pwc-archive/papers-with-abstracts huggingface dataset:
Error Processing of Sparse Identification of Nonlinear Dynamical Systems via $L_\infty$ Approximation
Authors: Yuqiang Wu
Date: 2021-07-12 00:00:00

This paper deals with the error processing problem of sparse identification of nonlinear dynamical systems(SINDy) through introducing the $L_\infty$ approximation to take place of the former $L_2$ approximation. The motivation is that the $L_\infty$ approximation could better describe the error phenomenon in the SINDy, which consists of the derivative approximation error and the measurement noise. Then, an iterative thresholding algorithm is proposed to solve the reformulated problem. 3 scenarios of possible errors are considered in the experiment. The results show that the $L_\infty$ approximation performs better or at least equal than the $L_2$ approximation in face of different error cases. Hence, it is reasonable to consider the $L_\infty$ approximation in the applications of the SINDy.
Dynamic Modeling and Control of a Two-Reactor Metal Hydride Energy Storage System
Authors: Patrick Krane, Austin L. Nash, Davide Ziviani, James E. Braun, Amy M. Marconnet, Neera Jain
Date: 2021-07-08 00:00:00

Metal hydrides have been studied for use in energy storage, hydrogen storage, and air-conditioning (A/C) systems. A common architecture for A/C and energy storage systems is two metal hydride reactors connected to each other so that hydrogen can flow between them, allowing for cyclic use of the hydrogen. This paper presents a nonlinear dynamic model and multivariate control strategy of such a system. Each reactor is modelled as a shell-and-tube heat exchanger connected to a circulating fluid, and a compressor drives hydrogen flow between the reactors. We further develop a linear state-space version of this model integrated with a model predictive controller to determine the fluid mass flow rates and compressor pressure difference required to achieve desired heat transfer rates between the metal hydride and the fluid. A series of case studies demonstrates that this controller can track desired heat transfer rates in each reactor, even in the presence of time-varying circulating fluid inlet temperatures, thereby enabling the use of a two-reactor system for energy storage or integration with a heat pump.
Barriers and Dynamical Paths in Alternating Gibbs Sampling of Restricted Boltzmann Machines
Authors: Clément Roussel, Simona Cocco, Rémi Monasson
Date: 2021-07-13 00:00:00

Restricted Boltzmann Machines (RBM) are bi-layer neural networks used for the unsupervised learning of model distributions from data. The bipartite architecture of RBM naturally defines an elegant sampling procedure, called Alternating Gibbs Sampling (AGS), where the configurations of the latent-variable layer are sampled conditional to the data-variable layer, and vice versa. We study here the performance of AGS on several analytically tractable models borrowed from statistical mechanics. We show that standard AGS is not more efficient than classical Metropolis-Hastings (MH) sampling of the effective energy landscape defined on the data layer. However, RBM can identify meaningful representations of training data in their latent space. Furthermore, using these representations and combining Gibbs sampling with the MH algorithm in the latent space can enhance the sampling performance of the RBM when the hidden units encode weakly dependent features of the data. We illustrate our findings on three datasets: Bars and Stripes and MNIST, well known in machine learning, and the so-called Lattice Proteins, introduced in theoretical biology to study the sequence-to-structure mapping in proteins.
Model of the Weak Reset Process in HfOx Resistive Memory for Deep Learning Frameworks
Authors: Atreya Majumdar, Marc Bocquet, Tifenn Hirtzlin, Axel Laborieux, Jacques-Olivier Klein, Etienne Nowak, Elisa Vianello, Jean-Michel Portal, Damien Querlioz
Date: 2021-07-02 00:00:00
Tasks: Deep Learning, Handwritten Digit Recognition

The implementation of current deep learning training algorithms is power-hungry, owing to data transfer between memory and logic units. Oxide-based RRAMs are outstanding candidates to implement in-memory computing, which is less power-intensive. Their weak RESET regime, is particularly attractive for learning, as it allows tuning the resistance of the devices with remarkable endurance. However, the resistive change behavior in this regime suffers many fluctuations and is particularly challenging to model, especially in a way compatible with tools used for simulating deep learning. In this work, we present a model of the weak RESET process in hafnium oxide RRAM and integrate this model within the PyTorch deep learning framework. Validated on experiments on a hybrid CMOS/RRAM technology, our model reproduces both the noisy progressive behavior and the device-to-device (D2D) variability. We use this tool to train Binarized Neural Networks for the MNIST handwritten digit recognition task and the CIFAR-10 object classification task. We simulate our model with and without various aspects of device imperfections to understand their impact on the training process and identify that the D2D variability is the most detrimental aspect. The framework can be used in the same manner for other types of memories to identify the device imperfections that cause the most degradation, which can, in turn, be used to optimize the devices to reduce the impact of these imperfections.
Impedance-based Capacity Estimation for Lithium-Ion Batteries Using Generative Adversarial Network
Authors: Seongyoon Kim, Yun Young Choi, Jung-Il Choi
Date: 2021-07-02 00:00:00
Tasks: Capacity Estimation, Generative Adversarial Network

This paper proposes a fully unsupervised methodology for the reliable extraction of latent variables representing the characteristics of lithium-ion batteries (LIBs) from electrochemical impedance spectroscopy (EIS) data using information maximizing generative adversarial networks. Meaningful representations can be obtained from EIS data even when measured with direct current and without relaxation, which are difficult to express when using circuit models. The extracted latent variables were investigated as capacity degradation progressed and were used to estimate the discharge capacity of the batteries by employing Gaussian process regression. The proposed method was validated under various conditions of EIS data during charging and discharging. The results indicate that the proposed model provides more robust capacity estimations than the direct capacity estimations obtained from EIS. We demonstrate that the latent variables extracted from the EIS data measured with direct current and without relaxation reliably represent the degradation characteristics of LIBs.
Single-shot structured illumination microscopy
Authors: Qinnan Zhang, En Bo, Jiawei Chen, Jiaosheng Li, Heming Jiang, Xiaoxu Lu, Liyun Zhong, Jindong Tian
Date: 2021-07-13 00:00:00
Tasks: Super-Resolution

Structured illumination microscopy (SIM) can double the resolution beyond the light diffraction limit, but it comes at the cost of multiple camera exposures and the heavy computation burden of multiple Fourier transforms. In this paper, we report a novel technique termed single-shot SIM, to overcome these limitations. A multi-task joint deep-learning strategy is proposed. Generative adversative networks (GAN) are employed to generate five structured illumination images based on the single-shot structured illumination image. U-Net is employed to reconstruct the super-resolution image from these six generated images without time-consuming Fourier transform. By imaging a self-assembling DNB array, we experimentally verified that this technique could perform single-shot super-resolution reconstruction comparing favorably with conventional SIM. This single-shot SIM technique may ultimately overcome the limitations of multiple exposures and Fourier transforms and is potentially applied for high-throughput gene sequencing.
Dynamics of the market states in the space of correlation matrices with applications to financial markets
Authors: Hirdesh K. Pharasi, Suchetana Sadhukhan, Parisa Majari, Anirban Chakraborti, Thomas H. Seligman
Date: 2021-07-12 00:00:00

The concept of states of financial markets based on correlations has gained increasing attention during the last 10 years. We propose to retrace some important steps up to 2018, and then give a more detailed view of recent developments that attempt to make the use of this more practical. Finally, we try to give a glimpse to the future proposing the analysis of trajectories in correlation matrix space directly or in terms of symbolic dynamics as well as attempts to analyze the clusters that make up the states in a random matrix context.
MinP Score Tests with an Inequality Constrained Parameter Space
Authors: Giuseppe Cavaliere, Zeng-Hua Lu, Anders Rahbek, Yuhong Yang
Date: 2021-07-13 00:00:00

Score tests have the advantage of requiring estimation alone of the model restricted by the null hypothesis, which often is much simpler than models defined under the alternative hypothesis. This is typically so when the alternative hypothesis involves inequality constraints. However, existing score tests address only jointly testing all parameters of interest; a leading example is testing all ARCH parameters or variances of random coefficients being zero or not. In such testing problems rejection of the null hypothesis does not provide evidence on rejection of specific elements of parameter of interest. This paper proposes a class of one-sided score tests for testing a model parameter that is subject to inequality constraints. Proposed tests are constructed based on the minimum of a set of $p$-values. The minimand includes the $p$-values for testing individual elements of parameter of interest using individual scores. It may be extended to include a $p$-value of existing score tests. We show that our tests perform better than/or perform as good as existing score tests in terms of joint testing, and has furthermore the added benefit of allowing for simultaneously testing individual elements of parameter of interest. The added benefit is appealing in the sense that it can identify a model without estimating it. We illustrate our tests in linear regression models, ARCH and random coefficient models. A detailed simulation study is provided to examine the finite sample performance of the proposed tests and we find that our tests perform well as expected.
Testability of Reverse Causality Without Exogenous Variation
Authors: Christoph Breunig, Patrick Burauel
Date: 2021-07-13 00:00:00

This paper shows that testability of reverse causality is possible even in the absence of exogenous variation, such as in the form of instrumental variables. Instead of relying on exogenous variation, we achieve testability by imposing relatively weak model restrictions and exploiting that a dependence of residual and purported cause is informative about the causal direction. Our main assumption is that the true functional relationship is nonlinear and that error terms are additively separable. We extend previous results by incorporating control variables and allowing heteroskedastic errors. We build on reproducing kernel Hilbert space (RKHS) embeddings of probability distributions to test conditional independence and demonstrate the efficacy in detecting the causal direction in both Monte Carlo simulations and an application to German survey data.
Identification of Average Marginal Effects in Fixed Effects Dynamic Discrete Choice Models
Authors: Victor Aguirregabiria, Jesus M. Carro
Date: 2021-07-12 00:00:00
Tasks: Discrete Choice Models

In nonlinear panel data models, fixed effects methods are often criticized because they cannot identify average marginal effects (AMEs) in short panels. The common argument is that identifying AMEs requires knowledge of the distribution of unobserved heterogeneity, but this distribution is not identified in a fixed effects model with a short panel. In this paper, we derive identification results that contradict this argument. In a panel data dynamic logit model, and for $T$ as small as three, we prove the point identification of different AMEs, including causal effects of changes in the lagged dependent variable or the last choice's duration. Our proofs are constructive and provide simple closed-form expressions for the AMEs in terms of probabilities of choice histories. We illustrate our results using Monte Carlo experiments and with an empirical application of a dynamic structural model of consumer brand choice with state dependence.
National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?
Authors: Juyong Lee, Youngsang Cho
Date: 2021-06-30 00:00:00
Tasks: BIG-bench Machine Learning, Load Forecasting, Time Series, Time Series Analysis

As the volatility of electricity demand increases owing to climate change and electrification, the importance of accurate peak load forecasting is increasing. Traditional peak load forecasting has been conducted through time series-based models; however, recently, new models based on machine or deep learning are being introduced. This study performs a comparative analysis to determine the most accurate peak load-forecasting model for Korea, by comparing the performance of time series, machine learning, and hybrid models. Seasonal autoregressive integrated moving average with exogenous variables (SARIMAX) is used for the time series model. Artificial neural network (ANN), support vector regression (SVR), and long short-term memory (LSTM) are used for the machine learning models. SARIMAX-ANN, SARIMAX-SVR, and SARIMAX-LSTM are used for the hybrid models. The results indicate that the hybrid models exhibit significant improvement over the SARIMAX model. The LSTM-based models outperformed the others; the single and hybrid LSTM models did not exhibit a significant performance difference. In the case of Korea's highest peak load in 2019, the predictive power of the LSTM model proved to be greater than that of the SARIMAX-LSTM model. The LSTM, SARIMAX-SVR, and SARIMAX-LSTM models outperformed the current time series-based forecasting model used in Korea. Thus, Korea's peak load-forecasting performance can be improved by including machine learning or hybrid models.
Sissy That Walk: Transportation to Work by Sexual Orientation
Authors: Sonia Oreffice, Dario Sansone
Date: 2021-07-13 00:00:00
Tasks: Survey

We analyze differences in mode of transportation to work by sexual orientation, using the American Community Survey 2008-2019. Individuals in same-sex couples are significantly less likely to drive to work than men and women in different-sex couples. This gap is particularly stark among men: on average, almost 12 percentage point (or 13%) lower likelihood of driving to work for men in same-sex couples. Individuals in same-sex couples are also more likely to use public transport, walk, or bike to work: on average, men and women are 7 and 3 percentage points more likely, respectively, to take public transportation to work than those in different-sex couples. These differences persist after controlling for demographic characteristics, partner's characteristics, location, fertility, and marital status. Additional evidence from the General Social Survey 2008-2018 suggests that these disparities by sexual orientation may be due to lesbian, gay, and bisexual individuals caring more for the environment than straight individuals.
The climate in climate economics
Authors: Doris Folini, Felix Kübler, Aleksandra Malova, Simon Scheidegger
Date: 2021-07-13 00:00:00

To analyze climate change mitigation strategies, economists rely on simplified climate models - climate emulators. We propose a generic and transparent calibration and evaluation strategy for these climate emulators that is based on Coupled Model Intercomparison Project, Phase 5 (CMIP5). We demonstrate that the appropriate choice of the free model parameters can be of key relevance for the predicted social cost of carbon. We propose to use four different test cases: two tests to separately calibrate and evaluate the carbon cycle and temperature response, a test to quantify the transient climate response, and a final test to evaluate the performance for scenarios close to those arising from economic models. We re-calibrate the climate part of the widely used DICE-2016: the multi-model mean as well as extreme, but still permissible climate sensitivities and carbon cycle responses. We demonstrate that the functional form of the climate emulator of the DICE-2016 model is fit for purpose, despite its simplicity, but its carbon cycle and temperature equations are miscalibrated. We examine the importance of the calibration for the social cost of carbon in the context of a partial equilibrium setting where interest rates are exogenous, as well as the simple general equilibrium setting from DICE-2016. We find that the model uncertainty from different consistent calibrations of the climate system can change the social cost of carbon by a factor of four if one assumes a quadratic damage function. When calibrated to the multi-model mean, our model predicts similar values for the social cost of carbon as the original DICE-2016, but with a strongly reduced sensitivity to the discount rate and about one degree less long-term warming. The social cost of carbon in DICE-2016 is oversensitive to the discount rate, leading to extreme comparative statics responses to changes in preferences.
Economic development and the structure of cross-technology interactions
Authors: Anton Bondarev, Frank C. Krysiak
Date: 2021-07-13 00:00:00

Most explanations of economic growth are based on knowledge spillovers, where the development of some technologies facilitates the enhancement of others. Empirical studies show that these spillovers can have a heterogeneous and rather complex structure. But, so far, little attention has been paid to the consequences of different structures of such cross-technology interactions: Is economic development more easily fostered by homogenous or heterogeneous interactions, by uni- or bidirectional spillovers? Using a detailed description of an r&d sector with cross-technology interactions embedded in a simple growth model, we analyse how the structure of spillovers influences growth prospects and growth patterns. We show that some type of interactions (e.g., one-way interactions) cannot induce exponential growth, whereas other structures can. Furthermore, depending on the structure of interactions, all or only some technologies will contribute to growth in the long run. Finally, some spillover structures can lead to complex growth patterns, such as technology transitions, where, over time, different technology clusters are the main engine of growth.
Micro-level dynamics in hidden action situations with limited information
Authors: Stephan Leitner, Friederike Wall
Date: 2021-07-13 00:00:00

The hidden-action model provides an optimal sharing rule for situations in which a principal assigns a task to an agent who makes an effort to carry out the task assigned to him. However, the principal can only observe the task outcome but not the agent's actual action, which is why the sharing rule can only be based on the outcome. The hidden-action model builds on somewhat idealized assumptions about the principal's and the agent's capabilities related to information access. We propose an agent-based model that relaxes some of these assumptions. Our analysis lays particular focus on the micro-level dynamics triggered by limited access to information. For the principal's sphere, we identify the so-called Sisyphus effect that explains why the sharing rule that provides the agent with incentives to take optimal action is difficult to achieve if the information is limited, and we identify factors that moderate this effect. In addition, we analyze the behavioral dynamics in the agent's sphere. We show that the agent might make even more of an effort than optimal under unlimited access to information, which we refer to as excess effort. Interestingly, the principal can control the probability of making an excess effort via the incentive mechanism. However, how much excess effort the agent finally makes is out of the principal's direct control.
Time-Division Multiplexing Light Field Display with Learned Coded Aperture
Authors: Chun-Hao Chao, Chang-Le Liu, Homer Chen
Date: 2021-07-13 00:00:00
Tasks: SSIM

Conventional stereoscopic displays suffer from vergence-accommodation conflict and cause visual fatigue. Integral-imaging-based displays resolve the problem by directly projecting the sub-aperture views of a light field into the eyes using a microlens array or a similar structure. However, such displays have an inherent trade-off between angular and spatial resolutions. In this paper, we propose a novel coded time-division multiplexing technique that projects encoded sub-aperture views to the eyes of a viewer with correct cues for vergence-accommodation reflex. Given sparse light field sub-aperture views, our pipeline can provide a perception of high-resolution refocused images with minimal aliasing by jointly optimizing the sub-aperture views for display and the coded aperture pattern. This is achieved via deep learning in an end-to-end fashion by simulating light transport and image formation with Fourier optics. To our knowledge, this work is among the first that optimize the light field display pipeline with deep learning. We verify our idea with objective image quality metrics (PSNR, SSIM, and LPIPS) and perform an extensive study on various customizable design variables in our display pipeline. Experimental results show that light fields displayed using the proposed technique indeed have higher quality than that of baseline display designs.
A 125$μ$W 8kS/s Sub-pA Area-Efficient Current Sensing 45nm CMOS ADC for Biosensing
Authors: Mitra Saeidi, Luke Theogarajan
Date: 2021-07-13 00:00:00

This paper presents a 125$\mu$W, area efficient (0.042mm2) 81dB DR, 8kS/s current sensing ADC in 45nm CMOS capable of sensing sub-pA currents. Our approach combines the transimpedance amplifier (TIA) and ADC into a unified structure by folding a low-noise capacitive TIA into the first stage integrator of a 2nd order Delta-Sigma modulator. The dominant DAC feedback noise is mitigated by utilizing current scaling via slope modification by an integrator and differentiator pair.
On the Realization of Impulse Invariant Bilinear Volterra Kernels
Authors: Phillip M. S. Burt, José Henrique de Morais Goulart
Date: 2021-07-12 00:00:00

As previously shown, the direct extension of the impulse invariance principle to Volterra kernels has to be modified in order to provide a condition for the exact modeling of mixed-signal chains. At first sight this would seem to seriously complicate the otherwise simple discrete-time realization of separable kernels (among which bilinear kernels are of particular importance). We show here, however, that this not the case. By defining a cascade operator, the structure of a generalized impulse invariance can be unveiled, leading to a realization without an inordinate increase in computational complexity.