pwc-archive/papers-with-abstracts huggingface dataset:
Dynamic graph and polynomial chaos based models for contact tracing data analysis and optimal testing prescription
Authors: Shashanka Ubaru, Lior Horesh, Guy Cohen
Date: 2020-09-10 00:00:00
Tasks: Uncertainty Quantification

In this study, we address three important challenges related to disease transmissions such as the COVID-19 pandemic, namely, (a) providing an early warning to likely exposed individuals, (b) identifying individuals who are asymptomatic, and (c) prescription of optimal testing when testing capacity is limited. First, we present a dynamic-graph based SEIR epidemiological model in order to describe the dynamics of the disease propagation. Our model considers a dynamic network that accounts for the interactions between individuals over time, such as the ones obtained by manual or automated contact tracing, and uses a diffusion-reaction mechanism to describe the state dynamics. This dynamic graph model helps identify likely exposed/infected individuals to whom we can provide early warnings, even before they display any symptoms and/or are asymptomatic. Moreover, when the testing capacity is limited compared to the population size, reliable estimation of individual's health state and disease transmissibility using epidemiological models is extremely challenging. Thus, estimation of state uncertainty is paramount for both eminent risk assessment, as well as for closing the tracing-testing loop by optimal testing prescription. Therefore, we propose the use of arbitrary Polynomial Chaos Expansion, a popular technique used for uncertainty quantification, to represent the states, and quantify the uncertainties in the dynamic model. This design enables us to assign uncertainty of the state of each individual, and consequently optimize the testing as to reduce the overall uncertainty given a constrained testing budget. These tools can also be used to optimize vaccine distribution to curb the disease spread when limited vaccines are available. We present a few simulation results that illustrate the performance of the proposed framework, and estimate the impact of incomplete contact tracing data.
Deep Replication of a Runoff Portfolio
Authors: Thomas Krabichler, Josef Teichmann
Date: 2020-09-10 00:00:00
Tasks: Decision Making, Management

To the best of our knowledge, the application of deep learning in the field of quantitative risk management is still a relatively recent phenomenon. This article presents the key notions of Deep Asset Liability Management (Deep~ALM) for a technological transformation in the management of assets and liabilities along a whole term structure. The approach has a profound impact on a wide range of applications such as optimal decision making for treasurers, optimal procurement of commodities or the optimisation of hydroelectric power plants. As a by-product, intriguing aspects of goal-based investing or Asset Liability Management (ALM) in abstract terms concerning urgent challenges of our society are expected alongside. We illustrate the potential of the approach in a stylised case.
Tuning of Constant in gain Lead in phase (CgLp) Reset Controller using higher-order sinusoidal input describing function (HOSIDF)
Date: 2020-09-10 00:00:00

Due to development of technology, linear controllers cannot satisfy
requirements of high-tech industry. One solution is using nonlinear controllers
such as reset elements to overcome this big barrier. In literature, the
Constant in gain Lead in phase (CgLp) compensator is a novel reset element
developed to overcome the inherent linear controller limitations. However, a
tuning guideline for these controllers has not been proposed so far. In this
paper, a recently developed method named higher-order sinusoidal input
describing function (HOSIDF), which gives deeper insight into the frequency
behaviour of non-linear controllers compared to sinusoidal input describing
function (DF), is used to obtain a straight-forward tuning method for CgLp
compensators. In this respect, comparative analyses on tracking performance of
these compensators are carried out. Based on these analyses, tuning guidelines
for CgLp compensators are developed and validated on a high-tech precision
positioning stage. The results show the effectiveness of the developed tuning
method.
Adaptive Distributed Transceiver Synchronization Over a 90 Meter Microwave Wireless Link
Authors: Serge R. Mghabghab, Anton Schlegel, Jeffrey A. Nanzer
Date: 2020-09-10 00:00:00

We present an adaptive approach for synchronizing both the phase and frequency of radio-frequency transceivers over long-range wireless links to support distributed antenna array applications. To enable distributed beamforming between separate wireless nodes, the oscillators in the transceivers must operate at the same frequency, and their phases must be appropriately aligned to support phase-coherent beamsteering. Based on a spectrally-sparse waveform, a self-mixing circuit, and an adaptive control loop, we present a system capable of synchronizing the RF oscillators in separate transceivers over distances of nearly 100 m. The approach is based on a spectrally-sparse waveform for joint inter-node ranging and frequency transfer. A frequency reference is modulated onto one signal of a two-tone waveform transmitted by the primary node which is demodulated and used to lock the oscillator of the secondary node. The secondary node retransmits the two-tone signal which the primary node uses for a high-accuracy range measurement. From this range, the phase of the two transceivers can be aligned to support beamforming. We furthermore implemented an adaptive phase control approach to support high-accuracy phase coordination in changing environmental conditions. We demonstrate continuous high accuracy links over a 90 m distance in an outdoor environment for durations up to seven days, demonstrating sufficient phase coordination in changing weather conditions to support distributed beamforming at frequencies up to 3 GHz.
Inference for high-dimensional exchangeable arrays
Authors: Harold D. Chiang, Kengo Kato, Yuya Sasaki
Date: 2020-09-10 00:00:00
Tasks: Density Estimation, regression, Vocal Bursts Intensity Prediction

We consider inference for high-dimensional separately and jointly exchangeable arrays where the dimensions may be much larger than the sample sizes. For both exchangeable arrays, we first derive high-dimensional central limit theorems over the rectangles and subsequently develop novel multiplier bootstraps with theoretical guarantees. These theoretical results rely on new technical tools such as Hoeffding-type decomposition and maximal inequalities for the degenerate components in the Hoeffiding-type decomposition for the exchangeable arrays. We exhibit applications of our methods to uniform confidence bands for density estimation under joint exchangeability and penalty choice for $\ell_1$-penalized regression under separate exchangeability. Extensive simulations demonstrate precise uniform coverage rates. We illustrate by constructing uniform confidence bands for international trade network densities.
STEP-GAN: A Step-by-Step Training for Multi Generator GANs with application to Cyber Security in Power Systems
Authors: Mohammad Adiban, Arash Safari, Giampiero Salvi
Date: 2020-09-11 00:00:00
Tasks: One-Class Classification

In this study, we introduce a novel unsupervised countermeasure for smart grid power systems, based on generative adversarial networks (GANs). Given the pivotal role of smart grid systems (SGSs) in urban life, their security is of particular importance. In recent years, however, advances in the field of machine learning, have raised concerns about cyber attacks on these systems. Power systems, among the most important components of urban infrastructure, have, for example, been widely attacked by adversaries. Attackers disrupt power systems using false data injection attacks (FDIA), resulting in a breach of availability, integrity, or confidential principles of the system. Our model simulates possible attacks on power systems using multiple generators in a step-by-step interaction with a discriminator in the training phase. As a consequence, our system is robust to unseen attacks. Moreover, the proposed model considerably reduces the well-known mode collapse problem of GAN-based models. Our method is general and it can be potentially employed in a wide range of one of one-class classification tasks. The proposed model has low computational complexity and outperforms baseline systems about 14% and 41% in terms of accuracy on the highly imbalanced publicly available industrial control system (ICS) cyber attack power system dataset.
Scenario Forecast of Cross-border Electric Interconnection towards Renewables in South America
Authors: Wenhao Wang, Jing Meng, Duan Chen, Wei Cong
Date: 2020-09-11 00:00:00

Cross-border Electric Interconnection towards renewables is a promising solution for electric sector under the UN 2030 sustainable development goals which is widely promoted in emerging economies. This paper comprehensively investigates state of art in renewable resources and cross-border electric interconnection in South America. Based on the raw data collected from typical countries, a long-term scenario forecast methodology is applied to estimate key indicators of electric sector in target years, comparing the prospects of active promoting cross-border Interconnections Towards Renewables (ITR) scenario with Business as Usual (BAU) scenario in South America region. Key indicators including peak load, installed capacity, investment, and generation cost are forecasted and comparative analyzed by year 2035 and 2050. The comparative data analysis shows that by promoting cross-border interconnection towards renewables in South America, renewable resources can be highly utilized for energy supply, energy matrix can be optimized balanced, economics can be obviously driven and generation cost can be greatly reduced.
A 6.3-Nanowatt-per-Channel 96-Channel Neural Spike Processor for a Movement-Intention-Decoding Brain-Computer-Interface Implant
Authors: Zhewei Jiang, Jiangyi Li, Pavan K. Chundi, Sung Justin Kim, Minhao Yang, Joonseong Kang, Seungchul Jung, Sang Joon Kim, Mingoo Seok
Date: 2020-09-11 00:00:00
Tasks: Brain Computer Interface, State Estimation

This paper presents microwatt end-to-end neural signal processing hardware for deployment-stage real-time upper-limb movement intent decoding. This module features intercellular spike detection, sorting, and decoding operations for a 96-channel prosthetic implant. We design the algorithms for those operations to achieve minimal computation complexity while matching or advancing the accuracy of state-of-art Brain-Computer-Interface sorting and movement decoding. Based on those algorithms, we devise the architect of the neural signal processing hardware with the focus on hardware reuse and event-driven operation. The design achieves among the highest levels of integration, reducing wireless data rate by more than four orders of magnitude. The chip prototype in a 180-nm high-VTH, achieving the lowest power dissipation of 0.61 uW for 96 channels, 21X lower than the prior art at a comparable/better accuracy even with integration of kinematic state estimation computation.
Deep Transfer Learning for Signal Detection in Ambient Backscatter Communications
Authors: Chang Liu, Zhiqiang Wei, Derrick Wing Kwan Ng, Jinhong Yuan, Ying-Chang Liang
Date: 2020-09-11 00:00:00
Tasks: TAG, Transfer Learning

Tag signal detection is one of the key tasks in ambient backscatter communication (AmBC) systems. However, obtaining perfect channel state information (CSI) is challenging and costly, which makes AmBC systems suffer from a high bit error rate (BER). To eliminate the requirement of channel estimation and to improve the system performance, in this paper, we adopt a deep transfer learning (DTL) approach to implicitly extract the features of channel and directly recover tag symbols. To this end, we develop a DTL detection framework which consists of offline learning, transfer learning, and online detection. Specifically, a DTL-based likelihood ratio test (DTL-LRT) is derived based on the minimum error probability (MEP) criterion. As a realization of the developed framework, we then apply convolutional neural networks (CNN) to intelligently explore the features of the sample covariance matrix, which facilitates the design of a CNN-based algorithm for tag signal detection. Exploiting the powerful capability of CNN in extracting features of data in the matrix formation, the proposed method is able to further improve the system performance. In addition, an asymptotic explicit expression is also derived to characterize the properties of the proposed CNN-based method when the number of samples is sufficiently large. Finally, extensive simulation results demonstrate that the BER performance of the proposed method is comparable to that of the optimal detection method with perfect CSI.
Reforms meet fairness concerns in school and college admissions
Authors: Somouaoga Bonkoungou, Alexander Nesterov
Date: 2020-09-11 00:00:00
Tasks: Blocking, Fairness

Recently, many matching systems around the world have been reformed. These reforms responded to objections that the matching mechanisms in use were unfair and manipulable. Surprisingly, the mechanisms remained unfair even after the reforms: the new mechanisms may induce an outcome with a blocking student who desires and deserves a school which she did not receive. However, as we show in this paper, the reforms introduced matching mechanisms which are more fair compared to the counterfactuals. First, most of the reforms introduced mechanisms that are more fair by stability: whenever the old mechanism does not have a blocking student, the new mechanism does not have a blocking student either. Second, some reforms introduced mechanisms that are more fair by counting: the old mechanism always has at least as many blocking students as the new mechanism. These findings give a novel rationale to the reforms and complement the recent literature showing that the same reforms have introduced less manipulable matching mechanisms. We further show that the fairness and manipulability of the mechanisms are strongly logically related.
Strategy-proof allocation with outside option
Date: 2020-09-11 00:00:00

Strategy-proof mechanisms are widely used in market design. In an abstract allocation framework where outside options are available to agents, we obtain two results for strategy-proof mechanisms. They provide a unified foundation for several existing results in distinct models and imply new results in some models. The first result proves that, for individually rational and strategy-proof mechanisms, pinning down every agent's probability of choosing his outside option is equivalent to pinning down a mechanism. The second result provides a sufficient condition for two strategy-proof mechanisms to be equivalent when the number of possible allocations is finite.
Energy-Efficient Design of IRS-NOMA Networks
Authors: Fang Fang, Yanqing Xu, Quoc-Viet Pham, Zhiguo Ding
Date: 2020-09-11 00:00:00

Combining intelligent reflecting surface (IRS) and non-orthogonal multiple access (NOMA) is an effective solution to enhance communication coverage and energy efficiency. In this paper, we focus on an IRS-assisted NOMA network and propose an energy-efficient algorithm to yield a good tradeoff between the sum-rate maximization and total power consumption minimization. We aim to maximize the system energy efficiency by jointly optimizing the transmit beamforming at the BS and the reflecting beamforming at the IRS. Specifically, the transmit beamforming and the phases of the low-cost passive elements on the IRS are alternatively optimized until the convergence. Simulation results demonstrate that the proposed algorithm in IRS-NOMA can yield superior performance compared with the conventional OMA-IRS and NOMA with a random phase IRS.
Distributed Density Filtering for Large-Scale Systems Using Mean-Filed Models
Authors: Tongjia Zheng, Hai Lin
Date: 2020-09-10 00:00:00
Tasks: Density Estimation, Scheduling

This work studies distributed (probability) density estimation of large-scale systems. Such problems are motivated by many density-based distributed control tasks in which the real-time density of the swarm is used as feedback information, such as sensor deployment and city traffic scheduling. This work is built upon our previous work [1] which presented a (centralized) density filter to estimate the dynamic density of large-scale systems through a novel integration of mean-field models, kernel density estimation (KDE), and infinite-dimensional Kalman filters. In this work, we further study how to decentralize the density filter such that each agent can estimate the global density only based on its local observation and communication with neighbors. This is achieved by noting that the global observation constructed by KDE is an average of the local kernels. Hence, dynamic average consensus algorithms are used for each agent to track the global observation in a distributed way. We present a distributed density filter which requires very little information exchange, and study its stability and optimality using the notion of input-to-state stability. Simulation results suggest that the distributed filter is able to converge to the centralized filter and remain close to it.
L2-Constrained RemNet for Camera Model Identification and Image Manipulation Detection
Authors: Abdul Muntakim Rafi, Jonathan Wu, Md. Kamrul Hasan
Date: 2020-09-10 00:00:00
Tasks: General Classification, Image Forensics, Image Manipulation, Image Manipulation Detection

Source camera model identification (CMI) and image manipulation detection are of paramount importance in image forensics. In this paper, we propose an L2-constrained Remnant Convolutional Neural Network (L2-constrained RemNet) for performing these two crucial tasks. The proposed network architecture consists of a dynamic preprocessor block and a classification block. An L2 loss is applied to the output of the preprocessor block, and categorical crossentropy loss is calculated based on the output of the classification block. The whole network is trained in an end-to-end manner by minimizing the total loss, which is a combination of the L2 loss and the categorical crossentropy loss. Aided by the L2 loss, the data-adaptive preprocessor learns to suppress the unnecessary image contents and assists the classification block in extracting robust image forensics features. We train and test the network on the Dresden database and achieve an overall accuracy of 98.15%, where all the test images are from devices and scenes not used during training to replicate practical applications. The network also outperforms other state-of-the-art CNNs even when the images are manipulated. Furthermore, we attain an overall accuracy of 99.68% in image manipulation detection, which implies that it can be used as a general-purpose network for image forensic tasks.
Investigation of analog signal distortion introduced by a fiber phase sensitive amplifier
Authors: Debanuj Chatterjee, Yousra Bouasria, Weilin Xie, Tarek Labidi, Fabienne Goldfarb, Ihsan Fsaifes, Fabien Bretenaker
Date: 2020-09-10 00:00:00

We numerically simulate the distortion of an analog signal carried in a microwave photonics link containing a phase sensitive amplifier (PSA), focusing mainly on amplitude modulation format. The numerical model is validated by comparison with experimental measurements. By using the well known two-tone test, we compare the situations in which a standard intensity modulator is used with the one where a perfectly linear modulator would be employed. We also investigate the role of gain saturation on the nonlinearity of the PSA. Finally, we establish the conditions, in which the signal nonlinearity introduced by the PSA itself can be extremely small.
Non-linear fitting with joint spatial regularization in Arterial Spin Labeling
Authors: Oliver Maier, Stefan M Spann, Daniela Pinter, Thomas Gattringer, Nicole Hinteregger, Gerhard G. Thallinger, Christian Enzinger, Josef Pfeuffer, Kristian Bredies, Rudolf Stollberger
Date: 2020-09-10 00:00:00
Tasks: Bayesian Inference

Multi-Delay single-shot arterial spin labeling (ASL) imaging provides accurate cerebral blood flow (CBF) and, in addition, arterial transit time (ATT) maps but the inherent low SNR can be challenging. Especially standard fitting using non-linear least squares often fails in regions with poor SNR, resulting in noisy estimates of the quantitative maps. State-of-the-art fitting techniques improve the SNR by incorporating prior knowledge in the estimation process which typically leads to spatial blurring. To this end, we propose a new estimation method with a joint spatial total generalized variation regularization on CBF and ATT. This joint regularization approach utilizes shared spatial features across maps to enhance sharpness and simultaneously improves noise suppression in the final estimates. The proposed method is evaluated at three levels, first on synthetic phantom data including pathologies, followed by in vivo acquisitions of healthy volunteers, and finally on patient data following an ischemic stroke. The quantitative estimates are compared to two reference methods, non-linear least squares fitting and a state-of-the-art ASL quantification algorithm based on Bayesian inference. The proposed joint regularization approach outperforms the reference implementations, substantially increasing the SNR in CBF and ATT while maintaining sharpness and quantitative accuracy in the estimates.
On the estimation of spatial density from mobile network operator data
Authors: Fabio Ricciato, Angelo Coluccia
Date: 2020-09-10 00:00:00
Tasks: Density Estimation

We tackle the problem of estimating the spatial distribution of mobile phones from Mobile Network Operator (MNO) data, namely Call Detail Record (CDR) or signalling data. The process of transforming MNO data to a density map requires geolocating radio cells to determine their spatial footprint. Traditional geolocation solutions rely on Voronoi tessellations and approximate cell footprints by mutually disjoint regions. Recently, some pioneering work started to consider more elaborate geolocation methods with partially overlapping (non-disjoint) cell footprints coupled with a probabilistic model for phone-to-cell association. Estimating the spatial density in such a probabilistic setup is currently an open research problem and is the focus of the present work. We start by reviewing three different estimation methods proposed in literature and provide novel analytical insights that unveil some key aspects of their mutual relationships and properties. Furthermore, we develop a novel estimation approach for which a closed-form solution can be given. Numerical results based on semi-synthetic data are presented to assess the relative accuracy of each method. Our results indicate that the estimators based on overlapping cells have the potential to improve spatial accuracy over traditional approaches based on Voronoi tessellations.