem in computational finance is the
choice of an appropriate epoch over which the empirical cross-correlation
return matrix is computed. A long epoch would smoothen the fluctuations in the
return time series and suffers from non-stationarity, whereas a short epoch
results in noisy fluctuations in the return time series and the correlation
matrices turn out to be highly singular. An effective method to tackle this
issue is the use of the power mapping, where a non-linear distortion is applied
to a short epoch correlation matrix. The value of distortion parameter controls
the noise-suppression. The distortion also removes the degeneracy of zero
eigenvalues. Depending on the correlation structures, interesting properties of
the eigenvalue spectra are found. We simulate different correlated Wishart
matrices to compare the results with empirical return matrices computed using
the S&P 500 (USA) market data for the period 1985-2016. We also briefly review
two recent applications of RMT in financial stock markets: (i) Identification
of "market states" and long-term precursor to a critical state; (ii)
Characterization of catastrophic instabilities (market crashes).
Direct Reconstruction of Saturated Samples in Band-Limited OFDM Signals
Date: 2018-09-19 00:00:00

Given a set of samples, a few of them being possibly saturated, we propose an
efficient algorithm in order to cancel saturation while reconstructing
band-limited signals. Our method satisfies a minimum-loss constraint and relies
on sinc-related bases. It involves matrix inversion and is a direct,
non-iterative approach. It consists of two main steps: (i) regression, to
estimate the expansion coefficients of the signal model; (ii) interpolation, to
restore an estimated value for those samples that are saturated. Because the
proposed method is free from tuning parameters, it is hardware-friendly and we
expect that it will be particularly useful in the context of orthogonal
frequency-division multiplexing. There, the high peak-to-average power ratio of
the transmitted signal results in a challenging decoding stage in the presence
of saturation, which causes significant decoding errors due to the nonlinearity
of amplifiers and receivers, ultimately resulting in band distortion and
information loss. Our experiments on realistic simulations confirm that our
proposed reconstruction of the saturated samples can significantly reduce
transmission errors in modern high-throughput digital-communication receivers.
Enabling Scientific Crowds: The Theory of Enablers for Crowd-Based Scientific Investigation
Date: 2018-09-18 00:00:00

Evidence shows that in a significant number of cases the current methods of
research do not allow for reproducible and falsifiable procedures of scientific
investigation. As a consequence, the majority of critical decisions at all
levels, from personal investment choices to overreaching global policies, rely
on some variation of try-and-error and are mostly non-scientific by definition.
We lack transparency for procedures and evidence, proper explanation of market
events, predictability on effects, or identification of causes. There is no
clear demarcation of what is inherently scientific, and as a consequence, the
line between fake and genuine is blurred. This paper presents highlights of the
Theory of Enablers for Crowd-Based Scientific Investigation, or Theory of
Enablers for short. The Theory of Enablers assumes the use of a next-generation
investigative approach leveraging forces of human diversity, micro-specialized
crowds, and proper computer-assisted control methods associated with
accessibility, reproducibility, communication, and collaboration. This paper
defines the set of very specific cognitive and non-cognitive enablers for
crowd-based scientific investigation: methods of proof, large-scale
collaboration, and a domain-specific computational representation. These
enablers allow the application of procedures of structured scientific
investigation powered by crowds, a collective brain in which neurons are human
collaborators
Time to Stop Telling Biophysics Students that Light is Primarily a Wave
Authors: Philip C. Nelson
Date: 2018-09-15 00:00:00

Standard pedagogy introduces optics as though it were a consequence of Maxwell's equations, and only grudgingly admits, usually in a rushed aside, that light has a particulate character that can somehow be reconciled with the wave picture. Recent revolutionary advances in optical imaging, however, make this approach more and more unhelpful: How are we to describe two-photon imaging, FRET, localization microscopy, and a host of related techniques to students who think of light primarily as a wave? I was surprised to find that everything I wanted my biophysics students to know about light, including image formation, x-ray diffraction, and even Bessel beams, could be expressed as well (or better) from the quantum viewpoint pioneered by Richard Feynman. Even my undergraduate students grasp this viewpoint as well as (or better than) the traditional one, and by mid-semester they are already well positioned to integrate the latest advances into their understanding. Moreover, I have found that this approach clarifies my own understanding of new techniques.
Audio Based Disambiguation Of Music Genre Tags
Authors: Hennequin Romain, Royo-Letelier Jimena, Moussallam Manuel
Date: 2018-09-19 00:00:00
Tasks: TAG, Translation

In this paper, we propose to infer music genre embeddings from audio datasets
carrying semantic information about genres. We show that such embeddings can be
used for disambiguating genre tags (identification of different labels for the
same genre, tag translation from a tag system to another, inference of
hierarchical taxonomies on these genre tags). These embeddings are built by
training a deep convolutional neural network genre classifier with large audio
datasets annotated with a flat tag system. We show empirically that they makes
it possible to retrieve the original taxonomy of a tag system, spot duplicates
tags and translate tags from a tag system to another.
On the reaction rate constants that enable multistationarity in the two-site phosphorylation cycle
Date: 2018-09-18 00:00:00

Parametrized polynomial ordinary differential equation systems are broadly
used for modeling, specially in the study of biochemical reaction networks
under the assumption of mass-action kinetics. Understanding the qualitative
behavior of the solutions with respect to the parameter values gives rise to
complex problems within real algebraic geometry, concerning the study of the
signs of multivariate polynomials over the positive orthant. In this work we
provide further insight into the number of positive steady states of a
benchmark model, namely the two-site phosphorylation cycle. In particular, we
provide new conditions on the reaction rate constants for the existence of one
or three positive steady states, partially filling a gap left in previous
works.
Pricing American Options by Exercise Rate Optimization
Authors: Christian Bayer, Raúl Tempone, Sören Wolfers
Date: 2018-09-19 00:00:00
Tasks: Stochastic Optimization

We present a novel method for the numerical pricing of American options based on Monte Carlo simulation and the optimization of exercise strategies. Previous solutions to this problem either explicitly or implicitly determine so-called optimal exercise regions, which consist of points in time and space at which a given option is exercised. In contrast, our method determines the exercise rates of randomized exercise strategies. We show that the supremum of the corresponding stochastic optimization problem provides the correct option price. By integrating analytically over the random exercise decision, we obtain an objective function that is differentiable with respect to perturbations of the exercise rate even for finitely many sample paths. The global optimum of this function can be approached gradually when starting from a constant exercise rate. Numerical experiments on vanilla put options in the multivariate Black-Scholes model and a preliminary theoretical analysis underline the efficiency of our method, both with respect to the number of time-discretization steps and the required number of degrees of freedom in the parametrization of the exercise rates. Finally, we demonstrate the flexibility of our method through numerical experiments on max call options in the classical Black-Scholes model, and vanilla put options in both the Heston model and the non-Markovian rough Bergomi model.
Clustering students' open-ended questionnaire answers
Authors: Hämäläinen Wilhelmiina, Joy Mike, Berger Florian, Huttunen Sami
Date: 2018-09-19 00:00:00
Tasks: Clustering

Open responses form a rich but underused source of information in educational
data mining and intelligent tutoring systems. One of the major obstacles is the
difficulty of clustering short texts automatically. In this paper, we
investigate the problem of clustering free-formed questionnaire answers. We
present comparative experiments on clustering ten sets of open responses from
course feedback queries in English and Finnish. We also evaluate how well the
main topics could be extracted from clusterings with the HITS algorithm. The
main result is that, for English data, affinity propagation performed well
despite frequent outliers and considerable overlapping between real clusters.
However, for Finnish data, the performance was poorer and none of the methods
clearly outperformed the others. Similarly, topic extraction was very
successful for the English data but only satisfactory for the Finnish data. The
most interesting discovery was that stemming could actually deteriorate the
clustering quality significantly.
Ordinal Synchronization: Using ordinal patterns to capture interdependencies between time series
Date: 2018-09-19 00:00:00
Tasks: Time Series, Time Series Analysis

We introduce Ordinal Synchronization ($OS$) as a new measure to quantify
synchronization between dynamical systems. $OS$ is calculated from the
extraction of the ordinal patterns related to two time series, their
transformation into $D$-dimensional ordinal vectors and the adequate
quantification of their alignment. $OS$ provides a fast and robust-to noise
tool to assess synchronization without any implicit assumption about the
distribution of data sets nor their dynamical properties, capturing in-phase
and anti-phase synchronization. Furthermore, varying the length of the ordinal
vectors required to compute $OS$ it is possible to detect synchronization at
different time scales. We test the performance of $OS$ with data sets coming
from unidirectionally coupled electronic Lorenz oscillators and brain imaging
datasets obtained from magnetoencephalographic recordings, comparing the
performance of $OS$ with other classical metrics that quantify synchronization
between dynamical systems.
New insights on the optimality of parameterized wiener filters for speech enhancement applications
Date: 2018-09-19 00:00:00
Tasks: Speech Enhancement

This work presents a unified framework for defining a family of noise
reduction techniques for speech enhancement applications. The proposed approach
provides a unique theoretical foundation for some widely-applied soft and hard
time-frequency masks, which encompasses the well-known Wiener filter and the
heuristically-designed Binary mask. These techniques can now be considered as
optimal solutions of the same minimization problem. The proposed cost function
is defined by two design parameters that not only establish a desired trade-off
between noise reduction and speech distortion, but also provide an insightful
relationship with the mask morphology. Such characteristic may be useful for
applications that require online adaptation of the suppression function
according to variations of the acoustic scenario. Simulation examples indicate
that the derived conformable suppression mask has approximately the same
quality and intelligibility performance capability of the classical
heuristically-defined parametric Wiener filter. The proposed approach may be of
special interest for real-time embedded speech enhancement applications such as
hearing aids and cochlear implants.
Joint Routing and Resource Allocation for Millimeter Wave Picocellular Backhaul
Date: 2018-09-20 00:00:00
Tasks: PICO

Picocellular architectures are essential for providing the spatial reuse
required to satisfy the ever-increasing demand for mobile data. A key
deployment challenge is to provide backhaul connections with sufficiently high
data rate. Providing wired support (e.g., using optical fiber) to pico base
stations deployed opportunistically on lampposts and rooftops is impractical,
hence wireless backhaul becomes an attractive approach. A multihop mesh network
comprised of directional millimeter wave links is considered here for this
purpose. The backhaul design problem is formulated as one of joint routing and
resource allocation, accounting for mutual interference across simultaneously
active links. A computationally tractable formulation is developed by
leveraging the localized nature of interference and the provable existence of a
sparse optimal allocation. Numerical results are provided for millimeter (mm)
wave mesh networks, which are well suited for scaling backhaul data rates due
to abundance of spectrum, and the ability to form highly directional,
electronically steerable beams.
Compact Low-Profile Wearable Antennas For Breast Cancer Detection
Date: 2018-09-20 00:00:00
Tasks: Breast Cancer Detection

Many lives can be saved if tumors are detected in early stages, which can
result in a bigger chance for recovery. Many patients find it irritating to get
regular checkups due to the fact that the majority of the monitoring systems
are complicated, not available everywhere and not mobile. Furthermore, for
medical field applications, micro-strip antennas are efficient and have
flexible properties that are utilized in imaging, diagnosis and treatment. It
is known that breast cancer is the most common type of cancer in the world, and
the earlier its been detected the better. In the early stages of breast cancer,
getting rid of from the tumors is much easier and more guaranteed. Nowadays,
the main method that is used in the hospitals for breast cancer detection is
the Ultra-Wideband method (UWB). However, Many patients find it irritating to
get regular check ups due to the fact that the majority of the monitoring
systems are complicated and not mobile.
On the quasi-sure superhedging duality with frictions
Date: 2019-09-18 00:00:00
Tasks: Math

We prove the superhedging duality for a discrete-time financial market with
proportional transaction costs under model uncertainty. Frictions are modeled
through solvency cones as in the original model of [Kabanov, Y., Hedging and
liquidation under transaction costs in currency markets. Fin. Stoch.,
3(2):237-248, 1999] adapted to the quasi-sure setup of [Bouchard, B. and Nutz,
M., Arbitrage and duality in nondominated discrete-time models. Ann. Appl.
Probab., 25(2):823-859, 2015]. Our approach allows to remove the restrictive
assumption of No Arbitrage of the Second Kind considered in [Bouchard, B.,
Deng, S. and Tan, X., Super-replication with proportional transaction cost
under model uncertainty, Math. Fin., 29(3):837-860, 2019] and to show the
duality under the more natural condition of No Strict Arbitrage. In addition,
we extend the results to models with portfolio constraints.
Insider Trading with Penalties
Date: 2018-09-20 00:00:00
Tasks: Fairness

We consider a one-period Kyle (1985) framework where the insider can be
subject to a penalty if she trades. We establish existence and uniqueness of
equilibrium for virtually any penalty function when noise is uniform. In
equilibrium, the demand of the insider and the price functions are in general
non-linear and remain analytically tractable because the expected price
function is linear. We use this result to investigate the trade off between
price efficiency and 'fairness': we consider a regulator that wants to minimise
post-trade standard deviation for a given level of uninformed traders' losses.
The minimisation is over the function space of penalties; for each possible
penalty, our existence and uniqueness theorem allows to define unambiguously
the post-trade standard deviation and the uninformed traders' losses that
prevail in equilibrium.Optimal penalties are characterized in closed-form. They
must increase quickly with the magnitude of the insider's order for small
orders and become flat for large orders: in cases where the fundamental
realizes at very high or very low values, the insider finds it optimal to trade
despite the high penalty. Although such trades-if they occur-are costly for
liquidity traders, they signal extreme events and therefore incorporate a lot
of information into prices. We generalize this result in two directions by
imposing a budget constraint on the regulator and considering the cases of
either non-pecuniary or pecuniary penalties. In the first case, we establish
that optimal penalties are a subset of the previously optimal penalties: the
patterns of equilibrium trade volumes and prices is unchanged. In the second
case, we also fully characterize the constrained efficient points and penalties
and show that new patterns emerge in the demand schedules of the insider trader
and the associated price functions.
Dynamic Adaptive Computation: Tuning network states to task requirements
Date: 2018-09-20 00:00:00
Tasks: Sensitivity

Neural circuits are able to perform computations under very diverse
conditions and requirements. The required computations impose clear constraints
on their fine-tuning: a rapid and maximally informative response to stimuli in
general requires decorrelated baseline neural activity. Such network dynamics
is known as asynchronous-irregular. In contrast, spatio-temporal integration of
information requires maintenance and transfer of stimulus information over
extended time periods. This can be realized at criticality, a phase transition
where correlations, sensitivity and integration time diverge. Being able to
flexibly switch, or even combine the above properties in a task-dependent
manner would present a clear functional advantage. We propose that cortex
operates in a "reverberating regime" because it is particularly favorable for
ready adaptation of computational properties to context and task. This
reverberating regime enables cortical networks to interpolate between the
asynchronous-irregular and the critical state by small changes in effective
synaptic strength or excitation-inhibition ratio. These changes directly adapt
computational properties, including sensitivity, amplification, integration
time and correlation length within the local network. We review recent
converging evidence that cortex in vivo operates in the reverberating regime,
and that various cortical areas have adapted their integration times to
processing requirements. In addition, we propose that neuromodulation enables a
fine-tuning of the network, so that local circuits can either decorrelate or
integrate, and quench or maintain their input depending on task. We argue that
this task-dependent tuning, which we call "dynamic adaptive computation",
presents a central organization principle of cortical networks and discuss
first experimental evidence.
Inferring Multiplex Diffusion Network via Multivariate Marked Hawkes Process
Authors: Suny Peiyuan, Li Jianxin, Mao Yongyi, Zhang Richong, Wang Lihong
Date: 2018-08-24 00:00:00

Understanding the diffusion in social network is an important task. However,
this task is challenging since (1) the network structure is usually hidden with
only observations of events like "post" or "repost" associated with each node,
and (2) the interactions between nodes encompass multiple distinct patterns
which in turn affect the diffusion patterns. For instance, social interactions
seldom develop on a single channel, and multiple relationships can bind pairs
of people due to their various common interests. Most previous work considers
only one of these two challenges which is apparently unrealistic. In this
paper, we study the problem of \emph{inferring multiplex network} in social
networks. We propose the Multiplex Diffusion Model (MDM) which incorporates the
multivariate marked Hawkes process and topic model to infer the multiplex
structure of social network. A MCMC based algorithm is developed to infer the
latent multiplex structure and to estimate the node-related parameters. We
evaluate our model based on both synthetic and real-world datasets. The results
show that our model is more effective in terms of uncovering the multiplex
network structure.
Optimal flow analysis, prediction and application
Authors: Zhang Weili
Date: 2018-08-11 00:00:00
Tasks: Prediction, regression

This thesis employs statistical learning technique to analyze, predict and
solve the fixed charge network flow (FCNF) problem, which is common encountered
in many real-world network problems. The cost structure for flows in the FCNF
involves both fixed and variable costs. The FCNF problem is modeled mixed
binary linear programs and can be solved with standard commercial solvers,
which use branch and bound algorithm. This problem is important for its widely
applications and solving challenges. There does not exist a efficient algorithm
to solve this problem optimally due to lacking tight bounds. To the best of our
knowledge, this is the first work that employs statistical learning technique
to analyze the optimal flow of the FCNF problem. Most algorithms developed to
solve the FCNF problem are based on the cost structure, relaxation, etc. We
start from the network characteristics and explore the relationship between
properties of nodes, arcs and networks and the optimal flow. This is a
bi-direction approach and the findings can be used to locate the features that
affect the optimal flow most significantly, predict the optimal arcs and
provide information to solve the FCNF problem. In particular, we define 33
features based on the network characteristics, from which using step wise
regression, we identify 26 statistical significant predictors for logistic
regression to predict which arcs will have positive flow in the optimal
solutions. The predictive model achieves 88% accuracy and the area under
receiver operating characteristic curve is 0.95. Two applications are
investigated. Firstly, the predictive results can be used directly as component
critical index. The failure of arcs with higher critical index result in more
cost increase over the entire network.