pwc-archive/papers-with-abstracts huggingface dataset:
Interference Management in UAV-assisted Integrated Access and Backhaul Cellular Networks
Date: 2019-07-25 00:00:00
Tasks: Management

An integrated access and backhaul (IAB) network architecture can enable
flexible and fast deployment of next-generation cellular networks. However,
mutual interference between access and backhaul links, small inter-site
distance and spatial dynamics of user distribution pose major challenges in the
practical deployment of IAB networks. To tackle these problems, we leverage the
flying capabilities of unmanned aerial vehicles (UAVs) as hovering IAB-nodes
and propose an interference management algorithm to maximize the overall sum
rate of the IAB network. In particular, we jointly optimize the user and base
station associations, the downlink power allocations for access and backhaul
transmissions, and the spatial configurations of UAVs. We consider two spatial
configuration modes of UAVs: distributed UAVs and drone antenna array (DAA),
and show how they are intertwined with the spatial distribution of ground
users. Our numerical results show that the proposed algorithm achieves an
average of $2.9\times$ and $6.7\times$ gains in the received downlink
signal-to-interference-plus-noise ratio (SINR) and overall network sum rate,
respectively. Finally, the numerical results reveal that UAVs cannot only be
used for coverage improvement but also for capacity boosting in IAB cellular
networks.
IoT evolution towards a super-connected world
Date: 2019-06-30 00:00:00
Tasks: Management

Internet of Things (IoT) is one of the key components of Digital
Transformation, along with big data and analytics. IoT, together with the
cloud, big data, analytics, machine learning (ML), and deep ML, can help create
numerous possibilities and new opportunities. These possibilities will impact
our daily lives substantially and open new business models for consumers and
enterprises where the number of connected IoT devices could go up to 50 Billion
by 2022. The ecosystem of IoT consists mainly of sensors/devices layer,
connectivity layer and IoT platform. The main value of IoT is in creating use
cases for efficiency, monitoring and management of the things/devices. IoT
connects the things through the Internet to the IoT platform which equipped
with device management and with the possibility of creating new use cases along
with data analytics and ML that provide 360 view through data insight
Spherical Large Intelligent Surfaces
Date: 2019-10-16 00:00:00

As an emerging technology and evolution that goes beyond massive multi-input
multi-output (MIMO), large intelligent surface (LIS) has gained much interest
recently. LIS acts as an electromagnetic surface and can transmit, redirect,
and receive radiating signals across its entire contiguous surface. It allows
for unprecedented energy-focusing, data-transmission and terminal-positioning,
and can fulfill the most grand visions for future communication systems.
Earlier proposed LISs are in two-dimensional (2D), i.e., planar shapes. In this
paper, we extend LIS to be three-dimensional (3D) and deployed as spherical
surfaces. Compared to 2D shapes, spherical LISs have advantages of wide
coverage, simple positioning techniques, and flexible deployments as
reflectors.
Application of Information Spectrum Method on Small Molecules and Target Recognition
Date: 2020-04-15 00:00:00
Tasks: Drug Discovery, Molecular Docking

Current methods for investigation of receptor - ligand interactions in drug
discovery are based on three-dimensional complementarity of receptor and ligand
surfaces, and they include pharmacophore modelling, QSAR, molecular docking
etc. Those methods only consider short-range molecular interactions (distances
<5A), and not include long-range interactions (distances >5A) which are
essential for kinetic of biochemical reactions because they influence the
number of productive collisions between interacting molecules. Previously was
shown that the electron-ion interaction potential (EIIP) represents the
physical property which determines the long-range properties of biological
molecules. This molecular descriptor served as a base for development of the
informational spectrum method (ISM), a virtual spectroscopy method for
investigation of protein-protein interactions. In this paper, we proposed a new
approach to treat small molecules as linear entities, allowing study of the
small molecule - protein interaction by ISM. We analyzed here 21 sets of KEGG
drug-protein interactions and showed that this new approach allows an efficient
discrimination between biologically active and inactive ligands, and
consistence with AA regions of their binding site on the target protein.
Mathematical Models of Consciousness
Date: 2020-04-20 00:00:00

In recent years, promising mathematical models have been suggested which aim
to describe conscious experience and its relation to the physical domain.
Whereas the axioms and metaphysical ideas of these theories have been carefully
motivated, their mathematical formalism has not. In this article we aim to
remedy this situation. We give an account of what warrants mathematical
representation of phenomenal experience, derive a general mathematical
framework which takes into account consciousness' epistemic context and study
which mathematical structures some of the key characteristics of conscious
experience imply, showing precisely where mathematical approaches allow to go
beyond what the standard methodology can do. The result is a general
mathematical framework for models of consciousness that can be employed in the
theory-building process.
Vertex-Frequency Graph Signal Processing: A review
Date: 2019-12-26 00:00:00

Graph signal processing deals with signals which are observed on an irregular
graph domain. While many approaches have been developed in classical graph
theory to cluster vertices and segment large graphs in a signal independent
way, signal localization based approaches to the analysis of data on graph
represent a new research direction which is also a key to big data analytics on
graphs. To this end, after an overview of the basic definitions in graphs and
graph signals, we present and discuss a localized form of the graph Fourier
transform. To establish an analogy with classical signal processing, spectral-
and vertex-domain definitions of the localization window are given next. The
spectral and vertex localization kernels are then related to the wavelet
transform, followed by a study of filtering and inversion of the localized
graph Fourier transform. For rigour, the analysis of energy representation and
frames in the localized graph Fourier transform is extended to the energy forms
of vertex-frequency distributions, which operate even without the need to apply
localization windows. Another link with classical signal processing is
established through the concept of local smoothness, which is subsequently
related to the particular paradigm of signal smoothness on graphs. This all
represents a comprehensive account of the relation of general vertex-frequency
analysis with classical time-frequency analysis, and important but missing link
for more advanced applications of graphs signal processing. The theory is
supported by illustrative and practically relevant examples.
Exact Crystalline Structure Recovery in X-ray Crystallography from Coded Diffraction Patterns
Date: 2019-06-29 00:00:00

X-ray crystallography (XC) is an experimental technique used to determine
three-dimensional crystalline structures. The acquired data in XC, called
diffraction patterns, is the Fourier magnitudes of the unknown crystalline
structure. To estimate the crystalline structure from its diffraction patterns,
we propose to modify the traditional system by including an optical element
called coded aperture which modulates the diffracted field to acquire coded
diffraction patterns (CDP). For the proposed coded system, in contrast with the
traditional, we derive exact reconstruction guarantees for the crystalline
structure from CDP (up to a global shift phase). Additionally, exploiting the
fact that the crystalline structure can be sparsely represented in the Fourier
domain, we develop an algorithm to estimate the crystal structure from CDP. We
show that this method requires 50% fewer measurements to estimate the crystal
structure in comparison with its competitive alternatives. Specifically, the
proposed method is able to reduce the exposition time of the crystal, implying
that under the proposed setup, its structural integrity is less affected in
comparison with the traditional. We discuss further implementation of imaging
devices that exploits this theoretical coded system.
Discovering the Computational Relevance of Brain Network Organization
Date: 2019-10-21 00:00:00

Understanding neurocognitive computations will require not just localizing
cognitive information distributed throughout the brain but also determining how
that information got there. We review recent advances in linking empirical and
simulated brain network organization with cognitive information processing.
Building on these advances, we offer a new framework for understanding the role
of connectivity in cognition - network coding (encoding/decoding) models. These
models utilize connectivity to specify the transfer of information via neural
activity flow processes, successfully predicting the formation of cognitive
representations in empirical neural data. The success of these models supports
the possibility that localized neural functions mechanistically emerge (are
computed) from distributed activity flow processes that are specified primarily
by connectivity patterns.
Quantitative Immunology for Physicists
Authors: Grégoire Altan-Bonnet, Thierry Mora, Aleksandra M. Walczak
Date: 2019-07-08 00:00:00

The adaptive immune system is a dynamical, self-organized multiscale system that protects vertebrates from both pathogens and internal irregularities, such as tumours. For these reason it fascinates physicists, yet the multitude of different cells, molecules and sub-systems is often also petrifying. Despite this complexity, as experiments on different scales of the adaptive immune system become more quantitative, many physicists have made both theoretical and experimental contributions that help predict the behaviour of ensembles of cells and molecules that participate in an immune response. Here we review some recent contributions with an emphasis on quantitative questions and methodologies. We also provide a more general methods section that presents some of the wide array of theoretical tools used in the field.
UAV Access Point Placement for Connectivity to a User with Unknown Location Using Deep RL
Date: 2019-07-13 00:00:00
Tasks: Deep Reinforcement Learning

In recent years, unmanned aerial vehicles (UAVs) have been considered for
telecommunications purposes as relays, caches, or IoT data collectors. In
addition to being easy to deploy, their maneuverability allows them to adjust
their location to optimize the capacity of the link to the user equipment on
the ground or of the link to the basestation. The majority of the previous work
that analyzes the optimal placement of such a UAV makes at least one of two
assumptions: the channel can be predicted using a simple model or the locations
of the users on the ground are known. In this paper, we use deep reinforcement
learning (deep RL) to optimally place a UAV serving a ground user in an urban
environment, without the previous knowledge of the channel or user location.
Our algorithm relies on signal-to-interference-plus-noise ratio (SINR)
measurements and a 3D map of the topology to account for blockage and
scatterers. Furthermore, it is designed to operate in any urban environment.
Results in conditions simulated by a ray tracing software show that with the
constraint on the maximum number of iterations our algorithm has a 90% success
rate in converging to a target SINR.
FarSense: Pushing the Range Limit of WiFi-based Respiration Sensing with CSI Ratio of Two Antennas
Date: 2019-08-10 00:00:00

The past few years have witnessed the great potential of exploiting channel
state information retrieved from commodity WiFi devices for respiration
monitoring. However, existing approaches only work when the target is close to
the WiFi transceivers and the performance degrades significantly when the
target is far away. On the other hand, most home environments only have one
WiFi access point and it may not be located in the same room as the target.
This sensing range constraint greatly limits the application of the proposed
approaches in real life.
  This paper presents FarSense--the first real-time system that can reliably
monitor human respiration when the target is far away from the WiFi transceiver
pair. FarSense works well even when one of the transceivers is located in
another room, moving a big step towards real-life deployment. We propose two
novel schemes to achieve this goal: (1) Instead of applying the raw CSI
readings of individual antenna for sensing, we employ the ratio of CSI readings
from two antennas, whose noise is mostly canceled out by the division operation
to significantly increase the sensing range; (2) The division operation further
enables us to utilize the phase information which is not usable with one single
antenna for sensing. The orthogonal amplitude and phase are elaborately
combined to address the "blind spots" issue and further increase the sensing
range. Extensive experiments show that FarSense is able to accurately monitor
human respiration even when the target is 8 meters away from the transceiver
pair, increasing the sensing range by more than 100%. We believe this is the
first system to enable through-wall respiration sensing with commodity WiFi
devices and the proposed method could also benefit other sensing applications.
Impact of climate change on the cost-optimal mix of decentralised heat pump and gas boiler technologies in Europe
Date: 2019-12-17 00:00:00

Residential demands for space heating and hot water account for 31% of the
total European energy demand. Space heating is highly dependent on ambient
conditions and susceptible to climate change. We adopt a techno-economic
standpoint and assess the impact of climate change on decentralised heating
demand and the cost-optimal mix of heat pump and gas boiler technologies.
Temperature data with high spatial resolution from nine climate models
implementing three Representative Concentration Pathways from IPCC are used to
estimate climate induced changes in the European demand side for heating. The
demand side is modelled by the proxy of heating-degree days. The supply side is
modelled by using a screening curve approach to the economics of heat
generation. We find that space heating demand decreases by about 16%, 24% and
42% in low, intermediate and extreme global warming scenarios. When considering
historic weather data, we find a heterogeneous mix of technologies are
cost-optimal, depending on the heating load factor (number of full-load hours
per year). Increasing ambient temperatures toward the end-century improve the
economic performance of heat pumps in all concentration pathways. Cost optimal
technologies broadly correspond to heat markets and policies in Europe, with
some exceptions
Cooperative Localization with Angular Measurements and Posterior Linearization
Date: 2019-07-10 00:00:00

The application of cooperative localization in vehicular networks is
attractive to improve accuracy and coverage. Conventional distance measurements
between vehicles are limited by the need for synchronization and provide no
heading information of the vehicle. To address this, we present a cooperative
localization algorithm using posterior linearization belief propagation (PLBP)
utilizing angle-of-arrival (AoA)-only measurements. Simulation results show
that both directional and positional root mean squared error (RMSE) of vehicles
can be decreased significantly and converge to a low value in a few iterations.
Furthermore, the influence of parameters for the vehicular network, such as
vehicle density, communication radius, prior uncertainty and AoA measurements
noise, is analyzed.
Evaluating bird collision risk of a high-speed railway crossing the habitat of the crested ibis (Nipponia nippon) in Qinling Mountains, China
Date: 2019-07-10 00:00:00

Bird collisions with high-speed transport modes is a vital topic on vehicle
safety and wildlife protection, especially when high-speed trains, with an
average speed of 250km/h, have to run across the habitat of an endangered bird
species. This paper evaluates the bird-train collision risk associated with a
recent high-speed railway project in Qinling Mountains, China, for the crested
ibis (Nipponia nippon) and other local bird species. Using line transect
surveys and walking monitoring techniques, we surveyed the population
abundance, spatial-temporal distributions, and bridge-crossing behaviors of the
birds in the study area. The results show that: (1) The crested ibis and the
egret were the two most abundant waterfowl species in the study area. The RAI
of these two species were about 43.69% and 42.91%, respectively; (2) Crested
ibises overall habitat closer to the railway bridge. 91.63% of them were
firstly detected within the range of 0m to 25m of the vicinity of the bridge;
(3) the ratio between crossing over and under the railway bridge was about 7:3.
Crested ibises were found to prefer to fly over the railway bridge (89.29% of
the total crossing activities observed for this species). Egrets were more
likely to cross the railway below the bridge, and they accounted for 60.27% of
the total observations of crossing under the bridge. We recommend that, while
the collision risk of crested ibises could be low, barrier-like structures,
such as fences, should still be considered to promote the conservation of
multiple bird species in the area. This paper provides a practical case for
railway ecology studies in China. To our best knowledge, this is the first
high-speed railway project that takes protecting crested ibises as one of the
top priorities, and exemplifies the recent nationwide initiative towards the
construction of "eco-civilization" in the country.
Optimizing defence, counter-defence and counter-counter defence in parasitic and trophic interactions -- A modelling study
Authors: Stefan Schuster, Jan Ewald, Thomas Dandekar, Sybille Dühring
Date: 2019-07-10 00:00:00

In host-pathogen interactions, often the host (attacked organism) defends itself by some toxic compound and the parasite, in turn, responds by producing an enzyme that inactivates that compound. In some cases, the host can respond by producing an inhibitor of that enzyme, which can be considered as a counter-counter defence. An example is provided by cephalosporins, beta-lactamases and clavulanic acid (an inhibitor of beta-lactamases). Here, we tackle the question under which conditions it pays, during evolution, to establish a counter-counter defence rather than to intensify or widen the defence mechanisms. We establish a mathematical model describing this phenomenon, based on enzyme kinetics for competitive inhibition. We use an objective function based on Haber's rule, which says that the toxic effect is proportional to the time integral of toxin concentration. The optimal allocation of defence and counter-counter defence can be calculated in an analytical way despite the nonlinearity in the underlying differential equation. The calculation provides a threshold value for the dissociation constant of the inhibitor. Only if the inhibition constant is below that threshold, that is, in the case of strong binding of the inhibitor, it pays to have a counter-counter defence. This theoretical prediction accounts for the observation that not for all defence mechanisms, a counter-counter defence exists. Our results should be of interest for computing optimal mixtures of beta-lactam antibiotics and beta-lactamase inhibitors such as sulbactam, as well as for plant-herbivore and other molecular-ecological interactions and to fight antibiotic resistance in general.
Identification and Estimation of Discrete Choice Models with Unobserved Choice Sets
Authors: Victor H. Aguiar, Nail Kashaev
Date: 2019-07-09 00:00:00
Tasks: Discrete Choice Models

We propose a framework for nonparametric identification and estimation of discrete choice models with unobserved choice sets. We recover the joint distribution of choice sets and preferences from a panel dataset on choices. We assume that either the latent choice sets are sparse or that the panel is sufficiently long. Sparsity requires the number of possible choice sets to be relatively small. It is satisfied, for instance, when the choice sets are nested, or when they form a partition. Our estimation procedure is computationally fast and uses mixed-integer optimization to recover the sparse support of choice sets. Analyzing the ready-to-eat cereal industry using a household scanner dataset, we find that ignoring the unobservability of choice sets can lead to biased estimates of preferences due to significant latent heterogeneity in choice sets.
Short-term ASV Collision Avoidance with Static and Moving Obstacles
Date: 2019-07-10 00:00:00
Tasks: Collision Avoidance, Model Predictive Control

This article considers collision avoidance (COLAV) for both static and moving
obstacles using the branching-course model predictive control (BC-MPC)
algorithm, which is designed for use by autonomous surface vehicles (ASVs). The
BC-MPC algorithm originally only considered COLAV of moving obstacles, so in
order to make the algorithm also be able to avoid static obstacles, we
introduce an extra term in the objective function based on an occupancy grid.
In addition, other improvements are made to the algorithm resulting in
trajectories with less wobbling. The modified algorithm is verified through
full-scale experiments in the Trondheimsfjord in Norway with both virtual
static obstacles and a physical moving obstacle. A radar-based tracking system
is used to detect and track the moving obstacle, which enables the algorithm to
avoid obstacles without depending on vessel-to-vessel communication. The
experiments show that the algorithm is able to simultaneously avoid both static
and moving obstacles, while providing clear and readily observable maneuvers.
The BC-MPC algorithm is compliant with rules 8, 13 and 17 of the the
International Regulations for Preventing Collisions at Sea (COLREGs), and
favors maneuvers following rules 14 and 15.
Maximum Entropy approach to multivariate time series randomization
Authors: Riccardo Marcaccioli, Giacomo Livan
Date: 2019-07-01 00:00:00
Tasks: Time Series, Time Series Analysis

Natural and social multivariate systems are commonly studied through sets of simultaneous and time-spaced measurements of the observables that drive their dynamics, i.e., through sets of time series. Typically, this is done via hypothesis testing: the statistical properties of the empirical time series are tested against those expected under a suitable null hypothesis. This is a very challenging task in complex interacting systems, where statistical stability is often poor due to lack of stationarity and ergodicity. Here, we describe an unsupervised, data-driven framework to perform hypothesis testing in such situations. This consists of a statistical mechanical approach - analogous to the configuration model for networked systems - for ensembles of time series designed to preserve, on average, some of the statistical properties observed on an empirical set of time series. We showcase its possible applications with a case study on financial portfolio selection.