pwc-archive/papers-with-abstracts huggingface dataset:
Deep Learning for Wireless Communications: An Emerging Interdisciplinary Paradigm
Authors: Linglong Dai, Ruicheng Jiao, Fumiyuki Adachi, H. Vincent Poor, Lajos Hanzo
Date: 2020-07-12 00:00:00
Tasks: Deep Learning

Wireless communications are envisioned to bring about dramatic changes in the future, with a variety of emerging applications, such as virtual reality (VR), Internet of things (IoT), etc., becoming a reality. However, these compelling applications have imposed many new challenges, including unknown channel models, low-latency requirement in large-scale super-dense networks, etc. The amazing success of deep learning (DL) in various fields, particularly in computer science, has recently stimulated increasing interest in applying it to address those challenges. Hence, in this review, a pair of dominant methodologies of using DL for wireless communications are investigated. The first one is DL-based architecture design, which breaks the classical model-based block design rule of wireless communications in the past decades. The second one is DL-based algorithm design, which will be illustrated by several examples in a series of typical techniques conceived for 5G and beyond. Their principles, key features, and performance gains will be discussed. Furthermore, open problems and future research opportunities will also be pointed out, highlighting the interplay between DL and wireless communications. We expect that this review can stimulate more novel ideas and exciting contributions for intelligent wireless communications.
Contracting over persistent information
Authors: Wei Zhao, Claudio Mezzetti, Ludovic Renou, Tristan Tomala
Date: 2020-07-12 00:00:00

We consider a dynamic moral hazard problem between a principal and an agent, where the sole instrument the principal has to incentivize the agent is the disclosure of information. The principal aims at maximizing the (discounted) number of times the agent chooses a particular action, e.g., to work hard. We show that there exists an optimal contract, where the principal stops disclosing information as soon as its most preferred action is a static best reply for the agent or else continues disclosing information until the agent perfectly learns the principal's private information. If the agent perfectly learns the state, he learns it in finite time with probability one; the more patient the agent, the later he learns it.
Standoff Through-the-Wall Sensing at Ka-Band Microwave
Date: 2020-07-12 00:00:00

Conventional microwave remote sensing/imaging of through-the-wall objects
made of different materials is usually performed at frequencies below 3 GHz
that provide relatively low spatial resolution. In this paper, we evaluate the
ability and sensitivity of high-frequency microwave or millimeter wave standoff
sensing of through-the-wall objects to achieve high spatial resolution. The
target under study is a sandwich structure consisting of different object
materials placed between two wall blocks. An Agilent PNA-X series (model
N5245A) vector network analyzer is used to sweep over the entire Ka-band (26.5
GHz to 40 GHz). The beam is then directed to a standard rectangular horn
antenna and collimated by a 6-inch-diameter Gaussian lens towards the sandwich
structure (wall block/object/wall block). The reflected electromagnetic wave is
picked up by the same system as the complex S-parameter S11. Both amplitude and
phase of the reflected signal are used to recognize different materials
sandwiched between the cement blocks. The experimental results are compared
with the theoretical calculations, which show satisfactory agreement for the
cases evaluated in this work.
Unified Virtual Oscillator Control for Grid-Forming and Grid-Following Converters
Date: 2020-07-12 00:00:00

A unified virtual oscillator controller (uVOC) is proposed, which enables a
unified analysis, design, and implementation framework for both grid-forming
(GFM) and grid-following (GFL) voltage source converters (VSCs). Oscillator
based GFM controllers, such as dispatchable virtual oscillator control (dVOC),
offer rigorous analytical framework with enhanced synchronization, but lack
effective fault handling capability which severely limits practical
application. The proposed uVOC facilitates synchronization with an arbitrarily
low grid voltage and fast over-current limiting; this enables effective fault
ride-through unlike existing GFM controllers which typically switch to a
back-up controller during fault. GFM operation with uVOC is achieved in both
grid connected and islanded modes with seamless transition between the two. In
GFL converters, bidirectional power flow control and DC bus voltage regulation
is achieved with uVOC. No phase-locked-loop (PLL) is required for either GFL or
GFM operation circumventing the synchronization issues associated with PLLs in
weak grid applications. Detail small signal models for GFM and GFL operation
have been developed and systematic design guidelines for controller parameters
are provided. The proposed controller is validated through hardware experiments
in a hybrid AC-DC microgrid.
Elevated LiDAR Placement under Energy and Throughput Capacity Constraints
Authors: Michael C. Lucic, Hakim Ghazzai, Yehia Massoud
Date: 2020-07-12 00:00:00
Tasks: Autonomous Vehicles

Elevated LiDAR (ELiD) has the potential to hasten the deployment of Autonomous Vehicles (AV), as ELiD can reduce energy expenditures associated with AVs, and can also be utilized for other intelligent Transportation Systems applications such as urban 3D mapping. In this paper, we address the need for a planning framework in order for ITS operators to have an effective tool for determining what resources are required to achieve a desired level of coverage of urban roadways. To this end, we develop a mixed-integer nonlinear constrained optimization problem, with the aim of maximizing effective area coverage of a roadway, while satisfying energy and throughput capacity constraints. Due to the non-linearity of the problem, we utilize Particle Swarm Optimization (PSO) to solve the problem. After demonstrating its effectiveness in finding a solution for a realistic scenario, we perform a sensitivity analysis to test the model's general ability.
Configurational Entropy of Folded Proteins and its Importance for Intrinsically Disordered Proteins
Authors: Meili Liu, Akshaya K. Das, James Lincoff, Sukanya Sasmal, Sara Y. Cheng, Robert Vernon, Julie Forman-Kay, Teresa Head-Gordon
Date: 2020-07-13 00:00:00

Many pairwise additive force fields are in active use for intrinsically disordered proteins (IDPs) and regions (IDRs), some of which modify energetic terms to improve description of IDPs/IDRs, but are largely in disagreement with solution experiments for the disordered states. We have evaluated representative pairwise and many-body protein and water force fields against experimental data on representative IDPs and IDRs, a peptide that undergoes a disorder-to-order transition, and for seven globular proteins ranging in size from 130-266 amino acids. We find that force fields with the largest statistical fluctuations consistent with the radius of gyration and universal Lindemann values for folded states simultaneously better describe IDPs and IDRs and disorder to order transitions. Hence the crux of what a force field should exhibit to well describe IDRs/IDPs is not just the balance between protein and water energetics, but the balance between energetic effects and configurational entropy of folded states of globular proteins.
Fast approximate reciprocal approximations for iterative algorithms
Authors: Michael Lunglmayr, Oliver Ploder
Date: 2020-07-13 00:00:00

The reciprocal function, 1/x, is important for many real-time algorithms. It is used in a large variety of algorithms from areas ranging from iterative estimation to machine learning. Many of these algorithms are iterative in nature and require the online computation of the reciprocal. Such an iterative structure often prevents effective use of pipelining for implementation of the reciprocal. For this reason, a reciprocal algorithm requiring only a low amount of clock cycles is desired. Many real-time algorithms, often being of approximate nature, can tolerate the use of only an approximate solution of the reciprocal. For this reason, we present a low complexity non-iterative approximation of the reciprocal function. This approximation can be calculated using only combinatorial logic. We present synthesis results showing that the proposed approach can be implemented with low area requirements at high clock frequencies. We analytically describe the error of the approximation and show that by optimizing a constant value used in the approximation, different variants with different error behaviors can be obtained. We furthermore present performance results of application examples that, when using our proposed method, show only negligible performance degradation compared to when using the exact reciprocal function, demonstrating the versatility of our proposed approach.
Exponential Signal Reconstruction with Deep Hankel Matrix Factorization
Authors: Yihui Huang, Jinkui Zhao, Zi Wang, Vladislav Orekhov, Di Guo, Xiaobo Qu
Date: 2020-07-13 00:00:00
Tasks: Rolling Shutter Correction

Exponential is a basic signal form, and how to fast acquire this signal is one of the fundamental problems and frontiers in signal processing. To achieve this goal, partial data may be acquired but result in the severe artifacts in its spectrum, which is the Fourier transform of exponentials. Thus, reliable spectrum reconstruction is highly expected in the fast sampling in many applications, such as chemistry, biology, and medical imaging. In this work, we propose a deep learning method whose neural network structure is designed by unrolling the iterative process in the model-based state-of-the-art exponentials reconstruction method with low-rank Hankel matrix factorization. With the experiments on synthetic data and realistic biological magnetic resonance signals, we demonstrate that the new method yields much lower reconstruction errors and preserves the low-intensity signals much better.
A micro-to-macro approach to returns, volumes and waiting times
Authors: Guglielmo D'Amico, Filippo Petroni
Date: 2020-07-13 00:00:00

Fundamental variables in financial market are not only price and return but a very important role is also played by trading volumes. Here we propose a new multivariate model that takes into account price returns, logarithmic variation of trading volumes and also waiting times, the latter to be intended as the time interval between changes in trades, price, and volume of stocks. Our approach is based on a generalization of semi-Markov chains where an endogenous index process is introduced. We also take into account the dependence structure between the above mentioned variables by means of copulae. The proposed model is motivated by empirical evidences which are known in financial literature and that are also confirmed in this work by analysing real data from Italian stock market in the period August 2015 - August 2017. By using Monte Carlo simulations, we show that the model reproduces all these empirical evidences.
On the Effectiveness of Tracking and Testing in SEIR Models
Date: 2020-07-13 00:00:00

We study the effectiveness of tracking and testing in mitigating or
suppressing epidemic outbreaks, in combination with or as an alternative to
quarantines and global lockdowns. We study these intervention methods on a
network-based SEIR model, augmented with an additional probability to model
symptomatic, asymptomatic and pre-symptomatic cases. Our focus is on the basic
trade-offs between economic costs and human lives lost, and how these
trade-offs change under different lockdown, quarantine, tracking and testing
policies.
  Our main findings are as follows: (i) Tests combined with patient quarantines
reduce both economic costs and mortality, but require a large-scale testing
capacity to achieve a significant improvement; (ii) Tracking significantly
reduces both economic costs and mortality; (iii) Tracking combined with a
limited number of tests can achieve containment without lockdowns; (iv) If
there is a small flow of new incoming infections, dynamic "On-Off" lockdowns
are more efficient than fixed lockdowns.
  Our simulation results underline the extreme effectiveness of tracking and
testing policies in reducing both economic costs and mortality and their
potential to contain epidemic outbreaks without imposing social distancing
restrictions. This highlights the difficult social question of trading-off
these gains with the privacy loss that tracking necessarily entails.
Stabilizing a spherical pendulum on a quadrotor
Authors: Aradhana Nayak, Ravi Banavar, D. H. S Maithripala
Date: 2020-07-13 00:00:00
Tasks: Position

In this article we design a backstepping control law based on geometric principles to swing up a spherical pendulum mounted on a moving quadrotor. The available degrees of freedom in the control vector also permit us to position the plane of the quadrotor parallel to the ground. The problem addressed here is, indeed, novel and has many practical applications which arise during the transport of a payload mounted on top of a quadrotor. The modeling and control law are coordinate-free and thus avoid singularity issues. The geometric treatment of the problem greatly simplifies both the modeling and control law for the system. The control action is verified and supported by numerical experiments for aggressive manoeuvres starting very close to the downward stable equilibrium position of the pendulum.
On the importance of block randomisation when designing proteomics experiments
Date: 2020-07-13 00:00:00
Tasks: Blocking, Experimental Design

Randomisation is used in experimental design to reduce the prevalence of
unanticipated confounders. Complete randomisation can however create unbalanced
designs, for example, grouping all samples of the same condition in the same
batch. Block randomisation is an approach that can prevent severe imbalances in
sample allocation with respect to both known and unknown confounders. This
feature provides the reader with an introduction to blocking and randomisation,
insights into how to effectively organise samples during experimental design,
with special considerations with respect to proteomics.
A novel random access scheme for M2M communication in crowded asynchronous massive MIMO systems
Authors: Huimei Han, Wenchao Zhai, Zhefu Wu, Ying Li, Jun Zhao, Mingda Chen
Date: 2020-07-13 00:00:00

A new random access scheme is proposed to solve the intra-cell pilot collision for M2M communication in crowded asynchronous massive multiple-input multiple-output (MIMO) systems. The proposed scheme utilizes the proposed estimation of signal parameters via rotational invariance technique enhanced (ESPRIT-E) method to estimate the effective timing offsets, and then active UEs obtain their timing errors from the effective timing offsets for uplink message transmission. We analyze the mean squared error of the estimated effective timing offsets of UEs, and the uplink throughput. Simulation results show that, compared to the exiting random access scheme for the crowded asynchronous massive MIMO systems, the proposed scheme can improve the uplink throughput and estimate the effective timing offsets accurately at the same time.
Equilibrium Refinement in Finite Action Evidence Games
Authors: Shaofei Jiang
Date: 2020-07-13 00:00:00

Evidence games study situations where a sender persuades a receiver by selectively disclosing hard evidence about an unknown state of the world. Evidence games often have multiple equilibria. Hart et al. (2017) propose to focus on truth-leaning equilibria, i.e., perfect Bayesian equilibria where the sender discloses truthfully when indifferent, and the receiver takes off-path disclosure at face value. They show that a truth-leaning equilibrium is an equilibrium of a perturbed game where the sender has an infinitesimal reward for truth-telling. We show that, when the receiver's action space is finite, truth-leaning equilibrium may fail to exist, and it is not equivalent to equilibrium of the perturbed game. To restore existence, we introduce a disturbed game with a small uncertainty about the receiver's payoff. A purifiable truthful equilibrium is the limit of a sequence of truth-leaning equilibria in the disturbed games as the disturbances converge to zero. It exists and features a simple characterization. A truth-leaning equilibrium that is also purifiable truthful is an equilibrium of the perturbed game. Moreover, purifiable truthful equilibria are receiver optimal and give the receiver the same payoff as the optimal deterministic mechanism.
Improving P300 Speller performance by means of optimization and machine learning
Authors: Luigi Bianchi, Chiara Liti, Giampaolo Liuzzi, Veronica Piccialli, Cecilia Salvatore
Date: 2020-07-13 00:00:00
Tasks: BIG-bench Machine Learning, EEG, Electroencephalogram (EEG), ERP, General Classification

Brain-Computer Interfaces (BCIs) are systems allowing people to interact with the environment bypassing the natural neuromuscular and hormonal outputs of the peripheral nervous system (PNS). These interfaces record a user's brain activity and translate it into control commands for external devices, thus providing the PNS with additional artificial outputs. In this framework, the BCIs based on the P300 Event-Related Potentials (ERP), which represent the electrical responses recorded from the brain after specific events or stimuli, have proven to be particularly successful and robust. The presence or the absence of a P300 evoked potential within the EEG features is determined through a classification algorithm. Linear classifiers such as SWLDA and SVM are the most used for ERPs' classification. Due to the low signal-to-noise ratio of the EEG signals, multiple stimulation sequences (a.k.a. iterations) are carried out and then averaged before the signals being classified. However, while augmenting the number of iterations improves the Signal-to-Noise Ratio (SNR), it also slows down the process. In the early studies, the number of iterations was fixed (no stopping), but recently, several early stopping strategies have been proposed in the literature to dynamically interrupt the stimulation sequence when a certain criterion is met to enhance the communication rate. In this work, we explore how to improve the classification performances in P300 based BCIs by combining optimization and machine learning. First, we propose a new decision function that aims at improving classification performances in terms of accuracy and Information Transfer Rate both in a no stopping and early stopping environment. Then, we propose a new SVM training problem that aims to facilitate the target-detection process. Our approach proves to be effective on several publicly available datasets.
Epidemic modelling of bovine tuberculosis in cattle herds and badgers in Ireland
Date: 2020-07-13 00:00:00

Bovine tuberculosis, a disease that affects cattle and badgers in Ireland,
was studied via stochastic epidemic modeling using incidence data from the Four
Area Project (Griffin et al., 2005). The Four Area Project was a large scale
field trial conducted in four diverse farming regions of Ireland over a
five-year period (1997-2002) to evaluate the impact of badger culling on bovine
tuberculosis incidence in cattle herds.
  Based on the comparison of several models, the model with no between-herd
transmission and badger-to-herd transmission proportional to the total number
of infected badgers culled was best supported by the data.
  Detailed model validation was conducted via model prediction, identifiability
checks and sensitivity analysis.
  The results suggest that badger-to-cattle transmission is of more importance
than between-herd transmission and that if there was no badger-to-herd
transmission, levels of bovine tuberculosis in cattle herds in Ireland could
decrease considerably.
Relative Entropy Regularised TDLAS Tomography for Robust Temperature Imaging
Date: 2020-07-13 00:00:00
Tasks: Image Retrieval, Retrieval

Tunable Diode Laser Absorption Spectroscopy (TDLAS) tomography has been
widely used for in situ combustion diagnostics, yielding images of both species
concentration and temperature. The temperature image is generally obtained from
the reconstructed absorbance distributions for two spectral transitions, i.e.
two-line thermometry. However, the inherently ill-posed nature of tomographic
data inversion leads to noise in each of the reconstructed absorbance
distributions. These noise effects propagate into the absorbance ratio and
generate artefacts in the retrieved temperature image. To address this problem,
we have developed a novel algorithm, which we call Relative Entropy Tomographic
RecOnstruction (RETRO), for TDLAS tomography. A relative entropy regularisation
is introduced for high-fidelity temperature image retrieval from jointly
reconstructed two-line absorbance distributions. We have carried out numerical
simulations and proof-of-concept experiments to validate the proposed
algorithm. Compared with the well-established Simultaneous Algebraic
Reconstruction Technique (SART), the RETRO algorithm significantly improves the
quality of the tomographic temperature images, exhibiting excellent robustness
against TDLAS tomographic measurement noise. RETRO offers great potential for
industrial field applications of TDLAS tomography, where it is common for
measurements to be performed in very harsh environments.
Government intervention modeling in microeconomic company market evolution
Authors: Michał Chorowski, Ryszard Kutner
Date: 2020-06-22 00:00:00

Modern technology and innovations are becoming more crucial than ever for the survival of companies in the market. Therefore, it is significant both from theoretical and practical points of view to understand how governments can influence technology growth and innovation diffusion (TGID) processes. We propose a simple but essential extension of Ausloos-Clippe-P\c{e}kalski and related Cichy numerical models of the TGID in the market. Both models are inspired by the nonlinear non-equilibrium statistical physics. Our extension involves a parameter describing the probability of government intervention in the TGID process in the company market. We show, using Monte Carlo simulations, the effects interventionism can have on the companies' market, depending on the segment of firms that are supported. The high intervention probability can result, paradoxically, in the destabilization of the market development. It lowers the market's technology level in the long-time limit compared to markets with a lower intervention parameter. We found that the intervention in the technologically weak and strong segments of the company market does not substantially influence the market dynamics, compared to the intervention helping the middle-level companies. However, this is still a simple model which can be extended further and made more realistic by including other factors. Namely, the cost and risk of innovation or limited government resources and capabilities to support companies.
A Sampling Algorithm for Diffusion Networks
Authors: Daniel Gilio Tiglea, Renato Candido, Magno T. M. Silva
Date: 2020-07-13 00:00:00

In this paper, we propose a sampling mechanism for adaptive diffusion networks that adaptively changes the amount of sampled nodes based on mean-squared error in the neighborhood of each node. It presents fast convergence during transient and a significant reduction in the number of sampled nodes in steady state. Besides reducing the computational cost, the proposed mechanism can also be used as a censoring technique, thus saving energy by reducing the amount of communication between nodes. We also present a theoretical analysis to obtain lower and upper bounds for the number of network nodes sampled in steady state.