A reassessment of non-union employee representation in the UK: developments since the 'ICE' age

In light of new legislation pertaining to information and consultation in the UK, the need to reassess the realms of employee involvement and participation, especially non-union forms, is critically apparent. This article explores the character of non-union employee representation arrangements established in the context of the Information and Consultation of Employees Regulations 2004, drawing on case study evidence from two UK-based manufacturing organisations. The findings highlight some important dynamics concerning the use of non-union employee representation; not least that the Information and Consultation of Employees Regulations have impacted in very different ways from those anticipated by the legislation.

Assessment of habitat representation across a network of marine protected areas with implications for the spatial design of monitoring

Networks of marine protected areas (MPAs) are being adopted globally to protect ecosystems and supplement fisheries management. The state of California recently implemented a coast-wide network of MPAs, a statewide seafloor mapping program, and ecological characterizations of species and ecosystems targeted for protection by the network. The main goals of this study were to use these data to evaluate how well seafloor features, as proxies for habitats, are represented and replicated across an MPA network and how well ecological surveys representatively sampled fish habitats inside MPAs and adjacent reference sites. Seafloor data were classified into broad substrate categories (rock and sediment) and finer scale geomorphic classifications standard to marine classification schemes using surface analyses (slope, ruggedness, etc.) done on the digital elevation model derived from multibeam bathymetry data. These classifications were then used to evaluate the representation and replication of seafloor structure within the MPAs and across the ecological surveys. Both the broad substrate categories and the finer scale geomorphic features were proportionately represented for many of the classes with deviations of 1-6% and 0-7%, respectively. Within MPAs, however, representation of seafloor features differed markedly from original estimates, with differences ranging up to 28%. Seafloor structure in the biological monitoring design had mismatches between sampling in the MPAs and their corresponding reference sites and some seafloor structure classes were missed entirely. The geomorphic variables derived from multibeam bathymetry data for these analyses are known determinants of the distribution and abundance of marine species and for coastal marine biodiversity. Thus, analyses like those performed in this study can be a valuable initial method of evaluating and predicting the conservation value of MPAs across a regional network.

The interior of our memories: a history of Melbourne's Jewish holocaust centre

The Interior of Our Memories describes the development of the Centre within global Holocaust memorial activity, both during the Holocaust and in the following decades when many survivors made new lives for themselves in Melbourne. The story begins, not in March 1984 when it first opened its doors, but during the Holocaust, when survivors began gathering documents. The book provides a history of the Centre’s early days and examines its transformation from a collection of photos, documents and material objects into the modern, educationally focused organisation it is today. The book situates the Jewish Holocaust Centre within a broader context, exploring issues of memory, testimony, the role of the museum within contemporary society, and what we can learn from one of the worst tragedies in human history.

Indonesia's national parties and the representation of a diverse society

 This study explores laws that promote the nationalisation of Indonesia’s political parties, and considers what this means for the representation of a diverse society. Overall, the research finds that the laws have restricted the development of political parties, but not for the reasons commonly expected.

Imagination, performance and affect: a critical pedagogy of the Holocaust?

Based on video testimony interviews held in the Shoah FoundationInstitute’s Visual History archive and the Melbourne JewishHolocaust Centre’s archive, this paper examines Holocaust survivortestimony as it relates to their return to the sites of atrocity,particularly Auschwitz-Birkenau. It analyses how survivor’s (re)encounters with material and imaginative landscapes revealsconceptions of, inter alia, agency, community, absence andbelonging in the performance of self. It uses these tensionsbetween landscapes of the past and present to develop thetheoretical relationship between performativity and ideas ofaffect. In doing so, it explores how these ideas can be used toengage students in a critical pedagogy of the Holocaust throughanalysis of survivor video-testimony and in visiting landscapes ofthe Holocaust.

Structured representation learning from complex data

This thesis advances several theoretical and practical aspects of the recently introduced restricted Boltzmann machine - a powerful probabilistic and generative framework for modelling data and learning representations. The contributions of this study represent a systematic and common theme in learning structured representations from complex data.

Mixed-norm sparse representation for multi view face recognition

Face recognition with multiple views is a challenging research problem. Most of the existing works have focused on extracting shared information among multiple views to improve recognition. However, when the pose variation is too large or missing, 'shared information' may not be properly extracted, leading to poor recognition results. In this paper, we propose a novel method for face recognition with multiple view images to overcome the large pose variation and missing pose issue. By introducing a novel mixed norm, the proposed method automatically selects candidates from the gallery to best represent a group of highly correlated face images in a query set to improve classification accuracy. This mixed norm combines the advantages of both sparse representation based classification (SRC) and joint sparse representation based classification (JSRC). A trade off between the ℓ1-norm from SRC and ℓ2,1-norm from JSRC is introduced to achieve this goal. Due to this property, the proposed method decreases the influence when a face image is unseen and has large pose variation in the recognition process. And when some face images with a certain degree of unseen pose variation appear, this mixed norm will find an optimal representation for these query images based on the shared information induced from multiple views. Moreover, we also address an open problem in robust sparse representation and classification which is using ℓ1-norm on the loss function to achieve a robust solution. To solve this formulation, we derive a simple, yet provably convergent algorithm based on the powerful alternative directions method of multipliers (ADMM) framework. We provide extensive comparisons which demonstrate that our method outperforms other state-of-the-arts algorithms on CMU-PIE, Yale B and Multi-PIE databases for multi-view face recognition.

Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (eNRBM)

Electronic medical record (EMR) offers promises for novel analytics. However, manual feature engineering from EMR is labor intensive because EMR is complex - it contains temporal, mixed-type and multimodal data packed in irregular episodes. We present a computational framework to harness EMR with minimal human supervision via restricted Boltzmann machine (RBM). The framework derives a new representation of medical objects by embedding them in a low-dimensional vector space. This new representation facilitates algebraic and statistical manipulations such as projection onto 2D plane (thereby offering intuitive visualization), object grouping (hence enabling automated phenotyping), and risk stratification. To enhance model interpretability, we introduced two constraints into model parameters: (a) nonnegative coefficients, and (b) structural smoothness. These result in a novel model called eNRBM (EMR-driven nonnegative RBM). We demonstrate the capability of the eNRBM on a cohort of 7578 mental health patients under suicide risk assessment. The derived representation not only shows clinically meaningful feature grouping but also facilitates short-term risk stratification. The F-scores, 0.21 for moderate-risk and 0.36 for high-risk, are significantly higher than those obtained by clinicians and competitive with the results obtained by support vector machines.