pwc-archive/papers-with-abstracts huggingface dataset:
Wideband Collaborative Spectrum Sensing using Massive MIMO Decision Fusion
Authors: I. Dey, D. Ciuonzo, P. Salvo Rossi
Date: 2020-06-10 00:00:00

In this paper, in order to tackle major challenges of spectrum exploration \& allocation in Cognitive Radio (CR) networks, we apply the general framework of Decision Fusion (DF) to wideband collaborative spectrum sensing based on Orthogonal Frequency Division Multiplexing (OFDM) reporting. At the transmitter side, we employ OFDM without Cyclic Prefix (CP) in order to improve overall bandwidth efficiency of the reporting phase in networks with high user density. On the other hand, at the receiver side (of the reporting channel) we device the Time-Reversal Widely Linear (TR-WL), Time-Reversal Maximal Ratio Combining (TR-MRC) and modified TR-MRC (TR-mMRC) rules for DF. The DF Center (DFC) is assumed to be equipped with a large antenna array, serving a number of unauthorized users competing for the spectrum, thereby resulting in a ``virtual'' massive Multiple-Input Multiple-Output (MIMO) channel. The effectiveness of the proposed TR-based rules in combating ($a$) inter-symbol and ($b$) inter-carrier interference over conventional (non-TR) counterparts is then examined, as a function of the Signal-to-Interference-plus-Noise Ratio (SINR). Closed-form performance, in terms of system false-alarm and detection probabilities, is derived for the formulated fusion rules. Finally, the impact of large-scale channel effects on the proposed fusion rules is also investigated, via Monte-Carlo simulations.
Multi-Agent Reinforcement Learning in a Realistic Limit Order Book Market Simulation
Authors: Michaël Karpe, Jin Fang, Zhongyao Ma, Chen Wang
Date: 2020-06-10 00:00:00
Tasks: Multi-agent Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning (RL)

Optimal order execution is widely studied by industry practitioners and academic researchers because it determines the profitability of investment decisions and high-level trading strategies, particularly those involving large volumes of orders. However, complex and unknown market dynamics pose significant challenges for the development and validation of optimal execution strategies. In this paper, we propose a model-free approach by training Reinforcement Learning (RL) agents in a realistic market simulation environment with multiple agents. First, we configure a multi-agent historical order book simulation environment for execution tasks built on an Agent-Based Interactive Discrete Event Simulation (ABIDES) [arXiv:1904.12066]. Second, we formulate the problem of optimal execution in an RL setting where an intelligent agent can make order execution and placement decisions based on market microstructure trading signals in High Frequency Trading (HFT). Third, we develop and train an RL execution agent using the Double Deep Q-Learning (DDQL) algorithm in the ABIDES environment. In some scenarios, our RL agent converges towards a Time-Weighted Average Price (TWAP) strategy. Finally, we evaluate the simulation with our RL agent by comparing it with a market replay simulation using real market Limit Order Book (LOB) data.
Deep reinforcement learning for optical systems: A case study of mode-locked lasers
Authors: Chang Sun, Eurika Kaiser, Steven L. Brunton, J. Nathan Kutz
Date: 2020-06-10 00:00:00
Tasks: Deep Reinforcement Learning, Navigate, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Trajectory Planning, Transfer Learning

We demonstrate that deep reinforcement learning (deep RL) provides a highly effective strategy for the control and self-tuning of optical systems. Deep RL integrates the two leading machine learning architectures of deep neural networks and reinforcement learning to produce robust and stable learning for control. Deep RL is ideally suited for optical systems as the tuning and control relies on interactions with its environment with a goal-oriented objective to achieve optimal immediate or delayed rewards. This allows the optical system to recognize bi-stable structures and navigate, via trajectory planning, to optimally performing solutions, the first such algorithm demonstrated to do so in optical systems. We specifically demonstrate the deep RL architecture on a mode-locked laser, where robust self-tuning and control can be established through access of the deep RL agent to its waveplates and polarizers. We further integrate transfer learning to help the deep RL agent rapidly learn new parameter regimes and generalize its control authority. Additionally, the deep RL learning can be easily integrated with other control paradigms to provide a broad framework to control any optical system.
Speaker Diarization: Using Recurrent Neural Networks
Date: 2020-06-10 00:00:00
Tasks: speaker-diarization, Speaker Diarization

Speaker Diarization is the problem of separating speakers in an audio. There
could be any number of speakers and final result should state when speaker
starts and ends. In this project, we analyze given audio file with 2 channels
and 2 speakers (on separate channel). We train Neural Network for learning when
a person is speaking. We use different type of Neural Networks specifically,
Single Layer Perceptron (SLP), Multi Layer Perceptron (MLP), Recurrent Neural
Network (RNN) and Convolution Neural Network (CNN) we achieve $\sim$92\% of
accuracy with RNN. The code for this project is available at
https://github.com/vishalshar/SpeakerDiarization_RNN_CNN_LSTM
Max-Min Optimal Beamforming for Cell-Free Massive MIMO
Authors: Andong Zhou, Jingxian Wu, Erik G. Larsson, Pingzhi Fan
Date: 2020-06-10 00:00:00

This letter develops an optimum beamforming method for downlink transmissions in cell-free massive multiple-input multiple-output (MIMO) systems, which employ a massive number of distributed access points to provide concurrent services to multiple users. The optimum design is formulated as a max-min problem that maximizes the minimum signal-to-interference-plus-noise ratio of all users. It is shown analytically that the problem is quasi-concave, and the optimum solution is obtained with the second-order cone programming. The proposed method identifies the best achievable beamforming performance in cell-free massive MIMO systems. The results can be used as benchmarks for the design of practical low complexity beamformers.
Quant Bust 2020
Authors: Zura Kakushadze
Date: 2020-06-10 00:00:00
Tasks: Quant Trading

We explain in a nontechnical fashion why dollar-neutral quant trading strategies, such as equities Statistical Arbitrage, suffered substantial losses (drawdowns) during the COVID-19 market selloff. We discuss: (i) why these strategies work during "normal" times; (ii) the market regimes when they work best; and (iii) their limitations and the reasons for why they "break" during extreme market events. An accompanying appendix (with a link to freely accessible source code) includes backtests for various strategies, which put flesh on and illustrate the discussion in the main text.
Distributed Optimization for Massive Connectivity
Authors: Yuning Jiang, Junyan Su, Yuanming Shi, Boris Houska
Date: 2020-06-10 00:00:00
Tasks: Action Detection, Activity Detection, Distributed Optimization

Massive device connectivity in Internet of Thing (IoT) networks with sporadic traffic poses significant communication challenges. To overcome this challenge, the serving base station is required to detect the active devices and estimate the corresponding channel state information during each coherence block. The corresponding joint activity detection and channel estimation problem can be formulated as a group sparse estimation problem, also known under the name "Group Lasso". This letter presents a fast and efficient distributed algorithm to solve such Group Lasso problems, which alternates between solving small-scaled problems in parallel and dealing with a linear equation for consensus. Numerical results demonstrate the speedup of this algorithm compared with the state-of-the-art methods in terms of convergence speed and computation time.
Bailout Stigma
Authors: Yeon-Koo Che, Chongwoo Choe, Keeyoung Rhee
Date: 2020-06-10 00:00:00

We develop a model of bailout stigma where accepting a bailout signals a firm's balance-sheet weakness and worsens its funding prospect. To avoid stigma, high-quality firms either withdraw from subsequent financing after receiving bailouts or refuse bailouts altogether to send a favorable signal. The former leads to a short-lived stimulation with a subsequent market freeze even worse than if there were no bailouts. The latter revives the funding market, albeit with delay, to the level achievable without any stigma, and implements a constrained optimal outcome. A menu of multiple bailout programs also compounds bailout stigma and worsens market freeze.
Online PMU-Based Wide-Area Damping Control for Multiple Inter-Area Modes
Date: 2020-06-10 00:00:00

This paper presents a new phasor measurement unit (PMU)-based wide-area
damping control (WADC) method to suppress the critical inter-area modes of
large-scale power systems. Modal participation factors, estimated by a
practically model-free system identification approach, are used to select the
most suitable synchronous generators for control through the proposed WADC
algorithm. It is shown that multiple inter-area modes can be sufficiently
damped by the proposed approach without affecting the rest of the modes, while
only a few machines are needed to perform the control. The proposed technique
is applied to the IEEE 68-bus and the IEEE 145-bus systems, including the test
cases with PMU measurement noise and with missing PMUs. The simulation results
clearly demonstrate the good adaptivity of the control strategy subjected to
network model changes, its effective damping performance comparing to power
system stabilizers (PSSs), and its great potential for near real-time
implementation.
Off-Policy Risk-Sensitive Reinforcement Learning Based Constrained Robust Optimal Control
Authors: Cong Li, Qingchen Liu, Zhehua Zhou, Martin Buss, Fangzhou Liu
Date: 2020-06-10 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

This paper proposes an off-policy risk-sensitive reinforcement learning based control framework for stabilization of a continuous-time nonlinear system that subjects to additive disturbances, input saturation, and state constraints. By introducing pseudo controls and risk-sensitive input and state penalty terms, the constrained robust stabilization problem of the original system is converted into an equivalent optimal control problem of an auxiliary system. Then, aiming at the transformed optimal control problem, we adopt adaptive dynamic programming (ADP) implemented as a single critic structure to get the approximate solution to the value function of the Hamilton-Jacobi-Bellman (HJB) equation, which results in the approximate optimal control policy that is able to satisfy both input and state constraints under disturbances. By replaying experience data to the off-policy weight update law of the critic artificial neural network, the weight convergence is guaranteed. Moreover, to get experience data to achieve a sufficient excitation required for the weight convergence, online and offline algorithms are developed to serve as principled ways to record informative experience data. The equivalence proof demonstrates that the optimal control strategy of the auxiliary system robustly stabilizes the original system without violating input and state constraints. The proofs of system stability and weight convergence are provided. Simulation results reveal the validity of the proposed control framework.
Efficient Joint Estimation of Tracer Distribution and Background Signals in Magnetic Particle Imaging using a Dictionary Approach
Authors: Tobias Knopp, Mirco Grosser, Matthias Graeser, Timo Gerkmann, Martin Möddel
Date: 2020-06-10 00:00:00

Background signals are a primary source of artifacts in magnetic particle imaging and limit the sensitivity of the method since background signals are often not precisely known and vary over time. The state-of-the art method for handling background signals uses one or several background calibration measurements with an empty scanner bore and subtracts a linear combination of these background measurements from the actual particle measurement. This approach yields satisfying results in case that the background measurements are taken in close proximity to the particle measurement and when the background signal drifts linearly. In this work, we propose a joint estimation of particle distribution and background signal based on a dictionary that is capable of representing typical background signals and allows for precise estimation of the background even when the latter is drifting non-linearly over time. Using a singular-value decomposition, the dictionary is derived from a large number of background calibration scans that do not need to be recorded in close proximity to the particle measurement. The dictionary is sufficiently expressive and represented by its principle components. The proposed joint estimation of particle distribution and background signal is expressed as a linear Tikhonov-regularized least squares problem, which can be efficiently solved. In phantom experiments it is shown that the method strongly suppresses background artifacts and even allows to estimate and remove the direct feed-through of the excitation field.
On the Influence of Noise in Randomized Consensus Algorithms
Date: 2020-06-10 00:00:00

In this paper we study the influence of additive noise in randomized
consensus algorithms. Assuming that the update matrices are symmetric, we
derive a closed form expression for the mean square error induced by the noise,
together with upper and lower bounds that are simpler to evaluate. Motivated by
the study of Open Multi-Agent Systems, we concentrate on Randomly Induced
Discretized Laplacians, a family of update matrices that are generated by
sampling subgraphs of a large undirected graph. For these matrices, we express
the bounds by using the eigenvalues of the Laplacian matrix of the underlying
graph or the graph's average effective resistance, thereby proving their
tightness. Finally, we derive expressions for the bounds on some examples of
graphs and numerically evaluate them.
Third DIHARD Challenge Evaluation Plan
Date: 2020-10-30 00:00:00
Tasks: speaker-diarization, Speaker Diarization

This paper introduces the third DIHARD challenge, the third in a series of
speaker diarization challenges intended to improve the robustness of
diarization systems to variation in recording equipment, noise conditions, and
conversational domain. The challenge comprises two tracks evaluating
diarization performance when starting from a reference speech segmentation
(track 1) and diarization from raw audio scratch (track 2). We describe the
task, metrics, datasets, and evaluation protocol.
North Sea Wind Power Hub: System Configurations, Grid Implementation and Techno-economic Assessment
Date: 2020-06-10 00:00:00

In 2017, Energinet and TenneT, the Danish and Dutch Transmission System
Operators (TSOs), have announced the North Sea Wind Power Hub (NSWPH) project.
The project aims at increasing by 36 GW the North Sea offshore wind capacity,
with an artificial island collecting all the power produced by wind turbines
and several HVDC links transmitting this power to the onshore grids. This
project brings together new opportunities and new challenges, both from a
technical and economic point of view. In this regard, this paper presents three
analyses regarding the design and operation of such an offshore system. First,
we perform a techno-economic assessment of different grid configurations for
the collection of the power produced by wind farms and its transmission to the
hub. In this analysis, two frequencies and two voltage levels for the operation
of the offshore grid are investigated. Our findings show that the
nominal-frequency high-voltage option is the more suitable, as low-frequency
does not bring any advantage and low-voltage would results in higher costs. The
second analysis is related to the differences in operating the system with low-
or zero-inertia; different dynamic studies are performed for each configuration
to identify proper control actions and their stability properties. Comparing
the outcomes of the simulations, we observed that voltage and frequency
oscillations are better damped in the zero-inertia system; however, the risk of
propagating offshore faults in the connected onshore grids is mitigated with
the inclusion of the synchronous condensers. Lastly, a comparison of
ElectroMagnetic Transient (EMT) and phasor-mode (also known as RMS) models is
presented, in order to understand their appropriateness of simulating low- and
zero- inertia systems. The results show that phasor approximation modelling can
be used, as long as eigen-frequencies in power network are well damped.
Optimal trade execution in an order book model with stochastic liquidity parameters
Authors: Julia Ackermann, Thomas Kruse, Mikhail Urusov
Date: 2020-06-10 00:00:00
Tasks: Position

We analyze an optimal trade execution problem in a financial market with stochastic liquidity. To this end we set up a limit order book model in which both order book depth and resilience evolve randomly in time. Trading is allowed in both directions and at discrete points in time. We derive an explicit recursion that, under certain structural assumptions, characterizes minimal execution costs. We also discuss several qualitative aspects of optimal strategies, such as existence of profitable round trips or closing the position in one go, and compare our findings with the literature.
Q-greedyUCB: a New Exploration Policy for Adaptive and Resource-efficient Scheduling
Date: 2020-06-10 00:00:00
Tasks: Decision Making, Q-Learning, Reinforcement Learning (RL), Scheduling

This paper proposes a learning algorithm to find a scheduling policy that
achieves an optimal delay-power trade-off in communication systems.
Reinforcement learning (RL) is used to minimize the expected latency for a
given energy constraint where the environments such as traffic arrival rates or
channel conditions can change over time. For this purpose, this problem is
formulated as an infinite-horizon Markov Decision Process (MDP) with
constraints. To handle the constrained optimization problem, we adopt the
Lagrangian relaxation technique to solve it. Then, we propose a variant of
Q-learning, Q-greedyUCB that combines Q-learning for \emph{average} reward
algorithm and Upper Confidence Bound (UCB) policy to solve this decision-making
problem. We prove that the Q-greedyUCB algorithm is convergent through
mathematical analysis. Simulation results show that Q-greedyUCB finds an
optimal scheduling strategy, and is more efficient than Q-learning with the
$\varepsilon$-greedy and Average-payoff RL algorithm in terms of the cumulative
reward (i.e., the weighted sum of delay and energy) and the convergence speed.
We also show that our algorithm can reduce the regret by up to 12% compared to
the Q-learning with the $\varepsilon$-greedy and Average-payoff RL algorithm.
VLSI Implementation of TDC Architectures Used in PET Imaging Systems
Authors: Mehmet Akif Ozdemir, Ali Tangel
Date: 2020-06-10 00:00:00

Positron emission tomography (PET) is a medical imaging method based on the measurement of concentrations of positron-emitting radionuclides in a living body. In the PET imaging system, glucose is labeled with a positron-emitting radionuclide and injected intravenously. Then, the positrons move through the tissue and collide with the electrons of the cells in which they interact. As a result of this interaction, two gamma rays are emitted in the opposite direction. Gama rays emitted from cancerous tissue that has retained radioactive glucose are detected through ring-shaped detectors. And the detected signals are converted into an electrical response. Subsequently, these responses are sampled with electronic circuits and recorded as histogram matrix to generate the image set. The gamma rays may not reach the detectors located in the opposite position in equal time. In PETs having TOF characteristics, it is aimed to obtain better positioning information by a method based on the principle of measuring the difference between the reach time of the two photons to detectors. The measurement of the flight time is carried out with TDC structures. The measurement of this time difference at the ps level is directly related to the spatial resolution of the PET system. In this study, 45 nm CMOS VLSI simulations of TDC structures that have various architectural approaches were performed for use in PET systems. With the designed TDC architectures, two gamma photons time reach to detectors have been simulated and the time difference has been successfully digitized. In addition, various performance metrics such as input and output voltages, time resolutions, measurement ranges, and power analysis of TDC architectures have been determined. Proposed Vernier oscillator-based TDC architecture has been reached 25 ps time resolution with a low power consumption of 1.62681 mW at 1V supply voltage.
Optimal Group Size in Microlending
Authors: Philip Protter, Alejandra Quintos
Date: 2020-06-10 00:00:00

Microlending, where a bank lends to a small group of people without credit histories, began with the Grameen Bank in Bangladesh, and is widely seen as the creation of Muhammad Yunus, who received the Nobel Peace Prize in recognition of his largely successful efforts. Since that time the modeling of microlending has received a fair amount of academic attention. One of the issues not yet addressed in full detail, however, is the issue of the size of the group. Some attention has nevertheless been paid using an experimental and game theory approach. We, instead, take a mathematical approach to the issue of an optimal group size, where the goal is to minimize the probability of default of the group. To do this, one has to create a model with interacting forces, and to make precise the hypotheses of the model. We show that the original choice of Muhammad Yunus, of a group size of five people, is, under the right, and, we believe, reasonable hypotheses, either close to optimal, or even at times exactly optimal, i.e., the optimal group size is indeed five people.
Black to Negative: Embedded optionalities in commodities markets
Authors: Richard J. Martin, Aldous Birchall
Date: 2020-06-10 00:00:00

We address the modelling of commodities that are supposed to have positive price but, on account of a possible failure in the physical delivery mechanism, may turn out not to. This is done by explicitly incorporating a `delivery liability' option into the contract. As such it is a simple generalisation of the established Black model.
Detection and Locating Cyber and Physical Stresses in Smart Grids using Graph Signal Processing
Authors: Md Abul Hasnat, Mahshid Rahnamay-Naeini
Date: 2020-06-10 00:00:00

Smart grids are large and complex cyber physical infrastructures that require real-time monitoring for ensuring the security and reliability of the system. Monitoring the smart grid involves analyzing continuous data-stream from various measurement devices deployed throughout the system, which are topologically distributed and structurally interrelated. In this paper, graph signal processing (GSP) has been used to represent and analyze the power grid measurement data. It is shown that GSP can enable various analyses for the power grid's structured data and dynamics of its interconnected components. Particularly, the effects of various cyber and physical stresses in the power grid are evaluated and discussed both in the vertex and the graph-frequency domains of the signals. Several techniques for detecting and locating cyber and physical stresses based on GSP techniques have been presented and their performances have been evaluated and compared. The presented study shows that GSP can be a promising approach for analyzing the power grid's data.