Miracle-dz/UniFAR-FTUs huggingface dataset:
The faint host galaxies of C IV absorbers at z > 5

We explore the expected galaxy environments of $\mathrm{C\, \small {IV}}$ absorbers at z > 5 using the Technicolor Dawn simulations. These simulations reproduce the observed history of reionization, the z ∼ 6 galaxy stellar mass function, the Ly α forest transmission at z > 5, and the $\mathrm{Si\, \small {IV}}$ column density distribution (CDD) at z ≈ 5.5. None the less, the $\mathrm{C\, \small {IV}}$ CDD remains underproduced. Comparison with observed $\mathrm{C\, \small {II}}/\mathrm{Si\, \small {II}}$ equivalent width ratios and the $\mathrm{C\, \small {II}}$ line incidence suggests that a low carbon yield accounts for some, but not all, of the $\mathrm{C\, \small {IV}}$ discrepancy. Alternatively, a density-bounded escape scenario could harden the metagalactic ionizing background more dramatically even than binary stellar evolution, boosting the $\mathrm{C\, \small {IV}}$ CDD into near agreement with observations. In this case, galaxies ionize more efficiently and fewer are required to host a given high-ionization absorber. Absorbers’ environments therefore constrain ionizing escape. Regardless of the escape scenario, galaxies correlate with $\mathrm{C\, \small {IV}}$ absorbers out to 300 proper kpc (pkpc). The correlation strengthens independently with galaxy luminosity and $\mathrm{C\, \small {IV}}$ column density. Around strong systems ($\log (N_\mathrm{C\, \small {IV}}/{\rm cm}^{-2})\gt 14)$), the overdensity of galaxies with MUV < −18 or log (LLy α/erg s−1) > 41.9 declines from 200–300 within 100 pkpc to 40–60 within 250 pkpc. The previously suggested association between strong $\mathrm{C\, \small {IV}}$ absorbers and Ly α emitters at z > 5 is not expected. It may arise if both populations inhabit large-scale voids, but for different reasons. Although most neighbouring galaxies are too faint for HST, JWST will, with a single pointing, identify ∼10 neighbouring galaxies per strong $\mathrm{C\, \small {IV}}$ absorber at z > 5. Ground-based tests of these predictions are possible via deep surveys for Ly α emission using integral field units.
The $\mathbf{Q}$-tensor Model with Uniaxial Constraint

This chapter is about the modeling of nematic liquid crystals (LCs) and their numerical simulation. We begin with an overview of the basic physics of LCs and discuss some of their many applications. Next, we delve into the modeling arguments needed to obtain macroscopic order parameters which can be used to formulate a continuum model. We then survey different continuum descriptions, namely the Oseen-Frank, Ericksen, and Landau-deGennes ($\mathbf{Q}$-tensor) models, which essentially model the LC material like an anisotropic elastic material. In particular, we review the mathematical theory underlying the three different continuum models and highlight the different trade-offs of using these models. Next, we consider the numerical simulation of these models with a survey of various methods, with a focus on the Ericksen model. We then show how techniques from the Ericksen model can be combined with the Landau-deGennes model to yield a $\mathbf{Q}$-tensor model that exactly enforces uniaxiality, which is relevant for modeling many nematic LC systems. This is followed by an in-depth numerical analysis, using tools from $\Gamma$-convergence, to justify our discrete method. We also show several numerical experiments and comparisons with the standard Landau-deGennes model.
6DLS: Modeling Non-Planar Frictional Surface Contacts for Grasping using 6D Limit Surfaces.

Robot grasping with deformable gripper jaws often results in non-planar surface contacts as the jaws deform to the object geometry and may lead to six-dimensional (6D) friction. We present a friction computation for curved surfaces by combining concepts of differential geometry and Coulomb friction. We propose two 6D limit surface (6DLS) models generalized from well-known three-dimensional LS (3DLS) models, which describe the friction-motion constraints for a single non-planar contact. We further introduce an algorithm to predict multi-contact grasp success by building a grasp wrench space with the 6DLS of each contact. Simulation results show that the 6DLS models reach up to 2.71% average fitting error with friction samples obtained from six parametric surfaces and 2473 meshed contacts from finite element method simulations of 24 objects and two types of gripper jaws. To evaluate the proposed 6DLS-based algorithm for grasp success prediction, we collect 280 physical grasps of ten 3D printed objects with a KUKA robot and a SCHUNK parallel-jaw gripper. Physical experiments suggest that the algorithm achieves 97.2% precision, a metric inversely related to false positive predictions, and 56.3% recall, a metric inversely related to false negative predictions. The 6DLS models increase recall by up to 15.3% over the 3DLS models without decreasing precision.
Generalized L$_p$-norm joint inversion of gravity and magnetic data using cross-gradient constraint

A generalized unifying approach for $L_{p}$-norm joint inversion of gravity and magnetic data using the cross-gradient constraint is presented. The presented framework incorporates stabilizers that use $L_{0}$, $L_{1}$, and $L_{2}$-norms of the model parameters, and/or the gradient of the model parameters. Furthermore, the formulation is developed from standard approaches for independent inversion of single data sets, and, thus, also facilitates the inclusion of necessary model and data weighting matrices that provide, for example, depth weighting and imposition of hard constraint data. The developed efficient algorithm can, therefore, be employed to provide physically-relevant smooth, sparse, or blocky target(s) which are relevant to the geophysical community. Here, the nonlinear objective function, that describes the inclusion of all stabilizing terms and the fit to data measurements, is minimized iteratively by imposing stationarity on the linear equation that results from applying linearization of the objective function about a starting model. To numerically solve the resulting linear system, at each iteration, the conjugate gradient algorithm is used. The general framework is then validated for three-dimensional synthetic models for both sparse and smooth reconstructions, and the results are compared with those of individual gravity and magnetic inversions. It is demonstrated that the presented joint inversion algorithm is practical and significantly improves reconstructed models obtained by independent inversion.
Censored Quantile Regression Forest

Random forests are powerful non-parametric regression method but are severely limited in their usage in the presence of randomly censored observations, and naively applied can exhibit poor predictive performance due to the incurred biases. Based on a local adaptive representation of random forests, we develop its regression adjustment for randomly censored regression quantile models. Regression adjustment is based on a new estimating equation that adapts to censoring and leads to quantile score whenever the data do not exhibit censoring. The proposed procedure named {\it censored quantile regression forest}, allows us to estimate quantiles of time-to-event without any parametric modeling assumption. We establish its consistency under mild model specifications. Numerical studies showcase a clear advantage of the proposed procedure.
The Very Best of Perfect Non-crossing Matchings

Given a set of points in the plane, we are interested in matching them with straight line segments. We focus on perfect (all points are matched) non-crossing (no two edges intersect) matchings. Apart from the well known MinMax variation, where the length of the longest edge is minimized, we extend work by looking into different optimization variants such as MaxMin, MinMin, and MaxMax. We consider both the monochromatic and bichromatic versions of these problems and provide efficient algorithms for various input point configurations.
Spectral gap and exponential mixing on geometrically finite hyperbolic manifolds

Let $\mathcal{M}=\Gamma\backslash\mathbb{H}^{d+1}$ be a geometrically finite hyperbolic manifold with critical exponent exceeding $d/2$. We obtain a precise asymptotic expansion of the matrix coefficients for the geodesic flow in $L^2(\mathrm{T}^1(\mathcal{M}))$, with exponential error term essentially as good as the one given by the spectral gap for the Laplace operator on $L^2(\mathcal{M})$ due to Lax and Phillips. Combined with the work of Bourgain, Gamburd, and Sarnak and its generalization by Golsefidy and Varju on expanders, this implies uniform exponential mixing for congruence covers of $\mathcal{M}$ when $\Gamma$ is a thin subgroup of $\mathrm{SO}^{\circ}(d+1,1)$. Our result implies that, with respect to the Bowen-Margulis-Sullivan measure, the geodesic flow on $\mathrm{T}^1(\mathcal{M})$ is exponentially mixing, uniformly over congruence covers in the case when $\Gamma$ is a thin subgroup.
Decentralized Optimization of Vehicle Route Planning—A Cross-City Comparative Study

The introduction of connected and autonomous vehicles enables new possibilities in vehicle routing: Knowing the origin and destination of each vehicle in the network can allow for coordinated real-time routing of the vehicles to optimize network performance. However, this relies on individual vehicles being “altruistic,” i.e., willing to accept alternative less-preferred routes. We conduct a study to compare different levels of agent altruism in decentralized vehicles coordination and the effect on the network-level traffic performance. This work introduces novel load-balancing scenarios of traffic flow in real-world cities for varied levels of agent altruism. We show evidence that the new decentralized optimization router is more effective with networks of high load.
Time-Varying Graph Learning with Constraints on Graph Temporal Variation

We propose a novel framework for learning time-varying graphs from spatiotemporal measurements. Given an appropriate prior on the temporal behavior of signals, our proposed method can estimate time-varying graphs from a small number of available measurements. To achieve this, we introduce two regularization terms in convex optimization problems that constrain sparseness of temporal variations of the time-varying networks. Moreover, a computationally-scalable algorithm is introduced to efficiently solve the optimization problem. The experimental results with synthetic and real datasets (point cloud and temperature data) demonstrate our proposed method outperforms the existing state-of-the-art methods.
A First-Order Optimization Algorithm for Statistical Learning with Hierarchical Sparsity Structure

In many statistical learning problems, it is desired that the optimal solution conform to an a priori known sparsity structure represented by a directed acyclic graph. Inducing such structures by means of convex regularizers requires nonsmooth penalty functions that exploit group overlapping. Our study focuses on evaluating the proximal operator of the latent overlapping group lasso developed by Jacob et al. in 2009. We implemented an alternating direction method of multiplier with a sharing scheme to solve large-scale instances of the underlying optimization problem efficiently. In the absence of strong convexity, global linear convergence of the algorithm is established using the error bound theory. More specifically, the paper contributes to establishing primal and dual error bounds when the nonsmooth component in the objective function does not have a polyhedral epigraph. We also investigate the effect of the graph structure on the speed of convergence of the algorithm. Detailed numerical simulation studies over different graph structures supporting the proposed algorithm and two applications in learning are provided. Summary of Contribution: The paper proposes a computationally efficient optimization algorithm to evaluate the proximal operator of a nonsmooth hierarchical sparsity-inducing regularizer and establishes its convergence properties. The computationally intensive subproblem of the proposed algorithm can be fully parallelized, which allows solving large-scale instances of the underlying problem. Comprehensive numerical simulation studies benchmarking the proposed algorithm against five other methods on the speed of convergence to optimality are provided. Furthermore, performance of the algorithm is demonstrated on two statistical learning applications related to topic modeling and breast cancer classification. The code along with the simulation studies and benchmarks are available on the corresponding author’s GitHub website for evaluation and future use.
Endoscopic clipping of a big periampullary perforation during endoscopic retrograde cholangiopancreatography

Abstract Perforation is an uncommon but serious complication during endoscopic retrograde cholangiopancreatography (ERCP). The incidence of perforation though small but is the one that requires immediate management. Delay in identifying this complication can lead to severe morbidity and even mortality in selected cases. If one can identify a perforation during the procedure (ERCP), then the minimally invasive technique such as clipping of the perforation in expert hands can save a lot of additional operative procedures and additional hospital stay costs. We present one such case of large perforation during precut sphincterotomy in a case of difficult cannulation. The perforation was identified and successfully treated by clipping the perforation during the procedure which leads to quick recovery and short hospital stay.
The chemical compositions of accreted and in situ galactic globular clusters according to SDSS/APOGEE

Studies of the kinematics and chemical compositions of Galactic globular clusters (GCs) enable the reconstruction of the history of star formation, chemical evolution, and mass assembly of the Galaxy. Using the latest data release (DR16) of the SDSS/APOGEE survey, we identify 3090 stars associated with 46 GCs. Using a previously defined kinematic association, we break the sample down into eight separate groups and examine how the kinematics-based classification maps into chemical composition space, considering only α (mostly Si and Mg) elements and Fe. Our results show that (i) the loci of both in situ and accreted subgroups in chemical space match those of their field counterparts; (ii) GCs from different individual accreted subgroups occupy the same locus in chemical space. This could either mean that they share a similar origin or that they are associated with distinct satellites which underwent similar chemical enrichment histories; (iii) the chemical compositions of the GCs associated with the low orbital energy subgroup defined by Massari and collaborators is broadly consistent with an in situ origin. However, at the low-metallicity end, the distinction between accreted and in situ populations is blurred; (iv) regarding the status of GCs whose origin is ambiguous, we conclude the following: the position in Si–Fe plane suggests an in situ origin for Liller 1 and a likely accreted origin for NGC 5904 and NGC 6388. The case of NGC 288 is unclear, as its orbital properties suggest an accretion origin, its chemical composition suggests it may have formed in situ.
Detrending Exoplanetary Transit Light Curves with Long Short-term Memory Networks

The precise derivation of transit depths from transit light curves is a key component for measuring exoplanet transit spectra, and henceforth for the study of exoplanet atmospheres. However, it is still deeply affected by various kinds of systematic errors and noise. In this paper we propose a new detrending method by reconstructing the stellar flux baseline during transit time. We train a probabilistic long short-term memory (LSTM) network to predict the next data point of the light curve during the out-of-transit, and use this model to reconstruct a transit-free light curve—i.e., including only the systematics—during the in-transit. By making no assumption about the instrument, and using only the transit ephemeris, this provides a general way to correct the systematics and perform a subsequent transit fit. The name of the proposed model is TLCD-LSTM, standing for transit light-curve detrending-LSTM. Here we present the first results on data from six transit observations of HD 189733b with the IRAC camera on board the Spitzer Space Telescope, and discuss some of its possible further applications.
Tidal Disruptions of Main-sequence Stars. V. The Varieties of Disruptions

Tidal disruption events (TDEs) are generally imagined as the complete disruption of a star when it passes close to a supermassive black hole (SMBH). Relativistic apsidal precession is thought to quickly “circularize” the bound debris, forming a compact accretion disk, which then emits a flare of standardized light curve and spectrum. We show here that this picture holds in only a minority of cases. TDEs are more diverse and can be grouped into several categories distinguished by stellar pericenter distance rp; we estimate the relative frequency of these categories. Rapid circularization is rare both because it requires ( ) and because most events with lead to direct capture. For larger pericenter distances, (for MBH = 106 M⊙), main-sequence stars with M* ≲ 3 are completely disrupted, but the bound debris orbits are highly eccentric and possess semimajor axes ∼100× the scale of the expected compact disk. Partial disruptions with fractional mass loss ≳10% occur with a rate similar to that of total disruptions; for fractional mass loss ≳50%, the rate is ≈1/3 as large. Partial disruptions—which must precede total disruptions when the stars’ angular momenta evolve in the “empty loss-cone” regime—change the orbital energy by factors ≳O(1). Partial disruption remnants are in general far from thermal equilibrium. Depending on its orbital energy and conditions within the stellar cluster surrounding the SMBH, a remnant may return after ∼O(100)–O(1000) yr and be fully disrupted, or it may rejoin the stellar cluster.
A Deep Neural Networks Approach for Pixel-Level Runway Pavement Crack Segmentation Using Drone-Captured Images

Pavement conditions are a critical aspect of asset management and directly affect safety. This study introduces a deep neural network method called U-Net for pavement crack segmentation based on drone-captured images to reduce the cost and time needed for airport runway inspection. The proposed approach can also be used for highway pavement conditions assessment during off-peak periods when there are few vehicles on the road. In this study, runway pavement images are collected using drone at various heights from the Fitchburg Municipal Airport (FMA) in Massachusetts to evaluate their quality and applicability for crack segmentation, from which an optimal height is determined. Drone images captured at the optimal height are then used to evaluate the crack segmentation performance of the U-Net model. Deep learning methods typically require a huge set of annotated training datasets for model development, which can be a major obstacle for their applications. An online annotated pavement image dataset is used together with the FMA data to train the U-Net model. The results show that U-Net performs well on the FMA testing data even with limited FMA training images, suggesting that it has good generalization ability and great potential to be used for both airport runways and highway pavements.
Downlink Fronthaul Compression in Frequency Domain using OpenAirInterface

This paper presents a compression scheme developed for the transport of downlink radio signals in packet fronthaul of centralized-radio access networks (C-RAN). The technique is tailored to frequency-domain functional splits, in which inactive LTE (or 5G NR) resource elements may not be transmitted in the packets. This allows decreasing the link data rate, especially when the cell load is low. The compression scheme is based on two parts: transmission of side information to indicate active and inactive resource elements of the LTE, and nonuniform scalar quantization to compress the QAM symbols of the active resource elements. The method was implemented in the OpenAirInterface (OAI) software for real-time evaluation. The testbed results show a significant reduction in link usage with a low computational cost.
Video Coding for Machines: A Paradigm of Collaborative Compression and Intelligent Analytics

Video coding, which targets to compress and reconstruct the whole frame, and feature compression, which only preserves and transmits the most critical information, stand at two ends of the scale. That is, one is with compactness and efficiency to serve for machine vision, and the other is with full fidelity, bowing to human perception. The recent endeavors in imminent trends of video compression, e.g. deep learning based coding tools and end-to-end image/video coding, and MPEG-7 compact feature descriptor standards, i.e. Compact Descriptors for Visual Search and Compact Descriptors for Video Analysis, promote the sustainable and fast development in their own directions, respectively. In this article, thanks to booming AI technology, e.g. prediction and generation models, we carry out exploration in the new area, Video Coding for Machines (VCM), arising from the emerging MPEG standardization efforts.1 Towards collaborative compression and intelligent analytics, VCM attempts to bridge the gap between feature coding for machine vision and video coding for human vision. Aligning with the rising Analyze then Compress instance Digital Retina, the definition, formulation, and paradigm of VCM are given first. Meanwhile, we systematically review state-of-the-art techniques in video compression and feature compression from the unique perspective of MPEG standardization, which provides the academic and industrial evidence to realize the collaborative compression of video and feature streams in a broad range of AI applications. Finally, we come up with potential VCM solutions, and the preliminary results have demonstrated the performance and efficiency gains. Further direction is discussed as well.1https://lists.aau. at/mailman/listinfo/mpeg-vcm
The evolution of mechanisms involved in vertebrate endothermy

Endothermy, i.e. the endogenous production of metabolic heat, has evolved multiple times among vertebrates, and several strategies of heat production have been studied extensively by physiologists over the course of the twentieth century. The independent acquisition of endothermy by mammals and birds has been the subject of many hypotheses regarding their origin and associated evolutionary constraints. Many groups of vertebrates, however, are thought to possess other mechanisms of heat production, and alternative ways to regulate thermogenesis that are not always considered in the palaeontological literature. Here, we perform a review of the mechanisms involved in heat production, with a focus on cellular and molecular mechanisms, in a phylogenetic context encompassing the entire vertebrate diversity. We show that endothermy in mammals and birds is not as well defined as commonly assumed by evolutionary biologists and consists of a vast array of physiological strategies, many of which are currently unknown. We also describe strategies found in other vertebrates, which may not always be considered endothermy, but nonetheless correspond to a process of active thermogenesis. We conclude that endothermy is a highly plastic character in vertebrates and provides a guideline on terminology and occurrences of the different types of heat production in vertebrate evolution. This article is part of the theme issue ‘Vertebrate palaeophysiology’.