The Role of Private International Law in Corporate Social Responsibility

__Abstract__ This contribution firstly reviews developments in the EU and in the United States on corporate social responsibility and conflict of laws. It concludes with reference to some related themes, in particular on the piercing of the corporate veil and with some remarks on compliance strategy, and compliance reality, for corporations

Governing Nanomedicine: Lessons from within, and for the EU medical technology regulatory framework.

Rapidly emerging technologies, such as nanotechnologies, are posing significant\ud challenges to regulatory governance due to the uncertainties of development\ud trajectories, product properties, and potential risk problems (Davies\ud 2009). While nanotechnology-based products and processes fall within the\ud scope of current regulatory instruments (European Commission 2008a;\ud Ludlow, Bowman, and Hodge 2007; van Calster 2006), there is increasing\ud concern that such frameworks may not be appropriate for adequately or\ud effectively regulating all dimensions of the technology (see, for example,\ud House of Lords Science and Technology Committee 2010; European Parliament\ud 2009a; Chaudhry et al. 2006; Taylor 2008, 2006; Royal Society and\ud Royal Academy of Engineering 2004). The traditional approach of evidencebased\ud regulation is not equipped to cope with myriad uncertainties surrounding\ud the development and commercialisation of nanotechnologies. This does\ud not mean that public policy can wait and see, however. Even in the case of\ud evidence deficiencies, public responsibility goes beyond a laissez-faire\ud approach to risk regulation. In the European Union and some other countries,\ud precautionary regulatory action is required when basic values like\ud human dignity, health, safety, environment, property, and privacy are at risk\ud (Fisher 2007; European Commission 2000)

Common Limitations of Image Processing Metrics:A Picture Story

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide.Comment: This is a dynamic paper on limitations of commonly used metrics. The current version discusses metrics for image-level classification, semantic segmentation, object detection and instance segmentation. For missing use cases, comments or questions, please contact [email protected] or [email protected]. Substantial contributions to this document will be acknowledged with a co-authorshi

Understanding metric-related pitfalls in image analysis validation

Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. To facilitate comprehension, illustrations and specific examples accompany each pitfall. As a structured body of information accessible to researchers of all levels of expertise, this work enhances global comprehension of a key topic in image analysis validation.Comment: Shared first authors: Annika Reinke, Minu D. Tizabi; shared senior authors: Paul F. J\"ager, Lena Maier-Hei

Wat betekent het begrip ‘huwelijksvermogensrecht in het IPR en welke praktische implicaties heeft de regeling inzake het huwelijksvermogensrecht in het WIPR

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China and the Challenge of Global Climate Change’: law and policy

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The Brussels International Business Court. A Carrot Sunk by Caviar

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Toegankelijkheid van informatie en rechterlijke bevoegdheid

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