Transfer hashing with privileged information

Most existing learning to hash methods assume that there are sufficient data, either labeled or unlabeled, on the domain of interest (i.e., the target domain) for training. However, this assumption cannot be satisfied in some real-world applications. To address this data sparsity issue in hashing, inspired by transfer learning, we propose a new framework named Transfer Hashing with Privileged Information (THPI). Specifically, we extend the standard learning to hash method, Iterative Quantization (ITQ), in a transfer learning manner, namely ITQ+. In ITQ+, a new slack function is learned from auxiliary data to approximate the quantization error in ITQ. We developed an alternating optimization approach to solve the resultant optimization problem for ITQ+. We further extend ITQ+ to LapITQ+ by utilizing the geometry structure among the auxiliary data for learning more precise binary codes in the target domain. Extensive experiments on several benchmark datasets verify the effectiveness of our proposed approaches through comparisons with several state-of-the-art baselines

Pharmacokinetics and safety of ixazomib plus lenalidomide–dexamethasone in Asian patients with relapsed/refractory myeloma: a phase 1 study

published_or_final_versio

Sc2Net: Sparse LSTMs for sparse coding ^∗

Copyright © 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. The iterative hard-thresholding algorithm (ISTA) is one of the most popular optimization solvers to achieve sparse codes. However, ISTA suffers from following problems: 1) ISTA employs non-adaptive updating strategy to learn the parameters on each dimension with a fixed learning rate. Such a strategy may lead to inferior performance due to the scarcity of diversity; 2) ISTA does not incorporate the historical information into the updating rules, and the historical information has been proven helpful to speed up the convergence. To address these challenging issues, we propose a novel formulation of ISTA (named as adaptive ISTA) by introducing a novel adaptive momentum vector. To efficiently solve the proposed adaptive ISTA, we recast it as a recurrent neural network unit and show its connection with the well-known long short term memory (LSTM) model. With a new proposed unit, we present a neural network (termed SC2Net) to achieve sparse codes in an end-to-end manner. To the best of our knowledge, this is one of the first works to bridge the 1-solver and LSTM, and may provide novel insights in understanding model-based optimization and LSTM. Extensive experiments show the effectiveness of our method on both unsupervised and supervised tasks

Associations between sleep trajectories up to 54 months and cognitive school readiness in 4 year old preschool children

10.3389/fpsyg.2023.1136448Front Psychol1

Trajectories of reported sleep duration associate with early childhood cognitive development

10.1093/sleep/zsac264Slee

The Singapore National Precision Medicine Strategy

10.1038/s41588-022-01274-xNature Genetics552178-18

Federated benchmarking of medical artificial intelligence with MedPerf

Funder: NIH (U01CA242871) NIH (U24CA189523)AbstractMedical artificial intelligence (AI) has tremendous potential to advance healthcare by supporting and contributing to the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving both healthcare provider and patient experience. Unlocking this potential requires systematic, quantitative evaluation of the performance of medical AI models on large-scale, heterogeneous data capturing diverse patient populations. Here, to meet this need, we introduce MedPerf, an open platform for benchmarking AI models in the medical domain. MedPerf focuses on enabling federated evaluation of AI models, by securely distributing them to different facilities, such as healthcare organizations. This process of bringing the model to the data empowers each facility to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status and real-world deployment, our roadmap and, importantly, the use of MedPerf with multiple international institutions within cloud-based technology and on-premises scenarios. Finally, we welcome new contributions by researchers and organizations to further strengthen MedPerf as an open benchmarking platform.</jats:p

Severe acute respiratory syndrome

Severe acute respiratory syndrome (SARS) was caused by a previously unrecognized animal coronavirus that exploited opportunities provided by 'wet markets' in southern China to adapt to become a virus readily transmissible between humans. Hospitals and international travel proved to be 'amplifiers' that permitted a local outbreak to achieve global dimensions. In this review we will discuss the substantial scientific progress that has been made towards understanding the virus - SARS coronavirus (SARS-CoV) - and the disease. We will also highlight the progress that has been made towards developing vaccines and therapies The concerted and coordinated response that contained SARS is a triumph for global public health and provides a new paradigm for the detection and control of future emerging infectious disease threats.link_to_subscribed_fulltex