pwc-archive/papers-with-abstracts huggingface dataset:
Shift in house price estimates during COVID-19 reveals effect of crisis on collective speculation
Authors: Alexander M. Petersen
Date: 2022-07-21 00:00:00
Tasks: counterfactual, Decision Making, Descriptive

We exploit a city-level panel comprised of individual house price estimates to estimate the impact of COVID-19 on both small and big real-estate markets in California USA. Descriptive analysis of spot house price estimates, including contemporaneous price uncertainty and 30-day price change for individual properties listed on the online real-estate platform Zillow.com, together facilitate quantifying both the excess valuation and valuation confidence attributable to this global socio-economic shock. Our quasi-experimental pre-/post-COVID-19 design spans several years around 2020 and leverages contemporaneous price estimates of rental properties - i.e., real estate entering the habitation market, just not for purchase (off-market) and hence free of speculation - as an appropriate counterfactual to properties listed for sale, which are subject to on-market speculation. Combining unit-level matching and multivariate difference-in-difference regression approaches, we obtain consistent estimates regarding the sign and magnitude of excess price growth observed after the pandemic onset. Specifically, our results indicate that properties listed for sale appreciated an additional 1% per month above what would be expected in the absence of the pandemic. This corresponds to an excess annual price growth of roughly 12.7 percentage points, which accounts for more than half of the actual annual price growth in 2021 observed across the studied regions. Simultaneously, uncertainty in price estimates decreased, signaling the irrational confidence characteristic of prior asset bubbles. We explore how these two trends are related to market size, local market supply and borrowing costs, which altogether lend support for the counterintuitive roles of uncertainty and interruptions in decision-making.
Modified Causal Forest
Authors: Michael Lechner, Jana Mareckova
Date: 2022-09-08 00:00:00

Uncovering the heterogeneity of causal effects of policies and business decisions at various levels of granularity provides substantial value to decision makers. This paper develops estimation and inference procedures for multiple treatment models in a selection-on-observed-variables framework by modifying the Causal Forest approach (Wager and Athey, 2018) in several dimensions. The new estimators have desirable theoretical, computational, and practical properties for various aggregation levels of the causal effects. While an Empirical Monte Carlo study suggests that they outperform previously suggested estimators, an application to the evaluation of an active labour market pro-gramme shows their value for applied research.
What You See is What You Get: Local Labor Markets and Skill Acquisition
Authors: Benjamin Niswonger
Date: 2022-09-08 00:00:00

This paper highlights the potential for negative dynamic consequences of recent trends towards the formation of "skill-hubs". I first show evidence that skill acquisition is biased towards skills which are in demand in local labor markets. This fact along with large heterogeneity in outcomes by major and recent reductions in migration rates implies a significant potential for inefficient skill upgrading over time. To evaluate the impact of local bias in education in the context of standard models which focus on agglomeration effects, I develop a structural spatial model which includes educational investment. The model focuses on two sources of externalities: productivity through agglomeration and signaling. Both of these affect educational decisions tilting the balance of aggregate skill composition. Signaling externalities can provide a substantial wedge in the response to changes in skill demand and skill concentration with the potential for substantial welfare gains from a more equal distribution of skills.
Probabilistic risk aversion for generalized rank-dependent functions
Authors: Ruodu Wang, Qinyu Wu
Date: 2022-09-07 00:00:00
Tasks: Management

Probabilistic risk aversion, defined through quasi-convexity in probabilistic mixtures, is a common useful property in decision analysis. We study a general class of non-monotone mappings, called the generalized rank-dependent functions, which includes the preference models of expected utilities, dual utilities, and rank-dependent utilities as special cases, as well as signed Choquet functions used in risk management. Our results fully characterize probabilistic risk aversion for generalized rank-dependent functions: This property is determined by the distortion function, which is precisely one of the two cases: those that are convex and those that correspond to scaled quantile-spread mixtures. Our result also leads to seven equivalent conditions for quasi-convexity in probabilistic mixtures of dual utilities and signed Choquet functions. As a consequence, although probabilistic risk aversion is quite different from the classic notion of strong risk aversion for generalized rank-dependent functions, these two notions coincide for dual utilities under an additional continuity assumption.
An Efficient Radar Receiver for OFDM-based Joint Radar and Communication Systems
Authors: Mamady Delamou, Kawtar Zerhouni, Guevara Noubir, El Mehdi Amhoud
Date: 2022-09-08 00:00:00

We propose in this work a radar detection system for orthogonal-frequency division multiplexing (OFDM) transmission. We assume that the transmitting antenna Tx is colocated with a monostatic radar. The latter knows the transmitted signal and listens to echoes coming from the reflection of fixed or moving targets. We estimate the targets parameters (range and velocity) using a 2D-Periodogram. Moreover, we improve the estimation performance in low signal to noise ratio (SNR) conditions using the discrete Fourier transform channel estimation (DFT-CE) and we show that Zadoff-Chu precoding (ZCP) adopted for communication, does not degrade the radar estimation in good SNR conditions. Furthermore, since the dimensions of the data matrix can be much larger than the number of targets to be detected, we propose a sparse Fourier transform based Fourier projection-slice (FPS-SFT) algorithm to reduce the computational complexity of the 2D-Periodogram. An appropriate system parameterization in the 77GHz industrial, scientific and medical (ISM) band, allows to achieve a range resolution of 30.52 cm and a velocity resolution of 0.67 m/s and to reduce the periodogram computation time up to around 98.84%.
Ambiguity Function Shaping based on Alternating Direction Riemannian Optimal Algorithm
Authors: Haoyu Yi, Xinyu Zhang, Weidong Jiang, Kai Huo
Date: 2022-09-08 00:00:00

In order to improve the ability of cognitive radar (CR) to adapt to the environment, the required ambiguity function (AF) can be synthesized by designing the waveform. The key to this problem is how to minimize the interference power. Suppressing the interference power is equivalent to minimize the expectation of slow-time ambiguity function (STAF) over range-Doppler bins. From a technical point of view, this is actually an optimization problem of a non-convex quartic function with constant modulus constraints (CMC). In this paper, we proposed a novel method to design a waveform to synthesize the STAF based on suppressing the interference power. We put forward an iterative algorithm within an alternating direction penalty method (ADPM) framework. In each iteration, this problem is split into two sub-problems by introducing auxiliary variables. In the first sub-problem, we solved the convex problem directly with a closed-form solution, then utilized the Riemannian trust region (RTR) algorithm in the second sub-problem. Simulation results demonstrate that the proposed algorithm outperforms other advanced algorithms in the aspects of STAF, range-cut and signal-to-interference-ratio (SIR) value.
Ziv-Zakai Bound for DOAs Estimation
Authors: Zongyu Zhang, Zhiguo Shi, Yujie Gu
Date: 2022-09-08 00:00:00
Tasks: valid

Lower bounds on the mean square error (MSE) play an important role in evaluating the direction-of-arrival (DOA) estimation performance. Among numerous bounds for DOA estimation, the local Cramer-Rao bound (CRB) is only tight asymptotically. By contrast, the existing global tight Ziv-Zakai bound (ZZB) is appropriate for evaluating the single source estimation only. In this paper, we derive an explicit ZZB applicable for evaluating hybrid coherent/incoherent multiple sources DOA estimation. It is first shown that, a straightforward generalization of ZZB from single source estimation to multiple sources estimation cannot keep the bound valid in the a priori performance region. To derive a global tight ZZB, we then introduce order statistics to describe the change of the a priori distribution of DOAs caused by ordering process during the MSE calculation. The derived ZZB is for the first time formulated as a function of coherent coefficients between coherent sources, and reveals the relationship between the MSE convergency in the a priori performance region and the number of sources. Moreover, the derived ZZB also provides a unified tight bound for both overdetermined and underdetermined DOA estimation. Simulation results demonstrate the obvious advantages of the derived ZZB over the CRB on evaluating and predicting the estimation performance for multiple sources DOA.
Deep Learning for Hierarchical Beam Alignment in mmWave Communication Systems
Authors: Junyi Yang, Weifeng Zhu, Meixia Tao
Date: 2022-09-08 00:00:00
Tasks: Deep Learning

Fast and precise beam alignment is crucial to support high-quality data transmission in millimeter wave (mmWave) communication systems. In this work, we propose a novel deep learning based hierarchical beam alignment method that learns two tiers of probing codebooks (PCs) and uses their measurements to predict the optimal beam in a coarse-to-fine searching manner. Specifically, the proposed method first performs coarse channel measurement using the tier-1 PC, then selects a tier-2 PC for fine channel measurement, and finally predicts the optimal beam based on both coarse and fine measurements. The proposed deep neural network (DNN) architecture is trained in two steps. First, the tier-1 PC and the tier-2 PC selector are trained jointly. After that, all the tier-2 PCs together with the optimal beam predictors are trained jointly. The learned hierarchical PCs can capture the features of propagation environment. Numerical results based on realistic ray-tracing datasets demonstrate that the proposed method is superior to the state-of-art beam alignment methods in both alignment accuracy and sweeping overhead.
SOM-Net: Unrolling the Subspace-based Optimization for Solving Full-wave Inverse Scattering Problems
Authors: Yu Liu, Hao Zhao, Rencheng Song, Xudong Chen, Chang Li, Xun Chen
Date: 2022-09-08 00:00:00
Tasks: Rolling Shutter Correction

In this paper, an unrolling algorithm of the iterative subspace-based optimization method (SOM) is proposed for solving full-wave inverse scattering problems (ISPs). The unrolling network, named SOM-Net, inherently embeds the Lippmann- Schwinger physical model into the design of network structures. The SOM-Net takes the deterministic induced current and the raw permittivity image obtained from back-propagation (BP) as the input. It then updates the induced current and the permittivity successively in sub-network blocks of the SOM- Net by imitating iterations of the SOM. The final output of the SOM-Net is the full predicted induced current, from which the scattered field and the permittivity image can also be deduced analytically. The parameters of the SOM-Net are optimized in a supervised manner with the total loss to simultaneously ensure the consistency of the induced current, the scattered field, and the permittivity in the governing equations. Numerical tests on both synthetic and experimental data verify the superior performance of the proposed SOM-Net over typical ones. The results on challenging examples like scatterers with tough profiles or high permittivity demonstrate the good generalization ability of the SOM-Net. With the use of deep unrolling technology, this work builds a bridge between traditional iterative methods and deep learning methods for solving ISPs.
Towards Multidimensional Textural Perception and Classification Through Whisker
Authors: Prasanna Kumar Routray, Aditya Sanjiv Kanade, Pauline Pounds, Manivannan Muniyandi
Date: 2022-09-01 00:00:00
Tasks: Classification

Texture-based studies and designs have been in focus recently. Whisker-based multidimensional surface texture data is missing in the literature. This data is critical for robotics and machine perception algorithms in the classification and regression of textural surfaces. In this study, we present a novel sensor design to acquire multidimensional texture information. The surface texture's roughness and hardness were measured experimentally using sweeping and dabbing. Three machine learning models (SVM, RF, and MLP) showed excellent classification accuracy for the roughness and hardness of surface textures. We show that the combination of pressure and accelerometer data, collected from a standard machined specimen using the whisker sensor, improves classification accuracy. Further, we experimentally validate that the sensor can classify texture with roughness depths as low as $2.5\mu m$ at an accuracy of $90\%$ or more and segregate materials based on their roughness and hardness. We present a novel metric to consider while designing a whisker sensor to guarantee the quality of texture data acquisition beforehand. The machine learning model performance was validated against the data collected from the laser sensor from the same set of surface textures. As part of our work, we are releasing two-dimensional texture data: roughness and hardness to the research community.
Deep Multi-Scale Representation Learning with Attention for Automatic Modulation Classification
Authors: Xiaowei Wu, Shengyun Wei, Yan Zhou
Date: 2022-08-31 00:00:00
Tasks: Classification, Ensemble Learning, Representation Learning

Currently, deep learning methods with stacking small size convolutional filters are widely used for automatic modulation classification (AMC). In this report, we find some experienced improvements by using large kernel size for convolutional deep convolution neural network based AMC, which is more efficient in extracting multi-scale features of the raw signal I/Q sequence data. Also, Squeeze-and-Excitation (SE) mechanisms can significantly help AMC networks to focus on the more important features of the signal. As a result, we propose a multi-scale feature network with large kernel size and SE mechanism (SE-MSFN) in this paper. SE-MSFN achieves state-of-the-art classification performance on the public well-known RADIOML 2018.01A dataset, with average classification accuracy of 64.50%, surpassing CLDNN by 1.42%, maximum classification accuracy of 98.5%, and an average classification accuracy of 85.53% in the lower SNR range 0dB to 10dB, surpassing CLDNN by 2.85%. In addition, we also verified that ensemble learning can help further improve classification performance. We hope this report can provide some references for developers and researchers in practical scenes.
Representation Learning for Appliance Recognition: A Comparison to Classical Machine Learning
Authors: Matthias Kahl, Daniel Jorde, Hans-Arno Jacobsen
Date: 2022-08-26 00:00:00
Tasks: Information Retrieval, Non-Intrusive Load Monitoring, Representation Learning, Retrieval

Non-intrusive load monitoring (NILM) aims at energy consumption and appliance state information retrieval from aggregated consumption measurements, with the help of signal processing and machine learning algorithms. Representation learning with deep neural networks is successfully applied to several related disciplines. The main advantage of representation learning lies in replacing an expert-driven, hand-crafted feature extraction with hierarchical learning from many representations in raw data format. In this paper, we show how the NILM processing-chain can be improved, reduced in complexity and alternatively designed with recent deep learning algorithms. On the basis of an event-based appliance recognition approach, we evaluate seven different classification models: a classical machine learning approach that is based on a hand-crafted feature extraction, three different deep neural network architectures for automated feature extraction on raw waveform data, as well as three baseline approaches for raw data processing. We evaluate all approaches on two large-scale energy consumption datasets with more than 50,000 events of 44 appliances. We show that with the use of deep learning, we are able to reach and surpass the performance of the state-of-the-art classical machine learning approach for appliance recognition with an F-Score of 0.75 and 0.86 compared to 0.69 and 0.87 of the classical approach.
Too Fine or Too Coarse? The Goldilocks Composition of Data Complexity for Robust Left-Right Eye-Tracking Classifiers
Authors: Brian Xiang, Abdelrahman Abdelmonsef
Date: 2022-08-24 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

The differences in distributional patterns between benchmark data and real-world data have been one of the main challenges of using electroencephalogram (EEG) signals for eye-tracking (ET) classification. Therefore, increasing the robustness of machine learning models in predicting eye-tracking positions from EEG data is integral for both research and consumer use. Previously, we compared the performance of classifiers trained solely on finer-grain data to those trained solely on coarse-grain. Results indicated that despite the overall improvement in robustness, the performance of the fine-grain trained models decreased, compared to coarse-grain trained models, when the testing and training set contained the same distributional patterns \cite{vectorbased}. This paper aims to address this case by training models using datasets of mixed data complexity to determine the ideal distribution of fine- and coarse-grain data. We train machine learning models utilizing a mixed dataset composed of both fine- and coarse-grain data and then compare the accuracies to models trained using solely fine- or coarse-grain data. For our purposes, finer-grain data refers to data collected using more complex methods whereas coarser-grain data refers to data collected using more simple methods. We apply covariate distributional shifts to test for the susceptibility of each training set. Our results indicated that the optimal training dataset for EEG-ET classification is not composed of solely fine- or coarse-grain data, but rather a mix of the two, leaning towards finer-grain.
Estimation of f-wave Dominant Frequency Using a Voting Scheme
Authors: Shany Biton, Mahmoud Suleiman, Noam Ben Moshe, Leif Sörnmo, Joachim A. Behar
Date: 2022-08-23 00:00:00

Introduction: Atrial fibrillation (AF) is the most common heart arrhythmia, characterized by the presence of fibrillatory waves (f-waves) in the ECG. We introduce a voting scheme to estimate the dominant atrial frequency (DAF) of f-waves. Methods: We analysed a subset of Holter recordings obtained from the University of Virginia AF Database. 100 Holter recordings with manually annotated AF events, resulting in a total 363 AF events lasting more than 1 min. The f-waves were extracted using four different template subtraction (TS) algorithms and the DAF was estimated from the first 1-min window of each AF event. A random forest classifier was used. We hypothesized that better extraction of the f-wave meant better AF/non-AF classification using the DAF as the single input feature of the RF model. Results: Performance on the test set, expressed in terms of AF/non-AF classification, was the best when the DAF was computed computed the three best-performing extraction methods. Using these three algorithms in a voting scheme, the classifier obtained AUC=0.60 and the DAFs were mostly spread around 6 Hz, 5.66 (4.83-7.47). Conclusions: This study has two novel contributions: (1) a method for assessing the performance of f-wave extraction algorithms, and (2) a voting scheme for improved DAF estimation.
Self-Supervised Multimodal Fusion Transformer for Passive Activity Recognition
Authors: Armand K. Koupai, Mohammud J. Bocus, Raul Santos-Rodriguez, Robert J. Piechocki, Ryan McConville
Date: 2022-08-15 00:00:00
Tasks: Activity Recognition, Self-Supervised Learning, Sensor Fusion

The pervasiveness of Wi-Fi signals provides significant opportunities for human sensing and activity recognition in fields such as healthcare. The sensors most commonly used for passive Wi-Fi sensing are based on passive Wi-Fi radar (PWR) and channel state information (CSI) data, however current systems do not effectively exploit the information acquired through multiple sensors to recognise the different activities. In this paper, we explore new properties of the Transformer architecture for multimodal sensor fusion. We study different signal processing techniques to extract multiple image-based features from PWR and CSI data such as spectrograms, scalograms and Markov transition field (MTF). We first propose the Fusion Transformer, an attention-based model for multimodal and multi-sensor fusion. Experimental results show that our Fusion Transformer approach can achieve competitive results compared to a ResNet architecture but with much fewer resources. To further improve our model, we propose a simple and effective framework for multimodal and multi-sensor self-supervised learning (SSL). The self-supervised Fusion Transformer outperforms the baselines, achieving a F1-score of 95.9%. Finally, we show how this approach significantly outperforms the others when trained with as little as 1% (2 minutes) of labelled training data to 20% (40 minutes) of labelled training data.
Maximization of User Association Deploying IRS in 6G Networks
Authors: Mobasshir Mahbub, Raed M. Shubair
Date: 2022-08-12 00:00:00

Prospective mobile networks will be heterogeneous multi-tier networks, with different classes of base stations (BS) installed dependent on user demand. Multi-tier networks enable operators to enhance system capacity and coverage through flexible implementations. Intelligent reflecting surfaces (IRS) or reconfigurable intelligent surfaces (RIS) is a novel and disruptive technology that comprises a vast number of cost-efficient passive components, each of which can intelligently reflect the incident wave or signal with a reconfigurable phase shift. The objective of this work is to maximize the probability of user association for millimeter-wave (mmWave) carriers in a two-tier 6G network consisting of micro and macro cell tiers. Therefore, the work analyzed and compared the probability of user association for conventional and IRS-assisted micro base stations. The research derived that, the deployment of IRS in a micro cell significantly enhances or maximizes the probability of user association of the entire coverage region including the cell edge. Moreover, the deployment of IRS reduces the transmit power consumption of the micro base station which assures a notable energy efficiency.
Sensitivity of steady states in networks with application to Markov chains and chemical reaction networks
Authors: Robin Chemnitz
Date: 2022-09-08 00:00:00
Tasks: Sensitivity

We consider steady states of dynamics that have an underlying network structure. We study how a steady state responds to small perturbations in the network parameters and how this sensitivity is connected to the network structure. We introduce a prototypical linear response equation and determine its sensitivity. This abstract result is applied to study the sensitivity of steady states in two common dynamics on networks: continuous-time Markov chains and deterministically modelled chemical reaction networks. For continuous-time Markov chains, we are able to efficiently compute the signs of the response in terms of the underlying network structure. The study of chemical reaction networks extends the sensitivity analysis to open systems with more complex network structures.