PQL: A Declarative Query Language over Dynamic Biological Schemata

We introduce the PQL query language (PQL) used in the GeneSeek genetic data integration project. PQL incorporates many features of query languages for semi-structured data. To this we add the ability to express metadata constraints like intended semantics and database curation approach. These constraints guide the dynamic generation of potential query plans. This allows a single query to remain relevant even in the presence of source and mediated schemas that are continually evolving, as is often the case in data integration

Projection of two biphoton qutrits onto a maximally entangled state

Bell state measurements, in which two quantum bits are projected onto a maximally entangled state, are an essential component of quantum information science. We propose and experimentally demonstrate the projection of two quantum systems with three states (qutrits) onto a generalized maximally entangled state. Each qutrit is represented by the polarization of a pair of indistinguishable photons - a biphoton. The projection is a joint measurement on both biphotons using standard linear optics elements. This demonstration enables the realization of quantum information protocols with qutrits, such as teleportation and entanglement swapping.Comment: 4 pages, 3 figures, published versio

Right for the Right Reason: Training Agnostic Networks

We consider the problem of a neural network being requested to classify images (or other inputs) without making implicit use of a "protected concept", that is a concept that should not play any role in the decision of the network. Typically these concepts include information such as gender or race, or other contextual information such as image backgrounds that might be implicitly reflected in unknown correlations with other variables, making it insufficient to simply remove them from the input features. In other words, making accurate predictions is not good enough if those predictions rely on information that should not be used: predictive performance is not the only important metric for learning systems. We apply a method developed in the context of domain adaptation to address this problem of "being right for the right reason", where we request a classifier to make a decision in a way that is entirely 'agnostic' to a given protected concept (e.g. gender, race, background etc.), even if this could be implicitly reflected in other attributes via unknown correlations. After defining the concept of an 'agnostic model', we demonstrate how the Domain-Adversarial Neural Network can remove unwanted information from a model using a gradient reversal layer.Comment: Author's original versio

Cooperation across multiple healthcare clinics on the cloud

Many healthcare units are creating cloud strategies and mi- gration plans in order to exploit the benefits of cloud based computing. This generally involves collaboration between healthcare specialists and data management researchers to create a new wave of healthcare tech- nology and services. However, in many cases the technology pioneers are ahead of government policies as cloud based storage of healthcare data is not yet permissible in many jurisdictions. One approach is to store anonymised data on the cloud and maintain all identifying data locally. At login time, a simple protocol can be developed to allow clinicians to combine both sets of data for selected patients for the current session. However, the management of o↵-cloud identifying data requires a frame- work to ensure sharing and availability of data within clinics and the ability to share data between users in remote clinics. In this paper, we introduce the PACE healthcare architecture which uses a combination of Cloud and Peer-to-Peer technologies to model healthcare units or clin- ics where o↵-cloud data is accessible to all, and where exchange of data between remote healthcare units is also facilitated