Inverse design and implementation of a wavelength demultiplexing grating coupler

Nanophotonics has emerged as a powerful tool for manipulating light on chips. Almost all of today's devices, however, have been designed using slow and ineffective brute-force search methods, leading in many cases to limited device performance. In this article, we provide a complete demonstration of our recently proposed inverse design technique, wherein the user specifies design constraints in the form of target fields rather than a dielectric constant profile, and in particular we use this method to demonstrate a new demultiplexing grating. The novel grating, which has not been developed using conventional techniques, accepts a vertical-incident Gaussian beam from a free-space and separates O-band $(1300\mathrm{nm})$ and C-band $(1550\mathrm{nm})$ light into separate waveguides. This inverse design concept is simple and extendable to a broad class of highly compact devices including frequency splitters, mode converters, and spatial mode multiplexers.Comment: 17 pages, 4 figures, 1 table. A supplementary section describing the inverse-design algorithm in detail has been added, in addition to minor corrections and updated reference

Airborne observations of the infrared emission bands

Earlier airborne studies of the infrared bands between 5 and 8 microns have now been extended to a sample of southern sources selected from the IRAS Low Resolution Spectra (LRS) atlas. The correlation between the strongest bands at 6.2 and 7.7 microns is now based on a total sample of 40 sources and is very strong. A new emission band at 5.2 microns, previously predicted for polycyclic aromatic hydrocarbons (PAHs), is recognized in 27 sources; it too correlates with the dominant 7.7 micron band, showing that the 5.2 micron feature also belongs to the generic spectrum of PAH features at 3.3, 5.6, 6.2, 6.2, 7.7, 8.7, 11.3, and 12.7 microns. Sufficient sources are had now to define the relative strengths of most of these bands in three separate nebular environments: planetaries, H II regions, and reflection nebulae. Significant variations are detected in the generic spectra of PAHs in these different environments which are echoed by variations in the exact wavelength of the strong 7.7 micron peak. The earlier suggestion that, in planetaries, the fraction of total emission observed by IRAS that is carried by the PAH emissions is correlated with nebular gas-phase C/O ratio is supported by the addition of newly-observed southern planetaries, including the unusually carbon-rich (WC10) nebular nuclei. These (WC10) nuclei also exhibit a strong plateau of emission linking the 6.2 and 7.7 micron features

Individual differences in the sensitivity to serotonergic drugs: a pharmacobehavioural approach using rats selected on the basis of their response to novelty

Contains fulltext : 76036.pdf (publisher's version ) (Closed access)15 p

A Brief Prehistory of Double Descent

In their thought-provoking paper [1], Belkin et al. illustrate and discuss the shape of risk curves in the context of modern high-complexity learners. Given a fixed training sample size $n$, such curves show the risk of a learner as a function of some (approximate) measure of its complexity $N$. With $N$ the number of features, these curves are also referred to as feature curves. A salient observation in [1] is that these curves can display, what they call, double descent: with increasing $N$, the risk initially decreases, attains a minimum, and then increases until $N$ equals $n$, where the training data is fitted perfectly. Increasing $N$ even further, the risk decreases a second and final time, creating a peak at $N=n$. This twofold descent may come as a surprise, but as opposed to what [1] reports, it has not been overlooked historically. Our letter draws attention to some original, earlier findings, of interest to contemporary machine learning

Pleiotropy of FRIGIDA enhances the potential for multivariate adaptation.

An evolutionary response to selection requires genetic variation; however, even if it exists, then the genetic details of the variation can constrain adaptation. In the simplest case, unlinked loci and uncorrelated phenotypes respond directly to multivariate selection and permit unrestricted paths to adaptive peaks. By contrast, 'antagonistic' pleiotropic loci may constrain adaptation by affecting variation of many traits and limiting the direction of trait correlations to vectors that are not favoured by selection. However, certain pleiotropic configurations may improve the conditions for adaptive evolution. Here, we present evidence that the Arabidopsis thaliana gene FRI (FRIGIDA) exhibits 'adaptive' pleiotropy, producing trait correlations along an axis that results in two adaptive strategies. Derived, low expression FRI alleles confer a 'drought escape' strategy owing to fast growth, low water use efficiency and early flowering. By contrast, a dehydration avoidance strategy is conferred by the ancestral phenotype of late flowering, slow growth and efficient water use during photosynthesis. The dehydration avoidant phenotype was recovered when genotypes with null FRI alleles were transformed with functional alleles. Our findings indicate that the well-documented effects of FRI on phenology result from differences in physiology, not only a simple developmental switch

Content-based microarray search using differential expression profiles

<p>Abstract</p> <p>Background</p> <p>With the expansion of public repositories such as the Gene Expression Omnibus (GEO), we are rapidly cataloging cellular transcriptional responses to diverse experimental conditions. Methods that query these repositories based on gene expression content, rather than textual annotations, may enable more effective experiment retrieval as well as the discovery of novel associations between drugs, diseases, and other perturbations.</p> <p>Results</p> <p>We develop methods to retrieve gene expression experiments that differentially express the same transcriptional programs as a query experiment. Avoiding thresholds, we generate differential expression profiles that include a score for each gene measured in an experiment. We use existing and novel dimension reduction and correlation measures to rank relevant experiments in an entirely data-driven manner, allowing emergent features of the data to drive the results. A combination of matrix decomposition and <it>p</it>-weighted Pearson correlation proves the most suitable for comparing differential expression profiles. We apply this method to index all GEO DataSets, and demonstrate the utility of our approach by identifying pathways and conditions relevant to transcription factors Nanog and FoxO3.</p> <p>Conclusions</p> <p>Content-based gene expression search generates relevant hypotheses for biological inquiry. Experiments across platforms, tissue types, and protocols inform the analysis of new datasets.</p