pwc-archive/papers-with-abstracts huggingface dataset:
Output, Employment, and Price Effects of U.S. Narrative Tax Changes: A Factor-Augmented Vector Autoregression Approach
Authors: Masud Alam
Date: 2021-06-21 00:00:00

This paper examines the short- and long-run effects of U.S. federal personal income and corporate income tax cuts on a wide array of economic policy variables in a data-rich environment. Using a panel of U.S. macroeconomic data set, made up of 132 quarterly macroeconomic series for 1959-2018, the study estimates factor-augmented vector autoregression (FAVARs) models where an extended narrative tax changes dataset combined with unobserved factors. The narrative approach classifies if tax changes are exogenous or endogenous. This paper identifies narrative tax shocks in the vector autoregression model using the sign restrictions with Uhlig's (2005) penalty function. Empirical findings show a significant expansionary effect of tax cuts on the macroeconomic variables. Cuts in personal and corporate income taxes cause a rise in output, investment, employment, and consumption; however, cuts in personal taxes appear to be a more effective fiscal policy tool than the cut in corporate income taxes. Real GDP, employment, investment, and industrial production increase significantly and reach their maximum response values two years after personal income tax cuts. The effects of corporate tax cuts have relatively smaller effects on output and consumption but show immediate and higher effects on fixed investment and price levels.
Entitled to Property: How Breaking the Gender Barrier Improves Child Health in India
Authors: Md Shahadath Hossain, Plamen Nikolov
Date: 2021-06-21 00:00:00

Non-unitary household models posit that increasing women's bargaining power can affect child health, a significant predictor of human capital and economic status throughout adulthood. We study how a policy change, the Hindu Succession Act Amendment (HSAA), which provided inheritance rights to unmarried women in India, impacts child health. We find evidence that the HSAA improved the height and weight of children. In addition, we find evidence consistent with a channel that the policy improved women's intra-household bargaining power, leading to downstream benefits through better parental care for children and improved child health. The findings confirm that children do better when their mothers control a more significant fraction of the family resources. Thus, policies that empower women can have additional positive spillovers for children's human capital.
The Knowledge Mobility of Renewable Energy Technology
Authors: P. G. J. Persoon, R. N. A. Bekkers, F. Alkemade
Date: 2021-06-19 00:00:00

In the race to achieve climate goals, many governments and organizations are encouraging the local development of Renewable Energy Technology (RET). The spatial innovation dynamics of the development of a technology partly depends on the characteristics of the knowledge base on which this technology builds, in particular the analyticity and cumulativeness of knowledge. Theoretically, greater analyticity and lesser cumulativeness are positively associated with more widespread development. In this study, we first empirically evaluate these relations for general technology and then systematically determine the knowledge base characteristics for a set of 14 different RETs. We find that, while several RETs (photovoltaics, fuel-cells, energy storage) have a highly analytic knowledge base and develop more widespread, there are also important RETs (wind turbines, solar thermal, geothermal and hydro energy) for which the knowledge base is less analytic and which develop less widespread. Likewise, the technological cumulativeness tends to be lower for the former than for the latter group. This calls for regional and country-level policies to be specific for different RETs, taking for a given RET into account both the type of knowledge it builds on as well as the local presence of this knowledge.
A Concavification Approach to Ambiguous Persuasion
Authors: Xiaoyu Cheng
Date: 2021-06-21 00:00:00

This note shows that the value of ambiguous persuasion characterized in Beauchene, Li and Li(2019) can be given by a concavification program as in Bayesian persuasion (Kamenica and Gentzkow, 2011). In addition, it implies that an ambiguous persuasion game can be equivalently formalized as a Bayesian persuasion game by distorting the utility functions. This result is obtained under a novel construction of ambiguous persuasion.
Conflicts, Assortative Matching, and the Evolution of Signaling Norms
Authors: Ethan Holdahl, Jiabin Wu
Date: 2021-06-20 00:00:00

This paper proposes a model to explain the potential role of inter-group conflicts in determining the rise and fall of signaling norms. Individuals in a population are characterized by high and low productivity types and they are matched in pairs to form social relationships such as mating or foraging relationships. In each relationship, an individual's payoff is increasing in its own type and its partner's type. Hence, the payoff structure of a relationship does not resemble a dilemma situation. Assume that types are not observable. In one population, assortative matching according to types is sustained by signaling. In the other population, individuals do not signal and they are randomly matched. Types evolve within each population. At the same time, the two populations may engage in conflicts. Due to assortative matching, high types grow faster in the population with signaling, yet they bear the cost of signaling, which lowers their population's fitness in the long run. Through simulations, we show that the survival of the signaling population depends crucially on the timing and the efficiency of weapon used in inter-group conflicts.
Cylindrical coordinates for LiDAR point cloud compression
Authors: Shashank N. Sridhara, Eduardo Pavez, Antonio Ortega
Date: 2021-06-21 00:00:00
Tasks: Attribute, Autonomous Vehicles

We present an efficient voxelization method to encode the geometry and attributes of 3D point clouds obtained from autonomous vehicles. Due to the circular scanning trajectory of sensors, the geometry of LiDAR point clouds is inherently different from that of point clouds captured from RGBD cameras. Our method exploits these specific properties to representing points in cylindrical coordinates instead of conventional Cartesian coordinates. We demonstrate thatRegion Adaptive Hierarchical Transform (RAHT) can be extended to this setting, leading to attribute encoding based on a volumetric partition in cylindrical coordinates. Experimental results show that our proposed voxelization outperforms conventional approaches based on Cartesian coordinates for this type of data. We observe a significant improvement in attribute coding performance with 5-10%reduction in bitrate and octree representation with 35-45% reduction in bits.
Fusion of Complex Networks-based Global and Local Features for Texture Classification
Authors: Zhengrui Huang
Date: 2021-06-20 00:00:00
Tasks: Texture Classification

To realize accurate texture classification, this article proposes a complex networks (CN)-based multi-feature fusion method to recognize texture images. Specifically, we propose two feature extractors to detect the global and local features of texture images respectively. To capture the global features, we first map a texture image as an undirected graph based on pixel location and intensity, and three feature measurements are designed to further decipher the image features, which retains the image information as much as possible. Then, given the original band images (BI) and the generated feature images, we encode them based on the local binary patterns (LBP). Therefore, the global feature vector is obtained by concatenating four spatial histograms. To decipher the local features, we jointly transfer and fine-tune the pre-trained VGGNet-16 model. Next, we fuse and connect the middle outputs of max-pooling layers (MP), and generate the local feature vector by a global average pooling layer (GAP). Finally, the global and local feature vectors are concatenated to form the final feature representation of texture images. Experiment results show that the proposed method outperforms the state-of-the-art statistical descriptors and the deep convolutional neural networks (CNN) models.
Classification of Documents Extracted from Images with Optical Character Recognition Methods
Authors: Omer Aydin
Date: 2021-06-15 00:00:00
Tasks: BIG-bench Machine Learning, Optical Character Recognition, Optical Character Recognition (OCR)

Over the past decade, machine learning methods have given us driverless cars, voice recognition, effective web search, and a much better understanding of the human genome. Machine learning is so common today that it is used dozens of times a day, possibly unknowingly. Trying to teach a machine some processes or some situations can make them predict some results that are difficult to predict by the human brain. These methods also help us do some operations that are often impossible or difficult to do with human activities in a short time. For these reasons, machine learning is so important today. In this study, two different machine learning methods were combined. In order to solve a real-world problem, the manuscript documents were first transferred to the computer and then classified. We used three basic methods to realize the whole process. Handwriting or printed documents have been digitalized by a scanner or digital camera. These documents have been processed with two different Optical Character Recognition (OCR) operation. After that generated texts are classified by using Naive Bayes algorithm. All project was programmed in Microsoft Visual Studio 12 platform on Windows operating system. C# programming language was used for all parts of the study. Also, some prepared codes and DLLs were used.
Mobile Optical Communications Using Second Harmonic of Intra-Cavity Laser
Authors: Mingliang Xiong, Qingwen Liu, Xin Wang, Shengli Zhou, Bin Zhou, Zhiyong Bu
Date: 2021-06-21 00:00:00

Optical wireless communication (OWC) meets the demands of the future six-generation mobile network (6G) as it operates at several hundreds of Terahertz and has the potential to enable data rate in the order of Tbps. However, most beam steering OWC technologies require high-accuracy positioning and high-speed control. Resonant beam communication (RBCom), as one kind of non-positioning OWC technologies, has been proposed for high-rate mobile communications. The mobility of RBCom relies on its self-alignment characteristic where no positioning is required. In a previous study, an external-cavity second-harmonic-generation (SHG) RBCom system has been proposed for eliminating the echo interference inside the resonator. However, its energy conversion efficiency and complexity are of concern. In this paper, we propose an intra-cavity SHG RBCom system to simplify the system design and improve the energy conversion efficiency. We elaborate the system structure and establish an analytical model. Numerical results show that the energy consumption of the proposed intra-cavity design is reduced to reach the same level of channel capacity at the receiver compared with the external-cavity one.
Channel Estimation for Hybrid RIS Aided MIMO Communications via Atomic Norm Minimization
Authors: Rafaela Schroeder, Jiguang He, Markku Juntti
Date: 2021-06-21 00:00:00
Tasks: Compressive Sensing

Reconfigurable intelligent surfaces (RISs) have been introduced as a remedy for mitigating frequent blockages in millimeter wave (mmWave) multiple-input multiple-output (MIMO) communication networks. However, perfect or nearly perfect channel state information (CSI) is fundamental in order to achieve their full potential. Traditionally, an RIS is fully passive without any baseband processing capabilities, which poses great challenges for CSI acquisition. Thus, we focus on the hybrid RIS architecture, where a small portion of RIS elements are active and able to processing the received pilot signals for estimating the corresponding channel. The channel estimation (CE) is done by resorting to off-the-grid compressive sensing technique, i.e., atomic norm minimization, for exacting channel parameters through two stages. Simulation results show that the proposed scheme outperforms the passive RIS CE under the same training overhead.
EM-based Solutions for Covariance Structure Detection and Classification in Polarimetric SAR Images
Authors: Pia Addabbo, Filippo Biondi, Carmine Clemente, Sudan Han, Danilo Orlando, Giuseppe Ricci
Date: 2021-06-21 00:00:00

This paper addresses the challenge of classifying polarimetric SAR images by leveraging the peculiar characteristics of the polarimetric covariance matrix (PCM). To this end, a general framework to solve a multiple hypothesis test is introduced with the aim to detect and classify contextual spatial variations in polarimetric SAR images. Specifically, under the null hypothesis, only an unknown structure is assumed for data belonging to a 2-dimensional spatial sliding window, whereas under each alternative hypothesis, data are partitioned into subsets sharing different structures. The problem of partition estimation is solved by resorting to hidden random variables representative of covariance structure classes and the expectation-maximization algorithm. The effectiveness of the proposed detection strategies is demonstrated on both simulated and real polarimetric SAR data also in comparison with existing classification algorithms.
Random Paraunitary Projections
Authors: Ricardo L. De Queiroz
Date: 2021-06-20 00:00:00

Transforms using random matrices have been found to have many applications. We are concerned with the projection of a signal onto Gaussian-distributed random orthogonal bases. We also would like to easily invert the process through transposes in order to facilitate iterative reconstruction. We derive an efficient method to implement random unitary matrices of larger sizes through a set of Givens rotations. Random angles are hierarchically generated on-the-fly and the inverse merely requires traversing the angles in reverse order. Hierarchical randomization of angles also enables reduced storage. Using the random unitary matrices as building blocks we introduce random paraunitary systems (filter banks). We also highlight an efficient implementation of the paraunitary system and of its inverse. We also derive an adaptive under-decimated system, wherein one can control and adapt the amount of projections the signal undergoes, in effect, varying the sampling compression ratio as we go along the signal, without segmenting it. It may locally range from very compressive sampling matrices to (para) unitary random ones. One idea is to adapt to local sparseness characteristics of non-stationary signals.
A New Signal Representation Using Complex Conjugate Pair Sums
Authors: Shaik Basheeruddin Shah, Vijay Kumar Chakka, Arikatla Satyanarayana Reddy
Date: 2021-06-20 00:00:00

This letter introduces a real valued summation known as Complex Conjugate Pair Sum (CCPS). The space spanned by CCPS and its one circular downshift is called {\em Complex Conjugate Subspace (CCS)}. For a given positive integer $N\geq3$, there exists $\frac{\varphi(N)}{2}$ CCPSs forming $\frac{\varphi(N)}{2}$ CCSs, where $\varphi(N)$ is the Euler's totient function. We prove that these CCSs are mutually orthogonal and their direct sum form a $\varphi(N)$ dimensional subspace $s_N$ of $\mathbb{C}^N$. We propose that any signal of finite length $N$ is represented as a linear combination of elements from a special basis of $s_d$, for each divisor $d$ of $N$. This defines a new transform named as Complex Conjugate Periodic Transform (CCPT). Later, we compared CCPT with DFT (Discrete Fourier Transform) and RPT (Ramanujan Periodic Transform). It is shown that, using CCPT we can estimate the period, hidden periods and frequency information of a signal. Whereas, RPT does not provide the frequency information. For a complex valued input signal, CCPT offers computational benefit over DFT. A CCPT dictionary based method is proposed to extract non-divisor period information.
A Scalable 256-Elements E-Band Phased-Array Transceiver for Broadband Communication
Authors: Xu Li, Wenyao Zhai, Morris Repeta, Hua Cai, Tyler Ross, Kimia Ansari, Sam Tiller, Hari Krishna Pothula, Dong Liang, Fan Yang, Yibo Lyu, Songlin Shuai, Guangjian Wang, Wen Tong
Date: 2021-06-20 00:00:00

For E-band wireless communications, a high gain steerable antenna with sub-arrays is desired to reduce the implementation complexity. This paper presents an E-band communication link with 256-elements antennas based on 8-elements sub-arrays and four beam-forming chips in silicon germanium (SiGe) bipolar complementary metal-oxide-semiconductor (BiCMOS), which is packaged on a 19-layer low temperature co-fired ceramic (LTCC) substrate. After the design and manufacture of the 256-elements antenna, a fast near-field calibration method is proposed for calibration, where a single near-field measurement is required. Then near-field to far-field (NFFF) transform and far-field to near-field (FFNF) transform are used for the bore-sight calibration. The comparison with high frequency structure simulator (HFSS) is utilized for the non-bore-sight calibration. Verified on the 256-elements antenna, the beam-forming performance measured in the chamber is in good agreement with the simulations. The communication in the office environment is also realized using a fifth generation (5G) new radio (NR) system, whose bandwidth is 400 megahertz (MHz) and waveform format is orthogonal frequency division multiplexing (OFDM) with 120 kilohertz (kHz) sub-carrier spacing.
Enhancing and Localizing Surface Wave Propagation with Reconfigurable Surfaces
Authors: Z. Chu, K. K. Wong, K. F. Tong
Date: 2021-06-19 00:00:00

As an attempt to develop a reconfigurable surface architecture that can use liquid metal such as Galinstan to shape surface channels on demand, this paper considers a punctured surface where cavities are evenly distributed and can be filled with liquid metal potentially via digitally controlled pumps. In this paper, we look at the benefits of such architecture in terms of surface-wave signal enhancement and isolation, and examine how various system parameters impact the performance using full wave 3-dimensional electromagnetic simulations. It is shown that extraordinary signal shaping can be obtained.
Cooperative Magneto-Inductive Localization
Authors: Henry Schulten, Gregor Dumphart, Antonios Koskinas, Armin Wittneben
Date: 2021-06-19 00:00:00
Tasks: Position

Wireless localization is a key requirement for many applications. It concerns position estimation of mobile nodes (agents) relative to fixed nodes (anchors) from wireless channel measurements. Cooperative localization is an advanced concept that considers the joint estimation of multiple agent positions based on channel measurements of all agent-anchor links together with all agent-agent links. In this paper we present the first study of cooperative localization for magneto-inductive wireless sensor networks, which are of technological interest due to good material penetration and channel predictability. We demonstrate significant accuracy improvements (a factor of 3 for 10 cooperating agents) over the non-cooperative scheme. The evaluation uses the Cram\'er-Rao lower bound on the cooperative position estimation error, which is derived herein. To realize this accuracy, the maximum-likelihood estimate (MLE) must be computed by solving a high-dimensional least-squares problem, whereby convergence to local minima proves to be problematic. A proposed cooperative localization algorithm addresses this issue: first, preliminary estimates of the agent positions and orientations are computed, which then serve as initial values for a gradient search. In all our test cases, this method yields the MLE and the associated high accuracy (comprising the cooperation gain) from a single solver run. The preliminary estimates use novel closed-form MLE formulas of the distance, direction and orientation for single links between three-axis coils, which are given in detail.
A Survey on Machine Learning Algorithms for Applications in Cognitive Radio Networks
Authors: Akshay Upadhye, Purushothaman Saravanan, Shreeram Suresh Chandra, Sanjeev Gurugopinath
Date: 2021-06-19 00:00:00
Tasks: BIG-bench Machine Learning

In this paper, we present a survey on the utility of machine learning (ML) algorithms for applications in cognitive radio networks (CRN). We start with a high-level overview of some of the major challenges in CRNs, and mention the ML architectures and algorithms that can be used to alleviate them. In particular, our focus is on two fundamental applications in CRNs, namely spectrum sensing -- with non-cooperative and cooperative scenarios, and dynamic spectrum access -- with spectrum auction and prediction. We present a detailed study of recent advancements in the field of ML in CRNs for these applications, and briefly discuss the set of challenges in real-time implementation of ML techniques for CRNs.
Cascaded Channel Estimation for RIS Assisted mmWave MIMO Transmissions
Authors: Yushan Liu, Shun Zhang, Feifei Gao, Jie Tang, Octavia A. Dobre
Date: 2021-06-19 00:00:00

Channel estimation is challenging for the reconfigurable intelligence surface (RIS) assisted millimeter wave (mmWave) communications. Since the number of coefficients of the cascaded channels in such systems is closely dependent on the product of the number of base station antennas and the number of RIS elements, the pilot overhead would be prohibitively high. In this letter, we propose a cascaded channel estimation framework for an RIS assisted mmWave multiple-input multiple-output system, where the wideband effect on transmission model is considered. Then, we transform the wideband channel estimation into a parameter recovery problem and use a few pilot symbols to detect the channel parameters by the Newtonized orthogonal matching pursuit algorithm. Moreover, the Cramer-Rao lower bound on the channel estimation is introduced. Numerical results show the effectiveness of the proposed channel estimation scheme.
Transformer-based Spatial-Temporal Feature Learning for EEG Decoding
Authors: Yonghao Song, Xueyu Jia, Lie Yang, Longhan Xie
Date: 2021-06-11 00:00:00
Tasks: Brain Computer Interface, EEG, Eeg Decoding, Electroencephalogram (EEG)

At present, people usually use some methods based on convolutional neural networks (CNNs) for Electroencephalograph (EEG) decoding. However, CNNs have limitations in perceiving global dependencies, which is not adequate for common EEG paradigms with a strong overall relationship. Regarding this issue, we propose a novel EEG decoding method that mainly relies on the attention mechanism. The EEG data is firstly preprocessed and spatially filtered. And then, we apply attention transforming on the feature-channel dimension so that the model can enhance more relevant spatial features. The most crucial step is to slice the data in the time dimension for attention transforming, and finally obtain a highly distinguishable representation. At this time, global averaging pooling and a simple fully-connected layer are used to classify different categories of EEG data. Experiments on two public datasets indicate that the strategy of attention transforming effectively utilizes spatial and temporal features. And we have reached the level of the state-of-the-art in multi-classification of EEG, with fewer parameters. As far as we know, it is the first time that a detailed and complete method based on the transformer idea has been proposed in this field. It has good potential to promote the practicality of brain-computer interface (BCI). The source code can be found at: \textit{https://github.com/anranknight/EEG-Transformer}.