32,857 research outputs found

    Organizational Chart Inference

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    Nowadays, to facilitate the communication and cooperation among employees, a new family of online social networks has been adopted in many companies, which are called the "enterprise social networks" (ESNs). ESNs can provide employees with various professional services to help them deal with daily work issues. Meanwhile, employees in companies are usually organized into different hierarchies according to the relative ranks of their positions. The company internal management structure can be outlined with the organizational chart visually, which is normally confidential to the public out of the privacy and security concerns. In this paper, we want to study the IOC (Inference of Organizational Chart) problem to identify company internal organizational chart based on the heterogeneous online ESN launched in it. IOC is very challenging to address as, to guarantee smooth operations, the internal organizational charts of companies need to meet certain structural requirements (about its depth and width). To solve the IOC problem, a novel unsupervised method Create (ChArT REcovEr) is proposed in this paper, which consists of 3 steps: (1) social stratification of ESN users into different social classes, (2) supervision link inference from managers to subordinates, and (3) consecutive social classes matching to prune the redundant supervision links. Extensive experiments conducted on real-world online ESN dataset demonstrate that Create can perform very well in addressing the IOC problem.Comment: 10 pages, 9 figures, 1 table. The paper is accepted by KDD 201

    Label-driven weakly-supervised learning for multimodal deformable image registration

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    Spatially aligning medical images from different modalities remains a challenging task, especially for intraoperative applications that require fast and robust algorithms. We propose a weakly-supervised, label-driven formulation for learning 3D voxel correspondence from higher-level label correspondence, thereby bypassing classical intensity-based image similarity measures. During training, a convolutional neural network is optimised by outputting a dense displacement field (DDF) that warps a set of available anatomical labels from the moving image to match their corresponding counterparts in the fixed image. These label pairs, including solid organs, ducts, vessels, point landmarks and other ad hoc structures, are only required at training time and can be spatially aligned by minimising a cross-entropy function of the warped moving label and the fixed label. During inference, the trained network takes a new image pair to predict an optimal DDF, resulting in a fully-automatic, label-free, real-time and deformable registration. For interventional applications where large global transformation prevails, we also propose a neural network architecture to jointly optimise the global- and local displacements. Experiment results are presented based on cross-validating registrations of 111 pairs of T2-weighted magnetic resonance images and 3D transrectal ultrasound images from prostate cancer patients with a total of over 4000 anatomical labels, yielding a median target registration error of 4.2 mm on landmark centroids and a median Dice of 0.88 on prostate glands.Comment: Accepted to ISBI 201

    Standardising neonatal and paediatric antibiotic clinical trial design and conduct: the PENTA-ID network view.

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    Antimicrobial development for children remains challenging due to multiple barriers to conducting randomised clinical trials (CTs). There is currently considerable heterogeneity in the design and conduct of paediatric antibiotic studies, hampering comparison and meta-analytic approaches. The board of the European networks for paediatric research at the European Medicines Agency (EMA), in collaboration with the Paediatric European Network for Treatments of AIDS-Infectious Diseases network (www.penta-id.org), recently developed a Working Group on paediatric antibiotic CT design, involving academic, regulatory and industry representatives. The evidence base for any specific criteria for the design and conduct of efficacy and safety antibiotic trials for children is very limited and will evolve over time as further studies are conducted. The suggestions being put forward here are based on the adult EMA guidance, adapted for neonates and children. In particular, this document provides suggested guidance on the general principles of harmonisation between regulatory and strategic trials, including (1) standardised key inclusion/exclusion criteria and widely applicable outcome measures for specific clinical infectious syndromes (CIS) to be used in CTs on efficacy of antibiotic in children; (2) key components of safety that should be reported in paediatric antibiotic CTs; (3) standardised sample sizes for safety studies. Summarising views from a range of key stakeholders, specific criteria for the design and conduct of efficacy and safety antibiotic trials in specific CIS for children have been suggested. The recommended criteria are intended to be applicable to both regulatory and clinical investigator-led strategic trials and could be the basis for harmonisation in the design and conduct of CTs on antibiotics in children. The next step is further discussion internationally with investigators, paediatric CTs networks and regulators
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