Do general innovation policy tools fit all? Analysis of the regional impact of the Norwegian Skattefunn scheme

Background: The paper examines the regional effects of a general innovation policy, i.e. a policy tool that does not target specific industries or subnational regions. General policy tools are an important part of the portfolio of innovation policy measures. However, there is a question over whether general tools are equally relevant for all types of firms, irrespective of their size, sector and location. Findings: The economic geography and innovation study literature, as well as the EU’s Smart Specialization approach, are based on the view that innovation policy tools must be adapted to specific regional conditions. General policy tools are insufficient unless they are adapted to individual regions. This paper examines the regional distribution of support from the Norwegian Skattefunn scheme, which is a tax incentive scheme designed to stimulate R&D activity in all types of enterprises, which has supported more than 24,000 approved R&D projects between 2002 and 2013. Based on our regression analysis, we observe that regional innovation system (RIS) variables are important for explaining the region’s ability to attract Skattefunn funding. Conclusions: Skattefunn projects are quite evenly spread across labour market regions, which are grouped into a geographical centre–periphery pattern. That is, being in a peripheral location is not a disadvantage. However, at a more detailed regional level, the Skattefunn scheme tends to favour firms in specific industries and in regions with a relatively developed regional innovation system

The Parallel Distributed Image Search Engine (ParaDISE)

Image retrieval is a complex task that differs according to the context and the user requirements in any specific field, for example in a medical environment. Search by text is often not possible or optimal and retrieval by the visual content does not always succeed in modelling high-level concepts that a user is looking for. Modern image retrieval techniques consists of multiple steps and aim to retrieve information from large–scale datasets and not only based on global image appearance but local features and if possible in a connection between visual features and text or semantics. This paper presents the Parallel Distributed Image Search Engine (ParaDISE), an image retrieval system that combines visual search with text–based retrieval and that is available as open source and free of charge. The main design concepts of ParaDISE are flexibility, expandability, scalability and interoperability. These concepts constitute the system, able to be used both in real–world applications and as an image retrieval research platform. Apart from the architecture and the implementation of the system, two use cases are described, an application of ParaDISE in retrieval of images from the medical literature and a visual feature evaluation for medical image retrieval. Future steps include the creation of an open source community that will contribute and expand this platform based on the existing parts

Zimbabwe’s foreign policy under Mnangagwa

Under the presidency of Mnangagwa, Zimbabwe’s foreign policy is characterized by the desire to ‘re-engage’ with the West with a view to securing the removal of sanctions and encouraging investment. In this, it has received the backing of the African Union and Southern African Development Community states. Simultaneously, the violence of the Mnangagwa regime has reinforced the reluctance of the West to remove sanctions, and Zimbabwe has even begun to test the patience of its neighbours. The government has placed renewed faith in the ‘Look East Policy’, but China is seeking to match its investments with tighter control.https://journals.sagepub.com/home/jashj2021Political Science

Overview of the ImageCLEF 2016 Medical Task

ImageCLEF is the image retrieval task of the Conference and Labs of the Evaluation Forum (CLEF). ImageCLEF has historically focused on the multimodal and language–independent retrieval of images. Many tasks are related to image classification and the annotation of image data as well. The medical task has focused more on image retrieval in the beginning and then retrieval and classification tasks in subsequent years. In 2016 a main focus was the creation of meta data for a collection of medical images taken from articles of the the biomedical scientific literature. In total 8 teams participated in the four tasks and 69 runs were submitted. No team participated in the caption prediction task, a totally new task. Deep learning has now been used for several of the ImageCLEF tasks and by many of the participants obtaining very good results. A majority of runs was submitting using deep learning and this follows general trends in machine learning. In several of the tasks multimodal approaches clearly led to best results

Using Crowdsourcing for Multi-label Biomedical Compound Figure Annotation

Information analysis or retrieval for images in the biomedical literature needs to deal with a large amount of compound figures (figures containing several subfigures), as they constitute probably more than half of all images in repositories such as PubMed Central, which was the data set used for the task. The ImageCLEFmed benchmark proposed among other tasks in 2015 and 2016 a multi-label classification task, which aims at evaluating the automatic classification of figures into 30 image types. This task was based on compound figures and thus the figures were distributed to participants as compound figures but also in a separated form. Therefore, the generation of a gold standard was required, so that algorithms of participants can be evaluated and compared. This work presents the process carried out to generate the multi-labels of ∼2650 compound figures using a crowdsourcing approach. Automatic algorithms to separate compound figures into subfigures were used and the results were then validated or corrected via crowdsourcing. The image types (MR, CT, X–ray, ...) were also annotated by crowdsourcing including detailed quality control. Quality control is necessary to insure quality of the annotated data as much as possible. ∼625 h were invested with a cost of ∼870$

Assessment of a newly designed double-barreled bullet-shooting stunner for adequate stunning of water buffaloes

To ensure animal welfare at slaughter, rapid stunning is required to render the animal deeply unconscious. In cattle, captive-bolt stunners are typically used for this purpose. However, with regard to their impact force and maximum length of approximately 120 mm, such captive-bolt stunners are not suitable for stunning water buffaloes due to anatomical characteristics of the skull. In water buffaloes the bone layer is thicker and the distance from the point of attachment of the captive-bolt stunner to the relevant brain region is longer. For this reason, a special bullet-shooting stunner was developed, which is similar in size and handling to a standard captive-bolt stunner, but instead of a bolt, it fires a bullet. Actually, even two bullets can be loaded so that a follow-up shot can be fired immediately if necessary. In this study, the bullet-shooting stunner was tested using two different types of hunting ammunition for stunning water buffaloes during regular slaughter

Comparing Fusion Techniques for the ImageCLEF 2013 Medical Case Retrieval Task

Retrieval systems can supply similar cases with a proven diagnosis to a new example case under observation to help clinicians during their work. The ImageCLEFmed evaluation campaign proposes a framework where research groups can compare case-based retrieval approaches. This paper focuses on the case-based task and adds results of the compound figure separation and modality classification tasks. Several fusion approaches are compared to identify the approaches best adapted to the heterogeneous data of the task. Fusion of visual and textual features is analyzed, demonstrating that the selection of the fusion strategy can improve the best performance on the case-based retrieval task

BigBovid- Evaluation of a Newly Developed 9 mm Bullet-Shooting Stunner for Adequate Stunning of Heavy Cattle

The stunning of heavy cattle and water buffalo is an animal welfare problem, as conventional cartridge fired captive-bolt stunners are not suitable due to the thicker skull bones and the greater depth of penetration required to reach and damage the relevant brain regions for deep unconsciousness. This current animal welfare problem requires a suitable and feasible as well as commercially available and legally approved stunning device to ensure deep unconsciousness of these animals. In this study, the use of a newly developed bullet-shooting stunner, the BigBovid, with two different types of hunting ammunition, namely .38 SPL FMJ-TC and .357 MAG FTX ® bullets, was evaluated on 22 heavy cattle (mean weight: 1062.27 kg, standard deviation: 124.09 kg). In ballistic experiments, the BigBovid reached a mean energy density of 8.18 J/mm2 (mean error: 0.45 J/mm2) for the .38 SPL FMJ-TC and 17.56 J/mm2 (mean error: 2.67 J/mm2) for the .357 MAG FTX ®. In in vivo experiments, the use of the .38 SPL FMJ-TC resulted in overpenetration three times. The .357 MAG FTX ® bullets showed to be more advantageous, because on the one hand no overpenetration occurred and on the other hand the bullets fragmented into small parts after penetration into the skull. The fragments were scattered in the brain tissue, such as the thalamus and the brain stem, and thus there is a high probability to damage the brain regions relevant for deep unconsciousness. Based on the results of this study, the use of the BigBovid in combination with the .357 MAG FTX ® bullet is found to be suitable for stunning heavy cattle. Keywords: animal welfare; concussion; desensitization; heavy bulls; slaughterin

The medGIFT Group in ImageCLEFmed 2013

This article presents the participation of the medGIFT groupin ImageCLEFmed 2013. Since 2004, the group has participated in themedical image retrieval tasks of ImageCLEF each year. There are fourtypes of tasks for ImageCLEFmed 2013: modality classi cation, image{based retrieval, case{based retrieval and a new task on compound gureseparation. The medGIFT group participated in all four tasks. MedGIFTis developing a system named ParaDISE (Parallel Distributed ImageSearch Engine), which is the successor of GIFT (GNU Image FindingTool). The alpha version of ParaDISE was used to run the experimentsin the competition. The focus was on the use of multiple features incombinations with novel strategies, i.e, compound gure separation formodality classi cation or modality ltering for ad{hoc image and case{based retrieval