Miracle-dz/UniFAR-FTUs huggingface dataset:
The Inside-out Growth of the Galactic Disk

We quantify the inside-out growth of the Milky Way’s low-α stellar disk, modeling the ages, metallicities, and Galactocentric radii of APOGEE red clump stars with 6 kpc < R < 13 kpc. The current stellar distribution differs significantly from that expected from the star formation history due to the redistribution of stars through radial orbit mixing. We propose and fit a global model for the Milky Way disk, specified by an inside-out star formation history, radial orbit mixing, and an empirical, parametric model for its chemical evolution. We account for the spatially complex survey selection function, and find that the model fits all data well. We find distinct inside-out growth of the Milky Way disk; the best-fit model implies that the half-mass radius of the Milky Way disk has grown by 43% over the last 7 Gyr. Yet, such inside-out growth still results in a present-day age gradient weaker than 0.1 Gyr kpc−1. Our model predicts the half-mass and half-light sizes of the Galactic disk at earlier epochs, which can be compared to the observed redshift–size relations of disk galaxies. We show that radial orbit migration can reconcile the distinct disk-size evolution with redshift, also expected from cosmological simulations, with the modest present-day age gradients seen in the Milky Way and other galaxies.
Human self-determination within algorithmic sociotechnical systems

In order to investigate the protection of human self-determination within algorithmic sociotechnical systems, we study the relationships between the concepts of mutability, bias, feedback loops, and power dynamics. We focus on the interactions between people and algorithmic systems in the case of Recommender Systems (RS) and provide novel theoretical analysis informed by human-in-the-loop system design and Supervisory Control, in order to question the dynamics in our interactions with RSs. We explore what meaningful reliability monitoring means in the context of RSs and elaborate on the need for metrics that encompass human-algorithmic interaction. We derive a metric we call a barrier-to-exit which is a proxy to the amount of effort a user needs to expend in order for the system to recognize their change in preference. Our goal is to highlight the assumptions and limitations of RSs and introduce a human-centered method of combating deterministic design.
Photoemission signatures of nonequilibrium carrier dynamics from first principles

Time- and angle-resolved photoemission spectroscopy (tr-ARPES) constitutes a powerful tool to inspect the dynamics and thermalization of hot carriers. The identification of the processes that drive the dynamics, however, is challenging even for the simplest systems owing to the coexistence of several relaxation mechanisms. Here, we devise a Green's function formalism for predicting the tr-ARPES spectral function and establish the origin of carrier thermalization entirely from first principles. The predictive power of this approach is demonstrated by an excellent agreement with experiments for graphene over time scales ranging from a few tens of femtoseconds up to several picoseconds. Our work provides compelling evidence of a non-equilibrium dynamics dominated by the establishment of a hot-phonon regime.
X-shooter spectroscopy of young stars with disks

Context. Measurements of the fraction of disk-bearing stars in clusters as a function of age indicate protoplanetary disk lifetimes ≲10 Myr. However, our knowledge of the time evolution of mass accretion in young stars over the disk lifespans is subject to many uncertainties, especially at the lowest stellar masses (M⋆). Aims. We investigate ongoing accretion activity in young stars in the TW Hydrae association (TWA). The age of the association (∼8–10 Myr) renders it an ideal target for probing the final stages of disk accretion, and its proximity (∼50 pc) enables a detailed assessment of stellar and accretion properties down to brown dwarf masses. Methods. Our sample comprises eleven TWA members with infrared excess, amounting to 85% of the total TWA population with disks. Our targets span spectral types between M0 and M9, and masses between 0.58 M⊙ and 0.02 M⊙. We employed homogeneous spectroscopic data from 300 nm to 2500 nm, obtained synoptically with the X-shooter spectrograph, to derive the individual extinction, stellar parameters, and accretion parameters for each object simultaneously. We then examined the luminosity of Balmer lines and forbidden emission lines to probe the physics of the star–disk interaction environment. Results. Disk-bearing stars represent around 24% of the total TWA population. We detected signatures of ongoing accretion for 70% of our TWA targets for which accurate measurements of the stellar parameters could be derived. This implies a fraction of accretors between 13–17% across the entire TWA (that accounts for the disk-bearing and potentially accreting members not included in our survey). The spectral emission associated with these stars reveals a more evolved stage of these accretors compared to younger PMS populations studied with the same instrument and analysis techniques (e.g., Lupus): first, a large fraction (∼50%) exhibit nearly symmetric, narrow Hα line profiles; second, over 80% of them exhibit Balmer decrements that are consistent with moderate accretion activity and optically thin emission; third, less than a third exhibit forbidden line emission in [O I] 6300 Å, which is indicative of winds and outflows activity; and fourth, only one sixth exhibit signatures of collimated jets. However, the distribution in accretion rates (Ṁacc) derived for the TWA sample closely follows that of younger regions (Lupus, Chamaeleon I, σ Orionis) over the mass range of overlap (M⋆ ∼ 0.1–0.3 M⊙). An overall correlation between Ṁacc and M⋆ is detected and best reproduced by the function Ṁacc ∝ M∝2.1±0.5. Conclusion. At least in the lowest M⋆ regimes, stars that still retain a disk at ages ∼8–10 Myr are found to exhibit statistically similar, albeit moderate, accretion levels as those measured around younger objects. This “slow” Ṁacc evolution that is apparent at the lowest masses may be associated with longer evolutionary timescales of disks around low-mass stars, which is suggested by the mass-dependent disk fractions reported in the literature within individual clusters.
Resolved Lyman-α properties of a luminous Lyman-break galaxy in a large ionized bubble at z = 6.53

The observed properties of the Lyman-α (Ly α) emission line are a powerful probe of neutral gas in and around galaxies. We present spatially resolved Ly α spectroscopy with VLT/MUSE targeting VR7, a UV-luminous galaxy at z = 6.532 with moderate Ly α equivalent width (EW0 ≈ 38 Å). These data are combined with deep resolved [CII]158μm spectroscopy obtained with ALMA and UV imaging from HST and we also detect UV continuum with MUSE. Ly α emission is clearly detected with S/N ≈ 40 and FWHM of 374 km s−1. Ly α and [C ii] are similarly extended beyond the UV, with effective radius reff = 2.1 ± 0.2 kpc for a single exponential model or r$_{\rm eff, Ly\alpha , halo} = 3.45^{+1.08}_{-0.87}$ kpc when measured jointly with the UV continuum. The Ly α profile is broader and redshifted with respect to the [C ii] line (by 213 km s−1), but there are spatial variations that are qualitatively similar in both lines and coincide with resolved UV components. This suggests that the emission originates from two components with plausibly different H i column densities. We place VR7 in the context of other galaxies at similar and lower redshift. The Ly α halo scale length is similar at different redshifts and velocity shifts with respect to the systemic are typically smaller. Overall, we find little indications of a more neutral vicinity at higher redshift. This means that the local (∼10 kpc) neutral gas conditions that determine the observed Ly α properties in VR7 resemble the conditions in post-reionization galaxies.
Real-Time, Highly Accurate Robotic Grasp Detection using Fully Convolutional Neural Network with Rotation Ensemble Module

Rotation invariance has been an important topic in computer vision tasks. Ideally, robot grasp detection should be rotation-invariant. However, rotation-invariance in robotic grasp detection has been only recently studied by using rotation anchor box that are often time-consuming and unreliable for multiple objects. In this paper, we propose a rotation ensemble module (REM) for robotic grasp detection using convolutions that rotates network weights. Our proposed REM was able to outperform current state-of-the-art methods by achieving up to 99.2% (image-wise), 98.6% (object-wise) accuracies on the Cornell dataset with real-time computation (50 frames per second). Our proposed method was also able to yield reliable grasps for multiple objects and up to 93.8% success rate for the real-time robotic grasping task with a 4-axis robot arm for small novel objects that was significantly higher than the baseline methods by 11-56%.
Hermitian metrics on the anti-canonical bundle of the blow-up of the projective plane at nine points

We investigate Hermitian metrics on the anti-canonical bundle of a rational surface obtained by blowing up the projective plane at nine points. For that purpose, we pose a modified variant of an argument made by Ueda on the complex analytic structure of a neighborhood of a subvariety by considering the deformation of the complex structure.
Direct images of pluricanonical bundles and Frobenius stable canonical rings of fibers

In this paper, we study direct images of pluricanonical bundles in positive characteristic, assuming that generic fibers have finitely generated canonical rings and sufficiently large Frobenius stable canonical rings. We treat a Fujita-type conjecture due to Popa and Schnell, and prove it under some additional hypotheses. As its application, we show the subadditivity of Kodaira dimensions in some new cases. We also prove an analog of Fujino's result concerning his Fujita-type conjecture.
Zero temperature momentum distribution of an impurity in a polaron state of one-dimensional Fermi and Tonks-Girardeau gases

We investigate the momentum distribution function of a single distinguishable impurity particle which formed a polaron state in a gas of either free fermions or Tonks-Girardeau bosons in one spatial dimension. We obtain a Fredholm determinant representation of the distribution function for the Bethe ansatz solvable model of an impurity-gas \deltaδ-function interaction potential at zero temperature, in both repulsive and attractive regimes. We deduce from this representation the fourth power decay at a large momentum, and a weakly divergent (quasi-condensate) peak at a finite momentum. We also demonstrate that the momentum distribution function in the limiting case of infinitely strong interaction can be expressed through a correlation function of the one-dimensional impenetrable anyons.
RVH: Range-Vector Hash for Fast Online Packet Classification

Packet classification according to multi-field ruleset is a key component for many network applications. Emerging software defined networking and cloud computing need to update the rulesets frequently for flexible policy configuration. Their success depends on the availability of the new generation of classifiers that can support both fast ruleset updating and high-speed packet classification. However, existing packet classification approaches focus either on high-speed packet classification or fast rule update, but no known scheme meets both requirements. In this paper, we propose Range-vector Hash (RVH) to effectively accelerate the packet classification with a hash-based algorithm while ensuring the fast rule update. RVH is built on our key observation that the number of distinct combinations of each field prefix lengths is not evenly distributed. To reduce the number of hash tables for fast classification, we introduce a novel concept range-vector with each specified the length range of each field prefix of the projected rules. RVH can overcome the major obstacle that hinders hash-based packet classification by balancing the number of hash tables and the probability of hash collision. Experimental results demonstrate that RVH can achieve the classification speed up to 15.7 times and the update speed up to 2.3 times that of the state-of-the-art algorithms on average, while only consuming 44% less memory.
Machine Discovery of Partial Differential Equations from Spatiotemporal Data

The study presents a general framework for discovering underlying Partial Differential Equations (PDEs) using measured spatiotemporal data. The method, called Sparse Spatiotemporal System Discovery ($\text{S}^3\text{d}$), decides which physical terms are necessary and which can be removed (because they are physically negligible in the sense that they do not affect the dynamics too much) from a pool of candidate functions. The method is built on the recent development of Sparse Bayesian Learning; which enforces the sparsity in the to-be-identified PDEs, and therefore can balance the model complexity and fitting error with theoretical guarantees. Without leveraging prior knowledge or assumptions in the discovery process, we use an automated approach to discover ten types of PDEs, including the famous Navier-Stokes and sine-Gordon equations, from simulation data alone. Moreover, we demonstrate our data-driven discovery process with the Complex Ginzburg-Landau Equation (CGLE) using data measured from a traveling-wave convection experiment. Our machine discovery approach presents solutions that has the potential to inspire, support and assist physicists for the establishment of physical laws from measured spatiotemporal data, especially in notorious fields that are often too complex to allow a straightforward establishment of physical law, such as biophysics, fluid dynamics, neuroscience or nonlinear optics.
A Real-Time Cross-Modality Correlation Filtering Method for Referring Expression Comprehension

Referring expression comprehension aims to localize the object instance described by a natural language expression. Current referring expression methods have achieved good performance. However, none of them is able to achieve real-time inference without accuracy drop. The reason for the relatively slow inference speed is that these methods artiï¬cially split the referring expression comprehension into two sequential stages including proposal generation and proposal ranking. It does not exactly conform to the habit of human cognition. To this end, we propose a novel Realtime Cross-modality Correlation Filtering method (RCCF). RCCF reformulates the referring expression comprehension as a correlation ï¬ltering process. The expression is ï¬rst mapped from the language domain to the visual domain and then treated as a template (kernel) to perform correlation ï¬ltering on the image feature map. The peak value in the correlation heatmap indicates the center points of the target box. In addition, RCCF also regresses a 2-D object size and 2-D offset. The center point coordinates, object size and center point offset together to form the target bounding box. Our method runs at 40 FPS while achieving leading performance in RefClef, RefCOCO, RefCOCO+ and RefCOCOg benchmarks. In the challenging RefClef dataset, our methods almost double the state-of-the-art performance (34.70% increased to 63.79%). We hope this work can arouse more attention and studies to the new cross-modality correlation ï¬ltering framework as well as the one-stage framework for referring expression comprehension.
Homogenization of random convolution energies in heterogeneous and perforated domains

We prove a homogenization theorem for a class of quadratic convolution energies with random coefficients. Under suitably stated hypotheses of ergodicity and stationarity we prove that the $\Gamma$-limit of such energy is almost surely a deterministic quadratic Dirichlet-type integral functional, whose integrand can be characterized through an asymptotic formula. The proof of this characterization relies on results on the asymptotic behaviour of subadditive processes. The proof of the limit theorem uses a blow-up technique common for local energies, that can be extended to this `asymptotically-local' case. As a particular application we derive a homogenization theorem on random perforated domains.
Incorporating Dynamicity of Transportation Network With Multi-Weight Traffic Graph Convolutional Network for Traffic Forecasting

Traffic forecasting problem remains a challenging task in the intelligent transportation system due to its spatio-temporal complexity. Although temporal dependency has been well studied and discussed, spatial dependency is relatively less explored due to its large variations, especially in the urban environment. In this study, a novel graph convolutional network model, Multi-Weight Traffic Graph Convolutional (MW-TGC) network, is proposed and applied to two urban networks with contrasting geometric constraints. The model conducts graph convolution operations on speed data with multi-weighted adjacency matrices to combine the features, including speed limit, distance, and angle. The spatially isolated dimension reduction operation is conducted on the combined features to learn the dependencies among the features and reduce the size of the output to a computationally feasible level. The output of multi-weight graph convolution is applied to the sequence-to-sequence model with Long Short-Term Memory units to learn temporal dependencies. When applied to two urban sites, urban-core and urban-mix, MW-TGC network not only outperformed the comparative models in both sites but also reduced variance in the heterogeneous urban-mix network. We conclude that MW-TGC network can provide a robust traffic forecasting performance across the variations in spatial complexity, which can be a strong advantage in urban traffic forecasting.
Highly Dynamic Quadruped Locomotion via Whole-Body Impulse Control and Model Predictive Control

Dynamic legged locomotion is a challenging topic because of the lack of established control schemes which can handle aerial phases, short stance times, and high-speed leg swings. In this paper, we propose a controller combining whole-body control (WBC) and model predictive control (MPC). In our framework, MPC finds an optimal reaction force profile over a longer time horizon with a simple model, and WBC computes joint torque, position, and velocity commands based on the reaction forces computed from MPC. Unlike existing WBCs, which attempt to track commanded body trajectories, our controller is focused more on the reaction force command, which allows it to accomplish high speed dynamic locomotion with aerial phases. The newly devised WBC is integrated with MPC and tested on the Mini-Cheetah quadruped robot. To demonstrate the robustness and versatility, the controller is tested on six different gaits in a number of different environments, including outdoors and on a treadmill, reaching a top speed of 3.7 m/s.
Putting the Horse before the Cart: A Generator-Evaluator Framework for Question Generation from Text

Automatic question generation (QG) is a useful yet challenging task in NLP. Recent neural network-based approaches represent the state-of-the-art in this task. In this work, we attempt to strengthen them significantly by adopting a holistic and novel generator-evaluator framework that directly optimizes objectives that reward semantics and structure. The generator is a sequence-to-sequence model that incorporates the structure and semantics of the question being generated. The generator predicts an answer in the passage that the question can pivot on. Employing the copy and coverage mechanisms, it also acknowledges other contextually important (and possibly rare) keywords in the passage that the question needs to conform to, while not redundantly repeating words. The evaluator model evaluates and assigns a reward to each predicted question based on its conformity to the structure of ground-truth questions. We propose two novel QG-specific reward functions for text conformity and answer conformity of the generated question. The evaluator also employs structure-sensitive rewards based on evaluation measures such as BLEU, GLEU, and ROUGE-L, which are suitable for QG. In contrast, most of the previous works only optimize the cross-entropy loss, which can induce inconsistencies between training (objective) and testing (evaluation) measures. Our evaluation shows that our approach significantly outperforms state-of-the-art systems on the widely-used SQuAD benchmark as per both automatic and human evaluation.
Phonon Thermal Hall Effect in Strontium Titanate.

It has been known for more than a decade that phonons can produce an off-diagonal thermal conductivity in the presence of a magnetic field. Recent studies of thermal Hall conductivity, κ_{xy}, in a variety of contexts, however, have assumed a negligibly small phonon contribution. We present a study of κ_{xy} in quantum paraelectric SrTiO_{3}, which is a nonmagnetic insulator and find that its peak value exceeds what has been reported in any other insulator, including those in which the signal has been qualified as "giant. " Remarkably, κ_{xy}(T) and κ(T) peak at the same temperature and the former decreases faster than the latter at both sides of the peak. Interestingly, in the case of La_{2}CuO_{4} and α-RuCl_{3}, κ_{xy}(T) and κ(T) peak also at the same temperature. We also studied KTaO_{3} and found a small signal, indicating that a sizable κ_{xy}(T) is not a generic feature of quantum paraelectrics. Combined to other observations, this points to a crucial role played by antiferrodistortive domains in generating κ_{xy} of this solid.
Discovery and Rapid Follow-up Observations of the Unusual Type II SN 2018ivc in NGC 1068

We present the discovery and high-cadence follow-up observations of SN 2018ivc, an unusual SNe II that exploded in NGC 1068 (D = 10.1 Mpc). The light curve of SN 2018ivc declines piecewise-linearly, changing slope frequently, with four clear slope changes in the first 30 days of evolution. This rapidly changing light curve indicates that interaction between the circumstellar material and ejecta plays a significant role in the evolution. Circumstellar interaction is further supported by a strong X-ray detection. The spectra are rapidly evolving and dominated by hydrogen, helium, and calcium emission lines. We identify a rare high-velocity emission-line feature blueshifted at ∼7800 (in Hα, Hβ, Pβ, Pγ, He i, and Ca ii), which is visible from day 18 until at least day 78 and could be evidence of an asymmetric progenitor or explosion. From the overall similarity between SN 2018ivc and SN 1996al, the Hα equivalent width of its parent H ii region, and constraints from pre-explosion archival Hubble Space Telescope images, we find that the progenitor of SN 2018ivc could be as massive as 52 but is more likely <12 . SN 2018ivc demonstrates the importance of the early discovery and rapid follow-up observations of nearby supernovae to study the physics and progenitors of these cosmic explosions.