The History Major and Undergraduate Liberal Education

Argues that the study of history integrates disciplinary knowledge, methods, and principles into a broad education and civic engagement. Recommends that departments set goals for student outcomes, diversify course requirements, and emphasize teaching

Bayesian Doubly Robust Causal Inference via Loss Functions

Frequentist inference has a well-established supporting theory for doubly robust causal inference based on the potential outcomes framework, which is realized via outcome regression (OR) and propensity score (PS) models. The Bayesian counterpart, however, is not obvious as the PS model loses its balancing property in joint modeling. In this paper, we propose a natural and formal Bayesian solution by bridging loss-type Bayesian inference with a utility function derived from the notion of a pseudo-population via the change of measure. Consistency of the posterior distribution is shown with correctly specified and misspecified OR models. Simulation studies suggest that our proposed method can estimate the true causal effect more efficiently and achieve the frequentist coverage if either the OR model is correctly specified or fit with a flexible function of the confounders, compared to the previous Bayesian approach via the Bayesian bootstrap. Finally, we apply this novel Bayesian method to assess the impact of speed cameras on the reduction of car collisions in England

Online Scheduling with Predictions

Online scheduling is the process of allocating resources to tasks to achieve objectives with uncertain information about future conditions or task characteristics. This thesis presents a new online scheduling framework named online scheduling with predictions. The framework uses predictions about unknowns to manage uncertainty in decision-making. It considers that the predictions may be imperfect and include errors, surpassing the traditional assumptions of either complete information in online clairvoyant scheduling or zero information in online non-clairvoyant scheduling. The goal is to create algorithms with predictions that perform better with quality predictions while having bounded performance with poor predictions. The framework includes metrics such as consistency, robustness, and smoothness to evaluate algorithm performance. We prove the fundamental theorems that give tight lower bounds for these metrics. We apply the framework to central scheduling problems and cyber-physical system applications, including minimizing makespan in uniform machine scheduling with job size predictions, minimizing mean response time in single and parallel identical machine scheduling with job size predictions, and maximizing energy output in pulsed power load scheduling with normal load predictions. Analysis and simulations show that this framework outperforms state-of-the-art methods by leveraging predictions

Learning with Single View Co-training and Marginalized Dropout

The generalization properties of most existing machine learning techniques are predicated on the assumptions that 1) a sufficiently large quantity of training data is available; 2) the training and testing data come from some common distribution. Although these assumptions are often met in practice, there are also many scenarios in which training data from the relevant distribution is insufficient. We focus on making use of additional data, which is readily available or can be obtained easily but comes from a different distribution than the testing data, to aid learning. We present five learning scenarios, depending on how the distribution we used to sample the additional training data differs from the testing distribution: 1) learning with weak supervision; 2) domain adaptation; 3) learning from multiple domains; 4) learning from corrupted data; 5) learning with partial supervision. We introduce two strategies and manifest them in five ways to cope with the difference between the training and testing distribution. The first strategy, which gives rise to Pseudo Multi-view Co-training: PMC) and Co-training for Domain Adaptation: CODA), is inspired by the co-training algorithm for multi-view data. PMC generalizes co-training to the more common single view data and allows us to learn from weakly labeled data retrieved free from the web. CODA integrates PMC with an another feature selection component to address the feature incompatibility between domains for domain adaptation. PMC and CODA are evaluated on a variety of real datasets, and both yield record performance. The second strategy marginalized dropout leads to marginalized Stacked Denoising Autoencoders: mSDA), Marginalized Corrupted Features: MCF) and FastTag: FastTag). mSDA diminishes the difference between distributions associated with different domains by learning a new representation through marginalized corruption and reconstruciton. MCF learns from a known distribution which is created by corrupting a small set of training data, and improves robustness of learned classifiers by training on ``infinitely\u27\u27 many data sampled from the distribution. FastTag applies marginalized dropout to the output of partially labeled data to recover missing labels for multi-label tasks. These three algorithms not only achieve the state-of-art performance in various tasks, but also deliver orders of magnitude speed up at training and testing comparing to competing algorithms

A Multiple Case Study Exploring Communities of Practice Led by Rural Secondary School Science Teachers to Overcome Community Isolation in a Research-Science, Dually-Enrolled, Program of Studies

This multiple case study focused on a research science dually-enrolled program of study and the unique challenges rural school educators face due to a lack of human and social capital. Some geographically-isolated rural secondary schools strategically use dual-enrollment programs to develop stronger social capital networks and communities of practice. Participants included five science research educators from rural, geographically-isolated secondary schools. Each case was examined individually, which allowed the researcher to explore the phenomenon within the context of the rural school research science teaching and learning environment. A cross-case analysis was conducted across all five cases using the inductive framework. The following research question guided this study: How do geographically-isolated rural secondary school Science Research in the High School (SRHS) educators utilize social capital and human action to establish, support, and facilitate communities of practice within their teaching and learning environment for student knowledge acquisition? This research study provided insight into the mutually beneficial roles communities and schools have in developing the social and human capital available to them in their community. By establishing partnerships through purposeful planning, community members, practitioners, and leaders can successfully work to address the student equity issues, often plaguing geographical-isolated rural schools. The results of this study reveal and communicate identified best educational practices used by SRHS educators in establishing communities of practice within their geographically isolated secondary schools. The identified need to prepare our students for a more global, technology, knowledge-driven society upon their graduation from secondary schools makes this study valuable and timely

A Survey of the Attitudes and Opinions of Industrial Arts Teachers Concerning the Modular-flexible Schedule in Selected Public Secondary Schools

This thesis is concerned with an examination of modular-flexible scheduling and the attitudes and opinions of industrial arts teachers employing this type of scheduling

Digital Me Ontology and Ethics

Digital me ontology and ethics. 21 December 2020. Ljupco Kocarev and Jasna Koteska. This paper addresses ontology and ethics of an AI agent called digital me. We define digital me as autonomous, decision-making, and learning agent, representing an individual and having practically immortal own life. It is assumed that digital me is equipped with the big-five personality model, ensuring that it provides a model of some aspects of a strong AI: consciousness, free will, and intentionality. As computer-based personality judgments are more accurate than those made by humans, digital me can judge the personality of the individual represented by the digital me, other individuals’ personalities, and other digital me-s. We describe seven ontological qualities of digital me: a) double-layer status of Digital Being versus digital me, b) digital me versus real me, c) mind-digital me and body-digital me, d) digital me versus doppelganger (shadow digital me), e) non-human time concept, f) social quality, g) practical immortality. We argue that with the advancement of AI’s sciences and technologies, there exist two digital me thresholds. The first threshold defines digital me having some (rudimentarily) form of consciousness, free will, and intentionality. The second threshold assumes that digital me is equipped with moral learning capabilities, implying that, in principle, digital me could develop their own ethics which significantly differs from human’s understanding of ethics. Finally we discuss the implications of digital me metaethics, normative and applied ethics, the implementation of the Golden Rule in digital me-s, and we suggest two sets of normative principles for digital me: consequentialist and duty based digital me principles. Authors are ordered alphabetically and equally contributed to the paper