pwc-archive/papers-with-abstracts huggingface dataset:
Privacy-Cost Management in Smart Meters with Mutual Information-Based Reinforcement Learning
Authors: Mohammadhadi Shateri, Francisco Messina, Pablo Piantanida, Fabrice Labeau
Date: 2020-06-10 00:00:00
Tasks: Deep Reinforcement Learning, Management, Q-Learning, reinforcement-learning, Reinforcement Learning (RL)

The rapid development and expansion of the Internet of Things (IoT) paradigm has drastically increased the collection and exchange of data between sensors and systems, a phenomenon that raises serious privacy concerns in some domains. In particular, Smart Meters (SMs) share fine-grained electricity consumption of households with utility providers that can potentially violate users' privacy as sensitive information is leaked through the data. In order to enhance privacy, the electricity consumers can exploit the availability of physical resources such as a rechargeable battery (RB) to shape their power demand as dictated by a Privacy-Cost Management Unit (PCMU). In this paper, we present a novel method to learn the PCMU policy using Deep Reinforcement Learning (DRL). We adopt the mutual information (MI) between the user's demand load and the masked load seen by the power grid as a reliable and general privacy measure. Unlike previous studies, we model the whole temporal correlation in the data to learn the MI in its general form and use a neural network to estimate the MI-based reward signal to guide the PCMU learning process. This approach is combined with a model-free DRL algorithm known as the Deep Double Q-Learning (DDQL) method. The performance of the complete DDQL-MI algorithm is assessed empirically using an actual SMs dataset and compared with simpler privacy measures. Our results show significant improvements over state-of-the-art privacy-aware demand shaping methods.
Uncertainty promotes neuroreductionism: A behavioral online study on folk psychological causal inference from neuroimaging data
Authors: Jona Carmon, Moritz Bammel, Peter Brugger, Bigna Lenggenhager
Date: 2020-06-10 00:00:00
Tasks: Attribute, Causal Inference

Introduction. Increased efforts in neuroscience try to understand mental disorders as brain disorders. In the present study we investigate how common a neuroreductionist inclination is among highly educated people. In particular, we shed light on implicit presuppositions of mental disorders little is known about in the public, exemplified here by the case of Body Integrity Dysphoria (BID) that is considered a mental disorder for the first time in ICD-11. Methods. Identically graphed, simulated data of mind-brain correlations were shown in three contexts with presumably different presumptions about causality. 738 highly-educated laymen rated plausibility of causality attribution from brain to mind and from mind to brain for correlations between brain structural properties and mental phenomena. We contrasted participants' plausibility ratings of causality in the contexts of commonly perceived brain-lesion induced behavior (aphasia), behavior-induced training effects (piano playing), and a newly described mental disorder (BID). Results. The findings reveal the expected context-dependent modulation of causality attributions in the contexts of aphasia and piano playing. Furthermore, we observed a significant tendency to more readily attribute causal inference from brain to mind than vice versa with respect to BID. Conclusion. In some contexts, exemplified here by aphasia and piano playing, unidirectional causality attributions may be justified. However, with respect to BID, we critically discuss presumably unjustified neuroreductionist inclinations under causal uncertainty. Finally, we emphasize the need for a presupposition-free approach in psychiatry.
An Objective Measure of Quality for Time-Scale Modification of Audio
Date: 2020-06-11 00:00:00

Objective evaluation of audio processed with Time-Scale Modification (TSM)
remains an open problem. Recently, a dataset of time-scaled audio with
subjective quality labels was published and used to create an initial objective
measure of quality. In this paper, an improved objective measure of quality for
time-scaled audio is proposed. The measure uses hand-crafted features and a
fully connected network to predict subjective mean opinion scores. Basic and
Advanced Perceptual Evaluation of Audio Quality features are used in addition
to nine features specific to TSM artefacts. Six methods of alignment are
explored, with interpolation of the reference magnitude spectrum to the length
of the test magnitude spectrum giving the best performance. The proposed
measure achieves a mean Root Mean Squared Error of 0.487 and a mean Pearson
correlation of 0.865, equivalent to 98th and 82nd percentiles of subjective
sessions respectively. The proposed measure is used to evaluate time-scale
modification algorithms, finding that Elastique gives the highest objective
quality for Solo instrument and voice signals, while the Identity Phase-Locking
Phase Vocoder gives the highest objective quality for music signals and the
best overall quality. The objective measure is available at
https://www.github.com/zygurt/TSM.
Secrecy Performance for Finite-Alphabet Inputs Over Fluctuating Two-Ray Channels in FDA Communications
Authors: Chongjun Ouyang, Sheng Wu, Chunxiao Jiang, Derrick Wing Kwan Ng, Hongwen Yang
Date: 2020-06-11 00:00:00

To provide system design insights for practical communication systems equipped with the frequency diverse array (FDA), this paper investigates the secrecy performance of FDA systems exploiting finite-alphabet inputs over fluctuating two-ray (FTR) fading channels. More specifically, closed-form expressions for the average secrecy rate (ASR) and the secrecy outage probability (SOP) are derived, while their correctness is confirmed by numerical simulations. In addition, we perform asymptotic analysis to quantify the secrecy performance gap between Gaussian and finite-alphabet inputs, for a sufficiently large average signal-to-noise ratio (SNR) of the main channel. Compared with Gaussian inputs-based research, this letter focuses on practical scenarios which sheds lights on properties of FDA systems.
Satellite-Terrestrial Channel Characterization in High-Speed Railway Environment at 22.6 GHz
Authors: Lei Ma, Ke Guan, Dong Yan, Danping He, Nuno R. Leonor, Bo Ai, Junhyeong Kim
Date: 2020-06-11 00:00:00

The integration of satellite and terrestrial communication systems plays a vital role in the fifth-generation mobile communication system (5G) for the ubiquitous coverage, reliable service and flexible networking. Moreover, the millimeter wave (mmWave) communication with large bandwidth is a key enabler for 5G intelligent rail transportation. In this paper, the satellite-terrestrial channel at 22.6 GHz is characterized for a typical high-speed railway (HSR) environment. The three-dimensional model of the railway scenario is reconstructed and imported into the Cloud Ray-Tracing (CloudRT) simulation platform. Based on extensive ray-tracing simulations, the channel for the terrestrial HSR system and the satellite-terrestrial system with two weather conditions are characterized, and the interference between them are evaluated. The results of this paper can help for the design and evaluation for the satellite-terrestrial communication system enabling future intelligent rail transportation.
Influence of Small Molecule Property on Antibody Response
Date: 2020-06-11 00:00:00

Antibodies with high titer and affinity to small molecule are critical in the
field for the development of vaccines against drugs of abuse, antidotes to
toxins and immunoassays for compounds. However, little is known regarding how
properties of small molecule influence and which chemical descriptor could
indicate the degree of the antibody response. Based on our previous study, we
designed and synthesized two groups of small molecules, called haptens, with
varied hydrophobicities to investigate the relationship between properties of
small molecules and antibody response in term of titer and affinity. We found
that the magnitude of the antibody response is positively correlated with the
degree of molecular hydrophobicity and related chemical descriptors. This study
provides insight into the immunological characteristics of small molecules
themselves and useful clues to produce high quality antibodies against small
molecules.
Innovation and imitation
Date: 2020-08-26 00:00:00

We study several models of growth driven by innovation and imitation by a
continuum of firms, focusing on the interaction between the two. We first
investigate a model on a technology ladder where innovation and imitation
combine to generate a balanced growth path (BGP) with compact support, and with
productivity distributions for firms that are truncated power-laws. We start
with a simple model where firms can adopt technologies of other firms with
higher productivities according to exogenous probabilities. We then study the
case where the adoption probabilities depend on the probability distribution of
productivities at each time. We finally consider models with a finite number of
firms, which by construction have firm productivity distributions with bounded
support. Stochastic imitation and innovation can make the distance of the
productivity frontier to the lowest productivity level fluctuate, and this
distance can occasionally become large. Alternatively, if we fix the length of
the support of the productivity distribution because firms too far from the
frontier cannot survive, the number of firms can fluctuate randomly.
A Simple Traffic Signal Control Using Queue Length Information
Date: 2020-06-11 00:00:00
Tasks: Autonomous Vehicles, Traffic Signal Control

Developments in sensor technologies, especially emerging connected and
autonomous vehicles, facilitate better queue length (QL) measurements on
signalized intersection approaches in real time. Currently there are very
limited methods that utilize QL information in real-time to enhance the
performance of signalized intersections. In this paper we present methods for
QL estimation and a control algorithm that adjusts maximum green times in
actuated signals at each cycle based on QLs. The proposed method is implemented
at a single intersection with random and platoon arrivals, and evaluated in
VISSIM (a microscopic traffic simulation environment) assuming 100 % accurate
cycle-by-cycle queue length information is available. To test the robustness of
the method, numerical experiments are performed where traffic demand is
increased and by 20\% relative to the demand levels for which signal timing
parameters are optimized. Compared to the typical fully-actuated signal
control, the proposed QL-based method improves average delay, number of stops,
and QL for random arrivals, by 6 %, 9 %, and 10 % respectively. In addition,
the method improves average delay, number of stops, and QL by 3 %, 3 %, and 11
% respectively for platoon vehicle arrivals.
Path-dependent Kyle equilibrium model
Authors: José M. Corcuera, Giulia Di Nunno
Date: 2020-06-11 00:00:00
Tasks: model

We consider an auction type equilibrium model with an insider in line with the one originally introduced by Kyle in 1985 and then extended to the continuous time setting by Back in 1992. The novelty introduced with this paper is that we deal with a general price functional depending on the whole past of the aggregate demand, i.e. we work with path-dependency. By using the functional It\^o calculus, we provide necessary and sufficient conditions for the existence of an equilibrium. Furthermore, we consider both the cases of a risk-neutral and a risk-averse insider.
The Consequences of Switching Strategies in a Two-Player Iterated Survival Game
Date: 2020-06-11 00:00:00

We consider two-player iterated survival games in which players may switch
from a more cooperative behavior to a less cooperative one at some step of the
game. Payoffs are survival probabilities and lone individuals have to finish
the game on their own. We explore the potential of these games to support
cooperation, focusing on the case in which each single step is a Prisoner's
Dilemma. We find that incentives for or against cooperation depend on the
number of defections at the end of the game, as opposed to the number of steps
in the game. Broadly, cooperation is supported when the survival prospects of
lone individuals are relatively bleak. Specifically, we find three critical
values or cutoffs for the loner survival probability which, in concert with
other survival parameters, determine the incentives for or against cooperation.
One cutoff determines the existence of an optimal number of defections against
a fully cooperative partner, one determines whether additional defections
eventually become disfavored as the number of defections by the partner
increases, and one determines whether additional cooperations eventually become
favored as the number of defections by the partner increases. We obtain
expressions for these switch-points and for optimal numbers of defections
against partners with various strategies. These typically involve small numbers
of defections even in very long games. We show that potentially long stretches
of equilibria may exist, in which there is no incentive to defect more or
cooperate more. We describe how individuals find equilibria in best-response
walks among strategies, and establish that evolutionary stability requires
there be just one such equilibrium. Otherwise, equilibria are not protected
against invasion by strategies with fewer defections.
String Stability of a Vehicular Platoon with the use of Macroscopic Information
Authors: Marco Mirabilio, Alessio Iovine, Elena De Santis, Maria Domenica Di Benedetto, Giordano Pola
Date: 2020-06-10 00:00:00
Tasks: Autonomous Vehicles

We investigate the possibility to use macroscopic information to improve control performance of a vehicular platoon composed of autonomous vehicles. A general mesoscopic traffic modeling is described, and a closed loop String Stability analysis is performed using Input-to-State Stability (ISS) results. Examples of mesoscopic control laws are provided and shown to ensure String Stability properties. Simulations are implementedin order to validate the control laws and to show the efficacy of the proposed approach.
Text as data: a machine learning-based approach to measuring uncertainty
Date: 2020-06-11 00:00:00
Tasks: BIG-bench Machine Learning

The Economic Policy Uncertainty index had gained considerable traction with
both academics and policy practitioners. Here, we analyse news feed data to
construct a simple, general measure of uncertainty in the United States using a
highly cited machine learning methodology. Over the period January 1996 through
May 2020, we show that the series unequivocally Granger-causes the EPU and
there is no Granger-causality in the reverse direction
Self-consumption for energy communities in Spain: a regional analysis under the new legal framework
Date: 2020-06-11 00:00:00

European climate polices acknowledge the role that energy communities can play in the energy transition. Self-consumption installations shared among those living in the same building are a good example of such energy communities. In this work, we perform a regional analysis of optimal self-consumption installations under the new legal framework recently passed in Spain. Results show that the optimal sizing of the installation leads to economic savings for self-consumers in all the territory, for both options with and without remuneration for energy surplus. A sensitivity analysis on technology costs revealed that batteries still require noticeably cost reductions to be cost-effective in a behind the meter self-consumption environment. In addition, solar compensation mechanisms make batteries less attractive in a scenario of low PV costs, since feeding PV surplus into the grid, yet less efficient, becomes more cost-effective. An improvement for the current energy surplus remuneration policy was proposed and analysed. It consists in the inclusion of the economic value of the avoided power losses in the remuneration.
Revolutionizing Future Healthcare using Wireless on the Walls (WoW)
Authors: Jalil ur Rehman Kazim, Tie Jun Cui, Ahmed Zoha, Lianlin Li, Syed Aziz Shah, Akram Alomainy, Muhammad Ali Imran, Qammer H. Abbasi
Date: 2020-06-11 00:00:00

Following the standardization and deployment of fifth generation (5G) network, researchers have shifted their focus to beyond 5G communication. Existing technologies have brought forth a plethora of applications that could not have been imagined in the past years. Beyond 5G will enable us to rethink the capability, it will offer in various sectors including agriculture, search and rescue and more specifically in the delivery of health care services. Unobtrusive and non-invasive measurements using radio frequency (RF) sensing, monitoring and control of wearable medical devices are the areas that would potentially benefit from beyond 5G. Applications such as RF sensing, device charging and remote patient monitoring will be a key challenge using millimetre (mmWave) communication. The mmWaves experience multi-path induced fading, where the rate of attenuation is larger as compared to the microwaves. Eventually, mmWave communication systems would require range extenders and guided surfaces. A proposed solution is the use of intelligent reflective surfaces, which will have the ability to manipulate electromagnetic (EM) signals. These intelligent surfaces mounted and/or coated on walls aka - Intelligent Walls are planar and active surfaces, which will be a key element in beyond 5G and 6G communication. These intelligent walls equipped with machine learning algorithm and computation power would have the ability to manipulate EM waves and act as gateways in the heterogeneous network environment. The article presents the application and vision of intelligent walls for next-generation healthcare in the era of beyond 5G.
Towards Expedited Impedance Tuning of a Robotic Prosthesis for Personalized Gait Assistance by Reinforcement Learning Control
Authors: Minhan Li, Yue Wen, Xiang Gao, Jennie Si, He Helen Huang
Date: 2020-06-11 00:00:00

Personalizing medical devices such as lower limb wearable robots is challenging. While the initial feasibility of automating the process of knee prosthesis control parameter tuning has been demonstrated in a principled way, the next critical issue is to improve tuning efficiency and speed it up for the human user, in clinic settings, while maintaining human safety. We, therefore, propose a policy iteration with constraint embedded (PICE) method as an innovative solution to the problem under the framework of reinforcement learning. Central to PICE is the use of a projected Bellman equation with a constraint of assuring positive semidefiniteness of performance values during policy evaluation. Additionally, we developed both online and offline PICE implementations that provide additional flexibility for the designer to fully utilize measurement data, either from on-policy or off-policy, to further improve PICE tuning efficiency. Our human subject testing showed that the PICE provided effective policies with significantly reduced tuning time. For the first time, we also experimentally evaluated and demonstrated the robustness of the deployed policies by applying them to different tasks and users. Putting it together, our new way of problem solving has been effective as PICE has demonstrated its potential toward truly automating the process of control parameter tuning for robotic knee prosthesis users.
Data and Incentives
Authors: Annie Liang, Erik Madsen
Date: 2020-06-11 00:00:00

"Big data" gives markets access to previously unmeasured characteristics of individual agents. Policymakers must decide whether and how to regulate the use of this data. We study how new data affects incentives for agents to exert effort in settings such as the labor market, where an agent's quality is initially unknown but is forecast from an observable outcome. We show that measurement of a new covariate has a systematic effect on the average effort exerted by agents, with the direction of the effect determined by whether the covariate is informative about long-run quality or about a shock to short-run outcomes. For a class of covariates satisfying a statistical property we call strong homoskedasticity, this effect is uniform across agents. More generally, new measurements can impact agents unequally, and we show that these distributional effects have a first-order impact on social welfare.
Efficient democratic decisions via nondeterministic proportional consensus
Date: 2020-06-10 00:00:00

Are there voting methods which (i) give everyone, including minorities, an
equal share of effective power even if voters act strategically, (ii) promote
consensus rather than polarization and inequality, and (iii) do not favour the
status quo or rely too much on chance?
  We show the answer is yes by describing two nondeterministic voting methods,
one based on automatic bargaining over lotteries, the other on conditional
commitments to approve compromise options. Our theoretical analysis and
agent-based simulation experiments suggest that with these, majorities cannot
consistently suppress minorities as with deterministic methods, proponents of
the status quo cannot block decisions as in consensus-based approaches, the
resulting aggregate welfare is comparable to existing methods, and average
randomness is lower than for other nondeterministic methods.
Confidence sets for dynamic poverty indexes
Date: 2020-06-11 00:00:00

In this study, we extend the research on the dynamic poverty indexes, namely
the dynamic Headcount ratio, the dynamic income-gap ratio, the dynamic Gini and
the dynamic Sen, proposed in D'Amico and Regnault (2018). The contribution is
twofold. First, we extend the computation of the dynamic Gini index, thus the
Sen index accordingly, with the inclusion of the inequality within each class
of poverty where people are classified according to their income. Second, for
each poverty index, we establish a central limit theorem that gives us the
possibility to determine the confidence sets. An application to the Italian
income data from 1998 to 2012 confirms the effectiveness of the considered
approach and the possibility to determine the evolution of poverty and
inequality in real economies.
Stochastic approach to study control strategies of Covid-19 pandemic in India
Date: 2020-06-11 00:00:00

India is one of the worst affected countries by the Covid-19 pandemic at
present. We studied publicly available data of the Covid-19 patients in India
and analyzed possible impacts of quarantine and social distancing within the
stochastic framework of the SEQIR model to illustrate the controlling strategy
of the pandemic. Our simulation results clearly show that proper quarantine and
social distancing should be maintained from an early time just at the start of
the pandemic and should be continued till its end to effectively control the
pandemic. This calls for a more socially disciplined lifestyle in this
perspective in future. The demographic stochasticity, which is quite visible in
the system dynamics, has a critical role in regulating and controlling the
pandemic.