pwc-archive/papers-with-abstracts huggingface dataset:
Optical Gaze Tracking with Spatially-Sparse Single-Pixel Detectors
Date: 2020-09-15 00:00:00

Gaze tracking is an essential component of next generation displays for virtual reality and augmented reality applications. Traditional camera-based gaze trackers used in next generation displays are known to be lacking in one or multiple of the following metrics: power consumption, cost, computational complexity, estimation accuracy, latency, and form-factor. We propose the use of discrete photodiodes and light-emitting diodes (LEDs) as an alternative to traditional camera-based gaze tracking approaches while taking all of these metrics into consideration. We begin by developing a rendering-based simulation framework for understanding the relationship between light sources and a virtual model eyeball. Findings from this framework are used for the placement of LEDs and photodiodes. Our first prototype uses a neural network to obtain an average error rate of 2.67{\deg} at 400Hz while demanding only 16mW. By simplifying the implementation to using only LEDs, duplexed as light transceivers, and more minimal machine learning model, namely a light-weight supervised Gaussian process regression algorithm, we show that our second prototype is capable of an average error rate of 1.57{\deg} at 250 Hz using 800 mW.
Which Trading Agent is Best? Using a Threaded Parallel Simulation of a Financial Market Changes the Pecking-Order
Authors: Michael Rollins, Dave Cliff
Date: 2020-09-15 00:00:00

This paper presents novel results generated from a new simulation model of a contemporary financial market, that cast serious doubt on the previously widely accepted view of the relative performance of various well-known public-domain automated-trading algorithms. Various public-domain trading algorithms have been proposed over the past 25 years in a kind of arms-race, where each new trading algorithm was compared to the previous best, thereby establishing a "pecking order", i.e. a partially-ordered dominance hierarchy from best to worst of the various trading algorithms. Many of these algorithms were developed and tested using simple minimal simulations of financial markets that only weakly approximated the fact that real markets involve many different trading systems operating asynchronously and in parallel. In this paper we use BSE, a public-domain market simulator, to run a set of experiments generating benchmark results from several well-known trading algorithms. BSE incorporates a very simple time-sliced approach to simulating parallelism, which has obvious known weaknesses. We then alter and extend BSE to make it threaded, so that different trader algorithms operate asynchronously and in parallel: we call this simulator Threaded-BSE (TBSE). We then re-run the trader experiments on TBSE and compare the TBSE results to our earlier benchmark results from BSE. Our comparison shows that the dominance hierarchy in our more realistic experiments is different from the one given by the original simple simulator. We conclude that simulated parallelism matters a lot, and that earlier results from simple simulations comparing different trader algorithms are no longer to be entirely trusted.
Data driven value-at-risk forecasting using a SVR-GARCH-KDE hybrid
Authors: Marius Lux, Wolfgang Karl Härdle, Stefan Lessmann
Date: 2020-09-15 00:00:00
Tasks: Density Estimation, Management

Appropriate risk management is crucial to ensure the competitiveness of financial institutions and the stability of the economy. One widely used financial risk measure is Value-at-Risk (VaR). VaR estimates based on linear and parametric models can lead to biased results or even underestimation of risk due to time varying volatility, skewness and leptokurtosis of financial return series. The paper proposes a nonlinear and nonparametric framework to forecast VaR that is motivated by overcoming the disadvantages of parametric models with a purely data driven approach. Mean and volatility are modeled via support vector regression (SVR) where the volatility model is motivated by the standard generalized autoregressive conditional heteroscedasticity (GARCH) formulation. Based on this, VaR is derived by applying kernel density estimation (KDE). This approach allows for flexible tail shapes of the profit and loss distribution, adapts for a wide class of tail events and is able to capture complex structures regarding mean and volatility. The SVR-GARCH-KDE hybrid is compared to standard, exponential and threshold GARCH models coupled with different error distributions. To examine the performance in different markets, one-day-ahead and ten-days-ahead forecasts are produced for different financial indices. Model evaluation using a likelihood ratio based test framework for interval forecasts and a test for superior predictive ability indicates that the SVR-GARCH-KDE hybrid performs competitive to benchmark models and reduces potential losses especially for ten-days-ahead forecasts significantly. Especially models that are coupled with a normal distribution are systematically outperformed.
The impact of social influence in Australian real-estate: market forecasting with a spatial agent-based model
Authors: Benjamin Patrick Evans, Kirill Glavatskiy, Michael S. Harré, Mikhail Prokopenko
Date: 2020-09-15 00:00:00
Tasks: Decision Making

Housing markets are inherently spatial, yet many existing models fail to capture this spatial dimension. Here we introduce a new graph-based approach for incorporating a spatial component in a large-scale urban housing agent-based model (ABM). The model explicitly captures several social and economic factors that influence the agents' decision-making behaviour (such as fear of missing out, their trend following aptitude, and the strength of their submarket outreach), and interprets these factors in spatial terms. The proposed model is calibrated and validated with the housing market data for the Greater Sydney region. The ABM simulation results not only include predictions for the overall market, but also produce area-specific forecasting at the level of local government areas within Sydney as arising from individual buy and sell decisions. In addition, the simulation results elucidate agent preferences in submarkets, highlighting differences in agent behaviour, for example, between first-time home buyers and investors, and between both local and overseas investors.
Robust MPC with data-driven demand forecasting for frequency regulation with heat pumps
Authors: Felix Bünning, Joseph Warrington, Philipp Heer, Roy S. Smith, John Lygeros
Date: 2020-09-15 00:00:00
Tasks: Demand Forecasting, Model Predictive Control

With the increased amount of volatile renewable energy sources connected to the electricity grid, and the phase-out of fossil fuel based power plants, there is an increased need for frequency regulation. On the demand side, frequency regulation services can be offered by buildings or districts that are equipped with electric heating or cooling systems, by exploiting their thermal inertia. Existing approaches for tapping into this potential typically rely on dynamic building models, which in practice can be challenging to obtain and maintain. As a result, practical implementations of such systems are scarce. Moreover, actively controlling buildings requires extensive control infrastructure and may cause privacy concerns in district energy systems. Motivated by this, we exploit the thermal inertia of buffer storage for reserves, reducing the building models to demand forecasts here. By combining a control scheme based on Robust Model Predictive Control, with affine policies, and heating demand forecasting based on Artificial Neural Networks with online correction methods, we offer frequency regulation reserves and maintain user comfort with a system comprising a heat pump and buffer storage. While the robust approach ensures occupant comfort, the use of affine policies reduces the effect of disturbance uncertainty on the system state. In a first-of-its-kind experiment with a real district-like building energy system, we demonstrate that the scheme is able to offer reserves in a variety of conditions and track a regulation signal while meeting the heating demand of the connected buildings. 13.4% of the consumed electricity is flexible. In additional numerical studies, we demonstrate that using affine policies significantly decreases the cost function and increases the amount of offered reserves and we investigate the suboptimality in comparison to an omniscient control system.
COVID-19 Impact on Global Maritime Mobility
Date: 2020-09-15 00:00:00

To prevent the outbreak of the Coronavirus disease (COVID-19), many countries around the world went into lockdown and imposed unprecedented containment measures. These restrictions progressively produced changes to social behavior and global mobility patterns, evidently disrupting social and economic activities. Here, using maritime traffic data collected via a global network of AIS receivers, we analyze the effects that the COVID-19 pandemic and containment measures had on the shipping industry, which accounts alone for more than 80% of the world trade. We rely on multiple data-driven maritime mobility indexes to quantitatively assess ship mobility in a given unit of time. The mobility analysis here presented has a worldwide extent and is based on the computation of: CNM of all ships reporting their position and navigational status via AIS, number of active and idle ships, and fleet average speed. To highlight significant changes in shipping routes and operational patterns, we also compute and compare global and local density maps. We compare 2020 mobility levels to those of previous years assuming that an unchanged growth rate would have been achieved, if not for COVID-19. Following the outbreak, we find an unprecedented drop in maritime mobility, across all categories of commercial shipping. With few exceptions, a generally reduced activity is observable from March to June, when the most severe restrictions were in force. We quantify a variation of mobility between -5.62% and -13.77% for container ships, between +2.28% and -3.32% for dry bulk, between -0.22% and -9.27% for wet bulk, and between -19.57% and -42.77% for passenger traffic. This study is unprecedented for the uniqueness and completeness of the employed dataset, which comprises a trillion AIS messages broadcast worldwide by 50000 ships, a figure that closely parallels the documented size of the world merchant fleet.
Deep Contextual Bandits for Fast Initial Access in mmWave Based User-Centric Ultra-Dense Networks
Authors: Insaf Ismath, K. B. Shashika Manosha, Samad Ali, Nandana Rajatheva, Matti Latva-aho
Date: 2020-09-15 00:00:00
Tasks: Management, Multi-Armed Bandits

Millimeter wave (mmWave) based multiple-input multiple-output (MIMO) capable user-centric (UC) ultra-dense (UD) networks are suggested to facilitate high throughput requirements of future networks. Due to the high blockage susceptibility of mmWave, the connections may drop frequently. Hence efficient and fast beam management in initial access (IA) is essential. Current cellular systems use beam sweeping based IA mechanisms. UC UD concept requires all of its access points (APs) to perform IA. This leads to a shortage of orthogonal radio resources. Nonorthogonal resource allocation causes interference which leads to a higher misdetection probability. In this paper, we propose a novel deep contextual bandit (DCB) based approach to perform fast and efficient IA in mmWave based UC UD networks. The DCB model uses one reference signal from the user to predict the IA beam. The reduced use of reference signals improves beam discovery delay and relaxes the requirement for radio resources. Ray-tracing and stochastic channel model-based simulations show that the suggested system outperforms its beam sweeping counterpart in terms of probability of beam misdetection and beam discovery delay in mmWave based UC UD networks.
Mapping horizontal and vertical urban densification in Denmark with Landsat time-series from 1985 to 2018: a semantic segmentation solution
Date: 2020-09-21 00:00:00
Tasks: Semantic Segmentation, Time Series, Time Series Analysis

Landsat imagery is an unparalleled freely available data source that allows
reconstructing horizontal and vertical urban form. This paper addresses the
challenge of using Landsat data, particularly its 30m spatial resolution, for
monitoring three-dimensional urban densification. We compare temporal and
spatial transferability of an adapted DeepLab model with a simple fully
convolutional network (FCN) and a texture-based random forest (RF) model to map
urban density in the two morphological dimensions: horizontal (compact, open,
sparse) and vertical (high rise, low rise). We test whether a model trained on
the 2014 data can be applied to 2006 and 1995 for Denmark, and examine whether
we could use the model trained on the Danish data to accurately map other
European cities. Our results show that an implementation of deep networks and
the inclusion of multi-scale contextual information greatly improve the
classification and the model's ability to generalize across space and time.
DeepLab provides more accurate horizontal and vertical classifications than FCN
when sufficient training data is available. By using DeepLab, the F1 score can
be increased by 4 and 10 percentage points for detecting vertical urban growth
compared to FCN and RF for Denmark. For mapping the other European cities with
training data from Denmark, DeepLab also shows an advantage of 6 percentage
points over RF for both the dimensions. The resulting maps across the years
1985 to 2018 reveal different patterns of urban growth between Copenhagen and
Aarhus, the two largest cities in Denmark, illustrating that those cities have
used various planning policies in addressing population growth and housing
supply challenges. In summary, we propose a transferable deep learning approach
for automated, long-term mapping of urban form from Landsat images.
Cross Layer Resource Allocation in H-CRAN with Spectrum and Energy Cooperation
Authors: Nazanin Moosavi, Mahnaz Sinaie, Paeiz Azmi, Pin-Hsun Lin, Eduard Jorswieck
Date: 2020-09-15 00:00:00

5G and beyond wireless networks are the upcoming evolution for the current cellular networks to provide the essential requirement of future demands such as high data rate, low energy consumption, and low latency to provide seamless communication for the emerging applications. Heterogeneous cloud radio access network (H-CRAN) is envisioned as a new trend of 5G that uses the advantages of heterogeneous and cloud radio access networks to enhance both the spectral and energy efficiency. In this paper, building on the notion of effective capacity (EC), we propose a framework in non-orthogonal multiple access (NOMA)-based H-CRAN to meet these demands simultaneously. Our proposed approach is to maximize the effective energy efficiency (EEE) while considering spectrum and power cooperation between macro base station (MBS) and radio remote heads (RRHs). To solve the formulated problem and to make it more tractable, we transform the original problem into an equivalent subtractive form via Dinkelbach algorithm. Afterwards, the combinational framework of distributed stable matching and successive convex algorithm (SCA) is then adopted to obtain the solution of the equivalent problem. Hereby, we propose an efficient resource allocation scheme to maximize energy efficiency while maintaining the delay quality of service (QoS) requirements for the all users. The simulation results show that the proposed algorithm can provide a non-trivial trade-off between delay and energy efficiency in NOMA H-CRAN systems in terms of EC and EEE and the spectrum and power cooperation improves EEE of the proposed network. Moreover, our proposed solution complexity is much lower than the optimal solution and it suffers a very limited gap compared to the optimal method.
On Spatial Multiplexing Using Reconfigurable Intelligent Surfaces
Authors: Mohamed A. ElMossallamy, Hongliang Zhang, Radwa Sultan, Karim G. Seddik, Lingyang Song, Geoffery Ye Li, Zhu Han
Date: 2020-09-15 00:00:00

We consider an uplink multi-user scenario and investigate the use of reconfigurable intelligent surfaces (RIS) to optimize spatial multiplexing performance when a linear receiver is used. We study two different formulations of the problem, namely maximizing the effective rank and maximizing the minimum singular value of the RIS-augmented channel. We employ gradient-based optimization to solve the two problems and compare the solutions in terms of the sum-rate achievable when a linear receiver is used. Our results show that the proposed criteria can be used to optimize the RIS to obtain effective channels with favorable properties and drastically improve performance even when the propagation through the RIS contributes a small fraction of the received power.
Optimal Bidding Strategy for Maker Auctions
Authors: Michael Darlin, Nikolaos Papadis, Leandros Tassiulas
Date: 2020-09-15 00:00:00

The Maker Protocol is a decentralized finance application that enables collateralized lending. The application uses open-bid, second-price auctions to complete its loan liquidation process. In this paper, we develop a bidding function for these auctions, focusing on the costs incurred to participate in the auctions. We then optimize these costs using parameters from historical auction data, and compare our optimal bidding prices to the historical auction prices. We find that the majority of auctions end at higher prices than our recommended optimal prices, and we propose several theories for these results.
What factors have caused Japanese prefectures to attract a larger population influx?
Date: 2020-09-15 00:00:00

Regional promotion and centralized correction in Tokyo have long been the
goals of the Government of Japan. Furthermore, in the wake of the recent new
coronavirus (COVID-19) epidemic, the momentum for rural migration is
increasing, to prevent the risk of infection with the help of penetration of
remote work. However, there is not enough debate about what kind of land will
attract the population. Therefore, in this paper, we will consider this problem
by performing correlation analysis and multiple regression analysis with the
inflow rate and the excess inflow rate of the population as the dependent
variables, using recent government statistics for each prefecture. As a result
of the analysis, in addition to economic factor variables, variables of
climatic, amenity, and human factors correlated with the inflow rate, and it
was shown that the model has the greatest explanatory power when multiple
factors were used in addition to specific factors. Therefore, local prefectures
are required to take regional promotion measures focusing on not only economic
factors but also multifaceted factors to attract the outside population.
Utility Maximization for Large-Scale Cell-Free Massive MIMO Downlink
Authors: Muhammad Farooq, Hien Quoc Ngo, Een-Kee Hong, Le-Nam Tran
Date: 2020-09-15 00:00:00
Tasks: Fairness, Second-order methods

We consider the system-wide utility maximization problem in the downlink of a cell-free massive multiple-input multiple-output (MIMO) system whereby a very large number of access points (APs) simultaneously serve a group of users. Specifically, four fundamental problems with increasing order of user fairness are of interest: (i) to maximize the average spectral efficiency (SE), (ii) to maximize the proportional fairness, (iii) to maximize the harmonic-rate of all users, and lastly (iv) to maximize the minimum SE of all users, subject to a sum power constraint at each AP. As the considered problems are non-convex, existing solutions normally rely on successive convex approximation to find a sub-optimal solution. More specifically, these known methods use off-the-shelf convex solvers, which basically implement an interior-point algorithm, to solve the derived convex problems. The main issue of such methods is that their complexity does not scale favorably with the problem size, limiting previous studies to cell-free massive MIMO of moderate scales. Thus the potential of cell-free massive MIMO has not been fully understood. To address this issue, we propose a unified framework based on an accelerated projected gradient method to solve the considered problems. Particularly, the proposed solution is found in closed-form expressions and only requires the first order oracle of the objective, rather than the Hessian matrix as in known solutions, and thus is much more memory efficient. Numerical results demonstrate that our proposed solution achieves the same utility performance but with far less run-time, compared to other second-order methods. Simulation results for large-scale cell-free massive MIMO show that the four utility functions can deliver nearly uniformed services to all users. In other words, user fairness is not a great concern in large-scale cell-free massive MIMO.
Network Structures of Collective Intelligence: The Contingent Benefits of Group Discussion
Date: 2020-09-15 00:00:00

Research on belief formation has produced contradictory findings on whether and when communication between group members will improve the accuracy of numeric estimates such as economic forecasts, medical diagnoses, and job candidate assessments. While some evidence suggests that carefully mediated processes such as the "Delphi method" produce more accurate beliefs than unstructured discussion, others argue that unstructured discussion outperforms mediated processes. Still others argue that independent individuals produce the most accurate beliefs. This paper shows how network theories of belief formation can resolve these inconsistencies, even when groups lack apparent structure as in informal conversation. Emergent network structures of influence interact with the pre-discussion belief distribution to moderate the effect of communication on belief formation. As a result, communication sometimes increases and sometimes decreases the accuracy of the average belief in a group. The effects differ for mediated processes and unstructured communication, such that the relative benefit of each communication format depends on both group dynamics as well as the statistical properties of pre-interaction beliefs. These results resolve contradictions in previous research and offer practical recommendations for teams and organizations.
A Review of 5G Front-End Systems Package Integration
Authors: Atom O. Watanabe, Muhammad Ali, Sk Yeahia Been Sayeed, Rao R. Tummala, P. Markondeya Raj
Date: 2020-09-15 00:00:00

Increasing data rates, spectrum efficiency and energy efficiency have been driving major advances in the design and hardware integration of RF communication networks. In order to meet the data rate and efficiency metrics, 5G networks have emerged as a follow-on to 4G, and projected to have 100X higher wireless date rates and 100X lower latency than those with current 4G networks. Major challenges arise in the packaging of radio-frequency front-end modules because of the stringent low signal-loss requirements in the millimeter-wave frequency bands, and precision-impedance designs with smaller footprints and thickness. Heterogeneous integration in 3D ultra-thin packages with higher component densities and performance than with the existing 2D packages is needed to realize such 5G systems. This paper reviews the key building blocks of 5G systems and the underlying advances in packaging technologies to realize them.