A Call for an Intersectional Approach to Bias Harassment policies

During this presentation, I discuss the failures of anti-harassment policies, specifically hate crime polices, in protecting transwomen of color. I also discuss the FBI Hate Crimes statistics, along with self-reported survey results from the National Transgender Survey, the recorded instances of hate on communtiesagainsthate.org, and how these make visible the failures of the current policies, laws, and their enforcement. This presentation then highlights the need for an intersectional approach to harassment policies to better protect individuals from facing bias. Before analyzing the reports, I provide an overview of hate crime policy in the United States. Each state determines what constitutes a hate crime. This is evident in several diverse ways, the most common being the varied types of bias that are protected against, types of offenses that can be considered hate crimes, and the sentencing guidelines that are imposed. States offer protection based on race, religion, ethnicity, and gender; yet as we look at other aspects of identity such as ability, sexual orientation and gender identity, protection becomes more limited across the country. My research shows that without an intersectional approach to policy development, individuals who are supposedly guaranteed protection based on multiple aspects of their identity will continue to fall victim to bias discrimination. This harassment affects them economically, mentally, and limits their ability to fully interact within society

International Olympic Committee consensus statement on pain management in elite athletes

Pain is a common problem among elite athletes and is frequently associated with sport injury. Both pain and injury interfere with the performance of elite athletes. There are currently no evidence-based or consensus-based guidelines for the management of pain in elite athletes. Typically, pain management consists of the provision of analgesics, rest and physical therapy. More appropriately, a treatment strategy should address all contributors to pain including underlying pathophysiology, biomechanical abnormalities and psychosocial issues, and should employ therapies providing optimal benefit and minimal harm. To advance the development of a more standardised, evidence-informed approach to pain management in elite athletes, an IOC Consensus Group critically evaluated the current state of the science and practice of pain management in sport and prepared recommendations for a more unified approach to this important topic

Is Cannabis Actually "Legal" In Washington State Following The Passage Of Initiative 502 And, If So, For Whom?

Thesis (Master's)--University of Washington, 2021In 2012, Washington state passed Initiative 502 legalizing recreational adult use of cannabis, changing the legal landscape for the citizens of the state that wished to consume cannabis without fear of the legality of consuming. Yet, an individual can be convicted of misdemeanor cannabis possession in 2021, nine years later. Examining the convictions of misdemeanor cannabis possession in Pierce County between 2013-2018, reveals that a Black individual is three times as likely as a White individual to receive a conviction for misdemeanor cannabis possession. This disparity in convictions continues the negative impact on the Black community perpetrated by the War on Drugs and continues a misconception of the legalization of recreational cannabis use: that it is legal to possess. The disparity in convictions is discussed in the context of the impact of the War on Drugs on the Black community and the use of prosecutorial and officer discretion in deciding what statutes to pursue

Evolving Sensor Suites For Enemy Radar Detection

Designing optimal teams of sensors to detect the enemy radars for military operations is a challenging design problem. Many applications require the need to manage sensor resources. There is a tradeoff between the need to decrease the cost and to increase the capabilities of a sensor suite. In this paper, we address this design problem using genetic algorithms. We attempt to evolve the characteristics, size, and arrangement of a team of sensors, focusing on minimizing the size of sensor suite while maximizing its detection capabilities. The genetic algorithm we have developed has produced promising results for different environmental configurations as well as varying sensor resources. © Springer-Verlag Berlin Heidelberg 2003

Designing teams of unattended ground sensors using genetic algorithms

Improvements in sensor capabilities have driven the need for automated sensor allocation and management systems. Such systems provide a penalty-free test environment and valuable input to human operators by offering candidate solutions. These abilities lead, in turn, to savings in manpower and time. Determining an optimal team of cooperating sensors for military operations is a challenging task. There is a tradeoff between the desire to decrease the cost and the need to increase the sensing capabilities of a sensor suite. This work focuses on unattended ground sensor networks consisting of teams of small, inexpensive sensors. Given a possible configuration of enemy radar, our goal is to generate sensor suites that monitor as many enemy radar as possible while minimizing cost. In previous work, we have shown that genetic algorithms (GAs) can be used to evolve successful teams of sensors for this problem. This work extends our previous work in two ways: we use an improved simulator containing a more accurate model of radar and sensor capabilities for out fitness evaluations and we introduce two new genetic operators, insertion and deletion, that are expected to improve the GA\u27s fine tuning abilities. Empirical results show that our GA approach produces near optimal results under a variety of enemy radar configurations using sensors with varying capabilities. Detection percentage remains stable regardless of changes in the enemy radar placements

Designing Teams Of Unattended Ground Sensors Using Genetic Algorithms

Improvements in sensor capabilities have driven the need for automated sensor allocation and management systems. Such systems provide a penalty-free test environment and valuable input to human operators by offering candidate solutions. These abilities lead, in turn, to savings in manpower and time. Determining an optimal team of cooperating sensors for military operations is a challenging task. There is a tradeoff between the desire to decrease the cost and the need to increase the sensing capabilities of a sensor suite. This work focuses on unattended ground sensor networks consisting of teams of small, inexpensive sensors. Given a possible configuration of enemy radar, our goal is to generate sensor suites that monitor as many enemy radar as possible while minimizing cost. In previous work, we have shown that genetic algorithms (GAs) can be used to evolve successful teams of sensors for this problem. This work extends our previous work in two ways: we use an improved simulator containing a more accurate model of radar and sensor capabilities for out fitness evaluations and we introduce two new genetic operators, insertion and deletion, that are expected to improve the GA\u27s fine tuning abilities. Empirical results show that our GA approach produces near optimal results under a variety of enemy radar configurations using sensors with varying capabilities. Detection percentage remains stable regardless of changes in the enemy radar placements

Designing Teams of Unattended Ground Sensors Using Genetic Algorithms

Improvements in sensor capabilities have driven the need for automated sensor allocation and management systems. Such systems provide a penalty-free test environment and valuable input to human operators by offering candidate solutions. These abilities lead, in turn, to savings in manpower and time. Determining an optimal team of cooperating sensors for military operations is a challenging task. There is a tradeoff between the desire to decrease the cost and the need to increase the sensing capabilities of a sensor suite. This work focuses on unattended ground sensor networks consisting of teams of small, inexpensive sensors. Given a possible configuration of enemy radar, our goal is to generate sensor suites that monitor as many enemy radar as possible while minimizing cost. In previous work, we have shown that genetic algorithms (GAs) can be used to evolve successful teams of sensors for this problem. This work extends our previous work in two ways: we use an improved simulator containing a more accurate model of radar and sensor capabilities for out fitness evaluations and we introduce two new genetic operators, insertion and deletion, that are expected to improve the GA’s fine tuning abilities. Empirical results show that our GA approach produces near optimal results under a variety of enemy radar configurations using sensors with varying capabilities. Detection percentage remains stable regardless of changes in the enemy radar placements

Evolving Sensor Suites For Enemy Radar

Designing optimal teams of sensors to detect the enemy radars for military operations is a challenging design problem. Many applications require the need to manage sensor resources. There is a tradeoff between the need to decrease the cost and to increase the capabilities of a sensor suite. In this paper, we address this design problem using genetic algorithms. We attempt to evolve the characteristics, size, and arrangement of a team of sensors, focusing on minimizing the size of sensor suite while maximizing its detection capabilities. The genetic algorithm we have developed has produced promising results for different environmental configurations as well as varying sensor resources