pwc-archive/papers-with-abstracts huggingface dataset:
Grid-connected Soft Switching Partial Resonance Inverter for Distributed Generation
Authors: Farid Naghavi, Hamid Toliyat
Date: 2022-04-08 00:00:00

This paper presents current control method for a grid-connected partial resonant soft switching inverter. This inverter does not use an electrolytic capacitor and is capable of boosting and bucking the voltage. Grid-connected inverters are used to integrate distributed energy sources to the grid. Current control is vital in meeting the standards and requirements when connecting to the grid. The closed-loop current regulation for this type of converters is analyzed and design guidelines are provided. The control is implemented in the synchronous frame. In addition active damping techniques using capacitor voltage and inductor voltage feedback is used to mitigate CL filter resonance at the output. The mentioned control strategies are implemented on a 400W lab prototype and the results are presented
Exergetic Port-Hamiltonian Systems: Navier-Stokes-Fourier Fluid
Authors: Markus Lohmayer, Sigrid Leyendecker
Date: 2022-04-01 00:00:00

The Exergetic Port-Hamiltonian Systems modeling language combines a graphical syntax inspired by bond graphs with a port-Hamiltonian semantics akin to the GENERIC formalism. The syntax enables the modular and hierarchical specification of the composition pattern of lumped and distributed-parameter models. The semantics reflects the first and second law of thermodynamics as structural properties. Interconnected and hierarchically defined models of multiphysical thermodynamic systems can thus be expressed in a formal language accessible to humans and computers alike. We discuss a composed model of the Navier-Stokes-Fourier fluid on a fixed spatial domain as an example of an open distributed-parameter system. At the top level, the system comprises five subsystems which model kinetic energy storage, internal energy storage, thermal conduction, bulk viscosity, and shear viscosity.
Temporal and spatial evolution of the distribution related to the number of COVID-19 pandemic
Authors: Peng Liu, Yanyan Zheng
Date: 2022-04-08 00:00:00

This work systematically conducts a data analysis based on the numbers of both cumulative and daily confirmed COVID-19 cases and deaths in a time span through April 2020 to June 2022 for over 200 countries around the world. Such research feature aims to reveal the temporal and spatial evolution of the country-level distribution observed in COVID-19 pandemic, and obtains some interesting results as follows. (1) The distributions of the numbers for cumulative confirmed cases and deaths obey power-law in early stages of COVID-19 and stretched exponential function in subsequent course. (2) The distributions of the numbers for daily confirmed cases and deaths obey power-law in early and late stages of COVID-19 and stretched exponential function in middle stages. The crossover region between power-law and stretched exponential behaviour seems to depend on the evolution of "infection" event and "death" event. Such observation implies a kind of important symmetry related to the dynamics process of COVID-19 spreading. (3) The distributions of the normalized numbers for each metric show a temporal scaling behaviour in 2-year period, and are well described by stretched exponential function. The observation of power-law and stretched exponential behaviour in such country-level distributions suggests underlying intrinsic dynamics of a virus spreading process in human interconnected society. And thus it is important for understanding and mathematically modeling the COVID-19 pandemic.
Two-step estimation in linear regressions with adaptive learning
Authors: Alexander Mayer
Date: 2022-04-11 00:00:00
Tasks: regression, Vocal Bursts Valence Prediction

Weak consistency and asymptotic normality of the ordinary least-squares estimator in a linear regression with adaptive learning is derived when the crucial, so-called, `gain' parameter is estimated in a first step by nonlinear least squares from an auxiliary model. The singular limiting distribution of the two-step estimator is normal and in general affected by the sampling uncertainty from the first step. However, this `generated-regressor' issue disappears for certain parameter combinations.
Bootstrap Cointegration Tests in ARDL Models
Authors: Stefano Bertelli, Gianmarco Vacca, Maria Grazia Zoia
Date: 2022-04-11 00:00:00

The paper proposes a new bootstrap approach to the Pesaran, Shin and Smith's bound tests in a conditional equilibrium correction model with the aim to overcome some typical drawbacks of the latter, such as inconclusive inference and distortion in size. The bootstrap tests are worked out under several data generating processes, including degenerate cases. Monte Carlo simulations confirm the better performance of the bootstrap tests with respect to bound ones and to the asymptotic F test on the independent variables of the ARDL model. It is also proved that any inference carried out in misspecified models, such as unconditional ARDLs, may be misleading. Empirical applications highlight the importance of employing the appropriate specification and provide definitive answers to the inconclusive inference of the bound tests when exploring the long-term equilibrium relationship between economic variables.
Educational Inequality
Authors: Jo Blanden, Matthias Doepke, Jan Stuhler
Date: 2022-04-10 00:00:00

This chapter provides new evidence on educational inequality and reviews the literature on the causes and consequences of unequal education. We document large achievement gaps between children from different socio-economic backgrounds, show how patterns of educational inequality vary across countries, time, and generations, and establish a link between educational inequality and social mobility. We interpret this evidence from the perspective of economic models of skill acquisition and investment in human capital. The models account for different channels underlying unequal education and highlight how endogenous responses in parents' and children's educational investments generate a close link between economic inequality and educational inequality. Given concerns over the extended school closures during the Covid-19 pandemic, we also summarize early evidence on the impact of the pandemic on children's education and on possible long-run repercussions for educational inequality.
Describing Sen's Transitivity Condition in Inequalities and Equations
Authors: Fujun Hou
Date: 2022-04-08 00:00:00
Tasks: Position

In social choice theory, Sen's value restriction condition is a sufficiency condition restricted to individuals' ordinal preferences so as to obtain a transitive social preference under the majority decision rule. In this article, Sen's transitivity condition is described by use of inequality and equation. First, for a triple of alternatives, an individual's preference is represented by a preference map, whose entries are sets containing the ranking position or positions derived from the individual's preference over that triple of those alternatives. Second, by using the union operation of sets and the cardinality concept, Sen's transitivity condition is described by inequalities. Finally, by using the membership function of sets, Sen's transitivity condition is further described by equations.
On Locally Rationalizable Social Choice Functions
Authors: Felix Brandt, Chris Dong
Date: 2022-04-11 00:00:00
Tasks: Relation

We consider a notion of rationalizability, where the rationalizing relation may depend on the set of feasible alternatives. More precisely, we say that a choice function is locally rationalizable if it is rationalized by a family of rationalizing relations such that a strict preference between two alternatives in some feasible set is preserved when removing other alternatives. Tyson (2008) has shown that a choice function is locally rationalizable if and only if it satisfies Sen's $\gamma$. We expand the theory of local rationalizability by proposing a natural strengthening of $\gamma$ that precisely characterizes local rationalizability via PIP-transitive relations and by introducing the $\gamma$-hull of a choice function as its finest coarsening that satisfies $\gamma$. Local rationalizability permits a unified perspective on social choice functions that satisfy $\gamma$, including classic ones such as the top cycle and the uncovered set as well as new ones such as two-stage majoritarian choice and split cycle. We give simple axiomatic characterizations of some of these using local rationalizability and propose systematic procedures to define social choice functions that satisfy $\gamma$.
From Modern CNNs to Vision Transformers: Assessing the Performance, Robustness, and Classification Strategies of Deep Learning Models in Histopathology
Authors: Maximilian Springenberg, Annika Frommholz, Markus Wenzel, Eva Weicken, Jackie Ma, Nils Strodthoff
Date: 2022-04-11 00:00:00
Tasks: Benchmarking, Cancer Classification, Classification, Image-to-Image Translation, whole slide images

While machine learning is currently transforming the field of histopathology, the domain lacks a comprehensive evaluation of state-of-the-art models based on essential but complementary quality requirements beyond a mere classification accuracy. In order to fill this gap, we developed a new methodology to extensively evaluate a wide range of classification models, including recent vision transformers, and convolutional neural networks such as: ConvNeXt, ResNet (BiT), Inception, ViT and Swin transformer, with and without supervised or self-supervised pretraining. We thoroughly tested the models on five widely used histopathology datasets containing whole slide images of breast, gastric, and colorectal cancer and developed a novel approach using an image-to-image translation model to assess the robustness of a cancer classification model against stain variations. Further, we extended existing interpretability methods to previously unstudied models and systematically reveal insights of the models' classifications strategies that can be transferred to future model architectures.
Learning-based Lossless Point Cloud Geometry Coding using Sparse Tensors
Authors: Dat Thanh Nguyen, Andre Kaup
Date: 2022-04-11 00:00:00

Most point cloud compression methods operate in the voxel or octree domain which is not the original representation of point clouds. Those representations either remove the geometric information or require high computational power for processing. In this paper, we propose a context-based lossless point cloud geometry compression that directly processes the point representation. Operating on a point representation allows us to preserve geometry correlation between points and thus to obtain an accurate context model while significantly reduce the computational cost. Specifically, our method uses a sparse convolution neural network to estimate the voxel occupancy sequentially from the x,y,z input data. Experimental results show that our method outperforms the state-of-the-art geometry compression standard from MPEG with average rate savings of 52% on a diverse set of point clouds from four different datasets.
A Dual Sensor Computational Camera for High Quality Dark Videography
Authors: Yuxiao Cheng, Runzhao Yang, Zhihong Zhang, Jinli Suo, Qionghai Dai
Date: 2022-04-11 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Videos captured under low light conditions suffer from severe noise. A variety of efforts have been devoted to image/video noise suppression and made large progress. However, in extremely dark scenarios, extensive photon starvation would hamper precise noise modeling. Instead, developing an imaging system collecting more photons is a more effective way for high-quality video capture under low illuminations. In this paper, we propose to build a dual-sensor camera to additionally collect the photons in NIR wavelength, and make use of the correlation between RGB and near-infrared (NIR) spectrum to perform high-quality reconstruction from noisy dark video pairs. In hardware, we build a compact dual-sensor camera capturing RGB and NIR videos simultaneously. Computationally, we propose a dual-channel multi-frame attention network (DCMAN) utilizing spatial-temporal-spectral priors to reconstruct the low-light RGB and NIR videos. In addition, we build a high-quality paired RGB and NIR video dataset, based on which the approach can be applied to different sensors easily by training the DCMAN model with simulated noisy input following a physical-process-based CMOS noise model. Both experiments on synthetic and real videos validate the performance of this compact dual-sensor camera design and the corresponding reconstruction algorithm in dark videography.
Image Reconstruction for MRI using Deep CNN Priors Trained without Groundtruth
Authors: Weijie Gan, Cihat Eldeniz, Jiaming Liu, Sihao Chen, Hongyu An, Ulugbek S. Kamilov
Date: 2022-04-10 00:00:00
Tasks: Image Reconstruction

We propose a new plug-and-play priors (PnP) based MR image reconstruction method that systematically enforces data consistency while also exploiting deep-learning priors. Our prior is specified through a convolutional neural network (CNN) trained without any artifact-free ground truth to remove undersampling artifacts from MR images. The results on reconstructing free-breathing MRI data into ten respiratory phases show that the method can form high-quality 4D images from severely undersampled measurements corresponding to acquisitions of about 1 and 2 minutes in length. The results also highlight the competitive performance of the method compared to several popular alternatives, including the TGV regularization and traditional UNet3D.
Denoiser-based projections for 2-D super-resolution multi-reference alignment
Authors: Jonathan Shani, Tom Tirer, Raja Giryes, Tamir Bendory
Date: 2022-04-10 00:00:00
Tasks: GPU, Super-Resolution

We study the 2-D super-resolution multi-reference alignment (SR-MRA) problem: estimating an image from its down-sampled, circularly-translated, and noisy copies. The SR-MRA problem serves as a mathematical abstraction of the structure determination problem for biological molecules. Since the SR-MRA problem is ill-posed without prior knowledge, accurate image estimation relies on designing priors that well-describe the statistics of the images of interest. In this work, we build on recent advances in image processing, and harness the power of denoisers as priors of images. In particular, we suggest to use denoisers as projections, and design two computational frameworks to estimate the image: projected expectation-maximization and projected method of moments. We provide an efficient GPU implementation, and demonstrate the effectiveness of these algorithms by extensive numerical experiments on a wide range of parameters and images.
NeoRS: a neonatal resting state fMRI data preprocessing pipeline
Authors: V. Enguix, J. Kenley, D. Luck, J. Cohen-Adad, G. A. Lodygensky
Date: 2022-04-08 00:00:00
Tasks: Functional Connectivity, Specificity

Resting state fMRI (rsfMRI) has been shown to be a promising tool to study intrinsic functional connectivity and assess its integrity in cerebral development. In neonates, where fMRI is limited to few paradigms, rsfMRI was shown to be a relevant tool to explore regional interactions of brain networks. However, to identify the resting state networks, data needs to be carefully processed. Because of the non-collaborative nature of the neonates, the differences in brain size and the reversed contrast compared to adults, neonates can't be processed with the existing adult pipelines. Therefore, we developed NeoRS. The main processing steps include atlas registration, skull tripping, segmentation, slice timing and head motion correction and confounds regression. To address the specificity of neonatal brain imaging, particular attention was given to registration including neonatal atlas type and parameters, such as brain size variations, and contrast differences compared to adults. Furthermore, head motion was scrutinized and optimized, as it is a major issue when processing neonatal data. The pipeline includes visual quality control assessment checkpoints. To assess its effectiveness, we used the data from the Baby Connectome Project including 10 neonates. NeoRS was designed to work on both multi-band and single-band acquisitions and is applicable on smaller datasets. It also includes popular functional connectivity analysis features such as seed based correlations. Language, default mode, dorsal attention, visual, ventral attention, motor and fronto parietal networks were evaluated. The different analyzed networks were in agreement with previously published studies in the neonate. NeoRS is coded in Matlab, it is open-source and available on https://github.com/venguix/NeoRS. NeoRS allows robust image processing of the neonatal rsfMRI data that can be readily customized to different datasets.
Blind Orthogonal Least Squares based Compressive Spectrum Sensing
Authors: Liyang Lu, Wenbo Xu, Yue Wang, Zhi Tian
Date: 2022-04-11 00:00:00
Tasks: Compressive Sensing

As an enabling technique of cognitive radio (CR), compressive spectrum sensing (CSS) based on compressive sensing (CS) can detect the spectrum opportunities from wide frequency bands efficiently and accurately by using sub-Nyquist sampling rate. However, the sensing performance of most existing CSS excessively relies on the prior information such as spectrum sparsity or noise variance. Thus, a key challenge in practical CSS is how to work effectively even in the absence of such information. In this paper, we propose a blind orthogonal least squares based CSS algorithm (B-OLS-CSS), which functions properly without the requirement of prior information. Specifically, we develop a novel blind stopping rule for the OLS algorithm based on its probabilistic recovery condition. This innovative rule gets rid of the need of the spectrum sparsity or noise information, but only requires the computational-feasible mutual incoherence property of the given measurement matrix. Our theoretical analysis indicates that the signal-to-noise ratio required by the proposed B-OLS-CSS for achieving a certain sensing accuracy is relaxed than that by the benchmark CSS using the OMP algorithm, which is verified by extensive simulation results.
Double Nonstationarity: Blind Extraction of Independent Nonstationary Vector/Component from Nonstationary Mixtures -- Algorithms
Authors: Zbyněk Koldovský, Václav Kautský, Petr Tichavský
Date: 2022-04-11 00:00:00

In this article, nonstationary mixing and source models are combined for developing new fast and accurate algorithms for Independent Component or Vector Extraction (ICE/IVE), one of which stands for a new extension of the well-known FastICA. This model allows for a moving source-of-interest (SOI) whose distribution on short intervals can be (non-)circular (non-)Gaussian. A particular Gaussian source model assuming tridiagonal covariance matrix structures is proposed. It is shown to be beneficial in the frequency-domain speaker extraction problem. The algorithms are verified in simulations. In comparison to the state-of-the-art algorithms, they show superior performance in terms of convergence speed and extraction accuracy.
A General Compressive Sensing Construct using Density Evolution
Authors: Hang Zhang, Afshin Abdi, Faramarz Fekri
Date: 2022-04-11 00:00:00
Tasks: Compressive Sensing

This paper proposes a general framework to design a sparse sensing matrix $\ensuremath{\mathbf{A}}\in \mathbb{R}^{m\times n}$, in a linear measurement system $\ensuremath{\mathbf{y}} = \ensuremath{\mathbf{Ax}}^{\natural} + \ensuremath{\mathbf{w}}$, where $\ensuremath{\mathbf{y}} \in \mathbb{R}^m$, $\ensuremath{\mathbf{x}}^{\natural}\in \RR^n$, and $\ensuremath{\mathbf{w}}$ denote the measurements, the signal with certain structures, and the measurement noise, respectively. By viewing the signal reconstruction from the measurements as a message passing algorithm over a graphical model, we leverage tools from coding theory in the design of low density parity check codes, namely the density evolution, and provide a framework for the design of matrix $\ensuremath{\mathbf{A}}$. Particularly, compared to the previous methods, our proposed framework enjoys the following desirable properties: ($i$) Universality: the design supports both regular sensing and preferential sensing, and incorporates them in a single framework; ($ii$) Flexibility: the framework can easily adapt the design of $\bA$ to a signal $\ensuremath{\mathbf{x}}^{\natural}$ with different underlying structures. As an illustration, we consider the $\ell_1$ regularizer, which correspond to Lasso, for both the regular sensing and preferential sensing scheme. Noteworthy, our framework can reproduce the classical result of Lasso, i.e., $m\geq c_0 k\log(n/k)$ (the regular sensing) with regular design after proper distribution approximation, where $c_0 > 0$ is some fixed constant. We also provide numerical experiments to confirm the analytical results and demonstrate the superiority of our framework whenever a preferential treatment of a sub-block of vector $\bx^{\natural}$ is required.
Design and Experimental Verification of a Novel Error-Backpropagation-Based Background Calibration for Time Interleaved ADC in Digital Communication Receivers
Authors: Fredy Solis, Benjamín T. Reyes, Damián A. Morero, Mario R. Hueda
Date: 2022-04-11 00:00:00

A novel background calibration technique for Time-Interleaved Analog-to-Digital Converters (TI-ADCs) is presented in this paper. This technique is applicable to equalized digital communication receivers. As shown by Tsai et al. [1] and Luna et al. [2], in a digital receiver it is possible to treat the TI-ADC errors as part of the communication channel and take advantage of the adaptive equalizer to compensate them. Therefore calibration becomes an integral part of the channel equalization. No special purpose analog or digital calibration blocks or algorithms are required. However, there is a large class of receivers where the equalization technique cannot be directly applied because other signal processing blocks are located between the TI-ADC and the equalizer. The technique presented here generalizes earlier works to this class of receivers. The error backpropagation algorithm, traditionally used in machine learning, is applied to the error computed at the receiver slicer and used to adapt an auxiliary equalizer adjacent to the TI-ADC, called the Compensation Equalizer (CE). Simulations using a dual polarization optical coherent receiver model demonstrate accurate and robust mismatch compensation across different application scenarios. Several Quadrature Amplitude Modulation (QAM) schemes are tested in simulations and experimentally. Measurements on an emulation platform which includes an 8 bit, 4 GS/s TI-ADC prototype chip fabricated in 130nm CMOS technology, show an almost ideal mitigation of the impact of the mismatches on the receiver performance when 64-QAM and 256-QAM schemes are tested. An absolute improvement in the TI-ADC performance of $\sim$15 dB in both SNDR and SFDR is measured.