전신령 교수

Abstract Government trade actions are an increasing source of supply chain risk. This research provides empirical evidence of the stock market reaction to trade actions against a targeted firm on other firms in the targeted firm's supply chain eco‐system. We test our hypothesized stock price effects using the case of the 2018 US government ban on US firms from supplying to ZTE, a Chinese telecommunications manufacturer. We estimate the ban's effects on ZTE's tier‐one US and non‐US suppliers, as

Service excellence is viewed as firms providing high levels of service quality that in turn generate high customer satisfaction. Studies have empirically linked service excellence and firm performance. We add to the understanding of this link by examining the association between delivering service excellence and shareholder value. Delivering service excellence is proxied by announcements of winning service awards and shareholder value is assessed by the stock market reaction to such announcement

Deep-learning-augmented single-pixel imaging (SPI) provides an efficient solution for target compressive sensing. However, the conventional supervised strategy suffers from laborious training and poor generalization. In this Letter, we report a self-supervised learning method for SPI reconstruction. It introduces dual-domain constraints to integrate the SPI physics model into a neural network. Specifically, in addition to the traditional measurement constraint, an extra transformation constraint

This article investigates the stock market reaction to appointments of newly created chief digital or data officer (CDO) positions. The analysis is based on a sample of 112 CDO appointment announcements by publicly traded companies listed in the US stock market from 2004 to 2017. We ground our arguments in signaling theory along with the institutional entrepreneurship and synergy and redundancy perspective to understand the factors that could influence the market reaction to CDO appointments. Al

Planar object tracking plays an important role in AI applications, such as robotics, visual servoing, and visual SLAM. Although the previous planar trackers work well in most scenarios, it is still a challenging task due to the rapid motion and large transformation between two consecutive frames. The essential reason behind this problem is that the condition number of such a non-linear system changes unstably when the searching range of the homography parameter space becomes larger. To this end,

Single-pixel sensing offers low-cost detection and reliable perception, and the image-free sensing technique enhances its efficiency by extracting high-level features directly from compressed measurements. However, the conventional methods have great limitations in practical applications, due to their high dependence on large labelled data sources and incapability to do complex tasks. In this Letter, we report an image-free semi-supervised sensing framework based on GAN and achieve an end-to-end

Reducing the imaging time while maintaining reconstruction accuracy remains challenging for single-pixel imaging. One cost-effective approach is nonuniform sparse sampling. The existing methods lack intuitive and intrinsic analysis in sparsity. The lack impedes our comprehension of the form's adjustable range and may potentially limit our ability to identify an optimal distribution form within a confined adjustable range, consequently impacting the method's overall performance. In this Letter, w

The novel single-pixel sensing technique that uses an end-to-end neural network for joint optimization achieves high-level semantic sensing, which is effective but computation-consuming for varied sampling rates. In this Letter, we report a weighted optimization technique for sampling-adaptive single-pixel sensing, which only needs to train the network once for any dynamic sampling rate. Specifically, we innovatively introduce a weighting scheme in the encoding process to characterize different

Single-pixel encryption is a recently developed encryption technique enabling the ciphertext amount to be decreased. It adopts modulation patterns as secret keys and uses reconstruction algorithms for image recovery in the decryption process, which are time-consuming and can easily be illegally deciphered if the patterns are exposed. We report an image-free single-pixel semantic encryption technique that significantly enhances security. The technique extracts semantic information directly from t

With the explosive development of internet, enterprises are facing the challenge of dealing with massive data and coping with growing digital requirements that both are continuously generated by the customers. A new breed of executive- CDO, chief digital officer and chief data officer- is emerging as a senior manager to solve the above problems thus creating firm value. This paper provides empirical evidence on the performance effects of appointments of CDOs. The analysis is based on a sample of

ABSTRACT In the rapidly advancing digital era, technological diversification (TD) emerges as a pivotal strategy to enhance firms' innovation performance. This study explores the nonlinear dynamics of TD and innovation performance, positing an inverted U‐shaped relationship and examining the moderating effects of governance mechanisms—management shareholding, board size, board meeting frequency, and analyst coverage—from the perspectives of incentive mechanism design, internal control, and extern

Purpose Supply chain revamping (SCR) is an important strategy for firms to improve their supply chain operations in a rapidly changing environment. The purpose of this study is to shed light on the impact of SCR on shareholder value. Design/methodology/approach Based on Signaling Theory and 184 SCR announcements published by US-listed firms from 2013 to 2018, this study employs event study methodology and empirically examines three issues: Antecedents of SCRs; Primary purposes and actions of SCR

Abstract Image sensing often relies on a high-quality machine vision system with a large field of view and high resolution. It requires fine imaging optics, has high computational costs, and requires large communication bandwidth between image sensors and computing units. In this paper, we propose a novel image-free sensing framework for resource-efficient image classification, where the required number of measurements can be reduced by up to two orders of magnitude. In the proposed framework fo

Image sensing often relies on a high-quality machine vision system with a large field of view and high resolution. It requires fine imaging optics, has high computational costs, and requires a large communication bandwidth between image sensors and computing units. In this paper, we propose a novel image-free sensing framework for resource-efficient image classification, where the required number of measurements can be reduced by up to two orders of magnitude. In the proposed framework for singl