Miracle-dz/UniFAR-FTUs huggingface dataset:
AGL

Machine learning over graphs has been emerging as powerful learning tools for graph data. However, it is challenging for industrial communities to leverage the techniques, such as graph neural networks (GNNs), and solve real-world problems at scale because of inherent data dependency in the graphs. As such, we cannot simply train a GNN with classic learning systems, for instance, parameter server that assumes data parallelism. Existing systems store the graph data in-memory for fast accesses either in a single machine or graph stores from remote. The major drawbacks are three-fold. First, they cannot scale because of the limitations on the volume of the memories, or the bandwidth between graph stores and workers. Second, they require extra development of graph stores without well exploiting mature infrastructures such as MapReduce that guarantee good system properties. Third, they focus on training but ignore optimizing the performance of inference over graphs, thus makes them an unintegrated system. In this paper, we design AGL, a scalable and integrated system, with fully-functional training and inference for GNNs. Our system design follows the message passing scheme underlying the computations of GNNs. We design to generate the K-hop neighborhood, an information-complete subgraph for each node, as well as do the inference simply by merging values from in-edge neighbors and propagating values to out-edge neighbors via MapReduce. In addition, the K-hop neighborhood contains information-complete subgraphs for each node, thus we simply do the training on parameter servers due to data independence. Our system AGL, implemented on mature infrastructures, can finish the training of a 2-layer GNN on a graph with billions of nodes and hundred billions of edges in 14 hours, and complete the inference in 1.2 hours.
On the spatial and temporal non-locality of dynamo mean-field effects in supersonic interstellar turbulence

The interstellar medium of the Milky Way and nearby disk galaxies harbours large-scale coherent magnetic fields of Microgauss strength, that can be explained via the action of a mean-field dynamo. As in our previous work, we aim to quantify dynamo effects that are self-consistently emerging in realistic direct magnetohydrodynamic simulations, but we generalise our approach to the case of a non-local (non-instantaneous) closure relation, described by a convolution integral in space (time). To this end, we leverage our comprehensive simulation framework for the supernova-regulated turbulent multi-phase interstellar medium. By introducing spatially (temporally) modulated mean fields, we extend the previously used test-field method to the spectral realm -- providing the Fourier representation of the convolution kernels. The resulting spectra of the dynamo mean-field coefficients that we obtain broadly match expectations and allow to rigorously constrain the degree of scale separation in the Galactic dynamo. A surprising result is found for the diamagnetic pumping term, which increases in amplitude when going to smaller scales. Our results amount to the most comprehensive description of dynamo mean-field effects in the Galactic context to date. Surveying the relevant parameter space and quenching behaviour, this will ultimately enable the development of assumption-free sub-grid prescriptions for otherwise unresolved global galaxy simulations.
Branching fractions for 
P3/2
 decays in 
Ba+

Branching fractions for decays from the $P_{3/2}$ level in $^{138}$Ba$^+$ have been measured with a single laser-cooled ion. Decay probabilities to $S_{1/2}$, $D_{3/2}$ and $D_{5/2}$ are determined to be $0.741716(71)$, $0.028031(23)$ and $0.230253(61)$, respectively, which are an order of magnitude improvement over previous results. Our methodology only involves optical pumping and state detection, and is hence relatively free of systematic effects. Measurements are carried out in two different ways to check for consistency. Our analysis also includes a measurement of the $D_{5/2}$ lifetime, for which we obtain 30.14(40)\,s.
Quantifying the Projected Suppression of Cluster Escape Velocity Profiles

The 3D radial escape-velocity profile of galaxy clusters has been suggested to be a promising and competitive tool for constraining mass profiles and cosmological parameters in an accelerating universe. However, the observed line-of-sight escape profile is known to be suppressed compared to the underlying 3D radial (or tangential) escape profile. Past work has suggested that velocity anisotropy in the phase-space data is the root cause. Instead, we find that the observed suppression is from the statistical undersampling of the phase spaces and that the 3D radial escape edge can be accurately inferred from projected data. We build an analytical model for this suppression that only requires the number of observed galaxies N in the phase-space data within the sky-projected range 0.3 ≤ r ⊥/R 200,critical ≤ 1. The radially averaged suppression function is an inverse power law 〈Zv〉=1+(N0/N)λ with N 0 = 17.818 and λ = 0.362. We test our model with N-body simulations, using dark matter particles, subhalos, and semianalytic galaxies as the phase-space tracers, and find excellent agreement. We also assess the model for systematic biases from cosmology (ΩΛ, H 0), cluster mass (M 200,critical), and velocity anisotropy (β). We find that varying these parameters over large ranges can impart a maximal additional fractional change in 〈Z v〉 of 2.7%. These systematics are highly subdominant (by at least a factor of 13.7) to the suppression from N.
Augmented Transformer Achieves 97% and 85% for Top5 Prediction of Direct and Classical Retro-Synthesis

We investigated the effect of different augmentation scenarios on predicting (retro)synthesis of chemical compounds using SMILES representation. We showed that augmentation of not only input sequences but also, importantly, of the target data eliminated the effect of data memorization by neural networks and improved their generalization performance for prediction of new sequences. The Top-5 accuracy was 85.4% for the prediction of the largest fragment (thus identifying principal transformation for classical retro-synthesis) for USPTO-50k test dataset and was achieved by a combination of SMILES augmentation and beam search. The same approach also outperformed best published results for prediction of direct reactions from the USPTO-MIT test set. Our model achieved 90.4% Top-1 and 96.5% Top-5 accuracy for its most challenging mixed set and 97% Top-5 accuracy for the USPTO-MIT separated set. The appearance frequency of the most abundantly generated SMILES was well correlated with the prediction outcome and can be used as a measure of the quality of reaction prediction.
End-to-End Trainable One-Stage Parking Slot Detection Integrating Global and Local Information

This paper proposes an end-to-end trainable one-stage parking slot detection method for around view monitor (AVM) images. The proposed method simultaneously acquires global information (entrance, type, and occupancy of parking slot) and local information (location and orientation of junction) by using a convolutional neural network (CNN), and integrates them to detect parking slots with their properties. This method divides an AVM image into a grid and performs a CNN-based feature extraction. For each cell of the grid, the global and local information of the parking slot is obtained by applying convolution filters to the extracted feature map. Final detection results are produced by integrating the global and local information of the parking slot through non-maximum suppression (NMS). Since the proposed method obtains most of the information of the parking slot using a fully convolutional network without a region proposal stage, it is an end-to-end trainable one-stage detector. In experiments, this method was quantitatively evaluated using the public dataset (PS2.0) and outperforms previous methods by showing both recall and precision of 99.77%, type classification accuracy of 100%, and occupancy classification accuracy of 99.31% while processing 60 frames per second.
Doppler tomographic measurement of the nodal precession of WASP-33b

WASP-33b is a retrograde hot Jupiter with a period of 1.2 days orbiting around a rapidly rotating and pulsating A-type star. A previous study found that the transit chord of WASP-33b had changed slightly from 2008 to 2014 based on Doppler tomographic measurements. They attributed the change to orbital precession caused by the non-zero oblateness of the host star and the misaligned orbit. We aim to confirm and more precisely model the precession behavior using additional Doppler tomographic data of WASP-33b obtained with the High Dispersion Spectrograph on the 8.2m Subaru telescope in 2011, as well as the datasets used in the previous study. Using equations of a long-term orbital precession, we constrain the stellar gravitational quadrupole moment $J_{2}=(9.14\pm 0.51)\times 10^{-5}$ and the angle between the stellar spin axis and the line of sight $i_{\star}=96^{+10}_{-14}$ deg. These values update that the host star is more spherical and viewed more equator than the previous study. We also estimate that the precession period is $\sim$840 years. We also find that the precession amplitude of WASP-33b is $\sim$67 deg and WASP-33b transits in front of the host star for only $\sim$20\% of the whole precession period.
PointLoc: Deep Pose Regressor for LiDAR Point Cloud Localization

In this paper, we present a novel end-to-end learning-based LiDAR sensor relocalization framework, termed PointLoc, which infers 6-DoF poses directly using only a single point cloud as input. Compared to visual sensor-based relocalization, LiDAR sensors can provide rich and robust geometric information about a scene. However, point clouds of LiDAR sensors are unordered and unstructured making it difficult to apply traditional deep learning regression models for this task. We address this issue by proposing a novel PointNet-style architecture with self-attention to efficiently estimate 6-DoF poses from 360° LiDAR sensor frames. Extensive experiments on recently released challenging Oxford Radar RobotCar dataset and real-world robot experiments demonstrate that the proposed method can achieve accurate relocalization performance.
The evolution of primordial black holes and their final observable spins

Primordial black holes in the mass range of ground-based gravitational-wave detectors can comprise a significant fraction of the dark matter. Mass and spin measurements from coalescences can be used to distinguish between an astrophysical or a primordial origin of the binary black holes. In standard scenarios the spin of primordial black holes is very small at formation. However, the mass and spin can evolve through the cosmic history due to accretion. We show that the mass and spin of primordial black holes are correlated in a redshift-dependent fashion, in particular primordial black holes with masses below 𝒪(30)M· are likely non-spinning at any redshift, whereas heavier black holes can be nearly extremal up to redshift z∼10. The dependence of the mass and spin distributions on the redshift can be probed with future detectors such as the Einstein Telescope. The mass and spin evolution affect the gravitational waveform parameters, in particular the distribution of the final mass and spin of the merger remnant, and that of the effective spin of the binary. We argue that, compared to the astrophysical-formation scenario, a primordial origin of black hole binaries might better explain the spin distribution of merger events detected by LIGO-Virgo, in which the effective spin parameter of the binary is compatible to zero except possibly for few high-mass events. Upcoming results from LIGO-Virgo third observation run might reinforce or weaken these predictions.
Connection between galactic downsizing and the most fundamental galactic scaling relations

Context. In their evolution, star-forming galaxies are known to follow scaling relations between some fundamental physical quantities, such as the relation between mass metallicity and star formation main sequence. Aims. We study the evolution of galaxies that at a given redshift, lie simultaneously on the mass-metallicity and main-sequence relations (MZR, MSR). Methods. To this aim, we used the analytical leaky-box chemical evolution model, in which galaxy evolution is described by the infall timescale τ and the wind efficiency λ. We provide a detailed analysis of the temporal evolution of their metallicity, stellar mass, mass-weighted age, and gas fraction. Results. The evolution of the galaxies lying on the MZR and MSR at z ∼ 0.1 suggests that the average infall timescale in two different bins of stellar masses (M⋆ < 1010 M⊙ and M⋆ > 1010 M⊙) decreases with decreasing redshift through the addition of new galaxies with shorter timescales. This means that at each redshift, only the youngest galaxies can be assembled on the shortest timescales and still belong to the star-forming MSR. In the lowest mass bin, a decrease in median τ is accompanied by an increase in the median λ value. This implies that systems that formed at more recent times will need to eject a larger amount of mass to retain their low metallicity values. Another important result is that galactic downsizing, as traced by the age-mass relation, is naturally recovered by imposing the local MZR and MSR for star-forming galaxies. This result is retained even when a constant star formation efficiency for different galactic masses is assumed (without imposing the observed scaling relation between stellar mass and gas-depletion time-scales). Finally, we study the evolution of the hosts of C IV-selected active galactic nuclei, which at z ∼ 2 follow a flat MZR. When we impose that these systems lie on the MSR, we find an “inverted” MZR at lower redshifts, meaning that some additional processes must be at play in their evolution. Conclusions. In our model, galactic downsizing is a direct consequence of the MZR and MSR for star-forming galaxies. This poses a challenge for models of galaxy evolution within a cosmological framework.
Study of hepatic enzyme activity as a predictor of perinatal asphyxia and its severity and outcome

Background: In spite of major advances in the knowledge of fetal and perinatal medicine, perinatal asphyxia is one of the significant causes of mortality and long-term morbidity. Outcome of asphyxiated babies depends on the severity of hypoxia which adversely affects the liver, kidney, brain, heart, and other organs leading to leakage of intracellular enzymes. Aims and Objectives: We undertook this study to estimate hepatic enzyme activity in perinatally asphyxiated babies in relation to nonasphyxiated babies and to know whether hepatic enzyme activity correlates with the severity and outcome of asphyxia. Materials and Methods: This cohort study was conducted in the Department of Pediatrics of our hospital from June 2012 to May 2013. The study included 75 asphyxiated neonates as case and 75 healthy neonates as controls. Serum aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), and lactate dehydrogenase (LDH) levels were estimated by IFCC method. Data gathered from perinatal asphyxia patients and control patients in a preformed pro forma were analyzed by appropriate statistical methods. Results: Serum AST, ALT, LDH, and ALP were found significantly higher in asphyxiated babies compared to the control group (P < 0.05). The rise of AST, ALT, and LDH also showed a significant positive correlation with the severity and outcome of asphyxia. Conclusion: Estimation of hepatic enzymes can be used as a marker to diagnose the presence of perinatal asphyxia and also to assess its severity and outcome.
Cross-GCN: Enhancing Graph Convolutional Network with <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="feng-ieq1-3077524.gif"/></alternatives></inline-formula>-Order Feature Interactions

Graph Convolutional Network (GCN) is an emerging technique that performs learning and reasoning on graph data. It operates feature learning on the graph structure, through aggregating the features of the neighbor nodes to obtain the embedding of each target node. Owing to the strong representation power, recent research shows that GCN achieves state-of-the-art performance on several tasks such as recommendation and linked document classification. Despite its effectiveness, we argue that existing designs of GCN forgo modeling cross features, making GCN less effective for tasks or data where cross features are important. Although neural network can approximate any continuous function, including the multiplication operator for modeling feature crosses, it can be rather inefficient to do so (i.e., wasting many parameters at the risk of overfitting) if there is no explicit design. To this end, we design a new operator named Cross-feature Graph Convolution, which explicitly models the arbitrary-order cross features with complexity linear to feature dimension and order size. We term our proposed architecture as Cross-GCN, and conduct experiments on three graphs to validate its effectiveness. Extensive analysis validates the utility of explicitly modeling cross features in GCN, especially for feature learning at lower layers.
Wave propagation in semiconvective regions of giant planets

Recent observations of Jupiter and Saturn suggest that heavy elements may be diluted in the gaseous envelope, providing a compositional gradient that could stabilise ordinary convection and produce a stably-stratified layer near the core of these planets. This region could consist of semi-convective layers with a staircase-like density profile, which have multiple convective zones separated by thin stably-stratified interfaces, as a result of double-diffusive convection. These layers could have important effects on wave propagation and tidal dissipation that have not been fully explored. We analyse the effects of these layers on the propagation and transmission of internal waves within giant planets, extending prior work in a local Cartesian model. We adopt a simplified global Boussinesq planetary model in which we explore the internal waves in a non-rotating spherical body. We begin by studying the free modes of a region containing semi-convective layers. We then analyse the transmission of internal waves through such a region. The free modes depend strongly on the staircase properties, and consist of modes with both internal and interfacial gravity wave-like behaviour. We determine the frequency shifts of these waves as a function of the number of steps to explore their potential to probe planetary internal structures. We also find that wave transmission is strongly affected by the presence of a staircase. Very large-wavelength waves are transmitted efficiently, but small-scale waves are only transmitted if they are resonant with one of the free modes. The effective size of the core is therefore larger for non-resonant modes.
Level statistics of extended states in random non-Hermitian Hamiltonians

Absence of level repulsion between extended states in random non-Hermitian systems is demonstrated. As a result, the general Wigner-Dyson distributions of level spacing of diffusive metals in the usual Hermitian systems is replaced by the Poisson distribution for quasiparticle level spacing of non-Hermitian disordered metals in the thermodynamic limit of infinite system size. This is a very surprising result because Poisson statistics is universally true for the Anderson insulators where energy eigenstates do not overlap with each other so that energy levels are independent from each other.For disordered metals where different eigenstates overlap with each other, one should expect different levels trying to stay away from each other so that the Poisson distribution should not apply there. Our results show that the larger non-Hermitian energy (dissipation) can invalidate level repulsion principle that holds dearly in quantum mechanics. Thus, our theory provides a unified picture for recent discovery of so called "level attraction" in various systems. It provides also a theoretical basis for manipulating energy levels.
Deep Learning in Memristive Nanowire Networks

Analog crossbar architectures for accelerating neural network training and inference have made tremendous progress over the past several years. These architectures are ideal for dense layers with fewer than roughly a thousand neurons. However, for large sparse layers, crossbar architectures are highly inefficient. A new hardware architecture, dubbed the MN3 (Memristive Nanowire Neural Network), was recently described as an efficient architecture for simulating very wide, sparse neural network layers, on the order of millions of neurons per layer. The MN3 utilizes a high-density memristive nanowire mesh to efficiently connect large numbers of silicon neurons with modifiable weights. Here, in order to explore the MN3's ability to function as a deep neural network, we describe one algorithm for training deep MN3 models and benchmark simulations of the architecture on two deep learning tasks. We utilize a simple piecewise linear memristor model, since we seek to demonstrate that training is, in principle, possible for randomized nanowire architectures. In future work, we intend on utilizing more realistic memristor models, and we will adapt the presented algorithm appropriately. We show that the MN3 is capable of performing composition, gradient propagation, and weight updates, which together allow it to function as a deep neural network. We show that a simulated multilayer perceptron (MLP), built from MN3 networks, can obtain a 1.61% error rate on the popular MNIST dataset, comparable to equivalently sized software-based network. This work represents, to the authors' knowledge, the first randomized nanowire architecture capable of reproducing the backpropagation algorithm.
BERT as a Teacher: Contextual Embeddings for Sequence-Level Reward

Measuring the quality of a generated sequence against a set of references is a central problem in many learning frameworks, be it to compute a score, to assign a reward, or to perform discrimination. Despite great advances in model architectures, metrics that scale independently of the number of references are still based on n-gram estimates. We show that the underlying operations, counting words and comparing counts, can be lifted to embedding words and comparing embeddings. An in-depth analysis of BERT embeddings shows empirically that contextual embeddings can be employed to capture the required dependencies while maintaining the necessary scalability through appropriate pruning and smoothing techniques. We cast unconditional generation as a reinforcement learning problem and show that our reward function indeed provides a more effective learning signal than n-gram reward in this challenging setting.
Enhanced anomalous Hall effect in the magnetic topological semimetal 
Co3Sn2−xInxS2

We study the anomalous Hall effect (AHE) of single-crystalline ${\mathrm{Co}}_{3}{\mathrm{Sn}}_{2\ensuremath{-}x}{\mathrm{In}}_{x}{\mathrm{S}}_{2}$ over a large range of indium concentration $x$ from 0 to 1. Their magnetization reduces progressively with increasing $x$ while their ground state evolves from a ferromagnetic Weyl semimetal into a nonmagnetic insulator. Remarkably, after systematically scaling the AHE, we find that their intrinsic anomalous Hall conductivity (AHC) features an unexpected maximum at around $x=0.15$. The change of the intrinsic AHC corresponds with the doping evolution of Berry curvature and the maximum arises from the magnetic topological nodal-ring gap. Our experimental results show a larger AHC in a fundamental nodal-ring gap than that of Weyl nodes.