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"Zheng, Fang"
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Inference on Directionally Differentiable Functions
This article studies an asymptotic framework for conducting inference on parameters of the form ϕ(θ₀), where ϕ is a known directionally differentiable function and θ₀ is estimated by θ̂n
. In these settings, the asymptotic distribution of the plug-in estimator ϕ(θ̂n
) can be derived employing existing extensions to the Delta method. We show, however, that (full) differentiability of ϕ is a necessary and sufficient condition for bootstrap consistency whenever the limiting distribution of θ̂n
is Gaussian. An alternative resampling scheme is proposed that remains consistent when the bootstrap fails, and is shown to provide local size control under restrictions on the directional derivative of ϕ. These results enable us to reduce potentially challenging statistical problems to simple analytical calculations—a feature we illustrate by developing a test of whether an identified parameter belongs to a convex set. We highlight the empirical relevance of our results by conducting inference on the qualitative features of trends in (residual) wage inequality in the U.S.
Journal Article
بروس لي : أسطورة الشرق الخالدة /
أسطورة الشرق الخالدة، التنين الصيني، علامة الووشو، مؤسس الجيت كون دو، معلم الفنون القتالية الأشهر في التاريخ، كلها ألقاب حظي بها الرجل الأشهر في الفنون القتالية في تاريخنا المعاصر \"لي تشن فان\" وهو الاسم الأصلي للتنين الصيني \"بروس لي\". يرتبط اسم بروس لي بالأساطير القتالية التي لا تزال يتناقلها الصغار والشباب المهتمون بفنون القتال، ذلك المصارع ذو الأصول الصينية المولود بالولايات المتحدة. تلك الأساطير التي ارتبطت بمولده ونشأته، وتعلمه فنون رياضتي الووشو والكونغ فو، تأسيسه لرياضة الجيت كون دو \"القبضة المعارضة\". حياته الشخصية، أساليبه القتالية الفريدة، أدواره السينمائية التي حققت أعلى الإيرادات في السينما الأمريكية في ذلك الوقت، مقولاته الشهيرة التي ألهمت الكثيرين، أسرار وفاته الغامضة، كل ذلك من أسرار يكشفها لنا المؤلف الصيني لهذا الكتاب تشنغ جيه الباحث المخضرم في كل ما يتعلق بيروس لي والذي أجرى تحليلا شاملا لكم ضخم من المعلومات الرسمية والموثوقة عن بروس لي، وبدأ منذ عام 2013 في كتابة قصة حياة بروس لي التي نعرض لها في هذا الكتاب والتي تضم الكثير من الوثائق المعلومات التي تنشر للمرة الأولى، ويرى مؤلف هذا الكتاب أن قصة حياة بروس لي التي يعرضها هذا الكتاب ليست محتوى أدبيا أو فكريا : ولكنها أقرب ما تكون إلى الحقيقة. كل هذا وأكثر، حول أسطورة الشرق الخالدة بروس لي. بين دفتي هذا الكتاب.
Book
Does lifelong learning matter for the subjective wellbeing of the elderly? A machine learning analysis on Singapore data
Our study explores whether lifelong learning is associated with the subjective wellbeing among the elderly in Singapore. Through a primary survey of 300 individuals aged 65 and above, we develop a novel index to capture three different aspects of subjective wellbeing, which we term “Quality of Life”, “Satisfaction with Life” and “Psychological Wellbeing”. Utilizing both supervised and unsupervised machine learning techniques, our findings reveal that attitudes towards lifelong learning are positively associated with quality of life, while participation in class activities is positively associated with all three measures of wellbeing. Although the study does not establish causality, it highlights a connection between lifelong learning and the perceived wellbeing of the elderly, offering support for policies that encourage lifelong learning among this population.
Journal Article
Artificial Intelligence Coaches for Sales Agents
Firms are exploiting artificial intelligence (AI) coaches to provide training to sales agents and improve their job skills. The authors present several caveats associated with such practices based on a series of randomized field experiments. Experiment 1 shows that the incremental benefit of the AI coach over human managers is heterogeneous across agents in an inverted-U shape: whereas middle-ranked agents improve their performance by the largest amount, both bottom- and top-ranked agents show limited incremental gains. This pattern is driven by a learning-based mechanism in which bottom-ranked agents encounter the most severe information overload problem with the AI versus human coach, while top-ranked agents hold the strongest aversion to the AI relative to a human coach. To alleviate the challenge faced by bottom-ranked agents, Experiment 2 redesigns the AI coach by restricting the training feedback level and shows a significant improvement in agent performance. Experiment 3 reveals that the AI–human coach assemblage outperforms either the AI or human coach alone. This assemblage can harness the hard data skills of the AI coach and soft interpersonal skills of human managers, solving both problems faced by bottom- and top-ranked agents. These findings offer novel insights into AI coaches for researchers and managers alike.
Journal Article
Impedance sources (Z sources) with inherent fault protection for resilient and fire-free electricity grids
Modern societies would not survive without electricity and at the same time electrical faults could cause and have caused many catastrophes—mainly deadly fires—to our societies. There are two types of electricity sources: the voltage source such as generators, charged batteries and capacitors, and the current source such as charged inductors, current-regulated rectifiers, and superconducting magnetic energy storage. An “ideal” voltage source—that is often-sought-or-intentionally engineered—generates a constant voltage irrespective of its load current, and an “ideal” current source injects a constant current irrespective of its load voltage. However, two problems exist: (1) voltage or current sources do not represent many emerging natural/renewable energy sources such as wind turbine generators, photovoltaic cells, and fuel cells, whose output voltage and current are strongly dependent on each other, and (2) a short-circuit fault to an artificially-made and controlled “ideal” voltage source or an open-circuit fault to an “ideal” current source can cause catastrophic failures of the source itself and its surrounding circuits due to large (theoretically infinite) short-circuit current or open-circuit voltage. Here we introduce an impedance source concept to represent, characterize, and model those electricity sources whose output voltage and current are strongly dependent on each other. First, we found that many electric sources with no feedback (or active) control of their output voltage and/or current are a natural impedance source with inherent fault protection at short-circuit or open-circuit faults. Second, any electrical source can be artificially controlled to mimic a natural impedance source. Finally, we show how to apply natural impedance sources and nature-mimicking artificially-controlled sources to the electricity grid—the most complex machine ever made by human beings—to realize electricity grids that are naturally stable, self-protected against electrical faults, and resilient to natural and human-made events.
Journal Article
The NF‐Y‐PYR module integrates the abscisic acid signal pathway to regulate plant stress tolerance
Summary Drought and salt stresses impose major constraints on soybean production worldwide. However, improving agronomically valuable soybean traits under drought conditions can be challenging due to trait complexity and multiple factors that influence yield. Here, we identified a nuclear factor Y C subunit (NF‐YC) family transcription factor member, GmNF‐YC14, which formed a heterotrimer with GmNF‐YA16 and GmNF‐YB2 to activate the GmPYR1‐mediated abscisic acid (ABA) signalling pathway to regulate stress tolerance in soybean. Notably, we found that CRISPR/Cas9‐generated GmNF‐YC14 knockout mutants were more sensitive to drought than wild‐type soybean plants. Furthermore, field trials showed that overexpression of GmNF‐YC14 or GmPYR1 could increase yield per plant, grain plumpness, and stem base circumference, thus indicating improved adaptation of soybean plants to drought conditions. Taken together, our findings expand the known functional scope of the NF‐Y transcription factor functions and raise important questions about the integration of ABA signalling pathways in plants. Moreover, GmNF‐YC14 and GmPYR1 have potential for application in the improvement of drought tolerance in soybean plants.
Journal Article
Application of Machine Learning Approaches to Predict the Strength Property of Geopolymer Concrete
Geopolymer concrete (GPC) based on fly ash (FA) is being studied as a possible alternative solution with a lower environmental impact than Portland cement mixtures. However, the accuracy of the strength prediction still needs to be improved. This study was based on the investigation of various types of machine learning (ML) approaches to predict the compressive strength (C-S) of GPC. The support vector machine (SVM), multilayer perceptron (MLP), and XGBoost (XGB) techniques have been employed to check the difference between the experimental and predicted results of the C-S for the GPC. The coefficient of determination (R2) was used to measure how accurate the results were, which usually ranged from 0 to 1. The results show that the XGB was a more accurate model, indicating an R2 value of 0.98, as opposed to SVM (0.91) and MLP (0.88). The statistical checks and k-fold cross-validation (CV) also confirm the high precision level of the XGB model. The lesser values of the errors for the XGB approach, such as mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE), were noted as 1.49 MPa, 3.16 MPa, and 1.78 MPa, respectively. These lesser values of the errors also indicate the high precision of the XGB model. Moreover, the sensitivity analysis was also conducted to evaluate the parameter’s contribution towards the anticipation of C-S of GPC. The use of ML techniques for the prediction of material properties will not only reduce the effort of experimental work in the laboratory but also minimize the cast and time for the researchers.
Journal Article