Machine Learning for K-adaptability in Two-stage Robust Optimization

Two-stage robust optimization problems constitute one of the hardest optimization problem classes. One of the solution approaches to this class of problems is K-adaptability. This approach simultaneously seeks the best partitioning of the uncertainty set of scenarios into K subsets, and optimizes decisions corresponding to each of these subsets. In general case, it is solved using the K-adaptability branch-and-bound algorithm, which requires exploration of exponentially-growing solution trees. To accelerate finding high-quality solutions in such trees, we propose a machine learning-based node selection strategy. In particular, we construct a feature engineering scheme based on general two-stage robust optimization insights that allows us to train our machine learning tool on a database of resolved B&B trees, and to apply it as-is to problems of different sizes and/or types. We experimentally show that using our learned node selection strategy outperforms a vanilla, random node selection strategy when tested on problems of the same type as the training problems, also in case the K-value or the problem size differs from the training ones

Making a Network Orchard by Adding Leaves

Phylogenetic networks are used to represent the evolutionary history of species. Recently, the new class of orchard networks was introduced, which were later shown to be interpretable as trees with additional horizontal arcs. This makes the network class ideal for capturing evolutionary histories that involve horizontal gene transfers. Here, we study the minimum number of additional leaves needed to make a network orchard. We demonstrate that computing this proximity measure for a given network is NP-hard and describe a tight upper bound. We also give an equivalent measure based on vertex labellings to construct a mixed integer linear programming formulation. Our experimental results, which include both real-world and synthetic data, illustrate the efficiency of our implementation

Reconstructing Phylogenetic Networks via Cherry Picking and Machine Learning

Combining a set of phylogenetic trees into a single phylogenetic network that explains all of them is a fundamental challenge in evolutionary studies. In this paper, we apply the recently-introduced theoretical framework of cherry picking to design a class of heuristics that are guaranteed to produce a network containing each of the input trees, for practical-size datasets. The main contribution of this paper is the design and training of a machine learning model that captures essential information on the structure of the input trees and guides the algorithms towards better solutions. This is one of the first applications of machine learning to phylogenetic studies, and we show its promise with a proof-of-concept experimental study conducted on both simulated and real data consisting of binary trees with no missing taxa

Neur2RO: Neural Two-Stage Robust Optimization

Robust optimization provides a mathematical framework for modeling and solving decision-making problems under worst-case uncertainty. This work addresses two-stage robust optimization (2RO) problems (also called adjustable robust optimization), wherein first-stage and second-stage decisions are made before and after uncertainty is realized, respectively. This results in a nested min-max-min optimization problem which is extremely challenging computationally, especially when the decisions are discrete. We propose Neur2RO, an efficient machine learning-driven instantiation of column-and-constraint generation (CCG), a classical iterative algorithm for 2RO. Specifically, we learn to estimate the value function of the second-stage problem via a novel neural network architecture that is easy to optimize over by design. Embedding our neural network into CCG yields high-quality solutions quickly as evidenced by experiments on two 2RO benchmarks, knapsack and capital budgeting. For knapsack, Neur2RO finds solutions that are within roughly $2\%$ of the best-known values in a few seconds compared to the three hours of the state-of-the-art exact branch-and-price algorithm; for larger and more complex instances, Neur2RO finds even better solutions. For capital budgeting, Neur2RO outperforms three variants of the $k$-adaptability algorithm, particularly on the largest instances, with a 5 to 10-fold reduction in solution time. Our code and data are available at https://github.com/khalil-research/Neur2RO

PlantRT : a Distributed Recommendation Tool for Citizen Science

International audienceLes utilisateurs du Web 2.0 sont de gros producteurs de données diverses qu'ils stockent dans une grande variété de systèmes. Dans ce travail, nous nous concentrons sur le cas particulier des botanistes. En effet, établir une connaissance précise de l'identité, de la distribution géographique et de l'évolution des espèces vivantes est essentiel pour la pérennité de cette biodiversité, tout autant que pour l'espèce humaine. L'émergence des sciences citoyennes et des réseaux sociaux sont des outils supplémentaires favorisant la création de grandes communautés d'observateurs de la nature, qui ont commencé a produire d'énormes collections de données multimédias. Cependant, la complexité inhérente à la réalisation de ces collections provoque une certaine méfiance des utilisateurs, ces dernier ne souhaitant pas stocker leurs données sur un serveur central. Dans ce travail, nous avons réalisé un prototype multi-sites, où chaque site, peut représenter 1 à n utilisateurs permettant la recherche et la recommandation d'observations de plantes diversifiées à grand échelle

Rational Design of Polymers for Selective CO2 Reduction Catalysis.

A series of copolymers comprising a terpyridine ligand and various functional groups were synthesized toward integrating a Co-based molecular CO2 reduction catalyst. Using porous metal oxide electrodes designed to host macromolecules, the Co-coordinated polymers were readily immobilized via phosphonate anchoring groups. Within the polymeric matrix, the outer coordination sphere of the Co terpyridine catalyst was engineered using hydrophobic functional moieties to improve CO2 reduction selectivity in the presence of water. Electrochemical and photoelectrochemical CO2 reduction were demonstrated with the polymer-immobilized hybrid cathodes, with a CO:H2 product ratio of up to 6:1 compared to 2:1 for a corresponding "monomeric" Co terpyridine catalyst. This versatile platform of polymer design demonstrates promise in controlling the outer-sphere environment of synthetic molecular catalysts, analogous to CO2 reductases.the Woolf Fisher Trust in New Zealand, the Winston Churchill Foundation of the United States, the Christian Doppler Research Association (Austrian Federal Ministry for Digital and Economic Affairs and the National Foundation for Research, Technology and Development), the OMV Grou

Genome-wide CRISPR screening identifies new regulators of glycoprotein secretion.

Background: The fundamental process of protein secretion from eukaryotic cells has been well described for many years, yet gaps in our understanding of how this process is regulated remain. Methods: With the aim of identifying novel genes involved in the secretion of glycoproteins, we used a screening pipeline consisting of a pooled genome-wide CRISPR screen, followed by secondary siRNA screening of the hits to identify and validate several novel regulators of protein secretion. Results: We present approximately 50 novel genes not previously associated with protein secretion, many of which also had an effect on the structure of the Golgi apparatus. We further studied a small selection of hits to investigate their subcellular localisation. One of these, GPR161, is a novel Golgi-resident protein that we propose maintains Golgi structure via an interaction with golgin A5. Conclusions: This study has identified new factors for protein secretion involved in Golgi homeostasis

A Computational-Experimental Approach to Unravel the Excited State Landscape in Heavy-Atom Free BODIPY-Related Dyes

We performed a time-gated laser-spectroscopy study in a set of heavy-atom free single BODIPY fluorophores, supported by accurate, excited-state computational simulations of the key low-lying excited states in these chromophores. Despite the strong fluorescence of these emitters, we observed a significant fraction of time-delayed (microseconds scale) emission associated with processes that involved passage through the triplet manifold. The accuracy of the predictions of the energy arrangement and electronic nature of the low-lying singlet and triplet excited states meant that an unambiguous assignment of the main deactivation pathways, including thermally activated delayed fluorescence and/or room temperature phosphorescence, was possible. The observation of triplet state formation indicates a breakthrough in the “classic” interpretation of the photophysical properties of the renowned BODIPY and its derivatives.This research was funded by Spanish Ministerio de Economia y Competitividad (project PID2020-114755GB-C31, C32 and C33) and Gobierno Vasco (IT1639-22)

A Changing Landscape for Lifelong Learning in Health Globally.

On 25 July 2022, the Continuing Professional Development (CPD) Special Interest Group of the Association for Medical Education in Europe came together to open up discussions during a live webinar on 'Exploring the Evolution of CPD'. The objective was to bring together global medical educators to consider perspectives of CPD from the role of global lifelong learners, the role of educators and the role of education providers and health regulators. The landscape of CPD is evolving, and the roles of each key player must include specific actions for facilitated change. Delivering competency outcomes-based learning, fit for purpose, to lifelong learners in health will require (1) learner agency, (2) leadership from educators and (3) providers of lifelong learning to come together to improve delivery of CPD that leads to meaningful change in practice care delivery

ThePlantGame: Actively Training Human Annotators for Domain-specific Crowdsourcing

International audienceIn a typical citizen science/crowdsourcing environment, the contributorslabel items. When there are few labels, it is straightforwardto train contributors and judge the quality of their labels bygiving a few examples with known answers. Neither is true whenthere are thousands of domain-specic labels and annotators withheterogeneous skills. This demo paper presents an Active UserTraining framework implemented as a serious game called The-PlantGame. It is based on a set of data-driven algorithms allowingto (i) actively train annotators, and (ii) evaluate the quality of contributors’answers on new test items to optimize predictions