18,975 research outputs found

    Slitless spectrophotometry with forward modelling: principles and application to atmospheric transmission measurement

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    In the next decade, many optical surveys will aim to tackle the question of dark energy nature, measuring its equation of state parameter at the permil level. This requires trusting the photometric calibration of the survey with a precision never reached so far, controlling many sources of systematic uncertainties. The measurement of the on-site atmospheric transmission for each exposure, or on average for each season or for the full survey, can help reach the permil precision for magnitudes. This work aims at proving the ability to use slitless spectroscopy for standard star spectrophotometry and its use to monitor on-site atmospheric transmission as needed, for example, by the Vera C. Rubin Observatory Legacy Survey of Space and Time supernova cosmology program. We fully deal with the case of a disperser in the filter wheel, which is the configuration chosen in the Rubin Auxiliary Telescope. The theoretical basis of slitless spectrophotometry is at the heart of our forward model approach to extract spectroscopic information from slitless data. We developed a publicly available software called Spectractor (https://github.com/LSSTDESC/Spectractor) that implements each ingredient of the model and finally performs a fit of a spectrogram model directly on image data to get the spectrum. We show on simulations that our model allows us to understand the structure of spectrophotometric exposures. We also demonstrate its use on real data, solving specific issues and illustrating how our procedure allows the improvement of the model describing the data. Finally, we discuss how this approach can be used to directly extract atmospheric transmission parameters from data and thus provide the base for on-site atmosphere monitoring. We show the efficiency of the procedure on simulations and test it on the limited data set available.Comment: 30 pages, 36 figures, submitted to Astronomy and Astrophysic

    Using knowledge graphs to infer gene expression in plants

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    IntroductionClimate change is already affecting ecosystems around the world and forcing us to adapt to meet societal needs. The speed with which climate change is progressing necessitates a massive scaling up of the number of species with understood genotype-environment-phenotype (G×E×P) dynamics in order to increase ecosystem and agriculture resilience. An important part of predicting phenotype is understanding the complex gene regulatory networks present in organisms. Previous work has demonstrated that knowledge about one species can be applied to another using ontologically-supported knowledge bases that exploit homologous structures and homologous genes. These types of structures that can apply knowledge about one species to another have the potential to enable the massive scaling up that is needed through in silico experimentation.MethodsWe developed one such structure, a knowledge graph (KG) using information from Planteome and the EMBL-EBI Expression Atlas that connects gene expression, molecular interactions, functions, and pathways to homology-based gene annotations. Our preliminary analysis uses data from gene expression studies in Arabidopsis thaliana and Populus trichocarpa plants exposed to drought conditions.ResultsA graph query identified 16 pairs of homologous genes in these two taxa, some of which show opposite patterns of gene expression in response to drought. As expected, analysis of the upstream cis-regulatory region of these genes revealed that homologs with similar expression behavior had conserved cis-regulatory regions and potential interaction with similar trans-elements, unlike homologs that changed their expression in opposite ways.DiscussionThis suggests that even though the homologous pairs share common ancestry and functional roles, predicting expression and phenotype through homology inference needs careful consideration of integrating cis and trans-regulatory components in the curated and inferred knowledge graph

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    A splitting property of the chromatic homology of the complete graph

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    Khovanov introduced a bigraded cohomology theory of links whose graded Euler characteristic is the Jones polynomial. The theory was subsequently applied to the chromatic polynomial of graph, resulting in a categorification known as the ``chromatic homology''. Much as in the Khovanov homology, the chromatic polynomial can be obtained by taking the Euler characteristic of the chromatic homology. In the present paper, we introduce a combinatorial description of enhanced states that can be applied to analysis of the homology in an explicit way by hand. Using the new combinatorial description, we show a splitting property of the chromatic homology for a certain class of graphs. Finally, as an application of the description, we compute the chromatic homology of the complete graph.Comment: 20 pages, 5 figure

    Form and Tonal Spectrum in 12-Tone Music: Approaches to Analysis in Schoenberg, Walker, and Webern

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    Approaches to analysis in 12-tone music have been predominantly focused around the concept of atonality. Building off of ideas first imagined by theorists such as Heinrich Schenker and Arnold Schoenberg, I propose that all music can be understood as tonal using nature’s model, the overtone series. Through a detailed description of the organic nature of tonality, my work suggests that what was once understood as a dissonance can be reimagined as a new type of consonance. Analyzing passages of 12-tone music from Arnold Schoenberg, George Walker, and Anton Webern, I provide a means for expanding upon traditional Schenkerian Analysis, which has been traditionally limited to music of the 18th and 19th century. I suggest that all music with “tones” can be considered tonal, and that background-level graphs representing higher partials can be used to categorize musical passages as “more” or “less” tonal, in a traditional sense

    Transferring Compactness

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    We demonstrate that the technology of Radin forcing can be used to transfer compactness properties at a weakly inaccessible but not strong limit cardinal to a strongly inaccessible cardinal. As an application, relative to the existence of large cardinals, we construct a model of set theory in which there is a cardinal κ\kappa that is nn-dd-stationary for all nωn\in \omega but not weakly compact. This is in sharp contrast to the situation in the constructible universe LL, where κ\kappa being (n+1)(n+1)-dd-stationary is equivalent to κ\kappa being Πn1\mathbf{\Pi}^1_n-indescribable. We also show that it is consistent that there is a cardinal κ2ω\kappa\leq 2^\omega such that Pκ(λ)P_\kappa(\lambda) is nn-stationary for all λκ\lambda\geq \kappa and nωn\in \omega, answering a question of Sakai.Comment: Corrected some typo

    Introduction to Facial Micro Expressions Analysis Using Color and Depth Images: A Matlab Coding Approach (Second Edition, 2023)

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    The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment. FMER is a subset of image processing and it is a multidisciplinary topic to analysis. So, it requires familiarity with other topics of Artifactual Intelligence (AI) such as machine learning, digital image processing, psychology and more. So, it is a great opportunity to write a book which covers all of these topics for beginner to professional readers in the field of AI and even without having background of AI. Our goal is to provide a standalone introduction in the field of MFER analysis in the form of theorical descriptions for readers with no background in image processing with reproducible Matlab practical examples. Also, we describe any basic definitions for FMER analysis and MATLAB library which is used in the text, that helps final reader to apply the experiments in the real-world applications. We believe that this book is suitable for students, researchers, and professionals alike, who need to develop practical skills, along with a basic understanding of the field. We expect that, after reading this book, the reader feels comfortable with different key stages such as color and depth image processing, color and depth image representation, classification, machine learning, facial micro-expressions recognition, feature extraction and dimensionality reduction. The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment.Comment: This is the second edition of the boo

    Topological Characterization of Consensus Solvability in Directed Dynamic Networks

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    Consensus is one of the most fundamental problems in distributed computing. This paper studies the consensus problem in a synchronous dynamic directed network, in which communication is controlled by an oblivious message adversary. The question when consensus is possible in this model has already been studied thoroughly in the literature from a combinatorial perspective, and is known to be challenging. This paper presents a topological perspective on consensus solvability under oblivious message adversaries, which provides interesting new insights. Our main contribution is a topological characterization of consensus solvability, which also leads to explicit decision procedures. Our approach is based on the novel notion of a communication pseudosphere, which can be seen as the message-passing analog of the well-known standard chromatic subdivision for wait-free shared memory systems. We further push the elegance and expressiveness of the "geometric" reasoning enabled by the topological approach by dealing with uninterpreted complexes, which considerably reduce the size of the protocol complex, and by labeling facets with information flow arrows, which give an intuitive meaning to the implicit epistemic status of the faces in a protocol complex

    On uniquely packable trees

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    An ii-packing in a graph GG is a set of vertices that are pairwise distance more than ii apart. A \emph{packing colouring} of GG is a partition X={X1,X2,,Xk}X=\{X_{1},X_{2},\ldots,X_{k}\} of V(G)V(G) such that each colour class XiX_{i} is an ii-packing. The minimum order kk of a packing colouring is called the packing chromatic number of GG, denoted by χρ(G)\chi_{\rho}(G). In this paper we investigate the existence of trees TT for which there is only one packing colouring using χρ(T)\chi_\rho(T) colours. For the case χρ(T)=3\chi_\rho(T)=3, we completely characterise all such trees. As a by-product we obtain sets of uniquely 33-χρ\chi_\rho-packable trees with monotone χρ\chi_{\rho}-coloring and non-monotone χρ\chi_{\rho}-coloring respectively
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