pwc-archive/papers-with-abstracts huggingface dataset:
Criticality in Reservoir Computer of Coupled Phase Oscillators
Authors: Liang Wang, Huawei Fan, Jinghua Xiao, Yueheng Lan, Xingang Wang
Date: 2021-07-23 00:00:00
Tasks: BIG-bench Machine Learning

Accumulating evidences show that the cerebral cortex is operating near a critical state featured by power-law size distribution of neural avalanche activities, yet evidence of this critical state in artificial neural networks mimicking the cerebral cortex is lacking. Here we design an artificial neural network of coupled phase oscillators and, by the technique of reservoir computing in machine learning, train it for predicting chaos. It is found that when the machine is properly trained, oscillators in the reservoir are synchronized into clusters whose sizes follow a power-law distribution. This feature, however, is absent when the machine is poorly trained. Additionally, it is found that despite the synchronization degree of the original network, once properly trained, the reservoir network is always developed to the same critical state, exemplifying the "attractor" nature of this state in machine learning. The generality of the results is verified in different reservoir models and by different target systems, and it is found that the scaling exponent of the distribution is independent on the reservoir details and the bifurcation parameter of the target system, but is modified when the dynamics of the target system is changed to a different type. The findings shed lights on the nature of machine learning, and are helpful to the design of high-performance machine in physical systems.
Backward CUSUM for Testing and Monitoring Structural Change with an Application to COVID-19 Pandemic Data
Authors: Sven Otto, Jörg Breitung
Date: 2020-03-05 00:00:00

It is well known that the conventional cumulative sum (CUSUM) test suffers from low power and large detection delay. In order to improve the power of the test, we propose two alternative statistics. The backward CUSUM detector considers the recursive residuals in reverse chronological order, whereas the stacked backward CUSUM detector sequentially cumulates a triangular array of backwardly cumulated residuals. A multivariate invariance principle for partial sums of recursive residuals is given, and the limiting distributions of the test statistics are derived under local alternatives. In the retrospective context, the local power of the tests is shown to be substantially higher than that of the conventional CUSUM test if a break occurs in the middle or at the end of the sample. When applied to monitoring schemes, the detection delay of the stacked backward CUSUM is found to be much shorter than that of the conventional monitoring CUSUM procedure. Furthermore, we propose an estimator of the break date based on the backward CUSUM detector and show that in monitoring exercises this estimator tends to outperform the usual maximum likelihood estimator. Finally, an application of the methodology to COVID-19 data is presented.
Dimensionality Reduction and State Space Systems: Forecasting the US Treasury Yields Using Frequentist and Bayesian VARs
Authors: Sudiksha Joshi
Date: 2021-08-14 00:00:00
Tasks: Dimensionality Reduction, Variable Selection

Using a state-space system, I forecasted the US Treasury yields by employing frequentist and Bayesian methods after first decomposing the yields of varying maturities into its unobserved term structure factors. Then, I exploited the structure of the state-space model to forecast the Treasury yields and compared the forecast performance of each model using mean squared forecast error. Among the frequentist methods, I applied the two-step Diebold-Li, two-step principal components, and one-step Kalman filter approaches. Likewise, I imposed the five different priors in Bayesian VARs: Diffuse, Minnesota, natural conjugate, the independent normal inverse: Wishart, and the stochastic search variable selection priors. After forecasting the Treasury yields for 9 different forecast horizons, I found that the BVAR with Minnesota prior generally minimizes the loss function. I augmented the above BVARs by including macroeconomic variables and constructed impulse response functions with a recursive ordering identification scheme. Finally, I fitted a sign-restricted BVAR with dummy observations.
Evidence Aggregation for Treatment Choice
Authors: Takuya Ishihara, Toru Kitagawa
Date: 2021-08-14 00:00:00
Tasks: Epidemiology

Consider a planner who has limited knowledge of the policy's causal impact on a certain local population of interest due to a lack of data, but does have access to the publicized intervention studies performed for similar policies on different populations. How should the planner make use of and aggregate this existing evidence to make her policy decision? Following Manski (2020; Towards Credible Patient-Centered Meta-Analysis, \textit{Epidemiology}), we formulate the planner's problem as a statistical decision problem with a social welfare objective, and solve for an optimal aggregation rule under the minimax-regret criterion. We investigate the analytical properties, computational feasibility, and welfare regret performance of this rule. We apply the minimax regret decision rule to two settings: whether to enact an active labor market policy based on 14 randomized control trial studies; and whether to approve a drug (Remdesivir) for COVID-19 treatment using a meta-database of clinical trials.
Causal Impact Of European Union Emission Trading Scheme On Firm Behaviour And Economic Performance: A Study Of German Manufacturing Firms
Authors: Nitish Gupta, Jay Shah, Satwik Gupta, Ruchir Kaul
Date: 2021-08-16 00:00:00

In this paper, we estimate the causal impact (i.e. Average Treatment Effect, ATT) of the EU ETS on GHG emissions and firm competitiveness (primarily measured by employment, turnover, and exports levels) by combining a difference-in-differences approach with semi-parametric matching techniques and estimators an to investigate the effect of the EU ETS on the economic performance of these German manufacturing firms using a Stochastic Production Frontier model.
Dynamic Monopoly Pricing With Multiple Varieties: Trading Up
Authors: Stefan Buehler, Nicolas Eschenbaum
Date: 2021-08-16 00:00:00

This paper studies dynamic monopoly pricing for a class of settings that includes multiple durable, multiple rental, or a mix of varieties. We show that the driving force behind pricing dynamics is the seller's incentive to switch consumers - buyers and non-buyers - to higher-valued consumption options by lowering prices ("trading up"). If consumers cannot be traded up from the static optimal allocation, pricing dynamics do not emerge in equilibrium. If consumers can be traded up, pricing dynamics arise until all trading-up opportunities are exhausted. We study the conditions under which pricing dynamics end in finite time and characterize the final prices at which dynamics end.
Study Of German Manufacturing Firms: Causal Impact Of European Union Emission Trading Scheme On Firm Behaviour And Economic Performance
Authors: Nitish Gupta, Ruchir Kaul, Satwik Gupta, Jay Shah
Date: 2021-08-16 00:00:00

The results based on the nonparametric nearest neighbor matching suggest a statistically significant positive effect of the EU ETS on the economic performance of the regulated firms during Phase I of the EU ETS. A year-by-year analysis shows that the effect was only significant during the first year of Phase I. The EU ETS, therefore, had a particularly strong effect when it was introduced. It is important to note that the EU ETS does not homogeneously affect firms in the manufacturing sector. We found a significant positive impact of EU ETS on the economic performance of regulated firms in the paper industry.
Logistics and trade flows in selected ECOWAS Countries: An empirical verification
Authors: Eriamiatoe Efosa Festus
Date: 2021-08-14 00:00:00
Tasks: Management

This study investigates the role of logistics and its six components on trade flows in selected Economic Community of West Africa States (ECOWAS) countries. The impact of other macro-economic variables on trade flows was also investigated. Ten countries were selected in eight years period. We decomposed trade flows into import and export trade. The World Bank Logistics performance index was used as a measure of logistics performance. The LPI has six components, and the impact of these components on trade flows were also examined. The fixed-effect model was used to explain the cross-country result that was obtained. The results showed that logistics has no significant impact on both Import and export, thus logistics play no role on trade flows among the selected ECOWAS countries. The components of logistics except Timeliness of shipments in reaching the final destination ( CRC ),have no impact on trade flows. Income was found to be positively related to imports. Exchange rate, consumption and money supply, reserve and tariff have no significant impact on imports. Relative import price has an inverse and significant relationship with imports. GDP has a positive and significant impact on export trade. The study also found FDI, savings, exchange rate and labour to have insignificant impact on exports. Finally, we found that logistics is not a driver of trade among the selected ECOWAS countries. The study recommended the introduction of the single window system and improvement in border management in order to reduce the cost associated with Logistics and thereby enhance trade.
Stable and extremely unequal
Authors: Alfred Galichon, Octavia Ghelfi, Marc Henry
Date: 2021-08-14 00:00:00

We highlight the tension between stability and equality in non transferable utility matching. We consider many to one matchings and refer to the two sides of the market as students and schools. The latter have aligned preferences, which in this context means that a school's utility is the sum of its students' utilities. We show that the unique stable allocation displays extreme inequality between matched pairs.
UWB Radar for through Foliage Imaging using Cyclic Prefix-based OFDM
Authors: G. Ba khadher, A. Zidouri, Ali Hussein Muqaibel
Date: 2021-08-16 00:00:00

This paper proposes the use of sufficient cyclic prefix (CP) OFDM synthetic aperture radar (SAR) for foliage penetration (FOPEN). The foliage introduces phase and amplitude fluctuation which cause the sidelobes to increase and affects the final image of the obscured targets. The wideband CP-based OFDM SAR inherently eliminates the sidelobes that arise from the interference between targets on the same range line. The integrated sidelobe level ratio (ISLR) of the CP-based OFDM signal along the range direction is lower than that of the random noise signal by 2 dB for foliage penetration application, while the peak sidelobe level ratio (PSLR) are almost the same of both of the two signals.
4-D Epanechnikov Mixture Regression in Light Field Image Compression
Authors: Boning Liu, Yan Zhao, Xiaomeng Jiang, Shigang Wang, Jian Wei
Date: 2021-08-14 00:00:00
Tasks: Image Compression, Model Selection, regression

With the emergence of light field imaging in recent years, the compression of its elementary image array (EIA) has become a significant problem. Our coding framework includes modeling and reconstruction. For the modeling, the covariance-matrix form of the 4-D Epanechnikov kernel (4-D EK) and its correlated statistics were deduced to obtain the 4-D Epanechnikov mixture models (4-D EMMs). A 4-D Epanechnikov mixture regression (4-D EMR) was proposed based on this 4-D EK, and a 4-D adaptive model selection (4-D AMLS) algorithm was designed to realize the optimal modeling for a pseudo video sequence (PVS) of the extracted key-EIA. A linear function based reconstruction (LFBR) was proposed based on the correlation between adjacent elementary images (EIs). The decoded images realized a clear outline reconstruction and superior coding efficiency compared to high-efficiency video coding (HEVC) and JPEG 2000 below approximately 0.05 bpp. This work realized an unprecedented theoretical application by (1) proposing the 4-D Epanechnikov kernel theory, (2) exploiting the 4-D Epanechnikov mixture regression and its application in the modeling of the pseudo video sequence of light field images, (3) using 4-D adaptive model selection for the optimal number of models, and (4) employing a linear function-based reconstruction according to the content similarity.
MetaSketch: Wireless Semantic Segmentation by Metamaterial Surfaces
Authors: Jingzhi Hu, Hongliang Zhang, Kaigui Bian, Zhu Han, H. Vincent Poor, Lingyang Song
Date: 2021-08-14 00:00:00
Tasks: Compressive Sensing, Object Recognition, Semantic Segmentation

Semantic segmentation is a process of partitioning an image into multiple segments for recognizing humans and objects, which can be widely applied in scenarios such as healthcare and safety monitoring. To avoid privacy violation, using RF signals instead of an image for human and object recognition has gained increasing attention. However, human and object recognition by using RF signals is usually a passive signal collection and analysis process without changing the radio environment, and the recognition accuracy is restricted significantly by unwanted multi-path fading, and/or the limited number of independent channels between RF transceivers in uncontrollable radio environments. This paper introduces MetaSketch, a novel RF-sensing system that performs semantic recognition and segmentation for humans and objects by making the radio environment reconfigurable. A metamaterial surface is incorporated into MetaSketch and diversifies the information carried by RF signals. Using compressive sensing techniques, MetaSketch reconstructs a point cloud consisting of the reflection coefficients of humans and objects at different spatial points, and recognizes the semantic meaning of the points by using symmetric multilayer perceptron groups. Our evaluation results show that MetaSketch is capable of generating favorable radio environments and extracting exact point clouds, and labeling the semantic meaning of the points with an average error rate of less than 1% in an indoor space.
Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond
Authors: Fan Liu, Yuanhao Cui, Christos Masouros, Jie Xu, Tony Xiao Han, Yonina C. Eldar, Stefano Buzzi
Date: 2021-08-16 00:00:00
Tasks: ISAC

As the standardization of 5G is being solidified, researchers are speculating what 6G will be. Integrating sensing functionality is emerging as a key feature of the 6G Radio Access Network (RAN), allowing to exploit the dense cell infrastructure of 5G for constructing a perceptive network. In this paper, we provide a comprehensive overview on the background, range of key applications and state-of-the-art approaches of Integrated Sensing and Communications (ISAC). We commence by discussing the interplay between sensing and communications (S&C) from a historical point of view, and then consider multiple facets of ISAC and its performance gains. By introducing both ongoing and potential use cases, we shed light on industrial progress and standardization activities related to ISAC. We analyze a number of performance tradeoffs between S&C, spanning from information theoretical limits, tradeoffs in physical layer performance, to the tradeoff in cross-layer designs. Next, we discuss signal processing aspects of ISAC, namely ISAC waveform design and receive signal processing. As a step further, we provide our vision on the deeper integration between S&C within the framework of perceptive networks, where the two functionalities are expected to mutually assist each other, i.e., communication-assisted sensing and sensing-assisted communications. Finally, we summarize the paper by identifying the potential integration between ISAC and other emerging communication technologies, and their positive impact on the future of wireless networks.
Cost-effective vibration analysis through data-backed pipeline optimisation
Authors: Artur Sokolovsky, David Hare, Jorn Mehnen
Date: 2021-08-16 00:00:00

Vibration analysis is an active area of research, aimed, among other targets, at an accurate classification of machinery failure modes. This often leads to complex and convoluted signal processing pipeline designs, which are computationally demanding and cannot be deployed in the Edge devices. In the current work, we address this issue by proposing a data-driven methodology that allows optimising and justifying the complexity of the signal processing pipelines. Additionally, aiming to make IoT vibration analysis systems more cost- and computationally effective, on the example of MAFAULDA vibration dataset, we assess the changes in the failure classification performance at low sampling rates as well as short observation time windows. We find out that a decrease of the sampling rate from 50 kHz to 1 kHz leads to a statistically significant classification performance drop. A statistically significant decrease is also observed for the 0.1 second time windows compared to the 5-second ones. However, the effect sizes are small to medium, suggesting that in certain settings lower sampling rates and shorter observation windows can be used. The proposed optimisation approach, as well as statistically supported findings of the study, allow a more efficient design of IoT vibration analysis systems, both in terms of complexity and costs, bringing us one step closer to the IoT/Edge-based vibration analysis.
Real-time Transmission of Geometrically-shaped Signals using a Software-defined GPU-based Optical Receiver
Authors: Sjoerd van der Heide, Ruben S. Luis, Sebastiaan Goossens, Benjamin J. Puttnam, Georg Rademacher, Ton Koonen, Satoshi Shinada, Yoshinari Awaji, Alex Alvarado, Hideaki Furukawa, Chigo Okonkwo
Date: 2021-08-16 00:00:00
Tasks: GPU

A software-defined optical receiver is implemented on an off-the-shelf commercial graphics processing unit (GPU). The receiver provides real-time signal processing functionality to process 1 GBaud minimum phase (MP) 4-, 8-, 16-, 32-, 64-, 128-ary quadrature amplitude modulation (QAM) as well as geometrically shaped (GS) 8- and 128-QAM signals using Kramers-Kronig (KK) coherent detection. Experimental validation of this receiver over a 91~km field-deployed optical fiber link between two Tokyo locations is shown with detailed optical signal-to-noise ratio (OSNR) investigations. A net data rate of 5 Gbps using 64-QAM is demonstrated.
Real-time 10,000 km Straight-line Transmission using a Software-defined GPU-Based Receiver
Authors: Sjoerd van der Heide, Ruben S. Luis, Benjamin J. Puttnam, Georg Rademacher, Ton Koonen, Satoshi Shinada, Yohinari Awaji, Hideaki Furukawa, Chigo Okonkwo
Date: 2021-08-16 00:00:00
Tasks: GPU

Real-time 10,000 km transmission over a straight-line link is achieved using a software-defined multi-modulation format receiver implemented on a commercial off-the-shelf general-purpose graphics processing unit (GPU). Minimum phase 1 GBaud 4-ary quadrature amplitude modulation (QAM) signals are transmitted over 10,000 km and successfully received after detection with a Kramers-Kronig (KK) coherent receiver. 8-, 16-, 32-, and 64-QAM are successfully transmitted over 7600, 5600, 3600, and 1600 km, respectively.
Rate-Splitting Multiple Access for Downlink MIMO: A Generalized Power Iteration Approach
Authors: Jeonghun Park, Jinseok Choi, Namyoon Lee, Wonjae Shin, H. Vincent Poor
Date: 2021-08-16 00:00:00

Rate-splitting multiple access (RSMA) is a general multiple access scheme for downlink multi-antenna systems embracing both classical spatial division multiple access and more recent non-orthogonal multiple access. Finding a linear precoding strategy that maximizes the sum spectral efficiency of RSMA is a challenging yet significant problem. In this paper, we put forth a novel precoder design framework that jointly finds the linear precoders for the common and private messages for RSMA. Our approach is first to approximate the non-smooth minimum function part in the sum spectral efficiency of RSMA using a LogSumExp technique. Then, we reformulate the sum spectral efficiency maximization problem as a form of the log-sum of Rayleigh quotients to convert it into a tractable form. By interpreting the first-order optimality condition of the reformulated problem as an eigenvector-dependent nonlinear eigenvalue problem, we reveal that the leading eigenvector of the derived optimality condition is a local optimal solution. To find the leading eigenvector, we propose an algorithm inspired by a power iteration. Simulation results show that the proposed RSMA transmission strategy provides significant improvement in the sum spectral efficiency compared to the state-of-the-art RSMA transmission methods.
Channel State Acquisition in Uplink NOMA for Cellular-Connected UAV: Exploitation of Doppler and Modulation Diversities
Authors: Donatella Darsena, Ivan Iudice, Francesco Verde
Date: 2021-08-15 00:00:00
Tasks: Diversity

Integration of unmanned aerial vehicles (UAVs) for surveillance or monitoring applications into fifth generation (5G) New Radio (NR) cellular networks is an intriguing problem that has recently tackled a lot of interest in both academia and industry. For an efficient spectrum usage, we consider a recently-proposed sky-ground nonorthogonal multiple access (NOMA) scheme, where a cellular-connected UAV acting as aerial user (AU) and a static terrestrial user (TU) are paired to simultaneously transmit their uplink signals to a base station (BS) in the same time-frequency resource blocks. In such a case, due to the highly dynamic nature of the UAV, the signal transmitted by the AU experiences both time dispersion due to multipath propagation effects and frequency dispersion caused by Doppler shifts. On the other hand, for a static ground network, frequency dispersion of the signal transmitted by the TU is negligible and only multipath effects have to be taken into account. To decode the superposed signals at the BS through successive interference cancellation, accurate estimates of both the AU and TU channels are needed. In this paper, we propose channel estimation procedures that suitably exploit the different circular/noncircular modulation formats (modulation diversity) and the different almost-cyclostationarity features (Doppler diversity) of the AU and TU by means of widely-linear time-varying processing. Our estimation approach is semi-blind since Doppler shifts and time delays of the AU are estimated based on the received data only, whereas the remaining relevant parameters of the AU and TU channels are acquired relying also on the available training symbols, which are transmitted by the AU and TU in a nonorthogonal manner.
Closed-Form Hybrid Beamforming Solution for Spectral Efficiency Upper Bound Maximization in mmWave MIMO-OFDM Systems
Authors: Mengyuan Ma, Nhan Thanh Nguyen, Markku Juntti
Date: 2021-08-15 00:00:00
Tasks: Form

Hybrid beamforming is considered a key enabler to realize millimeter wave (mmWave) multiple-input multiple-output (MIMO) communications due to its capability of considerably reducing the number of costly and power-hungry radio frequency chains in the transceiver. However, in mmWave MIMO orthogonal frequency-division multiplexing (MIMO-OFDM) systems, hybrid beamforming design is challenging because the analog precoder and combiner are required to be shared across the whole employed bandwidth. In this paper, we propose closed-form solutions to the problem of designing the analog precoder/combiner in a mmWave MIMO-OFDM system by maximizing the upper bound of the spectral efficiency. The closed-form solutions facilitate the design of analog beamformers while guaranteeing state-of-art performance. Numerical results show that the proposed algorithm attains a slightly improved performance with much lower computational complexity compared to the considered benchmarks.