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  1. ABSTRACT Biophysics is an interdisciplinary pursuit requiring researchers with knowledge and skills in several areas. Optical instruments and computers are fundamental tools in biophysics research to collect and analyze data. We developed a 1-semester Optical Engineering Laboratory course to teach image processing, optical engineering, and research skills to undergraduate students majoring in biology and biochemistry. With the use of development systems on students' laptops and in the cloud, students learned image processing with Python and OpenCV. Each student constructed a microprocessor-based lensless holographic microscope, gaining hands-on experience with optical engineering. The class culminated in original, student-designed research projects. All lectures, hands-on labs, and student research projects were performed both in person and remotely, in response to the COVID-19 pandemic. 
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    Free, publicly-accessible full text available August 1, 2024
  2. Schwartz, Russell (Ed.)
    Science students increasingly need programming and data science skills to be competitive in the modern workforce. However, at our university (San Francisco State University), until recently, almost no biology, biochemistry, and chemistry students (from here bio/chem students) completed a minor in computer science. To change this, a new minor in computing applications, which is informally known as the Promoting Inclusivity in Computing (PINC) minor, was established in 2016. Here, we present the lessons we learned from our experience in a set of 10 rules. The first 3 rules focus on setting up the program so that it interests students in biology, chemistry, and biochemistry. Rules 4 through 8 focus on how the classes of the program are taught to make them interesting for our students and to provide the students with the support they need. The last 2 rules are about what happens “behind the scenes” of running a program with many people from several departments involved. 
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  3. In order to achieve effective human-AI collaboration, it is necessary for an AI agent to align its behavior with the human's expectations. When the agent generates a task plan without such considerations, it may often result in inexplicable behavior from the human's point of view. This may have serious implications for the human, from increased cognitive load to more serious concerns of safety around the physical agent. In this work, we present an approach to generate explicable behavior by minimizing the distance between the agent's plan and the plan expected by the human. To this end, we learn a mapping between plan distances (distances between expected and agent plans) and human's plan scoring scheme. The plan generation process uses this learned model as a heuristic. We demonstrate the effectiveness of our approach in a delivery robot domain. 
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