pwc-archive/papers-with-abstracts huggingface dataset:
Integrating experimental data with molecular simulations to investigate RNA structural dynamics
Authors: Mattia Bernetti, Giovanni Bussi
Date: 2022-07-18 00:00:00

Conformational dynamics is crucial for ribonucleic acid (RNA) function. Techniques such as nuclear magnetic resonance, cryo-electron microscopy, small- and wide-angle X-ray scattering, chemical probing, single-molecule F\"orster resonance energy transfer or even thermal or mechanical denaturation experiments probe RNA dynamics at different time and space resolutions. Their combination with accurate atomistic molecular dynamics (MD) simulations paves the way for quantitative and detailed studies of RNA dynamics. First, experiments provide a quantitative validation tool for MD simulations. Second, available data can be used to refine simulated structural ensembles to match experiments. Finally, comparison with experiments allows for improving MD force fields that are transferable to new systems for which data is not available. Here we review the recent literature and provide our perspective on this field.
The Roads One Must Walk Down: Commute and Depression for Beijing's Residents
Authors: Xize Wang, Tao Liu
Date: 2022-07-16 00:00:00

As a vital aspect of individual's quality of life, mental health has been included as an important component of the U.N. Sustainable Development Goals. This study focuses on a specific aspect of mental health: depression, and examines its relationship with commute patterns. Using survey data from 1,528 residents in Beijing, China, we find that every 10 additional minutes of commute time is associated with 1.1% higher likelihood of depression. We test for the mechanisms of the commute-depression link and find that commute is associated with depression as a direct stressor rather than triggering higher work stress. When decomposing commute time into mode-specific time, we found that time on mopeds/motorcycles has the strongest association with depression. Moreover, the commute-depression associations are stronger for older workers and blue-collar workers. Hence, policies that could reduce commute time, encourage work from home, improve job-housing balance or increase motorcyclists' safety would help promote mental health.
Home-made blues: Residential crowding and mental health in Beijing, China
Authors: Xize Wang, Tao Liu
Date: 2022-07-16 00:00:00

Although residential crowding has many well-being implications, its connection to mental health is yet to be widely examined. Using survey data from 1613 residents in Beijing, China, we find that living in a crowded place - measured by both square metres per person and persons per bedroom - is significantly associated with a higher risk of depression. We test for the mechanisms of such associations and find that the residential crowding-depression link arises through increased living space-specific stress rather than increased life stress. We also identify the following subgroups that have relatively stronger residential crowding-depression associations: females, those living with children, those not living with parents, and those living in non-market housing units. Our findings show that inequality in living space among urban residents not only is an important social justice issue but also has health implications.
Estimating Continuous Treatment Effects in Panel Data using Machine Learning with a Climate Application
Authors: Sylvia Klosin, Max Vilgalys
Date: 2022-07-18 00:00:00

This paper introduces and proves asymptotic normality for a new semi-parametric estimator of continuous treatment effects in panel data. Specifically, we estimate the average derivative. Our estimator uses the panel structure of data to account for unobservable time-invariant heterogeneity and machine learning (ML) methods to preserve statistical power while modeling high-dimensional relationships. We construct our estimator using tools from double de-biased machine learning (DML) literature. Monte Carlo simulations in a nonlinear panel setting show that our method estimates the average derivative with low bias and variance relative to other approaches. Lastly, we use our estimator to measure the impact of extreme heat on United States (U.S.) corn production, after flexibly controlling for precipitation and other weather features. Our approach yields extreme heat effect estimates that are 50% larger than estimates using linear regression. This difference in estimates corresponds to an additional $3.17 billion in annual damages by 2050 under median climate scenarios. We also estimate a dose-response curve, which shows that damages from extreme heat decline somewhat in counties with more extreme heat exposure.
Testing for explosive bubbles: a review
Authors: Anton Skrobotov
Date: 2022-07-17 00:00:00
Tasks: Time Series, Time Series Analysis

This review discusses methods of testing for explosive bubbles in time series. A large number of recently developed testing methods under various assumptions about innovation of errors are covered. The review also considers the methods for dating explosive (bubble) regimes. Special attention is devoted to time-varying volatility in the errors. Moreover, the modelling of possible relationships between time series with explosive regimes is discussed.
Simultaneity in Binary Outcome Models with an Application to Employment for Couples
Authors: Bo E. Honoré, Luojia Hu, Ekaterini Kyriazidou, Martin Weidner
Date: 2022-07-15 00:00:00
Tasks: Econometrics

Two of Peter Schmidt's many contributions to econometrics have been to introduce a simultaneous logit model for bivariate binary outcomes and to study estimation of dynamic linear fixed effects panel data models using short panels. In this paper, we study a dynamic panel data version of the bivariate model introduced in Schmidt and Strauss (1975) that allows for lagged dependent variables and fixed effects as in Ahn and Schmidt (1995). We combine a conditional likelihood approach with a method of moments approach to obtain an estimation strategy for the resulting model. We apply this estimation strategy to a simple model for the intra-household relationship in employment. Our main conclusion is that the within-household dependence in employment differs significantly by the ethnicity composition of the couple even after one allows for unobserved household specific heterogeneity.
Flexible global forecast combinations
Authors: Ryan Thompson, Yilin Qian, Andrey L. Vasnev
Date: 2022-07-15 00:00:00

Forecast combination -- the aggregation of individual forecasts from multiple experts or models -- is a proven approach to economic forecasting. To date, research on economic forecasting has concentrated on local combination methods, which handle separate but related forecasting tasks in isolation. Yet, it has been known for over two decades in the machine learning community that global methods, which exploit task-relatedness, can improve on local methods that ignore it. Motivated by the possibility for improvement, this paper introduces a framework for globally combining forecasts while being flexible to the level of task-relatedness. Through our framework, we develop global versions of several existing forecast combinations. To evaluate the efficacy of these new global forecast combinations, we conduct extensive comparisons using synthetic and real data. Our real data comparisons, which involve forecasts of core economic indicators in the Eurozone, provide empirical evidence that the accuracy of global combinations of economic forecasts can surpass local combinations.
Job Prospects and Labour Mobility in China
Authors: Huaxin Wang-Lu, Octasiano Miguel Valerio Mendoza
Date: 2022-07-17 00:00:00

China's structural changes have brought new challenges to its regional employment structures, entailing labour redistribution. By now Chinese research on migration decisions with a forward-looking stand and on bilateral longitudinal determinants at the prefecture city level is almost non-existent. This paper investigates the effects of sector-based job prospects on individual migration decisions across prefecture boundaries. To this end, we created a proxy variable for job prospects, compiled a unique quasi-panel of 66,427 individuals from 283 cities during 1997--2017, introduced reference-dependence to the random utility maximisation model of migration in a sequential setting, derived empirical specifications with theoretical micro-foundations, and applied various monadic and dyadic fixed effects to address multilateral resistance to migration. Multilevel logit models and two-step system GMM estimation were adopted for the robustness check. Our primary findings are that a 10% increase in the ratio of sector-based job prospects in cities of destination to cities of origin raises the probability of migration by 1.281--2.185 percentage points, and the effects tend to be stronger when the scale of the ratio is larger. Having a family migration network causes an increase of approximately 6 percentage points in migratory probabilities. Further, labour migrants are more likely to be male, unmarried, younger, or more educated. Our results suggest that the ongoing industrial reform in China influences labour mobility between cities, providing important insights for regional policymakers to prevent brain drain and to attract relevant talent.
Adoption of Sustainable Agricultural Practices among Kentucky Farmers and Their Perception about Farm Sustainability
Authors: Bijesh Mishra
Date: 2022-07-17 00:00:00

The purpose of this research was to identify commonly adopted SAPs and their adoption among Kentucky farmers. The specific objectives were to explore farmers' Perceptions about farm and farming practice sustainability, to identify predictors of SAPs adoption using farm attributes, farmers' attitudes and behaviors, socioeconomic and demographic factors, and knowledge, and to evaluate adoption barriers of SAPs among Kentucky Farmers. Farmers generally perceive that their farm and farming activities attain the objectives of sustainable agriculture. Inadequate knowledge, perceived difficulty of implementation, lack of market, negative attitude about technologies, and lack of technologies were major adoption barriers of SAPs in Kentucky.
Debt Aversion: Theory and Measurement
Authors: Thomas Meissner, David Albrecht
Date: 2022-07-15 00:00:00
Tasks: Decision Making

Debt aversion can have severe adverse effects on financial decision-making. We propose a model of debt aversion, and design an experiment involving real debt and saving contracts, to elicit and jointly estimate debt aversion with preferences over time, risk and losses. Structural estimations reveal that the vast majority of participants (89%) are debt averse, and that this has a strong impact on choice. We estimate the "borrowing premium" - the compensation a debt averse person would require to accept getting into debt - to be around 16% of the principal for our average participant.
Assessing the Affordability of Nutrient-Adequate Diets
Authors: Kate R. Schneider, Luc Christiaensen, Patrick Webb, William A. Masters
Date: 2022-07-15 00:00:00

The cost and affordability of least-cost healthy diets by time and place are increasingly used as a proxy for access to nutrient-adequate diets. Recent work has focused on the nutrient requirements of individuals, although most food and anti-poverty programs target whole households. This raises the question of how the cost of a nutrient-adequate diet can be measured for an entire household. This study identifies upper and lower bounds on the feasibility, cost, and affordability of meeting all household members' nutrient requirements using 2013-2017 survey data from Malawi. Findings show only a minority of households can afford the nutrient-adequate diet at either bound, with 20% of households able to afford the (upper bound) shared diets and 38% the individualized (lower bound) diets. Individualized diets are more frequently feasible with locally available foods (90% vs. 60% of the time) and exhibit more moderate seasonal fluctuation. To meet all members' needs, a shared diet requires a more nutrient-dense combination of foods that is more costly and exhibits more seasonality in diet cost than any one food group or the individualized diets. The findings further help adjudicate the extent to which nutritional behavioral change programs versus broader agricultural and food policies can be relied upon to improve individual access to healthy diets.
Enhancing HDR Video Compression through CNN-based Effective Bit Depth Adaptation
Authors: Chen Feng, Zihao Qi, Duolikun Danier, Fan Zhang, Xiaozhong Xu, Shan Liu, David Bull
Date: 2022-07-18 00:00:00
Tasks: Decoder, Video Compression

It is well known that high dynamic range (HDR) video can provide more immersive visual experiences compared to conventional standard dynamic range content. However, HDR content is typically more challenging to encode due to the increased detail associated with the wider dynamic range. In this paper, we improve HDR compression performance using the effective bit depth adaptation approach (EBDA). This method reduces the effective bit depth of the original video content before encoding and reconstructs the full bit depth using a CNN-based up-sampling method at the decoder. In this work, we modify the MFRNet network architecture to enable multiple frame processing, and the new network, multi-frame MFRNet, has been integrated into the EBDA framework using two Versatile Video Coding (VVC) host codecs: VTM 16.2 and the Fraunhofer Versatile Video Encoder (VVenC 1.4.0). The proposed approach was evaluated under the JVET HDR Common Test Conditions using the Random Access configuration. The results show coding gains over both the original VVC VTM 16.2 and VVenC 1.4.0 (w/o EBDA) on JVET HDR tested sequences, with average bitrate savings of 2.9% (over VTM) and 4.8% (against VVenC) based on the Bjontegaard Delta measurement. The source code of multi-frame MFRNet has been released at https://github.com/fan-aaron-zhang/MF-MFRNet.
Pansharpening via Frequency-Aware Fusion Network with Explicit Similarity Constraints
Authors: Yinghui Xing, Yan Zhang, Houjun He, Xiuwei Zhang, Yanning Zhang
Date: 2022-07-18 00:00:00
Tasks: Pansharpening

The process of fusing a high spatial resolution (HR) panchromatic (PAN) image and a low spatial resolution (LR) multispectral (MS) image to obtain an HRMS image is known as pansharpening. With the development of convolutional neural networks, the performance of pansharpening methods has been improved, however, the blurry effects and the spectral distortion still exist in their fusion results due to the insufficiency in details learning and the frequency mismatch between MSand PAN. Therefore, the improvement of spatial details at the premise of reducing spectral distortion is still a challenge. In this paper, we propose a frequency-aware fusion network (FAFNet) together with a novel high-frequency feature similarity loss to address above mentioned problems. FAFNet is mainly composed of two kinds of blocks, where the frequency aware blocks aim to extract features in the frequency domain with the help of discrete wavelet transform (DWT) layers, and the frequency fusion blocks reconstruct and transform the features from frequency domain to spatial domain with the assistance of inverse DWT (IDWT) layers. Finally, the fusion results are obtained through a convolutional block. In order to learn the correspondence, we also propose a high-frequency feature similarity loss to constrain the HF features derived from PAN and MS branches, so that HF features of PAN can reasonably be used to supplement that of MS. Experimental results on three datasets at both reduced- and full-resolution demonstrate the superiority of the proposed method compared with several state-of-the-art pansharpening models.
ORB-based SLAM accelerator on SoC FPGA
Authors: Vibhakar Vemulapati, Deming Chen
Date: 2022-07-18 00:00:00
Tasks: Autonomous Navigation, CPU, Simultaneous Localization and Mapping

Simultaneous Localization and Mapping (SLAM) is one of the main components of autonomous navigation systems. With the increase in popularity of drones, autonomous navigation on low-power systems is seeing widespread application. Most SLAM algorithms are computationally intensive and struggle to run in real-time on embedded devices with reasonable accuracy. ORB-SLAM is an open-sourced feature-based SLAM that achieves high accuracy with reduced computational complexity. We propose an SoC based ORB-SLAM system that accelerates the computationally intensive visual feature extraction and matching on hardware. Our FPGA system based on a Zynq-family SoC runs 8.5x, 1.55x and 1.35x faster compared to an ARM CPU, Intel Desktop CPU, and a state-of-the-art FPGA system respectively, while averaging a 2x improvement in accuracy compared to prior work on FPGA.
Accelerating Magnetic Resonance T1\r{ho} Mapping Using Simultaneously Spatial Patch-based and Parametric Group-based Low-rank Tensors (SMART)
Authors: Yuanyuan Liu, Dong Liang, Zhuo-Xu Cui, Yuxin Yang, Chentao Cao, Qingyong Zhu, Jing Cheng, Caiyun Shi, Haifeng Wang, Yanjie Zhu
Date: 2022-07-17 00:00:00

Quantitative magnetic resonance (MR) T1\r{ho} mapping is a promising approach for characterizing intrinsic tissue-dependent information. However, long scan time significantly hinders its widespread applications. Recently, low-rank tensor has been employed and demonstrated good performance in accelerating MR T1\r{ho} mapping. In this study, we propose a novel method that uses spatial patch-based and parametric group-based low rank tensors simultaneously (SMART) to reconstruct images from highly undersampled k-space data. The spatial patch-based low-rank tensor exploits the high local and nonlocal redundancies and similarities between the contrast images in T1\r{ho} mapping. The parametric group based low-rank tensor, which integrates similar exponential behavior of the image signals, is jointly used to enforce the multidimensional low-rankness in the reconstruction process. In vivo brain datasets were used to demonstrate the validity of the proposed method. Experimental results have demonstrated that the proposed method achieves 11.7-fold and 13.21-fold accelerations in two-dimensional and three-dimensional acquisitions, respectively, with more accurate reconstructed images and maps than several state-of-the-art methods. Prospective reconstruction results further demonstrate the capability of the SMART method in accelerating MR T1\r{ho} imaging.
Adaptive t-vMF Dice Loss for Multi-class Medical Image Segmentation
Authors: Sota Kato, Kazuhiro Hotta
Date: 2022-07-16 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Semantic Segmentation

Dice loss is widely used for medical image segmentation, and many improvement loss functions based on such loss have been proposed. However, further Dice loss improvements are still possible. In this study, we reconsidered the use of Dice loss and discovered that Dice loss can be rewritten in the loss function using the cosine similarity through a simple equation transformation. Using this knowledge, we present a novel t-vMF Dice loss based on the t-vMF similarity instead of the cosine similarity. Based on the t-vMF similarity, our proposed Dice loss is formulated in a more compact similarity loss function than the original Dice loss. Furthermore, we present an effective algorithm that automatically determines the parameter $\kappa$ for the t-vMF similarity using a validation accuracy, called Adaptive t-vMf Dice loss. Using this algorithm, it is possible to apply more compact similarities for easy classes and wider similarities for difficult classes, and we are able to achieve an adaptive training based on the accuracy of the class. Through experiments conducted on four datasets using a five-fold cross validation, we confirmed that the Dice score coefficient (DSC) was further improved in comparison with the original Dice loss and other loss functions.
Averaged Deep Denoisers for Image Regularization
Authors: Pravin Nair, Kunal N. Chaudhury
Date: 2022-07-15 00:00:00
Tasks: Denoising, Image Reconstruction

Plug-and-Play (PnP) and Regularization-by-Denoising (RED) are recent paradigms for image reconstruction that leverage the power of modern denoisers for image regularization. In particular, they have been shown to deliver state-of-the-art reconstructions with CNN denoisers. Since the regularization is performed in an ad-hoc manner, understanding the convergence of PnP and RED has been an active research area. It was shown in recent works that iterate convergence can be guaranteed if the denoiser is averaged or nonexpansive. However, integrating nonexpansivity with gradient-based learning is challenging, the core issue being that testing nonexpansivity is intractable. Using numerical examples, we show that existing CNN denoisers tend to violate the nonexpansive property, which can cause PnP or RED to diverge. In fact, algorithms for training nonexpansive denoisers either cannot guarantee nonexpansivity or are computationally intensive. In this work, we construct contractive and averaged image denoisers by unfolding splitting-based optimization algorithms applied to wavelet denoising and demonstrate that their regularization capacity for PnP and RED can be matched with CNN denoisers. To our knowledge, this is the first work to propose a simple framework for training contractive denoisers using network unfolding.
Modulo Sampling of FRI Signals
Authors: Satish Mulleti, Yonina C. Eldar
Date: 2022-07-18 00:00:00

The dynamic range of an analog-to-digital converter (ADC) is critical during sampling of analog signals. A modulo operation prior to sampling can be used to enhance the effective dynamic range of the ADC. Further, sampling rate of ADC too plays a crucial role and it is desirable to reduce it. Finite-rate-of-innovation (FRI) signal model, which is ubiquitous in many applications, can be used to reduce the sampling rate. In the context of modulo folding for FRI sampling, existing works operate at a very high sampling rate compared to the rate of innovation (RoI) and require a large number of samples compared to the degrees of freedom (DoF) of the FRI signal. Moreover, these approaches use infinite length filters that are practically infeasible. We consider the FRI sampling problem with a compactly supported kernel under the modulo framework. We derive theoretical guarantees and show that FRI signals could be uniquely identified by sampling above the RoI. The number of samples for identifiability is equal to the DoF. We propose a practical algorithm to estimate the FRI parameters from the modulo samples. We show that the proposed approach has the lowest error in estimating the FRI parameters while operating with the lowest number of samples and sampling rates compared to existing techniques. The results are helpful in designing cost-effective, high-dynamic-range ADCs for FRI signals.