11131963 research outputs found
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Sugiyama, leather portfolio, [1941/1945]
No full textA leather portfolio belonging to Hiroshi Sugiyama, [1941/1945]. The portfolio is filled with Sugiyama's records and receipts pertaining to his life insurance policy and has "property of Hiroshi Sugiyama" written on the front flap. Includes: 1 insurance policy, 12 envelopes, 11 receipts, 4 premium notices, 2 advertisements, and 2 letters
Daily Trojan, vol. 196, no. 18, Feb 04, 2019
No full textDaily Trojan, vol. 196, no. 18, Feb 04, 2019
Daily Trojan, vol. 196, no. 29, Feb 21, 2019
No full textDaily Trojan, vol. 196, no. 29, Feb 21, 2019
Daily Trojan, vol. 196, no. 37, Mar 05, 2019
No full textDaily Trojan, vol. 196, no. 37, Mar 05, 2019
Daily Trojan, vol. 196, no. 47, Mar 28, 2019
No full textDaily Trojan, vol. 196, no. 47, Mar 28, 2019
Daily Trojan, vol. 196, no. 51, Apr 03, 2019
No full textDaily Trojan, vol. 196, no. 51, Apr 03, 2019
Daily Trojan, vol. 196, no. 55, Apr 09, 2019
No full textDaily Trojan, vol. 196, no. 55, Apr 09, 2019
Active state learning from surprises in stochastic and partially-observable environments
No full text2019-01-29There is an ever-increasing need for autonomous agents and robotic systems that are capable of adapting to and operating in challenging partially-observable and stochastic environments. Standard techniques for learning in such environments are typically fundamentally reliant on an a priori specification of the state space in which the agent will operate. Designing an appropriate state space demands extensive domain knowledge, and even relatively minor changes to the task or agent might necessitate an expensive manual re-engineering process. Clearly, imbuing agents with the ability to actively and incrementally learn task-independent representations of state in such environments directly from experience would reduce the manual effort required to deploy these systems and enable them to adapt to changes in environment, task, or even unexpected disruptions of their own sensing and actuation capabilities. ❧ As an important step toward this goal, we address, in this dissertation, the challenging and open problem of actively learning a representation of the state and dynamics of unknown, discrete, stochastic and partially-observable environments directly from a stream of agent experience (i.e., actions and observations). In particular, we present a novel family of nonparametric probabilistic models called Stochastic Distinguishing Experiments (SDEs) and a novel biologically-inspired framework for actively and incrementally learning these models from experience called Probabilistic Surprise Based Learning (PSBL). SDEs are hierarchically-organized key sequences of ordered actions and expected observations (along with associated probability distributions) that, taken together, form an approximate and task-independent representation of environment state and dynamics. The key idea behind PSBL is that the agent begins with a minimal set of SDEs and continuously designs and performs experiments that test whether extensions to these SDEs result in a model that causes the agent to be surprised less frequently by observations that do not match its predictions. PSBL can be understood as a procedure to minimize this surprise frequency. ❧ We provide formal proofs regarding the convergence and computational complexity of the PSBL algorithm for certain classes of SDE models. We formally prove the representational capacity of certain classes of SDE models with respect to deterministic environments and a useful subclass of POMDP environments. These constructive proofs lead to a provably-optimal procedure that enables an agent with perfect sampling capabilities to learn a perfect SDE model of such environments, provided that noise levels are bounded according to certain technical criteria, which we formally derive. Extensive simulation results are provided to validate these theoretical analyses and demonstrate the effectiveness and scalability of PSBL and SDE modeling on a variety of simulated prediction and decision-making tasks in a number of challenging environments
Future ready schools: how middle and high school principals support personalized and digital learning for teachers and students at a mid-sized urban middle/high school
No full text2019-02-15The purpose of this research was to uncover the strategies used by effective high school, middle school principals and superintendent in supporting personalized and digital learning to help prepare students with the 21st-century skills needed to be successful in college and career. More specifically, this study set out to uncover: (1) What leadership strategies do middle and high school principals at Future Ready Schools use to help provide access to technology for their teachers and students, (2) How do Future Ready Schools middle and high school principals encourage personalized learning for their teachers and students, (3) How do Future Ready Schools middle and high school principals create an innovative and adaptable culture to support teachers in becoming connected educators beyond their school community, (4) What skills and strategies do Future Ready Schools middle and high school principals need to wisely use time and resources to help lead successful Future Ready Schools, and (5) How do Future Ready Schools middle and high school principals evaluate strategies used to help lead successful Future Ready Schools. The study employed a mixed-methods design consisting of nine quantitative surveys and nine qualitative interviews completed by four high school principals, four middle school principals, and a superintendent in comprehensive mid-sized urban middle schools and high schools in Southern California/Los Angeles County. Through the process of triangulation, the study’s findings indicate that Future Ready Schools principals and superintendents find ways to build capacity in their teachers to embrace ever-changing technology in the classroom to help personalize learning for students to succeed beyond the classroom walls. The researchers found that effective leadership and the implications of the Future Ready Schools framework should encourage meaningful discussions on the actions needed to enhance the teaching and learning environments to prepare students for the jobs and careers of tomorrow
Graduation rates in college of students with disabilities: an innovation study
No full text2019-02-19The graduation rates of Students with Disabilities (SWDs) relies on factors that include organizational support for students’ academic achievement and faculty’s ability and beliefs related to providing an inclusive classroom and effective teaching strategies. The purpose of this study was to understand faculty’s current knowledge, motivation, and perceptions of organizational culture and resources regarding the implementation of a Universal Design for Learning (UDL) training by the Disabled Student Services (DSS) office at a moderate sized private institution in southern California. This study’s assumed influences were the result of a literature review. Data was collected via interviews, document analysis, and an observation. The study participants were faculty from various disciplines on a four-year private university campus. Data demonstrated that the most significant barrier to implementing a UDL was faculty’s insufficient knowledge of the basic framework, not believing in their own ability to teach SWDs, and a lack of support by the institution. This study provides recommendations developed by utilizing the New World Kirkpatrick Model (Kirkpatrick & Kirkpatrick, 2016). The recommendations identified will ensure that faculty have the necessary tools to support the academic achievement of SWDs