Lifelong Generative Modeling

Lifelong learning is the problem of learning multiple consecutive tasks in a sequential manner, where knowledge gained from previous tasks is retained and used to aid future learning over the lifetime of the learner. It is essential towards the development of intelligent machines that can adapt to their surroundings. In this work we focus on a lifelong learning approach to unsupervised generative modeling, where we continuously incorporate newly observed distributions into a learned model. We do so through a student-teacher Variational Autoencoder architecture which allows us to learn and preserve all the distributions seen so far, without the need to retain the past data nor the past models. Through the introduction of a novel cross-model regularizer, inspired by a Bayesian update rule, the student model leverages the information learned by the teacher, which acts as a probabilistic knowledge store. The regularizer reduces the effect of catastrophic interference that appears when we learn over sequences of distributions. We validate our model's performance on sequential variants of MNIST, FashionMNIST, PermutedMNIST, SVHN and Celeb-A and demonstrate that our model mitigates the effects of catastrophic interference faced by neural networks in sequential learning scenarios.Comment: 32 page

Continual Classification Learning Using Generative Models

Continual learning is the ability to sequentially learn over time by accommodating knowledge while retaining previously learned experiences. Neural networks can learn multiple tasks when trained on them jointly, but cannot maintain performance on previously learned tasks when tasks are presented one at a time. This problem is called catastrophic forgetting. In this work, we propose a classification model that learns continuously from sequentially observed tasks, while preventing catastrophic forgetting. We build on the lifelong generative capabilities of [10] and extend it to the classification setting by deriving a new variational bound on the joint log likelihood, $\log p(x; y)$.Comment: 5 pages, 4 figures, under review in Continual learning Workshop NIPS 201

Sparse Learning for Variable Selection with Structures and Nonlinearities

In this thesis we discuss machine learning methods performing automated variable selection for learning sparse predictive models. There are multiple reasons for promoting sparsity in the predictive models. By relying on a limited set of input variables the models naturally counteract the overfitting problem ubiquitous in learning from finite sets of training points. Sparse models are cheaper to use for predictions, they usually require lower computational resources and by relying on smaller sets of inputs can possibly reduce costs for data collection and storage. Sparse models can also contribute to better understanding of the investigated phenomenons as they are easier to interpret than full models.Comment: PhD thesi

Vector-Quantized Graph Auto-Encoder

In this work, we addresses the problem of modeling distributions of graphs. We introduce the Vector-Quantized Graph Auto-Encoder (VQ-GAE), a permutation-equivariant discrete auto-encoder and designed to model the distribution of graphs. By exploiting the permutation-equivariance of graph neural networks (GNNs), our autoencoder circumvents the problem of the ordering of the graph representation. We leverage the capability of GNNs to capture local structures of graphs while employing vector-quantization to prevent the mapping of discrete objects to a continuous latent space. Furthermore, the use of autoregressive models enables us to capture the global structure of graphs via the latent representation. We evaluate our model on standard datasets used for graph generation and observe that it achieves excellent performance on some of the most salient evaluation metrics compared to the state-of-the-art

Broad-range RNA modification analysis of complex biological samples using rapid C18-UPLC-MS

Post-transcriptional RNA modifications play an important role in cellular metabolism with homoeostatic disturbances manifesting as a wide repertoire of phenotypes, reduced stress tolerance and translational perturbation, developmental defects, and diseases, such as type II diabetes, leukaemia, and carcinomas. Hence, there has been an intense effort to develop various methods for investigating RNA modifications and their roles in various organisms, including sequencing-based approaches and, more frequently, liquid chromatography-mass spectrometry (LC-MS)-based methods. Although LC-MS offers numerous advantages, such as being highly sensitive and quantitative over a broad detection range, some stationary phase chemistries struggle to resolve positional isomers. Furthermore, the demand for detailed analyses of complex biological samples often necessitates long separation times, hampering sample-to-sample turnover and making multisample analyses time consuming. To overcome this limitation, we have developed an ultra-performance LC-MS (UPLC-MS) method that uses an octadecyl carbon chain (C18)-bonded silica matrix for the efficient separation of 50 modified ribonucleosides, including positional isomers, in a single 9-min sample-to-sample run. To validate the performance and versatility of our method, we analysed tRNA modification patterns of representative microorganisms from each domain of life, namely Archaea (Methanosarcina acetivorans), Bacteria (Pseudomonas syringae), and Eukarya (Saccharomyces cerevisiae). Additionally, our method is flexible and readily applicable for detection and relative quantification using stable isotope labelling and targeted approaches like multiple reaction monitoring (MRM). In conclusion, this method represents a fast and robust tool for broad-range exploration and quantification of ribonucleosides, facilitating future homoeostasis studies of RNA modification in complex biological samples.Peer reviewe

A comparative assessment of mandible shape in a consomic strain panel of the house mouse (Mus musculus) - implications for epistasis and evolvability of quantitative traits

<p>Abstract</p> <p>Background</p> <p>Expectations of repeatedly finding associations between given genes and phenotypes have been borne out by studies of parallel evolution, especially for traits involving absence or presence of characters. However, it has rarely been asked whether the genetic basis of quantitative trait variation is conserved at the intra- or even at the interspecific level. This question is especially relevant for shape, where the high dimensionality of variation seems to require a highly complex genetic architecture involving many genes.</p> <p>Results</p> <p>We analyse here the genetic effects of chromosome substitution strains carrying <it>M. m. musculus </it>chromosomes in a largely <it>M. m. domesticus </it>background on mandible shape and compare them to the results of previously published QTL mapping data between <it>M. m. domesticus </it>strains. We find that the distribution of genetic effects and effect sizes across the genome is consistent between the studies, while the specific shape changes associated with the chromosomes are different. We find also that the sum of the effects from the different <it>M. m. musculus </it>chromosomes is very different from the shape of the strain from which they were derived, as well as all known wild type shapes.</p> <p>Conclusions</p> <p>Our results suggest that the relative chromosome-wide effect sizes are comparable between the long separated subspecies <it>M. m. domesticus </it>and <it>M. m. musculus</it>, hinting at a relative stability of genes involved in this complex trait. However, the absolute effect sizes and the effect directions may be allele-dependent, or are context dependent, i.e. epistatic interactions appear to play an important role in controlling shape.</p

An improved RT-qPCR method for direct quantification of enveloped RNA viruses

Reverse transcription quantitative PCR (RT-qPCR) has emerged as the gold standard for virus detection and quantification, being utilized in numerous diagnostic and research applications. However, the direct detection of viruses has so far posed a challenge as the viral genome is often encapsidated by a proteinaceous layer surrounded by a lipid envelope. This necessitates an additional and undesired RNA extraction step prior to RT-qPCR amplification. To circumvent this limitation, we have developed a direct RT-qPCR method for the detection of RNA viruses. In our method, we provide a proof-of-concept using phage phi6, a safe-to-use proxy for pathogenic enveloped RNA viruses that is commonly utilized in e.g. aerosolization studies. First, the phage phi6 envelope is removed by 1% chloroform treatment and the virus is then directly quantified by RT-qPCR. To identify false negative results, firefly luciferase is included as a synthetic external control. Thanks to the duplex format, our direct RT-qPCR method reduces the reagents needed and provides an easy to implement and broadly applicable, fast, and cost-effective tool for the quantitative analysis of enveloped RNA viruses.center dot One-step direct RT-qPCR quantification of phage phi6 virus without prior RNA isolation.center dot Reduced reaction volume for sustainable and cost-effective analysis.(c) 2022 The Author(s). Published by Elsevier B.V.This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )Peer reviewe