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Energy management is a critical aspect of risk assessment for Uncrewed Aerial Vehicle (UAV) flights, as a depleted battery during a flight brings almost guaranteed vehicle damage and a high risk of human injuries or property damage. Predicting the amount of energy a flight will consume is challenging as routing, weather, obstacles, and other factors affect the overall consumption. We develop a deep energy model for a UAV that uses Temporal Convolutional Networks to capture the time varying features while incorporating static contextual information. Our energy model is trained on a real world dataset and does not require segregating flights into regimes. We illustrate an improvement in power predictions by $29\%$ on test flights when compared to a state-of-the-art analytical method. Using the energy model, we can predict the energy usage for a given trajectory and evaluate the risk of running out of battery during flight. We propose using Conditional Value-at-Risk (CVaR) as a metric for quantifying this risk. We show that CVaR captures the risk associated with worst-case energy consumption on a nominal path by transforming the output distribution of Monte Carlo forward simulations into a risk space. Computing the CVaR on the risk-space distribution provides a metric that can evaluate the overall risk of a flight before take-off. Our energy model and risk evaluation method can improve flight safety and evaluate the coverage area from a proposed takeoff location. The video and codebase are available at https://youtu.be/PHXGigqilOA and https://git.io/cvar-risk ., Comment: 7 pages, 8 figures, Submitted ICRA 2021

Recent observations from the MUSTANG2 instrument on the Green Bank Telescope have revealed evidence of enhanced long-wavelength emission in the dust spectral energy distribution (SED) in the Orion Molecular Cloud (OMC) 2/3 filament on 25" (0.1 pc) scales. Here we present a measurement of the SED on larger spatial scales (map size 0.5-3 degrees or 3-20 pc), at somewhat lower resolution (120", corresponding to 0.25 pc at 400 pc) using data from the Herschel satellite and Atacama Cosmology Telescope (ACT). We then extend the 120"-scale investigation to other regions covered in the Herschel Gould Belt Survey (HGBS) specifically: the dense filaments in the southerly regions of Orion A; Orion B; and Serpens-S. Our dataset in aggregate covers approximately 10 square degrees, with continuum photometry spanning from 160um to 3mm. These OMC 2/3 data display excess emission at 3mm, though less (10.9% excess) than what is seen at higher resolution. Strikingly, we find that the enhancement is present even more strongly in the other filaments we targeted, with an average excess of 42.4% and 30/46 slices showing an inconsistency with the modified blackbody to at least 4{\sigma}. Applying this analysis to the other targeted regions, we lay the groundwork for future high-resolution analyses. Additionally, we also consider a two-component dust model motivated by Planck results and an amorphous grain dust model. While both of these have been proposed to explain deviations in emission from a generic modified blackbody (MBB), we find that they have significant drawbacks, requiring many spectral points or lacking experimental data coverage.

One of the main obstacles to 3D semantic segmentation is the significant amount of endeavor required to generate expensive point-wise annotations for fully supervised training. To alleviate manual efforts, we propose GIDSeg, a novel approach that can simultaneously learn segmentation from sparse annotations via reasoning global-regional structures and individual-vicinal properties. GIDSeg depicts global- and individual- relation via a dynamic edge convolution network coupled with a kernelized identity descriptor. The ensemble effects are obtained by endowing a fine-grained receptive field to a low-resolution voxelized map. In our GIDSeg, an adversarial learning module is also designed to further enhance the conditional constraint of identity descriptors within the joint feature distribution. Despite the apparent simplicity, our proposed approach achieves superior performance over state-of-the-art for inferencing 3D dense segmentation with only sparse annotations. Particularly, with $5\%$ annotations of raw data, GIDSeg outperforms other 3D segmentation methods., Comment: 8 pages, 7 figures, Accepted in IEEE Robotics and Automation Letters, 2021

We present a method for localizing a single camera with respect to a point cloud map in indoor and outdoor scenes. The problem is challenging because correspondences of local invariant features are inconsistent across the domains between image and 3D. The problem is even more challenging as the method must handle various environmental conditions such as illumination, weather, and seasonal changes. Our method can match equirectangular images to the 3D range projections by extracting cross-domain symmetric place descriptors. Our key insight is to retain condition-invariant 3D geometry features from limited data samples while eliminating the condition-related features by a designed Generative Adversarial Network. Based on such features, we further design a spherical convolution network to learn viewpoint-invariant symmetric place descriptors. We evaluate our method on extensive self-collected datasets, which involve \textit{Long-term} (variant appearance conditions), \textit{Large-scale} (up to $2km$ structure/unstructured environment), and \textit{Multistory} (four-floor confined space). Our method surpasses other current state-of-the-arts by achieving around $3$ times higher place retrievals to inconsistent environments, and above $3$ times accuracy on online localization. To highlight our method's generalization capabilities, we also evaluate the recognition across different datasets. With a single trained model, i3dLoc can demonstrate reliable visual localization in random conditions., Comment: 8 Pages, 8 Figures, Accepted Robotics: Science and Systems 2021 paper

To improve the efficiency and reduce the labour cost of the renovation process, this study presents a lightweight Convolutional Neural Network (CNN)-based architecture to extract crack-like features, such as cracks and joints. Moreover, Transfer Learning (TF) method was used to save training time while offering comparable prediction results. For three different objectives: 1) Detection of the concrete cracks; 2) Detection of natural stone cracks; 3) Differentiation between joints and cracks in natural stone; We built a natural stone dataset with joints and cracks information as complementary for the concrete benchmark dataset. As the results show, our model is demonstrated as an effective tool for industry use., Comment: 10 pages, 11 figures

A central notion in systems theory is dissipativity, which has been introduced by Jan Willems with the explicit goal of arriving at a fundamental understanding of the stability properties of feedback interconnections. In robust control, the framework of integral quadratic constraints (IQCs) builds on the seminal contributions of Yakubovich and Zames in the 1960's. It provides a technique for analyzing the stability of an interconnection of some linear system in feedback with a whole class of systems, also refereed to as uncertainty. In this paper we survey the key ideas of exploiting dissipativity and integral quadratic constraints for the computational analysis of robust stability and performance properties of uncertain interconnections in terms of linear matrix inequalities. In particular for dynamic supply rates, the paper revolves around the notion of finite-horizon integral quadratic constraints with a terminal cost. We reveal that this provides a seamless link between the general IQC theorem and dissipativity theory that has been established only rather recently.

A result due to Williams, Stampfli and Fillmore shows that an essential isometry $T$ on a Hilbert space $\mathcal{H}$ is a compact perturbation of an isometry if and only if ind$(T)\le 0$. A recent result of S. Chavan yields an analogous characterization of essential spherical isometries $T=(T_1,\dots,T_n)\in\mathcal{B}(\mathcal{H})^n$ with dim($\bigcap_{i=1}^n\ker(T_i))\le$ dim$(\bigcap_{i=1}^n\ker(T_i^*))$. In the present note we show that in dimension $n>1$ the result of Chavan holds without any condition on the dimensions of the joint kernels of $T$ and $T^*$., Comment: 3 pages

We prove that there is an action of the cyclic group $\mathbf{C}_2$ on the $10$-dimensional Floyd manifold which turns it into a conjugation manifold. The submanifold of fixed points is the $5$-dimensional Floyd manifold, whose cohomology is isomorphic to that of the large one, scaled down by dividing the cohomological degree by a factor two., Comment: 11 pages

This paper presents a fundamental algorithm, called VDB-EDT, for Euclidean distance transform (EDT) based on the VDB data structure. The algorithm executes on grid maps and generates the corresponding distance field for recording distance information against obstacles, which forms the basis of numerous motion planning algorithms. The contributions of this work mainly lie in three folds. Firstly, we propose a novel algorithm that can facilitate distance transform procedures by optimizing the scheduling priorities of transform functions, which significantly improves the running speed of conventional EDT algorithms. Secondly, we for the first time introduce the memory-efficient VDB data structure, a customed B+ tree, to represent the distance field hierarchically. Benefiting from the special index and caching mechanism, VDB shows a fast (average \textit{O}(1)) random access speed, and thus is very suitable for the frequent neighbor-searching operations in EDT. Moreover, regarding the small scale of existing datasets, we release a large-scale dataset captured from subterranean environments to benchmark EDT algorithms. Extensive experiments on the released dataset and publicly available datasets show that VDB-EDT can reduce memory consumption by about 30%-85%, depending on the sparsity of the environment, while maintaining a competitive running speed with the fastest array-based implementation. The experiments also show that VDB-EDT can significantly outperform the state-of-the-art EDT algorithm in both runtime and memory efficiency, which strongly demonstrates the advantages of our proposed method. The released dataset and source code are available on https://github.com/zhudelong/VDB-EDT.

We study the performance scaling of three quantum algorithms for combinatorial optimization: measurement-feedback coherent Ising machines (MFB-CIM), discrete adiabatic quantum computation (DAQC), and the D\"urr-Hoyer algorithm for quantum minimum finding (DH-QMF) that is based on Grover's search. We use MaxCut problems as our reference for comparison, and time-to-solution (TTS) as a practical measure of performance for these optimization algorithms. We empirically observe a $\Theta(2^{\sqrt{n}})$ scaling for the median TTS for MFB-CIM, in comparison to the exponential scaling with the exponent $n$ for DAQC and the provable $\widetilde{\mathcal O}\left(\sqrt{2^n}\right)$ scaling for DH-QMF. We conclude that these scaling complexities result in a dramatic performance advantage for MFB-CIM in comparison to the other two algorithms for solving MaxCut problems., Comment: 24 pages, 20 figures

The combination of machine learning with control offers many opportunities, in particular for robust control. However, due to strong safety and reliability requirements in many real-world applications, providing rigorous statistical and control-theoretic guarantees is of utmost importance, yet difficult to achieve for learning-based control schemes. We present a general framework for learning-enhanced robust control that allows for systematic integration of prior engineering knowledge, is fully compatible with modern robust control and still comes with rigorous and practically meaningful guarantees. Building on the established Linear Fractional Representation and Integral Quadratic Constraints framework, we integrate Gaussian Process Regression as a learning component and state-of-the-art robust controller synthesis. In a concrete robust control example, our approach is demonstrated to yield improved performance with more data, while guarantees are maintained throughout.

Gaussian Process Regression is a popular nonparametric regression method based on Bayesian principles that provides uncertainty estimates for its predictions. However, these estimates are of a Bayesian nature, whereas for some important applications, like learning-based control with safety guarantees, frequentist uncertainty bounds are required. Although such rigorous bounds are available for Gaussian Processes, they are too conservative to be useful in applications. This often leads practitioners to replacing these bounds by heuristics, thus breaking all theoretical guarantees. To address this problem, we introduce new uncertainty bounds that are rigorous, yet practically useful at the same time. In particular, the bounds can be explicitly evaluated and are much less conservative than state of the art results. Furthermore, we show that certain model misspecifications lead to only graceful degradation. We demonstrate these advantages and the usefulness of our results for learning-based control with numerical examples., Comment: Contains supplementary material and corrections to the original version

Object encoding and identification are crucial for many robotic tasks such as autonomous exploration and semantic relocalization. Existing works heavily rely on the tracking of detected objects but have difficulty recalling revisited objects precisely. In this paper, we propose a novel object encoding method, which is named as AirCode, based on a graph of key-points. To be robust to the number of key-points detected, we propose a feature sparse encoding and object dense encoding method to ensure that each key-point can only affect a small part of the object descriptors, leading it to be robust to viewpoint changes, scaling, occlusion, and even object deformation. In the experiments, we show that it achieves superior performance for object identification than the state-of-the-art algorithms and is able to provide reliable semantic relocalization. It is a plug-and-play module and we expect that it will play an important role in various applications., Comment: IEEE Robotics and Automation Letters (RA-L), 2022

Galaxy clusters are an important tool for cosmology, and their detection and characterization are key goals for current and future surveys. Using data from the Wide-field Infrared Survey Explorer (WISE), the Massive and Distant Clusters of WISE Survey (MaDCoWS) located 2,839 significant galaxy overdensities at redshifts $0.7\lesssim z\lesssim 1.5$, which included extensive follow-up imaging from the Spitzer Space Telescope to determine cluster richnesses. Concurrently, the Atacama Cosmology Telescope (ACT) has produced large area mm-wave maps in three frequency bands along with a large catalog of Sunyaev-Zeldovich (SZ) selected clusters, as part of its Data Release 5 (DR5). Using the maps and cluster catalog from DR5, we explore the scaling between SZ mass and cluster richness. We use complementary radio survey data from the Very Large Array, submillimeter data from Herschel, and ACT 224~GHz data to assess the impact of contaminating sources on the SZ signals. We then use a hierarchical Bayesian model to fit the mass-richness scaling relation. We find that MaDCoWS clusters have submillimeter contamination which is consistent with a gray-body spectrum, while the ACT clusters are consistent with no submillimeter emission on average. We find the best fit ACT SZ mass vs. MaDCoWS richness scaling relation has a slope of $\kappa = 1.84^{+0.15}_{-0.14}$, where the slope is defined as $M\propto \lambda_{15}^{\kappa}$ where $\lambda_{15}$ is the richness. Additionally, we find that the approximate level of in-fill of the ACT and MaDCoWS cluster SZ signals to be at the percent level, Comment: 25 pages, 17 Figures; accepted for publication in A&A

We propose Super Odometry, a high-precision multi-modal sensor fusion framework, providing a simple but effective way to fuse multiple sensors such as LiDAR, camera, and IMU sensors and achieve robust state estimation in perceptually-degraded environments. Different from traditional sensor-fusion methods, Super Odometry employs an IMU-centric data processing pipeline, which combines the advantages of loosely coupled methods with tightly coupled methods and recovers motion in a coarse-to-fine manner. The proposed framework is composed of three parts: IMU odometry, visual-inertial odometry, and laser-inertial odometry. The visual-inertial odometry and laser-inertial odometry provide the pose prior to constrain the IMU bias and receive the motion prediction from IMU odometry. To ensure high performance in real-time, we apply a dynamic octree that only consumes 10 % of the running time compared with a static KD-tree. The proposed system was deployed on drones and ground robots, as part of Team Explorer's effort to the DARPA Subterranean Challenge where the team won $1^{st}$ and $2^{nd}$ place in the Tunnel and Urban Circuits, respectively.

Structures suffer from the emergence of cracks, therefore, crack detection is always an issue with much concern in structural health monitoring. Along with the rapid progress of deep learning technology, image semantic segmentation, an active research field, offers another solution, which is more effective and intelligent, to crack detection Through numerous artificial neural networks have been developed to address the preceding issue, corresponding explorations are never stopped improving the quality of crack detection. This paper presents a novel artificial neural network architecture named Full Attention U-net for image semantic segmentation. The proposed architecture leverages the U-net as the backbone and adopts the Full Attention Strategy, which is a synthesis of the attention mechanism and the outputs from each encoding layer in skip connection. Subject to the hardware in training, the experiments are composed of verification and validation. In verification, 4 networks including U-net, Attention U-net, Advanced Attention U-net, and Full Attention U-net are tested through cell images for a competitive study. With respect to mean intersection-over-unions and clarity of edge identification, the Full Attention U-net performs best in verification, and is hence applied for crack semantic segmentation in validation to demonstrate its effectiveness.

We use Atacama Cosmology Telescope (ACT) observations at 98 GHz (2015--2019), 150 GHz (2013--2019) and 229 GHz (2017--2019) to perform a blind shift-and-stack search for Planet 9. The search explores distances from 300 AU to 2000 AU and velocities up to 6.3 arcmin per year, depending on the distance. For a 5 Earth-mass Planet 9 the detection limit varies from 325 AU to 625 AU, depending on the sky location. For a 10 Earth-mass planet the corresponding range is 425 AU to 775 AU. The search covers the whole 18,000 square degrees of the ACT survey, though a slightly deeper search is performed for the parts of the sky consistent with Planet 9's expected orbital inclination. No significant detections are found, which is used to place limits on the mm-wave flux density of Planet 9 over much of its orbit. Overall we eliminate roughly 17% and 9% of the parameter space for a 5 and 10 Earth-mass Planet 9 respectively. We also provide a list of the 10 strongest candidates from the search for possible follow-up. More generally, we exclude (at 95% confidence) the presence of an unknown Solar system object within our survey area brighter than 4--12 mJy (depending on position) at 150 GHz with current distance $300 \text{ AU} < r < 600 \text{ AU}$ and heliocentric angular velocity $1.5'/\text{yr} < v \cdot \frac{500 \text{ AU}}{r} < 2.3'\text{yr}$, corresponding to low-to-moderate eccentricities. These limits worsen gradually beyond 600 AU, reaching 5--15 mJy by 1500 AU., Comment: 23 pages, 10 figures, 5 tables, submitted to ApJ

Moir\'e materials provide a highly tunable environment for the realization of band structures with engineered physical properties. Specifically, moir\'e structures with Fermi surface flat bands - a synthetic environment for the realization of correlated phases - have moir\'e unit cells containing thousands of atoms and tantalizingly complex bands structures. In this paper we show that statistical principles go a long way in explaining universal physical properties of these systems. Our approach builds on three conceptual elements: the presence of quantum chaos caused by the effective irregularity of the atomic configurations on short length scales, Anderson localization in momentum space, and the presence of approximate crystalline symmetries. Which of these principles dominates depends on material parameters such as the extension of the Fermi surface or the strength of the moir\'e lattice potential. The phenomenological consequences of this competition are predictions for the characteristic group velocity of moir\'e bands, a primary indicator for their average flatness. In addition to these generic features, we identify structures outside the statistical context, notably almost flat bands close to the extrema of the unperturbed spectra, and the celebrated zero energy `magic angle' flat bands, where the latter require exceptionally fine tuned material parameters., Comment: 15 pages, 10 figures

The Simons Observatory (SO) is a Cosmic Microwave Background (CMB) experiment to observe the microwave sky in six frequency bands from 30GHz to 290GHz. The Observatory -- at $\sim$5200m altitude -- comprises three Small Aperture Telescopes (SATs) and one Large Aperture Telescope (LAT) at the Atacama Desert, Chile. This research note describes the design and current status of the LAT along with its future timeline., Comment: 4 pages, 1 figure

We present PyTorch Geometric Temporal a deep learning framework combining state-of-the-art machine learning algorithms for neural spatiotemporal signal processing. The main goal of the library is to make temporal geometric deep learning available for researchers and machine learning practitioners in a unified easy-to-use framework. PyTorch Geometric Temporal was created with foundations on existing libraries in the PyTorch eco-system, streamlined neural network layer definitions, temporal snapshot generators for batching, and integrated benchmark datasets. These features are illustrated with a tutorial-like case study. Experiments demonstrate the predictive performance of the models implemented in the library on real world problems such as epidemiological forecasting, ridehail demand prediction and web-traffic management. Our sensitivity analysis of runtime shows that the framework can potentially operate on web-scale datasets with rich temporal features and spatial structure., Comment: Source code at: https://github.com/benedekrozemberczki/pytorch_geometric_temporal

Context. While the shapes of many observed bow shocks can be reproduced by simple astrosphere models, more elaborate approaches have recently been used to explain differing observable structures. Aims. By placing perturbations of an otherwise homogeneous interstellar medium in front of the astrospheric bow shock of the runaway blue supergiant $\lambda$ Cephei, the observable structure of the model astrosphere is significantly altered, providing insight into the origin of perturbed bow shock images. Methods. Three-dimensional single-fluid magnetohydrodynamic (MHD) models of stationary astrospheres were subjected to various types of perturbations and simulated until stationarity was reached again. As examples, simple perturbations of the available MHD parameters (number density, bulk velocity, temperature, and magnetic field) as well as a more complex perturbation were chosen. Synthetic observations were generated by line-of-sight integration of the model data, producing H$\alpha$, $70\,\mu$m dust emission, and bremsstrahlung maps of the perturbed astrosphere's evolution. Results. The resulting shock structures and observational images differ strongly depending on the type of the injected perturbation and the viewing angles, forming arc-like protrusions or bifurcations of the bow shock structure, as well as rings, arcs, and irregular structures detached from the bow shock., Comment: 13 pages, 12 figures. Accepted by and to be published in Astronomy & Astrophysics (A&A)

We investigate the axial-vector nucleon-to-delta transition form factors in the framework of relativistic baryon chiral perturbation theory at the one-loop order using the complex-mass renormalization scheme. We determine the available six free parameters by fitting to an empirical parametrization of the form factors obtained from the BNL neutrino bubble chamber experiments. A unique feature of our calculation is the prediction of a non-vanishing form factor $C_3^A(Q^2)$. Moreover, our results show a surprising sensitivity to the coupling constant $\texttt{g}_1$ of the leading-order Lagrangian ${\cal L}^{(1)}_{\pi \Delta}$., Comment: 22 pages, 8 figures

Many aerial robotic applications require the ability to land on moving platforms, such as delivery trucks and marine research boats. We present a method to autonomously land an Unmanned Aerial Vehicle on a moving vehicle. A visual servoing controller approaches the ground vehicle using velocity commands calculated directly in image space. The control laws generate velocity commands in all three dimensions, eliminating the need for a separate height controller. The method has shown the ability to approach and land on the moving deck in simulation, indoor and outdoor environments, and compared to the other available methods, it has provided the fastest landing approach. Unlike many existing methods for landing on fast-moving platforms, this method does not rely on additional external setups, such as RTK, motion capture system, ground station, offboard processing, or communication with the vehicle, and it requires only the minimal set of hardware and localization sensors. The videos and source codes are also provided., Comment: 18 pages. Published in Sensors Journal

Currently, state-of-the-art exploration methods maintain high-resolution map representations in order to optimize exploration goals in each step that maximizes information gain. However, during exploring, those "optimal" selections could quickly become obsolete due to the influx of new information, especially in large-scale environments, and result in high-frequency re-planning that hinders the overall exploration efficiency. In this paper, we propose a graph-based topological planning framework, building a sparse topological map in three-dimensional (3D) space to guide exploration steps with high-level intents so as to render consistent exploration maneuvers. Specifically, this work presents a novel method to estimate 3D space's geometry with convex polyhedrons. Then, the geometry information is utilized to group space into distinctive regions. And those regions are added as nodes into the topological map, directing the exploration process. We compared our method with the state-of-the-art in simulated environments. The proposed method achieves higher space coverage and outperforms exploration efficiency by more than 40% during experiments. Finally, a field experiment was conducted to further evaluate the applicability of our method to empower efficient and robust exploration in real-world environments., Comment: Preprint version for ICRA2021 final submission

We autonomously direct a small quadcopter package delivery Uncrewed Aerial Vehicle (UAV) or "drone" to take off, fly a specified route, and land for a total of 209 flights while varying a set of operational parameters. The vehicle was equipped with onboard sensors, including GPS, IMU, voltage and current sensors, and an ultrasonic anemometer, to collect high-resolution data on the inertial states, wind speed, and power consumption. Operational parameters, such as commanded ground speed, payload, and cruise altitude, are varied for each flight. This large data set has a total flight time of 10 hours and 45 minutes and was collected from April to October of 2019 covering a total distance of approximately 65 kilometers. The data collected were validated by comparing flights with similar operational parameters. We believe these data will be of great interest to the research and industrial communities, who can use the data to improve UAV designs, safety, and energy efficiency, as well as advance the physical understanding of in-flight operations for package delivery drones., Comment: 13 pages, 11 figures, submitted to Scientific Data

Stereo reconstruction models trained on small images do not generalize well to high-resolution data. Training a model on high-resolution image size faces difficulties of data availability and is often infeasible due to limited computing resources. In this work, we present the Occlusion-aware Recurrent binocular Stereo matching (ORStereo), which deals with these issues by only training on available low disparity range stereo images. ORStereo generalizes to unseen high-resolution images with large disparity ranges by formulating the task as residual updates and refinements of an initial prediction. ORStereo is trained on images with disparity ranges limited to 256 pixels, yet it can operate 4K-resolution input with over 1000 disparities using limited GPU memory. We test the model's capability on both synthetic and real-world high-resolution images. Experimental results demonstrate that ORStereo achieves comparable performance on 4K-resolution images compared to state-of-the-art methods trained on large disparity ranges. Compared to other methods that are only trained on low-resolution images, our method is 70% more accurate on 4K-resolution images., Comment: Submitted to International Conference on Intelligent Robots and Systems (IROS) 2021

The Simons Observatory (SO) Large Aperture Telescope Receiver (LATR) will be coupled to the Large Aperture Telescope located at an elevation of 5,200 m on Cerro Toco in Chile. The resulting instrument will produce arcminute-resolution millimeter-wave maps of half the sky with unprecedented precision. The LATR is the largest cryogenic millimeter-wave camera built to date with a diameter of 2.4 m and a length of 2.6 m. It cools 1200 kg of material to 4 K and 200 kg to 100 mk, the operating temperature of the bolometric detectors with bands centered around 27, 39, 93, 145, 225, and 280 GHz. Ultimately, the LATR will accommodate 13 40 cm diameter optics tubes, each with three detector wafers and a total of 62,000 detectors. The LATR design must simultaneously maintain the optical alignment of the system, control stray light, provide cryogenic isolation, limit thermal gradients, and minimize the time to cool the system from room temperature to 100 mK. The interplay between these competing factors poses unique challenges. We discuss the trade studies involved with the design, the final optimization, the construction, and ultimate performance of the system.

Li$_2$Sr[MnN]$_2$ single crystals were successfully grown out of Li rich flux. The crystal structure was determined by single crystal X-ray diffraction and revealed almost linear $-$N$-$Mn$-$N$-$Mn$-$ chains as central structural motif. Tetragonal columns of this air and moisture sensitive nitridomanganate were employed for electrical transport, heat capacity, and anisotropic magnetization measurements. Both the electronic and magnetic properties are most remarkable, in particular the linear increase of the magnetic susceptibility with temperature that is reminiscent of underdoped cuprate and Fe-based superconductors. Clear indications for antiferromagnetic ordering at $T_{\rm N} = 290$ K were obtained. Metallic transport behavior is experimentally observed in accordance with electronic band structure calculations., Comment: 12 pages, 7 figures