616 research outputs found

    Securing Space for Local Peacebuilding: the role of international and national civilian peacekeepers

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    While large multilateral peace operations arrive with agendas extending into governance, economics, and other reforms, unarmed civilian peacekeeping (UCP) interventions focus on contributing to sufficiently safe space for local efforts at peacebuilding to proceed, at the request of local partners. They use a variety of nonviolent methods to increase the safety for local leaders and everyday people to engage in (re)building peace infrastructures and governance, within their own culture and contexts. This paper examines the potential for international interveners to support local efforts based on local invitations, local staff, conflict and context analysis, and living in conflict affected communities, followed by a case study of the Nonviolent Peaceforce South Sudan project. This project is helping to revitalize or create community peace infrastructures in coordination with local partners, other peacekeepers and humanitarian agencies, local government, army and other armed actors. This has saved lives, contributed to improved policing, improved relations between ethnic groups, supported local peace actors, and increased the effectiveness of multilateral peace operations and humanitarian aid work focused on physical safety

    Influenza vaccination coverage among medical residents: An Italian multicenter survey

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    Although influenza vaccination is recognized to be safe and effective, recent studies have confirmed that immunization coverage among health care workers remain generally low, especially among medical residents (MRs). Aim of the present multicenter study was to investigate attitudes and determinants associated with acceptance of influenza vaccination among Italian MRs. A survey was performed in 2012 on MRs attending post-graduate schools of 18 Italian Universities. Each participant was interviewed via an anonymous, self-administered, web-based questionnaire including questions on attitudes regarding influenza vaccination. A total of 2506 MRs were recruited in the survey and 299 (11.9%) of these stated they had accepted influenza vaccination in 2011-2012 season. Vaccinated MRs were older (P = 0.006), working in clinical settings (P = 0.048), and vaccinated in the 2 previous seasons (P < 0.001 in both seasons). Moreover, MRs who had recommended influenza vaccination to their patients were significantly more compliant with influenza vaccination uptake in 2011-2012 season (P < 0.001). "To avoid spreading influenza among patients" was recognized as the main reason for accepting vaccination by less than 15% of vaccinated MRs. Italian MRs seem to have a very low compliance with influenza vaccination and they seem to accept influenza vaccination as a habit that is unrelated to professional and ethical responsibility. Otherwise, residents who refuse vaccination in the previous seasons usually maintain their behaviors. Promoting correct attitudes and good practice in order to improve the influenza immunization rates of MRs could represent a decisive goal for increasing immunization coverage among health care workers of the future. © 2014 Landes Bioscience

    PREGO: Online Mistake Detection in PRocedural EGOcentric Videos

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    Promptly identifying procedural errors from egocentric videos in an online setting is highly challenging and valuable for detecting mistakes as soon as they happen. This capability has a wide range of applications across various fields, such as manufacturing and healthcare. The nature of procedural mistakes is open-set since novel types of failures might occur, which calls for one-class classifiers trained on correctly executed procedures. However, no technique can currently detect open-set procedural mistakes online. We propose PREGO, the first online one-class classification model for mistake detection in PRocedural EGOcentric videos. PREGO is based on an online action recognition component to model the current action, and a symbolic reasoning module to predict the next actions. Mistake detection is performed by comparing the recognized current action with the expected future one. We evaluate PREGO on two procedural egocentric video datasets, Assembly101 and Epic-tent, which we adapt for online benchmarking of procedural mistake detection to establish suitable benchmarks, thus defining the Assembly101-O and Epic-tent-O datasets, respectively. The code is available at https://github.com/alef/abo/PREGO

    Single-cell analysis tools for drug discovery and development

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    The genetic, functional or compositional heterogeneity of healthy and diseased tissues presents major challenges in drug discovery and development. Such heterogeneity hinders the design of accurate disease models and can confound the interpretation of biomarker levels and of patient responses to specific therapies. The complex nature of virtually all tissues has motivated the development of tools for single-cell genomic, transcriptomic and multiplex proteomic analyses. Here, we review these tools and assess their advantages and limitations. Emerging applications of single cell analysis tools in drug discovery and development, particularly in the field of oncology, are discussed

    Rescaling Egocentric Vision: Collection, Pipeline and Challenges for EPIC-KITCHENS-100

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    This paper introduces the pipeline to extend the largest dataset in egocentric vision, EPIC-KITCHENS. The effort culminates in EPIC-KITCHENS-100, a collection of 100 hours, 20M frames, 90K actions in 700 variable-length videos, capturing long-term unscripted activities in 45 environments, using head-mounted cameras. Compared to its previous version (Damen in Scaling egocentric vision: ECCV, 2018), EPIC-KITCHENS-100 has been annotated using a novel pipeline that allows denser (54% more actions per minute) and more complete annotations of fine-grained actions (+128% more action segments). This collection enables new challenges such as action detection and evaluating the “test of time”—i.e. whether models trained on data collected in 2018 can generalise to new footage collected two years later. The dataset is aligned with 6 challenges: action recognition (full and weak supervision), action detection, action anticipation, cross-modal retrieval (from captions), as well as unsupervised domain adaptation for action recognition. For each challenge, we define the task, provide baselines and evaluation metrics.Published versionResearch at Bristol is supported by Engineering and Physical Sciences Research Council (EPSRC) Doctoral Training Program (DTP), EPSRC Fellowship UMPIRE (EP/T004991/1). Research at Catania is sponsored by Piano della Ricerca 2016-2018 linea di Intervento 2 of DMI, by MISE - PON I&C 2014-2020, ENIGMA project (CUP: B61B19000520008) and by MIUR AIM - Attrazione e Mobilita Internazionale Linea 1 - AIM1893589 - CUP E64118002540007
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