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This work presents the design of Stickbug, a six-armed, multi-agent, precision pollination robot that combines the accuracy of single-agent systems with swarm parallelization in greenhouses. Precision pollination robots have often been proposed to offset the effects of a decreasing population of natural pollinators, but they frequently lack the required parallelization and scalability. Stickbug achieves this by allowing each arm and drive base to act as an individual agent, significantly reducing planning complexity. Stickbug uses a compact holonomic Kiwi drive to navigate narrow greenhouse rows, a tall mast to support multiple manipulators and reach plant heights, a detection model and classifier to identify Bramble flowers, and a felt-tipped end-effector for contact-based pollination. Initial experimental validation demonstrates that Stickbug can attempt over 1.5 pollinations per minute with a 50% success rate. Additionally, a Bramble flower perception dataset was created and is publicly available alongside Stickbug's software and design files., Comment: 7 pages, 7 figures

In this paper we introduce the software package ProPane, written for the R data analysis language. ProPane combines the full range of wcslib projections with the C++ image manipulation routines provided by the CImg library. ProPane offers routines for image warping and combining (including stacking), and various related tasks such as image alignment tweaking and pixel masking. It can stack an effectively unlimited number of target frames using multiple parallel cores, and offers threading for many lower level routines. It has been used for a number of current and upcoming large surveys, and we present a range of its capabilities and features. ProPane is already available under a permissive open-source LGPL-3 license at github.com/asgr/ProPane (DOI: 10.5281/zenodo.10057053)., Comment: 19 pages, 17 figures, 5 tables, accepted to MNRAS

We present deep 1.4 GHz source counts from $\sim$5 deg$^2$ of the continuum Early Science data release of the MeerKAT International Gigahertz Tiered Extragalactic Exploration (MIGHTEE) survey down to $S_{1.4\textrm{GHz}}\sim$15 $\mu$Jy. Using observations over two extragalactic fields (COSMOS and XMM-LSS), we provide a comprehensive investigation into correcting the incompleteness of the raw source counts within the survey to understand the true underlying source count population. We use a variety of simulations that account for: errors in source detection and characterisation, clustering, and variations in the assumed source model used to simulate sources within the field and characterise source count incompleteness. We present these deep source count distributions and use them to investigate the contribution of extragalactic sources to the sky background temperature at 1.4 GHz using a relatively large sky area. We then use the wealth of ancillary data covering{a subset of the COSMOS field to investigate the specific contributions from both active galactic nuclei (AGN) and star forming galaxies (SFGs) to the source counts and sky background temperature. We find, similar to previous deep studies, that we are unable to reconcile the sky temperature observed by the ARCADE 2 experiment. We show that AGN provide the majority contribution to the sky temperature contribution from radio sources, but the relative contribution of SFGs rises sharply below 1 mJy, reaching an approximate 15-25% contribution to the total sky background temperature ($T_b\sim$100 mK) at $\sim$15 $\mu$Jy., Comment: 24 pages, 12 figures; Accepted for publication in MNRAS

We present the evolution of the star-formation dispersion - stellar mass relation ($\sigma_{SFR}$-M$_{\star}$) in the DEVILS D10 region using new measurements derived using the ProSpect spectral energy distribution fitting code. We find that $\sigma_{SFR}$-M$_{\star}$ shows the characteristic 'U-shape' at intermediate stellar masses from 0.1

Motivated by a recent series of diametrically opposed articles on the relative value of statistical methods for the analysis of path diagrams in the social sciences, we discuss from a primarily theoretical perspective selected fundamental aspects of path modeling and analysis based on a common re reflexive setting. Since there is a paucity of technical support evident in the debate, our aim is to connect it to mainline statistics literature and to address selected foundational issues that may help move the discourse. We do not intend to advocate for or against a particular method or analysis philosophy.

Huber regression (HR) is a popular robust alternative to the least squares regression when the error follows a heavy-tailed distribution. We propose a new method called the enveloped Huber regression (EHR) by considering the envelope assumption that there exists some subspace of the predictors that has no association with the response, which is referred to as the immaterial part. More efficient estimation is achieved via the removal of the immaterial part. Different from the envelope least squares (ENV) model whose estimation is based on maximum normal likelihood, the estimation of the EHR model is through Generalized Method of Moments. The asymptotic normality of the EHR estimator is established, and it is shown that EHR is more efficient than HR. Moreover, EHR is more efficient than ENV when the error distribution is heavy-tailed, while maintaining a small efficiency loss when the error distribution is normal. Moreover, our theory also covers the heteroscedastic case in which the error may depend on the covariates. Extensive simulation studies confirm the messages from the asymptotic theory. EHR is further illustrated on a real dataset.

We use the extended GALEX Arecibo SDSS Survey (xGASS) to quantify the relationship between atomic hydrogen (HI) reservoir and current star formation rate (SFR) for central disk galaxies. This is primarily motivated by recent claims for the existence, in this sample, of a large population of passive disks harbouring HI reservoirs as large as those observed in main sequence galaxies. Across the stellar mass range 10$^{9}<$M$_{*}$/M$_{\odot}<$10$^{11}$, we practically find no passive ($\gtrsim$2$\sigma$ below the star-forming main sequence) disk galaxies with HI reservoirs comparable to those typical of star-forming systems. Even including HI non detections at their upper limits, passive disks typically have $\geq$0.5 dex less HI than their active counterparts. We show that previous claims are due to the use of aperture-corrected SFR estimates from the MPA/JHU SDSS DR7 catalog, which do not provide a fair representation of the global SFR of HI-rich galaxies with extended star-forming disks. Our findings confirm that the bulk of the passive disk population in the local Universe is HI-poor. These also imply that the reduction of star formation, even in central disk galaxies, has to be accompanied by a reduction in their HI reservoir., Comment: 6 pages, 3 figures. Accepted for publication in Monthly Notices of the Royal Astronomical Society Letters

Sliced inverse regression is a popular tool for sufficient dimension reduction, which replaces covariates with a minimal set of their linear combinations without loss of information on the conditional distribution of the response given the covariates. The estimated linear combinations include all covariates, making results difficult to interpret and perhaps unnecessarily variable, particularly when the number of covariates is large. In this paper, we propose a convex formulation for fitting sparse sliced inverse regression in high dimensions. Our proposal estimates the subspace of the linear combinations of the covariates directly and performs variable selection simultaneously. We solve the resulting convex optimization problem via the linearized alternating direction methods of multiplier algorithm, and establish an upper bound on the subspace distance between the estimated and the true subspaces. Through numerical studies, we show that our proposal is able to identify the correct covariates in the high-dimensional setting.

This paper proposes a low order geometrically exact flexible beam formulation based on the utilisation of generic beam shape functions to approximate distributed kinematic properties of the deformed structure. The proposed nonlinear beam shapes approach is in contrast to the majority of geometrically nonlinear treatments in the literature in which element based --- and hence high order --- discretisations are adopted. The kinematic quantities approximated specifically pertain to shear and extensional gradients as well as local orientation parameters based on an arbitrary set of globally referenced attitude parameters. In developing the dynamic equations of motion, an Euler angle parameterisation is selected as it is found to yield fast computational performance. The resulting dynamic formulation is closed using an example shape function set satisfying the single generic kinematic constraint. The formulation is demonstrated via its application to the modelling of a series of static and dynamic test cases of both simple and non-prismatic structures; the simulated results are verified using MSC Nastran and an element-based intrinsic beam formulation. Through these examples it is shown that the nonlinear beam shapes approach is able to accurately capture the beam behaviour with a very minimal number of system states.

We investigate the application of sufficient dimension reduction (SDR) to a noiseless data set derived from a deterministic function of several variables. In this context, SDR provides a framework for ridge recovery. In this second part, we explore the numerical subtleties associated with using two inverse regression methods---sliced inverse regression (SIR) and sliced average variance estimation (SAVE)---for ridge recovery. This includes a detailed numerical analysis of the eigenvalues of the resulting matrices and the subspaces spanned by their columns. After this analysis, we demonstrate the methods on several numerical test problems., Comment: The content has been combined with arXiv:1702.02227

We have acquired radio continuum data between 70\,MHz and 48\,GHz for a sample of 19 southern starburst galaxies at moderate redshifts ($0.067 < z < 0.227$) with the aim of separating synchrotron and free-free emission components. Using a Bayesian framework we find the radio continuum is rarely characterised well by a single power law, instead often exhibiting low frequency turnovers below 500\,MHz, steepening at mid-to-high frequencies, and a flattening at high frequencies where free-free emission begins to dominate over the synchrotron emission. These higher order curvature components may be attributed to free-free absorption across multiple regions of star formation with varying optical depths. The decomposed synchrotron and free-free emission components in our sample of galaxies form strong correlations with the total-infrared bolometric luminosities. Finally, we find that without accounting for free-free absorption with turnovers between 90 to 500\,MHz the radio-continuum at low frequency ($\nu < 200$\,MHz) could be overestimated by upwards of a factor of twelve if a simple power law extrapolation is used from higher frequencies. The mean synchrotron spectral index of our sample is constrained to be $\alpha=-1.06$, which is steeper then the canonical value of $-0.8$ for normal galaxies. We suggest this may be caused by an intrinsically steeper cosmic ray distribution.

We consider the design of dimensional analysis experiments when there is more than a single response. We first give a brief overview of dimensional analysis experiments and the dimensional analysis (DA) procedure. The validity of the DA method for univariate responses was established by the Buckingham $\Pi$-Theorem in the early 20th century. We extend the theorem to the multivariate case, develop basic criteria for multivariate design of DA and give guidelines for design construction. Finally, we illustrate the construction of designs for DA experiments for an example involving the design of a heat exchanger.

Parameter reduction can enable otherwise infeasible design and uncertainty studies with modern computational science models that contain several input parameters. In statistical regression, techniques for sufficient dimension reduction (SDR) use data to reduce the predictor dimension of a regression problem. A computational scientist hoping to use SDR for parameter reduction encounters a problem: a computer prediction is best represented by a deterministic function of the inputs, so data comprised of computer simulation queries fail to satisfy the SDR assumptions. To address this problem, we interpret SDR methods sliced inverse regression (SIR) and sliced average variance estimation (SAVE) as estimating the directions of a ridge function, which is a composition of a low-dimensional linear transformation with a nonlinear function. Within this interpretation, SIR and SAVE estimate matrices of integrals whose column spaces are contained in the ridge directions' span; we analyze and numerically verify convergence of these column spaces as the number of computer model queries increases. Moreover, we show example functions that are not ridge functions but whose inverse conditional moment matrices are low-rank. Consequently, the computational scientist should beware when using SIR and SAVE for parameter reduction, since SIR and SAVE may mistakenly suggest that truly important directions are unimportant.

Precise estimation of expected Darwinian fitness, the expected lifetime number of offspring of organism, is a central component of life history analysis. The aster model serves as a defensible statistical model for distributions of Darwinian fitness. The aster model is equipped to incorporate the major life stages an organism travels through which separately may effect Darwinian fitness. Envelope methodology reduces asymptotic variability by establishing a link between unknown parameters of interest and the asymptotic covariance matrices of their estimators. It is known both theoretically and in applications that incorporation of envelope methodology reduces asymptotic variability. We develop an envelope framework, including a new envelope estimator, that is appropriate for aster analyses. The level of precision provided from our methods allows researchers to draw stronger conclusions about the driving forces of Darwinian fitness from their life history analyses than they could with the aster model alone. Our methods are illustrated on a simulated dataset and a life history analysis of \emph{Mimulus guttatus} flowers is provided. Useful variance reduction is obtained in both analyses., Comment: Title changed from "An Application of Envelope Methodology and Aster Models" to "Combining Envelope Methodology and Aster Models for Variance Reduction in Life History Analyses"

Envelope methodology can provide substantial efficiency gains in multivariate statistical problems, but in some applications the estimation of the envelope dimension can induce selection volatility that may mitigate those gains. Current envelope methodology does not account for the added variance that can result from this selection. In this article, we circumvent dimension selection volatility through the development of a weighted envelope estimator. Theoretical justification is given for our estimator and validity of the residual bootstrap for estimating its asymptotic variance is established. A simulation study and an analysis on a real data set illustrate the utility of our weighted envelope estimator.

Modern technology often generates data with complex structures in which both response and explanatory variables are matrix-valued. Existing methods in the literature are able to tackle matrix-valued predictors but are rather limited for matrix-valued responses. In this article, we study matrix-variate regressions for such data, where the response Y on each experimental unit is a random matrix and the predictor X can be either a scalar, a vector, or a matrix, treated as non-stochastic in terms of the conditional distribution Y|X. We propose models for matrix-variate regressions and then develop envelope extensions of these models. Under the envelope framework, redundant variation can be eliminated in estimation and the number of parameters can be notably reduced when the matrix-variate dimension is large, possibly resulting in significant gains in efficiency. The proposed methods are applicable to high dimensional settings., Comment: 28 pages, 4 figures

Magnetization and muon spin relaxation or rotation (muSR) measurements have been performed to study the superconducting and magnetic properties of Sr3Ir4Sn13. From magnetization measurements the lower and upper critical fields of Sr3Ir4Sn13 are found to be 81(1) Oe and 14.4(2) kOe, respectively. Zero-field muSR data show no sign of any magnetic ordering or weak magnetism in Sr3Ir4Sn13. Transverse-field muSR measurements in the vortex state provided the temperature dependence of the magnetic penetration depth. The dependence of penetration depth with temperature is consistent with the existence of single s-wave energy gap in the superconducting state of Sr3Ir4Sn13 with a gap value of 0.82(2) meV at absolute zero temperature. The magnetic penetration depth at zero temperature is 291(3) nm. The gap to Tc ratio is 2.1(1), indicates that Sr3Ir4Sn13 should be considered as a strong-coupling superconductor., Comment: 6 pages, 5 figures

Envelopes were recently proposed as methods for reducing estimative variation in multivariate linear regression. Estimation of an envelope usually involves optimization over Grassmann manifolds. We propose a fast and widely applicable one-dimensional (1D) algorithm for estimating an envelope in general. We reveal an important structural property of envelopes that facilitates our algorithm, and we prove both Fisher consistency and root-n-consistency of the algorithm., Comment: 30 pages, 2 figures, 2 tables

Sufficient dimension reduction (SDR) in regression, which reduces the dimension by replacing original predictors with a minimal set of their linear combinations without loss of information, is very helpful when the number of predictors is large. The standard SDR methods suffer because the estimated linear combinations usually consist of all original predictors, making it difficult to interpret. In this paper, we propose a unified method - coordinate-independent sparse estimation (CISE) - that can simultaneously achieve sparse sufficient dimension reduction and screen out irrelevant and redundant variables efficiently. CISE is subspace oriented in the sense that it incorporates a coordinate-independent penalty term with a broad series of model-based and model-free SDR approaches. This results in a Grassmann manifold optimization problem and a fast algorithm is suggested. Under mild conditions, based on manifold theories and techniques, it can be shown that CISE would perform asymptotically as well as if the true irrelevant predictors were known, which is referred to as the oracle property. Simulation studies and a real-data example demonstrate the effectiveness and efficiency of the proposed approach., Comment: Published in at http://dx.doi.org/10.1214/10-AOS826 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

We study the asymptotic behavior of a class of methods for sufficient dimension reduction in high-dimension regressions, as the sample size and number of predictors grow in various alignments. It is demonstrated that these methods are consistent in a variety of settings, particularly in abundant regressions where most predictors contribute some information on the response, and oracle rates are possible. Simulation results are presented to support the theoretical conclusion., Comment: Published in at http://dx.doi.org/10.1214/11-AOS962 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

We show that under a linearity condition on the distribution of the predictors, the coefficient in single-index regression can be estimated with the same efficiency as in the case when the link function is known. Thus, the linearity condition seems to substitute for knowing the exact conditional distribution of the response given the linear combinations of the predictors., Comment: 10 pages

(abridged) We present a new near-infrared survey covering the 2 deg sq COSMOS field. Combining our survey with Subaru B and z images we construct a deep, wide-field optical-infrared catalogue. At Ks<23 (AB magnitudes) our survey completeness is greater than 90% and 70% for stars and galaxies respectively and contains 143,466 galaxies and 13,254 stars. At z~2 our catalogues contain 3931 quiescent and 25,757 star-forming BzK-selected galaxies representing the largest and most secure sample of these objects to date. Our counts of quiescent galaxies turns over at Ks~22 an effect which we demonstrate cannot be due to sample incompleteness. In our survey both the number of faint and bright quiescent objects exceeds the predictions of a semi-analytic galaxy formation model, indicating potentially the need for further refinements in the amount of merging and AGN feedback at z~2 in these models. We measure the angular correlation function for each sample and find that at small scales the correlation function for passive BzK galaxies exceeds the clustering of dark matter. We use 30-band photometric redshifts to derive the spatial correlation length and the redshift distributions for each object class. At Ks<22 we find r_0^{\gamma/1.8}=7.0 +/-0.5h^{-1} Mpc for the passive BzK candidates and 4.7+/-0.8h^{-1} Mpc for the star-forming BzK galaxies. Our pBzK galaxies have an average photometric redshift of z_p~1.4, in approximate agreement with the limited spectroscopic information currently available. The stacked Ks image will be made publicly available from IRSA., Comment: Accepted for publication in Astrophysical Journal. 17 pages, 17 figures, minor revisions to match published version available at http://adsabs.harvard.edu/abs/2010ApJ...708..202M

We provide a remedy for two concerns that have dogged the use of principal components in regression: (i) principal components are computed from the predictors alone and do not make apparent use of the response, and (ii) principal components are not invariant or equivariant under full rank linear transformation of the predictors. The development begins with principal fitted components [Cook, R. D. (2007). Fisher lecture: Dimension reduction in regression (with discussion). Statist. Sci. 22 1--26] and uses normal models for the inverse regression of the predictors on the response to gain reductive information for the forward regression of interest. This approach includes methodology for testing hypotheses about the number of components and about conditional independencies among the predictors., Comment: Published in at http://dx.doi.org/10.1214/08-STS275 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)