pwc-archive/papers-with-abstracts huggingface dataset:
Decentralized Finance, Centralized Ownership? An Iterative Mapping Process to Measure Protocol Token Distribution
Authors: Matthias Nadler, Fabian Schär
Date: 2020-12-16 00:00:00

In this paper, we analyze various Decentralized Finance (DeFi) protocols in terms of their token distributions. We propose an iterative mapping process that allows us to split aggregate token holdings from custodial and escrow contracts and assign them to their economic beneficiaries. This method accounts for liquidity-, lending-, and staking-pools, as well as token wrappers, and can be used to break down token holdings, even for high nesting levels. We compute individual address balances for several snapshots and analyze intertemporal distribution changes. In addition, we study reallocation and protocol usage data, and propose a proxy for measuring token dependencies and ecosystem integration. The paper offers new insights on DeFi interoperability as well as token ownership distribution and may serve as a foundation for further research.
Exploring Narrative Economics: An Agent-Based-Modeling Platform that Integrates Automated Traders with Opinion Dynamics
Authors: Kenneth Lomas, Dave Cliff
Date: 2020-12-16 00:00:00

In seeking to explain aspects of real-world economies that defy easy understanding when analysed via conventional means, Nobel Laureate Robert Shiller has since 2017 introduced and developed the idea of Narrative Economics, where observable economic factors such as the dynamics of prices in asset markets are explained largely as a consequence of the narratives (i.e., the stories) heard, told, and believed by participants in those markets. Shiller argues that otherwise irrational and difficult-to-explain behaviors, such as investors participating in highly volatile cryptocurrency markets, are best explained and understood in narrative terms: people invest because they believe, because they have a heartfelt opinions, about the future prospects of the asset, and they tell to themselves and others stories (narratives) about those beliefs and opinions. In this paper we describe what is, to the best of our knowledge, the first ever agent-based modelling platform that allows for the study of issues in narrative economics. We have created this by integrating and synthesizing research in two previously separate fields: opinion dynamics (OD), and agent-based computational economics (ACE) in the form of minimally-intelligent trader-agents operating in accurately modelled financial markets. We show here for the first time how long-established models in OD and in ACE can be brought together to enable the experimental study of issues in narrative economics, and we present initial results from our system. The program-code for our simulation platform has been released as freely-available open-source software on GitHub, to enable other researchers to replicate and extend our work
Topological Learning for Brain Networks
Authors: Tananun Songdechakraiwut, Moo K. Chung
Date: 2020-11-25 00:00:00
Tasks: Diffusion  MRI

This paper proposes a novel topological learning framework that integrates networks of different sizes and topology through persistent homology. Such challenging task is made possible through the introduction of a computationally efficient topological loss. The use of the proposed loss bypasses the intrinsic computational bottleneck associated with matching networks. We validate the method in extensive statistical simulations to assess its effectiveness when discriminating networks with different topology. The method is further demonstrated in a twin brain imaging study where we determine if brain networks are genetically heritable. The challenge here is due to the difficulty of overlaying the topologically different functional brain networks obtained from resting-state functional MRI onto the template structural brain network obtained through diffusion MRI.
CHIMERA: A Hybrid Estimation Approach to Limit the Effects of False Data Injection Attacks
Authors: Xiaorui Liu, Yaodan Hu, Charalambos Konstantinou, Yier Jin
Date: 2021-03-25 00:00:00
Tasks: energy management, Management, State Estimation

The reliable operation of power grid is supported by energy management systems (EMS) that provide monitoring and control functionalities. Contingency analysis is a critical application of EMS to evaluate the impacts of outages and prepare for system failures. However, false data injection attacks (FDIAs) have demonstrated the possibility of compromising sensor measurements and falsifying the estimated power system states. As a result, FDIAs may mislead system operations and other EMS applications including contingency analysis and optimal power flow. In this paper, we assess the effect of FDIAs and demonstrate that such attacks can affect the resulted number of contingencies. In order to mitigate the FDIA impact, we propose CHIMERA, a hybrid attack-resilient state estimation approach that integrates model-based and data-driven methods. CHIMERA combines the physical grid information with a Long Short Term Memory (LSTM)-based deep learning model by considering a static loss of weighted least square errors and a dynamic loss of the difference between the temporal variations of the actual and the estimated active power. Our simulation experiments based on the load data from New York state demonstrate that CHIMERA can effectively mitigate 91.74% of the cases in which FDIAs can maliciously modify the contingencies.
Asymptotic Security by Model-based Incident Handlers for Markov Decision Processes
Authors: Hampei Sasahara, Henrik Sandberg
Date: 2021-03-24 00:00:00
Tasks: Decision Making

This study investigates general model-based incident handler's asymptotic behaviors in time against cyber attacks to control systems. The attacker's and the defender's dynamic decision making is modeled as an equilibrium of a dynamic signaling game. It is shown that the defender's belief on existence of an attacker converges over time for any attacker's strategy provided that the stochastic dynamics of the control system is known to the defender. This fact implies that the rational behavior of the attacker converges to a harmless action as long as the defender possesses an effective counteraction. The obtained result supports the powerful protection capability achieved by model-based defense mechanisms.
Learning Organization using Conversational Social Network for Social Customer Relationship Management Effort
Authors: Andry Alamsyah, Yahya Peranginangin, Gabriel Nurhadi
Date: 2021-03-10 00:00:00
Tasks: Management

The challenge of each organization is how they adapt to the shift of more complex technology such as mobile, big data, interconnected world, and the Internet of things. In order to achieve their objective, they must understand how to take advantage of the interconnected individuals inside and outside the organization. Learning organization continues to transform by listening and maintain the connection with their counterparts. Customer relationship management is an important source for business organizations to grow and to assure their future. The complex social network, where interconnected peoples get information and get influenced very quickly, certainly a big challenge for business organizations. The combination of these complex technologies provides intriguing insight such as the capabilities to listen to what the markets want, to understand their market competition, and to understand their market segmentation. In this paper, as a part of organization transformation, we show how a business organization mine online conversational in Twitter related to their brand issue and analyze them in the context of customer relationship management to extract several insights regarding their market.
Virtual Microgrid Management via Software-defined Energy Network for Electricity Sharing
Authors: Pedro H. J. Nardelli, Hafiz Majid Hussein, Arun Narayanan, Yongheng Yang
Date: 2021-02-01 00:00:00
Tasks: energy management, Management

Digitalization has led to radical changes in the distribution of goods across various sectors. The tendency is to move from traditional buyer-seller markets to subscription-based on-demand "smart" matching platforms enabled by pervasive ICTs. The driving force behind this lies in the fact that assets, which were scarce in the past, are readily abundant, approaching a regime of zero marginal costs. This is also becoming a reality in electrified energy systems due to the substantial growth of distributed renewable energy sources such as solar and wind; the increasing number of small-scale storage units such as batteries and heat pumps; and the availability of flexible loads that enable demand-side management. In this context, this article proposes a system architecture based on a logical (cyber) association of spatially distributed (physical) elements as an approach to build a virtual microgrid operated as a software-defined energy network (SDEN) that is enabled by packetized energy management. The proposed cyber-physical system presumes that electrical energy is shared among its members and that the energy sharing is enabled in the cyber domain by handshakes inspired by resource allocation methods utilized in computer networks, wireless communications, and peer-to-peer Internet applications (e.g., BitTorrent). The proposal has twofold benefits: (i) reducing the complexity of current market-based solutions by removing unnecessary and costly mediations and (ii) guaranteeing energy access to all virtual microgrid members according to their individual needs. This article concludes that the proposed solution generally complies with the existing regulations but has highly disruptive potential to organize a dominantly electrified energy system in the mid- to long-term, being a technical counterpart to the recently developed social-oriented microgrid proposals.
Policy-Aware Mobility Model Explains the Growth of COVID-19 in Cities
Authors: Zhenyu Han, Fengli Xu, Yong Li, Tao Jiang, Depeng Jin, Jianhua Lu, James A. Evans
Date: 2021-02-21 00:00:00

With the continued spread of coronavirus, the task of forecasting distinctive COVID-19 growth curves in different cities, which remain inadequately explained by standard epidemiological models, is critical for medical supply and treatment. Predictions must take into account non-pharmaceutical interventions to slow the spread of coronavirus, including stay-at-home orders, social distancing, quarantine and compulsory mask-wearing, leading to reductions in intra-city mobility and viral transmission. Moreover, recent work associating coronavirus with human mobility and detailed movement data suggest the need to consider urban mobility in disease forecasts. Here we show that by incorporating intra-city mobility and policy adoption into a novel metapopulation SEIR model, we can accurately predict complex COVID-19 growth patterns in U.S. cities ($R^2$ = 0.990). Estimated mobility change due to policy interventions is consistent with empirical observation from Apple Mobility Trends Reports (Pearson's R = 0.872), suggesting the utility of model-based predictions where data are limited. Our model also reproduces urban "superspreading", where a few neighborhoods account for most secondary infections across urban space, arising from uneven neighborhood populations and heightened intra-city churn in popular neighborhoods. Therefore, our model can facilitate location-aware mobility reduction policy that more effectively mitigates disease transmission at similar social cost. Finally, we demonstrate our model can serve as a fine-grained analytic and simulation framework that informs the design of rational non-pharmaceutical interventions policies.
Do the propensity and drivers of academics' engagement in research collaboration with industry vary over time?
Authors: Giovanni Abramo, Francesca Apponi, Ciriaco Andrea D'Angelo
Date: 2021-02-10 00:00:00

This study is about public-private research collaboration. In particular, we want to measure how the propensity of academics to collaborate with their colleagues from private firms varies over time and whether the typical profile of such academics change. Furthermore, we investigate the change of the weights of main drivers underlying the academics' propensity to collaborate with industry. In order to achieve such goals, we apply an inferential model on a dataset of professors working in Italian universities in two subsequent periods, 2010-2013 and 2014-2017. Results can be useful for supporting the definition of policies aimed at fostering public-private research collaborations, and should be taken into account when assessing their effectiveness afterwards.
A Simplified Multifractal Model for Self-Similar Traffic Flows in High-Speed Computer Networks Revisited
Authors: G. Millán, G. Lefranc
Date: 2021-03-09 00:00:00

In the context of the simulations carried out using a simplified multifractal model that is proposed to give an explanation to the locality phenomenon that appears in the estimation of the Hurst exponent in the second-order stationary series that represent the self-similar traffic flows in high-speed computer networks, its formulation is perfected to reduce the variability in the singularity limits and it is demonstrated through by its wavelet variant that this modification leads to a higher resolution in the interval of interest under study.
On the Hurst Exponent, Markov Processes, and Fractional Brownian Motion
Authors: G. Millán
Date: 2021-03-08 00:00:00

There is much confusion in the literature over Hurst exponent (H). The purpose of this paper is to illustrate the difference between fractional Brownian motion (fBm) on the one hand and Gaussian Markov processes where H is different to 1/2 on the other. The difference lies in the increments, which are stationary and correlated in one case and nonstationary and uncorrelated in the other. The two- and one-point densities of fBm are constructed explicitly. The two-point density does not scale. The one-point density for a semi-infinite time interval is identical to that for a scaling Gaussian Markov process with H different to 1/2 over a finite time interval. We conclude that both Hurst exponents and one-point densities are inadequate for deducing the underlying dynamics from empirical data. We apply these conclusions in the end to make a focused statement about nonlinear diffusion.
Reconfigurable Intelligent Surfaces in Challenging Environments: Underwater, Underground, Industrial and Disaster
Authors: Steven Kisseleff, Symeon Chatzinotas, Björn Ottersten
Date: 2020-11-10 00:00:00

Challenging environments comprise a range of scenarios, which share the fact that it is extremely difficult to establish a communication link using conventional technology due to many impairments typically associated with the propagation medium and increased signal scattering. Specifically, underwater and underground media are known to absorb electromagnetic radiation, which heavily affects the overall path loss. Industrial and disaster environments can be viewed as rich scattering environments with corresponding substantial multipath propagation leading to intersymbol interference and deterioration of signal quality. Although the challenges for the design of communication networks, and specifically the Internet of Things (IoT), in such environments are known, there is no common enabler or solution for all these applications. Reconfigurable intelligent surfaces (RISs) have been introduced to improve the signal propagation characteristics by focusing the signal power in the preferred direction, thus making the communication environment 'smart'. While the usual application of RIS is related to blockage avoidance, the very same technique can be used to reduce the effect of multipath and even partially compensate the signal absorption via passive beamforming. Due to the beneficial properties of RIS, its use in challenging environments can become the aforementioned enabler and a game changing technology. In this paper, we discuss potential use cases, deployment strategies and design aspects for RIS devices in underwater IoT, underground IoT as well as Industry 4.0 and emergency networks. Furthermore, we provide a potential hardware architecture and derive the expected signal quality. The numerical results reveal substantial performance gains of up to 20 dB per decade. In addition, novel research challenges to be addressed in this context are described.
Toward Localization in Terahertz-Operating Energy Harvesting Software-Defined Metamaterials: Context Analysis
Authors: F. Lemic, S. Abadal, J. Famaey
Date: 2020-12-21 00:00:00

Software-defined metamaterials (SDMs) represent a novel paradigm for real-time control of metamaterials. SDMs are envisioned to enable a variety of exciting applications in the domains such as smart textiles and sensing in challenging conditions. Many of these applications envisage deformations of the SDM structure (e.g., rolling, bending, stretching). This affects the relative position of the metamaterial elements and requires their localization relative to each other. The question of how to perform such localization is, however, yet to spark in the community. We consider that the metamaterial elements are controlled wirelessly through a Terahertz (THz)-operating nanonetwork. Moreover, we consider the elements to be energy constrained, with their sole powering option being to harvest environmental energy. For such a setup, we demonstrate sub-millimeter accuracy of the two-way Time of Flight (ToF)-based localization, as well as high availability of the service (i.e., consistently more than 80% of the time), which is a result of the low energy consumed in localization. Finally, we provide the localization context for a number of relevant system parameters such as operational frequency, bandwidth, and harvesting rate.
UAVs with Reconfigurable Intelligent Surfaces: Applications, Challenges, and Opportunities
Authors: Aly Sabri Abdalla, Talha Faizur Rahman, Vuk Marojevic
Date: 2020-12-08 00:00:00

A reconfigurable intelligent surface (RIS) is a metamaterial that can be integrated into walls and influence the propagation of electromagnetic waves. This, typically passive radio frequency (RF) technology is emerging for indoor and outdoor use with the potential of making wireless communications more reliable in increasingly challenging radio environments. This paper goes one step further and introduces mobile RIS, specifically, RIS carried by unmanned aerial vehicles (UAVs) to support cellular communications networks and services of the future. We elaborate on several use cases, challenges, and future research opportunities for designing and optimizing wireless systems at low cost and with low energy footprint.
Localization in Power-Constrained Terahertz-Operating Software-Defined Metamaterials
Authors: Filip Lemic, Sergi Abadal, Chong Han, Johann Marquez-Barja, Eduard Alarcón, Jeroen Famaey
Date: 2020-10-30 00:00:00

Software-Defined Metamaterials (SDMs) show a strong potential for advancing the engineered control of electromagnetic waves. As such, they are envisioned to enable a variety of exciting applications, among others in the domains of smart textiles, high-resolution structural monitoring, and sensing in challenging environments. Many of the applications envisage deformations of the SDM structures, such as their bending, stretching or rolling, which implies that the locations of metamaterial elements will be changing relative to one another. In this paper, we argue that if the metamaterial elements would be accurately localizable, this location information could potentially be utilized for enabling novel SDM applications, as well as for optimizing the control of the elements themselves. To enable their localization, we assume that these elements are controlled wirelessly through a Terahertz (THz)-operating nanonetwork. We consider the elements to be power-constrained, with their sole powering option being to harvest energy from different environmental sources. By means of simulation, we demonstrate sub-millimeter accuracy of the two-way Time of Flight (ToF)-based localization, as well as high availability of the service (i.e., consistently more than 80% of the time), which is a result of the low energy consumed in the localization process. Finally, we qualitatively characterize the latency of the proposed localization service, as well as outline several challenges and future research directions.
Intelligent Surfaces for 6G Wireless Networks: A Survey of Optimization and Performance Analysis Techniques
Authors: Rawan Alghamdi, Reem Alhadrami, Dalia Alhothali, Heba Almorad, Alice Faisal, Sara Helal, Rahaf Shalabi, Rawan Asfour, Noofa Hammad, Asmaa Shams, Nasir Saeed, Hayssam Dahrouj, Tareq Y. Al-Naffouri, Mohamed-Slim Alouini
Date: 2020-06-11 00:00:00

This paper surveys the optimization frameworks and performance analysis methods for large intelligent surfaces (LIS), which have been emerging as strong candidates to support the sixth-generation wireless physical platforms (6G). Due to their ability to adjust the behavior of interacting electromagnetic (EM) waves through intelligent manipulations of the reflections phase shifts, LIS have shown promising merits at improving the spectral efficiency of wireless networks. In this context, researchers have been recently exploring LIS technology in depth as a means to achieve programmable, virtualized, and distributed wireless network infrastructures. From a system level perspective, LIS have also been proven to be a low-cost, green, sustainable, and energy-efficient solution for 6G systems. This paper provides a unique blend that surveys the principles of operation of LIS, together with their optimization and performance analysis frameworks. The paper first introduces the LIS technology and its physical working principle. Then, it presents various optimization frameworks that aim to optimize specific objectives, namely, maximizing energy efficiency, sum-rate, secrecy-rate, and coverage. The paper afterwards discusses various relevant performance analysis works including capacity analysis, the impact of hardware impairments on capacity, uplink/downlink data rate analysis, and outage probability. The paper further presents the impact of adopting the LIS technology for positioning applications. Finally, we identify numerous exciting open challenges for LIS-aided 6G wireless networks, including resource allocation problems, hybrid radio frequency/visible light communication (RF-VLC) systems, health considerations, and localization.