pwc-archive/papers-with-abstracts huggingface dataset:
Bridging the Gap between Optimal Trajectory Planning and Safety-Critical Control with Applications to Autonomous Vehicles
Date: 2020-08-17 00:00:00
Tasks: Autonomous Vehicles, Trajectory Planning

We address the problem of optimizing the performance of a dynamic system
while satisfying hard safety constraints at all times. Implementing an optimal
control solution is limited by the computational cost required to derive it in
real time, especially when constraints become active, as well as the need to
rely on simple linear dynamics, simple objective functions, and ignoring noise.
The recently proposed Control Barrier Function (CBF) method may be used for
safety-critical control at the expense of sub-optimal performance. In this
paper, we develop a real-time control framework that combines optimal
trajectories generated through optimal control with the computationally
efficient CBF method providing safety guarantees. We use Hamiltonian analysis
to obtain a tractable optimal solution for a linear or linearized system, then
employ High Order CBFs (HOCBFs) and Control Lyapunov Functions (CLFs) to
account for constraints with arbitrary relative degrees and to track the
optimal state, respectively. We further show how to deal with noise in
arbitrary relative degree systems. The proposed framework is then applied to
the optimal traffic merging problem for Connected and Automated Vehicles (CAVs)
where the objective is to jointly minimize the travel time and energy
consumption of each CAV subject to speed, acceleration, and speed-dependent
safety constraints. In addition, when considering more complex objective
functions, nonlinear dynamics and passenger comfort requirements for which
analytical optimal control solutions are unavailable, we adapt the HOCBF method
to such problems. Simulation examples are included to compare the performance
of the proposed framework to optimal solutions (when available) and to a
baseline provided by human-driven vehicles with results showing significant
improvements in all metrics.
Massively Distributed Antenna Systems with Non-Ideal Optical Fiber Front-hauls: A Promising Technology for 6G Wireless Communication Systems
Authors: Lisu Yu, Jingxian Wu, Andong Zhou, Erik G. Larsson, Pingzhi Fan
Date: 2020-08-18 00:00:00

Employing massively distributed antennas brings radio access points (RAPs) closer to users, thus enables aggressive spectrum reuse that can bridge gaps between the scarce spectrum resource and extremely high connection densities in future wireless systems. Examples include cloud radio access network (C-RAN), ultra-dense network (UDN), and cell-free massive multiple-input multiple-output (MIMO) systems. These systems are usually designed in the form of fiber-wireless communications (FWC), where distributed antennas or RAPs are connected to a central unit (CU) through optical front-hauls. A large number of densely deployed antennas or RAPs requires an extensive infrastructure of optical front-hauls. Consequently, the cost, complexity, and power consumption of the network of optical front-hauls may dominate the performance of the entire system. This article provides an overview and outlook on the architecture, modeling, design, and performance of massively distributed antenna systems with non-ideal optical front-hauls. Complex interactions between optical front-hauls and wireless access links require optimum designs across the optical and wireless domains by jointly exploiting their unique characteristics. It is demonstrated that systems with analog radio-frequency-over-fiber (RFoF) links outperform their baseband-over-fiber (BBoF) or intermediate-frequency-over-fiber (IFoF) counterparts for systems with shorter fiber length and more RAPs, which are all desired properties for future wireless communication systems.
Open-Loop Distributed Beamforming Using Scalable High Accuracy Localization
Authors: Sean M. Ellison, Jeffrey A. Nanzer
Date: 2020-08-18 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We present a distributed antenna array supporting open-loop distributed beamforming at 1.5 GHz. Based on a scalable, high-accuracy internode ranging technique, we demonstrate open-loop beamforming experiments using three transmitting nodes. To support distributed beamforming without feedback from the destination, the relative positions of the nodes in the distributed array must be known with accuracies below $\lambda/15$ of the beamforming carrier frequency to ensure that the array maintains at least 90\% coherent beamforming gain at the receive location. For operations in the microwave range, this leads to range estimation accuracies of centimeters or lower. We present a scalable, high-accuracy waveform and new approaches to refine range measurements to significantly improve the estimation accuracy. Using this waveform with a three-node array, we demonstrate high-accuracy ranging simultaneously between multiple nodes, from which phase corrections on two secondary nodes are implemented to maintain beamforming with the primary node, thereby supporting open-loop distributed beamforming. Upon movement of the nodes, the range estimation is used to dynamically update the phase correction, maintaining beamforming as the nodes move. We show the first open-loop distributed beamforming at 1.5 GHz with two-node and three-node arrays, demonstrating the ability to implement and maintain phase-based beamforming without feedback from the destination.
Tdcgan: Temporal Dilated Convolutional Generative Adversarial Network for End-to-end Speech Enhancement
Date: 2020-09-30 00:00:00
Tasks: Generative Adversarial Network, Speech Enhancement

In this paper, in order to further deal with the performance degradation
caused by ignoring the phase information in conventional speech enhancement
systems, we proposed a temporal dilated convolutional generative adversarial
network (TDCGAN) in the end-to-end based speech enhancement architecture. For
the first time, we introduced the temporal dilated convolutional network with
depthwise separable convolutions into the GAN structure so that the receptive
field can be greatly increased without increasing the number of parameters. We
also first explored the effect of signal-to-noise ratio (SNR) penalty item as
regularization of the loss function of generator on improving the SNR of
enhanced speech. The experimental results demonstrated that our proposed method
outperformed the state-of-the-art end-to-end GAN-based speech enhancement.
Moreover, compared with previous GAN-based methods, the proposed TDCGAN could
greatly decreased the number of parameters. As expected, the work also
demonstrated that the SNR penalty item as regularization was more effective
than $L1$ on improving the SNR of enhanced speech.
Normalising phylogenetic networks
Authors: Andrew Francis, Daniel H. Huson, Mike Steel
Date: 2020-08-18 00:00:00

Rooted phylogenetic networks provide a way to describe species' relationships when evolution departs from the simple model of a tree. However, networks inferred from genomic data can be highly tangled, making it difficult to discern the main reticulation signals present. In this paper, we describe a natural way to transform any rooted phylogenetic network into a simpler canonical network, which has desirable mathematical and computational properties, and is based only on the 'visible' nodes in the original network. The method has been implemented and we demonstrate its application to some examples.
On the Dynamic Range of Digital Correlative Time Domain Radio Channel Measurements
Authors: Sven Wittig, Wilhelm Keusgen, Michael Peter, Taro Eichler
Date: 2020-08-18 00:00:00

For the concise characterization of radio channel measurement setups, different performance metrics like dynamic range and maximum measurable path loss have been proposed. This work further elaborates on these metrics and discusses practical bounds for the class of digital correlative time domain channel sounders. From a general instrumentation model, relevant nonidealities and the resulting effects on measurement performance are identified. These considerations yield the tools to select appropriate measurement parameters for a given scenario and assess the overall performance of a particular measurement setup. The findings are further illustrated with data acquired from an instrument-based measurement setup.
Adaptive trading strategies across liquidity pools
Authors: Bastien Baldacci, Iuliia Manziuk
Date: 2020-08-18 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

In this article, we provide a flexible framework for optimal trading in an asset listed on different venues. We take into account the dependencies between the imbalance and spread of the venues, and allow for partial execution of limit orders at different limits as well as market orders. We present a Bayesian update of the model parameters to take into account possibly changing market conditions and propose extensions to include short/long trading signals, market impact or hidden liquidity. To solve the stochastic control problem of the trader we apply the finite difference method and also develop a deep reinforcement learning algorithm allowing to consider more complex settings.
Long vs Short Time Scales: the Rough Dilemma and Beyond
Authors: Matthieu Garcin, Martino Grasselli
Date: 2020-08-18 00:00:00
Tasks: Time Series Analysis

Using a large dataset on major FX rates, we test the robustness of the rough fractional volatility model over different time scales, by including smoothing and measurement errors into the analysis. Our findings lead to new stylized facts in the log-log plots of the second moments of realized variance increments against lag which exhibit some convexity in addition to the roughness and stationarity of the volatility. The very low perceived Hurst exponents at small scales is consistent with the rough framework, while the higher perceived Hurst exponents for larger scales leads to a nonlinear behavior of the log-log plot that has not been described by models introduced so far.
Unveiling the directional network behind the financial statements data using volatility constraint correlation
Authors: Tomoshiro Ochiai, Jose C. Nacher
Date: 2020-08-18 00:00:00
Tasks: Management

Financial data, such as financial statements, contain valuable and critical information that may assist stakeholders and investors in optimizing their capital to maximize overall economic growth. Since there are many variables in financial statements, it is crucial to determine the causal relationships, that is, the directional influence between them in a structural way, as well as to understand the associated accounting mechanisms. However, the analysis of variable-to-variable relationships in financial information using standard correlation functions is not sufficient to unveil directionality. Here, we use the volatility constrained correlation (VC correlation) method to predict the directional relationship between two arbitrary variables. We apply the VC correlation method to five significant financial information variables (revenue, net income, operating income, own capital, and market capitalization) of 2321 firms listed on the Tokyo Stock Exchange over 28 years from 1990 to 2018. This study identifies which accounting variables are influential and which are susceptible. Our findings show that operating income is the most influential variable while market capitalization and revenue are the most susceptible variables. Surprisingly, the results differ from the existing intuitive understanding suggested by widely used investment strategy indicators, the price--earnings ratio and the price-to-book ratio, which report that net income and own capital are the most influential variables affecting market capitalization. This analysis may assist managers, stakeholders, and investors to improve financial management performance and optimize firms' financial strategies in future operations.
Fast Agent-Based Simulation Framework with Applications to Reinforcement Learning and the Study of Trading Latency Effects
Authors: Peter Belcak, Jan-Peter Calliess, Stefan Zohren
Date: 2020-08-18 00:00:00

We introduce a new software toolbox for agent-based simulation. Facilitating rapid prototyping by offering a user-friendly Python API, its core rests on an efficient C++ implementation to support simulation of large-scale multi-agent systems. Our software environment benefits from a versatile message-driven architecture. Originally developed to support research on financial markets, it offers the flexibility to simulate a wide-range of different (easily customisable) market rules and to study the effect of auxiliary factors, such as delays, on the market dynamics. As a simple illustration, we employ our toolbox to investigate the role of the order processing delay in normal trading and for the scenario of a significant price change. Owing to its general architecture, our toolbox can also be employed as a generic multi-agent system simulator. We provide an example of such a non-financial application by simulating a mechanism for the coordination of no-regret learning agents in a multi-agent network routing scenario previously proposed in the literature.
Elimination of Central Artefacts of L-SPECT with Modular Partial Ring Detectors by Shifting Center of Scanning
Date: 2020-08-18 00:00:00
Tasks: Lightfield

The Lightfield Single Photon Emission Computed Tomography (L-SPECT) system is
developed to overcome some of the drawbacks in conventional SPECT by applying
the idea of plenoptic imaging. This system displayed improved performance in
terms of reduced information loss and scanning time when compared to the SPECT
system which has a conventional collimator. The SPECT system is transformed
into L-SPECT system by replacing the conventional collimators with micro-range
multi-pinhole arrays. The field of view (FOV) of the L-SPECT system is enhanced
by reshaping the detector head into ring-type by tiling small detector modules.
The L-SPECT system with modular partial ring detectors (MPRD L-SPECT) exhibits
cylindrical artefacts during volumetric reconstruction. Hence, here the work is
focused to remove the cylindrical artefacts in the reconstruction of the
examined objects by changing the scanning orbit. The enhancement is done such
that the center of scanning of the L-SPECT system with MPRD L-SPECT is shifted
at different values. The reconstruction quality of MPRD L-SPECT with and
without center shifting is evaluated in terms of the Full Width at Half Maximum
(FWHM) and Modulation Transfer Function (MTF). Moreover, the visual comparison
is also examined. The results indicate that center shifting of MPRD L-SPECT
overcomes the problem of the central artefact with improved FWHM. By increasing
MPRD's scanning center shift gap, the spatial resolution can be further
improved.
A Sensitivity-based Approach for Optimal Siting of Distributed Energy Resources
Date: 2020-08-18 00:00:00
Tasks: Sensitivity

This paper presents a sensitivity-based approach for the placement of
distributed energy resources (DERs) in power systems. The approach is based on
the fact that most planning studies utilize some form of optimization, and
solutions to these optimization problems provide insights into the sensitivity
of many system variables to operating conditions and constraints. However, most
of the existing sensitivity-based planning criteria do not capture ranges of
effectiveness of these solutions (i.e., ranges of the effectiveness of Lagrange
multipliers). The proposed method detects the ranges of the effectiveness of
Lagrange multipliers and uses them to determine optimal solution alternatives.
Profiles for existing generation and loads, and transmission constraints are
taken into consideration. The proposed method is used to determine the impacts
of DERs at different locations, in the presence of a stochastic element (load
variability). This method consists of sequentially calculating Lagrange
multipliers of the dual solution of the optimization problem for various load
buses for all load scenarios. Optimal sizes and sites of resources are jointly
determined in a sequential manner based on the validity of active constraints.
The effectiveness of the proposed method is demonstrated through several case
studies on various test systems including the IEEE reliability test system
(IEEE RTS), the IEEE 14, and 30 bus systems. In comparison with conventional
sensitivity-based approaches (i.e., without considering ranges of validity of
Lagrange multipliers), the proposed approach provides more accurate results for
active constraints.
Accelerometric Method for Cuffless Continuous Blood Pressure Measurement
Authors: Mousumi Das, Tilendra Choudhary, L. N. Sharma, M. K. Bhuyan
Date: 2020-08-18 00:00:00

Pulse transit time (PTT) has been widely used for cuffless blood pressure (BP) measurement. But, it requires more than one cardiovascular signals involving more than one sensing device. In this paper, we propose a method for continuous cuffless blood pressure measurement with the help of left ventricular ejection time (LVET). The LVET is estimated using a signal obtained through a micro-electromechanical system (MEMS)-based accelerometric sensor. The sensor acquires a seismocardiogram (SCG) signal at the chest surface, and the LVET information is extracted. Both systolic blood pressure (SBP) and diastolic blood pressure (DBP) are estimated by calibrating the system with the original arterial blood pressure values of the subjects. The proposed method is evaluated using different quantitative measures on the signals collected from ten subjects under the supine position. The performance of the proposed method is also compared with two earlier approaches, where PTT intervals are estimated from electrocardiogram (ECG)-photoplethysmogram (PPG) and SCG-PPG, respectively. The performance results clearly show that the proposed method is comparable with the state-of-the-art methods. Also, the computed blood pressure is compared with the original one, measured through a CNAP system. It gives the mean errors of the estimated systolic BP and diastolic BP within the range of -0.19 +/- 3.3 mmHg and -1.29 +/- 2.6 mmHg, respectively. The mean absolute errors for systolic BP and diastolic BP are 3.2 mmHg and 2.6 mmHg, respectively. The accuracy of BPs estimated from the proposed method satisfies the requirements of the IEEE standard of 5 +/- 8 mmHg deviation, and thus, it may be used for ubiquitous long term blood pressure monitoring.
Volatility Depends on Market Trades and Macro Theory
Authors: Victor Olkhov
Date: 2020-08-15 00:00:00

We consider the randomness of market trade as the origin of price and return stochasticity. We look at time series of trade values and volumes as random variables during the averaging interval {\Delta} and describe the dependences of market-based volatilities of price and return on the volatilities and correlations of market trade values and volumes. We describe the market-based origin of the lower boundaries of the accuracy of macroeconomic variables and consider, as an example, the accuracy of macroeconomic investments. We highlight that current macroeconomic models describe relations between the 1st order variables determined by sums of trade values or volumes. To predict market-based volatilities of price, return, and volatilities of macroeconomic variables, one should develop econometric methodologies, collect data, and elaborate macroeconomic theories of the 2nd order that model the mutual dependence of the 1st and 2nd order economic variables. The absence of macroeconomic theories of the 2nd order means no economic basis for predictions of market-based volatilities of price and return, as well as volatilities of any macroeconomic variables. In turn, that limits the accuracy of forecasting probabilities of price, return, and the accuracy of macroeconomic variables in the best case by Gaussian distributions.
High Accurate Time-of-Arrival Estimation with Fine-Grained Feature Generation for Internet-of-Things Applications
Authors: Guangjin Pan, Tao Wang, Shunqing Zhang, Shugong Xu
Date: 2020-08-18 00:00:00

Conventional schemes often require extra reference signals or more complicated algorithms to improve the time-of-arrival (TOA) estimation accuracy. However, in this letter, we propose to generate fine-grained features from the full band and resource block (RB) based reference signals, and calculate the cross-correlations accordingly to improve the observation resolution as well as the TOA estimation results. Using the spectrogram-like cross-correlation feature map, we apply the machine learning technology with decoupled feature extraction and fitting to understand the variations in the time and frequency domains and project the features directly into TOA results. Through numerical examples, we show that the proposed high accurate TOA estimation with fine-grained feature generation can achieve at least 51% root mean square error (RMSE) improvement in the static propagation environments and 38 ns median TOA estimation errors for multipath fading environments, which is equivalently 36% and 25% improvement if compared with the existing MUSIC and ESPRIT algorithms, respectively.
Energy-Optimal Control of a Submarine-Launched Cruise Missile
Date: 2020-08-18 00:00:00

A typical mission profile of submarine-launched cruise missiles begins with
the launch phase which covers the motion of the missile from the launch to the
water-exit and continues with the boost phase which lasts from the water-exit
to the beginning of the cruise phase. In order to achieve the desired range of
the launch and boost phases, efficient utilization of available energy which
carries the missile to the beginning of the cruise phase is necessary. For this
purpose, this study presents a new approach for energy-optimal control of the
underwater and air motion of a submarine-launched cruise missile. In this
approach, the aforementioned problem is modeled and solved as a minimum-effort
optimal control problem. Then, the effects of initial and final conditions on
energy need are investigated, and the optimal conditions that result with the
minimum energy need are determined. Prior to the guidance and control design
steps, six degrees of freedom (6 DOF) motion equations are derived and the
hydrodynamic and aerodynamic parameters are retrieved. The nonlinear 6 DOF
motion model is simplified and linearized before minimum-effort optimal control
design part. Results of the designed guidance and control strategies are
presented through the nonlinear 6 DOF simulations. Finally, some comments are
made and future studies are mentioned based on theoretical and simulation
studies.
Bounds on Distributional Treatment Effect Parameters using Panel Data with an Application on Job Displacement
Date: 2020-08-18 00:00:00
Tasks: counterfactual

This paper develops new techniques to bound distributional treatment effect
parameters that depend on the joint distribution of potential outcomes -- an
object not identified by standard identifying assumptions such as selection on
observables or even when treatment is randomly assigned. I show that panel data
and an additional assumption on the dependence between untreated potential
outcomes for the treated group over time (i) provide more identifying power for
distributional treatment effect parameters than existing bounds and (ii)
provide a more plausible set of conditions than existing methods that obtain
point identification. I apply these bounds to study heterogeneity in the effect
of job displacement during the Great Recession. Using standard techniques, I
find that workers who were displaced during the Great Recession lost on average
34\% of their earnings relative to their counterfactual earnings had they not
been displaced. Using the methods developed in the current paper, I also show
that the average effect masks substantial heterogeneity across workers.