José Bento

Strain Dependent Genetic Networks for Antibiotic-Sensitivity in a Bacterial Pathogen with a Large Pan-Genome.

The interaction between an antibiotic and bacterium is not merely restricted to the drug and its direct target, rather antibiotic induced stress seems to resonate through the bacterium, creating selective pressures that drive the emergence of adaptive mutations not only in the direct target, but in genes involved in many different fundamental processes as well. Surprisingly, it has been shown that adaptive mutations do not necessarily have the same effect in all species, indicating that the genetic background influences how phenotypes are manifested. However, to what extent the genetic background affects the manner in which a bacterium experiences antibiotic stress, and how this stress is processed is unclear. Here we employ the genome-wide tool Tn-Seq to construct daptomycin-sensitivity profiles for two strains of the bacterial pathogen Streptococcus pneumoniae. Remarkably, over half of the genes that are important for dealing with antibiotic-induced stress in one strain are dispensable in another. By confirming over 100 genotype-phenotype relationships, probing potassium-loss, employing genetic interaction mapping as well as temporal gene-expression experiments we reveal genome-wide conditionally important/essential genes, we discover roles for genes with unknown function, and uncover parts of the antibiotic’s mode-of-action. Moreover, by mapping the underlying genomic network for two query genes we encounter little conservation in network connectivity between strains as well as profound differences in regulatory relationships. Our approach uniquely enables genome-wide fitness comparisons across strains, facilitating the discovery that antibiotic responses are complex events that can vary widely between strains, which suggests that in some cases the emergence of resistance could be strain specific and at least for species with a large pan-genome less predictable.

Identifying users from their rating patterns

This paper reports on our analysis of the 2011 CAMRa Challenge dataset (Track 2) for context-aware movie recommendation systems. The train dataset comprises 4 536 891 ratings provided by 171 670 users on 23 974 movies, as well as the household groupings of a subset of the users. The test dataset comprises 5 450 ratings for which the user label is missing, but the household label is provided. The challenge required to identify the user labels for the ratings in the test set. Our main finding is that temporal information (time labels of the ratings) is significantly more useful for achieving this objective than the user preferences (the actual ratings). Using a model that leverages on this fact, we are able to identify users within a known household with an accuracy of approximately 96% (i.e. misclassification rate around 4%).

An explicit rate bound for over-relaxed ADMM

The framework of Integral Quadratic Constraints of Lessard et al. (2014) reduces the computation of upper bounds on the convergence rate of several optimization algorithms to semi-definite programming (SDP). Follow up work by Nishihara et al. (2015) applies this technique to the entire family of over-relaxed Alternating Direction Method of Multipliers (ADMM). Unfortunately, they only provide an explicit error bound for sufficiently large values of some of the parameters of the problem, leaving the computation for the general case as a numerical optimization problem. In this paper we provide an exact analytical solution to this SDP and obtain a general and explicit upper bound on the convergence rate of the entire family of over-relaxed ADMM. Furthermore, we demonstrate that it is not possible to extract from this SDP a general bound better than ours. We end with a few numerical illustrations of our result and a comparison between the convergence rate we obtain for ADMM with known convergence rates for Gradient Descent (GD).

Information theoretic limits on learning stochastic differential equations

Consider the problem of learning the drift coefficient of a stochastic differential equation from a sample path. In this paper, we assume that the drift is parametrized by a high-dimensional vector. We address the question of how long the system needs to be observed in order to learn this vector of parameters. We prove a general lower bound on this time complexity by using a characterization of mutual information as time integral of conditional variance, due to Kadota, Zakai, and Ziv. This general lower bound is applied to specific classes of linear and non-linear stochastic differential equations. In the linear case, the problem under consideration is the one of learning a matrix of interaction coefficients. We evaluate our lower bound for ensembles of sparse and dense random matrices. The resulting estimates match the qualitative behavior of upper bounds achieved by computationally efficient procedures.

Markov Chain Lifting and Distributed ADMM

The time to converge to the steady state of a finite Markov chain can be greatly reduced by a lifting operation, which creates a new Markov chain on an expanded state space. For a class of quadratic objectives, we show an analogous behavior where a distributed alternating direction method of multipliers (ADMM) algorithm can be seen as a lifting of gradient descent. This provides a deep insight for its faster convergence rate under optimal parameter tuning. We conjecture that this gain is always present, as opposed to the lifting of a Markov chain, which sometimes only provides a marginal speedup.

Line-Drawing Video Stylization

We present a method to automatically convert videos and CG animations to stylized animated line drawings. Using a data‐driven approach, the animated drawings can follow the sketching style of a specific artist. Given an input video, we first extract edges from the video frames and vectorize them to curves. The curves are matched to strokes from an artists library, while following the artists stroke distribution and characteristics. The key challenge in this process is to match the large number of curves in the frames over time, despite topological and geometric changes, allowing to maintain temporal coherence in the output animation. We solve this problem using constrained optimization to build correspondences between tracked points and create smooth sheets over time. These sheets are then replaced with strokes from the artists database to render the final animation. We evaluate our tracking algorithm on various examples and show stylized animation results based on various artists.

Forecasting bacterial survival-success and adaptive evolution through multi-omics stress response-mapping, network analyses and machine learning

Whether a bacterial pathogen establishes an infection and/or evolves antibiotic resistance depends on successful survival while experiencing stress from for instance the host immune system and/or antibiotics. Predictions on bacterial survival and adaptive outcomes could thus have great prognostic value. However, it is unknown what information is required to enable such predictions. By developing a novel network-based analysis method, a bacteriums phenotypic and transcriptional response can be objectively quantified in temporal 3D-feature space. The resulting trajectories can be interpreted as a degree of coordination, where a focused and coordinated response predicts bacterial survival-success, and a random uncoordinated response predicts survival-failure. These predictions extend to both antibiotic resistance and in vivo infection conditions and are applicable to both Gram-positive and Gram-negative bacteria. Moreover, through experimental evolution we show that the degree of coordination is an adaptive outcome - an uncoordinated response evolves into a coordinated response when a bacterium adapts to its environment. Most surprisingly, it turns out that phenotypic and transcriptional response data, network features and genome plasticity data can be used to train a machine learning model that is able to predict which genes in the genome will adapt under nutrient or antibiotic selection. Importantly, this suggests that deterministic factors help drive adaptation and that evolution is, at least partially, predictable. This work demonstrates that with the right information predictions on bacterial short-term survival and long-term adaptive outcomes are feasible, which underscores that personalized infectious disease diagnostics and treatments are possible, and should be developed.

Testing Fine-Grained Parallelism for the ADMM on a Factor-Graph

There is an ongoing effort to develop tools that apply distributed computational resources to tackle large problems or reduce the time to solve them. In this context, the Alternating Direction Method of Multipliers (ADMM) arises as a method that can exploit distributed resources like the dual ascent method and has the robustness and improved convergence of the augmented Lagrangian method. Traditional approaches to accelerate the ADMM using multiple cores are problem-specific and often require multi-core programming. By contrast, we propose a problem-independent scheme of accelerating the ADMM that does not require the user to write any parallel code. We show that this scheme, an interpretation of the ADMM as a message-passing algorithm on a factor-graph, can automatically exploit fine-grained parallelism both in GPUs and shared-memory multi-core computers and achieves significant speedup in such diverse application domains as combinatorial optimization, machine learning, and optimal control. Specifically, we obtain 10-18x speedup using a GPU, and 5-9x using multiple CPU cores, over a serial, optimized C-version of the ADMM, which is similar to the typical speedup reported for existing GPU-accelerated libraries, including cuFFT (19x), cuBLAS (17x), and cuRAND (8x).

A Time and Space Efficient Algorithm for Contextual Linear Bandits

We consider a multi-armed bandit problem where payoffs are a linear function of an observed stochastic contextual variable. In the scenario where there exists a gap between optimal and suboptimal rewards, several algorithms have been proposed that achieve O(logT) regret after T time steps. However, proposed methods either have a computation complexity per iteration that scales linearly with T or achieve regrets that grow linearly with the number of contexts |i¾?|. We propose an e-greedy type of algorithm that solves both limitations. In particular, when contexts are variables in

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