Flipped classrooms: Using Google Groups to facilitate in-class communication

Foreword This report describes two mandatory one-semester ESP courses (English for International Relations) offered to second- and third-year undergraduates at the Institut National des Langues et Civilisations Orientales (INALCO) enrolled in the selective “international relations” track. The second-year course deals with topics related to jobs in international relations and the third-year course with topics related to recent issues in international relations and diplomacy. The common objecti..

Extending Explainable Boosting Machines to Scientific Image Data

As the deployment of computer vision technology becomes increasingly common in science, the need for explanations of the system and its output has become a focus of great concern. Driven by the pressing need for interpretable models in science, we propose the use of Explainable Boosting Machines (EBMs) for scientific image data. Inspired by an important application underpinning the development of quantum technologies, we apply EBMs to cold-atom soliton image data tabularized using Gabor Wavelet Transform-based techniques that preserve the spatial structure of the data. In doing so, we demonstrate the use of EBMs for image data for the first time and show that our approach provides explanations that are consistent with human intuition about the data.Comment: 7 pages, 2 figure

English Support to Academic Staff: A Pilot Study at the Department of Management

The internationalisation of Universities is a global phenomenon that has an impact on teaching and administrative staff, who, as a result of top-down decisions are required to perform their tasks in another language, usually English. Given that this process often leads to concerns regarding the quality of services and teaching, more research is needed to better analyse potential issues. Therefore, this project seeks to investigate the main linguistic and methodological difficulties experienced by the teaching and administrative staff in an Italian university when English is the medium of instruction and communication. The Department of Management at Ca’ Foscari University of Venice has instituted a pilot project running from November 2015 to May 2016, in which linguistic support is provided to the academic staff, in the form of a language help desk. To understand the specific needs of the department, diary entries of help desk activities were analysed through the lens of Grounded Theory. Initial results indicate that less proficient users would ask for a revision of some grammar points, while more proficient users would prefer having their articles and slides proofread, or rehearsing their lessons

Dynamic particle swarm optimization of biomolecular simulation parameters with flexible objective functions

Molecular simulations are a powerful tool to complement and interpret ambiguous experimental data on biomolecules to obtain structural models. Such data-assisted simulations often rely on parameters, the choice of which is highly non-trivial and crucial to performance. The key challenge is weighting experimental information with respect to the underlying physical model. We introduce FLAPS, a self-adapting variant of dynamic particle swarm optimization, to overcome this parameter selection problem. FLAPS is suited for the optimization of composite objective functions that depend on both the optimization parameters and additional, a priori unknown weighting parameters, which substantially influence the search-space topology. These weighting parameters are learned at runtime, yielding a dynamically evolving and iteratively refined search-space topology. As a practical example, we show how FLAPS can be used to find functional parameters for small-angle X-ray scattering-guided protein simulations

Forgiveness takes place on an attitudinal continuum from hostility to friendliness: Toward a closer union of forgiveness theory and measurement.

Researchers commonly conceptualize forgiveness as a rich complex of psychological changes involving attitudes, emotions, and behaviors. Psychometric work with the measures developed to capture this conceptual richness, however, often points to a simpler picture of the psychological dimensions in which forgiveness takes place. In an effort to better unite forgiveness theory and measurement, we evaluate several psychometric models for common measures of forgiveness. In doing so, we study people from the United States and Japan to understand forgiveness in both nonclose and close relationships. In addition, we assess the predictive utility of these models for several behavioral outcomes that traditionally have been linked to forgiveness motives. Finally, we use the methods of item response theory, which place person abilities and item responses on the same metric and, thus, help us draw psychological inferences from the ordering of item difficulties. Our results highlight models based on correlated factors models and bifactor (S-1) models. The bifactor (S-1) model evinced particular utility: Its general factor consistently predicts variation in relevant criterion measures, including 4 different experimental economic games (when played with a transgressor), and also suffuses a second self-report measure of forgiveness. Moreover, the general factor of the bifactor (S-1) model identifies a single psychological dimension that runs from hostility to friendliness while also pointing to other sources of variance that may be conceived of as method factors. Taken together, these results suggest that forgiveness can be usefully conceptualized as prosocial change along a single attitudinal continuum that ranges from hostility to friendliness. (PsycInfo Database Record (c) 2020 APA, all rights reserved)

Massively Parallel Genetic Optimization through Asynchronous Propagation of Populations

We present Propulate, an evolutionary optimization algorithm and software package for global optimization and in particular hyperparameter search. For efficient use of HPC resources, Propulate omits the synchronization after each generation as done in conventional genetic algorithms. Instead, it steers the search with the complete population present at time of breeding new individuals. We provide an MPI-based implementation of our algorithm, which features variants of selection, mutation, crossover, and migration and is easy to extend with custom functionality. We compare Propulate to the established optimization tool Optuna. We find that Propulate is up to three orders of magnitude faster without sacrificing solution accuracy, demonstrating the efficiency and efficacy of our lazy synchronization approach. Code and documentation are available at https://github.com/Helmholtz-AI-Energy/propulateComment: 18 pages, 5 figures submitted to ISC High Performance 202

Simulation of FRET dyes allows quantitative comparison against experimental data

Fully understanding biomolecular function requires detailed insight into the systems’ structural dynamics. Powerful experimental techniques such as single molecule Förster Resonance Energy Transfer (FRET) provide access to such dynamic information yet have to be carefully interpreted. Molecular simulations can complement these experiments but typically face limits in accessing slow time scales and large or unstructured systems. Here, we introduce a coarse-grained simulation technique that tackles these challenges. While requiring only few parameters, we maintain full protein flexibility and include all heavy atoms of proteins, linkers, and dyes. We are able to sufficiently reduce computational demands to simulate large or heterogeneous structural dynamics and ensembles on slow time scales found in, e.g., protein folding. The simulations allow for calculating FRET efficiencies which quantitatively agree with experimentally determined values. By providing atomically resolved trajectories, this work supports the planning and microscopic interpretation of experiments. Overall, these results highlight how simulations and experiments can complement each other leading to new insights into biomolecular dynamics and function

Acetyl-CoA synthetase 2 promotes acetate utilization and maintains cancer cell growth under metabolic stress

A functional genomics study revealed that the activity of acetyl-CoA synthetase 2 (ACSS2) contributes to cancer cell growth under low-oxygen and lipid-depleted conditions. Comparative metabolomics and lipidomics demonstrated that acetate is used as a nutritional source by cancer cells in an ACSS2-dependent manner, and supplied a significant fraction of the carbon within the fatty acid and phospholipid pools. ACSS2 expression is upregulated under metabolically stressed conditions and ACSS2 silencing reduced the growth of tumor xenografts. ACSS2 exhibits copy-number gain in human breast tumors, and ACSS2 expression correlates with disease progression. These results signify a critical role for acetate consumption in the production of lipid biomass within the harsh tumor microenvironment

Simple sequence repeat variation in the Daphnia pulex genome

Background: Simple sequence repeats (SSRs) are highly variable features of all genomes. Their rapid evolution makes them useful for tracing the evolutionary history of populations and investigating patterns of selection and mutation across gnomes. The recently sequenced Daphnia pulex genome provides us with a valuable data set to study the mode and tempo of SSR evolution, without the inherent biases that accompany marker selection. Results: Here we catalogue SSR loci in the Daphnia pulex genome with repeated motif sizes of 1-100 nucleotides with a minimum of 3 perfect repeats. We then used whole genome shotgun reads to determine the average heterozygosity of each SSR type and the relationship that it has to repeat number, motif size, motif sequence, and distribution of SSR loci. We find that SSR heterozygosity is motif specific, and positively correlated with repeat number as well as motif size. For non-repeat unit polymorphisms, we identify a motif-dependent end-nucleotide polymorphism bias that may contribute to the patterns of abundance for specific homopolymers, dimers, and trimers. Our observations confirm the high frequency of multiple unit variation (multistep) at large microsatellite loci, and further show that the occurrence of multiple unit variation is dependent on both repeat number and motif size. Using the Daphnia pulex genetic map, we show a positive correlation between dimer and trimer frequency and recombination. Conclusions: This genome-wide analysis of SSR variation in Daphnia pulex indicates that several aspects of SSR variation are motif dependent and suggests that a combination of unit length variation and end repeat biased base substitution contribute to the unique spectrum of SSR repeat loci

RNA contact prediction by data efficient deep learning

On the path to full understanding of the structure-function relationship or even design of RNA, structure prediction would offer an intriguing complement to experimental efforts. Any deep learning on RNA structure, however, is hampered by the sparsity of labeled training data. Utilizing the limited data available, we here focus on predicting spatial adjacencies ("contact maps") as a proxy for 3D structure. Our model, BARNACLE, combines the utilization of unlabeled data through self-supervised pre-training and efficient use of the sparse labeled data through an XGBoost classifier. BARNACLE shows a considerable improvement over both the established classical baseline and a deep neural network. In order to demonstrate that our approach can be applied to tasks with similar data constraints, we show that our findings generalize to the related setting of accessible surface area prediction