pwc-archive/papers-with-abstracts huggingface dataset:
Design, Implementation and Evaluation of a Variable Stiffness Transradial Hand Prosthesis
Date: 2019-10-28 00:00:00

We present the design, implementation, and experimental evaluation of a
low-cost, customizable, easy-to-use transradial hand prosthesis capable of
adapting its compliance. Variable stiffness actuation (VSA) of the prosthesis
is based on antagonistically arranged tendons coupled to nonlinear springs
driven through a Bowden cable-based power transmission. Bowden cable-based
antagonistic VSA can, not only regulate the stiffness and the position of the
prosthetic hand, but also enables a light-weight and low-cost design, by
opportunistic placement of motors, batteries and controllers on any convenient
location on the human body, while nonlinear springs are conveniently integrated
inside the forearm. The transradial hand prosthesis also features tendon driven
underactuated compliant fingers that allow natural adaption of the hand shape
to wrap around a wide variety of object geometries, while the modulation of the
stiffness of their drive tendons enables the prosthesis to perform various
tasks with high dexterity. The compliant fingers of the prosthesis add inherent
robustness and flexibility, even under impacts. The control of the variable
stiffness transradial hand prosthesis is achieved by an sEMG based natural
human-machine interface.
Power Allocation and User Assignment Scheme for Beyond 5G Heterogeneous Networks
Date: 2019-10-25 00:00:00

The issue of spectrum scarcity in wireless networks is becoming prominent and
critical with each passing year. Although several promising solutions have been
proposed to provide a solution to spectrum scarcity, most of them have many
associated tradeoffs. In this context, one of the emerging ideas relates to the
utilization of cognitive radios (CR) for future heterogeneous networks
(HetNets). This paper provides a marriage of two promising candidates (i.e., CR
and HetNets) for beyond fifth generation (5G) wireless networks. More
specifically, a joint power allocation and user assignment solution for the
multi-user underlay CR-based HetNets has been proposed and evaluated. To
counter the limiting factors in these networks, the individual power of
transmitting nodes and interference temperature protection constraints of the
primary networks have been considered. An efficient solution is designed from
the dual decomposition approach, where the optimal user assignment is obtained
for the optimized power allocation at each node. The simulation results
validate the superiority of the proposed optimization scheme against
conventional baseline techniques.
Super-resolved Localisation without Identifying LoS/NLoS Paths
Date: 2019-11-20 00:00:00
Tasks: Super-Resolution

This paper focuses on the problem of localising a transmitting mobile station
(MS) using multiple cooperative base stations (BSs). There are two technical
difficulties: one is the data association between intermediate parameters and
scatters; the other is to identify Line-of-Sight (LoS) and Non-Line-of-Sight
(NLoS) paths. Our main contribution is a unified approach bypassing both data
association and LoS/NLoS path identification. This is achieved by introducing
virtual scatters and then a direct localisation formulation. Modern
super-resolution technique is then adapted to cast the localisation problem as
convex programming and solve it. Our approach addresses long-standing issues
not completely solved in the literature, guarantees a global convergence,
achieves good localisation accuracy and demonstrates its robustness to noise in
our numerical tests.
Non-Centralized Navigation for Source Localization by Cooperative UAVs
Date: 2019-10-28 00:00:00
Tasks: Diversity, Motion Planning

In this paper, we propose a distributed solution to the navigation of a
population of unmanned aerial vehicles (UAVs) to best localize a static source.
The network is considered heterogeneous with UAVs equipped with received signal
strength (RSS) sensors from which it is possible to estimate the distance from
the source and/or the direction of arrival through ad-hoc rotations. This
diversity in gathering and processing RSS measurements mitigates the loss of
localization accuracy due to the adoption of low-complexity sensors. The UAVs
plan their trajectories on-the-fly and in a distributed fashion. The collected
data are disseminated through the network via multi-hops, therefore being
subject to latency. Since not all the paths are equal in terms of information
gathering rewards, the motion planning is formulated as a minimization of the
uncertainty of the source position under UAV kinematic and anti-collision
constraints and performed by 3D non-linear programming. The proposed analysis
takes into account non-line-of-sight (NLOS) channel conditions as well as
measurement age caused by the latency constraints in communication.
Joint Indoor Localization and Navigation of UAVs for Network Formation Control
Date: 2019-10-28 00:00:00
Tasks: Indoor Localization, Position

In this paper, we propose a joint indoor localization and navigation
algorithm to enable a swarm of unmanned aerial vehicles (UAVs) to deploy in a
specific spatial formation in indoor environments. In the envisioned scenario,
we consider a static user acting as a central unit whose main task is to
acquire all the UAV measurements carrying position-dependent information and to
estimate the UAV positions when there is no existing infrastructure for
positioning. Subsequently, the user exploits the estimated positions as inputs
for the navigation control with the aim of deploying the UAVs in a desired
formation in space (formation shaping). The user plans the trajectory of each
UAV in real time, guaranteeing a safe navigation in the presence of obstacles.
The proposed algorithm guides the UAVs to their desired final locations with
good accuracy.
Dynamic Radar Networks of UAVs
Date: 2019-10-28 00:00:00

In the incoming years, the low-aerial space will be crowded by unmanned
aerial vehicles (UAVs), which will be providing different services. In this
expected context, an emerging problem is to detect and track unauthorized or
malicious mini/micro UAVs. In contrast to current solutions mainly based on
fixed terrestrial radars, this article will put forth the idea of a dynamic
radar network (DRN) composed of UAVs able to smartly adapt their
formation-navigation control to best track malicious UAVs in real-time, with
high accuracy, and in a distributed fashion. To this end, some technological
solutions and the main methods for target detection and tracking will be
described. Further, an optimized navigation scheme will be developed according
to an information-seeking approach. Some examples of simulation results and
future directions of work will be finally presented highlighting the advantages
of dynamic and reconfigurable networks over static ones.
Waypoint Optimization Using Bayesian Optimization: A Case Study in Airborne Wind Energy Systems
Date: 2020-11-17 00:00:00
Tasks: Bayesian Optimization

We present a data-driven optimization framework that aims to address online
adaptation of the flight path shape for an airborne wind energy system (AWE)
that follows a repetitive path to generate power. Specifically, Bayesian
optimization, which is a data-driven algorithm for finding the optimum of an
unknown objective function, is utilized to solve the waypoint adaptation. To
form a computationally efficient optimization framework, we describe each
figure-$8$ flight via a compact set of parameters, termed as basis parameters.
We model the underlying objective function by a Gaussian Process (GP). Bayesian
optimization utilizes the predictive uncertainty information from the GP to
determine the best subsequent basis parameters. Once a path is generated using
Bayesian optimization, a path following mechanism is used to track the
generated figure-$8$ flight. The proposed framework is validated on a
simplified $2$-dimensional model that mimics the key behaviors of a
$3$-dimensional AWE system. We demonstrate the capability of the proposed
framework in a simulation environment for a simplified $2$-dimensional AWE
system model.
Optimization of Wireless Sensor Network Deployment for Spatiotemporal Reconstruction and Prediction
Date: 2019-10-28 00:00:00

This paper addresses the problem of optimizing sensor deployment locations to
reconstruct and also predict a spatiotemporal field. A novel deep learning
framework is developed to find a limited number of optimal sampling locations
and based on that, improve the accuracy of spatiotemporal field reconstruction
and prediction. The proposed approach first optimizes the sampling locations of
a wireless sensor network to retrieve maximum information from a spatiotemporal
field. A spatiotemporal reconstructor is then used to reconstruct and predict
the spatiotemporal field, using collected in-situ measurements. A simulation is
conducted using global climate datasets from the National Oceanic and
Atmospheric Administration, to implement and validate the developed
methodology. The results demonstrate a significant improvement made by the
proposed algorithm. Specifically, compared to traditional approaches, the
proposed method provides superior performance in terms of both reconstruction
error and long-term prediction robustness.
An Extended Integral Unit Commitment Formulation and an Iterative Algorithm for Convex Hull Pricing
Date: 2020-06-16 00:00:00
Tasks: Computational Efficiency

To increase market transparency, independent system operators (ISOs) have
been working on minimizing uplift payments based on convex hull pricing
theorems. However, the large-scale complex systems for ISOs bring computational
challenges to the existing convex hull pricing algorithms. In this paper, based
on the analysis of specific generator features in the Midcontinent ISO (MISO)
system, besides reviewing integral formulations for several special cases, we
develop two integral formulations of a single generator that can capture these
features. We then build a compact convex hull pricing formulation based on
these integral formulations. Meanwhile, to improve the computational
efficiency, we propose innovative iterative algorithms with convergence
properties, plus a complementary algorithm, to obtain a convex hull price. The
computational results indicate that our approach leads to an exact convex hull
price on MISO instances with and without transmission constraints and the
solutions can be obtained within 20 minutes.
Detection and Isolation of Adversaries in Decentralized Optimization for Non-Strongly Convex Objectives
Date: 2019-10-29 00:00:00

Decentralized optimization has found a significant utility in recent years,
as a promising technique to overcome the curse of dimensionality when dealing
with large-scale inference and decision problems in big data. While these
algorithms are resilient to node and link failures, they however, are not
inherently Byzantine fault-tolerant towards insider data injection attacks.
This paper proposes a decentralized robust subgradient push (RSGP) algorithm
for detection and isolation of malicious nodes in the network for optimization
non-strongly convex objectives. In the attack considered in this work, the
malicious nodes follow the algorithmic protocols, but can alter their local
functions arbitrarily. However, we show that in sufficiently structured
problems, the method proposed is effective in revealing their presence. The
algorithm isolates detected nodes from the regular nodes, thereby mitigating
the ill-effects of malicious nodes. We also provide performance measures for
the proposed method.
Characterizing Power Consumption of Dual-Frequency GNSS of a Smartphone
Authors: Bikram Karki, Myounggyu Won
Date: 2019-10-29 00:00:00

Location service is one of the most widely used features on a smartphone. More and more apps are built based on location services. As such, demand for accurate positioning is ever higher. Mobile brand Xiaomi has introduced Mi 8, the world's first smartphone equipped with a dual-frequency GNSS chipset which is claimed to provide up to decimeter-level positioning accuracy. Such unprecedentedly high location accuracy brought excitement to industry and academia for navigation research and development of emerging apps. On the other hand, there is a significant knowledge gap on the energy efficiency of smartphones equipped with a dual-frequency GNSS chipset. In this paper, we bridge this knowledge gap by performing an empirical study on power consumption of a dual-frequency GNSS phone. To the best our knowledge, this is the first experimental study that characterizes the power consumption of a smartphone equipped with a dual-frequency GNSS chipset and compares the energy efficiency with a single-frequency GNSS phone. We demonstrate that a smartphone with a dual-frequency GNSS chipset consumes 37% more power on average outdoors, and 28% more power indoors, in comparison with a singe-frequency GNSS phone.
Spoofing Speaker Verification Systems with Deep Multi-speaker Text-to-speech Synthesis
Date: 2019-10-29 00:00:00
Tasks: Speaker Verification, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

This paper proposes a deep multi-speaker text-to-speech (TTS) model for
spoofing speaker verification (SV) systems. The proposed model employs one
network to synthesize time-downsampled mel-spectrograms from text input and
another network to convert them to linear-frequency spectrograms, which are
further converted to the time domain using the Griffin-Lim algorithm. Both
networks are trained separately under the generative adversarial networks (GAN)
framework. Spoofing experiments on two state-of-the-art SV systems (i-vectors
and Google's GE2E) show that the proposed system can successfully spoof these
systems with a high success rate. Spoofing experiments on anti-spoofing systems
(i.e., binary classifiers for discriminating real and synthetic speech) also
show a high spoof success rate when such anti-spoofing systems' structures are
exposed to the proposed TTS system.
Utilization of Pulse Rate Variability between Post-sleep and Wake Cycles to increase Alarm Clock Efficiency using Arduino-based Non-invasive Pulse Detector
Date: 2019-10-29 00:00:00

The most common reason for students' tardiness is due to waking up late every
morning. The causes may vary from lack of rest and sleep, to failure of
ascertaining an alarm clock's noise or sometimes, falling back to sleep again
after snoozing an alarm. This paper's purpose is to address the problem
concerning student's inability to continue to stay awake after turning off
their alarms using Arduino-based pulse rate triggered alarm interrupt (APRTAI).
The proposed system will enable students to make use of a higher pulse rate
from their thumb to turn off an alarm which can be achieved by engaging
themselves in intensive physical activities that will eventually eradicate
drowsiness and therefore prevent them from falling back to sleep again.
Unsupervised Deep Basis Pursuit: Learning inverse problems without ground-truth data
Date: 2020-02-14 00:00:00
Proceeding: NeurIPS Workshop Deep_Invers 2019 12
Tasks: compressed sensing

Basis pursuit is a compressed sensing optimization in which the l1-norm is
minimized subject to model error constraints. Here we use a deep neural network
prior instead of l1-regularization. Using known noise statistics, we jointly
learn the prior and reconstruct images without access to ground-truth data.
During training, we use alternating minimization across an unrolled iterative
network and jointly solve for the neural network weights and training set image
reconstructions. At inference, we fix the weights and pass the measurements
through the network. We compare reconstruction performance between unsupervised
and supervised (i.e. with ground-truth) methods. We hypothesize this technique
could be used to learn reconstruction when ground-truth data are unavailable,
such as in high-resolution dynamic MRI.
Horse race of weekly idiosyncratic momentum strategies with respect to various risk metrics: Evidence from the Chinese stock market
Authors: Huai-Long Shi, Wei-Xing Zhou
Date: 2019-10-29 00:00:00

This paper focuses on the horse race of weekly idiosyncratic momentum (IMOM) with respect to various idiosyncratic risk metrics. Using the A-share individual stocks in the Chinese market from January 1997 to December 2017, we first evaluate the performance of the weekly momentum based on raw returns and idiosyncratic returns, respectively. After that the univariate portfolio analysis is conducted to investigate the return predictability with respect to various idiosyncratic risk metrics. Further, we perform a comparative study on the performance of the IMOM portfolios with respect to various risk metrics. At last, we explore the possible explanations to IMOM as well as risk based IMOM portfolios. We find that 1) there are prevailing contrarian effect and IMOM effect for the whole sample; 2) the negative relations exist between most of the idiosyncratic risk metrics and the cross-sectional stock returns, and better performance is linked to idiosyncratic volatility (IVol) and maximum drawdowns (IMDs); 3) additionally, the IVol-based and IMD-based IMOM portfolios exhibit better explanatory power to the IMOM portfolios with respect to other risk metrics; 4) finally, higher profitability of IMOM as well as IVol-based and IMD-based IMOM portfolios is found to be related to upside market states, high levels of liquidity and high levels of investor sentiment.
Estimating Centrality Blindly from Low-pass Filtered Graph Signals
Date: 2019-10-29 00:00:00

This paper considers blind methods for centrality estimation from graph
signals. We model graph signals as the outcome of an unknown low-pass graph
filter excited with influences governed by a sparse sub-graph. This model is
compatible with a number of data generation process on graphs, including stock
data and opinion dynamics. Based on the said graph signal model, we first prove
that the folklore heuristics based on PCA of data covariance matrix may fail
when the graph filter is not sufficiently low-pass. To remedy, we propose a
robust blind centrality estimation method which substantially improves the
centrality estimation performance. Numerical results on synthetic and real data
support our findings.
Deep reinforcement learning for market making in corporate bonds: beating the curse of dimensionality
Authors: Olivier Guéant, Iuliia Manziuk
Date: 2019-10-29 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning (RL)

In corporate bond markets, which are mainly OTC markets, market makers play a central role by providing bid and ask prices for a large number of bonds to asset managers from all around the globe. Determining the optimal bid and ask quotes that a market maker should set for a given universe of bonds is a complex task. Useful models exist, most of them inspired by that of Avellaneda and Stoikov. These models describe the complex optimization problem faced by market makers: proposing bid and ask prices in an optimal way for making money out of the difference between bid and ask prices while mitigating the market risk associated with holding inventory. While most of the models only tackle one-asset market making, they can often be generalized to a multi-asset framework. However, the problem of solving numerically the equations characterizing the optimal bid and ask quotes is seldom tackled in the literature, especially in high dimension. In this paper, our goal is to propose a numerical method for approximating the optimal bid and ask quotes over a large universe of bonds in a model \`a la Avellaneda-Stoikov. Because we aim at considering a large universe of bonds, classical finite difference methods as those discussed in the literature cannot be used and we present therefore a discrete-time method inspired by reinforcement learning techniques. More precisely, the approach we propose is a model-based actor-critic-like algorithm involving deep neural networks.
Lexicographic Choice Under Variable Capacity Constraints
Date: 2019-10-29 00:00:00
Tasks: Diversity

In several matching markets, in order to achieve diversity, agents'
priorities are allowed to vary across an institution's available seats, and the
institution is let to choose agents in a lexicographic fashion based on a
predetermined ordering of the seats, called a (capacity-constrained)
lexicographic choice rule. We provide a characterization of lexicographic
choice rules and a characterization of deferred acceptance mechanisms that
operate based on a lexicographic choice structure under variable capacity
constraints. We discuss some implications for the Boston school choice system
and show that our analysis can be helpful in applications to select among
plausible choice rules.
Optimal On-Off Transmission Schemes for Full Duplex Wireless Powered Communication Networks
Date: 2020-02-10 00:00:00
Tasks: Scheduling

In this paper, we consider a full duplex wireless powered communication
network where multiple users with radio frequency energy harvesting capability
communicate to an energy broadcasting hybrid access point. We investigate the
minimum length scheduling and sum throughput maximization problems considering
on-off transmission scheme in which users either transmit at a constant power
or remain silent. For minimum length scheduling problem, we propose a
polynomial-time optimal scheduling algorithm. For sum throughput maximization,
we first derive the characteristics of an optimal schedule and then to avoid
intractable complexity, we propose a polynomial-time heuristic algorithm which
is illustrated to perform nearly optimal through numerical analysis.
A Self-Exciting Modelling Framework for Forward Prices in Power Markets
Date: 2019-10-29 00:00:00
Tasks: Clustering

We propose and investigate two model classes for forward power price
dynamics, based on continuous branching processes with immigration, and on
Hawkes processes with exponential kernel, respectively. The models proposed
exhibit jumps clustering features. Models of this kind have been already
proposed for the spot price dynamics, but the main purpose of the present work
is to investigate the performances of such models in describing the forward
dynamics. We adopt a Heath-Jarrow-Morton approach in order to capture the whole
forward curve evolution. By examining daily data in the French power market, we
perform a goodness-of-fit test and we present our conclusions about the
adequacy of these models in describing the forward prices evolution.
Internet-Connected Residential Water Leak Monitor
Date: 2019-10-17 00:00:00

A device for passive monitoring of slow water leaks, especially periodic
leaks caused by faltering gaskets. The detection and notification solution is
composed of an algorithm tuned specifically for bathroom leaks, motivated by a
surprise water bill, and a hardware prototype connected to a serverless cloud
component. The package is essentially a microservice for slow-leak detection
and notification. Inexpensive commodity hardware is utilized to send alerts to
users over Amazon Simple Notification Service (SNS).
  Index Terms: home automation, water conservation, IoT, signal processing.
From microscopic price dynamics to multidimensional rough volatility models
Authors: Mehdi Tomas, Mathieu Rosenbaum
Date: 2019-10-18 00:00:00

Rough volatility is a well-established statistical stylised fact of financial assets. This property has lead to the design and analysis of various new rough stochastic volatility models. However, most of these developments have been carried out in the mono-asset case. In this work, we show that some specific multivariate rough volatility models arise naturally from microstructural properties of the joint dynamics of asset prices. To do so, we use Hawkes processes to build microscopic models that reproduce accurately high frequency cross-asset interactions and investigate their long term scaling limits. We emphasize the relevance of our approach by providing insights on the role of microscopic features such as momentum and mean-reversion on the multidimensional price formation process. We in particular recover classical properties of high-dimensional stock correlation matrices.