Rationale for tau aggregation inhibitor therapy in Alzheimer's disease and other tauopathies

Preprin

On the path to AI

This open access book explores machine learning and its impact on how we make sense of the world. It does so by bringing together two ‘revolutions’ in a surprising analogy: the revolution of machine learning, which has placed computing on the path to artificial intelligence, and the revolution in thinking about the law that was spurred by Oliver Wendell Holmes Jr in the last two decades of the 19th century. Holmes reconceived law as prophecy based on experience, prefiguring the buzzwords of the machine learning age—prediction based on datasets. On the path to AI introduces readers to the key concepts of machine learning, discusses the potential applications and limitations of predictions generated by machines using data, and informs current debates amongst scholars, lawyers and policy makers on how it should be used and regulated wisely. Technologists will also find useful lessons learned from the last 120 years of legal grappling with accountability, explainability, and biased data

On the path to AI

This open access book explores machine learning and its impact on how we make sense of the world. It does so by bringing together two ‘revolutions’ in a surprising analogy: the revolution of machine learning, which has placed computing on the path to artificial intelligence, and the revolution in thinking about the law that was spurred by Oliver Wendell Holmes Jr in the last two decades of the 19th century. Holmes reconceived law as prophecy based on experience, prefiguring the buzzwords of the machine learning age—prediction based on datasets. On the path to AI introduces readers to the key concepts of machine learning, discusses the potential applications and limitations of predictions generated by machines using data, and informs current debates amongst scholars, lawyers and policy makers on how it should be used and regulated wisely. Technologists will also find useful lessons learned from the last 120 years of legal grappling with accountability, explainability, and biased data

Taylorformer: Probabilistic Predictions for Time Series and other Processes

We propose the Taylorformer for time series and other random processes. Its two key components are: 1) the LocalTaylor wrapper to learn how and when to use Taylor series-based approximations for predictions, and 2) the MHA-X attention block which makes predictions in a way inspired by how Gaussian Processes' mean predictions are linear smoothings of contextual data. Taylorformer outperforms the state-of-the-art on several forecasting datasets, including electricity, oil temperatures and exchange rates with at least 14% improvement in MSE on all tasks, and better likelihood on 5/6 classic Neural Process tasks such as meta-learning 1D functions. Taylorformer combines desirable features from the Neural Process (uncertainty-aware predictions and consistency) and forecasting (predictive accuracy) literature, two previously distinct bodies.Comment: 18 pages, 6 figure

Tau Aggregation Inhibitor Therapy : An Exploratory Phase 2 Study in Mild or Moderate Alzheimer's Disease

ACKNOWLEDGMENTS We thank patients and their caregivers for their participation in the study and are indebted to all the investigators involved in the study, particularly Drs. Douglas Fowlie and Donald Mowat for their helpful contributions to the clinical execution of the study in Scotland. We thank Sharon Eastwood, Parexel, for assistance in preparing initial drafts of the manuscript. We acknowledge constructive comments provided by Professors G. Wilcock and S. Gauthier on drafts of the article. CMW, CRH, and JMDS are officers of, and hold beneficial interests in, TauRx Therapeutics. RTS, PB, KK, and DJW are paid consultants to TauRx Therapeutics. The study was financed entirely by TauRx TherapeuticsPeer reviewedPublisher PD

Fluid models of congestion collapse in overloaded switched networks

We consider a switched network (i.e. a queueing network in which there are constraints on which queues may be served simultaneously), in a state of overload. We analyse the behaviour of two scheduling algorithms for multihop switched networks: a generalized version of max-weight, and the α-fair policy. We show that queue sizes grow linearly with time, under either algorithm, and we characterize the growth rates. We use this characterization to demonstrate examples of congestion collapse, i.e. cases in which throughput drops as the switched network becomes more overloaded.We further show that the loss of throughput can be made arbitrarily small by the max-weight algorithm with weight function f (q) = q[superscript α] as α→0.National Science Foundation (U.S.) (Career CNS-0546590

Potential of Low Dose Leuco-Methylthioninium Bis(Hydromethanesulphonate) (LMTM) Monotherapy for Treatment of Mild Alzheimer’s Disease : Cohort Analysis as Modified Primary Outcome in a Phase III Clinical Trial

The supplementary material is available in the electronic version of this article: http://dx.doi.org/10.3233/JAD-170560. The study was sponsored by TauRx Therapeutics (Singapore). We thank Lon Schneider and Howard Feldman for their contribution to the Scientific Advisory Board. We gratefully acknowledge study investigators and the generosity of study participants. Authors’ disclosures available online (http://j-alz.com/manuscript disclosures/17-0560r3).Peer reviewedPublisher PD

Using Probabilistic Machine Learning to Better Model Temporal Patterns in Parameterizations: a case study with the Lorenz 96 model.

The modelling of small-scale processes is a major source of error in weather and climate models, hindering the accuracy of low-cost models which must approximate such processes through parameterization. Red noise is essential to many operational parameterization schemes, helping model temporal correlations. We show how to build on the successes of red noise by combining the known benefits of stochasticity with machine learning. This is done using a recurrent neural network within a probabilistic framework (L96-RNN). Our model is competitive and often superior to both a bespoke baseline and an existing probabilistic machine learning approach (GAN) when applied to the Lorenz 96 atmospheric simulation. This is due to its superior ability to model temporal patterns compared to standard first-order autoregressive schemes. It also generalises to unseen scenarios. We evaluate across a number of metrics from the literature, and also discuss the benefits of using the probabilistic metric of hold-out likelihood