Biology and Ecology of Crapemyrtle Bark Scale, Acanthococcus Lagerstroemiae (Kuwana) (Hemiptera: Eriococcidae)

The crapemyrtle bark scale, Acanthococcus lagerstroemiae (Kuwana) (Hemiptera: Eriococcidae), is an exotic pest on crapemyrtles, Lagerstroemia spp. (Myrtales: Lythraceae). Because of its recent arrival in the US, little is known about its biology and ecology. The purpose of my thesis was to improve the knowledge about A. lagerstroemiae in four aspects involving its thermal tolerance, physiological adaptations to cold temperatures, temperature-dependent development and host range. Thermal tolerance was determined to understand how temperature extremes constrain the distribution of A. lagerstroemiae in the US. Results suggested that A. lagerstroemiae can tolerant high heat, but its potential distribution to the northern US may be limited by cold temperatures. Based on laboratory experiments and local temperatures from reported infestations, A. lagerstroemiae can establish in areas south of 43 °N, which is similar to the northern distribution limit of crapemyrtles. Therefore, the temperature extremes cannot limit its distribution on crapemyrtles in the US. To adapt to winter, cold tolerance of A. lagerstroemiae nymphs was observed to increase since November. The mechanisms of this increase were investigated by measuring seasonal changes of biochemical variables. From November to February, A. lagerstroemiae had 20% less water and higher energy reserves, which could have contributed to the increased cold tolerance. A restructuring of fatty acid composition in the body fat of overwintering nymphs was reported indicating accumulation of fatty acids in shorter chains (C6:0, C8:0 and C10:0), resulting in lower melting points that can help maintain lipid fluidity for energy conversion. The development and host range of A. lagerstroemiae were also studied. Developmental time and survival of A. lagerstroemiae eggs and nymphs were assessed under different temperatures, and results can help IPM practitioners improve field sampling strategies and timing of control measures. Callicarpa americana L. (Lamiales: Lamiaceae), Heimia salicifolia Link, Lawsonia inermis L., Lythrum alatum Pursh, and Punica granatum L. (Myrtales: Lythraceae) supported life cycle development and reproduction of A. lagerstroemiae and thus determined as suitable hosts other than Lagerstroemia spp. Scouting is recommended on these host species, following immediate responses to avoid additional spread, economic loss, and ecological disturbance of this pest

Critical success factors of total quality management in autonomous driving business models

Autonomous driving is undoubtedly one of the most strategically relevant and financially promising developing industries. The requirements for autonomous driving systems’ reliability are dramatically higher than in the driver-based car industry. This study explores a model to identify the structure and evaluate the critical success factors (CSFs) of total quality management (TQM) in the autonomous driving industry. Fifteen CSFs are defined according to the expected ecosystem of autonomous driving. VDA and IATF 16,949 quality systems are used as starting points for deriving the CSFs for an autonomous driving TQM system (AD-TQM). The CSFs are integrated into a framework to reveal their effects and interdependencies. The framework is qualitatively empirically tested and designed to be employed as a model for future (quantitative empirical) research

Prioritising critical success factors of total quality management in autonomous driving business models : A comparison between Germany and China

As one of the most strategically relevant and financially promising developing industries, the requirements for autonomous driving (AD) systems' reliability are dramatically higher than in the driver-based car industry. Using the analytic hierarchy process method, this study conducts a quantitative empirical study to prioritise the 15 critical success factors (CSFs) of total quality management (TQM) in the AD-ecosystem. The CSFs are derived from VDA and IATF 16949, two widely accepted TQM-frameworks in the car industry. Comparisons are made between Germany and China as two of the most important places in the world for strategic marketing for autonomous driving

Summary Statistic Privacy in Data Sharing

We study a setting where a data holder wishes to share data with a receiver, without revealing certain summary statistics of the data distribution (e.g., mean, standard deviation). It achieves this by passing the data through a randomization mechanism. We propose summary statistic privacy, a metric for quantifying the privacy risk of such a mechanism based on the worst-case probability of an adversary guessing the distributional secret within some threshold. Defining distortion as a worst-case Wasserstein-1 distance between the real and released data, we prove lower bounds on the tradeoff between privacy and distortion. We then propose a class of quantization mechanisms that can be adapted to different data distributions. We show that the quantization mechanism's privacy-distortion tradeoff matches our lower bounds under certain regimes, up to small constant factors. Finally, we demonstrate on real-world datasets that the proposed quantization mechanisms achieve better privacy-distortion tradeoffs than alternative privacy mechanisms

Skeleton-of-Thought: Large Language Models Can Do Parallel Decoding

This work aims at decreasing the end-to-end generation latency of large language models (LLMs). One of the major causes of the high generation latency is the sequential decoding approach adopted by almost all state-of-the-art LLMs. In this work, motivated by the thinking and writing process of humans, we propose "Skeleton-of-Thought" (SoT), which guides LLMs to first generate the skeleton of the answer, and then conducts parallel API calls or batched decoding to complete the contents of each skeleton point in parallel. Not only does SoT provide considerable speed-up (up to 2.39x across 11 different LLMs), but it can also potentially improve the answer quality on several question categories in terms of diversity and relevance. SoT is an initial attempt at data-centric optimization for efficiency, and reveal the potential of pushing LLMs to think more like a human for answer quality.Comment: Technical report, work in progres