arXiv subjects

Sheng Dai

Publications and source records attributed to Sheng Dai.

At least 19 recordsLinked to original sources

arXiv2025

Quantile estimation of CO2 marginal abatement cost across emission-generating technologies

Marginal abatement cost (MAC) is a critical metric for designing efficient and cost-effective mitigation policies. However, existing MAC estimates are typically derived under different assumptions about emission-generating technologies, yet few studies have systematically compared these technologies. Moreover, conventional estimators often exhibit biases arising from limited abatement options, production inefficiencies, and data noise. To address these limitations, this paper analyzes the abatement behavior of three emission-generating technologies: by-production, joint disposability, and weak G-disposability, each consistent with the material balance principle. We employ both full and quantile frontier estimation methods to identify optimal abatement strategies. Using data from U.S. coal-fired power plants in 2022, the empirical results suggest that reducing electricity output, rather than cutting emission-generating inputs such as fossil fuels, provides a more cost-effective mitigation pathway. Furthermore, Monte Carlo simulations demonstrate that the quantile estimator consistently delivers more accurate results than the full frontier estimator.

stat.AP↗

arXiv2025

Orthogonality conditions for convex regression

Econometric identification generally relies on orthogonality conditions, which usually state that the random error term is uncorrelated with the explanatory variables. In convex regression, the orthogonality conditions for identification are unknown. Applying Lagrangian duality theory, we establish the sample orthogonality conditions for convex regression, including additive and multiplicative formulations of the regression model, with and without monotonicity and homogeneity constraints. We then propose a hybrid instrumental variable control function approach to mitigate the impact of potential endogeneity in convex regression. The superiority of the proposed approach is shown in a Monte Carlo study and examined in an empirical application to Chilean manufacturing data.

stat.ME↗

arXiv2025

Autonomous Electrochemistry Platform with Real-Time Normality Testing of Voltammetry Measurements Using ML

Electrochemistry workflows utilize various instruments and computing systems to execute workflows consisting of electrocatalyst synthesis, testing and evaluation tasks. The heterogeneity of the software and hardware of these ecosystems makes it challenging to orchestrate a complete workflow from production to characterization by automating its tasks. We propose an autonomous electrochemistry computing platform for a multi-site ecosystem that provides the services for remote experiment steering, real-time measurement transfer, and AI/ML-driven analytics. We describe the integration of a mobile robot and synthesis workstation into the ecosystem by developing custom hub-networks and software modules to support remote operations over the ecosystem's wireless and wired networks. We describe a workflow task for generating I-V voltammetry measurements using a potentiostat, and a machine learning framework to ensure their normality by detecting abnormal conditions such as disconnected electrodes. We study a number of machine learning methods for the underlying detection problem, including smooth, non-smooth, structural and statistical methods, and their fusers. We present experimental results to illustrate the effectiveness of this platform, and also validate the proposed ML method by deriving its rigorous generalization equations.

cs.DC↗

arXiv2024

Economic growth of cities: Does resource allocation matter?

We study how efficient resource reallocation across cities affects potential aggregate growth. Using optimal resource allocation models and data on 284 China's prefecture-level cities in the years 2003--2019, we quantitatively measure the cost of misallocation of resources. We show that average aggregate output gains from reallocating resources across nationwide cities to their efficient use are 1.349- and 1.287-fold in the perfect and imperfect allocation scenarios. We further provide evidence on the effects of administrative division adjustments and local allocation. This suggests that city-level adjustments can yield more aggregate gain and that the output gain from nationwide allocation is likely to be more substantial than that from local allocation. Policy implications are proposed to improve the resource allocation efficiency in China.

econ.GN↗

arXiv2024

Harnessing Zn-Volatility for Compositional Tuning in PtZn Nanoalloy Catalysts

Bimetallic nanoalloys have gained extensive attention due to their tunable properties and wide range of catalytic applications. However, achieving good compositional control in nanoalloy catalysts remains a formidable challenge. In this work, we demonstrate that heat treatment can be used to tune the composition of Pt-Zn nanoalloy catalysts, leveraging the volatile nature of zinc to enhance their performance in propane dehydrogenation. Through identical location (scanning) transmission electron microscopy (IL-(S)TEM) using an in-situ EM gas cell, as well as other complementary techniques, we observed that the zinc content of the Pt-Zn nanoalloy particles decreased over time of the heat treatment under hydrogen. The rate of change depends on the original composition of the particles, as well as the heat treatment conditions such as temperature and flow rate. Our experimental results and theoretical calculations suggest that Zn in the intermetallic phase might be more stable, providing an opportunity for precise tuning the nanoparticle compositions. This approach presents a viable strategy for developing better Pt-Zn catalysts for propane dehydrogenation.

physics.app-ph↗

arXiv2024

Overfitting Reduction in Convex Regression

Convex regression is a method for estimating the convex function from a data set. This method has played an important role in operations research, economics, machine learning, and many other areas. However, it has been empirically observed that convex regression produces inconsistent estimates of convex functions and extremely large subgradients near the boundary as the sample size increases. In this paper, we provide theoretical evidence of this overfitting behavior. To eliminate this behavior, we propose two new estimators by placing a bound on the subgradients of the convex function. We further show that our proposed estimators can reduce overfitting by proving that they converge to the underlying true convex function and that their subgradients converge to the gradient of the underlying function, both uniformly over the domain with probability one as the sample size is increasing to infinity. An application to Finnish electricity distribution firms confirms the superior performance of the proposed methods in predictive power over the existing methods.

stat.ME↗

arXiv2023

Modeling economies of scope in joint production: Convex regression of input distance function

Modeling of joint production has proved a vexing problem. This paper develops a radial convex nonparametric least squares (CNLS) approach to estimate the input distance function with multiple outputs. We document the correct input distance function transformation and prove that the necessary orthogonality conditions can be satisfied in radial CNLS. A Monte Carlo study is performed to compare the finite sample performance of radial CNLS and other deterministic and stochastic frontier approaches in terms of the input distance function estimation. We apply our novel approach to the Finnish electricity distribution network regulation and empirically confirm that the input isoquants become more curved. In addition, we introduce the weight restriction to radial CNLS to mitigate the potential overfitting and increase the out-of-sample performance in energy regulation.

stat.ME↗

arXiv2023

Optimal resource allocation: Convex quantile regression approach

Optimal allocation of resources across sub-units in the context of centralized decision-making systems such as bank branches or supermarket chains is a classical application of operations research and management science. In this paper, we develop quantile allocation models to examine how much the output and productivity could potentially increase if the resources were efficiently allocated between units. We increase robustness to random noise and heteroscedasticity by utilizing the local estimation of multiple production functions using convex quantile regression. The quantile allocation models then rely on the estimated shadow prices instead of detailed data of units and allow the entry and exit of units. Our empirical results on Finland's business sector reveal a large potential for productivity gains through better allocation, keeping the current technology and resources fixed.

stat.ME↗

arXiv2023

Normality of I-V Measurements Using ML

Electrochemistry ecosystems are promising for accelerating the design and discovery of electrochemical systems for energy storage and conversion, by automating significant parts of workflows that combine synthesis and characterization experiments with computations. They require the integration of flow controllers, solvent containers, pumps, fraction collectors, and potentiostats, all connected to an electrochemical cell. These are specialized instruments with custom software that is not originally designed for network integration. We developed network and software solutions for electrochemical workflows that adapt system and instrument settings in real-time for multiple rounds of experiments. We demonstrate this automated workflow by remotely operating the instruments and collecting their measurements to generate a voltammogram (I-V profile) of an electrolyte solution in an electrochemical cell. These measurements are made available at the remote computing system and used for subsequent analysis. In this paper, we focus on a novel, analytically validated machine learning (ML) method for an electrochemistry ecosystem to ensure that I-V measurements are consistent with the normal experimental conditions, and to detect abnormal conditions, such as disconnected electrodes or low cell content volume.

cs.ET↗

arXiv2023

Defect emission and its dipole orientation in layered ternary Znln2S4 semiconductor

Defect engineering is promising to tailor the physical properties of two-dimensional (2D) semiconductors for function-oriented electronics and optoelectronics. Compared with the extensively studied 2D binary materials, the origin of defects and their influence on physical properties of 2D ternary semiconductors have not been clarified. In this work, we thoroughly studied the effect of defects on the electronic structure and optical properties of few-layer hexagonal Znln2S4 via versatile spectroscopic tools in combination with theoretical calculations. It has been demonstrated that the Zn-In anti-structural defects induce the formation of a series of donor and acceptor levels inside the bandgap, leading to rich recombination paths for defect emission and extrinsic absorption. Impressively, the emission of donor-acceptor pair (DAP) in Znln2S4 can be significantly tailored by electrostatic gating due to efficient tunability of Fermi level (Ef). Furthermore, the layer-dependent dipole orientation of defect emission in Znln2S4 was directly revealed by back focal plane (BFP) imagining, where it presents obviously in-plane dipole orientation within a dozen layers thickness of Znln2S4. These unique features of defects in Znln2S4 including extrinsic absorption, rich recombination paths, gate tunability and in-plane dipole orientation will definitely benefit to the advanced orientation-functional optoelectronic applications.

cond-mat.mtrl-sci↗

arXiv2022

Convex Support Vector Regression

Nonparametric regression subject to convexity or concavity constraints is increasingly popular in economics, finance, operations research, machine learning, and statistics. However, the conventional convex regression based on the least squares loss function often suffers from overfitting and outliers. This paper proposes to address these two issues by introducing the convex support vector regression (CSVR) method, which effectively combines the key elements of convex regression and support vector regression. Numerical experiments demonstrate the performance of CSVR in prediction accuracy and robustness that compares favorably with other state-of-the-art methods.

stat.ME↗

arXiv2022

Partial frontiers are not quantiles

Quantile regression and partial frontier are two distinct approaches to nonparametric quantile frontier estimation. In this article, we demonstrate that partial frontiers are not quantiles. Both convex and nonconvex technologies are considered. To this end, we propose convexified order-$\alpha$ as an alternative to convex quantile regression (CQR) and convex expectile regression (CER), and two new nonconvex estimators: isotonic CQR and isotonic CER as alternatives to order-$\alpha$. A Monte Carlo study shows that the partial frontier estimators perform relatively poorly and even can violate the quantile property, particularly at low quantiles. In addition, the simulation evidence shows that the indirect expectile approach to estimating quantiles generally outperforms the direct quantile estimations. We further find that the convex estimators outperform their nonconvex counterparts owing to their global shape constraints. An illustration of those estimators is provided using a real-world dataset of U.S. electric power plants.

stat.ME↗

arXiv2022

Non-crossing convex quantile regression

Quantile crossing is a common phenomenon in shape constrained nonparametric quantile regression. A recent study by Wang et al. (2014) has proposed to address this problem by imposing non-crossing constraints to convex quantile regression. However, the non-crossing constraints may violate an intrinsic quantile property. This paper proposes a penalized convex quantile regression approach that can circumvent quantile crossing while better maintaining the quantile property. A Monte Carlo study demonstrates the superiority of the proposed penalized approach in addressing the quantile crossing problem.

stat.ME↗

arXiv2021

pyStoNED: A Python Package for Convex Regression and Frontier Estimation

Shape-constrained nonparametric regression is a growing area in econometrics, statistics, operations research, machine learning and related fields. In the field of productivity and efficiency analysis, recent developments in the multivariate convex regression and related techniques such as convex quantile regression and convex expectile regression have bridged the long-standing gap between the conventional deterministic-nonparametric and stochastic-parametric methods. Unfortunately, the heavy computational burden and the lack of powerful, reliable, and fully open access computational package has slowed down the diffusion of these advanced estimation techniques to the empirical practice. The purpose of the Python package pyStoNED is to address this challenge by providing a freely available and user-friendly tool for the multivariate convex regression, convex quantile regression, convex expectile regression, isotonic regression, stochastic nonparametric envelopment of data, and related methods. This paper presents a tutorial of the pyStoNED package and illustrates its application, focusing on the estimation of frontier cost and production functions.

stat.CO↗

arXiv2021

Variable selection in convex quantile regression: L1-norm or L0-norm regularization?

The curse of dimensionality is a recognized challenge in nonparametric estimation. This paper develops a new L0-norm regularization approach to the convex quantile and expectile regressions for subset variable selection. We show how to use mixed integer programming to solve the proposed L0-norm regularization approach in practice and build a link to the commonly used L1-norm regularization approach. A Monte Carlo study is performed to compare the finite sample performances of the proposed L0-penalized convex quantile and expectile regression approaches with the L1-norm regularization approaches. The proposed approach is further applied to benchmark the sustainable development performance of the OECD countries and empirically analyze the accuracy in the dimensionality reduction of variables. The results from the simulation and application illustrate that the proposed L0-norm regularization approach can more effectively address the curse of dimensionality than the L1-norm regularization approach in multidimensional spaces.

stat.ME↗

arXiv2019

Observation of perfect Andreev reflection due to Klein paradox in a topological superconducting state

In 1928, P. Dirac proposed a new wave equation to describe relativistic electrons. Shortly afterwards, O. Klein solved a simple potential step problem for the Dirac equation and stumbled upon an apparent paradox - the potential becomes transparent when the height is larger than the electron energy. For massless particles, backscattering is completely forbidden in Klein tunneling, leading to perfect transmission through any potential barrier. Recent advent of condensed matter systems with Dirac-like excitations, such as graphene and topological insulators (TIs), has opened the possibility of observing the Klein tunneling experimentally. In the surface states of TIs, fermions are bound by spin-momentum locking, and are thus immune to backscattering due to time-reversal symmetry. Here we report the observation of perfect Andreev reflection in point contact spectroscopy - a clear signature of Klein tunneling and a manifestation of the underlying relativistic physics of a proximity-induced superconducting state in a topological Kondo insulator.

cond-mat.mes-hall↗

arXiv2015

Aqueous Proton Transfer Across Single Layer Graphene

Proton transfer across single layer graphene is associated with large computed energy barriers and is therefore thought to be unfavorable at room temperature unless nanoscale holes or dopants are introduced, or a potential bias is applied. Here, we subject single layer graphene supported on fused silica to cycles of high and low pH and show that protons transfer reversibly from the aqueous phase through the graphene to the other side where they undergo acid-base chemistry with the silica hydroxyl groups. After ruling out diffusion through macroscopic pinholes, the protons are found to transfer through rare, naturally occurring atomic defects. Computer simulations reveal low energy barriers of 0.68 to 0.75 eV for aqueous proton transfer across hydroxyl-terminated atomic defects that participate in a Grotthuss-type relay, while pyrylium-like ether terminations shut down proton exchange. Unfavorable energy barriers to helium and hydrogen transfer indicate the transfer process is selective for aqueous protons.

cond-mat.mtrl-sci↗

arXiv2014

Interfacial Ionic Liquids: Connecting Static and Dynamic Structures

It is well-known that room temperature ionic liquids (RTILs) often adopt a charge-separated layered structure, i.e., with alternating cation- and anion-rich layers, at electrified interfaces. However, the dynamic response of the layered structure to temporal variations in applied potential is not well understood. We used in situ, real-time X-ray reflectivity (XR) to study the potential-dependent electric double layer (EDL) structure of an imidazolium-based RTIL on charged epitaxial graphene during potential cycling as a function of temperature. The results suggest that the graphene-RTIL interfacial structure is bistable in which the EDL structure at any intermediate potential can be described by the combination of two extreme-potential structures whose proportions vary depending on the polarity and magnitude of the applied potential. This picture is supported by the EDL structures obtained by fully atomistic molecular dynamics (MD) simulations at various static potentials. The potential-driven transition between the two structures is characterized by an increasing width but with an approximately fixed hysteresis magnitude as a function of temperature. The results are consistent with the coexistence of distinct anion and cation adsorbed structures separated by an energy barrier (~0.15 eV).

cond-mat.soft↗