pwc-archive/papers-with-abstracts huggingface dataset:
Dynamic Channel Selection in UAVs through Constellations in the Sky
Date: 2020-02-11 00:00:00
Tasks: channel selection

Wireless communication between an unmanned aerial vehicle (UAV) and the
ground base station (BS) is susceptible to adversarial jamming. In such
situations, it is important for the UAV to indicate a new channel to the BS.
This paper describes a method of creating spatial codes that map the chosen
channel to the motion and location of the UAVs in space, wherein the latter
physically traverses the space from a given so called "constellation point'' to
another. These points create patterns in the sky, analogous to modulation
constellations in classical wireless communications, and are detected at the BS
through a millimeter-wave (mmWave) radar sensor. A constellation point
represents a distinct n-bit field mapped to a specific channel, allowing
simultaneous frequency switching at both ends without any RF transmissions. The
main contributions of this paper are: (i) We conduct experimental studies to
demonstrate how such constellations may be formed using COTS UAVs and mmWave
sensors, given realistic sensing errors and hovering vibrations, (ii) We
develop a theoretical framework that maps a desired constellation design to
error and band switching time, considering again practical UAV movement
limitations, and (iii) We experimentally demonstrate jamming resilient
communications and validate system goodput for links formed by UAV-mounted
software defined radios.
Synergistic information in a dynamical model implemented on the human structural connectome reveals spatially distinct associations with age
Date: 2020-02-11 00:00:00

We implement the dynamical Ising model on the large scale architecture of
white matter connections of healthy subjects in the age range 4-85 years, and
analyze the dynamics in terms of the synergy, a quantity measuring the extent
to which the joint state of pairs of variables is projected onto the dynamics
of a target one. We find that the amount of synergy in explaining the dynamics
of the hubs of the structural connectivity (in terms of degree strength) peaks
before the critical temperature, and can thus be considered as a precursor of a
critical transition. Conversely the greatest amount of synergy goes into
explaining the dynamics of more central nodes. We also find that the aging of
the structural connectivity is associated to significant changes in the
simulated dynamics: there are brain regions whose synergy decreases with age,
in particular the frontal pole, the Subcallosal area and the Supplementary
Motor area; these areas could then be more likely to show a decline in terms of
the capability to perform higher order computation (if structural connectivity
was the sole variable). On the other hand, several regions in the temporal
cortex show a positive correlation with age in the first 30 years of life, i.e.
during brain maturation.
3D Point Cloud Enhancement using Graph-Modelled Multiview Depth Measurements
Date: 2020-02-11 00:00:00
Tasks: 3D geometry, Denoising, Metric Learning

A 3D point cloud is often synthesized from depth measurements collected by
sensors at different viewpoints. The acquired measurements are typically both
coarse in precision and corrupted by noise. To improve quality, previous works
denoise a synthesized 3D point cloud a posteriori after projecting the
imperfect depth data onto 3D space. Instead, we enhance depth measurements on
the sensed images a priori, exploiting inherent 3D geometric correlation across
views, before synthesizing a 3D point cloud from the improved measurements. By
enhancing closer to the actual sensing process, we benefit from optimization
targeting specifically the depth image formation model, before subsequent
processing steps that can further obscure measurement errors. Mathematically,
for each pixel row in a pair of rectified viewpoint depth images, we first
construct a graph reflecting inter-pixel similarities via metric learning using
data in previous enhanced rows. To optimize left and right viewpoint images
simultaneously, we write a non-linear mapping function from left pixel row to
the right based on 3D geometry relations. We formulate a MAP optimization
problem, which, after suitable linear approximations, results in an
unconstrained convex and differentiable objective, solvable using fast gradient
method (FGM). Experimental results show that our method noticeably outperforms
recent denoising algorithms that enhance after 3D point clouds are synthesized.
Mathematical Foundations of Regression Methods for the approximation of the Forward Initial Margin
Authors: Lucia Cipolina Kun, Simone Caenazzo, Ksenia Ponomareva
Date: 2020-02-11 00:00:00
Tasks: regression

Abundant literature has been published on approximation methods for the forward initial margin. The most popular ones being the family of regression methods. This paper describes the mathematical foundations on which these regression approximation methods lie. We introduce mathematical rigor to show that in essence, all the methods propose variations of approximations for the conditional expectation function, which is interpreted as an orthogonal projection on Hilbert spaces. We show that each method is simply choosing a different functional form to numerically estimate the conditional expectation. We cover in particular the most popular methods in the literature so far, Polynomial approximation, Kernel regressions and Neural Networks.
Inferring metric trees from weighted quartets via an intertaxon distance
Date: 2020-02-11 00:00:00

A metric phylogenetic tree relating a collection of taxa induces weighted
rooted triples and weighted quartets for all subsets of three and four taxa,
respectively. New intertaxon distances are defined that can be calculated from
these weights, and shown to exactly fit the same tree topology, but with edge
weights rescaled by certain factors dependent on the associated split size.
These distances are analogs for metric trees of similar ones recently
introduced for topological trees that are based on induced unweighted rooted
triples and quartets. The distances introduced here lead to new statistically
consistent methods of inferring a metric species tree from a collection of
topological gene trees generated under the multispecies coalescent model of
incomplete lineage sorting. Simulations provide insight into their potential.
Single supply of electrical energy in the supply distribution model: using GAMS to model the effects of network parameters on the level of consumer demand
Date: 2020-02-11 00:00:00

The proposed model of this study is a single supply of electrical energy and
is used in distribution systems. The objective of this study is to optimize the
distribution of active and reactive energy in the supply of sub-distribution or
the market. The model proposed in this paper accounts for the effects
associated with switching capacitors on distribution and dispatching centers.
Using capacitors in the power grid has an effect on bus voltage and
consequently the amount of active and reactive energy demand of the consumer.
The optimized supply is determined through the solution of a mathematical
operation model. In this study, the effect of voltage changes on consumer
demand for residential,commercial and industrial power are quantified and
embodied in an analytical model, which is then solved using General Algebraic
Modeling System (GAMS) software.ware.
Intra-Horizon Expected Shortfall and Risk Structure in Models with Jumps
Authors: Walter Farkas, Ludovic Mathys, Nikola Vasiljević
Date: 2020-02-11 00:00:00

The present article deals with intra-horizon risk in models with jumps. Our general understanding of intra-horizon risk is along the lines of the approach taken in Boudoukh, Richardson, Stanton and Whitelaw (2004), Rossello (2008), Bhattacharyya, Misra and Kodase (2009), Bakshi and Panayotov (2010), and Leippold and Vasiljevi\'c (2019). In particular, we believe that quantifying market risk by strictly relying on point-in-time measures cannot be deemed a satisfactory approach in general. Instead, we argue that complementing this approach by studying measures of risk that capture the magnitude of losses potentially incurred at any time of a trading horizon is necessary when dealing with (m)any financial position(s). To address this issue, we propose an intra-horizon analogue of the expected shortfall for general profit and loss processes and discuss its key properties. Our intra-horizon expected shortfall is well-defined for (m)any popular class(es) of L\'evy processes encountered when modeling market dynamics and constitutes a coherent measure of risk, as introduced in Cheridito, Delbaen and Kupper (2004). On the computational side, we provide a simple method to derive the intra-horizon risk inherent to popular L\'evy dynamics. Our general technique relies on results for maturity-randomized first-passage probabilities and allows for a derivation of diffusion and single jump risk contributions. These theoretical results are complemented with an empirical analysis, where popular L\'evy dynamics are calibrated to S&P 500 index data and an analysis of the resulting intra-horizon risk is presented.
Simultaneous Input and State Interval Observers for Nonlinear Systems with Full-Rank Direct Feedthrough
Date: 2020-02-12 00:00:00

A simultaneous input and state interval observer is presented for Lipschitz
continuous nonlinear systems with unknown inputs and bounded noise signals for
the case when the direct feedthrough matrix has full column rank. The observer
leverages the existence of bounding decomposition functions for mixed monotone
mappings to recursively compute the maximal and minimal elements of the
estimate intervals that are compatible with output/measurement signals, and are
proven to contain the true state and unknown input. Furthermore, we derive a
Lipschitz-like property for decomposition functions, which provides several
sufficient conditions for stability of the designed observer and boundedness of
the sequence of estimate interval widths. Finally, the effectiveness of our
approach is demonstrated using an illustrative example.
Combining LoRaWAN and a New 3D Motion Model for Remote UAV Tracking
Date: 2020-02-12 00:00:00
Tasks: Position

Over the last few years, the many uses of Unmanned Aerial Vehicles (UAVs)
have captured the interest of both the scientific and the industrial
communities. A typical scenario consists in the use of UAVs for surveillance or
target-search missions over a wide geographical area. In this case, it is
fundamental for the command center to accurately estimate and track the
trajectories of the UAVs by exploiting their periodic state reports. In this
work, we design an ad hoc tracking system that exploits the Long Range Wide
Area Network (LoRaWAN) standard for communication and an extended version of
the Constant Turn Rate and Acceleration (CTRA) motion model to predict drone
movements in a 3D environment. Simulation results on a publicly available
dataset show that our system can reliably estimate the position and trajectory
of a UAV, significantly outperforming baseline tracking approaches.
Guiding the guiders: Foundations of a market-driven theory of disclosure
Authors: M. Gietzmann, A. J. Ostaszewski, M. H. G. Schröder
Date: 2020-02-12 00:00:00
Tasks: Management, Relation

A foundational approach is developed for a mathematical theory of managerial disclosure in relation to asset pricing; this involves both the earnings guidance disclosed by firm management and market `trackers' pricing the firm's exposure to quotable risks.
A Line-of-Sight Channel Model for the 100-450 Gigahertz Frequency Band
Date: 2020-02-12 00:00:00
Tasks: Benchmarking

This paper documents a simple parametric polynomial line-of-sight channel
model for 100-450 GHz band. The band comprises two popular beyond fifth
generation (B5G) frequency bands, namely, the D band (110-170 GHz) and the
low-THz band (around 275-325 GHz). The main focus herein is to derive a simple,
compact, and accurate molecular absorption loss model for the 100-450 GHz band.
The derived model relies on simple absorption line shape functions that are
fitted to the actual response given by complex but exact database approach. The
model is also reducible for particular sub-bands within the full range of
100-450 GHz, further simplifying the absorption loss estimate. The proposed
model is shown to be very accurate by benchmarking it against the exact
response and the similar models given by International Telecommunication Union
Radio Communication Sector (ITU-R). The loss is shown to be within $\pm$2 dBs
from the exact response for one kilometer link in highly humid environment.
Therefore, the its accuracy is even much better in the case of usually
considered shorter range future B5G wireless systems.
Machine Learning-Assisted Anomaly Detection in Maritime Navigation Using AIS Data
Date: 2020-02-10 00:00:00
Tasks: Anomaly Detection, BIG-bench Machine Learning

The automatic identification system (AIS) reports vessels' static and dynamic
information, which are essential for maritime traffic situation awareness.
However, AIS transponders can be switched off to hide suspicious activities,
such as illegal fishing, or piracy. Therefore, this paper uses real world AIS
data to analyze the possibility of successful detection of various anomalies in
the maritime domain. We propose a multi-class artificial neural network
(ANN)-based anomaly detection framework to classify intentional and
non-intentional AIS on-off switching anomalies. The multi-class anomaly
framework captures AIS message dropouts due to various reasons, e.g., channel
effects or intentional one for carrying illegal activities. We extract
position, speed, course and timing information from real world AIS data, and
use them to train a 2-class (normal and anomaly) and a 3-class (normal, power
outage and anomaly) anomaly detection models. Our results show that the models
achieve around 99.9% overall accuracy, and are able to classify a test sample
in the order of microseconds.
AI Driven Heterogeneous MEC System with UAV Assistance for Dynamic Environment -- Challenges and Solutions
Authors: Feibo Jiang, Kezhi Wang, Li Dong, Cunhua Pan, Wei Xu, Kun Yang
Date: 2020-02-11 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, Edge-computing, Incremental Learning, Management, Scheduling

By taking full advantage of Computing, Communication and Caching (3C) resources at the network edge, Mobile Edge Computing (MEC) is envisioned as one of the key enablers for the next generation networks. However, current fixed-location MEC architecture may not be able to make real-time decision in dynamic environment, especially in large-scale scenarios. To address this issue, in this paper, a Heterogeneous MEC (H-MEC) architecture is proposed, which is composed of fixed unit, i.e., Ground Stations (GSs) as well as moving nodes, i.e., Ground Vehicles (GVs) and Unmanned Aerial Vehicles (UAVs), all with 3C resource enabled. The key challenges in H-MEC, i.e., mobile edge node management, real-time decision making, user association and resource allocation along with the possible Artificial Intelligence (AI)-based solutions are discussed. In addition, the AI-based joint Resource schEduling (ARE) framework with two different AI-based mechanisms, i.e., Deep neural network (DNN)-based and deep reinforcement learning (DRL)-based architectures are proposed. DNN-based solution with online incremental learning applies the global optimizer and therefore has better performance than the DRL-based architecture with online policy updating, but requires longer training time. The simulation results are given to verify the efficiency of our proposed ARE framework.
A Scalable Port-Hamiltonian Approach to Plug-and-Play Voltage Stabilization in DC Microgrids
Date: 2020-02-12 00:00:00

One of the major challenges of voltage stabilization in converter-based DC
microgrids are the multiple interacting units displaying intermittent supply
behavior. In this paper, we address this by a decentralized scalable,
plug-and-play voltage controller for voltage-source converters (VSCs) at
primary level. In contrast to existing approaches, we follow a systematic and
constructive design based on port-Hamiltonian systems (PHSs) which does neither
require the heuristic proposition of a Lyapunov function nor the computation of
auxilliary variables such as time-derivatives. By employing the Hamiltonian
naturally obtained from the PHS approach as Lyapunov function and using the
modularity of passive systems, we provide sufficient conditions under which the
designed VSC controllers achieve microgrid-wide asymptotic voltage stability.
Integral action (IA), which preserves the passive PHS structure, robustifies
the design against unknown disturbances and ensures zero voltage errors in the
steady-state. Numerical simulations illustrate the functionality of the
proposed voltage controller.
Towards Energy Positive Sensing using Kinetic Energy Harvesters
Date: 2020-02-11 00:00:00

Conventional systems for motion context detection rely on batteries to
provide the energy required for sampling a motion sensor. Batteries, however,
have limited capacity and, once depleted, have to be replaced or recharged.
Kinetic Energy Harvesting (KEH) allows to convert ambient motion and vibration
into usable electricity and can enable batteryless, maintenance free operation
of motion sensors. The signal from a KEH transducer correlates with the
underlying motion and may thus directly be used for context detection, saving
space, cost and energy by omitting the accelerometer. Previous work uses the
open circuit or the capacitor voltage for sensing without using the harvested
energy to power a load. In this paper, we propose to use other sensing points
in the KEH circuit that offer information rich sensing signals while the energy
from the harvester is used to power a load. We systematically analyse multiple
sensing signals available in different KEH architectures and compare their
performance in a transport mode detection case study. To this end, we develop
four hardware prototypes, conduct an extensive measurement campaign and use the
data to train and evaluate different classifiers. We show that sensing the
harvesting current signal from a transducer can be energy positive, delivering
up to ten times as much power as it consumes for signal acquisition, while
offering comparable detection accuracy to the accelerometer signal for most of
the considered transport modes.
New method for evaluating fitness using the waist-to-height ratio among Korean adults
Authors: Nam. Lyong Kang
Date: 2020-02-11 00:00:00

Objectives: This paper introduces a new method for evaluating fitness and determining effective exercises for reducing abdominal obesity in Korean adults using the new kind of waist-to-height ratio (WHT2R). Materials and Methods: The body mass index (BMI), body shape index (ABSI), and two other waist-to-height ratios (WHT.5R, WHTR) were considered as possible contenders for the WHT2R. The correlation coefficients were calculated by correlation analyses between the indices and four fitness tests for comparison. The LMV (lump mean value) and FSPW (fitness sensitivity percentage to WHT2R) were introduced to find the association between fitness and abdominal obesity using a linear regression method and to use as an indicator for the effective control of abdominal obesity. Results: The WHT2R is more suitable for assessing fitness than the other indices and can be controlled effectively by decreasing the 10-m shuttle run score for both males and females. Conclusions: The WHT2R can be used as a possible contender for evaluating fitness and is an effective indicator for the reduction of abdominal obesity. The LMV and FSPW can be used to establish personal exercise aims.
On the Secrecy Performance Over N-Wave with Diffuse Power Fading Channel
Date: 2020-03-15 00:00:00

We investigate the physical layer security over N-wave with diffuse power
(NWDP) fading model, which is typically encountered in realistic wireless
scenarios in the context of millimeter-wave communications and emerging 5G
technologies. More specifically, novel and exact expressions of the secrecy
outage probability (SOP) and the lower bound of the secrecy outage probability
are derived in terms of well-known elementary and special functions.
Furthermore, we provide very useful insights on the impact of increasing: the
number, the relative amplitudes and the power of the dominant specular
components for improving secrecy performance. In this context, we show that it
is possible to achieve a very good secrecy performance when: (i) the relative
amplitudes of the dominant specular components for the eavesdropper channel are
sufficiently large compared to those relative amplitudes of the dominant
components for the legitimate channel eavesdropper channel, and (ii) the power
of the Bob's dominant components is significantly greater than the power of the
Eve's dominant rays. The validity of the proposed expressions is confirmed via
Monte Carlo simulations.
Adaptive Control for Unknown Heterogeneous Vehicles Synchronization with Unstructured Uncertainty
Date: 2020-02-12 00:00:00
Tasks: Autonomous Vehicles

The cooperative control applied to vehicles allows the optimization of
traffic on the roads. There are many aspects to consider in the case of the
operation of autonomous vehicles on highways since there are different external
parameters that can be involved in the analysis of a network. In this paper, we
present the design and simulation of adaptive control for a platoon with
heterogeneous vehicles, taking into account that not all vehicles can
communicate their control input, and in turn include structured nonlinear
uncertainty input parameters.
Near-Optimal Interference Exploitation 1-Bit Massive MIMO Precoding via Partial Branch-and-Bound
Date: 2020-02-13 00:00:00

In this paper, we focus on 1-bit precoding for large-scale antenna systems in
the downlink based on the concept of constructive interference (CI). By
formulating the optimization problem that aims to maximize the CI effect
subject to the 1-bit constraint on the transmit signals, we mathematically
prove that, when relaxing the 1-bit constraint, the majority of the obtained
transmit signals already satisfy the 1-bit constraint. Based on this important
observation, we propose a 1-bit precoding method via a partial branch-and-bound
(P-BB) approach, where the BB procedure is only performed for the entries that
do not comply with the 1-bit constraint. The proposed P-BB enables the use of
the BB framework in large-scale antenna scenarios, which was not applicable due
to its prohibitive complexity. Numerical results demonstrate a near-optimal
error rate performance for the proposed 1-bit precoding algorithm.
Consumer Privacy Protection using Flexible Thermal Loads: Theoretical Limits and Practical Considerations
Date: 2020-10-28 00:00:00

The increasing adoption of smart meters introduces growing concerns about
consumer privacy risks stemming from high resolution metering data. To counter
these risks, there have been various works in actively shaping the grid-visible
energy consumption profile using controllable loads such as energy storage
systems (ESSs) and flexible consumer loads. In this paper, we compare the use
of flexible thermal-based consumer loads (FTLs) against ESSs for consumer
privacy protection. By first assuming ideal conditions, and subsequently
bringing them closer to reality, the limitations of using FTLs for privacy
protection are identified. Through theoretical analyses and realistic
simulations, it is shown that, due to the limitations in the operation of FTLs,
without significant over-sizing of systems and sacrifices in consumer comfort,
FTLs of much higher equivalent energy storage capacity are required to afford
the same level of protection as ESSs. Nonetheless, given their increasing
ubiquity, controllable FTLs should be considered for use in consumer privacy
protection.