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Task Interdependence and Shared Leadership: A Structural Perspective on the Distribution of Leadership in Teams
Nguyen, Thao P. H.; Bell, Bradford S. (SAGE, 2024)
Shared leadership has consistently been shown to predict team effectiveness. However, research also indicates that different teams may require different configurations of shared leadership, and achieving maximum sharedness in leadership does not always guarantee superior team outcomes. This reality underscores the need for a normative theory of shared leadership that can extend our understanding of the construct and facilitate its adoption in organizations. Despite the significance of such a theory, little attention has been given to its development and our understanding of how shared leadership should be distributed across different teams remains quite limited. In this article, we adopt a structural perspective to propose that the task interdependence network can serve as a robust foundation for devising effective shared leadership strategies. Our conceptual framework outlines the nuanced implications of the task interdependence network—from determining the optimal level of shared leadership necessary for performance to identifying potential members for shared leadership responsibilities. In doing so, we emphasize that the specific implications of the task interdependence network may vary, rather than remain uniform, across different dimensions of shared leadership.
CHEMICAL CONTROL OF SEMICONDUCTOR SURFACE: XPS AND STM
Zhu, Qingyuan (2024-08)
The chemical and physical control of semiconductor surfaces is crucial for various applications, including the performance enhancement of field-effect transistors, photocatalysts, and photocathodes. Despite significant advancements, there remains a need for in-depth research on various surface processes and characteristics of semiconductors. This thesis concentrates on the surface control of semiconductor photocatalysts and photocathodes, utilizing X-ray photoelectron spectroscopy and scanning tunneling microscopy.Investigation into the surface fluorination mechanism of rutile TiO2 (110) was performed. A mechanism akin to the Cabrera-Mott theory was proposed, where fluorination reduces surface charge density and induces an electric field. This field causes Ti cations to migrate to the surface, where they react with XeF2 and O2. Surface fluorination results in an atomically clean and non-stick surface, both before and after water rinsing. Additionally, this fluorination reaction is photo-switchable due to the photocatalyzed removal of the TiO2 surface carboxylate layer. Furthermore, the development of a method to protect photocathodes with atomically thin coatings, such as single-layer graphene and hexagonal boron nitride, was discussed. The feasibility of this method was proved by fabricating protected Mg photocathodes and detecting photoelectrons through the graphene layer. However, extending this approach to protect Cs3Sb photocathodes presented challenges, including the creation of clean substrates for photocathode growth and the nucleation of Cs3Sb on graphene and hexagonal boron nitride. These challenges require further investigation. Additionally, the surface chemistry of CsI-activated GaAs was investigated. Contrary to the conventional “yo-yo” activation method, the most stable oxide of Cs, Cs2O, was absent from the surface after annealing. Cs suboxides, such as Cs2O2 and CsO2, which possess lower work functions than Cs2O, were present in the activation layer. This hypothesis suggests a promising activation method for GaAs, potentially avoiding the formation of high work function Cs2O.
Optimizing Foundational System Building Blocks of Datacenter Applications
Zhou, Zhuangzhuang (2024-08)
Cloud computing has become the prevailing computing infrastructure for the majority of the world's computation. Computing platforms for cloud computing and large internet services are hosted in datacenters, and optimizing the performance of datacenter applications can result in significant cost savings. Given the diversity of datacenter workloads, optimizing a single application may not yield substantial improvements in the total system efficiency, as costs are spread across numerous independent workloads. In contrast, optimizing the foundational system building blocks of datacenter applications, including high-level system infrastructures to underlying system software libraries, can significantly improve the productivity of the datacenter fleet, since entire classes of datacenter applications can benefit from such optimizations. This dissertation proposes a series of optimizations in foundational system building blocks of datacenter applications. Applications running in datacenter are often built as collections of loosely coupled services that are deployed and executed through high-level system building blocks such as serverless workflow engines and microservice frameworks. First, we focus on optimizing such a system building block at the top of the computing stack, the serverless computing framework. Despite the benefits of ease of programming, fast elasticity, and fine-grained billing, serverless computing suffers from resource inefficiency. We designed Aquatope, a QoS-and-uncertainty-aware resource scheduler for end-to-end serverless workflows that takes into account the inherent uncertainty present in FaaS platforms, and improves performance predictability and resource efficiency. Aquatope uses a set of scalable and validated Bayesian models to create prewarmed containers ahead of function invocations, and to allocate appropriate resources at function granularity to meet a complex workflow’s end-to-end QoS, while minimizing resource cost. Aquatope demonstrates that a joint solution to cold start and resource management, taking into account uncertainty, can effectively improve the resource efficiency of serverless applications. However, serverless workflows still suffer from significant control plane and inter-function communication overheads, which make them unsuitable for latency-critical applications. We also designed Meteion, a fast and efficient serverless workflow engine for latency-critical interactive applications. Meteion decouples the control plane from the workflow execution, and leverages lightweight per-function engines to enable decentralized workflow orchestration and direct inter-function communication. Meteion's DAG scheduler utilizes the workflow's latency distribution and graph structure to provision containers promptly, ensuring that functions can execute seamlessly on worker servers without falling back to the control plane. Second, we delve into a foundational system library, the memory allocator. Datacenter applications typically share the usage of certain low-level software libraries, and memory allocation constitutes a substantial component of datacenter computation. Optimizing the memory allocator can improve application performance, leading to significant cost savings. We present the first comprehensive characterization of TCMalloc at warehouse scale. Our characterization reveals a profound diversity in the memory allocation patterns, allocated object sizes and lifetimes, for large-scale datacenter workloads, as well as in their performance on heterogeneous hardware platforms. Based on these insights, we optimize TCMalloc for warehouse-scale environments. Specifically, we propose optimizations for each level of its cache hierarchy that include usage-based dynamic sizing of allocator caches, leveraging hardware topology to mitigate inter-core communication overhead, and improving allocation packing algorithms based on statistical data. Evaluation results show that these optimizations significantly improve the productivity of the datacenter fleet.
Gerrymandering in the United States: Evolution, Measurement, and Political Consequences
Zhu, Zhiyang (2024-08)
This dissertation investigates the multifaceted issue of gerrymandering in the United States through three interconnected studies. The first study reviews the historical evolution of gerrymandering, assessing its origins, development, and the contemporary political and legal efforts to mitigate its impact. The second study evaluates quantitative measures like the efficiency gap and mean-median difference, highlighting significant flaws and limitations in their current implementations. The third study explores the relationship between gerrymandering and the rise of safe partisan congressional seats, attributing this phenomenon primarily to changes in political geography and voter behavior rather than gerrymandering itself. Together, these studies provide a comprehensive analysis of gerrymandering's historical context, methodological challenges, and contemporary implications, contributing to the discourse on electoral fairness and redistricting reforms.
Functional characterization of the human genome
Zhang, Junke (2024-08)
In this dissertation, I present my studies on functionally characterizing the human genome through a comprehensive evaluation of massively parallel reporter assays (MPRAs) for identifying human enhancers and prioritizing oncogenic gene fusions using a gene-based permutation test.Enhancers play crucial roles in regulating gene expression, and emerging evidence has revealed the association between genetic variants in enhancers and complex traits and diseases. This highlights the significance of identifying and characterizing enhancers for comprehending disease pathogenesis and developing new therapeutic approaches. The advances in high-throughput sequencing technologies have enabled the quantification of regulatory activities of millions of sequences simultaneously using MPRAs and self-transcribing active regulatory region sequencing (STARR-seq). Through comprehensive evaluation of MPRA/STARR-seq assays, I demonstrate factors affecting assay consistencies. By developing a uniform processing pipeline that addresses those factors, I identify more reliable enhancer regions and improve assay consistencies. The study provides valuable insights into areas for improvement in future applications of MPRA/STARR-seq to better characterize human enhancers. Gene fusions are potential products resulting from structural variants and have been recognized as an important class of somatic alterations in cancer. Previous experimental studies discovered and functionally characterized several oncogenic gene fusions, leading to the development of several drugs targeting gene fusion products. Recent advances in sequencing technologies and bioinformatics have enabled the detection of thousands of gene fusion events in cancer. I conduct a computational study to prioritize potential oncogenic fusions and provide functional characterization in the context of protein interactome networks. I identify a list of candidate driver fusions and map retained and lost protein-protein interactions through gene fusions. This serves as a valuable resource for future studies to understand how protein-interactome network rewiring by fusions can contribute to their oncogenic roles in cancer.