The tool switching problem revisited.

In this note we study the complexity of the tool switching problem with non-uniform tool sizes. More speci cally, we consider the problem where the job sequence is given as part of the input. We show that the resulting tooling problem is strongly NP-complete, even in case of unit loading and unloading costs. However, we show that if the capacity of the tool magazine is also given as part of the input, the problem is solvable in polynomial time.Research; Studies; Complexity; Job; Costs; Time;

A bootstrap version of the Hausman test to assess the impact of cluster-level endogeneity beyond the random intercept model

In the random intercept model for clustered data, the random effect is typically assumed to be independent of predictors. Violation of this assumption due to unmeasured cluster-level confounding (endogeneity) induces bias in the estimates of effects of within-cluster predictors. Treating cluster-specific intercepts as fixed rather than random avoids this bias. The Hausman test contrasts the fixed effect estimator with the traditional random effect estimator in the random intercept model to test for the presence of cluster-level endogeneity and has a known asymptotic -distribution under correct model specification. Unmeasured cluster-level heterogeneity may, however, interact with predictors as well, necessitating random slope models. Relying on either cluster or residual resampling in a bootstrap procedure, we propose two extensions of the Hausman test that can easily be used beyond the random intercept model. We compare the original Hausman test and its robust version to the newly proposed bootstrap tests in terms of empirical type I error rate and power. Under additive unmeasured heterogeneity, all methods perform equally well, whereas the original and robust Hausman tests are too liberal or too conservative under additional slope heterogeneity, both bootstrap Hausman tests maintain appropriate performance. Moreover, both bootstrap tests show robustness against misspecification in the presence of unit-level heteroscedasticity and temporal correlation

PyTorch-Hebbian : facilitating local learning in a deep learning framework

Recently, unsupervised local learning, based on Hebb's idea that change in synaptic efficacy depends on the activity of the pre- and postsynaptic neuron only, has shown potential as an alternative training mechanism to backpropagation. Unfortunately, Hebbian learning remains experimental and rarely makes it way into standard deep learning frameworks. In this work, we investigate the potential of Hebbian learning in the context of standard deep learning workflows. To this end, a framework for thorough and systematic evaluation of local learning rules in existing deep learning pipelines is proposed. Using this framework, the potential of Hebbian learned feature extractors for image classification is illustrated. In particular, the framework is used to expand the Krotov-Hopfield learning rule to standard convolutional neural networks without sacrificing accuracy compared to end-to-end backpropagation. The source code is available at https://github.com/Joxis/pytorch-hebbian.Comment: Presented as a poster at the NeurIPS 2020 Beyond Backpropagation worksho

beadarrayFilter : an R package to filter beads

Microarrays enable the expression levels of thousands of genes to be measured simultaneously. However, only a small fraction of these genes are expected to be expressed under different experimental conditions. Nowadays, filtering has been introduced as a step in the microarray preprocessing pipeline. Gene filtering aims at reducing the dimensionality of data by filtering redundant features prior to the actual statistical analysis. Previous filtering methods focus on the Affymetrix platform and can not be easily ported to the Illumina platform. As such, we developed a filtering method for Illumina bead arrays. We developed an R package, beadarrayFilter, to implement the latter method. In this paper, the main functions in the package are highlighted and using many examples, we illustrate how beadarrayFilter can be used to filter bead arrays

Using transcriptomics to guide lead optimization in drug discovery projects : lessons learned from the QSTAR project

The pharmaceutical industry is faced with steadily declining R&D efficiency which results in fewer drugs reaching the market despite increased investment. A major cause for this low efficiency is the failure of drug candidates in late-stage development owing to safety issues or previously undiscovered side-effects. We analyzed to what extent gene expression data can help to de-risk drug development in early phases by detecting the biological effects of compounds across disease areas, targets and scaffolds. For eight drug discovery projects within a global pharmaceutical company, gene expression data were informative and able to support go/no-go decisions. Our studies show that gene expression profiling can detect adverse effects of compounds, and is a valuable tool in early-stage drug discovery decision making

A random effects model for the identification of differential splicing (REIDS) using exon and HTA arrays

Background: Alternative gene splicing is a common phenomenon in which a single gene gives rise to multiple transcript isoforms. The process is strictly guided and involves a multitude of proteins and regulatory complexes. Unfortunately, aberrant splicing events do occur which have been linked to genetic disorders, such as several types of cancer and neurodegenerative diseases (Fan et al., Theor Biol Med Model 3:19, 2006). Therefore, understanding the mechanism of alternative splicing and identifying the difference in splicing events between diseased and healthy tissue is crucial in biomedical research with the potential of applications in personalized medicine as well as in drug development. Results: We propose a linear mixed model, Random Effects for the Identification of Differential Splicing (REIDS), for the identification of alternative splicing events. Based on a set of scores, an exon score and an array score, a decision regarding alternative splicing can be made. The model enables the ability to distinguish a differential expressed gene from a differential spliced exon. The proposed model was applied to three case studies concerning both exon and HTA arrays. Conclusion: The REIDS model provides a work flow for the identification of alternative splicing events relying on the established linear mixed model. The model can be applied to different types of arrays

High MIG (CXCL9) plasma levels favours response to peginterferon and ribavirin in HCV-infected patients regardless of DPP4 activity

Sustained virological response (SVR) following peginterferon (pegIFN) and ribavirin (RBV) treatment in hepatitis C virus (HCV) infected patients has been linked with the IL28B genotype and lower peripheral levels of the CXCR3-binding chemokine IP-10 (CXCL10). To further improve the understanding of these biomarkers we investigated plasma levels of the other CXCR3-binding chemokines and activity of the dipeptidyl peptidase IV (DPP4, CD26) protease, which cleaves IP-10, in relation to treatment response

Estimation of indirect effects in the presence of unmeasured confounding for the mediator-outcome relationship in a multilevel 2-1-1 mediation model

To assess the direct and indirect effect of an intervention, multilevel 2-1-1 studies with intervention randomized at the upper (class) level and mediator and outcome measured at the lower (student) level are frequently used in educational research. In such studies, the mediation process may flow through the student-level mediator (the within indirect effect) or a class-aggregated mediator (the contextual indirect effect). In this article, we cast mediation analysis within the counterfactual framework and clarify the assumptions that are needed to identify the within and contextual indirect effect. We show that unlike the contextual indirect effect, the within indirect effect can be unbiasedly estimated in linear models in the presence of unmeasured confounders of the mediator-outcome relationship at the upper level that exert additive effects on mediator and outcome. When unmeasured confounding occurs at the individual level, both indirect effects are no longer identified. We propose sensitivity analyses to assess the robustness of the within and contextual indirect effect under lower and upper-level confounding, respectively

Out of the Rock? Terracotta Figurines from Sagalassos in the Sadberk Hanım Museum in Istanbul

The contextualization of figurines that have been acquired by museums and that lack appropriate archaeological documentation is a growing trend in coroplastic research. This paper contributes to this trend by trying to reconstruct the context of some of the terracotta figurines kept at the Sadberk Hanım Museum in Istanbul. These objects could be identified as products of the coroplasts of Sagalassos (SW Turkey). What is more, there are several indications that suggest an exact origin for these figurines in a recently excavated cult site situated in the periphery of the Pisidian city. This in turn allows us to restore some of the social meaning of the terracottas and once again make them informative of the people that used them