Corrector theory for MsFEM and HMM in random media

We analyze the random fluctuations of several multi-scale algorithms such as the multi-scale finite element method (MsFEM) and the finite element heterogeneous multiscale method (HMM), that have been developed to solve partial differential equations with highly heterogeneous coefficients. Such multi-scale algorithms are often shown to correctly capture the homogenization limit when the highly oscillatory random medium is stationary and ergodic. This paper is concerned with the random fluctuations of the solution about the deterministic homogenization limit. We consider the simplified setting of the one dimensional elliptic equation, where the theory of random fluctuations is well understood. We develop a fluctuation theory for the multi-scale algorithms in the presence of random environments with short-range and long-range correlations. What we find is that the computationally more expensive method MsFEM captures the random fluctuations both for short-range and long-range oscillations in the medium. The less expensive method HMM correctly captures the fluctuations for long-range oscillations and strongly amplifies their size in media with short-range oscillations. We present a modified scheme with an intermediate computational cost that captures the random fluctuations in all cases.Comment: 41 page

Advertising Media and Target Audience Optimization via High-dimensional Bandits

We present a data-driven algorithm that advertisers can use to automate their digital ad-campaigns at online publishers. The algorithm enables the advertiser to search across available target audiences and ad-media to find the best possible combination for its campaign via online experimentation. The problem of finding the best audience-ad combination is complicated by a number of distinctive challenges, including (a) a need for active exploration to resolve prior uncertainty and to speed the search for profitable combinations, (b) many combinations to choose from, giving rise to high-dimensional search formulations, and (c) very low success probabilities, typically just a fraction of one percent. Our algorithm (designated LRDL, an acronym for Logistic Regression with Debiased Lasso) addresses these challenges by combining four elements: a multiarmed bandit framework for active exploration; a Lasso penalty function to handle high dimensionality; an inbuilt debiasing kernel that handles the regularization bias induced by the Lasso; and a semi-parametric regression model for outcomes that promotes cross-learning across arms. The algorithm is implemented as a Thompson Sampler, and to the best of our knowledge, it is the first that can practically address all of the challenges above. Simulations with real and synthetic data show the method is effective and document its superior performance against several benchmarks from the recent high-dimensional bandit literature.Comment: 39 pages, 8 figure

Type-Directed Operational Semantics for Gradual Typing (Artifact)

This artifact includes the Coq formalization associated with the paper Type-Directed Operational Semantics for Gradual Typing submitted in ECOOP 2021. The paper illustrates how to employ TDOS on gradually typed languages using two calculi. The first calculus, called ?B, is inspired by the semantics of the blame calculus(?B^g) and is sound with ?B^g. The second calculus, called ?B^r, explores a different design space in the semantics of gradually typed languages. This document explains how to run the Coq formalization. Artifact can either be compiled in the pre-built docker image with all the dependencies installed or it could be built from the scratch. Sections 1-7 explain the basic information about the artifact. Section 7 explains how to get the docker image for the artifact. Section 8 explains the prerequisites and the steps to run coq files from scratch. Section 9 explains coq files briefly. Section 10 shows the correspondence of important lemmas, definitions and pictures discussed in the paper with their respective Coq formalization

Type-Directed Operational Semantics for Gradual Typing

The semantics of gradually typed languages is typically given indirectly via an elaboration into a cast calculus. This contrasts with more conventional formulations of programming language semantics, where the semantics of a language is given directly using, for instance, an operational semantics. This paper presents a new approach to give the semantics of gradually typed languages directly. We use a recently proposed variant of small-step operational semantics called type-directed operational semantics (TDOS). In TDOS type annotations become operationally relevant and can affect the result of a program. In the context of a gradually typed language, such type annotations are used to trigger type-based conversions on values. We illustrate how to employ TDOS on gradually typed languages using two calculi. The first calculus, called ? B^g, is inspired by the semantics of the blame calculus, but it has implicit type conversions, enabling it to be used as a gradually typed language. The second calculus, called ? B^r, explores a different design space in the semantics of gradually typed languages. It uses a so-called blame recovery semantics, which enables eliminating some false positives where blame is raised but normal computation could succeed. For both calculi, type safety is proved. Furthermore we show that the semantics of ? B^g is sound with respect to the semantics of the blame calculus, and that ? B^r comes with a gradual guarantee. All the results have been mechanically formalized in the Coq theorem prover

The mechanism of Fe-rich intermetallic compound formation and growth on inoculants revealed by electron backscattered diffraction and X-ray imaging

Fe-rich intermetallics affect critically the mechanical properties and recyclability of aluminium alloys. Increasing effort has been spent on the inoculation of these intermetallics, hoping to promote a finer distribution. Recently Al-5Ti-1B (wt.%), originally developed to refine -Al, has been shown to refine Al13Fe4, an intermetallic phase present in a variety of Al alloys. However, mechanisms of the formation and growth of the intermetallics on the inoculants are unclear. In this paper, Ti is added to Fe-containing Al alloys to produce a large number of potent Al3Ti particles, the active inoculant in Al-5Ti-1B. We use a combination of electron backscattered diffraction, in situ synchrotron X-ray radiography and post-solidification X-ray computed tomography to investigate the formation and growth of primary Al13Fe4 on Al3Ti inoculants, first in a model Al-Fe alloy, with key insights then confirmed in a high Fe-containing, recycled 6xxx alloy. Crystallographic orientation relationships between Al13Fe4 and Al3Ti are analysed comprehensively, and the formation and growth dynamics of Al13Fe4 on Al3Ti is also unveiled. A strong link is revealed between the formation of Al13Fe4 on Al3Ti and a twinning-related pseudo-symmetry of Al13Fe4. Finally, a potential strategy to refine both intermetallics and -Al in recycled alloys with elevated Fe concentration is proposed

PACT: A pipeline for analysis of circulating tumor DNA

MOTIVATION: Detection of genomic alterations in circulating tumor DNA (ctDNA) is currently used for active clinical monitoring of cancer progression and treatment response. While methods for analysis of small mutations are more developed, strategies for detecting structural variants (SVs) in ctDNA are limited. Additionally, reproducibly calling small-scale mutations, copy number alterations, and SVs in ctDNA is challenging due to the lack to unified tools for these different classes of variants. RESULTS: We developed a unified pipeline for the analysis of ctDNA [Pipeline for the Analysis of ctDNA (PACT)] that accurately detects SVs and consistently outperformed similar tools when applied to simulated, cell line, and clinical data. We provide PACT in the form of a Common Workflow Language pipeline which can be run by popular workflow management systems in high-performance computing environments. AVAILABILITY AND IMPLEMENTATION: PACT is freely available at https://github.com/ChrisMaherLab/PACT

PRED_PPI: a server for predicting protein-protein interactions based on sequence data with probability assignment

<p>Abstract</p> <p>Background</p> <p>Protein-protein interactions (PPIs) are crucial for almost all cellular processes, including metabolic cycles, DNA transcription and replication, and signaling cascades. Given the importance of PPIs, several methods have been developed to detect them. Since the experimental methods are time-consuming and expensive, developing computational methods for effectively identifying PPIs is of great practical significance.</p> <p>Findings</p> <p>Most previous methods were developed for predicting PPIs in only one species, and do not account for probability estimations. In this work, a relatively comprehensive prediction system was developed, based on a support vector machine (SVM), for predicting PPIs in five organisms, specifically humans, yeast, <it>Drosophila</it>, <it>Escherichia coli</it>, and <it>Caenorhabditis elegans</it>. This PPI predictor includes the probability of its prediction in the output, so it can be used to assess the confidence of each SVM prediction by the probability assignment. Using a probability of 0.5 as the threshold for assigning class labels, the method had an average accuracy for detecting protein interactions of 90.67% for humans, 88.99% for yeast, 90.09% for <it>Drosophila</it>, 92.73% for <it>E. coli</it>, and 97.51% for <it>C. elegans</it>. Moreover, among the correctly predicted pairs, more than 80% were predicted with a high probability of ≥0.8, indicating that this tool could predict novel PPIs with high confidence.</p> <p>Conclusions</p> <p>Based on this work, a web-based system, Pred_PPI, was constructed for predicting PPIs from the five organisms. Users can predict novel PPIs and obtain a probability value about the prediction using this tool. Pred_PPI is freely available at <url>http://cic.scu.edu.cn/bioinformatics/predict_ppi/default.html</url>.</p

Urine cell-free DNA multi-omics to detect MRD and predict survival in bladder cancer patients

Circulating tumor DNA (ctDNA) sensitivity remains subpar for molecular residual disease (MRD) detection in bladder cancer patients. To remedy this problem, we focused on the biofluid most proximal to the disease, urine, and analyzed urine tumor DNA in 74 localized bladder cancer patients. We integrated ultra-low-pass whole genome sequencing (ULP-WGS) with urine cancer personalized profiling by deep sequencing (uCAPP-Seq) to achieve sensitive MRD detection and predict overall survival. Variant allele frequency, inferred tumor mutational burden, and copy number-derived tumor fraction levels in urine cell-free DNA (cfDNA) significantly predicted pathologic complete response status, far better than plasma ctDNA was able to. A random forest model incorporating these urine cfDNA-derived factors with leave-one-out cross-validation was 87% sensitive for predicting residual disease in reference to gold-standard surgical pathology. Both progression-free survival (HR = 3.00, p = 0.01) and overall survival (HR = 4.81, p = 0.009) were dramatically worse by Kaplan-Meier analysis for patients predicted by the model to have MRD, which was corroborated by Cox regression analysis. Additional survival analyses performed on muscle-invasive, neoadjuvant chemotherapy, and held-out validation subgroups corroborated these findings. In summary, we profiled urine samples from 74 patients with localized bladder cancer and used urine cfDNA multi-omics to detect MRD sensitively and predict survival accurately

Optically-Induced Antiferromagnetic Order in Mie-Resonant Dielectric Metasurfaces

We study silicon-based metasurfaces with complex unit cells composed of Mie-resonant dielectric nanodisks and nanorings and observe experimentally a signature of optical response with a staggered structure of optically-induced magnetic dipole moments, associated with the so-called optical antiferromagnetic order.The authors acknowledge a financial support from the Thuringian State Government within its ProExcellence Initiative (ACP2020), and the German Research Foundation DFG (STA 1426/2-1; project number 27 87 47 906). YK acknowledges a financial support from the Australian Research Council, the Alexander von Humboldt Foundation, and the Strategic Fund of the Australian National University, and also useful discussions with B. Lukyanchuk

Genetic and environmental determinants of diastolic heart function

Diastole is the sequence of physiological events that occur in the heart during ventricular filling and principally depends on myocardial relaxation and chamber stiffness. Abnormal diastolic function is related to many cardiovascular disease processes and is predictive of health outcomes, but its genetic architecture is largely unknown. Here, we use machine learning cardiac motion analysis to measure diastolic functional traits in 39,559 participants of the UK Biobank and perform a genome-wide association study. We identified 9 significant, independent loci near genes that are associated with maintaining sarcomeric function under biomechanical stress and genes implicated in the development of cardiomyopathy. Age, sex and diabetes were independent predictors of diastolic function and we found a causal relationship between genetically-determined ventricular stiffness and incident heart failure. Our results provide insights into the genetic and environmental factors influencing diastolic function that are relevant for identifying causal relationships and potential tractable targets