Developing a National Design Scoreboard

Recognising the growing importance of design, this paper reports on the development of an approach to measuring design at a national level. A series of measures is proposed, that are based around a simplified model of design as a system at a national level. This model was developed though insights from literature and a workshop with government, industry and design sector representatives. Detailed data on design in the UK is presented to highlight the difficulties in collecting reliable and robust data. Evidence is compared with four countries (Spain, Canada, Korea and Sweden). This comparison highlights the inherent difficulties in comparing performance and a revised set of measures is proposed. Finally, an approach to capturing design spend at a firm level is proposed, based on insights from literature and case studies. Keywords: National Design System, Design Performance</p

Reflecting on loss in Papua New Guinea

This article takes up the conundrum of conducting anthropological fieldwork with people who claim that they have 'lost their culture,' as is the case with Suau people in the Massim region of Papua New Guinea. But rather than claiming culture loss as a process of dispossession, Suau claim it as a consequence of their own attempts to engage with colonial interests. Suau appear to have responded to missionization and their close proximity to the colonial-era capital by jettisoning many of the practices characteristic of Massim societies, now identified as 'kastom.' The rejection of kastom in order to facilitate their relations with Europeans during colonialism, followed by the mourning for kastom after independence, both invite consideration of a kind of reflexivity that requires action based on the presumed perspective of another

Assessing European firms’ exports and productivity distributions: The CompNet trade module. National Bank of Belgium Working Paper No. 282

This paper provides a new cross-country evaluation of competitiveness, focusing on the linkages between productivity and export performance among European economies. We use the information compiled in the Trade module of CompNet to establish new stylized facts regarding the joint distributions of the firm-level exports performance and productivity in a panel of 15 countries, 23 manufacturing sectors during the 2000’s. We confirm that exporters are more productive than nonexporters. However, this productivity premium is rising with the export experience of firms, with permanent exporters being much more productive than starters. At the intensive margin, we show that both the level and the growth of firm-level exports rise with firm productivity, and that the bulk of aggregate exports in each country are made by a small number of highly productive firms. Finally, we show that during the crisis, the growth of exports by high productive firms sustained the current account adjustment of European “stressed” economies. This last result confirms that the shape of the productivity distribution within each country can have important consequences from the point of view of the dynamics of aggregate trade patterns

Information retrieval for the management of civil engineering design content

As more and more civil engineering work becomes digital, the amount of information that engineers need to handle is growing exponentially. While this presents opportunities for reusing designs and increasing productivity, there remains the challenging of navigating around huge repositories of digital content. This presentation outlines a research line spanning about one decade in Information Retrieval to manage civil engineering design content, such as drawings and building models. The field of Information Retrieval is concerned with systems that help users to fulfil their information needs. Common examples of Information Retrieval systems include web search engines and library catalogues. The basic aim of the research presented here can therefore be expressed informally as “to develop search engines for civil engineering design content”. However, in the case of civil engineering applications of Information Retrieval, understanding retrieved information is probably more informant than finding the information in the first place. The emergence of Building Information Models in recent years adds some urgency to this line of research

BIM search engine: Exploiting interrelations between objects when assessing relevance

An increasing amount of information is packed into Building Information Models (BIMs), with the 3D geometry intended to serve as a central index leading to other information. The Three-Dimensional Information Retrieval (3DIR) project investigated information retrieval from such environments, with the aim of developing a search engine for searching and retrieving information from a building model. Here, the 3D model of the building can be exploited to formulate queries, compute the relevance of information items to a given query, and visualize search results. The focus of this paper is the computing of relevance. Literature in BIM/CAD and information retrieval was reviewed as a precursor to developing the search engine. Based on earlier research which identified the needs and aspirations of the users of BIMs, a graph theoretic formulation is proposed here to inform the emerging retrieval mechanisms of a BIM search engine. This formulation distinguishes between 3D and textual information in the model (the vertices in the graph), and between different types of relationships linking model objects (the edges in the graph). The value is tested of exploiting a 3D object’s relations to other 3D objects when assessing that object’s relevance to a query. For example, if a user is searching for “glazing door internal wall”, such a holistic/contextual search would rate the relevance of a “glazing panel” object more highly if it was touching “internal wall” or “door” objects. This notion was tested using an Autodesk Revit model from an architectural industry partner, augmented with the 3DIR search toolset. The model contained just under 7k 3D elements. Relationships between the objects were either hosting, touching or intersecting relationships. A comparison of the retrieval performance for a handful of test queries with and without this holistic/contextual search function does not decisively highlight the benefit but demonstrates the promise of this approach particularly for more complex multiple search term queries, as well as the value of the underlying graph theoretic formulation for studying and developing such systems

More Hierarchy in Route Planning Using Edge Hierarchies

A highly successful approach to route planning in networks (particularly road networks) is to identify a hierarchy in the network that allows faster queries after some preprocessing that basically inserts additional "shortcut"-edges into a graph. In the past there has been a succession of techniques that infer a more and more fine grained hierarchy enabling increasingly more efficient queries. This appeared to culminate in contraction hierarchies that assign one hierarchy level to each vertex. In this paper we show how to identify an even more fine grained hierarchy that assigns one level to each edge of the network. Our findings indicate that this can lead to considerably smaller search spaces in terms of visited edges. Currently, this rarely implies improved query times so that it remains an open question whether edge hierarchies can lead to consistently improved performance. However, we believe that the technique as such is a noteworthy enrichment of the portfolio of available techniques that might prove useful in the future

Targeted Branching for the Maximum Independent Set Problem Using Graph Neural Networks

Identifying a maximum independent set is a fundamental NP-hard problem. This problem has several real-world applications and requires finding the largest possible set of vertices not adjacent to each other in an undirected graph. Over the past few years, branch-and-bound and branch-and-reduce algorithms have emerged as some of the most effective methods for solving the problem exactly. Specifically, the branch-and-reduce approach, which combines branch-and-bound principles with reduction rules, has proven particularly successful in tackling previously unmanageable real-world instances. This progress was largely made possible by the development of more effective reduction rules. Nevertheless, other key components that can impact the efficiency of these algorithms have not received the same level of interest. Among these is the branching strategy, which determines which vertex to branch on next. Until recently, the most widely used strategy was to choose the vertex of the highest degree. In this work, we present a graph neural network approach for selecting the next branching vertex. The intricate nature of current branch-and-bound solvers makes supervised and reinforcement learning difficult. Therefore, we use a population-based genetic algorithm to evolve the model’s parameters instead. Our proposed approach results in a speedup on 73% of the benchmark instances with a median speedup of 24%

Targeted Branching for the Maximum Independent Set Problem Using Graph Neural Networks

Identifying a maximum independent set is a fundamental NP-hard problem. This problem has several real-world applications and requires finding the largest possible set of vertices not adjacent to each other in an undirected graph. Over the past few years, branch-and-bound and branch-and-reduce algorithms have emerged as some of the most effective methods for solving the problem exactly. Specifically, the branch-and-reduce approach, which combines branch-and-bound principles with reduction rules, has proven particularly successful in tackling previously unmanageable real-world instances. This progress was largely made possible by the development of more effective reduction rules. Nevertheless, other key components that can impact the efficiency of these algorithms have not received the same level of interest. Among these is the branching strategy, which determines which vertex to branch on next. Until recently, the most widely used strategy was to choose the vertex of the highest degree. In this work, we present a graph neural network approach for selecting the next branching vertex. The intricate nature of current branch-and-bound solvers makes supervised and reinforcement learning difficult. Therefore, we use a population-based genetic algorithm to evolve the model’s parameters instead. Our proposed approach results in a speedup on 73% of the benchmark instances with a median speedup of 24%