What is the effect of technological shocks? A natural experiment from manufacturing industry in the United States of America from 2000 to 2010

This research studies from a firm level experiment in which we explored an exogenous technological change in firm's productivity. We present evidence from the manufacturing industry in the United States of America from 2000 to 2010, when the artificial intelligence are widely introduced across manufacturing sector. The result suggests that positive technological shocks affect the productivity in a positive way. Analysis of the data shows that the sector effect and the geographical effect exist but both are limited. These results highlight the universal impact of technological shocks and the interplay among innovations, firms and employees

AsterixDB: A Scalable, Open Source BDMS

AsterixDB is a new, full-function BDMS (Big Data Management System) with a feature set that distinguishes it from other platforms in today's open source Big Data ecosystem. Its features make it well-suited to applications like web data warehousing, social data storage and analysis, and other use cases related to Big Data. AsterixDB has a flexible NoSQL style data model; a query language that supports a wide range of queries; a scalable runtime; partitioned, LSM-based data storage and indexing (including B+-tree, R-tree, and text indexes); support for external as well as natively stored data; a rich set of built-in types; support for fuzzy, spatial, and temporal types and queries; a built-in notion of data feeds for ingestion of data; and transaction support akin to that of a NoSQL store. Development of AsterixDB began in 2009 and led to a mid-2013 initial open source release. This paper is the first complete description of the resulting open source AsterixDB system. Covered herein are the system's data model, its query language, and its software architecture. Also included are a summary of the current status of the project and a first glimpse into how AsterixDB performs when compared to alternative technologies, including a parallel relational DBMS, a popular NoSQL store, and a popular Hadoop-based SQL data analytics platform, for things that both technologies can do. Also included is a brief description of some initial trials that the system has undergone and the lessons learned (and plans laid) based on those early "customer" engagements

Emotion-Driven User Experience on Elderly Women’s Impulse Buying：A Kano Model study

This study examines how emotion-driven user experience (UX) design influences impulse buying among women aged 50-59 in online fashion shopping. Using the Kano model, it categorizes UX elements—visual appeal, social interaction, and personalized recommendations—into basic, performance, and attractive needs. A survey of 265 women reveals that emotionally engaging features significantly drive impulse purchases, though their impact on loyalty is limited. Key findings highlight the importance of visual aesthetics and personalized experiences. This research provides actionable insights for optimizing UX design to enhance engagement and purchasing behavior, with implications for future studies on other demographics and industries

SURE: SUrvey REcipes for building reliable and robust deep networks

In this paper, we revisit techniques for uncertainty estimation within deep neural networks and consolidate a suite of techniques to enhance their reliability. Our investigation reveals that an integrated application of diverse techniques--spanning model regularization, classifier and optimization--substantially improves the accuracy of uncertainty predictions in image classification tasks. The synergistic effect of these techniques culminates in our novel SURE approach. We rigorously evaluate SURE against the benchmark of failure prediction, a critical testbed for uncertainty estimation efficacy. Our results showcase a consistently better performance than models that individually deploy each technique, across various datasets and model architectures. When applied to real-world challenges, such as data corruption, label noise, and long-tailed class distribution, SURE exhibits remarkable robustness, delivering results that are superior or on par with current state-of-the-art specialized methods. Particularly on Animal-10N and Food-101N for learning with noisy labels, SURE achieves state-of-the-art performance without any task-specific adjustments. This work not only sets a new benchmark for robust uncertainty estimation but also paves the way for its application in diverse, real-world scenarios where reliability is paramount. Our code is available at \url{https://yutingli0606.github.io/SURE/}.Comment: Accepted to CVPR202

Contrasts in China and Soviet reform: sub-national and national causes

Why did reform in China and the former Soviet Union produce drastically different outcomes? Why did some provinces in China embrace faster economic reform than others? This article argues that the state sector and reform initiatives in the sub-national units, reform strategies, entrenchment and maturation of central planning, the size of the defence industry, policy choice and the historical context help explain the differences in Soviet and Chinese reform courses and outcomes. A predominant state sector in the former Soviet republics had stifled local reform initiatives. Gorbachev resorted to democratisation in order to unbolt the gate for popular support for marketisation, yet resulting in the breakup of the Soviet Union and destabilising the economy. In China, some provinces had sizable non-state sectors and were inclined to push forth marketization. Reform resulted in expanding non-state sectors, generating high growth and encouraging the regime to maintain its monopoly of power. China’s reform also benefited from a yet-to-be-entrenched and rudimentary central planning, a small defence sector, popular backlash against past policies, and reformist pragmatic strategy