Is Criminalizing Re-Homing the Best Solution? A Look Into Safe Adoption Policy

This paper will examine existing state policies of criminalizing re-homing, and their potential benefits and costs. Recommendations are made to mitigate the potential costs of criminalizing re-homing and to prevent families from reaching the point of re-homing their adopted child

Master of Science

thesisThis thesis is conducted to compare a crash-level severity model with an occupant-level severity model for single-vehicle crashes on rural, two-lane roads. A multinomial logit model is used to identify and quantify the main contributing factors to the severity of rural, two-lane highway, single-vehicle crashes including human, roadway, and environmental factors. A comprehensive analysis of 5 years of crashes on rural, two-lane highways in Illinois with roadway characteristics, vehicle information, and human factors will be provided. The modeling results show that lower crash severities are associated with wider lane widths, shoulder widths, and edge line widths, and larger traffic volumes, alcohol-impaired driving, no restraint use will increase crash severity significantly. It is also shown that the impacts of light condition and weather condition are counterintuitive but the results are consistent with some previous research. Goodness of fit test and IIA (independence of irrelevant alternatives) test are applied to examine the appropriateness of the multinomial logit model and to compare the fit of the crash-level model with the occupant-level model. It is found that there are consistent modeling results between the two models and the prediction of each severity level by crash-level model is more accurate than that of the occupant-level model

Architecting Data Centers for High Efficiency and Low Latency

Modern data centers, housing remarkably powerful computational capacity, are built in massive scales and consume a huge amount of energy. The energy consumption of data centers has mushroomed from virtually nothing to about three percent of the global electricity supply in the last decade, and will continuously grow. Unfortunately, a significant fraction of this energy consumption is wasted due to the inefficiency of current data center architectures, and one of the key reasons behind this inefficiency is the stringent response latency requirements of the user-facing services hosted in these data centers such as web search and social networks. To deliver such low response latency, data center operators often have to overprovision resources to handle high peaks in user load and unexpected load spikes, resulting in low efficiency. This dissertation investigates data center architecture designs that reconcile high system efficiency and low response latency. To increase the efficiency, we propose techniques that understand both microarchitectural-level resource sharing and system-level resource usage dynamics to enable highly efficient co-locations of latency-critical services and low-priority batch workloads. We investigate the resource sharing on real-system simultaneous multithreading (SMT) processors to enable SMT co-locations by precisely predicting the performance interference. We then leverage historical resource usage patterns to further optimize the task scheduling algorithm and data placement policy to improve the efficiency of workload co-locations. Moreover, we introduce methodologies to better manage the response latency by automatically attributing the source of tail latency to low-level architectural and system configurations in both offline load testing environment and online production environment. We design and develop a response latency evaluation framework at microsecond-level precision for data center applications, with which we construct statistical inference procedures to attribute the source of tail latency. Finally, we present an approach that proactively enacts carefully designed causal inference micro-experiments to diagnose the root causes of response latency anomalies, and automatically correct them to reduce the response latency.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144144/1/yunqi_1.pd

Bruce-Vincent transference numbers from molecular dynamics simulations

Transference number is a key design parameter for electrolyte materials used in electrochemical energy storage systems. However, the determination of the true transference number from experiments is rather demanding. On the other hand, the Bruce-Vincent method is widely used in the lab to measure transference numbers of polymer electrolytes approximately, which becomes exact in the limit of infinite dilution. Therefore, theoretical formulations to treat the Bruce-Vincent transference number and the true transference number on an equal footing are clearly needed. Here we show how the Bruce-Vincent transference number for concentrated electrolyte solutions can be derived in terms of the Onsager coefficients, without involving any extrathermodynamic assumptions. By demonstrating it for the case of PEO-LiTFSI system, this work opens the door to calibrating molecular dynamics (MD) simulations via reproducing the Bruce-Vincent transference number and using MD simulations as a predictive tool for determining the true transference number

Fairness of ChatGPT

Understanding and addressing unfairness in LLMs are crucial for responsible AI deployment. However, there is a limited availability of quantitative analyses and in-depth studies regarding fairness evaluations in LLMs, especially when applying LLMs to high-stakes fields. This work aims to fill this gap by providing a systematic evaluation of the effectiveness and fairness of LLMs using ChatGPT as a study case. We focus on assessing ChatGPT's performance in high-takes fields including education, criminology, finance and healthcare. To make thorough evaluation, we consider both group fairness and individual fairness and we also observe the disparities in ChatGPT's outputs under a set of biased or unbiased prompts. This work contributes to a deeper understanding of LLMs' fairness performance, facilitates bias mitigation and fosters the development of responsible artificial intelligence systems

Cash Demand and Financing Decisions

Recent literature starts to focus on the effects of the urgency of cash demand on the choice of financing sources. Extant studies use data from the U.S. and conclude that firms use debt financing to meet immediate cash demand and equity financing to meet longer-term cash demand. Using data from China, this paper uncovers opposite findings: Firms are more likely to use equity financing to meet immediate cash demand and debt financing to meet cash demand in longer terms. We discuss the possible mechanisms behind the pattern

Kadabra: Adapting Kademlia for the Decentralized Web

Blockchains have become the catalyst for a growing movement to create a more decentralized Internet. A fundamental operation of applications in a decentralized Internet is data storage and retrieval. As today's blockchains are limited in their storage functionalities, in recent years a number of peer-to-peer data storage networks have emerged based on the Kademlia distributed hash table protocol. However, existing Kademlia implementations are not efficient enough to support fast data storage and retrieval operations necessary for (decentralized) Web applications. In this paper, we present Kadabra, a decentralized protocol for computing the routing table entries in Kademlia to accelerate lookups. Kadabra is motivated by the multi-armed bandit problem, and can automatically adapt to heterogeneity and dynamism in the network. Experimental results show Kadabra achieving between 15-50% lower lookup latencies compared to state-of-the-art baselines.Comment: 26 pages, 19 figure

Effect of Physical Exercise on College Students’ Life Satisfaction: Mediating Role of Competence and Relatedness Needs

This study examined the effect of physical exercise on the life satisfaction among college students. On the basis of the Basic Psychological Need Theory, the mediating roles of competence and relatedness needs satisfaction and their differences among college students in physical education (PE) majors and non-PE majors were explored. The sample included 1,012 college students who were selected to participate in an online survey. Major findings were as follows: (1) The total effect of physical exercise commitment on college students’ life satisfaction was marginally significant while that of physical exercise adherence was not significant; (2) The effect of physical exercise commitment was observed exclusively through the mediating role of relatedness need satisfaction, while that of physical exercise adherence was through both competence and relatedness needs satisfaction; (3) In terms of differences caused by major, only one mediation path, that was, physical exercise → competence need satisfaction → college students’s life satisfaction was significant among PE majors. This study thus enriched the empirical research on the benefits of physical exercise to individual mental health, highlighted the particularity of college students majoring in PE, and provided targeted and sensible suggestions for the design of physical exercise intervention programs

Reducing Interaction Cost: A Mechanism Deisgn Approach

In this thesis we study the problem of requiring self-interested agents need to interact with some centralized mechanism where this interaction is costly. To improve their utility, agents may choose to interact with \emph{neighbours} in order to coordinate their actions, potentially resulting in savings with respect to total interaction costs for all involved. We highlight the issues that arise in such a setting for the mechanism as well as for the agents. We use a mechanism-design approach to study this problem and present a model for self-interested agents to form groups with neighbours in order to reduce the total interaction cost. Our model focuses on two aspects: reward-distribution and cost-sharing. We look at two scenarios for reward-distribution mechanisms and proposed a core-stable payoff as well as a fair payoff mechanism. We then propose a cost-sharing mechanism that agents can use to coordinate and reduce their interaction costs. We prove this mechanism to be incentive-compatible, cost-recovery and fair. We also discuss how agents might form groups in order to save on cost. We study how our final outcome (the total percentage of savings as a group) depends on the agents' interaction topology and analyze different topologies. In addition we carry out experiments which further validate our proposal

SK-Net: Deep Learning on Point Cloud via End-to-end Discovery of Spatial Keypoints

Since the PointNet was proposed, deep learning on point cloud has been the concentration of intense 3D research. However, existing point-based methods usually are not adequate to extract the local features and the spatial pattern of a point cloud for further shape understanding. This paper presents an end-to-end framework, SK-Net, to jointly optimize the inference of spatial keypoint with the learning of feature representation of a point cloud for a specific point cloud task. One key process of SK-Net is the generation of spatial keypoints (Skeypoints). It is jointly conducted by two proposed regulating losses and a task objective function without knowledge of Skeypoint location annotations and proposals. Specifically, our Skeypoints are not sensitive to the location consistency but are acutely aware of shape. Another key process of SK-Net is the extraction of the local structure of Skeypoints (detail feature) and the local spatial pattern of normalized Skeypoints (pattern feature). This process generates a comprehensive representation, pattern-detail (PD) feature, which comprises the local detail information of a point cloud and reveals its spatial pattern through the part district reconstruction on normalized Skeypoints. Consequently, our network is prompted to effectively understand the correlation between different regions of a point cloud and integrate contextual information of the point cloud. In point cloud tasks, such as classification and segmentation, our proposed method performs better than or comparable with the state-of-the-art approaches. We also present an ablation study to demonstrate the advantages of SK-Net