pwc-archive/papers-with-abstracts huggingface dataset:
Effects of a Hovering Unmanned Aerial Vehicle on Urban Soundscapes Perception
Date: 2019-11-29 00:00:00

Several industry leaders and governmental agencies are currently
investigating the use of Unmanned Aerial Vehicles (UAVs), or drones as commonly
known, for an ever-growing number of applications from blue light services to
parcel delivery. For the specific case of the delivery sector, drones can
alleviate road space usage and also lead to reductions in CO2 and air pollution
emissions, compared to traditional diesel-powered vehicles. However, due to
their unconventional acoustic characteristics and operational manoeuvres, it is
uncertain how communities will respond to drone operations. Noise has been
suggested as a major barrier to public acceptance of drone operations in urban
areas. In this paper, a series of audio-visual scenarios were created to
investigate the effects of drone noise on the reported loudness, annoyance and
pleasantness of seven different types of urban soundscapes. In soundscapes
highly impacted by road traffic noise, the presence of drone noise lead to
small changes in the perceived loudness, annoyance and pleasantness. In
soundscapes with reduced road traffic noise, the participants reported a
significantly higher perceived loudness and annoyance and a lower pleasantness
with the presence of the same drone noise. For instance, the reported annoyance
increased from 2.3 (without drone noise) to 6.8 (with drone noise), in an
11-point scale (0-not at all, 10-extremely). Based on these results, the
concentration of drone operations along flight paths through busy roads might
aid in the mitigation of the overall community noise impact caused by drones.
An Energy Efficient Distributed Gossip Algorithm for Wireless Sensor Networks based on a Randomized Markovian Duty-Cycling
Date: 2019-11-30 00:00:00

This paper proposes a novel asynchronous consensus algorithm which is based
on a continuous update rule and an energy efficient event triggered duty
(wake-sleep) cycle based on a discrete Markov chain model. The system model of
the proposed algorithm is formulated and analyzed. The conditions for
convergence and stability of the algorithm are derived and the algorithm is
proved to converge to an average consensus. Numerical simulations on random,
circular, chain and star graphs show stability in consensus and convergence to
network global state average.
Projective view at Optimization Problem for Multiband Filter
Date: 2020-01-21 00:00:00
Tasks: Electrical Engineering

The best uniform rational approximation of the \emph{sign} function on two
intervals separated by zero was explicitly solved by E.I. Zolotar\"ev in 1877.
This optimization problem is the initial step in the staircase of the so called
approximation problems for multiband filters which are of great importance for
electrical engineering. We show that known in the literature optimality
criterion for this problem may be contradictory since it does not take into
account the projective invariance of the problem. We propose a new consistently
projective formulation of this problem and give a constructive optimality
criterion for it.
Super-Nash performance in games
Authors: Mehmet S. Ismail
Date: 2019-11-30 00:00:00
Tasks: Decision Making

Since the 1990s, artificial intelligence (AI) systems have achieved 'superhuman performance' in major zero-sum games, where winning has an unambiguous definition. However, most economic and social interactions are non-zero-sum, where measuring 'performance' is a non-trivial task. In this paper, I introduce a novel benchmark, super-Nash performance, and a solution concept, optimin, whereby every player maximizes their minimal payoff under unilateral profitable deviations of the others. Optimin achieves super-Nash performance in that, for every Nash equilibrium, there exists an optimin where each player not only receives but also guarantees super-Nash payoffs, even if other players deviate unilaterally and profitably from the optimin. Further, optimin generalizes and unifies several key results across domains: it coincides with (i) the maximin strategies in zero-sum games, and (ii) the core in cooperative games when the core is nonempty, though it exists even if the core is empty; additionally, optimin generalizes (iii) Nash equilibrium in $n$-person constant-sum games. Finally, optimin is consistent with the direction of non-Nash deviations in games in which cooperation has been extensively studied, including the finitely repeated prisoner's dilemma, the centipede game, the traveler's dilemma, and the finitely repeated public goods game.
Learning poly-synaptic paths with traveling waves
Authors: Yoshiki Ito, Taro Toyoizumi
Date: 2019-11-30 00:00:00

Traveling waves are commonly observed across the brain. While previous studies have suggested the role of traveling waves in learning, the mechanism is still unclear. We adopted a computational approach to investigate the effect of traveling waves on synaptic plasticity. Our results indicate that traveling waves facilitate the learning of poly-synaptic network-paths when combined with a reward-dependent local synaptic plasticity rule. We also demonstrate that traveling waves expedite finding the shortest paths and learning nonlinear input/output-mapping, such as exclusive or (XOR) function.
Endogenous Liquidity Crises
Authors: Antoine Fosset, Jean-Philippe Bouchaud, Michael Benzaquen
Date: 2019-12-01 00:00:00

Empirical data reveals that the liquidity flow into the order book (depositions, cancellations andmarket orders) is influenced by past price changes. In particular, we show that liquidity tends todecrease with the amplitude of past volatility and price trends. Such a feedback mechanism inturn increases the volatility, possibly leading to a liquidity crisis. Accounting for such effects withina stylized order book model, we demonstrate numerically that there exists a second order phasetransition between a stable regime for weak feedback to an unstable regime for strong feedback,in which liquidity crises arise with probability one. We characterize the critical exponents, whichappear to belong to a new universality class. We then propose a simpler model for spread dynamicsthat maps onto a linear Hawkes process which also exhibits liquidity crises. If relevant for thereal markets, such a phase transition scenario requires the system to sit below, but very close tothe instability threshold (self-organised criticality), or else that the feedback intensity is itself timedependent and occasionally visits the unstable region. An alternative scenario is provided by a classof non-linear Hawkes process that show occasional "activated" liquidity crises, without having to bepoised at the edge of instability.
Joint and individual analysis of breast cancer histologic images and genomic covariates
Date: 2020-04-14 00:00:00

A key challenge in modern data analysis is understanding connections between
complex and differing modalities of data. For example, two of the main
approaches to the study of breast cancer are histopathology (analyzing visual
characteristics of tumors) and genetics. While histopathology is the gold
standard for diagnostics and there have been many recent breakthroughs in
genetics, there is little overlap between these two fields. We aim to bridge
this gap by developing methods based on Angle-based Joint and Individual
Variation Explained (AJIVE) to directly explore similarities and differences
between these two modalities. Our approach exploits Convolutional Neural
Networks (CNNs) as a powerful, automatic method for image feature extraction to
address some of the challenges presented by statistical analysis of
histopathology image data. CNNs raise issues of interpretability that we
address by developing novel methods to explore visual modes of variation
captured by statistical algorithms (e.g. PCA or AJIVE) applied to CNN features.
Our results provide many interpretable connections and contrasts between
histopathology and genetics.
Valuing Tradeability in Exponential Lévy Models
Authors: Ludovic Mathys
Date: 2019-12-01 00:00:00

The present article provides a novel theoretical way to evaluate tradeability in markets of ordinary exponential L\'evy type. We consider non-tradeability as a particular type of market illiquidity and investigate its impact on the price of the assets. Starting from an adaption of the continuous-time optional asset replacement problem initiated by McDonald and Siegel (1986), we derive tradeability premiums and subsequently characterize them in terms of free-boundary problems. This provides a simple way to compute non-tradeability values, e.g. by means of standard numerical techniques, and, in particular, to express the price of a non-tradeable asset as a percentage of the price of a tradeable equivalent. Our approach is illustrated via numerical examples where we discuss various properties of the tradeability premiums.
Hybrid Beamforming for Active Sensing using Sparse Arrays
Authors: Robin Rajamäki, Sundeep Prabhakar Chepuri, Visa Koivunen
Date: 2019-12-02 00:00:00

This paper studies hybrid beamforming for active sensing applications, such as millimeter-wave or ultrasound imaging. Hybrid beamforming can substantially lower the cost and power consumption of fully digital sensor arrays by reducing the number of active front ends. Sparse arrays can be used to further reduce hardware costs. We consider phased arrays and employ linear beamforming with possibly sparse array configurations at both the transmitter and receiver. The quality of the acquired images is improved by adding together several component images corresponding to different transmissions and receptions. In order to limit the acquisition time of an image, we formulate an optimization problem for minimizing the number of component images subject to achieving a desired point spread function. Since this problem is not convex, we propose algorithms for finding approximate solutions in the fully digital beamforming case, as well as in the more challenging hybrid and analog beamforming cases that employ quantized phase shifters. We also determine upper bounds on the number of component images needed for achieving the fully digital solution using fully analog and hybrid architectures, and derive closed-form expressions for the beamforming weights in these cases. Simulations demonstrate that a hybrid sparse array with very few elements, and even fewer front ends, can achieve the resolution of a fully digital uniform array at the expense of a longer image acquisition time.
Measurement and Characterization of the Stationary Noise in Narrowband Power Line Communication
Date: 2019-11-27 00:00:00

Understanding the interference scenario in power lines network is a key step
to characterize the power line communication (PLC) system. This paper focuses
on the characterization and modelling of the stationary noise in Narrowband
PLC. Measurement and analysis of noise is carried out in the Tunisian outdoor
Low Voltage (LV) power line network in the frequency band below 500 kHz. Based
on existing models and measurements results, a parametric model of noise is
proposed; the model parameters are statistically studied.
Time-activity budget of urban-adapted free-ranging dogs
Date: 2019-11-29 00:00:00

The domestic dog is known to have evolved from gray wolves, about 15,000
years ago. They majorly exist as free-ranging populations across the world.
They are typically scavengers and well adapted to living among humans. Most
canids living in and around urban habitats tend to avoid humans and show
crepuscular activity peaks. In this study, we carried out a detailed
population-level survey on free-ranging dogs in West Bengal, India, to
understand the activity patterns of free-ranging dogs in relation to human
activity. Using 5669 sightings of dogs, over a period of 1 year, covering the
24 hours of the day, we carried out an analysis of the time-activity budget of
free-ranging dogs to conclude that they are generalists in their habit. They
remain active when humans are active. Their activity levels are affected
significantly by age class and time of the day. Multivariate analysis revealed
the presence of certain behavioural clusters on the basis of time of the day
and energy expenditure in the behaviours. In addition, we provide a detailed
ethogram of free-ranging dogs. This, to our knowledge, is the first study of
this kind, which might be used to further study the eco-ethology of these dogs.
Multiframe-based Adaptive Despeckling Algorithm for Ultrasound B-mode Imaging with Superior Edge and Texture
Authors: Jayanta Dey, Md. Kamrul Hasan
Date: 2019-12-02 00:00:00
Tasks: Diagnostic, Image Restoration, Noise Estimation

Removing speckle noise from medical ultrasound images while preserving image features without introducing artifact and distortion is a major challenge in ultrasound image restoration. In this paper, we propose a multiframe-based adaptive despeckling (MADS) algorithm to reconstruct a high-resolution B-mode image from raw radio-frequency (RF) data that is based on a multiple input single output (MISO) model. As a prior step to despeckling, the speckle pattern in each frame is estimated using a novel multiframe-based adaptive approach for ultrasonic speckle noise estimation (MSNE) based on a single input multiple output (SIMO) modeling of consecutive deconvolved ultrasound image frames. The elegance of the proposed despeckling algorithm is that it addresses the despeckling problem by completely following the signal generation model unlike conventional ad-hoc smoothening or filtering based approaches, and therefore, it is likely to maximally preserve the image features. As deconvolution is a necessary pre-processing step to despeckling, we describe here a 2-D extension of the SIMO model-based 1-D deconvolution method. Finally, a complete framework for the generation of high-resolution ultrasound B-mode image has been also established in this paper. The results show 8.55-15.91 dB, 8.24-14.94 dB improvement in terms of SNR and PSNR, respectively, for simulation data and 2.22-3.17, 13.24-32.85 improvement in terms of NIQE and BRISQUE, respectively, for in-vivo data compared to the traditional despeckling algorithms. Visual comparison shows superior texture, resolution, details of B-mode images offered by our method compared to those by a commercial scanner, and hence, it may significantly improve the diagnostic quality of ultrasound images.
Voice Biomarker Identification for Effects of Deep-Brain Stimulation on Parkinson's Disease
Date: 2019-11-25 00:00:00
Tasks: Sentence

Deep-Brain Stimulation (DBS) is a therapy used in conjunction with medication
to help alleviate the motor symptoms of Parkinson's Disease (PD). However, the
monitoring and adjustment of DBS settings is tedious and expensive, requiring
long programming appointments every few months. We investigated the possible
correlation between PD motor score severity and digitally extracted patient
voice features to potentially aid clinicians in their monitoring and treatment
of PD with DBS, and eventually enable a closed-loop DBS system. 5 DBS PD
patients were enrolled. Voice samples were collected for various voice tasks
(single phoneme vocalization, free speech task, sentence reading task, counting
backward task, categorical fluency task) for DBS ON and OFF states. Motor
scores per the Unified Parkinson's Disease Rating Scale (UPDRS) were also
collected for DBS ON and OFF states. Voice samples were then analyzed to
extract voice features using publicly available voice feature library sets, and
statistically compared for DBS ON and OFF. Of the feature categories explored
(Acoustic, Prosodic, Linguistic) 6 features from the GeMAPS feature set for
acoustic features demonstrated significant differences with DBS ON and OFF
(p<0.05). Prosodic features such as pause length/percentage were found to be
negatively correlated with increased motor symptom severity. Non-significant
differences were found for linguistic features. These findings provide
preliminary evidence for acoustic and prosodic speech features to act as
potential biomarkers for PD disease severity in DBS patients. We hope to
explore further by expanding our data set, identifying other features, applying
machine learning models, and working towards a closed-loop DBS system that can
auto-tune itself based on changes in a patient's voice.
Clustering and External Validity in Randomized Controlled Trials
Authors: Antoine Deeb, Clément de Chaisemartin
Date: 2019-12-02 00:00:00
Tasks: Clustering

The randomization inference literature studying randomized controlled trials (RCTs) assumes that units' potential outcomes are deterministic. This assumption is unlikely to hold, as stochastic shocks may take place during the experiment. In this paper, we consider the case of an RCT with individual-level treatment assignment, and we allow for individual-level and cluster-level (e.g. village-level) shocks. We show that one can draw inference on the ATE conditional on the realizations of the cluster-level shocks, using heteroskedasticity-robust standard errors, or on the ATE netted out of those shocks, using cluster-robust standard errors.
Refuting Samuelson's Capitulation on the Re-switching of Techniques in the Cambridge Capital Controversy
Date: 2019-12-19 00:00:00

Paul A. Samuelson's (1966) capitulation during the so-called Cambridge
controversy on the re-switching of techniques in capital theory had
implications not only in pointing at supposed internal contradiction of the
marginal theory of production and distribution, but also in preserving vested
interests in the academic and political world. Based on a new non-switching
theorem, the present paper demonstrates that Samuelson's capitulation was
logically groundless from the point of view of the economic theory of
production.
Lithic techno-complexes in Italy from 50 to 39 thousand years BP: an overview of lithic technological changes across the Middle-Upper Palaeolithic boundary
Date: 2019-12-16 00:00:00

Defining the processes involved in the technical/cultural shifts from the
Late Middle to the Early Upper Palaeolithic in Europe (~50-39 thousand years
BP) is one of the most important tasks facing prehistoric studies. In this
debate Italy plays a pivotal role, due to its geographical position between
eastern and western Mediterranean Europe as well as to it being the location of
several sites showing Late Mousterian, Uluzzian and Protoaurignacian evidence
distributed across the Peninsula. Our study aims to provide a synthesis of the
available lithic evidence from this key area through a review of the evidence
collected from a number of reference sites. The main technical features of the
Late Mousterian, the Uluzzian and the Protoaurignacian traditions are examined
from a diachronic and spatial perspective. Our overview allows the
identification of major differences in the technological behaviour of these
populations, making it possible to propose a number of specific working
hypotheses on the basis of which further studies can be carried out. This study
presents a detailed comparative study of the whole corpus of the lithic
production strategies documented during this interval, and crucial element thus
emerge.These data are of primary importance in order to assess the nature of
the "transition" phenomenon in Italy, thus contributing to the larger debate
about the disappearance of Neandertals and the arrival of early Modern Humans
in Europe.
A New Approach to Pinning Control of Boolean Networks
Date: 2020-10-31 00:00:00

Boolean networks (BNs) are discrete-time systems where nodes are
inter-connected (here we call such connection rule among nodes as network
structure), and the dynamics of each gene node is determined by logical
functions. In this paper, we propose a new approach on pinning control design
for global stabilization of BNs based on BNs' network structure, named as
network-structure-based distributed pinning control. By deleting the minimum
number of edges, the network structure becomes acyclic. Then, an efficient
distributed pinning control is designed to achieve global stabilization.
Compared with existing literature, the design of pinning control is not based
on the state transition matrix of BNs. Hence, the computational complexity in
this paper is reduced from $O(2^n\times 2^n)$ to $O(2\times 2^K)$, where $n$ is
the number of nodes and $K\leq n$ is the largest number of in-neighbors of
nodes. In addition, without using state transition matrix, global state
information is no longer needed, the design of pinning control is just based on
neighbors' local information, which is easier to be implemented. The proposed
method is well demonstrated by several biological networks with different
sizes. The results are shown to be simple and concise, while the traditional
pinning control can not be applied for BNs with such a large dimension.
Neural network interpretation using descrambler groups
Authors: Jake L. Amey, Jake Keeley, Tajwar Choudhury, Ilya Kuprov
Date: 2019-12-02 00:00:00
Tasks: Network Interpretation

The lack of interpretability and trust is a much-criticised feature of deep neural networks. In fully connected nets, the signalling between inner layers is scrambled because backpropagation training does not require perceptrons to be arranged in any particular order. The result is a black box; this problem is particularly severe in scientific computing and digital signal processing (DSP), where neutral nets perform abstract mathematical transformations that do not reduce to features or concepts. We present here a group-theoretical procedure that attempts to bring inner layer signalling into a human-readable form, the assumption being that this form exists and has identifiable and quantifiable features - for example, smoothness or locality. We applied the proposed method to DEERNet (a DSP network used in electron spin resonance) and managed to descramble it. We found considerable internal sophistication: the network spontaneously invents a bandpass filter, a notch filter, a frequency axis rectifier, frequency division multiplexing, group embedding, spectral filtering regularisation, and a Fourier-like transform that maps harmonic functions into Chebyshev polynomials - in ten minutes of unattended training from a random initial guess.
Celebrating Three Decades of Worldwide Stock Market Manipulation
Authors: Bruce Knuteson
Date: 2019-11-20 00:00:00
Tasks: Misinformation

As the decade turns, we reflect on nearly thirty years of successful manipulation of the world's public equity markets. This reflection highlights a few of the key enabling ingredients and lessons learned along the way. A quantitative understanding of market impact and its decay, which we cover briefly, lets you move long-term market prices to your advantage at acceptable cost. Hiding your footprints turns out to be less important than moving prices in the direction most people want them to move. Widespread (if misplaced) trust of market prices -- buttressed by overestimates of the cost of manipulation and underestimates of the benefits to certain market participants -- makes price manipulation a particularly valuable and profitable tool. Of the many recent stories heralding the dawn of the present golden age of misinformation, the manipulation leading to the remarkable increase in the market capitalization of the world's publicly traded companies over the past three decades is among the best.