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The first very long baseline interferometry (VLBI) detections at 870$\mu$m wavelength (345$\,$GHz frequency) are reported, achieving the highest diffraction-limited angular resolution yet obtained from the surface of the Earth, and the highest-frequency example of the VLBI technique to date. These include strong detections for multiple sources observed on inter-continental baselines between telescopes in Chile, Hawaii, and Spain, obtained during observations in October 2018. The longest-baseline detections approach 11$\,$G$\lambda$ corresponding to an angular resolution, or fringe spacing, of 19$\mu$as. The Allan deviation of the visibility phase at 870$\mu$m is comparable to that at 1.3$\,$mm on the relevant integration time scales between 2 and 100$\,$s. The detections confirm that the sensitivity and signal chain stability of stations in the Event Horizon Telescope (EHT) array are suitable for VLBI observations at 870$\mu$m. Operation at this short wavelength, combined with anticipated enhancements of the EHT, will lead to a unique high angular resolution instrument for black hole studies, capable of resolving the event horizons of supermassive black holes in both space and time., Comment: Corresponding author: S. Doeleman

Models of the resolved Event Horizon Telescope (EHT) sources Sgr A* and M87* are constrained by observations at multiple wavelengths, resolutions, polarizations, and time cadences. In this paper we compare unresolved circular polarization (CP) measurements to a library of models, where each model is characterized by a distribution of CP over time. In the library we vary the spin of the black hole, the magnetic field strength at the horizon (i.e. both SANE and MAD models), the observer inclination, a parameter for the maximum ion-electron temperature ratio assuming a thermal plasma, and the direction of the magnetic field dipole moment. We find that ALMA observations of Sgr A* are inconsistent with all edge-on ($i = 90^\circ$) models. Restricting attention to the magnetically arrested disk (MAD) models favored by earlier EHT studies of Sgr A*, we find that only models with magnetic dipole moment pointing away from the observer are consistent with ALMA data. We also note that in 26 of the 27 passing MAD models the accretion flow rotates clockwise on the sky. We provide a table of the mean and standard deviation of the CP distributions for all model parameters along with their trends., Comment: 33 pages, 17 figures, 2 tables. Accepted for publication in ApJ

Recent advancements in Artificial Intelligence (AI) have largely been propelled by scaling. In Robotics, scaling is hindered by the lack of access to massive robot datasets. We advocate using realistic physical simulation as a means to scale environments, tasks, and datasets for robot learning methods. We present RoboCasa, a large-scale simulation framework for training generalist robots in everyday environments. RoboCasa features realistic and diverse scenes focusing on kitchen environments. We provide thousands of 3D assets across over 150 object categories and dozens of interactable furniture and appliances. We enrich the realism and diversity of our simulation with generative AI tools, such as object assets from text-to-3D models and environment textures from text-to-image models. We design a set of 100 tasks for systematic evaluation, including composite tasks generated by the guidance of large language models. To facilitate learning, we provide high-quality human demonstrations and integrate automated trajectory generation methods to substantially enlarge our datasets with minimal human burden. Our experiments show a clear scaling trend in using synthetically generated robot data for large-scale imitation learning and show great promise in harnessing simulation data in real-world tasks. Videos and open-source code are available at https://robocasa.ai/, Comment: RSS 2024

The Event Horizon Telescope (EHT) observed in 2017 the supermassive black hole at the center of the Milky Way, Sagittarius A* (Sgr A*), at a frequency of 228.1 GHz ($\lambda$=1.3 mm). The fundamental physics tests that even a single pulsar orbiting Sgr A* would enable motivate searching for pulsars in EHT datasets. The high observing frequency means that pulsars - which typically exhibit steep emission spectra - are expected to be very faint. However, it also negates pulse scattering, an effect that could hinder pulsar detections in the Galactic Center. Additionally, magnetars or a secondary inverse Compton emission could be stronger at millimeter wavelengths than at lower frequencies. We present a search for pulsars close to Sgr A* using the data from the three most-sensitive stations in the EHT 2017 campaign: the Atacama Large Millimeter/submillimeter Array, the Large Millimeter Telescope and the IRAM 30 m Telescope. We apply three detection methods based on Fourier-domain analysis, the Fast-Folding-Algorithm and single pulse search targeting both pulsars and burst-like transient emission; using the simultaneity of the observations to confirm potential candidates. No new pulsars or significant bursts were found. Being the first pulsar search ever carried out at such high radio frequencies, we detail our analysis methods and give a detailed estimation of the sensitivity of the search. We conclude that the EHT 2017 observations are only sensitive to a small fraction ($\lesssim$2.2%) of the pulsars that may exist close to Sgr A*, motivating further searches for fainter pulsars in the region., Comment: 33 pages, 7 figures, 6 Tables. Accepted for publication in ApJ

The Event Horizon Telescope (EHT) has produced images of M87* and Sagittarius A*, and will soon produce time sequences of images, or movies. In anticipation of this, we describe a technique to measure the rotation rate, or pattern speed $\Omega_p$, from movies using an autocorrelation technique. We validate the technique on Gaussian random field models with a known rotation rate and apply it to a library of synthetic images of Sgr A* based on general relativistic magnetohydrodynamics simulations. We predict that EHT movies will have $\Omega_p \approx 1^\circ$ per $GMc^{-3}$, which is of order $15\%$ of the Keplerian orbital frequency in the emitting region. We can plausibly attribute the slow rotation seen in our models to the pattern speed of inward-propagating spiral shocks. We also find that $\Omega_p$ depends strongly on inclination. Application of this technique will enable us to compare future EHT movies with the clockwise rotation of Sgr A* seen in near-infrared flares by GRAVITY. Pattern speed analysis of future EHT observations of M87* and Sgr A* may also provide novel constraints on black hole inclination and spin, as well as an independent measurement of black hole mass.

Interpretation of resolved polarized images of black holes by the Event Horizon Telescope (EHT) requires predictions of the polarized emission observable by an Earth-based instrument for a particular model of the black hole accretion system. Such predictions are generated by general relativistic radiative transfer (GRRT) codes, which integrate the equations of polarized radiative transfer in curved spacetime. A selection of ray-tracing GRRT codes used within the EHT collaboration is evaluated for accuracy and consistency in producing a selection of test images, demonstrating that the various methods and implementations of radiative transfer calculations are highly consistent. When imaging an analytic accretion model, we find that all codes produce images similar within a pixel-wise normalized mean squared error (NMSE) of 0.012 in the worst case. When imaging a snapshot from a cell-based magnetohydrodynamic simulation, we find all test images to be similar within NMSEs of 0.02, 0.04, 0.04, and 0.12 in Stokes I, Q, U , and V respectively. We additionally find the values of several image metrics relevant to published EHT results to be in agreement to much better precision than measurement uncertainties., Comment: Accepted for publication in ApJ

Real-world vision based applications require fine-grained classification for various area of interest like e-commerce, mobile applications, warehouse management, etc. where reducing the severity of mistakes and improving the classification accuracy is of utmost importance. This paper proposes a method to boost fine-grained classification through a hierarchical approach via learnable independent query embeddings. This is achieved through a classification network that uses coarse class predictions to improve the fine class accuracy in a stage-wise sequential manner. We exploit the idea of hierarchy to learn query embeddings that are scalable across all levels, thus making this a relevant approach even for extreme classification where we have a large number of classes. The query is initialized with a weighted Eigen image calculated from training samples to best represent and capture the variance of the object. We introduce transformer blocks to fuse intermediate layers at which query attention happens to enhance the spatial representation of feature maps at different scales. This multi-scale fusion helps improve the accuracy of small-size objects. We propose a two-fold approach for the unique representation of learnable queries. First, at each hierarchical level, we leverage cluster based loss that ensures maximum separation between inter-class query embeddings and helps learn a better (query) representation in higher dimensional spaces. Second, we fuse coarse level queries with finer level queries weighted by a learned scale factor. We additionally introduce a novel block called Cross Attention on Multi-level queries with Prior (CAMP) Block that helps reduce error propagation from coarse level to finer level, which is a common problem in all hierarchical classifiers. Our method is able to outperform the existing methods with an improvement of ~11% at the fine-grained classification., Comment: 6 pages, 7 figures Published in IEEE ICCE 2023

Deep networks for computer vision are not reliable when they encounter adversarial examples. In this paper, we introduce a framework that uses the dense intrinsic constraints in natural images to robustify inference. By introducing constraints at inference time, we can shift the burden of robustness from training to the inference algorithm, thereby allowing the model to adjust dynamically to each individual image's unique and potentially novel characteristics at inference time. Among different constraints, we find that equivariance-based constraints are most effective, because they allow dense constraints in the feature space without overly constraining the representation at a fine-grained level. Our theoretical results validate the importance of having such dense constraints at inference time. Our empirical experiments show that restoring feature equivariance at inference time defends against worst-case adversarial perturbations. The method obtains improved adversarial robustness on four datasets (ImageNet, Cityscapes, PASCAL VOC, and MS-COCO) on image recognition, semantic segmentation, and instance segmentation tasks. Project page is available at equi4robust.cs.columbia.edu., Comment: Published in ICML 2023

We introduce VIOLA, an object-centric imitation learning approach to learning closed-loop visuomotor policies for robot manipulation. Our approach constructs object-centric representations based on general object proposals from a pre-trained vision model. VIOLA uses a transformer-based policy to reason over these representations and attend to the task-relevant visual factors for action prediction. Such object-based structural priors improve deep imitation learning algorithm's robustness against object variations and environmental perturbations. We quantitatively evaluate VIOLA in simulation and on real robots. VIOLA outperforms the state-of-the-art imitation learning methods by $45.8\%$ in success rate. It has also been deployed successfully on a physical robot to solve challenging long-horizon tasks, such as dining table arrangement and coffee making. More videos and model details can be found in supplementary material and the project website: https://ut-austin-rpl.github.io/VIOLA ., Comment: Published at the 6th Conference on Robot Learning

We use the open source, community-driven, numerical relativity software, the Einstein Toolkit to study the physics of eccentric, spinning, nonprecessing binary black hole mergers with mass-ratios $q=\{2, 4, 6\}$, individual dimensionless spin parameters $\chi_{1z}=\pm0.6$, $\chi_{2z}=\pm0.3$, that include higher order gravitational wave modes $\ell\leq4$, except for memory modes. Assuming stellar mass binary black hole mergers that may be detectable by the advanced LIGO detectors, we find that including modes up to $\ell=4$ increases the signal-to-noise of compact binaries between $3.5\%$ to $35\%$, compared to signals that only include the $\ell=|m|=2$ mode. We use two waveform models, TEOBResumS and SEOBNRE, which incorporate spin and eccentricity corrections in the waveform dynamics, to quantify the orbital eccentricity of our numerical relativity catalog in a gauge-invariant manner through fitting factor calculations. Our findings indicate that the inclusion of higher order wave modes has a measurable effect in the recovery of moderately and highly eccentric black hole mergers, and thus it is essential to develop waveform models and signal processing tools that accurately describe the physics of these astrophysical sources., Comment: 13 pages, 5 figures, updated manuscript with added waveform comparisons to TEOBResumS. accepted in Phys. Rev. D

Detecting Out-of-Distribution (OOD) samples in real world visual applications like classification or object detection has become a necessary precondition in today's deployment of Deep Learning systems. Many techniques have been proposed, of which Energy based OOD methods have proved to be promising and achieved impressive performance. We propose semantic driven energy based method, which is an end-to-end trainable system and easy to optimize. We distinguish in-distribution samples from out-distribution samples with an energy score coupled with a representation score. We achieve it by minimizing the energy for in-distribution samples and simultaneously learn respective class representations that are closer and maximizing energy for out-distribution samples and pushing their representation further out from known class representation. Moreover, we propose a novel loss function which we call Cluster Focal Loss(CFL) that proved to be simple yet very effective in learning better class wise cluster center representations. We find that, our novel approach enhances outlier detection and achieve state-of-the-art as an energy-based model on common benchmarks. On CIFAR-10 and CIFAR-100 trained WideResNet, our model significantly reduces the relative average False Positive Rate(at True Positive Rate of 95%) by 67.2% and 57.4% respectively, compared to the existing energy based approaches. Further, we extend our framework for object detection and achieve improved performance., Comment: accepted at International Joint Conference on Neural Networks (IJCNN) 2022

In this article we are going to study the FEM solution to the Density Functional description of Helium. Solving self-consistently including electron-electron repulsion and exchange-correlation effects. This project will be split in four different consecutive task with different approaches: Numerical Hartree potential (Sect. I) and nuclear potential (Sect. II) for the hydrogen and resolution of Schrodinger equation for hydrogen (Sect. III) and helium (Sect. IV). The code is included in the format Sinajo with $n\in{0,1,2,3,4}$ corresponding with supporting material (code) to each task. The format of each section is standard in three cases. Beginning with a theoretical introduction, we mainly explain the code and show the principal results in form of values or graphics. Later on, we try to study the convergence and stability of the method proposed. Exception to this rule can be found in Section II, corresponding to the analytical deduction of a potential term.

The Event Horizon Telescope (EHT) has released analyses of reconstructed images of horizon-scale millimeter emission near the supermassive black hole at the center of the M87 galaxy. Parts of the analyses made use of a large library of synthetic black hole images and spectra, which were produced using numerical general relativistic magnetohydrodynamics fluid simulations and polarized ray tracing. In this article, we describe the PATOKA pipeline, which was used to generate the Illinois contribution to the EHT simulation library. We begin by describing the relevant accretion systems and radiative processes. We then describe the details of the three numerical codes we use, iharm, ipole, and igrmonty, paying particular attention to differences between the current generation of the codes and the originally published versions. Finally, we provide a brief overview of simulated data as produced by PATOKA and conclude with a discussion of limitations and future directions., Comment: 42 pages, 17 figures, accepted for publication in ApJS

Accurate radiative transfer coefficients (emissivities, absorptivities, and rotativities) are needed for modeling radiation from relativistically hot, magnetized plasmas such as those found in Event Horizon Telescope sources. Here we review, update, and correct earlier work on radiative transfer coefficients. We also describe an improved method for numerically evaluating rotativities and provide convenient fitting formulae for the relativistic $\kappa$ distribution of electron energies., Comment: 14 pages, 3 figures, Accepted to ApJ

Sustaining exotic quantum mechanical phases at high temperatures is a long-standing goal of condensed matter physics. Among them, half-metals are spin-polarized conductors that are essential for realizing room-temperature spin current sources. However, typical half-metals are low-temperature phases whose spin polarization rapidly deteriorates with temperature increase. Here, we first show that a low-temperature insulator with an unequal charge gap for the two spin channels can arise from competing Mott and band insulating tendencies. We establish that thermal fluctuations can drive this insulator to a half-metal through a first-order phase transition by closing the charge gap for one spin channel. This half-metal has 100% spin polarization at the onset temperature of metallization. Further, varying the strength of electron repulsion can enhance the onset temperature while preserving spin polarization. We outline experimental scenarios for realizing this tunable finite temperature half-metal., Comment: Published version