Miracle-dz/UniFAR-FTUs huggingface dataset:
Mining Domain Knowledge: Improved Framework Towards Automatically Standardizing Anatomical Structure Nomenclature in Radiotherapy

The automatic standardization of nomenclature for anatomical structures in radiotherapy (RT) clinical data is a critical prerequisite for data curation and data-driven research in the era of big data and artificial intelligence, but it is currently an unmet need. Existing methods either cannot handle cross-institutional datasets or suffer from heavy imbalance and poor-quality delineation in clinical RT datasets. To solve these problems, we propose an automated structure nomenclature standardization framework, 3D Non-local Network with Voting (3DNNV). This framework consists of an improved data processing strategy, namely, adaptive sampling and adaptive cropping (ASAC) with voting, and an optimized feature extraction module. The framework simulates clinicians’ domain knowledge and recognition mechanisms to identify small-volume organs at risk (OARs) with heavily imbalanced data better than other methods. We used partial data from an open-source head-and-neck cancer dataset to train the model, then tested the model on three cross-institutional datasets to demonstrate its generalizability. 3DNNV outperformed the baseline model, achieving higher average true positive rates (TPR) over all categories on the three test datasets (+8.27%, +2.39%, and +5.53%, respectively). More importantly, the 3DNNV outperformed the baseline on the test dataset, 28.63% to 91.17%, in terms of F1 score for a small-volume OAR with only 9 training samples. The results show that 3DNNV can be applied to identify OARs, even error-prone ones. Furthermore, we discussed the limitations and applicability of the framework in practical scenarios. The framework we developed can assist in standardizing structure nomenclature to facilitate data-driven clinical research in cancer radiotherapy.
The Pseudopotential Approach within Density-Functional Theory: The Case of Atomic Metallic Hydrogen

Internal energies, enthalpies, phonon dispersion curves, and superconductivity of atomic metallic hydrogen are calculated. The standard use of pseudopotentials in density-functional theory are compared with full Coulomb-potential, all-electron linear muffin-tin orbital calculations. Quantitatively similar results are found as far as internal energies are concerned. Larger differences are found for phase-transition pressures; significant enough to affect the phase diagram. Electron–phonon spectral functions α2F(ω) also show significant differences. Against expectation, the estimated superconducting critical-temperature Tc of the first atomic metallic phase I41/amd (Cs-IV) at 500 GPa is actually higher.
A Robust Self-Learning Method for Fully Unsupervised Cross-Lingual Mappings of Word Embeddings: Making the Method Robustly Reproducible as Well

In this paper, we reproduce the experiments of Artetxe et al. (2018b) regarding the robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings. We show that the reproduction of their method is indeed feasible with some minor assumptions. We further investigate the robustness of their model by introducing four new languages that are less similar to English than the ones proposed by the original paper. In order to assess the stability of their model, we also conduct a grid search over sensible hyperparameters. We then propose key recommendations that apply to any research project in order to deliver fully reproducible research.
Are inner disc misalignments common? ALMA reveals an isotropic outer disc inclination distribution for young dipper stars

Dippers are a common class of young variable star exhibiting day-long dimmings with depths of up to several tens of per cent. A standard explanation is that dippers host nearly edge-on (id ≈ 70°) protoplanetary discs that allow close-in (<1 au) dust lifted slightly out of the mid-plane to partially occult the star. The identification of a face-on dipper disc and growing evidence of inner disc misalignments brings this scenario into question. Thus, we uniformly (re)derive the inclinations of 24 dipper discs resolved with (sub-)mm interferometry from ALMA. We find that dipper disc inclinations are consistent with an isotropic distribution over id ≈ 0−75°, above which the occurrence rate declines (likely an observational selection effect due to optically thick disc mid-planes blocking their host stars). These findings indicate that the dipper phenomenon is unrelated to the outer (>10 au) disc resolved by ALMA and that inner disc misalignments may be common during the protoplanetary phase. More than one mechanism may contribute to the dipper phenomenon, including accretion-driven warps and ‘broken’ discs caused by inclined (sub-)stellar or planetary companions.
Evolution of the Alfvén Mach number associated with a coronal mass ejection shock

<p>The Sun regularly produces large-scale eruptive events, such as coronal mass ejections (CMEs) that can drive shock waves through the solar corona. Such shocks can result in electron acceleration and subsequent radio emission in the form of a type II radio burst. However, the early-phase evolution of shock properties and its relationship to type II burst evolution is still subject to investigation. Here we study the evolution of a CME-driven shock by comparing three commonly used methods of calculating the Alfv&#233;n Mach number (<span tabindex="0"><span><span><span><span>M</span><sub><span>A</span></sub></span></span></span></span>), namely: shock geometry, a comparison of CME speed to a model of the coronal Alfv&#233;n speed, and the type II band-splitting method. We applied the three methods to the 2017 September 2 event, focusing on the shock wave observed in extreme ultraviolet (EUV) by the Solar Ultraviolet Imager (SUVI) on board GOES-16, in white-light by the Large Angle and Spectrometric Coronagraph (LASCO) on board SOHO, and the type II radio burst observed by the Irish Low Frequency Array (I-LOFAR). We show that the three different methods of estimating shock <span tabindex="0"><span><span><span><span>M</span><sub><span>A</span></sub></span></span></span></span> yield consistent results and provide a means of relating shock property evolution to the type II emission duration. The type II radio emission emerged from near the nose of the CME when <span tabindex="0"><span><span><span><span>M</span><sub><span>A</span></sub></span></span></span></span>&#160;was in the range 1.4-2.4 at a heliocentric distance of&#160;<span tabindex="0"><span><span><span>&#8764;</span></span></span></span>1.6&#160;<span tabindex="0"><span><span><span><span>R<span tabindex="0"><span><span><span><sub><span>&#8857;</span></sub></span></span></span></span></span></span></span></span></span>. The emission ceased when the CME nose reached&#160;<span tabindex="0"><span><span><span>&#8764;</span></span></span></span>2.4&#160;<span tabindex="0"><span><span><span><span>R</span><sub><span>&#8857;</span></sub></span></span></span></span>, despite an increasing Alfv&#233;n Mach number (up to 4). We suggest the radio emission cessation is due to the lack of quasi-perpendicular geometry at this altitude, which inhibits efficient electron acceleration and subsequent radio emission. </p>
Conv-MPN: Convolutional Message Passing Neural Network for Structured Outdoor Architecture Reconstruction

This paper proposes a novel message passing neural (MPN) architecture Conv-MPN, which reconstructs an outdoor building as a planar graph from a single RGB image. Conv-MPN is specifically designed for cases where nodes of a graph have explicit spatial embedding. In our problem, nodes correspond to building edges in an image. Conv-MPN is different from MPN in that 1) the feature associated with a node is represented as a feature volume instead of a 1D vector; and 2) convolutions encode messages instead of fully connected layers. Conv-MPN learns to select a true subset of nodes (i.e., building edges) to reconstruct a building planar graph. Our qualitative and quantitative evaluations over 2,000 buildings show that Conv-MPN makes significant improvements over the existing fully neural solutions. We believe that the paper has a potential to open a new line of graph neural network research for structured geometry reconstruction.
The quantum random energy model as a limit of p-spin interactions

We consider the free energy of a mean-field quantum spin glass described by a [Formula: see text]-spin interaction and a transversal magnetic field. Recent rigorous results for the case [Formula: see text], i.e. the quantum random energy model (QREM), are reviewed. We show that the free energy of the [Formula: see text]-spin model converges in a joint thermodynamic and [Formula: see text] limit to the free energy of the QREM.
Neural Machine Translation: A Review

The field of machine translation (MT), the automatic translation of written text from one natural language into another, has experienced a major paradigm shift in recent years. Statistical MT, which mainly relies on various count-based models and which used to dominate MT research for decades, has largely been superseded by neural machine translation (NMT), which tackles translation with a single neural network. In this work we will trace back the origins of modern NMT architectures to word and sentence embeddings and earlier examples of the encoder-decoder network family. We will conclude with a short survey of more recent trends in the field.
Acquiring Knowledge from Pre-trained Model to Neural Machine Translation

Pre-training and fine-tuning have achieved great success in natural language process field. The standard paradigm of exploiting them includes two steps: first, pre-training a model, e.g. BERT, with a large scale unlabeled monolingual data. Then, fine-tuning the pre-trained model with labeled data from downstream tasks. However, in neural machine translation (NMT), we address the problem that the training objective of the bilingual task is far different from the monolingual pre-trained model. This gap leads that only using fine-tuning in NMT can not fully utilize prior language knowledge. In this paper, we propose an Apt framework for acquiring knowledge from pre-trained model to NMT. The proposed approach includes two modules: 1). a dynamic fusion mechanism to fuse task-specific features adapted from general knowledge into NMT network, 2). a knowledge distillation paradigm to learn language knowledge continuously during the NMT training process. The proposed approach could integrate suitable knowledge from pre-trained models to improve the NMT. Experimental results on WMT English to German, German to English and Chinese to English machine translation tasks show that our model outperforms strong baselines and the fine-tuning counterparts.
GraphPoseGAN: 3D Hand Pose Estimation from a Monocular RGB Image via Adversarial Learning on Graphs

—This paper addresses the problem of 3D hand pose estimation from a monocular RGB image. We are the ﬁrst to pro-pose a graph-based generative adversarial learning framework regularized by a hand model, aiming at realistic 3D hand pose estimation. Our model consists of a 3D hand pose generator and a multi-source discriminator. Taking one monocular RGB image as the input, the generator is essentially a residual graph convolution module with a parametric deformable hand model as prior for pose reﬁnement. Further, we design a multi-source discriminator with hand poses, bones and the input image as input to capture intrinsic features, which distinguishes the predicted 3D hand pose from the ground-truth and leads to anthropomorphically valid hand poses. In addition, we propose two novel bone-constrained loss functions, which characterize the morphable structure of hand poses explicitly. Extensive experiments demonstrate that our model sets new state-of-the-art performances in 3D hand pose estimation from a monocular image on standard benchmarks.
Evolution of the Radius Valley around Low-mass Stars from Kepler and K2

We present calculations of the occurrence rate of small close-in planets around low-mass dwarf stars using the known planet populations from the Kepler and K2 missions. Applying completeness corrections clearly reveals the radius valley in the maximum a posteriori occurrence rates as a function of orbital separation and planet radius. We measure the slope of the valley to be , which bears the opposite sign from that measured around Sun-like stars, thus suggesting that thermally driven atmospheric mass loss may not dominate the evolution of planets in the low stellar mass regime or that we are witnessing the emergence of a separate channel of planet formation. The latter notion is supported by the relative occurrence of rocky to non-rocky planets increasing from 0.5 ± 0.1 around mid-K dwarfs to 8.5 ± 4.6 around mid-M dwarfs. Furthermore, the center of the radius valley at 1.54 ± 0.16 R⊕ is shown to shift to smaller sizes with decreasing stellar mass, in agreement with physical models of photoevaporation, core-powered mass loss, and gas-poor formation. Although current measurements are insufficient to robustly identify the dominant formation pathway of the radius valley, such inferences may be obtained by the Transiting Exoplanet Survey Satellite with (85,000) mid-to-late M dwarfs observed with 2 minutes cadence. The measurements presented herein also precisely designate the subset of planetary orbital periods and radii that should be targeted in radial velocity surveys to resolve the rocky to non-rocky transition around low-mass stars.
Non-thermal emission in lobes of radio galaxies – III. 3C 98, Pictor A, DA 240, Cygnus A, 3C 326, and 3C 236

Recent analyses of the broad spectral energy distributions (SEDs) of extensive lobes of local radio galaxies have confirmed the leptonic origin of their Fermi/Large Area Telescope γ-ray emission, significantly constraining the level of hadronic contribution. SEDs of distant (DL > 125 Mpc) radio-galaxy lobes are currently limited to the radio and X-ray bands, hence give no information on the presence of non-thermal (NT) protons but are adequate to describe the properties of NT electrons. Modelling lobe radio and X-ray emission in 3C 98, Pictor A, DA 240, Cygnus A, 3C 326, and 3C 236, we fully determine the properties of intralobe NT electrons and estimate the level of the related γ-ray emission from Compton scattering of the electrons off the superposed cosmic microwave background, extragalactic background light, and source-specific radiation fields.
Pattern and Anomaly Detection in Urban Temporal Networks

Broad spectrum of urban activities including mobility can be modeled as temporal networks evolving over time. Abrupt changes in urban dynamics caused by events such as disruption of civic operations, mass crowd gatherings, holidays and natural disasters are potentially reflected in these temporal mobility networks. Identification and early detecting of such abnormal developments is of critical importance for transportation planning and security. Anomaly detection from high dimensional network data is a challenging task as edge level measurements often have low values and high variance resulting in high noise-to-signal ratio. In this study, we propose a generic three-phase pipeline approach to tackle curse of dimensionality and noisiness of the original data. Our pipeline consists of i) initial network aggregation leveraging community detection ii) unsupervised dimensionality reduction iii) clustering of the resulting representations for outlier detection. We perform extensive experiments to evaluate the proposed approach on mobility data collected from two major cities, New York City and Taipei. Our results empirically prove that proposed methodology outperforms traditional approaches for anomaly detection. We further argue that the proposed anomaly detection framework is potentially generalizable to various other types of temporal networks e.g. social interactions, information propagation and epidemic spread.
Enumerating the class of minimally path connected simplicial complexes

In 1983 Kalai proved an incredible generalisation of Cayley's formula for the number of trees on a labelled vertex set to a formula for a class of $r$-dimensional simplicial complexes. These simplicial complexes generalise trees by means of being homologically $\Bbb Q$-acyclic. In this text we consider a different generalisation of trees to the class of pure dimensional simplicial complexes that \textit{minimally connect a vertex set} (in the sense that the removal of any top dimensional face disconnects the complex). Our main result provides an upper and lower bound for the number of these minimally connected complexes on a labelled vertex set. We also prove that they are potentially vastly more topologically complex than the generalisation of Kalai. As an application of our bounds we compute the threshold probability for the connectivity of a random simplicial complex.
Clinical review of non-invasive intracranial pressure measurement in medical cases

Abstract Intracranial pressure (ICP) measurement plays a vital role in decision making in neurological emergencies. Invasive methods of ICP monitoring have been the gold standard. Advent of various non-invasive techniques has widened the option of ICP measurement in medical cases as well. We illustrate two cases where optic nerve sheath measurements helped in managing raised ICP and time the need for surgical intervention and review the literature on non-invasive ICP measurement.
Domain-Independent Dominance of Adaptive Methods

From a simplified analysis of adaptive methods, we derive AvaGrad, a new optimizer which outperforms SGD on vision tasks when its adaptability is properly tuned. We observe that the power of our method is partially explained by a decoupling of learning rate and adaptability, greatly simplifying hyperparameter search. In light of this observation, we demonstrate that, against conventional wisdom, Adam can also outperform SGD on vision tasks, as long as the coupling between its learning rate and adaptability is taken into account. In practice, AvaGrad matches the best results, as measured by generalization accuracy, delivered by any existing optimizer (SGD or adaptive) across image classification (CIFAR, ImageNet) and character-level language modelling (Penn Treebank) tasks. When training GANs, AvaGrad improves upon existing optimizers.1
Multi-Scan Implementation of the Trajectory Poisson Multi-Bernoulli Mixture Filter

The Poisson multi-Bernoulli mixture (PMBM) and the multi-Bernoulli mixture (MBM) are two multi-target distributions for which closed-form filtering recursions exist. The PMBM has a Poisson birth process, whereas the MBM has a multi-Bernoulli birth process. This paper considers a recently developed formulation of the multi-target tracking problem using a random finite set of trajectories, through which the track continuity is explicitly established. A multi-scan trajectory PMBM filter and a multi-scan trajectory MBM filter, with the ability to correct past data association decisions to improve current decisions, are presented. In addition, a multi-scan trajectory $\text{MBM}_{01}$ filter, in which the existence probabilities of all Bernoulli components are either 0 or 1, is presented. This paper proposes an efficient implementation that performs track-oriented $N$-scan pruning to limit computational complexity, and uses dual decomposition to solve the involved multi-frame assignment problem. The performance of the presented multi-target trackers, applied with an efficient fixed-lag smoothing method, are evaluated in a simulation study.
Plug and Play Language Models: A Simple Approach to Controlled Text Generation

Large transformer-based language models (LMs) trained on huge text corpora have shown unparalleled generation capabilities. However, controlling attributes of the generated language (e.g. switching topic or sentiment) is difficult without modifying the model architecture or fine-tuning on attribute-specific data and entailing the significant cost of retraining. We propose a simple alternative: the Plug and Play Language Model (PPLM) for controllable language generation, which combines a pretrained LM with one or more simple attribute classifiers that guide text generation without any further training of the LM. In the canonical scenario we present, the attribute models are simple classifiers consisting of a user-specified bag of words or a single learned layer with 100,000 times fewer parameters than the LM. Sampling entails a forward and backward pass in which gradients from the attribute model push the LM's hidden activations and thus guide the generation. Model samples demonstrate control over a range of topics and sentiment styles, and extensive automated and human annotated evaluations show attribute alignment and fluency. PPLMs are flexible in that any combination of differentiable attribute models may be used to steer text generation, which will allow for diverse and creative applications beyond the examples given in this paper.
Constraining the Milky Way Mass Profile with Phase-space Distribution of Satellite Galaxies

We estimate the Milky Way (MW) halo properties using satellite kinematic data including the latest measurements from Gaia DR2. With a simulation-based 6D phase-space distribution function (DF) of satellite kinematics, we can infer halo properties efficiently and without bias, and handle the selection function and measurement errors rigorously in the Bayesian framework. Applying our DF from the EAGLE simulation to 28 satellites, we obtain an MW halo mass of and a concentration of with the prior based on the M–c relation. The inferred mass profile is consistent with previous measurements but with better precision and reliability due to the improved methodology and data. Potential improvement is illustrated by combining satellite data and stellar rotation curves. Using our EAGLE DF and best-fit MW potential, we provide much more precise estimates of the kinematics for those satellites with uncertain measurements. Compared to the EAGLE DF, which matches the observed satellite kinematics very well, the DF from the semi-analytical model based on the dark-matter-only simulation Millennium II (SAM-MII) over-represents satellites with small radii and velocities. We attribute this difference to less disruption of satellites with small pericenter distances in the SAM-MII simulation. By varying the disruption rate of such satellites in this simulation, we estimate a ∼5% scatter in the inferred MW halo mass among hydrodynamics-based simulations.
StarGAN v2: Diverse Image Synthesis for Multiple Domains

A good image-to-image translation model should learn a mapping between different visual domains while satisfying the following properties: 1) diversity of generated images and 2) scalability over multiple domains. Existing methods address either of the issues, having limited diversity or multiple models for all domains. We propose StarGAN v2, a single framework that tackles both and shows significantly improved results over the baselines. Experiments on CelebA-HQ and a new animal faces dataset (AFHQ) validate our superiority in terms of visual quality, diversity, and scalability. To better assess image-to-image translation models, we release AFHQ, high-quality animal faces with large inter- and intra-domain differences. The code, pretrained models, and dataset are available at https://github.com/clovaai/stargan-v2.