An Analysis of Homicides in Oakland 2003, 2004 and 2005

This report summarizes changes in specific characteristics of homicides over the past three years, such as victim/suspect demographic characteristics, locations and methods of the crimes and the parole/probation status of victims and suspects. In addition to presenting annual data for 2003, 2004, and 2005, the tables and figures in this report show how characteristics of the homicides changed from year to year and over the three year period.In general there was an overall homicide increase of 7% from 2004 (88 homicides) to 2005 (94 homicides). While this increase does represent a troubling trend, total homicides remain well below the 20 year average of 111.8 homicides per year, and represents a decrease of 17% from 2003.Demographic trends also appear to have shifted from 2004 to 2005 for both the victims and suspects. The percentage of Black victims and suspects dropped from 2004, while Latino and Asian victims and suspects both increased. The number of identified Latino homicide suspects is especially troubling, rising from 7 in 2004 to 13 in 2005. Finally, the number of suspects who were known to be on probation/parole status decreased from 59% to 49%

The road to precision oncology

The ultimate goal of precision medicine is to use population-based molecular, clinical and other data to make individually tailored clinical decisions for patients, although the path to achieving this goal is not entirely clear. A new study shows how knowledge banks of patient data can be used to make individual treatment decisions in acute myeloid leukemia

Employing Moderate Resolution Sensors in Human Rights and International Humanitarian Law Monitoring

Organizations concerned with human rights are increasingly using remote sensing as a tool to improve their detection of human rights and international humanitarian law violations. However, as these organizations have transitioned to human rights monitoring campaigns conducted over large regions and extended periods of time, current methods of using fine- resolution sensors and manpower-intensive analyses have become cost- prohibitive. To support the continued growth of remote sensing in human rights and international humanitarian law monitoring campaigns, this study researches how moderate resolution land observatories can provide complementary data to operational human rights monitoring efforts. This study demonstrates the capacity of moderate resolutions to provide data to monitoring efforts by developing an approach that uses Landsat Enhanced Thematic Mapper Plus (ETM+) as part of a system for the detection of village destruction in Darfur, Sudan. Village destruction is an indicator of a human rights or international humanitarian law violations in Darfur during the 2004 study period. This analysis approach capitalizes on Landsat's historical archive and systematic observations by constructing a historic spectral baseline for each village in the study area that supports automated detection of a potentially destroyed village with each new overpass of the sensor. Using Landsat's near-infrared band, the approach demonstrates high levels of accuracy when compared with a U.S. government database documenting destroyed villages. This approach is then applied to the Darfur conflict from 2002 to 2008, providing new data on when and where villages were destroyed in this widespread and long-lasting conflict. This application to the duration of a real-world conflict illustrates the abilities and shortcomings of moderate resolution sensors in human rights monitoring efforts. This study demonstrates that moderate resolution satellites have the capacity to contribute complementary data to operational human rights monitoring efforts. While this study validates this capacity for the burning of villages in arid environments, this approach can be generalized to detect other human rights violations if an observable signal that represents the violation is identified

Effectiveness of Diet/Exercise in Prevention of Gestational Diabetes Mellitus and Associated Cesarean Section Delivery

Gestational diabetes mellitus (GDM) is becoming a more common diagnosis during pregnancy. GDM is defined as glucose intolerance diagnosed during pregnancy. Women diagnosed with GDM during pregnancy are at an increased risk for emergent or planned cesarean section delivery and the development of overt diabetes mellitus post pregnancy. Complications related to GDM include eclampsia, macrosomia, shoulder dystocia, stillbirth, and cesarean section delivery. Initial treatment consists of diet and exercise and if glucose can not be controlled then pharmacotherapy is introduced. A literature review was performed utilizing scientific databases, mesh terms, and keywords to gather statistically relevant research to analyze the effects of diet and exercise on the prevention of GDM and cesarean section delivery. Studies that met criteria for inclusion analyzed the effects of diet and exercise individually, as well as, combined effects on GDM prevention and cesarean section delivery. The current data available indicates that exercise is safe during pregnancy and when combined with diet prove beneficial in prevention of GDM and cesarean section delivery

Calibrating for Class Weights by Modeling Machine Learning

A much studied issue is the extent to which the confidence scores provided by machine learning algorithms are calibrated to ground truth probabilities. Our starting point is that calibration is seemingly incompatible with class weighting, a technique often employed when one class is less common (class imbalance) or with the hope of achieving some external objective (cost-sensitive learning). We provide a model-based explanation for this incompatibility and use our anthropomorphic model to generate a simple method of recovering likelihoods from an algorithm that is miscalibrated due to class weighting. We validate this approach in the binary pneumonia detection task of Rajpurkar, Irvin, Zhu, et al. (2017).Comment: 14 pages, 4 figure

The Power of Social Media in Supporting Warehouse Location Decisions for Online Retailers Using GIS

While online shopping is one of the fastest growing sectors in the U.S. economy and is quickly surpassing traditional retailers (Enright 2014), shopper demand data used to place warehouses is either proprietary or expensive. To address this, we present an alternative approach to identifying where online shopping demand occurs in Los Angeles County and therefore where to most efficiently place warehouses for online retailers. Twitter data was harvested identifying the location of tweets about Amazon or EBay. This information was used as a proxy to model location of online shoppers. When compared with U.S. Census population data for ages 18 to 40, the Twitter-derived data was found to be a much more effective means to model the location of online shoppers and more efficiently place online warehouses of goods