Sequential quasi-Monte Carlo: Introduction for Non-Experts, Dimension Reduction, Application to Partly Observed Diffusion Processes

SMC (Sequential Monte Carlo) is a class of Monte Carlo algorithms for filtering and related sequential problems. Gerber and Chopin (2015) introduced SQMC (Sequential quasi-Monte Carlo), a QMC version of SMC. This paper has two objectives: (a) to introduce Sequential Monte Carlo to the QMC community, whose members are usually less familiar with state-space models and particle filtering; (b) to extend SQMC to the filtering of continuous-time state-space models, where the latent process is a diffusion. A recurring point in the paper will be the notion of dimension reduction, that is how to implement SQMC in such a way that it provides good performance despite the high dimension of the problem.Comment: To be published in the proceedings of MCMQMC 201

Designing processing and fermentation conditions for long-life set yoghurt for made-in-transit (MIT) product

Extending yoghurt fermentations could facilitate yoghurt distribution by allowing the fermentation to occur during transportation - a concept known as "made-in-transit" (MIT). The objective was to determine the starter culture composition, inoculum size and fermentation temperature for extending yoghurt fermentations to 168 h. The yoghurt was processed using a milk base sterilized by ultra-high temperature (UHT) treatment at 138C for 6 s. Factorial experiments for yoghurt processing were designed with starter culture combinations of STLB (Streptococcus thermophilus with Lactobacillus delbrueckii subsp. bulgaricus) and STLA (S. thermophilus with L. acidophilus), inoculum sizes of 2.0 and 0.2% (v/v) and fermentation temperatures of 25 or 35C. The fermentation was monitored over 168 h using pH, starter culture concentration and firmness. The combination of STLA, and a 0.2% inoculum, fermented at 25C extended the yoghurt fermentation to 168 h; however, no gel formed. The best product was produced with a STLB starter combination of 2.0% inoculum fermented at 35C for 24 h. This shows the constraints and limitations of applying the MIT concept to a fermented food

Using Social Media to Promote STEM Education: Matching College Students with Role Models

STEM (Science, Technology, Engineering, and Mathematics) fields have become increasingly central to U.S. economic competitiveness and growth. The shortage in the STEM workforce has brought promoting STEM education upfront. The rapid growth of social media usage provides a unique opportunity to predict users' real-life identities and interests from online texts and photos. In this paper, we propose an innovative approach by leveraging social media to promote STEM education: matching Twitter college student users with diverse LinkedIn STEM professionals using a ranking algorithm based on the similarities of their demographics and interests. We share the belief that increasing STEM presence in the form of introducing career role models who share similar interests and demographics will inspire students to develop interests in STEM related fields and emulate their models. Our evaluation on 2,000 real college students demonstrated the accuracy of our ranking algorithm. We also design a novel implementation that recommends matched role models to the students.Comment: 16 pages, 8 figures, accepted by ECML/PKDD 2016, Industrial Trac

Local Interpretation Methods to Machine Learning Using the Domain of the Feature Space

As machine learning becomes an important part of many real world applications affecting human lives, new requirements, besides high predictive accuracy, become important. One important requirement is transparency, which has been associated with model interpretability. Many machine learning algorithms induce models difficult to interpret, named black box. Moreover, people have difficulty to trust models that cannot be explained. In particular for machine learning, many groups are investigating new methods able to explain black box models. These methods usually look inside the black models to explain their inner work. By doing so, they allow the interpretation of the decision making process used by black box models. Among the recently proposed model interpretation methods, there is a group, named local estimators, which are designed to explain how the label of particular instance is predicted. For such, they induce interpretable models on the neighborhood of the instance to be explained. Local estimators have been successfully used to explain specific predictions. Although they provide some degree of model interpretability, it is still not clear what is the best way to implement and apply them. Open questions include: how to best define the neighborhood of an instance? How to control the trade-off between the accuracy of the interpretation method and its interpretability? How to make the obtained solution robust to small variations on the instance to be explained? To answer to these questions, we propose and investigate two strategies: (i) using data instance properties to provide improved explanations, and (ii) making sure that the neighborhood of an instance is properly defined by taking the geometry of the domain of the feature space into account. We evaluate these strategies in a regression task and present experimental results that show that they can improve local explanations

Efficient calculation of the worst-case error and (fast) component-by-component construction of higher order polynomial lattice rules

We show how to obtain a fast component-by-component construction algorithm for higher order polynomial lattice rules. Such rules are useful for multivariate quadrature of high-dimensional smooth functions over the unit cube as they achieve the near optimal order of convergence. The main problem addressed in this paper is to find an efficient way of computing the worst-case error. A general algorithm is presented and explicit expressions for base~2 are given. To obtain an efficient component-by-component construction algorithm we exploit the structure of the underlying cyclic group. We compare our new higher order multivariate quadrature rules to existing quadrature rules based on higher order digital nets by computing their worst-case error. These numerical results show that the higher order polynomial lattice rules improve upon the known constructions of quasi-Monte Carlo rules based on higher order digital nets

Quality of life following fistulotomy - short term follow-up

INTRODUCTION: Anal fistula causes pain, discharge of pus and blood. Fistulotomy has the highest success, however, can risk continence; treatment balances cure with continence. This study assessed the impact of fistulotomy on Quality Of Life (QOL) and continence. METHODS: Patients selected for fistulotomy prospectively completed St Mark's Continence Score (full incontinence = 24) and Short Form - 36 questionnaires pre-operatively at two institutions with an interest in anal fistula, and reassessed 3 months post-operatively. RESULTS: There were 52 patients median age 44, range 19 - 82 years, 10 were women. Pre-operative continence scores were median 0, range 0 - 23, there was no significant difference compared to post-operative scores, median 1, range 0-24. Quality of life was significantly improved following fistulotomy in 4 of 8 domains: Bodily Pain (p<0.001); Vitality (p<0.01); Social Functioning (p<0.05); Mental Health (p<0.001) and returned to that of the general population. QOL for patients with intersphincteric fistula improved post fistulotomy, for those with trans-sphincteric fistula QOL remained the same. Data were further examined in two groups, with and without continence score deterioration. Where continence improved post-operatively, QOL improved in 3 domains; where continence deteriorated QOL also improved, in 2 domains (p<0.05). Patients with post-operative continence of <5 points had worse QOL than those scoring 4 or less. DISCUSSION: QOL at three months follow up significantly improved following fistulotomy where continence was maintained or a small reduction occurred

E-cadherin can limit the transforming properties of activating β-catenin mutations

Wnt pathway deregulation is a common characteristic of many cancers. But only Colorectal Cancer predominantly harbours mutations in APC, whereas other cancer types (hepatocellular carcinoma, solid pseudopapillary tumours of pancreas) have activating mutations in β-catenin (CTNNB1). We have compared the dynamics and the potency of β-catenin mutations in vivo. Within the murine small intestine (SI), an activating mutation of β-catenin took much longer to achieve a Wnt deregulation and acquire a crypt-progenitor-cell (CPC) phenotype than Apc or Gsk3 loss. Within the colon, a single activating mutation of β-catenin was unable to drive Wnt deregulation or induce the CPC phenotype. This ability of β-catenin mutation to differentially transform the SI versus the colon correlated with significantly higher expression of the β-catenin binding partner E-cadherin. This increased expression is associated with a higher number of E-cadherin:β-catenin complexes at the membrane. Reduction of E-cadherin synergised with an activating mutation of β-catenin so there was now a rapid CPC phenotype within the colon and SI. Thus there is a threshold of β-catenin that is required to drive transformation and E-cadherin can act as a buffer to prevent β-catenin accumulation

The Braincase and Neurosensory Anatomy of an Early Jurassic Marine Crocodylomorph: Implications for Crocodylian Sinus Evolution and Sensory Transitions

This is the pre-peer reviewed version of the following article: Brusatte, S. L., Muir, A. , Young, M. T., Walsh, S. , Steel, L. and Witmer, L. M. (2016), The Braincase and Neurosensory Anatomy of an Early Jurassic Marine Crocodylomorph: Implications for Crocodylian Sinus Evolution and Sensory Transitions. Anat. Rec., 299: 1511-1530., which has been published in final form at doi:10.1002/ar.23462. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving." You are advised to consult the published version if you wish to cite from it

Hot new directions for quasi-Monte Carlo research in step with applications

This article provides an overview of some interfaces between the theory of quasi-Monte Carlo (QMC) methods and applications. We summarize three QMC theoretical settings: first order QMC methods in the unit cube $[0,1]^s$ and in $\mathbb{R}^s$, and higher order QMC methods in the unit cube. One important feature is that their error bounds can be independent of the dimension $s$ under appropriate conditions on the function spaces. Another important feature is that good parameters for these QMC methods can be obtained by fast efficient algorithms even when $s$ is large. We outline three different applications and explain how they can tap into the different QMC theory. We also discuss three cost saving strategies that can be combined with QMC in these applications. Many of these recent QMC theory and methods are developed not in isolation, but in close connection with applications