62 research outputs found

    Transitioning to College: Experiences of Successful First-Generation College Students

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    This qualitative study explored the high school to college transition experiences of ten successful first-generation college students (FGCS). Participants were college seniors at an historically black university in the United States. A generic qualitative research design was used, including in-depth, semi-structured interviews to collect and analyze data. Participants reported that the transition experience led to confusion with academic and financial procedures, various emotions including anxiety and fear, the realization that they had deficits in academic skills, and the receipt of support from family members and others. Cultural and social capital appeared to play key roles in their success. Student affairs professionals are encouraged to explore targeted, individualized strategies that meet the needs of FGCS as they transition to college

    Reiter's syndrome following shigella flexneri 2a

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    Shigella flexneri 2a was isolated from a patient with Reiter's syndrome (RS) following a family outbreak of traveler's diarrhea. Among 3 members at risk, only the patient was positive for HLA-B27. Data from 3 similar families support the hypothesis that susceptibility to RS is genetically transmitted. It is urged that every effort be made to culture and subtype Shigella and other enteric pathogens in RS following diarrhea. Concurrently, the patient had hepatitis, interpreted as a parallel enteric infection.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/37731/1/1780200117_ftp.pd

    Optimistic Planning for Markov Decision Processes

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    International audienceThe reinforcement learning community has recently intensified its interest in online planning methods, due to their relative independence on the state space size. However, tight near-optimality guarantees are not yet available for the general case of stochastic Markov decision processes and closed-loop, state-dependent planning policies. We therefore consider an algorithm related to AO* that optimistically explores a tree representation of the space of closed-loop policies, and we analyze the near-optimality of the action it returns after n tree node expansions. While this optimistic planning requires a finite number of actions and possible next states for each transition, its asymptotic performance does not depend directly on these numbers, but only on the subset of nodes that significantly impact near-optimal policies. We characterize this set by introducing a novel measure of problem complexity, called the near-optimality exponent. Specializing the exponent and performance bound for some interesting classes of MDPs illustrates the algorithm works better when there are fewer near-optimal policies and less uniform transition probabilities

    Robots As Intentional Agents: Using Neuroscientific Methods to Make Robots Appear More Social

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    Robots are increasingly envisaged as our future cohabitants. However, while considerable progress has been made in recent years in terms of their technological realization, the ability of robots to interact with humans in an intuitive and social way is still quite limited. An important challenge for social robotics is to determine how to design robots that can perceive the user’s needs, feelings, and intentions, and adapt to users over a broad range of cognitive abilities. It is conceivable that if robots were able to adequately demonstrate these skills, humans would eventually accept them as social companions. We argue that the best way to achieve this is using a systematic experimental approach based on behavioral and physiological neuroscience methods such as motion/eye-tracking, electroencephalography, or functional near-infrared spectroscopy embedded in interactive human–robot paradigms. This approach requires understanding how humans interact with each other, how they perform tasks together and how they develop feelings of social connection over time, and using these insights to formulate design principles that make social robots attuned to the workings of the human brain. In this review, we put forward the argument that the likelihood of artificial agents being perceived as social companions can be increased by designing them in a way that they are perceived as intentional agents that activate areas in the human brain involved in social-cognitive processing. We first review literature related to social-cognitive processes and mechanisms involved in human–human interactions, and highlight the importance of perceiving others as intentional agents to activate these social brain areas. We then discuss how attribution of intentionality can positively affect human–robot interaction by (a) fostering feelings of social connection, empathy and prosociality, and by (b) enhancing performance on joint human–robot tasks. Lastly, we describe circumstances under which attribution of intentionality to robot agents might be disadvantageous, and discuss challenges associated with designing social robots that are inspired by neuroscientific principles

    Emotion regulation in borderline personality disorder: The role of self‐criticism, shame, and self‐compassion

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    Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/110727/1/pmh1290.pd

    Irrational Belief Tests: New Insights, New Directions

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