pwc-archive/papers-with-abstracts huggingface dataset:
Reconfigurable Intelligent Surface for Interference Alignment in MIMO Device-to-Device Networks
Authors: Min Fu, Yong Zhou, Yuanming Shi
Date: 2020-05-14 00:00:00

In multiple-input multiple-output (MIMO) device-to-device (D2D) networks, interference and rank-deficient channels are the critical bottlenecks for achieving high degrees of freedom (DoFs). In this paper, we propose a reconfigurable intelligent surface (RIS) assisted interference alignment strategy to simultaneous mitigate the co-channel interference and cope with rank-deficient channels, thereby improving the feasibility of interference alignment conditions and in turn increasing the achievable DoFs. The key enabler is a general low-rank optimization approach that maximizes the achievable DoFs by jointly designing the phase-shift and transceiver matrices. To address the unique challenges of the coupled optimization variables, we develop a block-structured Riemannian pursuit method by solving fixed-rank and unit modulus constrained least square subproblems along with rank increase. Finally, to reduce the computational complexity and achieve good DoF performance, we develop unified Riemannian conjugate gradient algorithms to alternately optimize the fixed-rank transceiver matrix and the unit modulus constrained phase shifter by exploiting the non-compact Stiefel manifold and the complex circle manifold, respectively. Numerical results demonstrate the effectiveness of deploying an RIS and the superiority of the proposed block-structured Riemannian pursuit method in terms of the achievable DoFs and the achievable sum rate.
Patterns of social mobility across social groups in India
Date: 2020-05-14 00:00:00
Tasks: STS

Social mobility captures the extent to which socio-economic status of
children, is independent of status of their respective parents. In order to
measure social mobility, most widely used indicators of socio-economic status
are income, education and occupation. While social mobility measurement based
on income is less contested, data availability in Indian context limits us to
observing mobility patterns along the dimensions of either education or
occupation. In this study we observe social mobility patterns for different
social groups along these two main dimensions, and find that while upward and
downward mobility prospects in education for SCs/STs is somewhat improving in
the recent times, occupational mobility patterns are rather worrisome. These
results motivate the need for reconciling disparate trends along education and
occupation, in order to get a more comprehensive picture of social mobility in
the country.
Continuous time mean-variance-utility portfolio problem and its equilibrium strategy
Authors: Ben-Zhang Yang, Xin-Jiang He, Song-Ping Zhu
Date: 2020-05-14 00:00:00

In this paper, we propose a new class of optimization problems, which maximize the terminal wealth and accumulated consumption utility subject to a mean variance criterion controlling the final risk of the portfolio. The multiple-objective optimization problem is firstly transformed into a single-objective one by introducing the concept of overall "happiness" of an investor defined as the aggregation of the terminal wealth under the mean-variance criterion and the expected accumulated utility, and then solved under a game theoretic framework. We have managed to maintain analytical tractability; the closed-form solutions found for a set of special utility functions enable us to discuss some interesting optimal investment strategies that have not been revealed before in literature.
High Precision Indoor Navigation for Autonomous Vehicles
Authors: Eduardo Sánchez Morales, Michael Botsch, Bertold Huber, Andrés García Higuera
Date: 2020-05-14 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, CPU, State Estimation, Vocal Bursts Intensity Prediction

Autonomous driving is an important trend of the automotive industry. The continuous research towards this goal requires a precise reference vehicle state estimation under all circumstances in order to develop and test autonomous vehicle functions. However, even when lane-accurate positioning is expected from oncoming technologies, like the L5 GPS band, the question of accurate positioning in roofed areas, e.\,g., tunnels or park houses, still has to be addressed. In this paper, a novel procedure for a reference vehicle state estimation is presented. The procedure includes three main components. First, a robust standstill detection based purely on signals from an Inertial Measurement Unit. Second, a vehicle state estimation by means of statistical filtering. Third, a high accuracy LiDAR-based positioning method that delivers velocity, position and orientation correction data with a mean error of 0.1 m/s, 4.7 cm and 1$^\circ$ respectively. Runtime tests on a CPU indicates the possibility of real-time implementation.
Worst-Case Detection Performance for Distributed SIMO Physical Layer Authentication
Authors: Henrik Forssell, Ragnar Thobaben
Date: 2020-05-14 00:00:00
Tasks: Position

Feature-based physical layer authentication (PLA) schemes, using position-specific channel characteristics as identifying features, can provide lightweight protection against impersonation attacks in overhead-limited applications like e.g., mission-critical and low-latency scenarios. However, with PLA-aware attack strategies, an attacker can maximize the probability of successfully impersonating the legitimate devices. In this paper, we provide worst-case detection performance bounds under such strategies for a distributed PLA scheme that is based on the channel-state information (CSI) observed at multiple distributed remote radio-heads. This distributed setup exploits the multiple-channel diversity for enhanced detection performance and mimics distributed antenna architectures considered for 4G and 5G radio access networks. We consider (i) a power manipulation attack, in which a single-antenna attacker adopts optimal transmit power and phase; and (ii) an optimal spatial position attack. Interestingly, our results show that the attacker can achieve close-to-optimal success probability with only statistical CSI, which significantly strengthens the relevance of our results for practical scenarios. Furthermore, our results show that, by distributing antennas to multiple radio-heads, the worst-case missed detection probability can be reduced by 4 orders of magnitude without increasing the total number of antennas, illustrating the superiority of distributed PLA over a co-located antenna setup.
Public Concern and the Financial Markets during the COVID-19 outbreak
Authors: Michele Costola, Matteo Iacopini, Carlo R. M. A. Santagiustina
Date: 2020-05-14 00:00:00

We measure the public concern during the outbreak of COVID-19 disease using three data sources from Google Trends (YouTube, Google News, and Google Search). Our findings are three-fold. First, the public concern in Italy is found to be a driver of the concerns in other countries. Second, we document that Google Trends data for Italy better explains the stock index returns of France, Germany, Great Britain, the United States, and Spain with respect to their country-based indicators. Finally, we perform a time-varying analysis and identify that the most severe impacts in the financial markets occur at each step of the Italian lock-down process.
High precision indoor positioning by means of LiDAR
Authors: Eduardo Sánchez Morales, Michael Botsch, Bertold Huber, Andrés García Higuera
Date: 2020-05-14 00:00:00
Tasks: Autonomous Driving, CPU, Trajectory Planning, Vocal Bursts Intensity Prediction

The trend towards autonomous driving and the continuous research in the automotive area, like Advanced Driver Assistance Systems (ADAS), requires an accurate localization under all circumstances. An accurate estimation of the vehicle state is a basic requirement for any trajectory-planning algorithm. Still, even when the introduction of the GPS L5 band promises lane-accuracy, coverage limitations in roofed areas still have to be addressed. In this work, a method for high precision indoor positioning using a LiDAR is presented. The method is based on the combination of motion models with LiDAR measurements, and uses infrastructural elements as positioning references. This allows to estimate the orientation, velocity over ground and position of a vehicle in a Local Tangent Plane (LTP) reference frame. When the outputs of the proposed method are compared to those of an Automotive Dynamic Motion Analyzer (ADMA), mean errors of 1 degree, 0.1 m/s and of 4.7 cm respectively are obtained. The method can be implemented by using a LiDAR sensor as a stand-alone unit. A median runtime of 40.77 us on an Intel i7-6820HQ CPU signals the possibility of real-time processing.
Joint Modeling of Received Power, Mean Delay, and Delay Spread for Wideband Radio Channels
Date: 2020-05-14 00:00:00

We propose a multivariate log-normal distribution to jointly model received power, mean delay, and root mean square (rms) delay spread of wideband radio channels, referred to as the standardized temporal moments. The model is validated using experimental data collected from five different measurement campaigns (four indoor and one outdoor scenario). We observe that the received power, mean delay and rms delay spread are correlated random variables and, therefore, should be simulated jointly. Joint models are able to capture the structure of the underlying process, unlike the independent models considered in the literature. The proposed model of the multivariate log-normal distribution is found to be a good fit for a large number of wideband data-sets.
Fault Tolerant Equilibria in Anonymous Games: best response correspondences and fixed points
Authors: Deepanshu Vasal, Randall Berry
Date: 2020-05-14 00:00:00

The notion of fault tolerant Nash equilibria has been introduced as a way of studying the robustness of Nash equilibria. Under this notion, a fixed number of players are allowed to exhibit faulty behavior in which they may deviate arbitrarily from an equilibrium strategy. A Nash equilibrium in a game with $N$ players is said to be $\alpha$-tolerant if no non-faulty user wants to deviate from an equilibrium strategy as long as $N-\alpha-1$ other players are playing the equilibrium strategies, i.e., it is robust to deviations from rationality by $\alpha$ faulty players. In prior work, $\alpha$-tolerance has been largely viewed as a property of a given Nash equilibria. Here, instead we consider following Nash's approach for showing the existence of equilibria, namely, through the use of best response correspondences and fixed-point arguments. In this manner, we provide sufficient conditions for the existence an $\alpha$-tolerant equilibrium. This involves first defining an $\alpha$-tolerant best response correspondence. Given a strategy profile of non-faulty agents, this correspondence contains strategies for a non-faulty player that are a best response given any strategy profile of the faulty players. We prove that if this correspondence is non-empty, then it is upper-hemi-continuous. This enables us to apply Kakutani's fixed-point theorem and argue that if this correspondence is non-empty for every strategy profile of the non-faulty players then there exists an $\alpha$-tolerant equilibrium. However, we also illustrate by examples, that in many games this best response correspondence will be empty for some strategy profiles even though $\alpha$-tolerant equilibira still exist.
How Does the Adoption of Ad Blockers Affect News Consumption?
Authors: Shunyao Yan, Klaus M. Miller, Bernd Skiera
Date: 2020-05-14 00:00:00
Tasks: Articles

Ad blockers allow users to browse websites without viewing ads. Online news providers that rely on advertising revenue tend to perceive users adoption of ad blockers purely as a threat to revenue. Yet, this perception ignores the possibility that avoiding ads, which users presumably dislike, may affect users online news consumption behavior in positive ways. Using 3.1 million anonymized visits from 79,856 registered users on a news website, we find that adopting an ad blocker has a robust positive effect on the quantity and variety of articles users consume (21.5% - 43.3% more articles and 13.4% - 29.1% more content categories). An increase in repeat user visits of the news website, rather than the number of page impressions per visit, drives the news consumption. These visits tend to start with direct navigation to the news website, indicating user loyalty. The increase in news consumption is more substantial for users who have less prior experience with the website. We discuss how news publishers could benefit from these findings, including exploring revenue models that consider users desire to avoid ads.
Joint User Grouping, Scheduling, and Precoding for Multicast Energy Efficiency in Multigroup Multicast Systems
Authors: Ashok Bandi, Bhavani Shankar Mysore R, Symeon Chatzinotas, Björn Ottersten
Date: 2020-05-14 00:00:00
Tasks: Scheduling

This paper studies the joint design of user grouping, scheduling (or admission control) and precoding to optimize energy efficiency (EE) for multigroup multicast scenarios in single-cell multiuser MISO downlink channels. Noticing that the existing definition of EE fails to account for group sizes, a new metric called multicast energy efficiency (MEE) is proposed. In this context, the joint design is considered for the maximization of MEE, EE, and scheduled users. Firstly, with the help of binary variables (associated with grouping and scheduling) the joint design problem is formulated as a mixed-Boolean fractional programming problem such that it facilitates the joint update of grouping, scheduling and precoding variables. Further, several novel optimization formulations are proposed to reveal the hidden difference of convex/ concave structure in the objective and associated constraints. Thereafter, we propose a convex-concave procedure framework based iterative algorithm for each optimization criteria where grouping, scheduling, and precoding variables are updated jointly in each iteration. Finally, we compare the performance of the three design criteria concerning three performance metrics namely MEE, EE, and scheduled users through Monte-Carlo simulations. These simulations establish the need for MEE and the improvement from the system optimization.
Detection and Isolation of Wheelset Intermittent Over-creeps for Electric Multiple Units Based on a Weighted Moving Average Technique
Authors: Yinghong Zhao, Xiao He, Donghua Zhou, Michael G. Pecht
Date: 2020-05-14 00:00:00

Wheelset intermittent over-creeps (WIOs), i.e., slips or slides, can decrease the overall traction and braking performance of Electric Multiple Units (EMUs). However, they are difficult to detect and isolate due to their small magnitude and short duration. This paper presents a new index called variable-to-minimum difference (VMD) and a new technique called weighted moving average (WMA). Their combination, i.e., the WMA-VMD index, is used to detect and isolate WIOs in real time. Different from the existing moving average (MA) technique that puts an equal weight on samples within a time window, WMA uses correlation information to find an optimal weight vector (OWV), so as to better improve the index's robustness and sensitivity. The uniqueness of the OWV for the WMA-VMD index is proven, and the properties of the OWV are revealed. The OWV possesses a symmetrical structure, and the equally weighted scheme is optimal when data are independent. This explains the rationale of existing MA-based methods. WIO detectability and isolability conditions of the WMA-VMD index are provided, leading to an analysis of the properties of two nonlinear, discontinuous operators, $\min$ and $\textrm{VMD}_i$. Experimental studies are conducted based on practical running data and a hardware-in-the-loop platform of an EMU to show that the developed methods are effective.
Efficient and fair trading algorithms in market design environments
Authors: Jingsheng Yu, Jun Zhang
Date: 2020-05-14 00:00:00
Tasks: Fairness

We propose a new method to define trading algorithms in market design environments. Dropping the traditional idea of clearing cycles in generated graphs, we use parameterized linear equations to define trading algorithms. Our method has two advantages. First, our method avoids discussing the details of who trades with whom and how, which can be a difficult question in complex environments. Second, by controlling parameter values in our equations, our method is flexible and transparent to satisfy various fairness criteria. We apply our method to several models and obtain new trading algorithms that are efficient and fair.
Effects of demand control on the complex dynamics of electric power system blackouts
Date: 2020-05-14 00:00:00

The propagation of failures and blackouts in electric networks is a complex
problem. Typical models, such as the ORNL-PSerc-Alaska (OPA), are based on a
combination of fast and a slow dynamics. The first describes the cascading
failures while the second the grid evolution though line and generation
upgrades as well as demand growth, all taking place in time scales from days to
years. The growing integration of renewable energy sources, whose power
fluctuates in time scales from seconds to hours, together with the increase in
demand, which also present fast fluctuations, require the incorporation of
distributed methods of control in the demand side to avoid the high cost of
ordinary control in conventional power plants. In this work, we extend the OPA
model to include fluctuations in the demand at time scales of the order of
minutes, intraday demand variations and the effect of demand control. We find
that demand control effectively reduces the number of blackouts without
increasing the probability of large-scale events.
Existence and Uniqueness of Recursive Utility Models in $L_p$
Date: 2020-05-13 00:00:00

Recursive preferences, of the sort developed by Epstein and Zin (1989), play
an integral role in modern macroeconomics and asset pricing theory.
Unfortunately, it is non-trivial to establish the unique existence of a
solution to recursive utility models. We show that the tightest known existence
and uniqueness conditions can be extended to (i) Schorfheide, Song and Yaron
(2018) recursive utilities and (ii) recursive utilities with `narrow framing'.
Further, we sharpen the solution space of Borovicka and Stachurski (2019) from
$L_1$ to $L_p$ so that the results apply to a broader class of modern asset
pricing models. For example, using $L_2$ Hilbert space theory, we find the
class of parameters which generate a unique $L_2$ solution to the Bansal and
Yaron (2004) and Schorfheide, Song and Yaron (2018) models.
You Only Search Once: A Fast Automation Framework for Single-Stage DNN/Accelerator Co-design
Authors: Weiwei Chen, Ying Wang, Shuang Yang, Chen Liu, Lei Zhang
Date: 2020-05-14 00:00:00

DNN/Accelerator co-design has shown great potential in improving QoR and performance. Typical approaches separate the design flow into two-stage: (1) designing an application-specific DNN model with high accuracy; (2) building an accelerator considering the DNN specific characteristics. However, it may fail in promising the highest composite score which combines the goals of accuracy and other hardware-related constraints (e.g., latency, energy efficiency) when building a specific neural-network-based system. In this work, we present a single-stage automated framework, YOSO, aiming to generate the optimal solution of software-and-hardware that flexibly balances between the goal of accuracy, power, and QoS. Compared with the two-stage method on the baseline systolic array accelerator and Cifar10 dataset, we achieve 1.42x~2.29x energy or 1.79x~3.07x latency reduction at the same level of precision, for different user-specified energy and latency optimization constraints, respectively.
The $α$-$η$-$\mathcal{F}$ and $α$-$κ$-$\mathcal{F}$ Composite Fading Distributions
Authors: Osamah. S. Badarneh
Date: 2020-05-14 00:00:00

In this paper, we present the $\alpha$-$\eta$-$\mathcal{F}$ and $\alpha$-$\kappa$-$\mathcal{F}$ composite fading distributions. The two distributions generalize the two well-known composite fading distributions, namely the $\eta$-$\mu$/inverse gamma and the $\kappa$-$\mu$/inverse gamma distributions. For both distributions, we derive new exact closed-form expressions for the probability density function (PDF) and cumulative density function (CDF) of the instantaneous signal-to-noise ratio (SNR). Additionally, the outage probabilities (OPs) of a wireless communication system operating over the new composite fading channels are obtained. Moreover, we derive asymptotic OPs, in the high SNR regime, to gain more insight into the influence of fading parameters. Some numerical results on the envelope PDFs and OPs are provided and compared with Monte-Carlo simulation results to validate the analyses.
Real-time and high-throughput Raman signal extraction and processing in CARS hyperspectral imaging
Authors: Charles H. Camp Jr., John S. Bender, Young Jong Lee
Date: 2020-05-12 00:00:00
Tasks: Denoising

We present a new collection of processing techniques, collectively "factorized Kramers--Kronig and error correction" (fKK-EC), for (a) Raman signal extraction, (b) denoising, and (c) phase- and scale-error correction in coherent anti-Stokes Raman scattering (CARS) hyperspectral imaging and spectroscopy. These new methods are orders-of-magnitude faster than conventional methods and are capable of real-time performance, owing to the unique core concept: performing all processing on a small basis vector set and using matrix/vector multiplication afterwards for direct and fast transformation of the entire dataset. Experimentally, we demonstrate that a 703026 spectra image of chicken cartilage can be processed in 70 s (approximately 0.1 ms / spectrum), which is approximately 70 times faster than with the conventional workflow (approximately 7.0 ms / spectrum). Additionally, we discuss how this method may be used for machine learning (ML) by re-using the transformed basis vector sets with new data. Using this ML paradigm, the same tissue image was processed (post-training) in approximately 33 s, which is a speed-up of approximately 150 times when compared with the conventional workflow.
Introducing Coherent MIMO Sensing, a fading-resilient, polarization-independent approach to phase-OTDR
Authors: Sterenn Guerrier, Christian Dorize, Elie Awwad, Jérémie Renaudier
Date: 2020-05-12 00:00:00
Tasks: Diversity

Nowadays, long distance optical fibre transmission systems use polarization diversity multiplexed signals to enhance transmission performance. Distributed acoustic sensors (DAS) use the same propagation medium ie. single mode optical fibre, and aims at comparable targets such as covering the highest distance with the best signal quality. In the case of sensors, a noiseless transmission enables to monitor a large quantity of mechanical events along the fibre. This paper aims at extending the perspectives of DAS systems with regard to technology breakthroughs introduced in long haul transmission systems over the last decade. We recently developed a sensor interrogation method based on coherent phase-sensitive optical time domain reflectometry ($\phi$-OTDR), with dual polarization multiplexing at the transmitter and polarization diversity at the receiver. We name this technique Coherent-MIMO sensing. A study is performed from a dual-polarization numerical model to compare several sensor interrogation techniques, including Coherent-MIMO. We demonstrate that dual-polarization probing of a fibre sensor makes it insensitive to polarization effects, decreases the risks of false alarms and thus strongly enhances its sensitivity. The simulations results are validated with an experiment, and finally quantitative data are given on the performance increase enabled by Coherent-MIMO sensing.