pwc-archive/papers-with-abstracts huggingface dataset:
Impulse Denoising From Hyper-Spectral Images: A Blind Compressed Sensing Approach
Date: 2019-12-11 00:00:00
Tasks: compressed sensing, Denoising, Dictionary Learning

In this work we propose a technique to remove sparse impulse noise from
hyperspectral images. Our algorithm accounts for the spatial redundancy and
spectral correlation of such images. The proposed method is based on the
recently introduced Blind Compressed Sensing (BCS) framework, i.e. it
empirically learns the spatial and spectral sparsifying dictionaries while
denoising the images. The BCS framework differs from existing CS techniques -
which assume the sparsifying dictionaries to be data independent, and from
prior dictionary learning studies which learn the dictionary in an offline
training phase. Our proposed formulation have shown over 5 dB improvement in
PSNR over other techniques.
Control of excitable systems is optimal near criticality
Date: 2019-12-13 00:00:00

Experiments suggest that cerebral cortex gains several functional advantages
by operating in a dynamical regime near the critical point of a phase
transition. However, a long-standing criticism of this hypothesis is that
critical dynamics are rather noisy, which might be detrimental to aspects of
brain function that require precision. If the cortex does operate near
criticality, how might it mitigate the noisy fluctuations? One possibility is
that other parts of the brain may act to control the fluctuations and reduce
cortical noise. To better understand this possibility, here we numerically and
analytically study a network of binary neurons. We determine how efficacy of
controlling the population firing rate depends on proximity to criticality as
well as different structural properties of the network. We found that control
is most effective - errors are minimal for the widest range of target firing
rates - near criticality. Optimal control is slightly away from criticality for
networks with heterogeneous degree distributions. Thus, while criticality is
the noisiest dynamical regime, it is also the regime that is easiest to
control, which may offer a way to mitigate the noise.
Data-driven Identification of Occupant Thermostat-Behavior Dynamics
Date: 2019-12-13 00:00:00

Building occupant behavior drives significant differences in building energy
use, even in automated buildings. Users' distrust in the automation causes them
to override settings. This results in responses that fail to satisfy both the
occupants' and/or the building automation's objectives. The transition toward
grid-interactive efficient buildings will make this evermore important as
complex building control systems optimize not only for comfort, but also
changing electricity costs. This paper presents a data-driven approach to study
thermal comfort behavior dynamics which are not captured by standard
steady-state comfort models such as predicted mean vote.
  The proposed model captures the time it takes for a user to override a
thermostat setpoint change as a function of the manual setpoint change
magnitude. The model was trained with the ecobee Donate Your Data dataset of 5
min. resolution data from 27,764 smart thermostats and occupancy sensors. The
resulting population-level model shows that, on average, a 2{\deg}F override
will occur after ~30 mins. and an 8{\deg}F override will occur in only ~15
mins., indicating the magnitude of discomfort as a key driver to the swiftness
of an override. Such models could improve demand response programs through
personalized controls.
Robustness and sensitivity analyses for stochastic volatility models under uncertain data structure
Authors: Jan Pospíšil, Tomáš Sobotka, Philipp Ziegler
Date: 2019-12-13 00:00:00
Tasks: Sensitivity

In this paper we perform robustness and sensitivity analysis of several continuous-time stochastic volatility (SV) models with respect to the process of market calibration. The analyses should validate the hypothesis on importance of the jump part in the underlying model dynamics. Also an impact of the long memory parameter is measured for the approximative fractional SV model. For the first time, the robustness of calibrated models is measured using bootstrapping methods on market data and Monte-Carlo filtering techniques. In contrast to several other sensitivity analysis approaches for SV models, the newly proposed methodology does not require independence of calibrated parameters - an assumption that is typically not satisfied in practice. Empirical study is performed on data sets of Apple Inc. equity options traded in April and May 2015.
Robust Quadratic Gaussian Control of Continuous-time Nonlinear Systems
Date: 2019-12-13 00:00:00

In this paper, we propose a new Robust Nonlinear Quadratic Gaussian (RNQG)
controller based on State-Dependent Riccati Equation (SDRE) scheme for
continuous-time nonlinear systems. Existing controllers do not account for
combined noise and disturbance acting on the system. The proposed controller is
based on a Lyapunov function and a cost function includes states, inputs,
outputs, disturbance, and the noise acting on the system. We express the RNQG
control law in the form of a traditional Riccati equation. Real-time
applications of the controller place high computational burden on system
implementation. This is mainly due to the nonlinear and complex form of the
cost function. In order to solve this problem, this cost function is
approximated by a weighted polynomial. The weights are found by using a
least-squares technique and a neural network. The approximate cost function is
incorporated into the controller by employing a method based on Bellman's
principle of optimality. Finally, an inertially stabilized inverted pendulum
example is used to verify the utility of the proposed control approach.
A simulation-based comparative analysis of PID and LQG control for closed-loop anesthesia delivery
Date: 2020-07-11 00:00:00
Tasks: Point Tracking

Closed loop anesthesia delivery (CLAD) systems can help anesthesiologists
efficiently achieve and maintain desired anesthetic depth over an extended
period of time. A typical CLAD system would use an anesthetic marker,
calculated from physiological signals, as real-time feedback to adjust
anesthetic dosage towards achieving a desired set-point of the marker. Since
control strategies for CLAD vary across the systems reported in recent
literature, a comparative analysis of common control strategies can be useful.
For a nonlinear plant model based on well-established models of compartmental
pharmacokinetics and sigmoid-Emax pharmacodynamics, we numerically analyze the
set-point tracking performance of three output-feedback linear control
strategies: proportional-integral-derivative (PID) control, linear quadratic
Gaussian (LQG) control, and an LQG with integral action (ILQG). Specifically,
we numerically simulate multiple CLAD sessions for the scenario where the plant
model parameters are unavailable for a patient and the controller is designed
based on a nominal model and controller gains are held constant throughout a
session. Based on the numerical analyses performed here, conditioned on our
choice of model and controllers, we infer that in terms of accuracy and bias
PID control performs better than ILQG which in turn performs better than LQG.
In the case of noisy observations, ILQG can be tuned to provide a smoother
infusion rate while achieving comparable steady-state response with respect to
PID. The numerical analyses framework and findings, reported here, can help
CLAD developers in their choice of control strategies. This paper may also
serve as a tutorial paper for teaching control theory for CLAD.
Path Loss in Reconfigurable Intelligent Surface-Enabled Channels
Authors: Steven W. Ellingson
Date: 2019-12-14 00:00:00

A reconfigurable intelligent surface (RIS) employs an array of individually-controllable elements to scatter incident signals in a desirable way; for example, to facilitate links between base stations and mobile stations that would otherwise be blocked. A principal consideration in the study of RIS-enabled propagation channels is path loss. This paper presents a simple yet broadly-applicable method for calculating the path loss of a channel consisting of a passive reflectarray-type RIS. This model is then used to characterize path loss as a function of RIS size, link geometry, and the method used to set the element states. Whereas previous work presumes either (1) an array of parameterizable element patterns and spacings (most useful for analysis of specific designs) or (2) a continuous electromagnetic surface (most useful for determining scaling laws and theoretical limits), this work begins with (1) and is then shown to be consistent with (2), making it possible to identify specific practical designs and scenarios that exhibit the performance predicted using (2). This model is used to further elucidate the matter of path loss of the RIS-enabled channel relative to that of the free space direct and specular reflection channels, which is an important consideration in the design of networks employing RIS technology.
A Dual Approach for Positive T-S Fuzzy Controller Design and Its Application to Cancer Treatment Under Immunotherapy and Chemotherapy
Date: 2019-12-14 00:00:00

This study proposes an effective positive control design strategy for cancer
treatment by resorting to the combination of immunotherapy and chemotherapy.
The treatment objective is to transfer the initial number of tumor cells and
immune-competent cells from the malignant region into the region of benign
growth where the immune system can inhibit tumor growth. In order to achieve
this goal, a new modeling strategy is used that is based on Takagi-Sugen. A
Takagi-Sugeno fuzzy model is derived based on the Stepanova nonlinear model
that enables a systematic design of the controller. Then, a positive Parallel
Distributed Compensation controller is proposed based on a linear copositive
Lyapunov Function so that the tumor volume and administration of the
chemotherapeutic and immunotherapeutic drugs is reduced, while the density of
the immune-competent cells is reached to an acceptable level. Thanks to the
proposed strategy, the entire control design is formulated as a Linear
Programming problem, which can be solved very efficiently. Finally, the
simulation results show the effectiveness of the proposed control approach for
the cancer treatment. Keywords: Co-positive linear Lyapunov function, Cancer,
Chemotherapy, Immunotherapy, Positive system, Takagi-Sugeno fuzzy system.
Analysis of the Conradi-Kahle Algorithm for Detecting Binomiality on Biological Models
Date: 2019-12-14 00:00:00

We analyze the Conradi-Kahle Algorithm for detecting binomiality. We present
experiments using two implementations of the algorithm in Macaulay2 and Maple
on biological models and assess the performance of the algorithm on these
models. We compare the two implementations with each other and with Gr\"obner
bases computations up to their performance on these biological models.
The Crawler: Three Equivalence Results for Object (Re)allocation Problems when Preferences Are Single-peaked
Authors: Yuki Tamura, Hadi Hosseini
Date: 2019-12-14 00:00:00

For object reallocation problems, if preferences are strict but otherwise unrestricted, the Top Trading Cycles rule (TTC) is the leading rule: It is the only rule satisfying efficiency, individual rationality, and strategy-proofness. However, on the subdomain of single-peaked preferences, Bade (2019) defines a new rule, the "crawler", which also satisfies these three properties. (i) The crawler selects an allocation by "visiting" agents in a specific order. A natural "dual" rule can be defined by proceeding in the reverse order. Our first theorem states that the crawler and its dual are actually the same. (ii) Single-peakedness of a preference profile may in fact hold for more than one order and its reverse. Our second theorem states that the crawler is invariant to the choice of the order. (iii) For object allocation problems (as opposed to reallocation problems), we define a probabilistic version of the crawler by choosing an endowment profile at random according to a uniform distribution, and applying the original definition. Our third theorem states that this rule is the same as the "random priority rule".
Set-Valued Risk Measures as Backward Stochastic Difference Inclusions and Equations
Authors: Çağın Ararat, Zachary Feinstein
Date: 2019-12-14 00:00:00

Scalar dynamic risk measures for univariate positions in continuous time are commonly represented as backward stochastic differential equations. In the multivariate setting, dynamic risk measures have been defined and studied as families of set-valued functionals in the recent literature. There are two possible extensions of scalar backward stochastic differential equations for the set-valued framework: (1) backward stochastic differential inclusions, which evaluate the risk dynamics on the selectors of acceptable capital allocations; or (2) set-valued backward stochastic differential equations, which evaluate the risk dynamics on the full set of acceptable capital allocations as a singular object. In this work, the discrete time setting is investigated with difference inclusions and difference equations in order to provide insights for such differential representations for set-valued dynamic risk measures in continuous time.
Compressed Sensing for Reconstructing Coherent Multidimensional Spectra
Date: 2019-12-14 00:00:00
Tasks: compressed sensing

We apply two sparse reconstruction techniques, the least absolute shrinkage
and selection operator (LASSO) and the sparse exponential mode analysis (SEMA),
to two-dimensional (2D) spectroscopy. The algorithms are first tested on model
data, showing that both are able to reconstruct the spectra using only a
fraction of the data required by the traditional Fourier-based estimator.
Through the analysis of a sparsely sampled experimental fluorescence detected
2D spectra of LH2 complexes, we conclude that both SEMA and LASSO can be used
to significantly reduce the required data, still allowing to reconstruct the
multidimensional spectra. Of the two techniques, it is shown that SEMA offers
preferable performance, providing more accurate estimation of the spectral line
widths and their positions. Furthermore, SEMA allows for off-grid components,
enabling the use of a much smaller dictionary than the LASSO, thereby improving
both the performance and lowering the computational complexity for
reconstructing coherent multidimensional spectra.
Gauge transformations in the dual space, and pricing and estimation in the long run in affine jump-diffusion models
Date: 2019-12-25 00:00:00

We suggest a simple reduction of pricing European options in affine
jump-diffusion models to pricing options with modified payoffs in diffusion
models. The procedure is based on the conjugation of the infinitesimal
generator of the model with an operator of the form $e^{i\Phi(-i\dd_x)}$ (gauge
transformation in the dual space). A general procedure for the calculation of
the function $\Phi$ is given, with examples. As applications, we consider
pricing in jump-diffusion models and their subordinated versions using the
eigenfunction expansion technique, and estimation of the extremely rare jumps
component. The beliefs of the market about yet unobserved extreme jumps and
pricing kernel can be recovered: the market prices allow one to see "the shape
of things to come".
A network-constrained rolling transactive energy model for EV aggregators participating in balancing marke
Date: 2019-12-15 00:00:00
Tasks: Management

The increasing adoption of renewable energy sources increases the need for
balancing power. The security concerns of the distribution system operators are
increasing due to fast adoption of distributed energy resources. So far,
various operational models are proposed to manage the distributed energy
resources for economic benefit. However, there is lack of an efficient
operational model that balances the security, market, and uncertainty
management. With the focus on the aggregators operation, this paper developed
an operational model for continuous operation of aggregators interactively with
the grid operators. A rolling optimization model is developed to manage the
uncertainties from prediction errors. The network security is ensured
continuously through an iterative price negotiation process between the
distribution network operator and the aggregators. The optimality of the
solution is guaranteed by convexification of the mixed integer formulation.
On the Feasibility of Multi-Mode Antennas in UWB and IoT Applications below 10 GHz
Authors: Nils L. Johannsen, Nikolai Peitzmeier, Peter A. Hoeher, Dirk Manteuffel
Date: 2019-12-15 00:00:00

While on the one hand 5G and B5G networks are challenged by ultra-high data rates in wideband applications like 100+ Gbps wireless Internet access, on the other hand they are expected to support reliable low-latency Internet of Things (IoT) applications with ultra-high connectivity. These conflicting challenges are addressed in a system proposal dealing with both extremes. In contrast to most recent publications, focus is on the frequency domain below 10~GHz. Towards this goal, multi-mode antenna technology is used and different realizations, offering up to eight uncorrelated ports per radiator element, are studied. Possible baseband architectures tailored to multi-mode antennas are discussed, enabling different options regarding precoding and beamforming.
Single-Element Beamforming using Multi-Mode Antenna Patterns
Authors: Nils L. Johannsen, Peter A. Hoeher
Date: 2019-12-15 00:00:00

Recently, multi-mode antennas have been studied for communication as well as localization purposes. In this work, the capabilities provided by a single planar multi-mode radiator as a steerable multi-port antenna are explored. As an original contribution, the radiation characteristics of individual groups of modes of the single radiator are combined to optimize beamforming performance. Three possible codebook realizations are studied and compared. A new optimization criterion, gain by element factor, is introduced.
Characterizing Generalized Rate-Distortion Performance of Video Coding: An Eigen Analysis Approach
Date: 2020-04-13 00:00:00
Tasks: Data Compression

Rate-distortion (RD) theory is at the heart of lossy data compression. Here
we aim to model the generalized RD (GRD) trade-off between the visual quality
of a compressed video and its encoding profiles (e.g., bitrate and spatial
resolution). We first define the theoretical functional space $\mathcal{W}$ of
the GRD function by analyzing its mathematical properties.We show that
$\mathcal{W}$ is a convex set in a Hilbert space, inspiring a computational
model of the GRD function, and a method of estimating model parameters from
sparse measurements. To demonstrate the feasibility of our idea, we collect a
large-scale database of real-world GRD functions, which turn out to live in a
low-dimensional subspace of $\mathcal{W}$. Combining the GRD reconstruction
framework and the learned low-dimensional space, we create a low-parameter
eigen GRD method to accurately estimate the GRD function of a source video
content from only a few queries. Experimental results on the database show that
the learned GRD method significantly outperforms state-of-the-art empirical RD
estimation methods both in accuracy and efficiency. Last, we demonstrate the
promise of the proposed model in video codec comparison.
On the Order of Gene Distribution on Chromosomes Across the Animal Kingdom
Authors: Abdullah A. Toor, Amir A. Toor MD
Date: 2019-12-16 00:00:00

Background. The large-scale pattern of distribution of genes on the chromosomes in the known animal genomes is not well characterized. We hypothesized that individual genes will be distributed on chromosomes in a mathematically ordered manner across the animal kingdom. Results. Twenty-one animal genomes reported in the NCBI database were examined. Numerically, there was a trend towards increasing overall gene content with increasing size of the genome as reflected by the chromosomal complement. Gene frequency on individual chromosomes in each animal genome was analyzed and demonstrated uniformity of proportions within each animal with respect to both average gene frequency on individual chromosomes and gene distribution across the unique genomes. Further, average gene distribution across animal species followed a relationship whereby it was, approximately, inversely proportional to the square root of the number of chromosomes in the unique animal genomes, consistent with the notion that there is an ordered increase in gene dispersion as the complexity of the genome increased. To further corroborate these findings a derived measure, termed gene spacing on chromosomes correlated with gene frequency and gene distribution. Conclusion. As animal species have evolved, the distribution of their genes on individual chromosomes and within their genomes, when viewed on a large scale is not random, but follows a mathematically ordered process, such that as the complexity of the organism increases, the genes become less densely distributed on the chromosomes and more dispersed across the genome.
An Economical Business-Cycle Model
Authors: Pascal Michaillat, Emmanuel Saez
Date: 2019-12-16 00:00:00
Tasks: model

This paper develops a new model of business cycles. The model is economical in that it is solved with an aggregate demand-aggregate supply diagram, and the effects of shocks and policies are obtained by comparative statics. The model builds on two unconventional assumptions. First, producers and consumers meet through a matching function. Thus, the model features unemployment, which fluctuates in response to aggregate demand and supply shocks. Second, wealth enters the utility function, so the model allows for permanent zero-lower-bound episodes. In the model, the optimal monetary policy is to set the interest rate at the level that eliminates the unemployment gap. This optimal interest rate is computed from the prevailing unemployment gap and monetary multiplier (the effect of the nominal interest rate on the unemployment rate). If the unemployment gap is exceedingly large, monetary policy cannot eliminate it before reaching the zero lower bound, but a wealth tax can.
Predicting intraday jumps in stock prices using liquidity measures and technical indicators
Authors: Ao Kong, Hongliang Zhu, Robert Azencott
Date: 2019-12-16 00:00:00
Tasks: BIG-bench Machine Learning

Predicting the intraday stock jumps is a significant but challenging problem in finance. Due to the instantaneity and imperceptibility characteristics of intraday stock jumps, relevant studies on their predictability remain limited. This paper proposes a data-driven approach to predict intraday stock jumps using the information embedded in liquidity measures and technical indicators. Specifically, a trading day is divided into a series of 5-minute intervals, and at the end of each interval, the candidate attributes defined by liquidity measures and technical indicators are input into machine learning algorithms to predict the arrival of a stock jump as well as its direction in the following 5-minute interval. Empirical study is conducted on the level-2 high-frequency data of 1271 stocks in the Shenzhen Stock Exchange of China to validate our approach. The result provides initial evidence of the predictability of jump arrivals and jump directions using level-2 stock data as well as the effectiveness of using a combination of liquidity measures and technical indicators in this prediction. We also reveal the superiority of using random forest compared to other machine learning algorithms in building prediction models. Importantly, our study provides a portable data-driven approach that exploits liquidity and technical information from level-2 stock data to predict intraday price jumps of individual stocks.