pwc-archive/papers-with-abstracts huggingface dataset:
COVID-19 and the Social Distancing Paradox: dangers and solutions
Authors: Massimo Marchiori
Date: 2020-05-26 00:00:00

Background: Without proven effect treatments and vaccines, Social Distancing is the key protection factor against COVID-19. Social distancing alone should have been enough to protect again the virus, yet things have gone very differently, with a big mismatch between theory and practice. What are the reasons? A big problem is that there is no actual social distancing data, and the corresponding people behavior in a pandemic is unknown. We collect the world-first dataset on social distancing during the COVID-19 outbreak, so to see for the first time how people really implement social distancing, identify dangers of the current situation, and find solutions against this and future pandemics. Methods: Using a sensor-based social distancing belt we collected social distance data from people in Italy for over two months during the most critical COVID-19 outbreak. Additionally, we investigated if and how wearing various Personal Protection Equipment, like masks, influences social distancing. Results: Without masks, people adopt a counter-intuitively dangerous strategy, a paradox that could explain the relative lack of effectiveness of social distancing. Using masks radically changes the situation, breaking the paradoxical behavior and leading to a safe social distance behavior. In shortage of masks, DIY (Do It Yourself) masks can also be used: even without filtering protection, they provide social distancing protection. Goggles should be recommended for general use, as they give an extra powerful safety boost. Generic Public Health policies and media campaigns do not work well on social distancing: explicit focus on the behavioral problems of necessary mobility are needed.
Range Value-at-Risk: Multivariate and Extreme Values
Authors: Roba Bairakdar, Lu Cao, Melina Mailhot
Date: 2020-05-26 00:00:00

The concept of univariate Range Value-at-Risk, presented by Cont et al. (2010), is extended in the multidimensional setting. Traditional risk measures are not well suited when dealing with heavy-tail distributions and infinite tail expectations. The multivariate definitions of robust truncated tail expectations are provided to overcome this problem. Robustness and other properties as well as empirical estimators are derived. Closed-form expressions and special cases in the extreme value framework are also discussed. Numerical and graphical examples are provided to examine the accuracy of the empirical estimators.
Mitigation of Human RF Exposure in Wearable Communications
Authors: Yakub Ahmed Sharif, Seungmo Kim
Date: 2020-05-26 00:00:00
Tasks: Management

A major concern regarding wearable communications is human biological safety under exposure to radio frequency (RF) radiation generated by wearable devices. The biggest challenge in the implementation of wearable devices is to reduce the usage of energy to minimize the harmful impacts of exposure to RF on human health. Power management is one of the key energy-saving strategies used in wearable networks. Signals enter the receiver (Rx) from a transmitter (Tx) through the human body in the form of electromagnetic field (EMF) radiation produced during the transmission of the packet. It may have a negative effect on human health as a result of specific absorption rate (SAR). SAR is the amount of radio frequency energy consumed by human tissue in mass units. The higher the body's absorption rate, the more radio frequency radiation. Therefore, SAR can be reduced by distributing the power over a greater mass or tissue volume equivalently larger. The Institute of Electrical and Electronics Engineers (IEEE) 802.15.6-supported multi-hop topology is particularly useful for low-power embedded devices that can reduce consumption of energy by communicating to the receiver (Rx) through nearby transmitted devices. In this paper, we suggest a relaying mechanism to minimize the transmitted power and, as a consequence, the power density (PD), a measure of SAR.
Exploiting Local and Cloud Sensor Fusion in Intermittently Connected Sensor Networks
Authors: Michal Yemini, Stephanie Gil, Andrea Goldsmith
Date: 2020-05-26 00:00:00
Tasks: Clustering, Event Detection, Sensor Fusion

We consider a detection problem where sensors experience noisy measurements and intermittent communication opportunities to a centralized fusion center (or cloud). The objective of the problem is to arrive at the correct estimate of event detection in the environment. The sensors may communicate locally with other sensors (local clusters) where they fuse their noisy sensor data to estimate the detection of an event locally. In addition, each sensor cluster can intermittently communicate to the cloud, where a centralized fusion center fuses estimates from all sensor clusters to make a final determination regarding the occurrence of the event across the deployment area. We refer to this hybrid communication scheme as a cloud-cluster architecture. Minimizing the expected loss function of networks where noisy sensors are intermittently connected to the cloud, as in our hybrid communication scheme, has not been investigated to our knowledge. We leverage recently improved concentration inequalities to arrive at an optimized decision rule for each cluster and we analyze the expected detection performance resulting from our hybrid scheme. Our analysis shows that clustering the sensors provides resilience to noise in the case of low communication probability with the cloud. For larger clusters, a steep improvement in detection performance is possible even for a low communication probability by using our cloud-cluster architecture.
When Full-Duplex Transmission Meets Intelligent Reflecting Surface: Opportunities and Challenges
Authors: Gaofeng Pan, Jia Ye, Jianping An, Mohamed-Slim Alouini
Date: 2020-05-26 00:00:00

Full-duplex (FD) transmission has already been regarded and developed as a promising method to improve the utilization efficiency of the limited spectrum resource, as transmitting and receiving are allowed to simultaneously occur on the same frequency band. Nowadays, benefiting from the recent development of intelligent reflecting surface (IRS), some unique electromagnetic (EM) functionalities, like wavefront shaping, focusing, anomalous reflection, absorption, frequency shifting, and nonreciprocity can be realized by soft-controlled elements at the IRS, showing the capability of reconfiguring the wireless propagation environment with no hardware cost and nearly zero energy consumption. To jointly exploit the virtues of both FD transmission and IRS, in this article we first introduce several EM functionalities of IRS that are profitable for FD transmission; then, some designs of FD-enabled IRS systems are proposed and discussed, followed by numerical results to demonstrate the obtained benefits. Finally, the challenges and open problems of realizing FD-enabled IRS systems are outlined and elaborated upon.
Entropy Martingale Optimal Transport and Nonlinear Pricing-Hedging Duality
Authors: Alessandro Doldi, Marco Frittelli
Date: 2020-05-26 00:00:00
Tasks: Math

The objective of this paper is to develop a duality between a novel Entropy Martingale Optimal Transport problem (A) and an associated optimization problem (B). In (A) we follow the approach taken in the Entropy Optimal Transport (EOT) primal problem by Liero et al. "Optimal entropy-transport problems and a new Hellinger-Kantorovic distance between positive measures", Invent. math. 2018, but we add the constraint, typical of Martingale Optimal Transport (MOT) theory, that the infimum of the cost functional is taken over martingale probability measures, instead of finite positive measures, as in Liero et al.. The Problem (A) differs from the corresponding problem in Liero et al. not only by the martingale constraint, but also because we admit less restrictive penalization terms $\mathcal{D}_{U}$, which may not have a divergence formulation. In Problem (B) the objective functional, associated via Fenchel conjugacy to the terms $\mathcal{D}_{U}$, is not any more linear, as in OT or in MOT. This leads to a novel optimization problem which also has a clear financial interpretation as a nonlinear subhedging value. Our theory allows us to establish a nonlinear robust pricing-hedging duality, which covers a wide range of known robust results. We also focus on Wasserstein-induced penalizations and we study how the duality is affected by variations in the penalty terms, with a special focus on the convergence of EMOT to the extreme case of MOT.
Real-Time Fault Detection and Process Control Based on Multi-channel Sensor Data Fusion
Authors: Feng Ye, Zhijie Xia, Min Dai, Zhisheng Zhang
Date: 2020-05-26 00:00:00
Tasks: Fault Detection, Fault Diagnosis

Sensor signals acquired in the industrial process contain rich information which can be analyzed to facilitate effective monitoring of the process, early detection of system anomalies, quick diagnosis of fault root causes, and intelligent system design and control. In many mechatronic systems, multiple signals are acquired by different sensor channels (i.e. multi-channel data) which can be represented by high-order arrays (tensorial data). The multi-channel data has a high-dimensional and complex cross-correlation structure. It is crucial to develop a method that considers the interrelationships between different sensor channels. This paper proposes a new process monitoring approach based on uncorrelated multilinear discriminant analysis that can effectively model the multi-channel data to achieve a superior monitoring and fault diagnosis performance compared to other competing methods. The proposed method is applied directly to the high-dimensional tensorial data. Features are extracted and combined with multivariate control charts to monitor multi-channel data. The effectiveness of the proposed method in quick detection of process changes is demonstrated with both the simulation and a real-world case study.
Computation of Expected Shortfall by fast detection of worst scenarios
Authors: Bruno Bouchard, Adil Reghai, Benjamin Virrion
Date: 2020-05-26 00:00:00

We consider a multi-step algorithm for the computation of the historical expected shortfall such as defined by the Basel Minimum Capital Requirements for Market Risk. At each step of the algorithm, we use Monte Carlo simulations to reduce the number of historical scenarios that potentially belong to the set of worst scenarios. The number of simulations increases as the number of candidate scenarios is reduced and the distance between them diminishes. For the most naive scheme, we show that the L p-error of the estimator of the Expected Shortfall is bounded by a linear combination of the probabilities of inversion of favorable and unfavorable scenarios at each step, and of the last step Monte Carlo error associated to each scenario. By using concentration inequalities, we then show that, for sub-gamma pricing errors, the probabilities of inversion converge at an exponential rate in the number of simulated paths. We then propose an adaptative version in which the algorithm improves step by step its knowledge on the unknown parameters of interest: mean and variance of the Monte Carlo estimators of the different scenarios. Both schemes can be optimized by using dynamic programming algorithms that can be solved off-line. To our knowledge, these are the first non-asymptotic bounds for such estimators. Our hypotheses are weak enough to allow for the use of estimators for the different scenarios and steps based on the same random variables, which, in practice, reduces considerably the computational effort. First numerical tests are performed.
The Race between Technology and Woman: Changes in Relative Wages and Labor Shares in OECD Countries
Authors: Hiroya Taniguchi, Ken Yamada
Date: 2020-05-26 00:00:00

The era of technological change entails complex patterns of changes in wages and employment. We develop a unified framework to evaluate the effects of capital-embodied technological change on, as well as the contributions of factor inputs to, the relative wages and income shares of different types of labor. We obtain the aggregate elasticities of substitution among different types of capital and labor by estimating and aggregating sectoral production function parameters with cross-country cross-industry panel data from OECD countries. We show that advances in information, communication, and computation technologies contribute significantly to narrowing the gender wage gap, widening the skill wage gap, and declining labor shares.
Reconfigurable Intelligent Surface-Aided Grant-Free Access for Uplink URLLC
Authors: Dick Carrillo Melgarejo, Charalampos Kalalas, Arthur Sousa de Sena, Pedro H. J. Nardelli
Date: 2020-05-26 00:00:00
Tasks: Diversity

Reconfigurable intelligent surfaces (RISs) have been recently considered as one of the emerging technologies for future communication systems by leveraging the tuning capabilities of their reflecting elements. In this paper, we investigate the potential of an RIS-based architecture for uplink sensor data transmission in an ultra-reliable low-latency communication (URLLC) context. In particular, we propose an RIS-aided grant-free access scheme for an industrial control scenario, aiming to exploit diversity and achieve improved reliability performance. We consider two different resource allocation schemes for the uplink transmissions, i.e., dedicated and shared slot assignment, and three different receiver types, namely the zero-forcing, the minimum mean squared error (MMSE), and the MMSE-successive interference cancellation receivers. Our extensive numerical evaluation in terms of outage probability demonstrates the gains of our approach in terms of reliability, resource efficiency, and capacity and for different configurations of the RIS properties. An RIS-aided grant-free access scheme combined with advanced receivers is shown to be a well-suited option for uplink URLLC.
Using Network Interbank Contagion in Bank Default Prediction
Authors: Riccardo Doyle
Date: 2020-05-26 00:00:00
Tasks: Prediction, regression

Interbank contagion can theoretically exacerbate losses in a financial system and lead to additional cascade defaults during downturn. In this paper we produce default analysis using both regression and neural network models to verify whether interbank contagion offers any predictive explanatory power on default events. We predict defaults of U.S. domiciled commercial banks in the first quarter of 2010 using data from the preceding four quarters. A number of established predictors (such as Tier 1 Capital Ratio and Return on Equity) are included alongside contagion to gauge if the latter adds significance. Based on this methodology, we conclude that interbank contagion is extremely explanatory in default prediction, often outperforming more established metrics, in both regression and neural network models. These findings have sizeable implications for the future use of interbank contagion as a variable of interest for stress testing, bank issued bond valuation and wider bank default prediction.
Nonlinear model reduction of dynamical power grid models using quadratization and balanced truncation
Date: 2020-05-26 00:00:00

In this work, we present a nonlinear model reduction approach for reducing
two commonly used nonlinear dynamical models of power grids: the effective
network (EN) model and the synchronous motor (SM) model. Such models are
essential in real-time security assessments of power grids. However, as power
grids are often large-scale, it is necessary to reduce the models in order to
utilize them in real-time. We reformulate the nonlinear power grid models as
quadratic systems and reduce them using balanced truncation based on
approximations of the reachability and observability Gramians. Finally, we
present examples involving numerical simulation of reduced EN and SM models of
the IEEE 57 bus and IEEE 118 bus systems.
Analysis of protrusion dynamics in amoeboid cell motility by means of regularized contour flows
Authors: Daniel Schindler, Ted Moldenhawer, Maike Stange, Valentino Lepro, Carsten Beta, Matthias Holschneider, Wilhelm Huisinga
Date: 2020-05-26 00:00:00

Amoeboid cell motility is essential for a wide range of biological processes including wound healing, embryonic morphogenesis, and cancer metastasis. It relies on complex dynamical patterns of cell shape changes that pose long-standing challenges to mathematical modeling and raise a need for automated and reproducible approaches to extract quantitative morphological features from image sequences. Here, we introduce a theoretical framework and a computational method for obtaining smooth representations of the spatiotemporal contour dynamics from stacks of segmented microscopy images. Based on a Gaussian process regression we propose a one-parameter family of regularized contour flows that allows us to continuously track reference points (virtual markers) between successive cell contours. We use this approach to define a coordinate system on the moving cell boundary and to represent different local geometric quantities in this frame of reference. In particular, we introduce the local marker dispersion as a measure to identify localized membrane expansions and provide a fully automated way to extract the properties of such expansions, including their area and growth time. The methods are available as an open-source software package called AmoePy, a Python-based toolbox for analyzing amoeboid cell motility (based on time-lapse microscopy data), including a graphical user interface and detailed documentation. Due to the mathematical rigor of our framework, we envision it to be of use for the development of novel cell motility models. We mainly use experimental data of the social amoeba Dictyostelium discoideum to illustrate and validate our approach.
Physical Layer Authentication for Non-Coherent Massive SIMO-Enabled Industrial IoT Communications
Authors: Zhifang Gu, He Chen, Pingping Xu, Yonghui Li, Branka Vucetic
Date: 2020-05-23 00:00:00
Tasks: Attribute, TAG

Achieving ultra-reliable, low-latency and secure communications is essential for realizing the industrial Internet of Things (IIoT). Non-coherent massive multiple-input multiple-output (MIMO) is one of promising techniques to fulfill ultra-reliable and low-latency requirements. In addition, physical layer authentication (PLA) technology is particularly suitable for secure IIoT communications thanks to its low-latency attribute. A PLA method for non-coherent massive single-input multiple-output (SIMO) IIoT communication systems is proposed in this paper. This method realizes PLA by embedding an authentication signal (tag) into a message signal, referred to as "message-based tag embedding". It is different from traditional PLA methods utilizing uniform power tags. We design the optimal tag embedding and optimize the power allocation between the message and tag signals to characterize the trade-off between the message and tag error performance. Numerical results show that the proposed message-based tag embedding PLA method is more accurate than the traditional uniform tag embedding method which has an unavoidable tag error floor close to 10%.
Output Controllability of a Linear Dynamical System with Sparse Controls
Authors: Geethu Joseph
Date: 2020-05-24 00:00:00

In this paper, we study the conditions to be satisfied by a discrete-time linear system to ensure output controllability using sparse control inputs. A set of necessary and sufficient conditions can be directly obtained by extending the Kalman rank test for output controllability. However, the verification of these conditions is computationally heavy due to their combinatorial nature. Therefore, we derive non-combinatorial conditions for output sparse controllability which can be verified with polynomial time complexity. Our results also provide bounds on the minimum sparsity level required to ensure output controllability of the system. This additional insight is useful for designing sparse control input that drives the system to any desired output.
Mean-Variance Portfolio Management with Functional Optimization
Authors: Ka Wai Tsang, Zhaoyi He
Date: 2020-05-26 00:00:00
Tasks: Management, Portfolio Optimization

This paper introduces a new functional optimization approach to portfolio optimization problems by treating the unknown weight vector as a function of past values instead of treating them as fixed unknown coefficients in the majority of studies. We first show that the optimal solution, in general, is not a constant function. We give the optimal conditions for a vector function to be the solution, and hence give the conditions for a plug-in solution (replacing the unknown mean and variance by certain estimates based on past values) to be optimal. After showing that the plug-in solutions are sub-optimal in general, we propose gradient-ascent algorithms to solve the functional optimization for mean-variance portfolio management with theorems for convergence provided. Simulations and empirical studies show that our approach can perform significantly better than the plug-in approach.
Group-In: Group Inference from Wireless Traces of Mobile Devices
Authors: Gürkan Solmaz, Jonathan Fürst, Samet Aytaç, Fang-Jing Wu
Date: 2020-05-26 00:00:00
Tasks: Clustering

This paper proposes Group-In, a wireless scanning system to detect static or mobile people groups in indoor or outdoor environments. Group-In collects only wireless traces from the Bluetooth-enabled mobile devices for group inference. The key problem addressed in this work is to detect not only static groups but also moving groups with a multi-phased approach based only noisy wireless Received Signal Strength Indicator (RSSIs) observed by multiple wireless scanners without localization support. We propose new centralized and decentralized schemes to process the sparse and noisy wireless data, and leverage graph-based clustering techniques for group detection from short-term and long-term aspects. Group-In provides two outcomes: 1) group detection in short time intervals such as two minutes and 2) long-term linkages such as a month. To verify the performance, we conduct two experimental studies. One consists of 27 controlled scenarios in the lab environments. The other is a real-world scenario where we place Bluetooth scanners in an office environment, and employees carry beacons for more than one month. Both the controlled and real-world experiments result in high accuracy group detection in short time intervals and sampling liberties in terms of the Jaccard index and pairwise similarity coefficient.