pwc-archive/papers-with-abstracts huggingface dataset:
Quantitative phase imaging of single particles from a cryoEM micrograph
Authors: Apoorv Pant, Manidipa Banerjee, Kedar Khare
Date: 2021-05-26 00:00:00
Tasks: Retrieval

We show that de-focused single particle images recorded using a cryo-electron microscope (cryoEM) system may be processed like a Fresnel zone in-line hologram to obtain physically meaningful quantitative phase maps associated with individual particles. In particular, a region-of-interest (ROI) of the de-focused image surrounding a particle can be numerically back-propagated, in order to determine accurate de-focus information based on the sparsity-of-gradient merit function. Further with the knowledge of de-focus information, an iterative Fresnel zone phase retrieval algorithm using image sparsity constraints can accurately estimate the quantitative phase information associated with a single particle. The proposed methodology which can correct for both de-focus and spherical aberrations is a deviation from the image processing chain currently used in single particle cryoEM reconstructions. Our illustrations as presented here suggest that the phase retrieval approach applies uniformly to de-focused image data recorded using the traditional CCD detectors as well as the newer direct electron detectors.
Machine learning predictions of COVID-19 second wave end-times in Indian states
Authors: Anvesh Reddy, Hanesh Koganti, Sai Krishna, Suhas Reddy, Soumyajyoti Biswas
Date: 2021-05-26 00:00:00
Tasks: BIG-bench Machine Learning

The estimate of the remaining time of an ongoing wave of epidemic spreading is a critical issue. Due to the variations of a wide range of parameters in an epidemic, for simple models such as Susceptible-Infected-Removed (SIR) model, it is difficult to estimate such a time scale. On the other hand, multidimensional data with a large set attributes are precisely what one can use in statistical learning algorithms to make predictions. Here we show, how the predictability of the SIR model changes with various parameters using a supervised learning algorithm. We then estimate the condition in which the model gives the least error in predicting the duration of the first wave of the COVID-19 pandemic in different states in India. Finally, we use the SIR model with the above mentioned optimal conditions to generate a training data set and use it in the supervised learning algorithm to estimate the end-time of the ongoing second wave of the pandemic in different states in India.
Identification and Estimation of Partial Effects in Nonlinear Semiparametric Panel Models
Authors: Laura Liu, Alexandre Poirier, Ji-Liang Shiu
Date: 2021-05-27 00:00:00

Average partial effects (APEs) are often not point identified in panel models with unrestricted unobserved individual heterogeneity, such as a binary response panel model with fixed effects and logistic errors as a special case. This lack of point identification occurs despite the identification of these models' common coefficients. We provide a unified framework to establish the point identification of various partial effects in a wide class of nonlinear semiparametric models under an index sufficiency assumption on the unobserved heterogeneity, even when the error distribution is unspecified and non-stationary. This assumption does not impose parametric restrictions on the unobserved heterogeneity and idiosyncratic errors. We also present partial identification results when the support condition fails. We then propose three-step semiparametric estimators for APEs, average structural functions, and average marginal effects, and show their consistency and asymptotic normality. Finally, we illustrate our approach in a study of determinants of married women's labor supply.
Ordered Reference Dependent Choice
Authors: Xi Zhi Lim
Date: 2021-05-27 00:00:00

This paper studies how violations of structural assumptions like expected utility and exponential discounting can be connected to basic rationality violations, even though these assumptions are typically regarded as independent building blocks in decision theory. A reference-dependent generalization of behavioral postulates captures preference shifts in various choice domains. When reference points are fixed, canonical models hold; otherwise, reference-dependent preference parameters (e.g., CARA coefficients, discount factors) give rise to "non-standard" behavior. The framework allows us to study risk, time, and social preferences collectively, where seemingly independent anomalies are interconnected through the lens of reference-dependent choice.
Corruption Determinants, Geography, and Model Uncertainty
Authors: Sajad Rahimian
Date: 2021-05-26 00:00:00
Tasks: model

This paper aims to identify the robust determinants of corruption after integrating out the effects of spatial spillovers in corruption levels between countries. In other words, we want to specify which variables play the most critical role in determining the corruption levels after accounting for the effects that neighbouring countries have on each other. We collected the annual data of 115 countries over the 1985-2015 period and used the averaged values to conduct our empirical analysis. Among 39 predictors of corruption, our spatial BMA models identify Rule of Law as the most persistent determinant of corruption.
Correlation Concern
Authors: Andrew Ellis
Date: 2021-05-27 00:00:00

In many choice problems, the interaction between several distinct variables determines the payoff of each alternative. I propose and axiomatize a model of a decision maker who recognizes that she may not accurately perceive the correlation between these variables, and who takes this into account when making her decision. She chooses as if she calculates each alternative's expected outcome under multiple possible correlation structures, and then evaluates it according to the worst expected outcome.
Conditional generator and multi-sourcecorrelation guided brain tumor segmentation with missing MR modalities
Authors: Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan
Date: 2021-05-27 00:00:00
Tasks: Brain Tumor Segmentation, Segmentation, Tumor Segmentation

Brain tumor is one of the most high-risk cancers which causes the 5-year survival rate of only about 36%. Accurate diagnosis of brain tumor is critical for the treatment planning. However, complete data are not always available in clinical scenarios. In this paper, we propose a novel brain tumor segmentation network to deal with the missing data issue. To compensate for missing data, we propose to use a conditional generator to generate the missing modality under the condition of the available modalities. As the multi-modality has a strong correlation in tumor region, we design a correlation constraint network to leverage the multi-source information. On the one hand, the correlation constraint network can help the conditional generator to generate the missing modality which should keep the multi-source correlation with the available modalities. On the other hand, it can guide the segmentation network to learn the correlated feature representations to improve the segmentation performance. The proposed network consists of a conditional generator, a correlation constraint network and a segmentation network. We carried out extensive experiments on BraTS 2018 dataset to evaluate the proposed method.The experimental results demonstrate the importance of the proposed components and the superior performance of the proposed method com-pared with the state-of-the-art methods
VeniBot: Towards Autonomous Venipuncture with Automatic Puncture Area and Angle Regression from NIR Images
Authors: Xu Cao, Zijie Chen, Bolin Lai, Yuxuan Wang, Yu Chen, Zhengqing Cao, Zhilin Yang, Nanyang Ye, Junbo Zhao, Xiao-Yun Zhou, Peng Qi
Date: 2021-05-27 00:00:00
Tasks: Navigate, regression

Venipucture is a common step in clinical scenarios, and is with highly practical value to be automated with robotics. Nowadays, only a few on-shelf robotic systems are developed, however, they can not fulfill practical usage due to varied reasons. In this paper, we develop a compact venipucture robot -- VeniBot, with four parts, six motors and two imaging devices. For the automation, we focus on the positioning part and propose a Dual-In-Dual-Out network based on two-step learning and two-task learning, which can achieve fully automatic regression of the suitable puncture area and angle from near-infrared(NIR) images. The regressed suitable puncture area and angle can further navigate the positioning part of VeniBot, which is an important step towards a fully autonomous venipucture robot. Validation on 30 VeniBot-collected volunteers shows a high mean dice coefficient(DSC) of 0.7634 and a low angle error of 15.58{\deg} on suitable puncture area and angle regression respectively, indicating its potentially wide and practical application in the future.
VeniBot: Towards Autonomous Venipuncture with Semi-supervised Vein Segmentation from Ultrasound Images
Authors: Yu Chen, Yuxuan Wang, Bolin Lai, Zijie Chen, Xu Cao, Nanyang Ye, Zhongyuan Ren, Junbo Zhao, Xiao-Yun Zhou, Peng Qi
Date: 2021-05-27 00:00:00
Tasks: Segmentation

In the modern medical care, venipuncture is an indispensable procedure for both diagnosis and treatment. In this paper, unlike existing solutions that fully or partially rely on professional assistance, we propose VeniBot -- a compact robotic system solution integrating both novel hardware and software developments. For the hardware, we design a set of units to facilitate the supporting, positioning, puncturing and imaging functionalities. For the software, to move towards a full automation, we propose a novel deep learning framework -- semi-ResNeXt-Unet for semi-supervised vein segmentation from ultrasound images. From which, the depth information of vein is calculated and used to enable automated navigation for the puncturing unit. VeniBot is validated on 40 volunteers, where ultrasound images can be collected successfully. For the vein segmentation validation, the proposed semi-ResNeXt-Unet improves the dice similarity coefficient (DSC) by 5.36%, decreases the centroid error by 1.38 pixels and decreases the failure rate by 5.60%, compared to fully-supervised ResNeXt-Unet.
MR elasticity reconstruction using statistical physical modeling and explicit data-driven denoising regularizer
Authors: Narges Mohammadi, Marvin M. Doyley, Mujdat Cetin
Date: 2021-05-27 00:00:00
Tasks: Denoising

Elasticity image, visualizing the quantitative map of tissue stiffness, can be reconstructed by solving an inverse problem. Classical methods for magnetic resonance elastography (MRE) try to solve a regularized optimization problem comprising a deterministic physical model and a prior constraint as data-fidelity term and regularization term, respectively. For improving the elasticity reconstructions, appropriate prior about the underlying elasticity distribution is required which is not unique. This article proposes an infused approach for MRE reconstruction by integrating the statistical representation of the physical laws of harmonic motions and learning-based prior. For data-fidelity term, we use a statistical linear-algebraic model of equilibrium equations and for the regularizer, data-driven regularization by denoising (RED) is utilized. In the proposed optimization paradigm, the regularizer gradient is simply replaced by the residual of learned denoiser leading to time-efficient computation and convex explicit objective function. Simulation results of elasticity reconstruction verify the effectiveness of the proposed approach.
Structurally Adaptive Multi-Derivative Regularization for Image Recovery from Sparse Fourier Samples
Authors: Sanjay Viswanath, Manu Ghulyani, Muthuvel Arigovindan
Date: 2021-05-26 00:00:00
Tasks: Compressive Sensing, Image Reconstruction

The importance of regularization has been well established in image reconstruction -- which is the computational inversion of imaging forward model -- with applications including deconvolution for microscopy, tomographic reconstruction, magnetic resonance imaging, and so on. Originally, the primary role of the regularization was to stabilize the computational inversion of the imaging forward model against noise. However, a recent framework pioneered by Donoho and others, known as compressive sensing, brought the role of regularization beyond the stabilization of inversion. It established a possibility that regularization can recover full images from highly undersampled measurements. However, it was observed that the quality of reconstruction yielded by compressive sensing methods falls abruptly when the under-sampling and/or measurement noise goes beyond a certain threshold. Recently developed learning-based methods are believed to outperform the compressive sensing methods without a steep drop in the reconstruction quality under such imaging conditions. However, the need for training data limits their applicability. In this paper, we develop a regularization method that outperforms compressive sensing methods as well as selected learning-based methods, without any need for training data. The regularization is constructed as a spatially varying weighted sum of first- and canonical second-order derivatives, with the weights determined to be adaptive to the image structure; the weights are determined such that the attenuation of sharp image features -- which is inevitable with the use of any regularization -- is significantly reduced. We demonstrate the effectiveness of the proposed method by performing reconstruction on sparse Fourier samples simulated from a variety of MRI images.
Wireless Link Scheduling via Interference-aware Symmetric Positive Definite Connectivity Manifolds
Authors: Ahmed S. Ibrahim
Date: 2021-05-27 00:00:00
Tasks: Scheduling

In this paper, we investigate the fundamental problem of wireless link scheduling in device-to-device (D2D) networks, through the lens of Riemannian geometry. Our goal is to find a novel metric to characterize interference among D2D pairs, which can pave the way towards efficient and fast scheduling algorithms. Towards achieving this goal, we first model the connectivity pattern of each D2D pair, including its interference links, as a positively-shifted Laplacian matrix, which is a symmetric positive definite (SPD) one. Noting that SPD matrices constitute a non-Euclidean manifold, we represent each of the D2D pairs as a point on the SPD (i.e., conic) manifold, which is analyzed via Riemannian geometry. Accordingly we employ Riemannian metrics (e.g., Log-Euclidean metric "LEM"), which are suitable measures of distances on manifolds, to characterize the interference among D2D points on the SPD manifold. To validate the effectiveness of the proposed LEM-based interference measure, we propose a sequential link selection algorithm that schedules D2D pairs in a descending order of their signal-to-noise ratio (SNR), while keeping their LEM distances towards the already-scheduled pairs on the Riemannian manifold to be greater than a certain LEM threshold. Such LEM-based condition is equivalent to limiting the interference from potential D2D pairs to be below certain threshold. We show that the proposed LEM-based scheduling algorithm achieves sum rate of more than 86% of state-of-the-art ones (e.g., FPLinQ), while only requiring spatial locations of D2D pairs, as opposed to requiring full channel state information (CSI).
Joint Channel Estimation and Device Activity Detection in Heterogeneous Networks
Authors: Leatile Marata, Onel Luis Alcaraz López, Eduardo Noboro Tominaga, Hirley Alves
Date: 2021-05-27 00:00:00
Tasks: Action Detection, Activity Detection

Internet of Things (IoT) has triggered a rapid increase in the number of connected devices and new use cases of wireless communications. To meet the new demands, the fifth generation (5G) of wireless communication systems features native machine type communication (MTC) services in addition to traditional human type communication (HTC) services. Some of the main challenges are the heterogeneous requirements and the sporadic traffic of massive MTC (mMTC), which makes the orthogonal allocation of resources infeasible. To overcome this problem, grant free non-orthogonal multiple access schemes have been proposed alongside with sparse signal recovery algorithms. While most of the related works have considered only homogeneous networks, we focus on a scenario where an enhanced mobile broadband (eMBB) device and multiple MTC devices share the same radio resources. We exploit the approximate message passing (AMP) algorithm for joint device activity detection and channel estimation of MTC devices in the presence of interference from eMBB, and evaluate the system performance in terms of receiver operating characteristics (ROC) and channel estimation errors. Moreover, we also propose two new pilot sequence generation strategies which improve the detection capabilities of the MTC receiver without affecting the eMBB service.
Beam Focusing for Near-Field Multi-User MIMO Communications
Authors: Haiyang Zhang, Nir Shlezinger, Francesco Guidi, Davide Dardari, Mohammadreza F. Imani, Yonina C. Eldar
Date: 2021-05-27 00:00:00

Large antenna arrays and high-frequency bands are two key features of future wireless communication systems. The combination of large-scale antennas with high transmission frequencies often results in the communicating devices operating in the near-field (Fresnel) region. In this paper, we study the potential of beam focusing, feasible in near-field operation, in facilitating high-rate multi-user downlink multiple-input multiple-output (MIMO) systems. As the ability to achieve beam focusing is dictated by the transmit antenna, we study near-field signaling considering different antenna structures, including fully-digital architectures, hybrid phase shifter-based precoders, and the emerging dynamic metasurface antenna (DMA) architecture for massive MIMO arrays. We first provide a mathematical model to characterize near-field wireless channels as well as the transmission pattern for the considered antenna architectures. Then, we formulate the beam focusing problem for the goal of maximizing the achievable sum-rate in multi-user networks. We propose efficient solutions based on the sum-rate maximization task for fully-digital, (phase shifters based-) hybrid and DMA architectures. Simulation results show the feasibility of the proposed beam focusing scheme for both single- and multi-user scenarios. In particular, the designed focused beams are such that users residing at the same angular direction can communicate reliably without interfering with each other, which is not achievable using conventional far-field beam steering.
IRS-Assisted Massive MIMO-NOMA Networks with Polarization Diversity
Authors: Arthur S. de Sena, Pedro H. J. Nardelli, Daniel B. da Costa, F. Rafael M. Lima, Liang Yang, Petar Popovski, Zhiguo Ding, Constantinos B. Papadias
Date: 2021-05-27 00:00:00
Tasks: Diversity

In this paper, the appealing features of a dual-polarized intelligent reflecting surface (IRS) are exploited to improve the performance of dual-polarized massive multiple-input multiple-output (MIMO) with non-orthogonal multiple access (NOMA) under imperfect successive interference cancellation (SIC). By considering the downlink of a multi-cluster scenario, the IRSs assist the base station (BS) to multiplex subsets of users in the polarization domain. Our novel strategy alleviates the impact of imperfect SIC and enables users to exploit polarization diversity with near-zero inter-subset interference. Our results show that when the IRSs are large enough, the proposed scheme always outperforms conventional massive MIMO-NOMA and MIMO-OMA systems even if SIC error propagation is present. It is also confirmed that dual-polarized IRSs can make cross-polar transmissions beneficial to the users, allowing them to improve their performance through polarization diversity.
MIG Median Detectors with Manifold Filter
Authors: Xiaoqiang Hua, Linyu Peng
Date: 2021-05-27 00:00:00

In this paper, we propose a class of median-based matrix information geometry (MIG) detectors with a manifold filter and apply them to signal detection in nonhomogeneous environments. As customary, the sample data is assumed to be modeled as Hermitian positive-definite (HPD) matrices, and the geometric median of a set of HPD matrices is interpreted as an estimate of the clutter covariance matrix (CCM). Then, the problem of signal detection can be reformulated as discriminating two points on the manifold of HPD matrices, one of which is the HPD matrix in the cell under test while the other represents the CCM. By manifold filter, we map a set of HPD matrices to another set of HPD matrices by weighting them, that consequently improves the discriminative power by reducing the intra-class distances while increasing the inter-class distances. Three MIG median detectors are designed by resorting to three geometric measures on the matrix manifold, and the corresponding geometric medians are shown to be robust to outliers. Numerical simulations show the advantage of the proposed MIG median detectors in comparison with their state-of-the-art counterparts as well as the conventional detectors in nonhomogeneous environments.
On the Concept of Frequency in Signal Processing
Authors: Móises Soto-Bajo, Andrés Fraguela Collar, Javier Herrera Vega, Raúl Felipe-Sosa
Date: 2021-05-24 00:00:00

Frequency is a central concept in Mathematics, Physics, and Signal Processing. It is the main tool for describing the oscillatory behavior of signals, which is usually argued to be the manifestation of some of their key features, depending on their nature. For instance, this is the case of Electroencephalographic signals. Hence, frequency is substantially present in the most common methodologies for analyzing signals, as the Fourier Analysis or the Time-Frequency Analysis. However, in spite of its importance as a keystone in Signal Processing, and its seemingly simple meaning, its mathematical foundation is not as straightforward as it may seem at first glance. A naive interpretation of the different mathematical concepts modelling frequency can be misleading, as their actual meanings essentially differ from the intuitive notion which are supposed to represent. In our opinion, this circumstance should be taken into account in order to develop appropriate signal analyzing and processing tools in some applications. In the current text we discuss this topic, with the main goal to draw the attention of the mathematical and engineering community to this point,often overlooked.
Charging A Smartphone Over the Air: The Resonant Beam Charging Method
Authors: Qingwen Liu, Mingqing Xiong, Mingqing Liu, Qingwei Jiang, Wen Fang, Yunfeng Bai
Date: 2021-05-24 00:00:00

Wireless charging for mobile Internet of Things (IoT) devices such as smartphones is extremely difficult. To reduce energy dissipation during wireless transmission in mobile scenarios, laser or narrow radio beams with sophisticated tracking control are typically required. However, reaching the necessary tracking accuracy and reliability is really difficult. In this paper, inspired by the features of optical resonators and retroreflectors, we develop an experiment on a self-aligned resonant beam charging system for long-distance mobile power transfer. It exploits light resonances inside a double-retroreflector-based spatially separated laser resonator (SSLR), which eliminates the requirement for any kind of tracking control. Focal telecentric cat's eye retroreflectors are employed here. The SSLR was investigated by both theoretical calculation and experiment. We also well assembled the transmitter and the receiver and demonstrated its application in mobile smartphone charging. The results show that above 5-W optical power (also obtained more than 0.6-W electrical power) transferring with negligible diffraction loss to a few-centimeter-size receiver is realized while the receiver moves arbitrarily within 2-m vertical distance and 6{\deg} field of view from the transmitter. The maximum horizontal moving range is up to 18cm. This wireless charging system empowers a smartphone in mobile operation with unlimited battery life without the need for a cable.