An evaluation of delay to reinforcement and variant responding

Children with Autism Spectrum Disorders and other developmental disabilities often exhibit invariant responding (i.e., restricted behavioral repertoires), deficits in communication, and challenging behavior. A variety of interventions have targeted increasing variant responding such as extinction, lag schedules of reinforcement, and percentile schedules of reinforcement. An additional variation studied in the basic literature entails the inclusion of a delay to reinforcement. Results of basic studies indicate that the inclusion of a delay to reinforcement leads to an increase in the variety of responses. The purpose of the current study was to evaluate the effects of a delay to reinforcement on the variability of communication responses during functional communication training with children with developmental disabilities with histories of engagement in challenging behavior. Results indicated that the delay to reinforcement increased variant communicative responding with all four participants.Educational Psycholog

Essays In Matching Markets

I present two experiments exploring failures in matching markets. In the first experiment, I introduce a new experimental paradigm to evaluate employer preferences, called Incentivized Resume Rating (IRR). Employers evaluate resumes they know to be hypothetical in order to be matched with real job seekers, preserving incentives while avoiding the deception necessary in audit studies. I deploy IRR with employers recruiting college seniors from a prestigious school, randomizing human capital characteristics and demographics of hypothetical candidates. I measure both employer preferences for candidates and employer beliefs about the likelihood candidates will accept job offers, avoiding a typical confound in audit studies. I discuss the costs, benefits, and future applications of this new methodology. In the second experiment, I examine out-of-equilibrium truth-telling in strategic matching markets. In two-sided settings, market designers tend to advocate for deferred acceptance (DA) over priority mechanisms, even though theory tells us that both types of mechanisms can yield unstable matches in incomplete information equilibrium. However, if match participants on the proposed-to side deviate from equilibrium by truth-telling, then DA yields stable outcomes. In a novel experimental setting, I find out-of-equilibrium truth-telling under DA but not under a priority mechanism, which could help to explain the success of DA in preventing unraveling in the field. I then attempt to explain the difference in behavior across mechanisms by estimating an experience-weighted learning model adapted to this complex strategic environment. I find that initial cognition and willingness to explore new strategies drive the difference in agents\u27 ability to find strategic equilibria

Physical and psychophysical analysis of progressive addition lens embodiments

The extent to which the surface parameters of Progressive Addition Lenses (PALs) affect successful patient tolerance was investigated. Several optico-physical evaluation techniques were employed, including a newly constructed surface reflection device which was shown to be of value for assessing semi-finished PAL blanks. Detailed physical analysis was undertaken using a computer-controlled focimeter and from these data, iso-cylindrical and mean spherical plots were produced for each PAL studied. Base curve power was shown to have little impact upon the distribution of PAL astigmatism. A power increase in reading addition primarily caused a lengthening and narrowing of the lens progression channel. Empirical measurements also indicated a marginal steepening of the progression power gradient with an increase in reading addition power. A sample of the PAL wearing population were studied using patient records and questionnaire analysis (90% were returned). This subjective analysis revealed the reading portion to be the most troublesome lens zone and showed that patients with high astigmatism (> 2.00D) adapt more readily to PALs than those with spherical or low cylindrical (2.00D) corrections. The psychophysical features of PALs were then investigated. Both grafting visual acuity (VA) and contrast sensitivity (CS) were shown to be reduced with an increase in eccentricity from the central umbilical line. Two sample populations (N= 20) of successful and unsuccessful PAL wearers were assessed for differences in their visual performance and their adaptation to optically induced distortion. The possibility of dispensing errors being the cause of poor patient tolerance amongst the unsuccessful wearer group was investigated and discounted. The contrast sensitivity of the successful group was significantly greater than that of the unsuccessful group. No differences in adaptation to or detection of curvature distortion were evinced between these presbyopic groups

MAPTree: Beating "Optimal" Decision Trees with Bayesian Decision Trees

Decision trees remain one of the most popular machine learning models today, largely due to their out-of-the-box performance and interpretability. In this work, we present a Bayesian approach to decision tree induction via maximum a posteriori inference of a posterior distribution over trees. We first demonstrate a connection between maximum a posteriori inference of decision trees and AND/OR search. Using this connection, we propose an AND/OR search algorithm, dubbed MAPTree, which is able to recover the maximum a posteriori tree. Lastly, we demonstrate the empirical performance of the maximum a posteriori tree both on synthetic data and in real world settings. On 16 real world datasets, MAPTree either outperforms baselines or demonstrates comparable performance but with much smaller trees. On a synthetic dataset, MAPTree also demonstrates greater robustness to noise and better generalization than existing approaches. Finally, MAPTree recovers the maxiumum a posteriori tree faster than existing sampling approaches and, in contrast with those algorithms, is able to provide a certificate of optimality. The code for our experiments is available at https://github.com/ThrunGroup/maptree.Comment: 19 page

Safety Stroller

ME450 Capstone Design and Manufacturing Experience: Winter 2010Kids in Danger desires to guide a team of engineers towards building a redesigned child stroller to address certain safety hazards. Common accidents from current strollers include rolling into traffic, invisibility at night, entrapment of the child, difficult setup, and pinching points. Nancy Cowles of Kids in Danger provided us with basic information regarding stroller malfunctions and what needs to be addressed. Our redesigned stroller targets these safety concerns and reduces the possibility of stroller accidents. In addition, this prototype introduces new technology that addresses safety concerns not currently covered by the ASTM F833 standards. The new features target safety hazards that are responsible for numerous child injuries each year, using an engineering approach to provide solutions.http://deepblue.lib.umich.edu/bitstream/2027.42/109379/1/me450w10project21_report.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/109379/2/me450w10project21_photo.jp

Bayesian Decision Trees via Tractable Priors and Probabilistic Context-Free Grammars

Decision Trees are some of the most popular machine learning models today due to their out-of-the-box performance and interpretability. Often, Decision Trees models are constructed greedily in a top-down fashion via heuristic search criteria, such as Gini impurity or entropy. However, trees constructed in this manner are sensitive to minor fluctuations in training data and are prone to overfitting. In contrast, Bayesian approaches to tree construction formulate the selection process as a posterior inference problem; such approaches are more stable and provide greater theoretical guarantees. However, generating Bayesian Decision Trees usually requires sampling from complex, multimodal posterior distributions. Current Markov Chain Monte Carlo-based approaches for sampling Bayesian Decision Trees are prone to mode collapse and long mixing times, which makes them impractical. In this paper, we propose a new criterion for training Bayesian Decision Trees. Our criterion gives rise to BCART-PCFG, which can efficiently sample decision trees from a posterior distribution across trees given the data and find the maximum a posteriori (MAP) tree. Learning the posterior and training the sampler can be done in time that is polynomial in the dataset size. Once the posterior has been learned, trees can be sampled efficiently (linearly in the number of nodes). At the core of our method is a reduction of sampling the posterior to sampling a derivation from a probabilistic context-free grammar. We find that trees sampled via BCART-PCFG perform comparable to or better than greedily-constructed Decision Trees in classification accuracy on several datasets. Additionally, the trees sampled via BCART-PCFG are significantly smaller -- sometimes by as much as 20x.Comment: 10 pages, 1 figur

Using electronic health records to support clinical trials: a report on stakeholder engagement for EHR4CR

Background. The conduct of clinical trials is increasingly challenging due to greater complexity and governance requirements as well as difficulties with recruitment and retention. Electronic Health Records for Clinical Research (EHR4CR) aims at improving the conduct of trials by using existing routinely collected data, but little is known about stakeholder views on data availability, information governance, and acceptable working practices. Methods. Senior figures in healthcare organisations across Europe were provided with a description of the project and structured interviews were subsequently conducted to elicit their views. Results. 37 structured interviewees in Germany, UK, Switzerland, and France indicated strong support for the proposed EHR4CR platform. All interviewees reported that using the platform for assessing feasibility would enhance the conduct of clinical trials and the majority also felt it would reduce workloads. Interviewees felt the platform could enhance trial recruitment and adverse event reporting but also felt it could raise either ethical or information governance concerns in their country. Conclusions. There was clear support for EHR4CR and a belief that it could reduce workloads and improve the conduct and quality of trials. However data security, privacy, and information governance issues would need to be carefully managed in the development of the platform