pwc-archive/papers-with-abstracts huggingface dataset:
Digital Watermarking of video streams: Review of the State-Of-The-Art
Date: 2019-08-23 00:00:00

Digital Watermarking is an extremely wide aspect of information security,
either by its applications, by its properties, or by its designs. In
particular, a lot of research has been made about video watermarking and it can
make it quite difficult to put into perspective the various schemes possible in
order to implement a watermarking process for a given application. This paper
presents an in-depth overview of the current video watermarking technologies
and how they each respond to certain criteria that may be imposed by the aimed
application. The goal being in first place to be able to define the desired
equilibrium point between invisibility, robustness and efficiency for an
application. Then, given this balance, being able to deduce the best location
of the information embedding as well as the method used to embed it. The
equilibrium point is to be found using the needed properties of the watermark
and by studying the threat model that the scheme will have to face. The
location describes whether the extra information should be added to the
metadata of the video, to its frames or to specific regions of its frames.
Finally, the method to embed the watermark refers to the insertion domain and
its coefficients to be altered in order to insert the wanted information.
Tail Option Pricing Under Power Laws
Authors: Nassim Nicholas Taleb, Brandon Yarckin, Chitpuneet Mann, Damir Delic, Mark Spitznagel
Date: 2019-08-06 00:00:00

We build a methodology that takes a given option price in the tails with strike $K$ and extends (for calls, all strikes > $K$, for puts all strikes $< K$) assuming the continuation falls into what we define as "Karamata Constant" over which the strong Pareto law holds. The heuristic produces relative prices for options, with for sole parameter the tail index $\alpha$, under some mild arbitrage constraints. Usual restrictions such as finiteness of variance are not required. The methodology allows us to scrutinize the volatility surface and test various theories of relative tail option overpricing (usually built on thin tailed models and minor modifications/fudging of the Black-Scholes formula).
ATSFFT: A Novel Sparse Fast Fourier Transform Enabled With Sparsity Detection
Date: 2020-02-24 00:00:00

The Fast Fourier Transform(FFT) is a classic signal processing algorithm that
is utilized in a wide range of applications. For image processing, FFT computes
on every pixel's value of an image, regardless of their properties in frequency
domain. The Sparse Fast Fourier Transform (SFFT) is an innovative algorithm for
discrete Fourier transforms on signals that possess characteristics of the
sparsity in frequency domain. A reference implementation of the algorithm has
been proven to be efficient than modern FFT library in cases of sufficient
sparsity. However, the SFFT implementation has a critical drawback that it only
works reliably for very specific input parameters, especially signal sparsity
$k$, which hinders the extensive application of SFFT. In this paper, we propose
an Adaptive Tuning Sparse Fast Fourier Transform (ATSFFT), which is a novel
sparse fast fourier transform enabled with sparsity detection. In the case of
unknown sparsity $k$, ATSFFT is capable of probing the sparsity $k$ via
adaptive dynamic tuning and completing the sparse Fourier transform.
Experimental results show that ATSFFT outperforms SFFT while it is able to
control the computation error better than SFFT. Furthermore, ATSFFT achieves an
order of magnitude of performance improvement than the state-of-the-art FFT
library, FFTW.
An instantaneous market volatility estimation
Authors: Oleh Danyliv, Bruce Bland
Date: 2019-08-07 00:00:00
Tasks: Algorithmic Trading

Working on different aspects of algorithmic trading we empirically discovered a new market invariant. It links together the volatility of the instrument with its traded volume, the average spread and the volume in the order book. The invariant has been tested on different markets and different asset classes. In all cases we did not find significant violation of the invariant. The formula for the invariant was used for the volatility estimation, which we called the instantaneous volatility. Quantitative comparison showed that it reproduces realised volatility better than one-day-ahead GARCH(1,1) prediction. Because of the short-term prediction nature, the instantaneous volatility could be used by algo developers, volatility traders and other market professionals.
Maximal Spectral Efficiency of OFDM with Index Modulation under Polynomial Space Complexity
Date: 2020-01-24 00:00:00

In this letter, we demonstrate a mapper that enables all waveforms of OFDM
with Index Modulation (OFDM-IM) while preserving polynomial time and space
computational complexities. Enabling all OFDM-IM waveforms maximizes the
spectral efficiency (SE) gain over the classic OFDM but, as far as we know, the
computational overhead of the resulting mapper remains conjectured as
prohibitive across the OFDM-IM literature. We show that the largest number of
binomial coefficient calculations performed by the original OFDM-IM mapper is
polynomial on the number of subcarriers, even under the setup that maximizes
the SE gain over OFDM. Also, such coefficients match the entries of the
so-called Pascal's triangle (PT). Thus, by assisting the OFDM-IM mapper with a
PT table, we show that the maximum SE gain over OFDM can be achieved under
polynomial (rather than exponential) time and space complexities.
A neural network approach to GOP-level rate control of x265 using Lookahead
Date: 2019-10-24 00:00:00

To optimize the perceived quality under a specific bitrate constraint,
multi-pass encoding is usually performed with the rate control mode of the
average bitrate (ABR) or the constant rate factor (CRF) to distribute bits as
reasonably as possible in terms of perceived quality, leading to high
computational complexity. In this paper, we propose to utilize the video
information generated during the encoding to adaptively adjust the CRF setting
at GOP level, ensuring the bits of frames in each GOP are allocated reasonably
under the bitrate constraint with a single-pass encoding framework. In
particular, due to the inherent relationship between CRF values and bitrates,
we adopt a shallow neural network (NN) to map video content features to the
CRF-bitrate model. The content-related features are collected from the
lookahead module inside the x265 encoder, including encoding cost estimation,
motion vector and so on. Further, a rate control method, called content
adaptive rate factor (CARF), is proposed to adjust the CRF value of each GOP
with the requirement of the target bitrate by using the predicted CRF-bitrate
models of each GOP. The experimental results show that the proposed approach
can make 84.5\% testing data within 20% bitrate error (or better) and
outperform the ABR mode in x265, leading to 5.23% BD-rate reduction on average.
Anomalous diffusions in option prices: connecting trade duration and the volatility term structure
Authors: Antoine Jacquier, Lorenzo Torricelli
Date: 2019-08-08 00:00:00

Anomalous diffusions arise as scaling limits of continuous-time random walks (CTRWs) whose innovation times are distributed according to a power law. The impact of a non-exponential waiting time does not vanish with time and leads to different distribution spread rates compared to standard models. In financial modelling this has been used to accommodate for random trade duration in the tick-by-tick price process. We show here that anomalous diffusions are able to reproduce the market behaviour of the implied volatility more consistently than usual L\'evy or stochastic volatility models. We focus on two distinct classes of underlying asset models, one with independent price innovations and waiting times, and one allowing dependence between these two components. These two models capture the well-known paradigm according to which shorter trade duration is associated with higher return impact of individual trades. We fully describe these processes in a semimartingale setting leading no-arbitrage pricing formulae, and study their statistical properties. We observe that skewness and kurtosis of the asset returns do not tend to zero as time goes by. We also characterize the large-maturity asymptotics of Call option prices, and find that the convergence rate is slower than in standard L\'evy regimes, which in turn yields a declining implied volatility term structure and a slower decay of the skew.
Demystifying the Power Scaling Law of Intelligent Reflecting Surfaces and Metasurfaces
Date: 2019-11-11 00:00:00

Intelligent reflecting surfaces (IRSs) have recently attracted the attention
of communication theorists as a means to control the wireless propagation
channel. It has been shown that the signal-to-noise ratio (SNR) of a
single-user IRS-aided transmission increases as $N^2$, with $N$ being the
number of passive reflecting elements in the IRS. This has been interpreted as
a major potential advantage of using IRSs, instead of conventional Massive MIMO
(mMIMO) whose SNR scales only linearly in $N$. This paper shows that this
interpretation is incorrect. We first prove analytically that mMIMO always
provides higher SNRs, and then show numerically that the gap is substantial; a
very large number of reflecting elements is needed for an IRS to obtain SNRs
comparable to mMIMO.
Fast Pricing of Energy Derivatives with Mean-reverting Jump-diffusion Processes
Authors: Nicola Cufaro Petroni, Piergiacomo Sabino
Date: 2019-08-08 00:00:00

Most energy and commodity markets exhibit mean-reversion and occasional distinctive price spikes, which results in demand for derivative products which protect the holder against high prices. To this end, in this paper we present exact and fast methodologies for the simulation of the spot price dynamics modeled as the exponential of the sum of an Ornstein-Uhlenbeck and an independent pure jump process, where the latter one is driven by a compound Poisson process with (bilateral) exponentially distributed jumps. These methodologies are finally applied to the pricing of Asian options, gas storages and swings under different combinations of jump-diffusion market models, and the apparent computational advantages of the proposed procedures are emphasized.
Received Signal Strength Based Wireless Source Localization with Inaccurate Anchor Position
Authors: Yang Liu, Guojun Han, Yonghua Wang, Zheng Xue, Jing Chen
Date: 2019-08-09 00:00:00
Tasks: Position

Received signal strength (RSS)-based wireless localization is easy to implement at low cost. In practice,exact positions of anchors may not be available. This paper focuses on determining the location of a source in the presence of inaccurate positions of anchors based on RSS directly. We first use Taylor expansion and a min-max approach to get a maximum likelihood estimator of the coordinates of the source. Then we propose a relaxed semi-definite programming model to circumvent the non-convexity. We also propose a rounding algorithm considering both inaccurate source location and inaccurate anchor locations.Simulation results together with analysis are presented to validate the proposed method.
The Economics of Enlightenment: Time Value of Knowledge and the Net Present Value (NPV) of Knowledge Machines, A Proposed Approach Adapted from Finance
Authors: Ravi Kashyap
Date: 2019-08-01 00:00:00

We formulate one methodology to put a value or price on knowledge using well accepted techniques from finance. We provide justifications for these finance principles based on the limitations of the physical world we live in. We start with the intuition for our method to value knowledge and then formalize this idea with a series of axioms and models. To the best of our knowledge this is the first recorded attempt to put a numerical value on knowledge. The implications of this valuation exercise, which places a high premium on any piece of knowledge, are to ensure that participants in any knowledge system are better trained to notice the knowledge available from any source. Just because someone does not see a connection does not mean that there is no connection. We need to try harder and be more open to acknowledging the smallest piece of new knowledge that might have been brought to light by anyone from anywhere about anything.
Committee Draft of JPEG XL Image Coding System
Date: 2019-08-13 00:00:00

JPEG XL is a practical approach focused on scalable web distribution and
efficient compression of high-quality images. It provides various benefits
compared to existing image formats: 60% size reduction at equivalent subjective
quality; fast, parallelizable decoding and encoding configurations; features
such as progressive, lossless, animation, and reversible transcoding of
existing JPEG with 22% size reduction; support for high-quality applications
including wide gamut, higher resolution/bit depth/dynamic range, and visually
lossless coding. The JPEG XL architecture is traditional block-transform coding
with upgrades to each component.
Monitoring Link Faults in Nonlinear Diffusively-coupled Networks
Date: 2020-07-14 00:00:00
Tasks: Fault Detection

Fault detection and isolation is an area of engineering dealing with
designing on-line protocols for systems that allow one to identify the
existence of faults, pinpoint their exact location, and overcome them. We
consider the case of multi-agent systems, where faults correspond to the
disappearance of links in the underlying graph, simulating a communication
failure between the corresponding agents. We study the case in which the agents
and controllers are maximal equilibrium-independent passive (MEIP), and use the
known connection between steady-states of these multi-agent systems and network
optimization theory. We first study asymptotic methods of differentiating the
faultless system from its faulty versions by studying their steady-state
outputs. We explain how to apply the asymptotic differentiation to detect and
isolate communication faults, with graph-theoretic guarantees on the number of
faults that can be isolated, assuming the existence of a "convergence assertion
protocol", a data-driven method of asserting that a multi-agent system
converges to a conjectured limit. We then construct two data-driven model-based
convergence assertion protocols. We demonstrate our results by a case study.
Shuyi, A Name After Dendritic Cell-mediated Immunological Memory
Authors: Yiqiqang Wang
Date: 2019-08-10 00:00:00

Immunological memory is a fundamental theory of modern immunology, which is traditionally believed to be mediated only by B and T lymphocytes that recognize antigen epitopes in a receptor-restricted manner. During the last decade data accumulated to show that monocytes and macrophages, the two main initiators of innate immune response, also built up a "memory" to antigens they encountered, though in most concerned publications a different wording (i.e. "train" or"educate") was utilized to describe this feature. More recently, Hole et al demonstrated a "memory-like" response of dendritic cells (DCs). In brief, if fungal-challenged mice could develop a protective immune response, DCs immediately (in 3 weeks) isolated from those mice would manifest a pro-inflammatory phenotype. Even after the mice were allowed to rest for 10 weeks, DCs from them still exhibited an enhanced immune activation profile in their transcriptome and cytokine productions upon re-challenge with same pathogens. Lastly, Hole showed that the "training" or memory-building in DCs was achieved by histone modification. All above findings obtained in monocytes, macrophages or DCs emphasized the necessity for rechecking the questions whether antigen presenting cells (APCs) as a whole could be classified the third class of cells that would mediate immunological memory. In this essay, the author described the effort he made in late 1990s to identify dendtitic cell-mediated memory, and how he named his daughter SHUYI to memorize that hypothesis.
Quasi-spectral characterization of intracellular regions in bright-field light microscopy images
Date: 2020-10-27 00:00:00
Tasks: Semantic Segmentation

Investigation of cell structure is hardly imaginable without bright-field
microscopy. Numerous modifications such as depth-wise scanning or
videoenhancement make this method being state-of-the-art. This raises a
question what maximal information can be extracted from ordinary (but well
acquired) bright-field images in a model-free way. Here we introduce a method
of a physically correct extraction of features for each pixel when these
features resemble a transparency spectrum. The method is compatible with
existent ordinary bright-field microscopes and requires mathematically
sophisticated data processing. Unsupervised clustering of the spectra yields
reasonable semantic segmentation of unstained living cells without any a priori
information about their structures. Despite the lack of reference data (to
prove strictly that the proposed feature vectors coincide with transparency),
we believe that this method is the right approach to an intracellular
(semi)quantitative and qualitative chemical analysis.
Fallback Strategies in Operation Control of Microgrids with Communication Failures
Date: 2019-12-10 00:00:00
Tasks: energy management, Management, Model Predictive Control

This paper proposes a model predictive control (MPC)-based energy management
system to address communication failures in an islanded microgrid (MG). The
energy management system relies on a communication network to monitor and
control power generation in the units. A communication failure to a unit
inhibits the ability of the MPC to change the power of the unit. However, this
unit is electrically connected to the network and ignoring this aspect could
have adverse effect in the operation of the microgrid. Therefore, this paper
considers an MPC design that includes the electrical connectivity of the unit
during communication failures. This paper also highlights the benefit of
including the lower layer control behaviour of the MG to withstand
communication failures. The proposed approaches are validated with a case
study.
Optimization-based Settingless Algorithm Combining Protection and Fault Identification
Date: 2019-08-10 00:00:00

The demand for faster protection algorithms is growing due to the
increasingly faster dynamics in the system. The majority of existing algorithms
require empirically selected set-points, which may reduce sensitivity to
internal faults and cause security problems. This paper addresses these
challenges by proposing a settingless time-domain unit protection algorithm for
medium-voltage lines. The main idea of the algorithm is to identify which model
of a protected line, i.e. healthy or with an internal fault, is more consistent
with the input measurements. This is done by solving a number of small-scale
convex optimization problems, which at the same time determine the
characteristics of an internal fault that best fit the measurements. Thus, the
proposed algorithm merges protection, fault location and fault type
identification functionalities. The algorithm's performance is extensively
tested on a grid model in MATLAB Simulink for different types of generation and
grid operating conditions. The results demonstrate that the algorithm can
operate quickly and reliably, and accurately estimate fault characteristics
even in the presence of noisy measurements and uncertain line parameters.
Portfolio Optimization Managing Value at Risk under Heavy Tail Return, using Stochastic Maximum Principle
Authors: Subhojit Biswas, Mrinal K. Ghosh, Diganta Mukherjee
Date: 2019-08-11 00:00:00
Tasks: Portfolio Optimization

We consider an investor, whose portfolio consists of a single risky asset and a risk free asset, who wants to maximize his expected utility of the portfolio subject to managing the Value at Risk (VaR) assuming a heavy tailed distribution of the stock prices return. We use a stochastic maximum principle to formulate the dynamic optimisation problem. The equations which we obtain does not have any explicit analytical solution, so we look for accurate approximations to estimate the value function and optimal strategy. As our calibration strategy is non-parametric in nature, no prior knowledge on the form of the distribution function is needed. We also provide detailed empirical illustration using real life data. Our results show close concordance with financial intuition.We expect that our results will add to the arsenal of the high frequency traders.
Discrete time portfolio optimisation managing value at risk under heavy tail return distribution
Authors: Subhojit Biswas, Diganta Mukherjee
Date: 2019-08-11 00:00:00
Tasks: Numerical Integration

We consider an investor, whose portfolio consists of a single risky asset and a risk free asset, who wants to maximize his expected utility of the portfolio subject to the Value at Risk assuming a heavy tail distribution of the stock prices return. We use Markov Decision Process and dynamic programming principle to get the optimal strategies and the value function which maximize the expected utility for parametric as well as non parametric distributions. Due to lack of explicit solution in the non parametric case, we use numerical integration for optimization
Stable spline identification of linear systems under missing data
Date: 2020-05-14 00:00:00
Tasks: Gaussian Processes

A different route to identification of time-invariant linear systems has been
recently proposed which does not require committing to a specific parametric
model structure. Impulse responses are described in a nonparametric Bayesian
framework as zero-mean Gaussian processes. Their covariances are given by the
so-called stable spline kernels encoding information on regularity and BIBO
stability. In this paper, we demonstrate that these kernels also lead to a new
family of radial basis functions kernels suitable to model system components
subject to disturbances given by filtered white noise. This novel class, in
cooperation with the stable spline kernels, paves the way to a new approach to
solve missing data problems in both discrete and continuous-time settings.
Numerical experiments show that the new technique may return models more
predictive than those obtained by standard parametric Prediction Error Methods,
also when these latter exploit the full data set.
Estimation of the excess mortality in chronic diseases from prevalence and incidence data
Date: 2019-08-12 00:00:00

Aggregated health data such as claims data from health insurances become more
and more available for research purposes. Estimates of excess mortality from
prevalence and incidence of a chronic condition have only been possible for
ages 50 years and older and have shown to be unstable in younger ages. The aim
of this article is to explore the reasons why estimates of excess mortality for
younger ages are prone to bias and what can be done to extend the age range to
ages below 50 years.