Miracle-dz/UniFAR-FTUs huggingface dataset:
3D-MiniNet: Learning a 2D Representation From Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmentation

LIDAR semantic segmentation is an essential task that provides 3D semantic information about the environment to robots. Fast and efficient semantic segmentation methods are needed to match the strong computational and temporal restrictions of many real-world robotic applications. This work presents 3D-MiniNet, a novel approach for LIDAR semantic segmentation that combines 3D and 2D learning layers. It first learns a 2D representation from the raw points through a novel projection which extracts local and global information from the 3D data. This representation is fed to an efficient 2D Fully Convolutional Neural Network (FCNN) that produces a 2D semantic segmentation. These 2D semantic labels are re-projected back to the 3D space and enhanced through a post-processing module. The main novelty in our strategy relies on the projection learning module. Our detailed ablation study shows how each component contributes to the final performance of 3D-MiniNet. We validate our approach on well known public benchmarks (SemanticKITTI and KITTI), where 3D-MiniNet gets state-of-the-art results while being faster and more parameter-efficient than previous methods.
Semileptonic decays of 
Λc+
 in dynamical approaches

We study the semileptonic decays of $\Lambda_c^+ \to \Lambda(n)\ell^+ \nu_{\ell}$ in two relativistic dynamical approaches of the light-front constituent quark model (LFCQM) and MIT bag model (MBM). By considering the Fermi statistic between quarks and determining spin-flavor structures in baryons along with the helicity formalism in the two different dynamical models, we calculate the branching ratios (${\cal B}$s) and averaged asymmetry parameters ($\alpha$s) in the decays. Explicitly, we find that ${\cal B}( \Lambda_c^+ \to \Lambda e^+ \nu_{e})=(3.36\pm0.87,3.48)\%$ and ${\alpha}( \Lambda_c^+ \to \Lambda e^+ \nu_{e})=(-0.97\pm0.03,-0.83)$ in (LFCQM, MBM), in comparison with the data of ${\cal B}( \Lambda_c^+ \to \Lambda e^+ \nu_{e})=(3.6\pm0.4)\%$ and ${\alpha}( \Lambda_c^+ \to \Lambda e^+ \nu_{e})=-0.86\pm 0.04$ given by the Particle Data Group, respectively. We also predict that ${\cal B}( \Lambda_c^+ \to n e^+ \nu_{e})=(0.57\pm0.15, 3.6\pm1.5)\times 10^{-3}$ and ${\alpha}( \Lambda_c^+ \to n e^+ \nu_{e})=(-0.98\pm0.02,-0.96\pm0.04)$ in LFCQM with two different scenarios for the momentum distributions of quarks in the neutron, and ${\cal B}( \Lambda_c^+ \to n e^+ \nu_{e})= 0.279\times 10^{-2}$ and ${\alpha}( \Lambda_c^+ \to n e^+ \nu_{e})=-0.87$ in MBM, which could be tested by the ongoing experiments at BESIII, LHCb and BELLEII.
Interactive, effort-aware library version harmonization

As a mixed result of intensive dependency on third-party libraries, flexible mechanisms to declare dependencies and increased number of modules in a project, different modules of a project directly depend on multiple versions of the same third-party library. Such library version inconsistencies could increase dependency maintenance cost, or even lead to dependency conflicts when modules are inter-dependent. Although automated build tools (e.g., Maven's enforcer plugin) provide partial support to detect library version inconsistencies, they do not provide any support to harmonize inconsistent library versions. We first conduct a survey with 131 Java developers from GitHub to retrieve first-hand information about the root causes, detection methods, reasons for fixing or not fixing, fixing strategies, fixing efforts, and tool expectations on library version inconsistencies. Then, based on the insights from our survey, we propose LibHarmo, an interactive, effort-aware library version harmonization technique, to detect library version inconsistencies, interactively suggest a harmonized version with the least harmonization efforts based on library API usage analysis, and refactor build configuration files. LibHarmo is currently developed for Java Maven projects. Our experimental study on 443 highly-starred Java Maven projects from GitHub shows that i) LibHarmo detected 621 library version inconsistencies in 152 (34.3%) projects with a false positive rate of 16.8%, while Maven's enforcer plugin only detected 219 of them; and ii) LibHarmo saved 87.5% of the harmonization efforts. Further, 31 library version inconsistencies have been confirmed, and 17 of them have been already harmonized by developers.
Exploring Learning Dynamics of DNNs via Layerwise Conditioning Analysis

Conditioning analysis uncovers the landscape of optimization objective by exploring the spectrum of its curvature matrix. It is well explored theoretically for linear models. We extend this analysis to deep neural networks (DNNs). To this end, we propose a layer-wise conditioning analysis that explores the optimization landscape with respect to each layer independently. Such an analysis is theoretically supported under mild assumptions that approximately hold in practice. Based on our analysis, we show that batch normalization (BN) can adjust the magnitude of the layer activations/gradients, and thus stabilizes the training. However, such a stabilization can result in a false impression of a local minimum, which sometimes has detrimental effects on the learning. Besides, we experimentally observe that BN can improve the layer-wise conditioning of the optimization problem. Finally, we observe that the last linear layer of very deep residual network has ill-conditioned behavior during training. We solve this problem by only adding one BN layer before the last linear layer, which achieves improved performance over the original residual networks, especially when the networks are deep.
Flowing emulsions through disorder: Critical depinning and smectic rivers

During the past sixty minutes only, oil companies have extracted six trillions liters of oil from the ground, i.e. the volume of about two hundreds Olympic swimming pools. This phenomenal number gives a striking illustration of the impact of multiphase flows on the world economy and environment. From a fundamental perspective, we now clearly understand the large-scale patterns formed when liquid interfaces are driven through heterogeneous environments. In stark contrast, the displacement of fragmented fluids through disordered media remains limited to isolated droplets and bubbles. Here, we elucidate the collective dynamics of emulsions hydrodynamically driven through disordered environments. Advecting hundreds of thousands of microfluidic droplets through random lattices of pinning sites, we establish that the mobilization of confined emulsions is a critical dynamical transition. Unlike contact-line depinning, emulsion mobilization is not triggered by large-scale avalanches but merely requires the coordinated motion of small groups of particles. Criticality arises from the correlations of seemingly erratic depinning events over system-spanning scales along smectic river networks. We elucidate the microscopic origin of these self-organized flow patterns: contact interactions and hydrodynamic focusing conspire to mobilize emulsion out of disorder. We close our article commenting on the similarities (and profound differences) with the plastic depinning transitions of driven flux lines in high-$T_{\rm c}$ superconductors, and grain transport in eroded sand beds.
Milky Way Satellites Shining Bright in Gravitational Waves

The population of Milky Way satellite galaxies is of great interest for cosmology, fundamental physics, and astrophysics. They represent the faint end of the galaxy luminosity function, are the most dark-matter-dominated objects in the local universe, and contain the oldest and most metal-poor stellar populations. Recent surveys have revealed around 60 satellites, but this could represent less than half of the total. Characterization of these systems remains a challenge due to their low luminosity. We consider the gravitational-wave observatory LISA as a potential tool for studying these satellites through observations of their short-period double white dwarf populations. LISA will observe the entire sky without selection effects due to dust extinction, complementing optical surveys, and could potentially discover massive satellites hidden behind the disk of the Galaxy.
An Empirical Study of Usages, Updates and Risks of Third-Party Libraries in Java Projects

Third-party libraries play a key role in software development as they can relieve developers of the heavy burden of re-implementing common functionalities. However, third-party libraries and client projects evolve asynchronously. As a result, out-dated third-party libraries might be used in client projects while developers are not aware of the potential risk (e.g., security bug). Outdated third-party libraries may be updated in client projects in a delayed way, and developers may be less aware of the potential risk (e.g., API incompatibility) in updates. Developers of third-party libraries may be unaware of how their third-party libraries are used or updated in client projects. Therefore, a quantitative and holistic study on usages, updates and risks of third-party libraries in open-source projects can provide concrete evidences on these problems, and practical insights to improve the ecosystem.In this paper, we contribute such a study in Java ecosystem. In particular, we conduct a library usage analysis (e.g., usage intensity and outdatedness) and library update analysis (e.g., update intensity and delay) on 806 open-source projects and 13,565 third- party libraries. Then, we carry out a library risk analysis (e.g., usage risk and update risk) on 806 open-source projects and 544 security bugs. These analyses aim to quantify the usage and update practices and the potential risk of using and updating outdated third-party libraries with respect to security bugs from two holistic perspectives (i.e., open-source projects and third-party libraries). Our findings suggest practical implications to developers and researchers on problems and potential solutions in maintaining third-party libraries (e.g., smart alerting and automated updating of outdated third-party libraries). To indicate the usefulness of our findings, we design a smart alerting system for assisting developers to make confident decisions when updating third-party libraries. 33 and 24 open-source projects have confirmed and updated third-party libraries after receiving our alerts.
Bigness of the tangent bundle of del Pezzo
surfaces and D-simplicity

We consider the question of simplicity of a ring $R$ under the action of its ring of differential operators $D_R$. We give examples to show that even when $R$ is Gorenstein and has rational singularities $R$ need not be a simple $D_R$-module; for example, this is the case when $R$ is the homogeneous coordinate ring of a smooth cubic surface. Our examples are homogeneous coordinate rings of smooth Fano varieties, and our proof proceeds by showing that the tangent bundle of such a variety need not be big. We also give a partial converse showing that when $R$ is the homogeneous coordinate ring of a smooth projective variety $X$, embedded by some multiple of its canonical divisor, then simplicity of $R$ as a $D_R$-module implies that $X$ is Fano and thus $R$ has rational singularities.
Dimensionality Reduction of SDSS Spectra with Variational Autoencoders

High-resolution galaxy spectra contain much information about galactic physics, but the high dimensionality of these spectra makes it difficult to fully utilize the information they contain. We apply variational autoencoders (VAEs), a nonlinear dimensionality reduction technique, to a sample of spectra from the Sloan Digital Sky Survey (SDSS). In contrast to principal component analysis (PCA), a widely used technique, VAEs can capture nonlinear relationships between latent parameters and the data. We find that a VAE can reconstruct the SDSS spectra well with only six latent parameters, outperforming PCA with the same number of components. Different galaxy classes are naturally separated in this latent space, without class labels having been given to the VAE. The VAE latent space is interpretable because the VAE can be used to make synthetic spectra at any point in latent space. For example, making synthetic spectra along tracks in latent space yields sequences of realistic spectra that interpolate between two different types of galaxies. Using the latent space to find outliers may yield interesting spectra: in our small sample, we immediately find unusual data artifacts and stars misclassified as galaxies. In this exploratory work, we show that VAEs create compact, interpretable latent spaces that capture nonlinear features of the data. While a VAE takes substantial time to train (≈1 day for 48,000 spectra), once trained, VAEs can enable the fast exploration of large astronomical data sets.
Fair and Truthful Mechanisms for Dichotomous Valuations

We consider the problem of allocating a set on indivisible items to players with private preferences in an efficient and fair way. We focus on valuations that have dichotomous marginals, in which the added value of any item to a set is either 0 or 1, and aim to design truthful allocation mechanisms (without money) that maximize welfare and are fair. For the case that players have submodular valuations with dichotomous marginals, we design such a deterministic truthful allocation mechanism. The allocation output by our mechanism is Lorenz dominating, and consequently satisfies many desired fairness properties, such as being envy-free up to any item (EFX), and maximizing the Nash Social Welfare (NSW). We then show that our mechanism with random priorities is envy-free ex-ante, while having all the above properties ex-post. Furthermore, we present several impossibility results precluding similar results for the larger class of XOS valuations.
CO, Water, and Tentative Methanol in η Carinae Approaching Periastron

The complex circumstellar environment around the massive binary and luminous blue variable η Carinae is known to harbor numerous light molecules, emitting most strongly in rotational states with upper level energies to ∼300 K. In circumstellar gas, the complex organic molecule methanol (CH3OH) has been found almost exclusively around young stellar objects, and thus regarded as a signpost of recent star formation. Here we report the first potential detection of methanol around a highly evolved high-mass star, while using the Atacama Large Millimeter Array to investigate molecular cloud conditions traced by CO (2–1) in an orbit phase preceding the 2020 periastron. The methanol emission originates from hot (Tgas ≃ 700 K) material, ∼2″ (0.02 pc) across, centered on the dust-obscured binary, and is accompanied by prominent absorption of continuum radiation in a cooler (Tgas ≃ 110 K) layer of gas. We also report a first detection of water in Herschel observations at 557 and 988 GHz. The methanol abundance is several to 50 times higher than observed toward several lower-mass stars, while water abundances are similar to those observed in cool, dense molecular clouds. The very high methanol:water abundance ratio in the core of η Car may suggest methanol formation processes similar to Fischer–Tropsch-type catalytic reactions on dust grains. These observations prove that complex molecule formation can occur in a chemically evolved massive stellar environment, given sufficient gas densities and shielding conditions as may occur in material around massive interacting companions and merger remnants.
Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs

Generative adversarial networks (GANs) have shown outstanding performance on a wide range of problems in computer vision, graphics, and machine learning, but often require numerous training data and heavy computational resources. To tackle this issue, several methods introduce a transfer learning technique in GAN training. They, however, are either prone to overfitting or limited to learning small distribution shifts. In this paper, we show that simple fine-tuning of GANs with frozen lower layers of the discriminator performs surprisingly well. This simple baseline, FreezeD, significantly outperforms previous techniques used in both unconditional and conditional GANs. We demonstrate the consistent effect using StyleGAN and SNGAN-projection architectures on several datasets of Animal Face, Anime Face, Oxford Flower, CUB-200-2011, and Caltech-256 datasets. The code and results are available at https://github.com/sangwoomo/FreezeD.
Learning Machines from Simulation to Real World

Learning Machines is developing a flexible, cross-industry, advanced analytics platform, targeted during stealth-stage at a limited number of specific vertical applications. In this paper, we aim to integrate a general machine system to learn a variant of tasks from simulation to real world. In such a machine system, it involves real-time robot vision, sensor fusion, and learning algorithms (reinforcement learning). To this end, we demonstrate the general machine system on three fundamental tasks including obstacle avoidance, foraging, and predator-prey robot. The proposed solutions are implemented on Robobo robots with mobile device (smartphone with camera) as interface and built-in infrared (IR) sensors. The agent is trained in a virtual environment. In order to assess its performance, the learned agent is tested in the virtual environment and reproduce the same results in a real environment. The results show that the reinforcement learning algorithm can be reliably used for a variety of tasks in unknown environments.
Hidden magnetic fields of young suns

Global magnetic fields of active solar-like stars are, nowadays, routinely detected with spectropolarimetric measurements and are mapped with Zeeman Doppler imaging (ZDI). However, due to the cancellation of opposite field polarities, polarimetry only captures a tiny fraction of the magnetic flux and cannot assess the overall stellar surface magnetic field if it is dominated by a small-scale component. The analysis of Zeeman broadening in high-resolution intensity spectra can reveal these hidden complex magnetic fields. Historically, there were very few attempts to obtain such measurements for G dwarf stars due to the difficulty of disentangling the Zeeman effect from other broadening mechanisms affecting spectral lines. Here, we developed a new magnetic field diagnostic method based on relative Zeeman intensification of optical atomic lines with different magnetic sensitivity. By using this technique, we obtained 78 field strength measurements for 15 Sun-like stars, including some of the best-studied young solar twins. We find that the average magnetic field strength Bf drops from 1.3−2.0 kG in stars younger than about 120 Myr to 0.2−0.8 kG in older stars. The mean field strength shows a clear correlation with the Rossby number and with the coronal and chromospheric emission indicators. Our results suggest that magnetic regions have roughly the same local field strength B ≈ 3.2 kG in all stars, with the filling factor f of these regions systematically increasing with stellar activity. In comparing our results with the spectropolarimetric analyses of global magnetic fields in the same stars, we find that ZDI recovers about 1% of the total magnetic field energy in the most active stars. This figure drops to just 0.01% for the least active targets.
CybORG: An Autonomous Cyber Operations Research Gym

Autonomous Cyber Operations (ACO) involves the consideration of blue team (defender) and red team (attacker) decision-making models in adversarial scenarios. To support the application of machine learning algorithms to solve this problem, and to encourage such practitioners to attend to problems in the ACO setting, a suitable gym (toolkit for experiments) is necessary. We introduce CybORG, a work-in-progress gym for ACO research. Driven by the need to efficiently support reinforcement learning to train adversarial decision-making models through simulation and emulation, our design differs from prior related work. Our early evaluation provides some evidence that CybORG is appropriate for our purpose and may provide a basis for advancing ACO research towards practical applications.
Efficient Rollout Strategies for Bayesian Optimization

Bayesian optimization (BO) is a class of sample-efficient global optimization methods, where a probabilistic model conditioned on previous observations is used to determine future evaluations via the optimization of an acquisition function. Most acquisition functions are myopic, meaning that they only consider the impact of the next function evaluation. Non-myopic acquisition functions consider the impact of the next $h$ function evaluations and are typically computed through rollout, in which $h$ steps of BO are simulated. These rollout acquisition functions are defined as $h$-dimensional integrals, and are expensive to compute and optimize. We show that a combination of quasi-Monte Carlo, common random numbers, and control variates significantly reduce the computational burden of rollout. We then formulate a policy-search based approach that removes the need to optimize the rollout acquisition function. Finally, we discuss the qualitative behavior of rollout policies in the setting of multi-modal objectives and model error.
Proceedings of the Society for Computation in Linguistics Proceedings of the Society for Computation in Linguistics Parsing Early Modern English for Linguistic Search Parsing Early Modern English for Linguistic Search

This work addresses the question of whether the output of a state-of-the-art parser is accurate enough to support research in theoretical linguistics. In order to build reliable models of syntactic change, we aim to eventually parse the 1.5-billion-word Early English Books Online (EEBO) corpus. But since EEBO is not yet parsed, we begin by constructing and testing a parser on the 1.7-million-word Penn-Helsinki Parsed Corpus of Early Modern English (PPCEME). In order to obtain robust results, we deﬁne an 8-fold split on PPCEME. We then evaluate the parser with evalb and, more relevantly for us, with a task-speciﬁc metric - namely, its accuracy in parsing 6 sentence types necessary to track the rise of auxiliary do (as in They did not come vs. its historical precursor They came not ). Retrieving the relevant sentences from the gold and test versions with CorpusSearch queries (Ran-dall, 2010), we ﬁnd that the parser’s accuracy promises to be sufﬁcient for our purposes. A remaining concern is the variability of the output, which we plan to address with three pieces of future work sketched in the conclusion.
Cosmic microwave background bounds on primordial black holes including dark matter halo accretion

Even if massive ($10\,M_\odot \lesssim M \lesssim 10^4 M_\odot$) primordial black holes (PBHs) can only account for a small fraction of the dark matter (DM) in the universe, they may still be responsible for a sizable fraction of the coalescence events measured by LIGO/Virgo, and/or act as progenitors of the supermassive black holes (SMBHs) observed already at high redshift ($z\gtrsim 6$). In presence of a dominant, non-PBH DM component, the bounds set by CMB via an altered ionization history are modified. We revisit the cosmological accretion of a DM halo around PBHs via toy models and dedicated numerical simulations, deriving updated CMB bounds which also take into account the last Planck data release. We prove that these constraints dominate over other constraints available in the literature at masses $M\gtrsim 20-50\,M_\odot$ (depending on uncertainty in accretion physics), reaching the level $f_{\rm PBH}<3\times 10^{-9}$ around $M\sim 10^{4}\,M_\odot$. These tight bounds are nonetheless consistent with the hypothesis of a primordial origin of the SMBH massive seeds.