Amenity or Necessity? Street Standards as Parking Policy, Research Report 11-23

This paper explores the rationales underlying the use of minimum street width requirements to mandate street parking. A survey of 97 cities reveals that this mandate is not a technical necessity based on safety concerns or an amenity reflecting market demand, two common beliefs held by decision-makers. Many residents are likely unwilling to pay for street parking if it is unbundled from housing. The hidden parking policies should be made transparent and subject to public oversight, the double standard between private and public streets should be eliminated, and parking on residential streets should be optional

Residential On-Site Carsharing and Off-Street Parking Policy in the San Francisco Bay Area, Research Report 11-28

In light of rising motorization, transportation planners have increasingly supported alternatives to the indiscriminate use of the car. Off-street parking policy and carsharing have emerged as credible alternatives for discouraging car ownership. This report explores an initiative that could connect these policy fields and build on their synergy: the provision of on-site carsharing service in residential developments. It evaluates the performance of on-site carsharing programs in the San Francisco Bay Area by interviewing developers, planners, and carsharing service providers. Interviews were conducted in four Bay Area cities that support the provision of carsharing as an alternative to the private automobile. Based on these interviews, this report identifies the principal factors contributing to the success or failure of on-site carsharing: the unbundling status of off-street parking in residential developments; ties to off-street parking standards; financial constraints; and the level of coordination among stakeholders. The interviews revealed that on-site carsharing has been accepted by developers, planners, and service providers, particularly in densely-populated, transit-rich communities. Nevertheless, there appears to be a gap between on-site carsharing programs and off-street parking standards, and between carsharing programs and carsharing business operations. The authors recommend that a few models for establishing carsharing policy be tested: a model designed to serve high-density cities with traditional carsharing; and another designed to serve moderately-dense communities, with new carsharing options (e.g., peer-to-peer). In the case of the latter, trip reduction can be achieved through the promotion of alternative modes along major corridors

Transcriptional landscape of epithelial and immune cell populations revealed through FACS-seq of healthy human skin.

Human skin consists of multiple cell types, including epithelial, immune, and stromal cells. Transcriptomic analyses have previously been performed from bulk skin samples or from epithelial and immune cells expanded in cell culture. However, transcriptomic analysis of bulk skin tends to drown out expression signals from relatively rare cells while cell culture methods may significantly alter cellular phenotypes and gene expression profiles. To identify distinct transcriptomic profiles of multiple cell populations without substantially altering cell phenotypes, we employed a fluorescence activated cell sorting method to isolate keratinocytes, dendritic cells, CD4+ T effector cells, and CD8+ T effector cells from healthy skin samples, followed by RNA-seq of each cell population. Principal components analysis revealed distinct clustering of cell types across samples, while differential expression and coexpression network analyses revealed transcriptional profiles of individual cell populations distinct from bulk skin, most strikingly in the least abundant CD8+ T effector population. Our work provides a high resolution view of cutaneous cellular gene expression and suggests that transcriptomic profiling of bulk skin may inadequately capture the contribution of less abundant cell types

Stop Glorifying Fashion Piracy: It is Time to Enact the Innovative Design Protection Act

The current low-IP regime in the United States fails to provide adequate protection for fashion designs. Multiple bills had been proposed in Congress to extend copyright protection to fashion designs, but none of these was passed. Proponents of the “Piracy Paradox” doctrine suggest that unregulated copying is paradoxically beneficial to fashion designers and can foster innovation. This paper shows that the doctrine reflects a clear misunderstanding of fashion theories and how fashion trends are formed. It further argues that the fashion industry requires a diverse portfolio of inspired works rather than line-by-line knockoffs to foster trend formation. The Innovative Design Protection Act is a well-thought-out bill that can maximize the welfare of fashion designers, copyists, and the public. Congress needs to extend limited sui generis copyright protection to fashion designs that can prohibit fashion piracy without interfering with the production of inspired works

Vertical distribution of radiocesium in soil deposits on the contaminated areas after the Fukushima Daiichi Nuclear Power Plant accident

2017 Fall.Includes bibliographical references.An accident at the Fukushima Daiichi Nuclear Power Plant (FDNPP) occurred on March 11, 2011 which resulted in an environmental contamination with the radiocesium species 134Cs and 137Cs. Vertical distribution of radiocesium is important as it impacts the area dose rate. The vertical distribution of radiocesium is sensitive to wash-off by surface runoff, wind resuspension, and soil to plant transfer. Soil core samples were extracted to develop soil profiles. The purpose of this research is to study the vertical distribution of radiocesium in different soils contaminated after the accident, and to characterize the mechanisms by which the element moves through the soil. The results were compared to data on radiocesium vertical migration observed in Fukushima contaminated area for the year 2015 (Konoplev et al. 1992; Konoplev et al. 2016). The hypothesis is that reliable predictions of future soil contamination can be made based on the results from our soil samples. Predictions regarding radiocesium movement in soils will assist and improve remediation efforts in the Fukushima District. The vertical distribution of radiocesium was found to have a rate of movement of up to 12 cm/y in fluvisol type soils of Inkyozaka, 1 cm/y in andosol soils (Funasawa) and 3 cm/y in terrestrial regosol soils (Kashiramori). The results compared well with previous studies. Movement of radiocesium in Fukushima soils is most likely due to the high precipitation rate, combined with the weak bonding of cesium to fluvisol type soils

Revisiting Topic-Guided Language Models

A recent line of work in natural language processing has aimed to combine language models and topic models. These topic-guided language models augment neural language models with topic models, unsupervised learning methods that can discover document-level patterns of word use. This paper compares the effectiveness of these methods in a standardized setting. We study four topic-guided language models and two baselines, evaluating the held-out predictive performance of each model on four corpora. Surprisingly, we find that none of these methods outperform a standard LSTM language model baseline, and most fail to learn good topics. Further, we train a probe of the neural language model that shows that the baseline's hidden states already encode topic information. We make public all code used for this study.Comment: Published in Transactions on Machine Learning Research (TMLR) (12/2023

Interpretable Machine Learning for the Social Sciences: Applications in Political Science and Labor Economics

Recent advances in machine learning offer social scientists a unique opportunity to use data-driven methods to uncover insights into human behavior. However, current machine learning methods are opaque, ineffective on small social science datasets, and tailored for predicting unseen values rather than estimating parameters from data. In this thesis, we develop interpretable machine learning techniques designed to uncover latent patterns and estimate critical quantities in the social sciences. We focus on two aspects of interpretability: explaining individual model predictions and discovering latent patterns from data. We describe a method for explaining the predictions of general, black-box sequence models. This method approximates a combinatorial objective to elucidate the decision-making processes of sequence models. Next, we narrow our focus to domain-specific applications. In political science, we develop the text-based ideal point model, a model that quantifies political positions from text. This model marries a classical idea from political science with a Bayesian matrix factorization technique to infer meaningful structure from text. In labor economics, we adapt a model from natural language processing to analyze career trajectories. We describe a transfer learning method that can overcome the constraints posed by small survey datasets. Finally, we adapt this predictive model to estimate an important quantity in labor economics: the history-adjusted gender wage gap