Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence

Incremental learning (IL) has received a lot of attention recently, however, the literature lacks a precise problem definition, proper evaluation settings, and metrics tailored specifically for the IL problem. One of the main objectives of this work is to fill these gaps so as to provide a common ground for better understanding of IL. The main challenge for an IL algorithm is to update the classifier whilst preserving existing knowledge. We observe that, in addition to forgetting, a known issue while preserving knowledge, IL also suffers from a problem we call intransigence, inability of a model to update its knowledge. We introduce two metrics to quantify forgetting and intransigence that allow us to understand, analyse, and gain better insights into the behaviour of IL algorithms. We present RWalk, a generalization of EWC++ (our efficient version of EWC [Kirkpatrick2016EWC]) and Path Integral [Zenke2017Continual] with a theoretically grounded KL-divergence based perspective. We provide a thorough analysis of various IL algorithms on MNIST and CIFAR-100 datasets. In these experiments, RWalk obtains superior results in terms of accuracy, and also provides a better trade-off between forgetting and intransigence

Modeling drying kinetics of thyme (thymus vulgaris l.): theoretical and empirical models, and neural networks

[EN] The drying kinetics of thyme was analyzed by considering different conditions: air temperature of between 40 C and 70 C, and air velocity of 1 m/s. A theoretical diffusion model and eight different empirical models were fitted to the experimental data. From the theoretical model application, the effective diffusivity per unit area of the thyme was estimated (between 3.68 10 5 and 2.12 10 4 s 1). The temperature dependence of the effective diffusivity was described by the Arrhenius relationship with activation energy of 49.42 kJ/mol. Eight different empirical models were fitted to the experimental data. Additionally, the dependence of the parameters of each model on the drying temperature was determined, obtaining equations that allow estimating the evolution of the moisture content at any temperature in the established range. Furthermore, artificial neural networks were developed and compared with the theoretical and empirical models using the percentage of the relative errors and the explained variance. The artificial neural networks were found to be more accurate predictors of moisture evolution with VAR 99.3% and ER 8.7%.The authors acknowledge the financial support from the 'Ministerio de Educacion y Ciencia' in Spain, CONSOLIDER INGENIO 2010 (CSD2007-00016).Rodríguez Cortina, J.; Clemente Polo, G.; Sanjuán Pellicer, MN.; Bon Corbín, J. (2014). Modeling drying kinetics of thyme (thymus vulgaris l.): theoretical and empirical models, and neural networks. Food Science and Technology International. 20(1):13-22. https://doi.org/10.1177/1082013212469614S132220

Effect of Preinjury Oral Anticoagulants on Outcomes Following Traumatic Brain Injury from Falls in Older Adults

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156128/2/phar2435_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156128/1/phar2435.pd

Validation of nonlinear PCA

Linear principal component analysis (PCA) can be extended to a nonlinear PCA by using artificial neural networks. But the benefit of curved components requires a careful control of the model complexity. Moreover, standard techniques for model selection, including cross-validation and more generally the use of an independent test set, fail when applied to nonlinear PCA because of its inherent unsupervised characteristics. This paper presents a new approach for validating the complexity of nonlinear PCA models by using the error in missing data estimation as a criterion for model selection. It is motivated by the idea that only the model of optimal complexity is able to predict missing values with the highest accuracy. While standard test set validation usually favours over-fitted nonlinear PCA models, the proposed model validation approach correctly selects the optimal model complexity.Comment: 12 pages, 5 figure

Developing combinatorial multi-component therapies (CMCT) of drugs that are more specific and have fewer side effects than traditional one drug therapies

Drugs designed for a specific target are always found to have multiple effects. Rather than hope that one bullet can be designed to hit only one target, nonlinear interactions across genomic and proteomic networks could be used to design Combinatorial Multi-Component Therapies (CMCT) that are more targeted with fewer side effects. We show here how computational approaches can be used to predict which combinations of drugs would produce the best effects. Using a nonlinear model of how the output effect depends on multiple input drugs, we show that an artificial neural network can accurately predict the effect of all 215 = 32,768 combinations of drug inputs using only the limited data of the output effect of the drugs presented one-at-a-time and pairs-at-a-time

An intense traveling airglow front in the upper mesosphere-lower thermosphere with characteristics of a bore observed over Alice Springs, Australia, during a strong 2 day wave episode

Extent: 13p.The Aerospace Corporation's Nightglow Imager observed a large step function change in airglow in the form of a traveling front in the OH Meinel (OHM) and O2atmospheric (O2A) airglow emissions over Alice Springs, Australia, on 2 February 2003. The front exhibited nearly a factor of 2 stepwise increase in the OHM brightness and a stepwise decrease in the O2A brightness. There was significant (∼25 K) cooling behind the airglow fronts. The OHM airglow brightness behind the front was among the brightest for Alice Springs that we have measured in 7 years of observations. The event was associated with a strong phase-locked 2 day wave (PL/TDW). We have analyzed the wave trapping conditions for the upper mesosphere and lower thermosphere using a combination of data and empirical models and found that the airglow layers were located in a region of ducting. The PL/TDW-disturbed wind profile was effective in supporting a high degree of ducting, whereas without the PL/TDW the ducting was minimal or nonexistent. The change in brightness in each layer was associated with a strong leading disturbance followed by a train of weak barely visible waves. In OHM the leading disturbance was an isolated disturbance resembling a solitary wave. The characteristics of the wave train suggest an undular bore with some turbulent dissipation at the leading edge.R. L. Walterscheid, J. H. Hecht, L. J. Gelinas, M. P. Hickey, and I. M. Rei

The unfolded protein response and its relevance to connective tissue diseases

The unfolded protein response (UPR) has evolved to counter the stresses that occur in the endoplasmic reticulum (ER) as a result of misfolded proteins. This sophisticated quality control system attempts to restore homeostasis through the action of a number of different pathways that are coordinated in the first instance by the ER stress-senor proteins IRE1, ATF6 and PERK. However, prolonged ER-stress-related UPR can have detrimental effects on cell function and, in the longer term, may induce apoptosis. Connective tissue cells such as fibroblasts, osteoblasts and chondrocytes synthesise and secrete large quantities of proteins and mutations in many of these gene products give rise to heritable disorders of connective tissues. Until recently, these mutant gene products were thought to exert their effect through the assembly of a defective extracellular matrix that ultimately disrupted tissue structure and function. However, it is now becoming clear that ER stress and UPR, because of the expression of a mutant gene product, is not only a feature of, but may be a key mediator in the initiation and progression of a whole range of different connective tissue diseases. This review focuses on ER stress and the UPR that characterises an increasing number of connective tissue diseases and highlights novel therapeutic opportunities that may arise

Linking ecosystem services, urban form and green space configuration using multivariate landscape metric analysis

Context: Landscape metrics represent powerful tools for quantifying landscape structure, but uncertainties persist around their interpretation. Urban settings add unique considerations, containing habitat structures driven by the surrounding built-up environment. Understanding urban ecosystems, however, should focus on the habitats rather than the matrix. Objectives: We coupled a multivariate approach with landscape metric analysis to overcome existing shortcomings in interpretation. We then explored relationships between landscape characteristics and modelled ecosystem service provision. Methods: We used principal component analysis and cluster analysis to isolate the most effective measures of landscape variability and then grouped habitat patches according to their attributes, independent of the surrounding urban form. We compared results to the modelled provision of three ecosystem services. Seven classes resulting from cluster analysis were separated primarily on patch area, and secondarily by measures of shape complexity and inter-patch distance. Results: When compared to modelled ecosystem services, larger patches up to 10 ha in size consistently stored more carbon per area and supported more pollinators, while exhibiting a greater risk of soil erosion. Smaller, isolated patches showed the opposite, and patches larger than 10 ha exhibited no additional areal benefit. Conclusions: Multivariate landscape metric analysis offers greater confidence and consistency than analysing landscape metrics individually. Independent classification avoids the influence of the urban matrix surrounding habitats of interest, and allows patches to be grouped according to their own attributes. Such a grouping is useful as it may correlate more strongly with the characteristics of landscape structure that directly affect ecosystem function

Neural networks for modeling gene-gene interactions in association studies

<p>Abstract</p> <p>Background</p> <p>Our aim is to investigate the ability of neural networks to model different two-locus disease models. We conduct a simulation study to compare neural networks with two standard methods, namely logistic regression models and multifactor dimensionality reduction. One hundred data sets are generated for each of six two-locus disease models, which are considered in a low and in a high risk scenario. Two models represent independence, one is a multiplicative model, and three models are epistatic. For each data set, six neural networks (with up to five hidden neurons) and five logistic regression models (the null model, three main effect models, and the full model) with two different codings for the genotype information are fitted. Additionally, the multifactor dimensionality reduction approach is applied.</p> <p>Results</p> <p>The results show that neural networks are more successful in modeling the structure of the underlying disease model than logistic regression models in most of the investigated situations. In our simulation study, neither logistic regression nor multifactor dimensionality reduction are able to correctly identify biological interaction.</p> <p>Conclusions</p> <p>Neural networks are a promising tool to handle complex data situations. However, further research is necessary concerning the interpretation of their parameters.</p