pwc-archive/papers-with-abstracts huggingface dataset:
One-shot learning for solution operators of partial differential equations
Authors: Anran Jiao, Haiyang He, Rishikesh Ranade, Jay Pathak, Lu Lu
Date: 2021-04-06 00:00:00
Tasks: One-Shot Learning, Operator learning

Learning and solving governing equations of a physical system, represented by partial differential equations (PDEs), from data is a central challenge in a variety of areas of science and engineering. Traditional numerical methods for solving PDEs can be computationally expensive for complex systems and require the complete PDEs of the physical system. On the other hand, current data-driven machine learning methods require a large amount of data to learn a surrogate model of the PDE solution operator, which could be impractical. Here, we propose the first solution operator learning method that only requires one PDE solution, i.e., one-shot learning. By leveraging the principle of locality of PDEs, we consider small local domains instead of the entire computational domain and define a local solution operator. The local solution operator is then trained using a neural network, and utilized to predict the solution of a new input function via mesh-based fixed-point iteration (FPI), meshfree local-solution-operator informed neural network (LOINN) or local-solution-operator informed neural network with correction (cLOINN). We test our method on diverse PDEs, including linear or nonlinear PDEs, PDEs defined on complex geometries, and PDE systems, demonstrating the effectiveness and generalization capabilities of our method across these varied scenarios.
Guiding large-scale management of invasive species using network metrics
Authors: Jaime Ashander, Kailin Kroetz, Rebecca S Epanchin-Niell, Nicholas B. D. Phelps, Robert G Haight, Laura E. Dee
Date: 2021-04-12 00:00:00
Tasks: Management

Complex socio-environmental interdependencies drive biological invasions, causing damages across large spatial scales. For widespread invasions, targeting of management activities based on optimization approaches may fail due to computational or data constraints. Here we evaluate an alternative approach that embraces complexity by representing the invasion as a network and using network structure to inform management locations. We compare optimal versus network-guided invasive species management at a landscape-scale, considering siting of boat decontamination stations targeting 1.6 million boater movements among 9,182 lakes in Minnesota, USA. Studying performance for 58 counties, we find that when full information is known on invasion status and boater movements, the best-performing network-guided metric achieves a median and lower quartile performance of 100% of optimal. We also find that performance remains relatively high using different network metrics or with less information (median above 80% and lower quartile above 60% of optimal for most metrics), but is more variable, particularly at the lower quartile. Additionally, performance is generally stable across counties with varying lake counts, suggesting viability for large-scale invasion management.
A novel Three-step Network-based Ecosystem Modelling Framework
Authors: Laurin Steidle, Inga Hense
Date: 2021-04-12 00:00:00

NEMF is a novel network-based ecosystem modelling framework. It is a flexible and easy-to-use tool for modelling ecosystems with low- to intermediate complexity. It is designed around the idea of visualizing an ecosystem through a network that implicitly defines a set of differential equations. These equations are then solved numerically and can also be inverse modelled. The framework offers the functionality to handle non-equilibrium, non-linear interactions.
Early systems change necessary for catalyzing long-term sustainability in a post-2030 agenda
Authors: Enayat A. Moallemi, Sibel Eker, Lei Gao, Michalis Hadjikakou, Qi Liu, Jan Kwakkel, Patrick M. Reed, Michael Obersteiner, Zhaoxia Guo, Brett A. Bryan
Date: 2020-12-08 00:00:00

Progress to-date towards the Sustainable Development Goals (SDGs) has fallen short of expectations and is unlikely to fully meet 2030 targets. Despite the little chance of imminent success, past assessments have mostly focused on short- and medium-term evaluations, limiting the ability to explore the longer-term effects of systemic interactions with time lags and delay. Here we undertake global systems modelling with a longer-term view than previous assessments to explore the drivers of sustainability progress and how they could emerge by 2030, 2050, and 2100 under different development pathways and towards quantitative targets. We find that early planning for systems change to shift from business-as-usual to more sustainable pathways is important for accelerating progress towards increasingly ambitious targets by 2030, 2050, and 2100. These findings indicate the importance of adopting longer-term timeframes and pathways to ensure that the necessary pre-conditions are in place for sustainability beyond the current 2030 Agenda.
Vaccine allocation to blue-collar workers
Authors: László Czaller, Gergő Tóth, Balázs Lengyel
Date: 2021-04-09 00:00:00

Vaccination may be the solution to the pandemic-induced health crisis, but the allocation of vaccines is a complex task in which economic and social considerations can be important. The central problem is to use the limited number of vaccines in a country to reduce the risk of infection and mitigate economic uncertainty at the same time. In this paper, we propose a simple economic model for vaccine allocation across two types of workers: white-collars can work from home; while blue-collars must work on site. These worker types are complementary to each other, thus a negative shock to the supply of either one decreases the demand for the other that leads to unemployment. Using parameters of blue and white-collar labor supply, their infection risks, productivity losses at home office during lock-down, and available vaccines, we express the optimal share of vaccines allocated to blue-collars. The model points to the dominance of blue-collar vaccination, especially during waves when their relative infection risks increase and when the number of available vaccines is limited. Taking labor supply data from 28 European countries, we quantify blue-collar vaccine allocation that minimizes unemployment across levels of blue- and white-collar infection risks. The model favours blue-collar vaccination identically across European countries in case of vaccine scarcity. As more vaccines become available, economies that host large-shares of employees in home-office shall increasingly immunize them in case blue-collar infection risks can be kept down. Our results highlight that vaccination plans should include workers and rank them by type of occupation. We propose that prioritizing blue-collar workers during infection waves and early vaccination can also favour economy besides helping the most vulnerable who can transmit more infection.
Identification of Dynamic Panel Logit Models with Fixed Effects
Authors: Christopher Dobronyi, Jiaying Gu, Kyoo il Kim, Thomas M. Russell
Date: 2021-04-09 00:00:00

We show that identification in a general class of dynamic panel logit models with fixed effects is related to the truncated moment problem from the mathematics literature. We use this connection to show that the identified set for structural parameters and functionals of the distribution of latent individual effects can be characterized by a finite set of conditional moment equalities subject to a certain set of shape constraints on the model parameters. In addition to providing a general approach to identification, the new characterization can deliver informative bounds in cases where competing methods deliver no identifying restrictions, and can deliver point identification in cases where competing methods deliver partial identification. We then present an estimation and inference procedure that uses semidefinite programming methods, is applicable with continuous or discrete covariates, and can be used for models that are either point- or partially-identified. Finally, we illustrate our identification result with a number of examples, and provide an empirical application to employment dynamics using data from the National Longitudinal Survey of Youth.
Assessing the practicability of the condition used for dynamic equilibrium in Pasinetti theory of distribution
Authors: A Jayakrishnan, Anil Lal S
Date: 2021-04-12 00:00:00
Tasks: valid

In this note an assessment of the condition \(K_w/K=S_w/S\) is made to interpret its meaning to the Passineti's theory of distribution\cite{pasinetti1962rate}. This condition leads the theory to enforce the result \(s_w\rightarrow0\) as \(P_w\rightarrow 0\), which is the Pasinetti's description about behavior of the workers. We find that the Pasinetti's claim, of long run worker's propensity to save as not influencing the distribution of income between profits and the wage can not be generalized. This claim is found to be valid only when \(W>>P_w\) or \(P_w=0\) with \(W\ne0\). In practice, the Pasinetti's condition imposes a restriction on the actual savings by one of the agents to a lower level compared to its full saving capacity. An implied relationship between the propensities to save by workers and capitalists shows that the Passineti's condition can be practiced only through a contract for a constant value of \(R=s_w/s_c\), to be agreed upon between the workers and the capitalists. It is showed that the Passineti's condition can not be described as a dynamic equilibrium of economic growth. Implementation of this condition (a) may lead to accumulation of unsaved income, (b) reduces growth of capital, (c)is not practicable and (d) is not warranted. We have also presented simple mathematical steps for the derivation of the Pasinetti's final equation compared to those presented in \cite{pasinetti1962rate}
Demand-pull, technology-push, and the direction of technological change
Authors: Kerstin Hötte
Date: 2021-04-10 00:00:00

This paper studies the impact of Demand-pull (DP) and Technology-push (TP) on growth, innovation, and the factor bias of technological change in a two-layer network of input-output (market) and patent citation (innovation) links among 307 6-digit US manufacturing industries in 1977-2012. Two types of TP and DP are distinguished: (1) DP and TP are between-layer spillovers when market demand shocks pull innovation and innovation pushes market growth. (2) Within-layer DP arises if downstream users trigger upstream innovation and growth, while TP effects spill over from up- to downstream industries. The results support between- and within-layer TP: Innovation spillovers from upstream industries drive market growth and innovation. Within the market, upstream supply shocks stimulate growth, but this effect differs across industries. DP is not supported but shows a factor bias favoring labor, while TP comes with a shift towards non-production work. The results are strongest after the 2000s and shed light on the drivers of recent technological change and its factor bias.
WTO GPA and Sustainable Procurement as Tools for Transitioning to a Circular Economy
Authors: Sareesh Rawat
Date: 2021-04-10 00:00:00

We live in an age of consumption with an ever-increasing demand of already scarce resources and equally fast growing problems of waste generation and climate change. To tackle these difficult issues, we must learn from mother nature. Just like waste does not exist in nature, we must strive to create circular ecosystems where waste is minimized and energy is conserved. This paper focuses on how public procurement can help us transition to a more circular economy, while navigating international trade laws that govern it.
Option to survive or surrender: carbon asset management and optimization in thermal power enterprises from China
Authors: Yue Liu, Lixin Tian, Zhuyun Xie, Zaili Zhen, Huaping Sun
Date: 2021-04-10 00:00:00
Tasks: Asset Management, Decision Making, Management

Carbon emission right allowance is a double-edged sword, one edge is to reduce emission as its original design intention, another edge has in practice slain many less developed coal-consuming enterprises, especially for those in thermal power industry. Partially governed on the hilt in hands of the authority, body of this sword is the prices of carbon emission right. How should the thermal power plants dance on the blade motivates this research. Considering the impact of price fluctuations of carbon emission right allowance, we investigate the operation of Chinese thermal power plant by modeling the decision-making with optimal stopping problem, which is established on the stochastic environment with carbon emission allowance price process simulated by geometric Brownian motion. Under the overall goal of maximizing the ultimate profitability, the optimal stopping indicates the timing of suspend or halt of production, hence the optimal stopping boundary curve implies the edge of life and death with regard to this enterprise. Applying this methodology, real cases of failure and survival of several Chinese representative thermal power plants were analyzed to explore the industry ecotope, which leads to the findings that: 1) The survival environment of existed thermal power plants becomes severer when facing more pressure from the newborn carbon-finance market. 2) Boundaries of survival environment is mainly drawn by the technical improvements for rising the utilization rate of carbon emission. Based on the same optimal stopping model, outlook of this industry is drawn with a demarcation surface defining the vivosphere of thermal power plants with different levels of profitability. This finding provides benchmarks for those enterprises struggling for survival and policy makers scheming better supervision and necessary intervene.
The Effect of Sport in Online Dating: Evidence from Causal Machine Learning
Authors: Daniel Boller, Michael Lechner, Gabriel Okasa
Date: 2021-04-07 00:00:00
Tasks: BIG-bench Machine Learning

Online dating emerged as a key platform for human mating. Previous research focused on socio-demographic characteristics to explain human mating in online dating environments, neglecting the commonly recognized relevance of sport. This research investigates the effect of sport activity on human mating by exploiting a unique data set from an online dating platform. Thereby, we leverage recent advances in the causal machine learning literature to estimate the causal effect of sport frequency on the contact chances. We find that for male users, doing sport on a weekly basis increases the probability to receive a first message from a woman by 50%, relatively to not doing sport at all. For female users, we do not find evidence for such an effect. In addition, for male users the effect increases with higher income.
The link between unemployment and real economic growth in developed countries
Authors: Ivan Kitov
Date: 2021-03-14 00:00:00

Ten years ago we presented a modified version of Okun law for the biggest developed economies and reported its excellent predictive power. In this study, we revisit the original models using the estimates of real GDP per capita and unemployment rate between 2010 and 2019. The initial results show that the change in unemployment rate can be accurately predicted by variations in the rate of real economic growth. There is a discrete version of the model which is represented by a piece wise linear dependence of the annual increment in unemployment rate on the annual rate of change in real GDP per capita. The lengths of the country-dependent time segments are defined by breaks in the GDP measurement units associated with definitional revisions to the nominal GDP and GDP deflator (dGDP). The difference between the CPI and dGDP indices since the beginning of measurements reveals the years of such breaks. Statistically, the link between the studied variables in the revised models is characterized by the coefficient of determination in the range from R2=0.866 (Australia) to R2=0.977 (France). The residual errors can be likely associated with the measurement errors, e.g. the estimates of real GDP per capita from various sources differ by tens of percent. The obtained results confirm the original finding on the absence of structural unemployment in the studied developed countries.
Echo Chambers: Voter-to-Voter Communication and Political Competition
Authors: Monica Anna Giovanniello
Date: 2021-04-10 00:00:00

I study how strategic communication among voters shapes both political outcomes and parties' advertising strategies in a model of informative campaign advertising. Two main results are derived. First, echo chambers arise endogenously. Surprisingly, a small ideological distance between voters is not sufficient to guarantee that a chamber is created, bias direction plays a crucial role. Second, when voters' network entails a significant waste of information, parties tailor their advertising to the opponent's supporters rather than to their own.
The DKU System Description for The Interspeech 2021 Auto-KWS Challenge
Authors: Yechen Wang, Yan Jia, Murong Ma, Zexin Cai, Ming Li
Date: 2021-04-11 00:00:00
Tasks: Dynamic Time Warping, Keyword Spotting, Speaker Verification, Template Matching, Word Embeddings

This paper introduces the system submitted by the DKU-SMIIP team for the Auto-KWS 2021 Challenge. Our implementation consists of a two-stage keyword spotting system based on query-by-example spoken term detection and a speaker verification system. We employ two different detection algorithms in our proposed keyword spotting system. The first stage adopts subsequence dynamic time warping for template matching based on frame-level language-independent bottleneck feature and phoneme posterior probability. We use a sliding window template matching algorithm based on acoustic word embeddings to further verify the detection from the first stage. As a result, our KWS system achieves an average score of 0.61 on the feedback dataset, which outperforms the baseline1 system by 0.25.
Soft then Hard: Rethinking the Quantization in Neural Image Compression
Authors: Zongyu Guo, Zhizheng Zhang, Runsen Feng, Zhibo Chen
Date: 2021-04-12 00:00:00
Tasks: Image Compression, Quantization

Quantization is one of the core components in lossy image compression. For neural image compression, end-to-end optimization requires differentiable approximations of quantization, which can generally be grouped into three categories: additive uniform noise, straight-through estimator and soft-to-hard annealing. Training with additive uniform noise approximates the quantization error variationally but suffers from the train-test mismatch. The other two methods do not encounter this mismatch but, as shown in this paper, hurt the rate-distortion performance since the latent representation ability is weakened. We thus propose a novel soft-then-hard quantization strategy for neural image compression that first learns an expressive latent space softly, then closes the train-test mismatch with hard quantization. In addition, beyond the fixed integer quantization, we apply scaled additive uniform noise to adaptively control the quantization granularity by deriving a new variational upper bound on actual rate. Experiments demonstrate that our proposed methods are easy to adopt, stable to train, and highly effective especially on complex compression models.
Blind Primed Supervised (BLIPS) Learning for MR Image Reconstruction
Authors: Anish Lahiri, Guanhua Wang, Saiprasad Ravishankar, Jeffrey A. Fessler
Date: 2021-04-11 00:00:00
Tasks: Dictionary Learning, Image Reconstruction

This paper examines a combined supervised-unsupervised framework involving dictionary-based blind learning and deep supervised learning for MR image reconstruction from under-sampled k-space data. A major focus of the work is to investigate the possible synergy of learned features in traditional shallow reconstruction using adaptive sparsity-based priors and deep prior-based reconstruction. Specifically, we propose a framework that uses an unrolled network to refine a blind dictionary learning-based reconstruction. We compare the proposed method with strictly supervised deep learning-based reconstruction approaches on several datasets of varying sizes and anatomies. We also compare the proposed method to alternative approaches for combining dictionary-based methods with supervised learning in MR image reconstruction. The improvements yielded by the proposed framework suggest that the blind dictionary-based approach preserves fine image details that the supervised approach can iteratively refine, suggesting that the features learned using the two methods are complementary
Transfer Learning for Neural Networks-based Equalizers in Coherent Optical Systems
Authors: Pedro J. Freire, Daniel Abode, Jaroslaw E. Prilepsky, Nelson Costa, Bernhard Spinnler, Antonio Napoli, Sergei K. Turitsyn
Date: 2021-04-11 00:00:00
Tasks: Transfer Learning

In this work, we address the question of the adaptability of artificial neural networks (NNs) used for impairments mitigation in optical transmission systems. We demonstrate that by using well-developed techniques based on the concept of transfer learning, we can efficaciously retrain NN-based equalizers to adapt to the changes in the transmission system, using just a fraction (down to 1%) of the initial training data or epochs. We evaluate the capability of transfer learning to adapt the NN to changes in the launch power, modulation format, symbol rate, or even fiber plants (different fiber types and lengths). The numerical examples utilize the recently introduced NN equalizer consisting of a convolutional layer coupled with bi-directional long-short term memory (biLSTM) recurrent NN element. Our analysis focuses on long-haul coherent optical transmission systems for two types of fibers: the standard single-mode fiber (SSMF) and the TrueWave Classic (TWC) fiber. We underline the specific peculiarities that occur when transferring the learning in coherent optical communication systems and draw the limits for the transfer learning efficiency. Our results demonstrate the effectiveness of transfer learning for the fast adaptation of NN architectures to different transmission regimes and scenarios, paving the way for engineering flexible and universal solutions for nonlinearity mitigation.
Deep Learning Based Channel Covariance Matrix Estimation with User Location and Scene Images
Authors: Weihua Xu, Feifei Gao, Jianhua Zhang, Xiaoming Tao, Ahmed Alkhateeb
Date: 2021-01-19 00:00:00
Tasks: Denoising

Channel covariance matrix (CCM) is one critical parameter for designing the communications systems. In this paper, a novel framework of the deep learning (DL) based CCM estimation is proposed that exploits the perception of the transmission environment without any channel sample or the pilot signals. Specifically, as CCM is affected by the user's movement, we design a deep neural network (DNN) to predict CCM from user location and user speed, and the corresponding estimation method is named as ULCCME. A location denoising method is further developed to reduce the positioning error and improve the robustness of ULCCME. For cases when user location information is not available, we propose an interesting way that uses the environmental 3D images to predict the CCM, and the corresponding estimation method is named as SICCME. Simulation results show that both the proposed methods are effective and will benefit the subsequent channel estimation.