Miracle-dz/UniFAR-FTUs huggingface dataset:
The Orbit of WASP-12b Is Decaying

WASP-12b is a transiting hot Jupiter on a 1.09 day orbit around a late-F star. Since the planet’s discovery in 2008, the time interval between transits has been decreasing by 29 ± 2 ms yr−1. This is a possible sign of orbital decay, although the previously available data left open the possibility that the planet’s orbit is slightly eccentric and is undergoing apsidal precession. Here, we present new transit and occultation observations that provide more decisive evidence for orbital decay, which is favored over apsidal precession by a of 22.3 or Bayes factor of 70,000. We also present new radial-velocity data that rule out the Rømer effect as the cause of the period change. This makes WASP-12 the first planetary system for which we can be confident that the orbit is decaying. The decay timescale for the orbit is . Interpreting the decay as the result of tidal dissipation, the modified stellar tidal quality factor is .
Star-forming Clumps in Local Luminous Infrared Galaxies

We present HST narrowband near-infrared imaging of Paα and Paβ emission of 48 local luminous infrared galaxies (LIRGs) from the Great Observatories All-Sky LIRG Survey. These data allow us to measure the properties of 810 spatially resolved star-forming regions (59 nuclei and 751 extranuclear clumps) and directly compare their properties to those found in both local and high-redshift star-forming galaxies. We find that in LIRGs the star-forming clumps have radii ranging from ∼90 to 900 pc and star formation rates (SFRs) of ∼1 × 10−3 to 10 M⊙ yr−1, with median values for extranuclear clumps of 170 pc and 0.03 M⊙ yr−1. The detected star-forming clumps are young, with a median stellar age of 8.7 Myr, and have a median stellar mass of 5 × 105 M⊙. The SFRs span the range of those found in normal local star-forming galaxies to those found in high-redshift star-forming galaxies at z = 1–3. The luminosity function of the LIRG clumps has a flatter slope than found in lower-luminosity, star-forming galaxies, indicating a relative excess of luminous star-forming clumps. In order to predict the possible range of star-forming histories and gas fractions, we compare the star-forming clumps to those measured in the MassiveFIRE high-resolution cosmological simulation. The star-forming clumps in MassiveFIRE cover the same range of SFRs and sizes found in the local LIRGs and have total gas fractions that extend from 10% to 90%. If local LIRGs are similar to these simulated galaxies, we expect that future observations with ALMA will find a large range of gas fractions, and corresponding star formation efficiencies, among the star-forming clumps in LIRGs.
LCD: Learned Cross-Domain Descriptors for 2D-3D Matching

In this work, we present a novel method to learn a local cross-domain descriptor for 2D image and 3D point cloud matching. Our proposed method is a dual auto-encoder neural network that maps 2D and 3D input into a shared latent space representation. We show that such local cross-domain descriptors in the shared embedding are more discriminative than those obtained from individual training in 2D and 3D domains. To facilitate the training process, we built a new dataset by collecting ≈ 1.4 millions of 2D-3D correspondences with various lighting conditions and settings from publicly available RGB-D scenes. Our descriptor is evaluated in three main experiments: 2D-3D matching, cross-domain retrieval, and sparse-to-dense depth estimation. Experimental results confirm the robustness of our approach as well as its competitive performance not only in solving cross-domain tasks but also in being able to generalize to solve sole 2D and 3D tasks. Our dataset and code are released publicly at https://hkust-vgd.github. io/lcd.
Unsupervised Object Segmentation with Explicit Localization Module

In this paper, we propose a novel architecture that iteratively discovers and segments out the objects of a scene based on the image reconstruction quality. Different from other approaches, our model uses an explicit localization module that localizes objects of the scene based on the pixel-level reconstruction qualities at each iteration, where simpler objects tend to be reconstructed better at earlier iterations and thus are segmented out first. We show that our localization module improves the quality of the segmentation, especially on a challenging background.
Deep neuroethology of a virtual rodent

Parallel developments in neuroscience and deep learning have led to mutually productive exchanges, pushing our understanding of real and artificial neural networks in sensory and cognitive systems. However, this interaction between fields is less developed in the study of motor control. In this work, we develop a virtual rodent as a platform for the grounded study of motor activity in artificial models of embodied control. We then use this platform to study motor activity across contexts by training a model to solve four complex tasks. Using methods familiar to neuroscientists, we describe the behavioral representations and algorithms employed by different layers of the network using a neuroethological approach to characterize motor activity relative to the rodent's behavior and goals. We find that the model uses two classes of representations which respectively encode the task-specific behavioral strategies and task-invariant behavioral kinematics. These representations are reflected in the sequential activity and population dynamics of neural subpopulations. Overall, the virtual rodent facilitates grounded collaborations between deep reinforcement learning and motor neuroscience.
A Dynamical Survey of Stellar-mass Black Holes in 50 Milky Way Globular Clusters

Recent numerical simulations of globular clusters (GCs) have shown that stellar-mass black holes (BHs) play a fundamental role in driving cluster evolution and shaping their present-day structure. Rapidly mass-segregating to the center of GCs, BHs act as a dynamical energy source via repeated superelastic scattering, delaying the onset of core collapse and limiting mass segregation for visible stars. While recent discoveries of BH candidates in Galactic and extragalactic GCs have further piqued interest in BH-mediated cluster dynamics, numerical models show that even if significant BH populations remain in today’s GCs, they are not typically in directly detectable configurations. We demonstrated in Weatherford et al. that an anticorrelation between a suitable measure of mass segregation (Δ) in observable stellar populations and the number of retained BHs in GC models can be applied to indirectly probe BH populations in real GCs. Here we estimate the number and total mass of BHs in 50 Milky Way GCs from the Advanced Camera for Surveys GC Survey. For each GC, Δ is measured between observed main-sequence populations and fed into correlations between Δ and BH retention found in our CMC Cluster Catalog’s models. We demonstrate that the range in measured Δ from our models matches that for observed GCs to a remarkable degree. Our results constitute the largest sample of GCs for which BH populations have been predicted to date using a self-consistent and robust statistical approach. We identify NGC 2808, 5927, 5986, 6101, and 6205 to retain especially large BH populations, each with a total BH mass exceeding 103 .
Correlations between mass, stellar kinematics, and gas metallicity in eagle galaxies

The metallicity of star-forming gas in galaxies from the EAGLE simulations increases with stellar mass. Here we investigate whether the scatter around this relation correlates with morphology and/or stellar kinematics. At redshift $z=0$, galaxies with more rotational support have lower metallicities on average when the stellar mass is below $M_\star\approx 10^{10}~{\rm M}_\odot$. This trend inverts at higher values of $M_\star$, when prolate galaxies show typically lower metallicity. At increasing redshifts, the trend between rotational support and metallicity becomes weaker at low stellar mass but more pronounced at high stellar mass. We argue that the secondary dependence of metallicity on stellar kinematics is another manifestation of the observed anti-correlation between metallicity and star formation rate at a given stellar mass. At low masses, such trends seem to be driven by the different star-formation histories of galaxies and stellar feedback. At high masses, feedback from active galactic nuclei and galaxy mergers play a dominant role.
Low damping and microstructural perfection of sub-40nm-thin yttrium iron garnet films grown by liquid phase epitaxy

The field of magnon spintronics is experiencing an increasing interest in the development of solutions for spin-wave-based data transport and processing technologies that are complementary or alternative to modern CMOS architectures. Nanometer-thin yttrium iron garnet (YIG) films have been the gold standard for insulator-based spintronics to date, but a potential process technology that can deliver perfect, homogeneous large-diameter films is still lacking. We report that liquid phase epitaxy (LPE) enables the deposition of nanometer-thin YIG films with low ferromagnetic resonance losses and consistently high magnetic quality down to a thickness of 20 nm. The obtained epitaxial films are characterized by an ideal stoichiometry and perfect film lattices, which show neither significant compositional strain nor geometric mosaicity, but sharp interfaces. Their magneto-static and dynamic behavior is similar to that of single crystalline bulk YIG. We found, that the Gilbert damping coefficient alpha is independent of the film thickness and close to 1 x 10-4, and that together with an inhomogeneous peak-to-peak linewidth broadening of delta H0|| = 0.4 G, these values are among the lowest ever reported for YIG films with a thickness smaller than 40 nm. These results suggest, that nanometer-thin LPE films can be used to fabricate nano- and micro-scaled circuits with the required quality for magnonic devices. The LPE technique is easily scalable to YIG sample diameters of several inches.
Massive spin-one-half one-particle states for the mass-dimension-one fermions

We study the conditions under which a non-standard Wigner class concerning discrete symmetries may arise for massive spin one-half states. The mass dimension one fermionic states are shown \textcolor{red}{to} constitute explicit examples. We also show how to conciliate these states with the current criticism due to the Lee and Wick, and Weinberg formulation.
Irreducible representations of the symmetric groups from slash homologies of p-complexes

In the 40s, Mayer introduced a construction of (simplicial) $p$-complex by using the unsigned boundary map and taking coefficients of chains modulo $p$. We look at such a $p$-complex associated to an $(n-1)$-simplex; in which case, this is also a $p$-complex of representations of the symmetric group of rank $n$ - specifically, of permutation modules associated to two-row compositions. In this article, we calculate the so-called slash homology - a homology theory introduced by Khovanov and Qi - of such a $p$-complex. We show that every non-trivial slash homology group appears as an irreducible representation associated two-row partitions, and how this calculation leads to a basis of these irreduicble representations given by the so-called $p$-standard tableaux.
Remarks on mass dimension one fermions: The underlying aspects, bilinear forms, Spinor Classification and RIM decomposition

In the present essay we review the underlying physical information behind the first concrete example describing a mass dimension one fermion - namely Elko spinors. We start the program exploring the physical information by evaluating the Elko bilinear forms, both within the proper orthochronous Lorentz subgroup as well as within the VSR theory. As we shall see, such structures do not hold the right observance of the Fierz-Pauli-Kofink quadratic relations. Thus, by the aforementioned reasons, we develop a deformation of the Clifford algebra basis. Such protocol can be accomplished by taking precisely the right Elko dual structure during the construction of the bilinear forms related to these spinors. With the appropriated bilinear forms at hands, we search for a real physical interpretation in order to achieve a deeper understanding of such spinor fields. Aiming an interesting application, we present a relation concerning Elko spinors and the neutrino physics via the Heisenberg non-linear theory by means of a bijective linear map between Elko spinors and the so-called Restricted Inomata-McKinley (RIM) spinors. Thus, we describe some of its properties. Some interesting results concerning the construction of RIM-decomposable spinors emerge from such prescription.
ID-aware Quality for Set-based Person Re-identification

Set-based person re-identification (SReID) is a matching problem that aims to verify whether two sets are of the same identity (ID). Existing SReID models typically generate a feature representation per image and aggregate them to represent the set as a single embedding. However, they can easily be perturbed by noises--perceptually/semantically low quality images--which are inevitable due to imperfect tracking/detection systems, or overfit to trivial images. In this work, we present a novel and simple solution to this problem based on ID-aware quality that measures the perceptual and semantic quality of images guided by their ID information. Specifically, we propose an ID-aware Embedding that consists of two key components: (1) Feature learning attention that aims to learn robust image embeddings by focusing on 'medium' hard images. This way it can prevent overfitting to trivial images, and alleviate the influence of outliers. (2) Feature fusion attention is to fuse image embeddings in the set to obtain the set-level embedding. It ignores noisy information and pays more attention to discriminative images to aggregate more discriminative information. Experimental results on four datasets show that our method outperforms state-of-the-art approaches despite the simplicity of our approach.
Computer Model Emulation with High-Dimensional Functional Output in Large-Scale Observing System Uncertainty Experiments

Abstract Observing system uncertainty experiments (OSUEs) have been recently proposed as a cost-effective way to perform probabilistic assessment of retrievals for NASA’s Orbiting Carbon Observatory-2 (OCO-2) mission. One important component in the OCO-2 retrieval algorithm is a full-physics forward model that describes the mathematical relationship between atmospheric variables such as carbon dioxide and radiances measured by the remote sensing instrument. This complex forward model is computationally expensive but large-scale OSUEs require evaluation of this model numerous times, which makes it infeasible for comprehensive experiments. To tackle this issue, we develop a statistical emulator to facilitate large-scale OSUEs in the OCO-2 mission. Within each distinct spectral band, the emulator represents radiance output at irregular wavelengths as a linear combination of basis functions and random coefficients. These random coefficients are then modeled with nearest-neighbor Gaussian processes with built-in input dimension reduction via active subspace. The proposed emulator reduces dimensionality in both input space and output space, so that fast computation is achieved within a fully Bayesian inference framework. Validation experiments demonstrate that this emulator outperforms other competing statistical methods and a reduced order model that approximates the full-physics forward model.
The flickering nuclear activity of Fornax A

We present new observations of Fornax A taken at ∼1 GHz with the MeerKAT telescope and at ∼6 GHz with the Sardinia Radio Telescope (SRT). The sensitive (noise ∼16 μJy beam−1), high-resolution (≲10″) MeerKAT images show that the lobes of Fornax A have a double-shell morphology, where dense filaments are embedded in a diffuse and extended cocoon. We study the spectral properties of these components by combining the MeerKAT and SRT observations with archival data between 84 MHz and 217 GHz. For the first time, we show that multiple episodes of nuclear activity must have formed the extended radio lobes. The modelling of the radio spectrum suggests that the last episode of injection of relativistic particles into the lobes started ∼24 Myr ago and stopped 12 Myr ago. More recently (∼3 Myr ago), a less powerful and short (≲1 Myr) phase of nuclear activity generated the central jets. Currently, the core may be in a new active phase. It appears that Fornax A is rapidly flickering. The dense environment around Fornax A has lead to a complex recent merger history for this galaxy, including mergers spanning a range of gas contents and mass ratios, as shown by the analysis of the galaxy’s stellar- and cold-gas phases. This complex recent history may be the cause of the rapid, recurrent nuclear activity of Fornax A.
REVAMP2T: Real-Time Edge Video Analytics for Multicamera Privacy-Aware Pedestrian Tracking

This article presents real-time edge video analytics for multicamera privacy-aware pedestrian tracking (REVAMP2T), as an integrated end-to-end Internet of Things (IoT) system for privacy built-in decentralized situational awareness. REVAMP2T presents novel algorithmic and system constructs to push deep learning and video analytics next to IoT devices (i.e., video cameras). On the algorithm side, REVAMP2T proposes a unified integrated computer vision pipeline for detection, reidentification, and tracking across multiple cameras without the need for storing the streaming data. At the same time, it avoids facial recognition and tracks and reidentifies the pedestrians based on their key features at runtime. On the IoT system side, REVAMP2T provides an infrastructure to maximize the hardware utilization on the edge, orchestrates global communications, and provides system-wide reidentification, without the use of personally identifiable information, for a distributed IoT network. For the results and evaluation, this article also proposes a new metric, accuracy ${\cdot}$ efficiency (Æ), for holistic evaluation of IoT systems for real-time video analytics based on accuracy, performance, and power efficiency. REVAMP2T outperforms the current state of the art by as much as 13-fold Æ improvement.
The politics of deceptive borders: ‘biomarkers of deceit’ and the case of iBorderCtrl

ABSTRACT This paper critically examines a recently developed proposal for a border control system called iBorderCtrl, designed to detect deception based on facial recognition technology and the measurement of micro-expressions, termed ‘biomarkers of deceit’. Funded under the European Commission’s Horizon 2020 programme, the system is analysed in relation to the wider political economy of ‘emotional AI’ and the history of deception detection technologies. We then move on to interrogate the design of iBorderCtrl using publicly available documents and assess the assumptions and scientific validation underpinning the project design. Finally, drawing on a Bayesian analysis we outline statistical fallacies in the foundational premise of mass screening and argue that it is very unlikely that the model that iBorderCtrl provides for deception detection would work in practice. By interrogating actual systems in this way, we argue that we can begin to question the very premise of the development of data-driven systems, and emotional AI and deception detection in particular, pushing back on the assumption that these systems are fulfilling the tasks they claim to be attending to and instead ask what function such projects carry out in the creation of subjects and management of populations. This function is not merely technical but, rather, we argue, distinctly political and forms part of a mode of governance increasingly shaping life opportunities and fundamental rights.
Controversial stimuli: pitting neural networks against each other as models of human recognition

Distinct scientific theories can make similar predictions. To adjudicate between theories, we must design experiments for which the theories make distinct predictions. Here we consider the problem of comparing deep neural networks as models of human visual recognition. To efficiently compare models' ability to predict human responses, we synthesize controversial stimuli: images for which different models produce distinct responses. We applied this approach to two visual recognition tasks, handwritten digits (MNIST) and objects in small natural images (CIFAR-10). For each task, we synthesized controversial stimuli to maximize the disagreement among models which employed different architectures and recognition algorithms. Human subjects viewed hundreds of these stimuli, as well as natural examples, and judged the probability of presence of each digit/object category in each image. We quantified how accurately each model predicted the human judgments. The best performing models were a generative Analysis-by-Synthesis model (based on variational autoencoders) for MNIST and a hybrid discriminative-generative Joint Energy Model for CIFAR-10. These DNNs, which model the distribution of images, performed better than purely discriminative DNNs, which learn only to map images to labels. None of the candidate models fully explained the human responses. Controversial stimuli generalize the concept of adversarial examples, obviating the need to assume a ground-truth model. Unlike natural images, controversial stimuli are not constrained to the stimulus distribution models are trained on, thus providing severe out-of-distribution tests that reveal the models' inductive biases. Controversial stimuli therefore provide powerful probes of discrepancies between models and human perception.
EnergyScout: A Consumer Oriented Dashboard for Smart Meter Data Analytics

The increasing popularity of smart meters provides energy consumers in households with unprecedented opportunities for understanding and modifying their energy use. However, while a variety of solutions, both commercial and academic,have been proposed, research on effective visual analysis tools is still needed to achieve widespread adoption of smart meters. In this paper we explore an interface that seeks to balance the tradeoff between complexity and usability. We worked with real household data and in close collaboration with consumer experts of a large local utility company. Based on their continued feedback we designed EnergyScout - a dashboard with a versatile set of highly interactive visual tools with which consumers can understand the energy consumption of their household devices, discover the impact of their usage patterns, compare them with usage patterns of the past, and see via what-if analysis what effects a modification of these patterns may have, also in the context of modulated incentivized pricing, social and personal events, outside temperature, and weather. All of these are events which could explain certain usage patterns and help motivate a modification of behavior. We tested EnergyScout with various groups of people, households, and energy bill responsibilities in order to gauge the merits of this system.