FREEZE! A manifesto for safeguarding and preserving born-digital heritage

Finding ways to preserve born-digital heritage has become a matter of urgency and growing concern. Websites, games and interactive documentaries each bring specific challenges that need to be addressed. It takes three to tango: Ensuring that our digital lives and digital creativity are not lost to future generations requires a joint effort by the principal players: creators, heritage professionals and policy makers. This manifesto lays out the actions they need to take today to safeguard born-digital heritage

Direct Learning for Parameter-Varying Feedforward Control: A Neural-Network Approach

The performance of a feedforward controller is primarily determined by the extent to which it can capture the relevant dynamics of a system. The aim of this paper is to develop an input-output linear parameter-varying (LPV) feedforward parameterization and a corresponding data-driven estimation method in which the dependency of the coefficients on the scheduling signal are learned by a neural network. The use of a neural network enables the parameterization to compensate a wide class of constant relative degree LPV systems. Efficient optimization of the neural-network-based controller is achieved through a Levenberg-Marquardt approach with analytic gradients and a pseudolinear approach generalizing Sanathanan-Koerner to the LPV case. The performance of the developed feedforward learning method is validated in a simulation study of an LPV system showing excellent performance.Comment: Final author version, accepted for publication at 62nd IEEE Conference on Decision and Control, Singapore, 202

Symbiosis between the TRECVid benchmark and video libraries at the Netherlands Institute for Sound and Vision

Audiovisual archives are investing in large-scale digitisation efforts of their analogue holdings and, in parallel, ingesting an ever-increasing amount of born- digital files in their digital storage facilities. Digitisation opens up new access paradigms and boosted re-use of audiovisual content. Query-log analyses show the shortcomings of manual annotation, therefore archives are complementing these annotations by developing novel search engines that automatically extract information from both audio and the visual tracks. Over the past few years, the TRECVid benchmark has developed a novel relationship with the Netherlands Institute of Sound and Vision (NISV) which goes beyond the NISV just providing data and use cases to TRECVid. Prototype and demonstrator systems developed as part of TRECVid are set to become a key driver in improving the quality of search engines at the NISV and will ultimately help other audiovisual archives to offer more efficient and more fine-grained access to their collections. This paper reports the experiences of NISV in leveraging the activities of the TRECVid benchmark

Open Culture Data: otwarcie danych GLAM od podstaw

Open Culture Data started as a grassroot movement at the end of 2011, with the aim to open up data in the cultural sector and stimulate (creative) re-use. In this context, we organised a hackathon, which resulted in the creation of 13 Open Culture Data apps. After this successful first half year, a solid network of cultural heritage professionals, copyright and open data experts and developers was formed. In April 2012, an Open Culture Data masterclass started in which 17 institutions got practical, technical and legal advice on how to open data for re-use. Furthermore, we organised an app competition and three hackathons, in which developers were stimulated to re-use Open Cultural Datasets in new and innovative ways. These activities resulted in 27 more apps and 34 open datasets. In this paper we share lessons-learned that will inform heritage institutions with real-life quantitative and qualitative experiences, best practices and guidelines from their peers for opening up data and the ways in which this data is reused. Since the open culture data field is still relatively young, this is highly relevant information needed to stimulate others to join the open data movement. To this end, we are already taking steps to cross the borders and let Europe know about the initiative, on both a practical and a policy level.Open Culture Data (dane otwartej kultury) to ruch, który zaczął rozwijać się pod koniec 2011 r. od obywatelskiej inicjatywy, mającej na celu otwarcie danych w sektorze kultury oraz rozbudzenie zainteresowania (twórczego) ich ponownym wykorzystaniem. W tych okolicznościach, zorganizowaliśmy maraton dla programistów (hackathon), w wyniku którego powstało 13 aplikacji Open Culture Data. Po tym pomyślnym pierwszym półroczu uformowała się trwała społeczność osób zawodowo związanych ze sferą dziedzictwa kulturowego, praw autorskich oraz specjalistów od otwartych danych i programistów. W kwietniu 2012 r. rozpoczął się kurs mistrzowski Open Culture Data, podczas którego 17 instytucji zdobyło praktyczną, techniczną i prawniczą wiedzę, jak otwierać dane w celu ich ponownego wykorzystania. Ponadto, zorganizowaliśmy konkurs na aplikacje oraz trzy maratony programistyczne, podczas których poproszono programistów o ponowne użycie zbioru danych otwartej kultury (Open Culture Dataset) w sposób nowatorski i innowacyjny. W wyniku tych działań powstało 27 kolejnych aplikacji oraz 34 otwarte zbiory danych. W niniejszym artykule chcemy podzielić się z instytucjami dziedzictwa kulturowego wiedzą zdobytą podczas rzeczywistych jakościowych i ilościowych doświadczeń, dobrych praktyk oraz wytycznych z otwierania danych i sposobów na ich ponowne wykorzystanie przez siostrzane instytucje. Póki dziedzina otwartych danych kultury jest stosunkowo świeża, ma to bardzo istotne znaczenie dla wzbudzania w innych chęci przyłączenia się do ruchu otwartych danych. Dlatego też, czynimy pierwsze kroki, by przekroczyć granice i rozpowszechnić tę inicjatywę w Europie zarówno pod względem praktycznym, jak i strategicznym

Kinetics of neuroendocrine differentiation in an androgen-dependent human prostate xenograft model

It was previously shown in the PC-295 xenograft that the number of chromogranin A (CgA)-positive neuroendocrine (NE) cells increased after androgen withdrawal. NE cells did not proliferate and differentiated from G0-phase-arrested cells. Here we further characterized NE differentiation, androgen receptor status, and apoptosis-associated Bcl-2 expression in the PC-295 model after androgen withdrawal to assess the origin of NE cells. PC-295 tumor volumes decreased by 50% in 4 days. Intraperitoneal bromodeoxyuridine (BrdU) incorporation and MIB-1 labeling decreased to 0%, and the apoptosis was maximal at day 4. Androgen receptor expression and prostate-specific antigen (PSA) serum levels decreased rapidly within 2 days. The number of NE cells increased 6-fold at day 4 and 30-fold at day 7. Five and ten percent of the CgA-positive cells were BrdU positive after continuous BrdU labeling for 2 and 4 days, respectively. However, no MIB-1 expression was observed in CgA-positive cells. NE cells expressed the regulated secretory pathway marker secretogranin III but were negative for androgen receptor and Bcl-2. Bcl-2 expression did increase in the non-NE tumor cells. In conclusion, androgen withdrawal leads to a rapid PC-295 tumor regression and a proliferation-independent induction of NE differentiation. The strictly androgen-independent NE cells that were still present after 21 days differentiated mainly from G0-phase-arrested cells