NamesforLife Semantic Resolution Services for the Life Sciences

A major challenge in bioinformatics, life sciences, and medicine is using correct and informative names. While this sounds simple enough, many different naming conventions exist in the life sciences and medicine that may be either complementary or competitive with other naming conventions. For a variety of reasons, proper names are not always used, leading to an accumulated semantic ambiguity that readers of the literature and end users of databases are left to resolve on their own. This ambiguity is a growing problem and the biocuration community is aware of its consequences. 

To assist those confronted with ambiguous names (which not only includes researchers but clinicians, manufacturers, patent attorneys, and others who use biological data in their routine work), we developed a generalizable semantic model that represents names, concepts, and exemplars (representations of biological entities) as distinct objects. By identifying each object with a Digital Object Identifier (DOI) it becomes possible to place forward-pointing links in the published literature, in databases, and vector graphics that can be used as part of a mechanism for resolving ambiguities, thereby “future proofing” a nomenclature or terminology. A full implementation of the N4L model for the _Bacteria_ and _Archaea_ was released in April, 2010. The system is professionally curated and represents a Tier III resource in Parkhill’s view of bioinformatic services. A variety of tools and web services have been developed for readers, publishers, and others (N4L Guide, N4L Autotagger, N4L Semantic Search, N4L Taxonomic Abstracts) and we are incorporating other taxonomies into the N4L data model, as well as adding additional phenotypic, genotypic, and genomic information to the existing exemplars to add greater value to end users

Bayesian reinforcement learning with exploration

We consider a general reinforcement learning problem and show that carefully combining the Bayesian optimal policy and an exploring policy leads to minimax sample-complexity bounds in a very general class of (history-based) environments. We also prove lower bounds and show that the new algorithm displays adaptive behaviour when the environment is easier than worst-case

Robustness and Generalization

We derive generalization bounds for learning algorithms based on their robustness: the property that if a testing sample is "similar" to a training sample, then the testing error is close to the training error. This provides a novel approach, different from the complexity or stability arguments, to study generalization of learning algorithms. We further show that a weak notion of robustness is both sufficient and necessary for generalizability, which implies that robustness is a fundamental property for learning algorithms to work

Endolacrimal laser assisted lacrimal surgery

AIMS—To utilise the improved optical qualities of newly developed lacrimal endoscopes and newly miniaturised laser fibres for diagnostic visualisation and laser surgery of the lacrimal system.
METHODS—A KTP laser (wavelength 532 nm, 10 W energy) was used for laser assisted dacryocystorhinostomy (DCR) with endolacrimal visualisation in 26 patients. Bicanalicular silicone intubation was placed in all patients for at least 3( )months.
RESULTS—After 3-9 months of follow up, the silicone tube in all 21 patients who underwent KTP laser DCR are still patent, three patients have eye watering in extremely cold weather and two required a conventional DCR.
CONCLUSIONS—The KTP laser generates enough power to open the bony window in DCR surgery. Precise endolacrimal visualisation via a specially designed miniendoscope is essential for surgical success.


Reinforcement learning of pareto-optimal multiobjective policies using steering

There has been little research into multiobjective reinforcement learning (MORL) algorithms using stochastic or non-stationary policies, even though such policies may Pareto-dominate deterministic stationary policies. One approach is steering which forms a nonstationary combination of deterministic stationary base policies. This paper presents two new steering algorithms designed for the task of learning Pareto-optimal policies. The first algorithm (w-steering) is a direct adaptation of previous approaches to steering, and therefore requires prior knowledge of recurrent states which are guaranteed to be revisited. The second algorithm (Q-steering) eliminates this requirement. Empirical results show that both algorithms perform well when given knowledge of recurrent states, but that Q-steering provides substantial performance improvements over w-steering when this knowledge is not available. © Springer International Publishing Switzerland 2015

Bias and Variance Approximation in Value Function Estimates

We consider a finite-state, finite-action, infinite-horizon, discounted reward Markov decision process and study the bias and variance in the value function estimates that result from empirical estimates of the model parameters. We provide closed-form approximations for the bias and variance, which can then be used to derive confidence intervals around the value function estimates. We illustrate and validate our findings using a large database describing the transaction and mailing histories for customers of a mail-order catalog firm.value function, confidence interval, variance, bias

Heterogeneous stream processing and crowdsourcing for traffic monitoring: Highlights

We give an overview of an intelligent urban traffic management system. Complex events related to congestions are detected from heterogeneous sources involving fixed sensors mounted on intersections and mobile sensors mounted on public transport vehicles. To deal with data veracity, sensor disagreements are resolved by crowdsourcing. To deal with data sparsity, a traffic model offers information in areas with low sensor coverage. We apply the system to a real-world use-case. © 2014 Springer-Verlag