Hyperbolic Interaction Model For Hierarchical Multi-Label Classification

Different from the traditional classification tasks which assume mutual exclusion of labels, hierarchical multi-label classification (HMLC) aims to assign multiple labels to every instance with the labels organized under hierarchical relations. Besides the labels, since linguistic ontologies are intrinsic hierarchies, the conceptual relations between words can also form hierarchical structures. Thus it can be a challenge to learn mappings from word hierarchies to label hierarchies. We propose to model the word and label hierarchies by embedding them jointly in the hyperbolic space. The main reason is that the tree-likeness of the hyperbolic space matches the complexity of symbolic data with hierarchical structures. A new Hyperbolic Interaction Model (HyperIM) is designed to learn the label-aware document representations and make predictions for HMLC. Extensive experiments are conducted on three benchmark datasets. The results have demonstrated that the new model can realistically capture the complex data structures and further improve the performance for HMLC comparing with the state-of-the-art methods. To facilitate future research, our code is publicly available

Substructure and Boundary Modeling for Continuous Action Recognition

This paper introduces a probabilistic graphical model for continuous action recognition with two novel components: substructure transition model and discriminative boundary model. The first component encodes the sparse and global temporal transition prior between action primitives in state-space model to handle the large spatial-temporal variations within an action class. The second component enforces the action duration constraint in a discriminative way to locate the transition boundaries between actions more accurately. The two components are integrated into a unified graphical structure to enable effective training and inference. Our comprehensive experimental results on both public and in-house datasets show that, with the capability to incorporate additional information that had not been explicitly or efficiently modeled by previous methods, our proposed algorithm achieved significantly improved performance for continuous action recognition.Comment: Detailed version of the CVPR 2012 paper. 15 pages, 6 figure

Modularity in Platform Competition

This paper explores how modularity logic facilitates platform competition to approach competitive advantages, in particular with context of a business ecosystem rather than a firm. The research adopts a case study approach. Data are collected through semi-structured interviews, and secondary resources including company annual reports, archives, and websites, and industry reports. The results indicated that product/service modularity should be aligned with business ecosystem modularity to facilitate network effects in platform competition. This research extends modularity research from the firm level to a business ecosystem context, and develops a two-layer modular architecture of platform competition in the business ecosystem

Drivers and patterns of supply chain collaboration in the pharmaceutical industry: A case study on SMEs in China

The objectives of this paper are to identify the supply chain collaboration models/patterns and its correspondent advantages on pharmaceutical supply chain. This paper aims to investigate how col-laborative activities could impact on the development of supply chain and industry. A case study methodology was adopted in this research, which involves pharmaceutical SMEs. The results indi-cate that collaborations are common in all phases of pharmaceutical supply chain, the different strength of barging power among collaborative partners will impact the advantages achieved at strategic, operational and political level

Nurturing business ecosystem with modular architecture

This paper aims to identify the structural elements of a business ecosystem from a view of modularity. The paper proposed that the architecture of the business ecosystem is consisted of three structural layers, including organization, product/service, and technology. Moreover, the structural elements in the business ecosystem can be divided into three categories, which are evolutional module, developmental module, and fundamental module. This paper extends the modularity research into the context of business ecosystem, and links the modularity in biology with the business studies. The three-layer modular architecture of the business ecosystem provides guidance to practitioners to nurture and evolve their business ecosystem. The identified modules clarify the roles of each actor and position themselves better in the business ecosystem. This paper proposes a modular logic to analyse the business ecosystem, which integrates the modularity theory both from ecology and technology into business/management studies

Structural analysis and evolutionary exploration based on the research topic network of a field: a case in high-frequency trading

This study aims to systematically analyze the distribution dynamics of research topics and uncover the development state of the research in the specific field, which will provide a practical reference for developing professional subject knowledge services in the era of big data. The research topic network is constructed and analyzed using methods and tools of scientometrics. Basic statistics on network characteristics are performed to reveal the research status. Community detection, node ordering, and other steps are conducted to generate the evolutionary alluvial diagram. Then, relevant results are analyzed to explore the knowledge structure of the specific field and evolutionary context of research topics. Visualization analysis on the network structure of the latest period is executed to distinguish related concepts and predict the research trends. Taking high-frequency trading (HFT) as a case, this study achieves diversified scientometrics analysis of the research topic network and multi-dimensional evolution exploration of the relevant research topics in the specific field, which obtaining some knowledge insights. (1) Six major topics in HFT: liquidity & market microstructure, market efficiency, financial market, incomplete market, cointegration & price discovery, and event study. (2) The research focus about markets gradually transferred from international to emerging, meanwhile continuous attention to volatility/risk related issues. (3) The emphasis will change from theory to practice, technologies (big data, etc.) and theories (behavioral finance, etc.) will have more interaction with HFT. An effective research idea is proposed to reveal the knowledge structure of field and analyze the evolutionary context of research topics, which demonstrating the knowledge insights

Supply chain collaboration in the pharmaceutical industry: A triadic view

Collaboration is a value-adding activity to achieve competitive advantages. The rise in outsourcing has led to a supply landscape increasingly rely on networks rather than vertical-integration. To explore collaborations in a triadic view is the first step towards network. This research aims to explore the drivers and patterns of triadic collaboration in adoption of case study methodology