pwc-archive/papers-with-abstracts huggingface dataset:
Improving Macroeconomic Model Validity and Forecasting Performance with Pooled Country Data using Structural, Reduced Form, and Neural Network Model
Authors: Cameron Fen, Samir Undavia
Date: 2022-03-13 00:00:00
Tasks: Form, model

We show that pooling countries across a panel dimension to macroeconomic data can improve by a statistically significant margin the generalization ability of structural, reduced form, and machine learning (ML) methods to produce state-of-the-art results. Using GDP forecasts evaluated on an out-of-sample test set, this procedure reduces root mean squared error by 12\% across horizons and models for certain reduced-form models and by 24\% across horizons for dynamic structural general equilibrium models. Removing US data from the training set and forecasting out-of-sample country-wise, we show that reduced-form and structural models are more policy-invariant when trained on pooled data, and outperform a baseline that uses US data only. Given the comparative advantage of ML models in a data-rich regime, we demonstrate that our recurrent neural network model and automated ML approach outperform all tested baseline economic models. Robustness checks indicate that our outperformance is reproducible, numerically stable, and generalizable across models.
Fast Simulation-Based Bayesian Estimation of Heterogeneous and Representative Agent Models using Normalizing Flow Neural Networks
Authors: Cameron Fen
Date: 2022-03-12 00:00:00

This paper proposes a simulation-based deep learning Bayesian procedure for the estimation of macroeconomic models. This approach is able to derive posteriors even when the likelihood function is not tractable. Because the likelihood is not needed for Bayesian estimation, filtering is also not needed. This allows Bayesian estimation of HANK models with upwards of 800 latent states as well as estimation of representative agent models that are solved with methods that don't yield a likelihood--for example, projection and value function iteration approaches. I demonstrate the validity of the approach by estimating a 10 parameter HANK model solved via the Reiter method that generates 812 covariates per time step, where 810 are latent variables, showing this can handle a large latent space without model reduction. I also estimate the algorithm with an 11-parameter model solved via value function iteration, which cannot be estimated with Metropolis-Hastings or even conventional maximum likelihood estimators. In addition, I show the posteriors estimated on Smets-Wouters 2007 are higher quality and faster using simulation-based inference compared to Metropolis-Hastings. This approach helps address the computational expense of Metropolis-Hastings and allows solution methods which don't yield a tractable likelihood to be estimated.
Spectral Modification Based Data Augmentation For Improving End-to-End ASR For Children's Speech
Authors: Vishwanath Pratap Singh, Hardik Sailor, Supratik Bhattacharya, Abhishek Pandey
Date: 2022-03-13 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Data Augmentation, speech-recognition, Speech Recognition

Training a robust Automatic Speech Recognition (ASR) system for children's speech recognition is a challenging task due to inherent differences in acoustic attributes of adult and child speech and scarcity of publicly available children's speech dataset. In this paper, a novel segmental spectrum warping and perturbations in formant energy are introduced, to generate a children-like speech spectrum from that of an adult's speech spectrum. Then, this modified adult spectrum is used as augmented data to improve end-to-end ASR systems for children's speech recognition. The proposed data augmentation methods give 6.5% and 6.1% relative reduction in WER on children dev and test sets respectively, compared to the vocal tract length perturbation (VTLP) baseline system trained on Librispeech 100 hours adult speech dataset. When children's speech data is added in training with Librispeech set, it gives a 3.7 % and 5.1% relative reduction in WER, compared to the VTLP baseline system.
SSCU-Net: Spatial-Spectral Collaborative Unmixing Network for Hyperspectral Images
Authors: Lin Qi, Feng Gao, Junyu Dong, Xinbo Gao, Qian Du
Date: 2022-03-12 00:00:00
Tasks: Hyperspectral Unmixing

Linear spectral unmixing is an essential technique in hyperspectral image processing and interpretation. In recent years, deep learning-based approaches have shown great promise in hyperspectral unmixing, in particular, unsupervised unmixing methods based on autoencoder networks are a recent trend. The autoencoder model, which automatically learns low-dimensional representations (abundances) and reconstructs data with their corresponding bases (endmembers), has achieved superior performance in hyperspectral unmixing. In this article, we explore the effective utilization of spatial and spectral information in autoencoder-based unmixing networks. Important findings on the use of spatial and spectral information in the autoencoder framework are discussed. Inspired by these findings, we propose a spatial-spectral collaborative unmixing network, called SSCU-Net, which learns a spatial autoencoder network and a spectral autoencoder network in an end-to-end manner to more effectively improve the unmixing performance. SSCU-Net is a two-stream deep network and shares an alternating architecture, where the two autoencoder networks are efficiently trained in a collaborative way for estimation of endmembers and abundances. Meanwhile, we propose a new spatial autoencoder network by introducing a superpixel segmentation method based on abundance information, which greatly facilitates the employment of spatial information and improves the accuracy of unmixing network. Moreover, extensive ablation studies are carried out to investigate the performance gain of SSCU-Net. Experimental results on both synthetic and real hyperspectral data sets illustrate the effectiveness and competitiveness of the proposed SSCU-Net compared with several state-of-the-art hyperspectral unmixing methods.
DURRNet: Deep Unfolded Single Image Reflection Removal Network
Authors: Jun-Jie Huang, Tianrui Liu, Zhixiong Yang, Shaojing Fu, Wentao Zhao, Pier Luigi Dragotti
Date: 2022-03-12 00:00:00
Tasks: blind source separation, Reflection Removal, Rolling Shutter Correction

Single image reflection removal problem aims to divide a reflection-contaminated image into a transmission image and a reflection image. It is a canonical blind source separation problem and is highly ill-posed. In this paper, we present a novel deep architecture called deep unfolded single image reflection removal network (DURRNet) which makes an attempt to combine the best features from model-based and learning-based paradigms and therefore leads to a more interpretable deep architecture. Specifically, we first propose a model-based optimization with transform-based exclusion prior and then design an iterative algorithm with simple closed-form solutions for solving each sub-problems. With the deep unrolling technique, we build the DURRNet with ProxNets to model natural image priors and ProxInvNets which are constructed with invertible networks to impose the exclusion prior. Comprehensive experimental results on commonly used datasets demonstrate that the proposed DURRNet achieves state-of-the-art results both visually and quantitatively.
Human Attention Detection Using AM-FM Representations
Authors: Wenjing Shi
Date: 2022-03-09 00:00:00
Tasks: Action Detection, Activity Detection

Human activity detection from digital videos presents many challenges to the computer vision and image processing communities. Recently, many methods have been developed to detect human activities with varying degree of success. Yet, the general human activity detection problem remains very challenging, especially when the methods need to work 'in the wild' (e.g., without having precise control over the imaging geometry). The thesis explores phase-based solutions for (i) detecting faces, (ii) back of the heads, (iii) joint detection of faces and back of the heads, and (iv) whether the head is looking to the left or the right, using standard video cameras without any control on the imaging geometry. The proposed phase-based approach is based on the development of simple and robust methods that rely on the use of Amplitude Modulation- Frequency Modulation (AM-FM) models. The approach is validated using video frames extracted from the Advancing Out-of-school Learning in Mathematics and Engineering (AOLME) project. The dataset consisted of 13,265 images from ten students looking at the camera, and 6,122 images from five students looking away from the camera. For the students facing the camera, the method was able to correctly classify 97.1% of them looking to the left and 95.9% of them looking to the right. For the students facing the back of the camera, the method was able to correctly classify 87.6% of them looking to the left and 93.3% of them looking to the right. The results indicate that AM-FM based methods hold great promise for analyzing human activity videos.
WSSAMNet: Weakly Supervised Semantic Attentive Medical Image Registration Network
Authors: Sahar Almahfouz Nasser, Nikhil Cherian Kurian, Saqib Shamsi, Mohit Meena, Amit Sethi
Date: 2022-03-05 00:00:00
Tasks: Image Registration, Medical Image Registration

We present WSSAMNet, a weakly supervised method for medical image registration. Ours is a two step method, with the first step being the computation of segmentation masks of the fixed and moving volumes. These masks are then used to attend to the input volume, which are then provided as inputs to a registration network in the second step. The registration network computes the deformation field to perform the alignment between the fixed and the moving volumes. We study the effectiveness of our technique on the BraTSReg challenge data against ANTs and VoxelMorph, where we demonstrate that our method performs competitively.
Constrained RIS Phase Profile Optimization and Time Sharing for Near-field Localization
Authors: Moustafa Rahal, Benoît Denis, Kamran Keykhosravi, Furkan Keskin, Bernard Uguen, Henk Wymeersch
Date: 2022-03-14 00:00:00

The rising concept of reconfigurable intelligent surface (RIS) has promising potential for Beyond 5G localization applications. We herein investigate different phase profile designs at a reflective RIS, which enable non-line-of-sight positioning in nearfield from downlink single antenna transmissions. We first derive the closed-form expressions of the corresponding Fisher information matrix (FIM) and position error bound (PEB). Accordingly, we then propose a new localization-optimal phase profile design, assuming prior knowledge of the user equipment location. Numerical simulations in a canonical scenario show that our proposal outperforms conventional RIS random and directional beam codebook designs in terms of PEB. We also illustrate the four beams allocated at the RIS (i.e., one directional beam, along with its derivatives with respect to space dimensions) and show how their relative weights according to the optimal solution can be practically implemented through time sharing (i.e., considering feasible beams sequentially).
Arbitrary Beam Pattern Approximation via RISs with Measured Element Responses
Authors: Moustafa Rahal, Benoît Denis, Kamran Keykhosravi, Furkan Keskin, Bernard Uguen, George C. Alexandropoulos, Henk Wymeersch
Date: 2022-03-14 00:00:00

Smart radio environments (SREs) are seen as a key rising concept of next generation wireless networks, where propagation channels between transmitters and receivers are purposely controlled. One promising approach to achieve such channel flexibility relies on semipassive reflective Reconfigurable intelligent surfaces (RISs), which can shape the bouncing multipath signals for enhancing communication quality of service, making localization feasible in adverse operating conditions, or reducing unwanted electromagnetic emissions. This paper introduces a generic framework that aims at optimizing the end-to-end precoder controlled by RISs, so that arbitrary beam patterns can be generated, given a predefined lookup table of RIS element-wise complex reflection coefficients. This method is validated and illustrated for different targeted beam patterns in both the far-field and the near-field regimes, while considering the prior characterization of real-life RIS hardware prototypes. These results show how, and to which extent, RIS configuration optimization can approximate the desired beams under realistic hardware limitations and low-complexity implementation practicability, or conversely, which RIS elements' lookup tables would be more suitable. The latter can provide useful guidelines for future RIS hardware designs.
Mixture Components Inference for Sparse Regression: Introduction and Application for Estimation of Neuronal Signal from fMRI BOLD
Authors: Anna Pidnebesna, Iveta Fajnerova, Jiri Horacek, Jaroslav Hlinka
Date: 2022-03-14 00:00:00
Tasks: Model Selection, regression

Sparse linear regression methods including the well-known LASSO and the Dantzig selector have become ubiquitous in the engineering practice, including in medical imaging. Among other tasks, they have been successfully applied for the estimation of neuronal activity from functional magnetic resonance data without prior knowledge of the stimulus or activation timing, utilizing an approximate knowledge of the hemodynamic response to local neuronal activity. These methods work by generating a parametric family of solutions with different sparsity, among which an ultimate choice is made using an information criteria. We propose a novel approach, that instead of selecting a single option from the family of regularized solutions, utilizes the whole family of such sparse regression solutions. Namely, their ensemble provides a first approximation of probability of activation at each time-point, and together with the conditional neuronal activity distributions estimated with the theory of mixtures with varying concentrations, they serve as the inputs to a Bayes classifier eventually deciding on the verity of activation at each time-point. We show in extensive numerical simulations that this new method performs favourably in comparison with standard approaches in a range of realistic scenarios. This is mainly due to the avoidance of overfitting and underfitting that commonly plague the solutions based on sparse regression combined with model selection methods, including the corrected Akaike Information Criterion. This advantage is finally documented in selected fMRI task datasets.
Pulses with Minimum Residual Intersymbol Interference for Faster than Nyquist Signaling
Authors: Youssef Jaffal, Alex Alvarado
Date: 2022-03-14 00:00:00

Faster than Nyquist signaling increases the spectral efficiency of pulse amplitude modulation by accepting intersymbol interference, where an equalizer is needed at the receiver. Since the complexity of an optimal equalizer increases exponentially with the number of the interfering symbols, practical truncated equalizers assume shorter memory. The power of the resulting residual interference depends on the transmit filter and limits the performance of truncated equalizers. In this paper, we use numerical optimizations and the prolate spheroidal wave functions to find optimal time-limited pulses that achieve minimum residual interference. Compared to root raised cosine pulses, the new pulses decrease the residual interference by an order of magnitude, for example, a decrease by 32 dB is achieved for an equalizer that considers four interfering symbols at 57% faster transmissions. As a proof of concept, for the 57% faster transmissions of binary symbols, we showed that using the new pulse with a 4-state equalizer has better bit error rate performance compared to using a root raised cosine pulse with a 128-state equalizer.
Energy Efficiency Maximization of Simultaneous Transmission and Reflection RIS Assisted Full-Duplex Communications
Authors: Pengxin Guan, Yiru Wang, Hongkang Yu, Yuping Zhao
Date: 2022-03-14 00:00:00

This work studies the effectiveness of a novel simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) aided Full-Duplex (FD) communication system. We aim to maximize the energy efficiency by jointly optimizing the transmit power and passive beamforming at the STAR-RIS. We propose an efficient algorithm to optimize them iteratively under the alternating optimization framework. The successive convex approximation (SCA) and Dinkelbach's method are used to solve the power optimization subproblem. The penalty-based method is used to design passive beamforming at the STAR-RIS. Numerical results verify the convergence and effectiveness of the proposed algorithm, and further reveal the benifits of the combining of the STAR-RIS and FD communication compared to benchmarks.
Robust 2-D DOA Estimation in a Polarization Sensitive Single User Environment
Authors: Md Imrul Hasan, Mohammad Saquib
Date: 2022-03-14 00:00:00
Tasks: All

Apart from the conventional parameters (such as signal-to-noise ratio, array geometry and size, sample size), several other factors (e.g. alignment of the antenna elements, polarization parameters) influence the performance of direction of arrival (DOA) estimating algorithms. When all the antenna elements are identically aligned, the polarization parameters do not affect the steering vectors, which is the underlying assumption of all the conventional DOA algorithms. Unfortunately, in this case, for a given set of DOA angles there exists a range of polarization parameters which could result in a very low signal-to-noise ratio (SNR) across all the antenna elements in the array. To avoid this type of catastrophic event, different antenna element needs to be aligned differently. However, this fact will make almost all commonly used DOA estimation algorithms non-operable, since the steering vectors are contaminated by the polarization parameters. To the best of our knowledge, no work in the literature addresses this issue even for a single user environment. In this paper, that line of inquiry is pursued. We consider a circular array with the minimum number of antenna elements and propose an antenna alignment scheme to ensure that at any given point no more than one element will suffer from significantly low SNR due to the contribution of polarization. A low complexity algorithm that estimates the DOA angles in a closed-form manner is developed. We treat MUSIC as the baseline algorithm and demonstrate how it can reliably operate in all possible DOA and polarization environments. Finally, a thorough performance and complexity analysis are illustrated for the above two algorithms.
Channel Charting Aided Pilot Reuse for Massive MIMO Systems with Spatially Correlated Channels
Authors: Lucas Ribeiro, Markus Leinonen, Hanan Al-Tous, Olav Tirkkonen, Markku Juntti
Date: 2022-03-13 00:00:00

Massive multiple-input multiple-output (mMIMO) technology is a way to increase the spectral efficiency of machine-type communications (MTC). To exploit the benefits from large antenna arrays, accurate channel estimation through pilot signals is needed. Massive MTC systems cannot avoid pilot reuse due to the enormous numbers of connected devices. We propose a pilot reuse algorithm based on channel charting (CC) to mitigate pilot contamination in a multi-sector single-cell massive MTC system having spatially correlated channels. We show that after creating an interference map via CC, a simple strategy to allocate the pilot sequences can be implemented. The simulation results show that the CC-based pilot reuse strategy improves channel estimation accuracy, which subsequently improves the symbol detection performance and increases the spectral efficiency compared to other existing schemes. Moreover, the performance of the CC pilot assignment method approaches that of exhaustive search pilot assignment for small network setups.
A Context-Aware Readout System for Sparse Touch Sensing Array Using Ultra-low-power Always-on Event Detection
Authors: Hyeri Roh, Woo-Seok Choi
Date: 2022-03-13 00:00:00
Tasks: compressed sensing, Event Detection

Increasing demand for larger touch screen panels (TSPs) places more energy burden to mobile systems with conventional sensing methods. To mitigate this problem, taking advantage of the touch event sparsity, this paper proposes a novel TSP readout system that can obtain huge energy saving by turning off the readout circuits when none of the sensors are activated. To this end, a novel ultra-low-power always-on event and region of interest detection based on lightweight compressed sensing is proposed. Exploiting the proposed event detector, the context-aware TSP readout system, which can improve the energy efficiency by up to 42x, is presented.
Emergent dynamics in an astrocyte-neuronal network coupled via nitric oxide
Authors: Bhanu Sharma, Spandan Kumar, Subhendu Ghosh, Vikram Singh
Date: 2022-03-14 00:00:00

In the brain, both neurons and glial cells work in conjunction with each other during information processing. Stimulation of neurons can cause calcium oscillations in astrocytes which in turn can affect neuronal calcium dynamics. The "glissandi" effect is one such phenomenon, associated with a decrease in infraslow fluctuations, in which synchronized calcium oscillations propagate as a wave in hundreds of astrocytes. Nitric oxide molecules released from the astrocytes contribute to synaptic functions on the basis of the underlying astrocyte-neuron interaction network. In this study, by defining an astrocyte-neuronal (A-N) unit as an integrated circuit of one neuron and one astrocyte, we developed a minimal model of neuronal stimulus-dependent and nitric oxide-mediated emergence of calcium waves in astrocytes. Incorporating inter-unit communication via nitric oxide molecules, a coupled network of 1,000 such A-N units is developed in which multiple stable regimes were found to emerge in astrocytes. We examined the ranges of neuronal stimulus strength and the coupling strength between A-N units that give rise to such dynamical behaviors. We also report that there exists a range of coupling strength, wherein units not receiving stimulus also start showing oscillations and become synchronized. Our results support the hypothesis that glissandi-like phenomena exhibiting synchronized calcium oscillations in astrocytes help in efficient synaptic transmission by reducing the energy demand of the process.
Quantifying active and resistive stresses in adherent cells
Authors: Hélène Delanoë-Ayari, Alice Nicolas
Date: 2022-03-12 00:00:00
Tasks: Bayesian Inference

To understand cell migration, it is crucial to gain knowledge on how cells exert and integrate forces on/from their environment. A quantity of prime interest for biophysicists interested in cell movements modeling is the intracellular stresses. Up to now, three different methods have been proposed to calculate it, they are all in the regime of the thin plate approximation. Two are based on solving the mechanical equilibrium equation inside the cell material (Monolayer Stress Microscopy, and Bayesian Inference Stress Microscopy) and one is based on the continuity of displacement at the cell/substrate interface (Intracellular Stress Microscopy). We show here using 3D FEM modeling that these techniques do not calculate the same quantities (as was previously assumed), the first techniques calculate the sum of the active and resistive stresses within the cell, whereas the last one only calculate the resistive component. Combining these techniques should in principle permit to get access to the active stress alone.