Standardization as situation-specific achievement: regulatory diversity and the production of value in intercontinental collaborations in stem cell medicine

The article examines the role and challenges of scientific self-governance and standardization in inter-continental clinical research partnerships in stem cell medicine. The paper shows that – due to a high level of regulatory diversity – the enactment of internationally recognized standards in multi-country stem cell trials is a complex and highly situation-specific achievement. Standardization is imposed on a background of regulatory, institutional and epistemic-cultural heterogeneity, and implemented exclusively in the context of select clinical projects. Based on ethnographic data from the first trans-continental clinical trial infrastructure in stem cell medicine between China and the USA, the article demonstrates that locally evolved and international forms of experimental clinical research practices often co-exist in the same medical institutions. Researchers switch back and forth between these schemas, depending on the purposes of their research, the partners they work with, the geographic scale of research projects, and the contrasting demands for regulatory review, that result from these differences. Drawing on Birch’s analysis of the role of standardization in international forms of capital production in the biosciences, the article argues that the integration of local knowledge institutions into the global bioeconomy does not necessarily result in the shutting down of localized forms of value production. In emerging fields of medical research, that are regulated in highly divergent ways across geographical regions, the coexistence of distinct modes of clinical translation allows also for the production of multiple forms of economic value, at varying spatial scales. This is especially so in countries with lenient regulations. As this paper shows, the long-standing absence of a regulatory framework for clinical stem cell applications in China, permits the situation-specific adoption of internationally recognized standards in some contexts, while enabling the continuation of localized forms of value production in others

Evolution of a supply chain management game for the trading agent competition

TAC SCM is a supply chain management game for the Trading Agent Competition (TAC). The purpose of TAC is to spur high quality research into realistic trading agent problems. We discuss TAC and TAC SCM: game and competition design, scientific impact, and lessons learnt

Continuation-Passing C: compiling threads to events through continuations

In this paper, we introduce Continuation Passing C (CPC), a programming language for concurrent systems in which native and cooperative threads are unified and presented to the programmer as a single abstraction. The CPC compiler uses a compilation technique, based on the CPS transform, that yields efficient code and an extremely lightweight representation for contexts. We provide a proof of the correctness of our compilation scheme. We show in particular that lambda-lifting, a common compilation technique for functional languages, is also correct in an imperative language like C, under some conditions enforced by the CPC compiler. The current CPC compiler is mature enough to write substantial programs such as Hekate, a highly concurrent BitTorrent seeder. Our benchmark results show that CPC is as efficient, while using significantly less space, as the most efficient thread libraries available.Comment: Higher-Order and Symbolic Computation (2012). arXiv admin note: substantial text overlap with arXiv:1202.324

Ontology mapping: the state of the art

Ontology mapping is seen as a solution provider in today's landscape of ontology research. As the number of ontologies that are made publicly available and accessible on the Web increases steadily, so does the need for applications to use them. A single ontology is no longer enough to support the tasks envisaged by a distributed environment like the Semantic Web. Multiple ontologies need to be accessed from several applications. Mapping could provide a common layer from which several ontologies could be accessed and hence could exchange information in semantically sound manners. Developing such mapping has beeb the focus of a variety of works originating from diverse communities over a number of years. In this article we comprehensively review and present these works. We also provide insights on the pragmatics of ontology mapping and elaborate on a theoretical approach for defining ontology mapping

A New Laboratory for Hands-on Teaching of Electrical Engineering

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This paper describes an innovative laboratory for students in Electrical Engineering courses, which is recently established at the Energy Department of Politecnico di Torino, Italy. The main peculiarities of the lab are the high ICT content of each test rig, the multidisciplinary experiences, and the hands-on teaching methodology, allowing the student to have access in overall safety to many complex electrical/electromechanical systems. Currently, eight courses of Bachelor and Master of Science degrees in electrical engineering carry out in-class exercises and hands-on experiments in the new lab, serving over 200 students in total per year. The innovative lab also allows for external collaborations with companies and institutions for specific (and in some cases permanent) training offers, like a one-day per month LabVIEW course for faculty and staff members of Politecnico di Torino

Basic Enhancement Strategies When Using Bayesian Optimization for Hyperparameter Tuning of Deep Neural Networks

Compared to the traditional machine learning models, deep neural networks (DNN) are known to be highly sensitive to the choice of hyperparameters. While the required time and effort for manual tuning has been rapidly decreasing for the well developed and commonly used DNN architectures, undoubtedly DNN hyperparameter optimization will continue to be a major burden whenever a new DNN architecture needs to be designed, a new task needs to be solved, a new dataset needs to be addressed, or an existing DNN needs to be improved further. For hyperparameter optimization of general machine learning problems, numerous automated solutions have been developed where some of the most popular solutions are based on Bayesian Optimization (BO). In this work, we analyze four fundamental strategies for enhancing BO when it is used for DNN hyperparameter optimization. Specifically, diversification, early termination, parallelization, and cost function transformation are investigated. Based on the analysis, we provide a simple yet robust algorithm for DNN hyperparameter optimization - DEEP-BO (Diversified, Early-termination-Enabled, and Parallel Bayesian Optimization). When evaluated over six DNN benchmarks, DEEP-BO mostly outperformed well-known solutions including GP-Hedge, BOHB, and the speed-up variants that use Median Stopping Rule or Learning Curve Extrapolation. In fact, DEEP-BO consistently provided the top, or at least close to the top, performance over all the benchmark types that we have tested. This indicates that DEEP-BO is a robust solution compared to the existing solutions. The DEEP-BO code is publicly available at <uri>https://github.com/snu-adsl/DEEP-BO</uri>