Learning liminality : a case of continuing education in Greece

This article addresses the issue of liminality in the making, as manifested by traineeships in the Greek tourism sector. Drawing from ethnographic data collected between 2016 and 2017, we examine the experiences of young trainees in tourism-related enterprises in a national context of mild economic recovery. Our primary focus is on the impact of the selected training scheme as regards both the trainees’ self-image and their perceptions of work, occupation and careers in the tourism sector, the so-called heavy industry of the Greek economy. Our findings suggest that instead of concluding with a meaningful and inspiring career path, the actors learn to live in an inbetween and transient state for long periods of time as they prepare themselves for navigating a deregulated labour market. Through the lens of liminality, we aim at a more complex understanding of the unsettling and disruptive condition that pertains to the threshold position of our informants, of the transient spatio-temporal characteristics of Continuing Education itself, but also aspects of the transformations and transitions that shook up Greek society and economy during the last decad

DnS: Distill-and-Select for Efficient and Accurate Video Indexing and Retrieval

In this paper, we address the problem of high performance and computationally efficient content-based video retrieval in large-scale datasets. Current methods typically propose either: (i) fine-grained approaches employing spatio-temporal representations and similarity calculations, achieving high performance at a high computational cost or (ii) coarse-grained approaches representing/indexing videos as global vectors, where the spatio-temporal structure is lost, providing low performance but also having low computational cost. In this work, we propose a Knowledge Distillation framework, which we call Distill-and-Select (DnS), that starting from a well-performing fine-grained Teacher Network learns: a) Student Networks at different retrieval performance and computational efficiency trade-offs and b) a Selection Network that at test time rapidly directs samples to the appropriate student to maintain both high retrieval performance and high computational efficiency. We train several students with different architectures and arrive at different trade-offs of performance and efficiency, i.e., speed and storage requirements, including fine-grained students that store index videos using binary representations. Importantly, the proposed scheme allows Knowledge Distillation in large, unlabelled datasets -- this leads to good students. We evaluate DnS on five public datasets on three different video retrieval tasks and demonstrate a) that our students achieve state-of-the-art performance in several cases and b) that our DnS framework provides an excellent trade-off between retrieval performance, computational speed, and storage space. In specific configurations, our method achieves similar mAP with the teacher but is 20 times faster and requires 240 times less storage space. Our collected dataset and implementation are publicly available: https://github.com/mever-team/distill-and-select

InDistill: Information flow-preserving knowledge distillation for model compression

In this paper we introduce InDistill, a model compression approach that combines knowledge distillation and channel pruning in a unified framework for the transfer of the critical information flow paths from a heavyweight teacher to a lightweight student. Such information is typically collapsed in previous methods due to an encoding stage prior to distillation. By contrast, InDistill leverages a pruning operation applied to the teacher's intermediate layers reducing their width to the corresponding student layers' width. In that way, we force architectural alignment enabling the intermediate layers to be directly distilled without the need of an encoding stage. Additionally, a curriculum learning-based training scheme is adopted considering the distillation difficulty of each layer and the critical learning periods in which the information flow paths are created. The proposed method surpasses state-of-the-art performance on three standard benchmarks, i.e. CIFAR-10, CUB-200, and FashionMNIST by 3.08%, 14.27%, and 1% mAP, respectively, as well as on more challenging evaluation settings, i.e. ImageNet and CIFAR-100 by 1.97% and 5.65% mAP, respectively

Knowledge-based semantic annotation and retrieval of multimedia content

aceMedia is a 4 year EC part-funded FP6 Integrated Project, ending in December 2007. The project has developed tools to enable users to manage and share both personal and purchased content across PC, STB and mobile platforms. Knowledge-based analysis and ontologies have been successfully exploited in an end-to-end system to enable automated semantic annotation and retrieval of multimedia content. The paper briefly describes the objectives of aceMedia and the application of knowledge-based analysis in the project

CERTH/CEA LIST at MediaEval Placing Task 2015

ABSTRACT We describe the participation of the CERTH/CEA LIST team in the Placing Task of MediaEval 2015. We submitted five runs in total to the Locale-based placing sub-task, providing the estimated locations for the test set released by the organisers. Out of five runs, two are based solely on textual information, using feature selection and weighting methods over an existing language model-based approach. One is based on visual content, using geo-spatial clustering over the most visually similar images, and two runs are based on hybrid approaches, using both visual and textual cues from the images. The best results (median error 22km, 27.5% at 1km) were obtained when both visual and textual features are combined, using external data for training

Functional Assessment Scales in a General Intensive Care Unit

The aims of this study were to describe the functional assessment scales for intensive care unit patients, to examine the psychometric evidence for reliability and validity and to summarize the strengths and the weaknesses of them. Several instruments have been used so far for the assessment of functional ability, impairment and/or disability in ICU patients, but all of them have specific limitations. These measurement tools include: Barthel Index, Functional Independence Measure, Functional Status Score for the ICU, Physical Function ICU Test Modified Rankin Scale, Karnofsky Scale Index, 4P questionnaire, Glasgow Outcome Scale, and Disability Rating Scale. The choice of the most appropriate assessment tool will depend on the specific patient population, its diagnosis and rehabilitation phase and the psychological properties of the available measurement. Future studies should examine additional types of reliability and validity with more sophisticated statistical analyses and to assess whether the tool is used for research and/or for clinical purposes