489 research outputs found

    A University Without Walls

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    During its formative years the development of prisoner education at the Open University (henceforth OU) was shaped by prisoners, prison and OU staff, and framed by a government desire to maintain and develop society through broadening prospects for social improvement. OU staff tended to see the university as part of a social democratic commitment to rehabilitation. Their pedagogy encouraged learners to be active in constructing knowledge by reflection on experience. For many prisoners, education was a means of escape, or at least engaging with ideas from beyond the walls

    Book Review of Learning Behind Bars: How IRA Prisoners Shaped the Peace Process in Ireland

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    This piece is a book review of Learning Behind Bars: How IRA Prisoners Shaped the Peace Process in Ireland, by Dieter Reinisch

    Dermatologists and radiotherapy

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    Oxidation status as a predictor of disease activity and response to therapy in pediatric patients with inflammatory bowel disease

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    INTRODUCTION: Physiologic and pathophysiologic inflammation is mediated, at least in part, by the generation and release of reactive oxygen species into the local tissue milieu. The chronic inflammation observed in patients with inflammatory bowel disease (IBD) is thought to begin in the lining of the intestine and may progress to involve the entire bowel wall.In an effort to assess disease activity, clinicians rely on costly and technically invasive procedures such as colonoscopies. As such, there is currently a need for the development of less invasive and more cost-effective methods for use in the diagnosis and interval assessment of children and adults with these chronic intestinal inflammatory disorders. OBJECTIVES: The objective of this study was to first determine if ambient redox status can be reliably measured in the stool of patients with IBD. A second aim of the study was to determine if ambient stool redox status was related to underlying diagnosis, clinical disease activity, or response to therapy in patients with IBD . METHODS: We first our ability to measure redox redox standards using three different commercially available devices. Once demonstrated, we then the process of performing sample analysis under various conditions (room tempererture, refrigerated, frozen, or spun/unspun) to determine the conditions under which we were able to achieve the most stable redox assessments. Finally, we conducted a small pilot cohort study in hospitalized pediatric patients with IBD to assess if stool redox status informed about disease activityWe collected stool samples from seven patients admitted to the inpatient gastrointestinal service at Boston Children’s Hospital during a period extending from November of 2018 to March of 2019. RESULTS: Preliminary studies confirmed our ability to accurately measure relative redox status (RRS) using three different apparatuses. Furthermore, we were able to generate dilution curves using juices known to include oxidants, with linear regression r2 values of 0.99. In our patient population, we confirmed our ability to generate a reliable readings and consistent RRS measurements over. Frozen samples displayed less stable and higher RRS than those either refrigerated or kept at room temperature for up to 8-hours. This suggests that freeze-thaw cycles may impact adversely on the stability of oxidants and antioxidants in our samples. The RRS measurements from stool samples collected from patients who were exhibiting active symptoms of their IBD measured about -400 mV, while samples collected from hospitalized patients without IBD manifest RRS readings of about 100 mV. CONCLUSION: This preliminary study demonstrates our ability to measure RRS in the stool of patients with and without IBD. The stability we observed in samples that were either stored at room temperature or refrigerated demonstrated that these represented optimal storage options. Additionally, measurements from homogenized stool samples appeared to be more variable when compared to the relatively smaller range from centrifuged samples. Initial studies indicated a strong difference in RRS measurements between patients with inflammatory and non-inflammatory GI disease or inactive IBD. This difference suggests that measurements of RRS could provide a quantitative real-time assessments of disease activity and response to therapy in patients with IBD

    PLOT-TAL -- Prompt Learning with Optimal Transport for Few-Shot Temporal Action Localization

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    This paper introduces a novel approach to temporal action localization (TAL) in few-shot learning. Our work addresses the inherent limitations of conventional single-prompt learning methods that often lead to overfitting due to the inability to generalize across varying contexts in real-world videos. Recognizing the diversity of camera views, backgrounds, and objects in videos, we propose a multi-prompt learning framework enhanced with optimal transport. This design allows the model to learn a set of diverse prompts for each action, capturing general characteristics more effectively and distributing the representation to mitigate the risk of overfitting. Furthermore, by employing optimal transport theory, we efficiently align these prompts with action features, optimizing for a comprehensive representation that adapts to the multifaceted nature of video data. Our experiments demonstrate significant improvements in action localization accuracy and robustness in few-shot settings on the standard challenging datasets of THUMOS-14 and EpicKitchens100, highlighting the efficacy of our multi-prompt optimal transport approach in overcoming the challenges of conventional few-shot TAL methods.Comment: Under Revie

    Two-Stream Transformer Architecture for Long Video Understanding

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    Pure vision transformer architectures are highly effective for short video classification and action recognition tasks. However, due to the quadratic complexity of self attention and lack of inductive bias, transformers are resource intensive and suffer from data inefficiencies. Long form video understanding tasks amplify data and memory efficiency problems in transformers making current approaches unfeasible to implement on data or memory restricted domains. This paper introduces an efficient Spatio-Temporal Attention Network (STAN) which uses a two-stream transformer architecture to model dependencies between static image features and temporal contextual features. Our proposed approach can classify videos up to two minutes in length on a single GPU, is data efficient, and achieves SOTA performance on several long video understanding tasks
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