Police Body Worn Cameras and Privacy: Retaining Benefits While Reducing Public Concerns

Recent high-profile incidents of police misconduct have led to calls for increased police accountability. One proposed reform is to equip police officers with body worn cameras, which provide more reliable evidence than eyewitness accounts. However, such cameras may pose privacy concerns for individuals who are recorded, as the footage may fall under open records statutes that would require the footage to be released upon request. Furthermore, storage of video data is costly, and redaction of video for release is time-consuming. While exempting all body camera video from release would take care of privacy issues, it would also prevent the public from using body camera footage to uncover misconduct. Agencies and lawmakers can address privacy problems successfully by using data management techniques to identify and preserve critical video evidence, and allowing non-critical video to be deleted under data-retention policies. Furthermore, software redaction may be used to produce releasable video that does not threaten the privacy of recorded individuals

A comparative study of Bayesian models for unsupervised sentiment detection

This paper presents a comparative study of three closely related Bayesian models for unsupervised document level sentiment classification, namely, the latent sentiment model (LSM), the joint sentimenttopic (JST) model, and the Reverse-JST model. Extensive experiments have been conducted on two corpora, the movie review dataset and the multi-domain sentiment dataset. It has been found that while all the three models achieve either better or comparable performance on these two corpora when compared to the existing unsupervised sentiment classification approaches, both JST and Reverse-JST are able to extract sentiment-oriented topics. In addition, Reverse-JST always performs worse than JST suggesting that the JST model is more appropriate for joint sentiment topic detection

ASSET FIXITY IN U.S. AGRICULTURE: ROBUSTNESS TO FUNCTIONAL FORM

The sensitivity of asset fixity conclusions, input adjustment rates, and elasticities to choice of functional form is examined using a dynamic dual model of U.S. agriculture. A very general initial specification allows tests of instantaneous adjustment to be performed for every input. Test results are mixed across functional forms for all inputs except real estate, which is consistently found to be quasi-fixed. Important differences in estimated adjustment rates and elasticities are also found among the functional forms. The translog has higher likelihood support than either the generalized Leontief or normalized quadratic functional forms for this dynamic model specification and data set.Agricultural Finance, Research Methods/ Statistical Methods, Q11, C51,

Existence of Good Sweepouts on Closed Manifolds

In this note we establish estimates for the harmonic map heat flow from $S^1$ into a closed manifold, and use it to construct sweepouts with the following good property: each curve in the tightened sweepout, whose energy is close to the maximal energy of curves in the sweepout, is itself close to a closed geodesic.Comment: 7 pages; added reference; corrected typo

AGGREGATION WITHOUT SEPARABILITY: TESTS OF U.S. AND MEXICAN AGRICULTURAL PRODUCTION DATA

The generalized composite commodity theorem (Lewbel 1996) is used to test for consistent aggregation of U.S. and Mexican agricultural production data in each of the categories for which earlier tests rejected homothetic separability. All U.S. agricultural outputs can be justifiably aggregated into as few as four categories. All Mexican agricultural outputs can be aggregated into as few as five categories. The aggregation of all outputs into a single output cannot be supported in either country by sufficient conditions provided by the generalized composite commodity theorem and/or a homothetically separable technology.aggregation, separability, generalized composite commodity theorem, Demand and Price Analysis,