pwc-archive/papers-with-abstracts huggingface dataset:
Inference in heavy-tailed non-stationary multivariate time series
Authors: Matteo Barigozzi, Giuseppe Cavaliere, Lorenzo Trapani
Date: 2021-07-29 00:00:00
Tasks: Time Series, Time Series Analysis

We study inference on the common stochastic trends in a non-stationary, $N$-variate time series $y_{t}$, in the possible presence of heavy tails. We propose a novel methodology which does not require any knowledge or estimation of the tail index, or even knowledge as to whether certain moments (such as the variance) exist or not, and develop an estimator of the number of stochastic trends $m$ based on the eigenvalues of the sample second moment matrix of $y_{t}$. We study the rates of such eigenvalues, showing that the first $m$ ones diverge, as the sample size $T$ passes to infinity, at a rate faster by $O\left(T \right)$ than the remaining $N-m$ ones, irrespective of the tail index. We thus exploit this eigen-gap by constructing, for each eigenvalue, a test statistic which diverges to positive infinity or drifts to zero according to whether the relevant eigenvalue belongs to the set of the first $m$ eigenvalues or not. We then construct a randomised statistic based on this, using it as part of a sequential testing procedure, ensuring consistency of the resulting estimator of $m$. We also discuss an estimator of the common trends based on principal components and show that, up to a an invertible linear transformation, such estimator is consistent in the sense that the estimation error is of smaller order than the trend itself. Finally, we also consider the case in which we relax the standard assumption of \textit{i.i.d.} innovations, by allowing for heterogeneity of a very general form in the scale of the innovations. A Monte Carlo study shows that the proposed estimator for $m$ performs particularly well, even in samples of small size. We complete the paper by presenting four illustrative applications covering commodity prices, interest rates data, long run PPP and cryptocurrency markets.
Machine Learning and Factor-Based Portfolio Optimization
Authors: Thomas Conlon, John Cotter, Iason Kynigakis
Date: 2021-07-29 00:00:00
Tasks: BIG-bench Machine Learning, Dimensionality Reduction, Portfolio Optimization, Sensitivity

We examine machine learning and factor-based portfolio optimization. We find that factors based on autoencoder neural networks exhibit a weaker relationship with commonly used characteristic-sorted portfolios than popular dimensionality reduction techniques. Machine learning methods also lead to covariance and portfolio weight structures that diverge from simpler estimators. Minimum-variance portfolios using latent factors derived from autoencoders and sparse methods outperform simpler benchmarks in terms of risk minimization. These effects are amplified for investors with an increased sensitivity to risk-adjusted returns, during high volatility periods or when accounting for tail risk.
Design-Robust Two-Way-Fixed-Effects Regression For Panel Data
Authors: Dmitry Arkhangelsky, Guido W. Imbens, Lihua Lei, Xiaoman Luo
Date: 2021-07-29 00:00:00
Tasks: regression, Vocal Bursts Valence Prediction

We propose a new estimator for average causal effects of a binary treatment with panel data in settings with general treatment patterns. Our approach augments the popular two-way-fixed-effects specification with unit-specific weights that arise from a model for the assignment mechanism. We show how to construct these weights in various settings, including the staggered adoption setting, where units opt into the treatment sequentially but permanently. The resulting estimator converges to an average (over units and time) treatment effect under the correct specification of the assignment model, even if the fixed effect model is misspecified. We show that our estimator is more robust than the conventional two-way estimator: it remains consistent if either the assignment mechanism or the two-way regression model is correctly specified. In addition, the proposed estimator performs better than the two-way-fixed-effect estimator if the outcome model and assignment mechanism are locally misspecified. This strong double robustness property underlines and quantifies the benefits of modeling the assignment process and motivates using our estimator in practice. We also discuss an extension of our estimator to handle dynamic treatment effects.
China, India, Myanmar: Playing Rohingya Roulette
Authors: Hossain Ahmed Taufiq
Date: 2021-07-29 00:00:00

The 2017 crackdown on Rakhine Rohingyas by the Myanmar army (Tatmadaw) pushed more than 600,000 refugees into Bangladesh. Both Western and Islamic countries denounced Aung Sang Suu Kyis government, but both Asian giants, China and India, supported Myanmars actions. Both also have high stakes in Myanmar given their long-term geopolitics and geoeconomic South and Southeast Asian plans. In spite of Myanmar-based commonalities, Chinas and Indias approaches differ significantly, predicting equally dissimilar outcomes. This chapter examines their foreign policy and stakes in Myanmar in order to draw a sketch of the future of Rakhine Rohingyas stuck in Bangladesh.
Role of NGOs in fostering equity and social inclusion in cities of Bangladesh: The Case of Dhaka
Authors: Hossain Ahmed Taufiq
Date: 2021-07-29 00:00:00

Non-governmental organisations have made a significant contribution in the development of Bangladesh. Today, Bangladesh has more than 2000 NGOs, and few of them are among the largest in the world. NGOs are claimed to have impacts on the sustainable development in Bangladesh. However, to what extent they have fostered equity and social inclusion in the urban cities of Bangladesh remains a subject of thorough examination. The 11th goal of the Sustainable Development Goals (SDG) advocates for making cities and human settlements inclusive, safe, resilient and sustainable. Bangladesh which is the most densely populated country in the world faces multifaceted urbanization challenges. The capital city Dhaka itself has experienced staggering population growth in last few decades. Today, Dhaka has become one of the fastest growing megacities in the world. Dhaka started its journey with a manageable population of 2.2 million in 1975 which now reached 14.54 million. The growth rate averaged 6 per cent each year. As this rapid growth of Dhaka City is not commensurate with its industrial development, a significant portion of its population is living in informal settlements or slums where they experience the highest level of poverty and vulnerability. Many NGOs have taken either concerted or individual efforts to address socio-economic challenges in the city. Earlier results suggest that programs undertaken by NGOs have shown potential to positively contribute to fostering equity and reducing social exclusion. This paper, attempts to explore what types of relevant NGO programs are currently in place taking the case of Dhaka city.
Heterogeneous Responses to the U.S. Narrative Tax Changes: Evidence from the U.S. States
Authors: Masud Alam
Date: 2021-07-29 00:00:00
Tasks: valid

This paper investigates the assumption of homogeneous effects of federal tax changes across the U.S. states and identifies where and why that assumption may not be valid. More specifically, what determines the transmission mechanism of tax shocks at the state level? How vital are states' fiscal structures, financial conditions, labor market rigidities, and industry mix? Do these economic and structural characteristics drive the transmission mechanism of the tax changes at the state level at different horizons? This study employs a panel factor-augmented vector autoregression (FAVAR) technique to answer these issues. The findings show that state economies respond homogeneously in terms of employment and price levels; however, they react heterogeneously in real GDP and personal income growth. In most states, these reactions are statistically significant, and the heterogeneity in the effects of tax cuts is significantly related to the state's fiscal structure, manufacturing and financial composition, and the labor market's rigidity. A cross-state regression analysis shows that states with higher tax elasticity, higher personal income tax, strict labor market regulation, and economic policy uncertainties are relatively less responsive to federal tax changes. In contrast, the magnitude of the response in real GDP, personal income, and employment to tax cuts is relatively higher in states with a larger share of finance, manufacturing, lower tax burdens, and flexible credit markets.
Artificial Intelligence-Based Image Enhancement in PET Imaging: Noise Reduction and Resolution Enhancement
Authors: Juan Liu, Masoud Malekzadeh, Niloufar Mirian, Tzu-An Song, Chi Liu, Joyita Dutta
Date: 2021-07-28 00:00:00
Tasks: Deblurring, Denoising, Image Denoising, Image Enhancement

High noise and low spatial resolution are two key confounding factors that limit the qualitative and quantitative accuracy of PET images. AI models for image denoising and deblurring are becoming increasingly popular for post-reconstruction enhancement of PET images. We present here a detailed review of recent efforts for AI-based PET image enhancement with a focus on network architectures, data types, loss functions, and evaluation metrics. We also highlight emerging areas in this field that are quickly gaining popularity, identify barriers to large-scale adoption of AI models for PET image enhancement, and discuss future directions.
Point-Cloud Deep Learning of Porous Media for Permeability Prediction
Authors: Ali Kashefi, Tapan Mukerji
Date: 2021-07-18 00:00:00
Tasks: Deep Learning

We propose a novel deep learning framework for predicting permeability of porous media from their digital images. Unlike convolutional neural networks, instead of feeding the whole image volume as inputs to the network, we model the boundary between solid matrix and pore spaces as point clouds and feed them as inputs to a neural network based on the PointNet architecture. This approach overcomes the challenge of memory restriction of graphics processing units and its consequences on the choice of batch size, and convergence. Compared to convolutional neural networks, the proposed deep learning methodology provides freedom to select larger batch sizes, due to reducing significantly the size of network inputs. Specifically, we use the classification branch of PointNet and adjust it for a regression task. As a test case, two and three dimensional synthetic digital rock images are considered. We investigate the effect of different components of our neural network on its performance. We compare our deep learning strategy with a convolutional neural network from various perspectives, specifically for maximum possible batch size. We inspect the generalizability of our network by predicting the permeability of real-world rock samples as well as synthetic digital rocks that are statistically different from the samples used during training. The network predicts the permeability of digital rocks a few thousand times faster than a Lattice Boltzmann solver with a high level of prediction accuracy.
Improvement of image classification by multiple optical scattering
Authors: Xinyu Gao, Yi Li, Yanqing Qiu, Bangning Mao, Miaogen Chen, Yanlong Meng, Chunliu Zhao, Juan Kang, Yong Guo, Changyu Shen
Date: 2021-07-12 00:00:00
Tasks: Classification, Edge-computing, image-classification, Image Classification

Multiple optical scattering occurs when light propagates in a non-uniform medium. During the multiple scattering, images were distorted and the spatial information they carried became scrambled. However, the image information is not lost but presents in the form of speckle patterns (SPs). In this study, we built up an optical random scattering system based on an LCD and an RGB laser source. We found that the image classification can be improved by the help of random scattering which is considered as a feedforward neural network to extracts features from image. Along with the ridge classification deployed on computer, we achieved excellent classification accuracy higher than 94%, for a variety of data sets covering medical, agricultural, environmental protection and other fields. In addition, the proposed optical scattering system has the advantages of high speed, low power consumption, and miniaturization, which is suitable for deploying in edge computing applications.
Tensor-Based Channel Estimation and Reflection Design for RIS-Aided Millimeter-Wave MIMO Communication Systems
Authors: Sepideh Gherekhloo, Khaled Ardah, André L. F. de Almeida, Martin Haardt
Date: 2021-07-29 00:00:00

In this work, we consider both channel estimation and reflection design problems in point-to-point reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) MIMO communication systems. First, we show that by exploiting the low-rank nature of mmWave MIMO channels, the received training signals can be written as a low-rank multi-way tensor admitting a canonical polyadic CP decomposition. Utilizing such a structure, a tensor-based RIS channel estimation method (termed TenRICE) is proposed, wherein the tensor factor matrices are estimated using an alternating least squares method. Using TenRICE, the transmitter-to-RIS and the RIS-to-receiver channels are efficiently and separately estimated, up to a trivial scaling factor. After that, we formulate the beamforming and RIS reflection design as a spectral efficiency maximization problem. Due to its non-convexity, we propose a heuristic non-iterative two-step method, where the RIS reflection vector is obtained in a closed form using a Frobenius-norm maximization (FroMax) strategy. Our numerical results show that TenRICE has a superior performance, compared to benchmark methods, approaching the Cram\'er-Rao lower bound with a low training overhead. Moreover, we show that FroMax achieves a comparable performance to benchmark methods with a lower complexity.
Sparse Joint Transmission for Cloud Radio Access Networks with Limited Fronthaul Capacity
Authors: Deokhwan Han, Jeonghun Park, Seok-Hwan Park, Namyoon Lee
Date: 2021-07-29 00:00:00
Tasks: Quantization

A cloud radio access network (C-RAN) is a promising cellular network, wherein densely deployed multi-antenna remote-radio-heads (RRHs) jointly serve many users using the same time-frequency resource. By extremely high signaling overheads for both channel state information (CSI) acquisition and data sharing at a baseband unit (BBU), finding a joint transmission strategy with a significantly reduced signaling overhead is indispensable to achieve the cooperation gain in practical C-RANs. In this paper, we present a novel sparse joint transmission (sparse-JT) method for C-RANs, where the number of transmit antennas per unit area is much larger than the active downlink user density. Considering the effects of noisy-and-incomplete CSI and the quantization errors in data sharing by a finite-rate fronthaul capacity, the key innovation of sparse-JT is to find a joint solution for cooperative RRH clusters, beamforming vectors, and power allocation to maximize a lower bound of the sum-spectral efficiency under the sparsity constraint of active RRHs. To find such a solution, we present a computationally efficient algorithm that guarantees to find a local-optimal solution for a relaxed sum-spectral efficiency maximization problem. By system-level simulations, we exhibit that sparse-JT provides significant gains in ergodic spectral efficiencies compared to existing joint transmissions.
Edge computing in 5G cellular networks for real-time analysis of electrocardiography recorded with wearable textile sensors
Authors: Nicolai Spicher, Arne Klingenberg, Valentin Purrucker, Thomas M. Deserno
Date: 2021-07-29 00:00:00
Tasks: Deep Learning, Edge-computing, Electrocardiography (ECG)

Fifth-generation (5G) cellular networks promise higher data rates, lower latency, and large numbers of interconnected devices. Thereby, 5G will provide important steps towards unlocking the full potential of the Internet of Things (IoT). In this work, we propose a lightweight IoT platform for continuous vital sign analysis. Electrocardiography (ECG) is acquired via textile sensors and continuously sent from a smartphone to an edge device using cellular networks. The edge device applies a state-of-the art deep learning model for providing a binary end-to-end classification if a myocardial infarction is at hand. Using this infrastructure, experiments with four volunteers were conducted. We compare 3rd, 4th-, and 5th-generation cellular networks (release 15) with respect to transmission latency, data corruption, and duration of machine learning inference. The best performance is achieved using 5G showing an average transmission latency of 110ms and data corruption in 0.07% of ECG samples. Deep learning inference took approximately 170ms. In conclusion, 5G cellular networks in combination with edge devices are a suitable infrastructure for continuous vital sign analysis using deep learning models. Future 5G releases will introduce multi-access edge computing (MEC) as a paradigm for bringing edge devices nearer to mobile clients. This will decrease transmission latency and eventually enable automatic emergency alerting in near real-time.
Three-dimensional instantaneous orbit map for rotor-bearing system based on a novel multivariate complex variational mode decomposition algorithm
Authors: Xiaolong Cui, Jie Huang, Chaoshun Li, Yujie Zhao
Date: 2021-07-29 00:00:00
Tasks: Fault Diagnosis

Full spectrum and holospectrum are homogenous information fusion technology developed for the fault diagnosis of rotating machinery, which is extensively exploited in the analysis of the orbits of rotor-bearing systems. However, they are not adapted for non-stationary signals, nor can they be used for fusion analysis of vibrations of multiple bearing sections. By drawing inspiration from the multivariate variational mode decomposition (MVMD) and the complex-valued signal decomposition, we propose a method called multivariate complex variational mode decomposition (MCVMD). It can simultaneously extract the forward and backward components of multiple bearing sections and realize non-stationary complex signal decomposition of multiple bearing sections of the rotor. To achieve the visualization goal of condition monitoring, we propose the three-dimensional instantaneous orbit map (3D-IOM). It enables more features of shaft vibration of a rotor system to be displayed and offers a new way for the fusion analysis of vibration signals of multiple bearing sections of rotating machinery. Furthermore, making the most of the joint information, we also provide a high-resolution time-full spectrum (Time-FS) to display the forward and backward frequency components of multiple bearing sections. The effectiveness of the proposed method through both the simulated experiment and the real-life complex-valued signals is demonstrated in this paper.
Sub-Nyquist Sampling with Optical Pulses for Photonic Blind Source Separation
Authors: Taichu Shi, Yang Qi, Weipeng Zhang, Paul Prucnal, Ben Wu
Date: 2021-07-25 00:00:00
Tasks: blind source separation

We proposed and demonstrated an optical pulse sampling method for photonic blind source separation. It can separate large bandwidth of mixed signals by small sampling frequency, which can reduce the workload of digital signal processing.
Photonic Interference Cancellation with Hybrid Free Space Optical Communication and MIMO Receiver
Authors: Taichu Shi, Yang Qi, Ben Wu
Date: 2021-07-25 00:00:00
Tasks: blind source separation

We proposed and demonstrated a hybrid blind source separation system which can switch between multiple-input and multi-output mode and free space optical communication mode depends on different situation to get best condition for separation.
Radio Frequency Interference Management with Free-Space Optical Communication and Photonic Signal Processing
Authors: Yang Qi, Ben Wu
Date: 2021-07-25 00:00:00
Tasks: Management

We design and experimentally demonstrate a radio frequency interference management system with free-space optical communication and photonic signal processing. The system provides real-time interference cancellation in 6 GHz wide bandwidth.
Modifications of FastICA in Convolutive Blind Source Separation
Authors: Yunpeng Li
Date: 2021-07-24 00:00:00
Tasks: blind source separation

Convolutive blind source separation (BSS) is intended to recover the unknown components from their convolutive mixtures. Contrary to the contrast functions used in instantaneous cases, the spatial-temporal prewhitening stage and the para-unitary filters constraint are difficult to implement in a convolutive context. In this paper, we propose several modifications of FastICA to alleviate these difficulties. Our method performs the simple prewhitening step on convolutive mixtures prior to the separation and optimizes the contrast function under the diagonalization constraint implemented by single value decomposition (SVD). Numerical simulations are implemented to verify the performance of the proposed method.
Vaccination Worldwide: Strategies, Distribution and Challenges
Authors: Chirag Samal, Kasia Jakimowicz, Krishnendu Dasgupta, Aniket Vashishtha, Francisco O., Arunakiry Natarajan, Haris Nazir, Alluri Siddhartha Varma, Tejal Dahake, Amitesh Anand Pandey, Ishaan Singh, John Sangyeob Kim, Mehrab Singh Gill, Saurish Srivastava, Orna Mukhopadhyay, Parth Patwa, Qamil Mirza, Sualeha Irshad, Sheshank Shankar, Rohan Iyer, Rohan Sukumaran, Ashley Mehra, Anshuman Sharma, Abhishek Singh, Maurizio Arseni, Sethuraman T V, Saras Agrawal, Vivek Sharma, Ramesh Raskar
Date: 2021-07-21 00:00:00

The Coronavirus 2019 (Covid-19) pandemic caused by the SARS-CoV-2 virus represents an unprecedented crisis for our planet. It is a bane of the \"uber connected world that we live in that this virus has affected almost all countries and caused mortality and economic upheaval at a scale whose effects are going to be felt for generations to come. While we can all be buoyed at the pace at which vaccines have been developed and brought to market, there are still challenges ahead for all countries to get their populations vaccinated equitably and effectively. This paper provides an overview of ongoing immunization efforts in various countries. In this early draft, we have identified a few key factors that we use to review different countries' current COVID-19 immunization strategies and their strengths and draw conclusions so that policymakers worldwide can learn from them. Our paper focuses on processes related to vaccine approval, allocation and prioritization, distribution strategies, population to vaccine ratio, vaccination governance, accessibility and use of digital solutions, and government policies. The statistics and numbers are dated as per the draft date [June 24th, 2021].