Consistent as-similar-as-possible non-isometric surface registration

© 2017 The Author(s)Non-isometric surface registration, aiming to align two surfaces with different sizes and details, has been widely used in computer animation industry. Various existing surface registration approaches have been proposed for accurate template fitting; nevertheless, two challenges remain. One is how to avoid the mesh distortion and fold over of surfaces during transformation. The other is how to reduce the amount of landmarks that have to be specified manually. To tackle these challenges simultaneously, we propose a consistent as-similar-as-possible (CASAP) surface registration approach. With a novel defined energy, it not only achieves the consistent discretization for the surfaces to produce accurate result, but also requires a small number of landmarks with little user effort only. Besides, CASAP is constrained as-similar-as-possible so that angles of triangle meshes are preserved and local scales are allowed to change. Extensive experimental results have demonstrated the effectiveness of CASAP in comparison with the state-of-the-art approaches

A Metric Framework for quantifying Data Concentration

Poor performance of artificial neural nets when applied to credit-related classification problems is investigated and contrasted with logistic regression classification. We propose that artificial neural nets are less successful because of the inherent structure of credit data rather than any particular aspect of the neural net structure. Three metrics are developed to rationalise the result with such data. The metrics exploit the distributional properties of the data to rationalise neural net results. They are used in conjunction with a variant of an established concentration measure that differentiates between class characteristics. The results are contrasted with those obtained using random data, and are compared with results obtained using logistic regression. We find, in general agreement with previous studies, that logistic regressions out-perform neural nets in the majority of cases. An approximate decision criterion is developed in order to explain adverse results

Phase II study of weekly vinorelbine and 24-h infusion of high-dose 5-fluorouracil plus leucovorin as first-line treatment of advanced breast cancer

We prospectively investigated the efficacy and safety of combining weekly vinorelbine (VNB) with weekly 24-h infusion of high-dose 5-fluorouracil (5-FU) and leucovorin (LV) in the treatment of patients with advanced breast cancer (ABC). Vinorelbine 25 mg m−2 30-min intravenous infusion, and high-dose 5-FU 2600 mg m−2 plus LV 300 mg m−2 24-h intravenous infusion (HDFL regimen) were given on days 1 and 8 every 3 weeks. Between June 1999 and April 2003, 40 patients with histologically confirmed recurrent or metastatic breast cancer were enrolled with a median age of 49 years (range: 36–68). A total of 25 patients had recurrent ABC, and 15 patients had primary metastatic diseases. The overall response rate for the intent-to-treat group was 70.0% (95% CI: 54–84%) with eight complete responses and 20 partial responses. All 40 patients were evaluated for survival and toxicities. Among a total of 316 cycles of VNB–HDFL given (average: 7.9: range: 4–14 cycles per patient), the main toxicity was Gr3/4 leucopenia and Gr3/4 neutropenia in 57 (18.0%) and 120 (38.0%) cycles, respectively. Gr1/2 infection and Gr1/2 stomatitis were noted in five (1.6%) and 59 (18.7%) cycles, respectively. None of the patients developed Gr3/4 stomatitis or Gr3/4 infection. Gr2/3 and Gr1 hand–foot syndrome was noted in two (5.0%) and 23 (57.5%) patients, respectively. Gr1 sensory neuropathy developed in three patients. The median time to progression was 8.0 months (range: 3–25.5 months), and the median overall survival was 25.0 months with a follow-up of 5.5 to 45+ months. This VNB–HDFL regimen is a highly active yet well-tolerated first-line treatment for ABC

Extremely long quasiparticle spin lifetimes in superconducting aluminium using MgO tunnel spin injectors

There has been an intense search in recent years for long-lived spin-polarized carriers for spintronic and quantum-computing devices. Here we report that spin polarized quasi-particles in superconducting aluminum layers have surprisingly long spin-lifetimes, nearly a million times longer than in their normal state. The lifetime is determined from the suppression of the aluminum's superconductivity resulting from the accumulation of spin polarized carriers in the aluminum layer using tunnel spin injectors. A Hanle effect, observed in the presence of small in-plane orthogonal fields, is shown to be quantitatively consistent with the presence of long-lived spin polarized quasi-particles. Our experiments show that the superconducting state can be significantly modified by small electric currents, much smaller than the critical current, which is potentially useful for devices involving superconducting qubits

Trading-off Data Fit and Complexity in Training Gaussian Processes with Multiple Kernels

This is the author accepted manuscript. The final version is available from Springer Verlag via the DOI in this recordLOD 2019: Fifth International Conference on Machine Learning, Optimization, and Data Science, 10-13 September 2019, Siena, ItalyGaussian processes (GPs) belong to a class of probabilistic techniques that have been successfully used in different domains of machine learning and optimization. They are popular because they provide uncertainties in predictions, which sets them apart from other modelling methods providing only point predictions. The uncertainty is particularly useful for decision making as we can gauge how reliable a prediction is. One of the fundamental challenges in using GPs is that the efficacy of a model is conferred by selecting an appropriate kernel and the associated hyperparameter values for a given problem. Furthermore, the training of GPs, that is optimizing the hyperparameters using a data set is traditionally performed using a cost function that is a weighted sum of data fit and model complexity, and the underlying trade-off is completely ignored. Addressing these challenges and shortcomings, in this article, we propose the following automated training scheme. Firstly, we use a weighted product of multiple kernels with a view to relieve the users from choosing an appropriate kernel for the problem at hand without any domain specific knowledge. Secondly, for the first time, we modify GP training by using a multi-objective optimizer to tune the hyperparameters and weights of multiple kernels and extract an approximation of the complete trade-off front between data-fit and model complexity. We then propose to use a novel solution selection strategy based on mean standardized log loss (MSLL) to select a solution from the estimated trade-off front and finalise training of a GP model. The results on three data sets and comparison with the standard approach clearly show the potential benefit of the proposed approach of using multi-objective optimization with multiple kernels.Natural Environment Research Council (NERC

Improved Weighted Random Forest for Classification Problems

Several studies have shown that combining machine learning models in an appropriate way will introduce improvements in the individual predictions made by the base models. The key to make well-performing ensemble model is in the diversity of the base models. Of the most common solutions for introducing diversity into the decision trees are bagging and random forest. Bagging enhances the diversity by sampling with replacement and generating many training data sets, while random forest adds selecting a random number of features as well. This has made the random forest a winning candidate for many machine learning applications. However, assuming equal weights for all base decision trees does not seem reasonable as the randomization of sampling and input feature selection may lead to different levels of decision-making abilities across base decision trees. Therefore, we propose several algorithms that intend to modify the weighting strategy of regular random forest and consequently make better predictions. The designed weighting frameworks include optimal weighted random forest based on ac-curacy, optimal weighted random forest based on the area under the curve (AUC), performance-based weighted random forest, and several stacking-based weighted random forest models. The numerical results show that the proposed models are able to introduce significant improvements compared to regular random forest

Lrp Acts as Both a Positive and Negative Regulator for Type 1 Fimbriae Production in Salmonella enterica Serovar Typhimurium

Leucine-responsive regulatory protein (Lrp) is known to be an indirect activator of type 1 fimbriae synthesis in Salmonella enterica serovar Typhimurium via direct regulation of FimZ, a direct positive regulator for type 1 fimbriae production. Using RT-PCR, we have shown previously that fimA transcription is dramatically impaired in both lrp-deletion (Δlrp) and constitutive-lrp expression (lrpC) mutant strains. In this work, we used chromosomal PfimA-lacZ fusions and yeast agglutination assays to confirm and extend our previous results. Direct binding of Lrp to PfimA was shown by an electrophoretic mobility shift assay (EMSA) and DNA footprinting assay. Site-directed mutagenesis revealed that the Lrp-binding motifs in PfimA play a role in both activation and repression of type 1 fimbriae production. Overproduction of Lrp also abrogates fimZ expression. EMSA data showed that Lrp and FimZ proteins independently bind to PfimA without competitive exclusion. In addition, both Lrp and FimZ binding to PfimA caused a hyper retardation (supershift) of the DNA-protein complex compared to the shift when each protein was present alone. Nutrition-dependent cellular Lrp levels closely correlated with the amount of type 1 fimbriae production. These observations suggest that Lrp plays important roles in type 1 fimbriation by acting as both a positive and negative regulator and its effect depends, at least in part, on the cellular concentration of Lrp in response to the nutritional environment

Active behaviour during early development shapes glucocorticoid reactivity

TGlucocorticoids are the final effectors of the stress axis, with numerous targets in the central nervous system and the periphery. They are essential for adaptation, yet currently it is unclear how early life events program the glucocorticoid response to stress. Here we provide evidence that involuntary swimming at early developmental stages can reconfigure the cortisol response to homotypic and heterotypic stress in larval zebrafish (Danio rerio), also reducing startle reactivity and increasing spontaneous activity as well as energy efficiency during active behaviour. Collectively, these data identify a role of the genetically malleable zebrafish for linking early life stress with glucocorticoid function in later life

Precise measurement of the W-boson mass with the CDF II detector

We have measured the W-boson mass MW using data corresponding to 2.2/fb of integrated luminosity collected in proton-antiproton collisions at 1.96 TeV with the CDF II detector at the Fermilab Tevatron collider. Samples consisting of 470126 W->enu candidates and 624708 W->munu candidates yield the measurement MW = 80387 +- 12 (stat) +- 15 (syst) = 80387 +- 19 MeV. This is the most precise measurement of the W-boson mass to date and significantly exceeds the precision of all previous measurements combined