Miracle-dz/UniFAR-FTUs huggingface dataset:
Updated parameters of 1743 open clusters based on Gaia DR2

In this study we follow up our recent paper (Monteiro et al. 2020) and present a homogeneous sample of fundamental parameters of open clusters in our Galaxy, entirely based onGaiaDR2 data. We used published membership probability of the stars derived from Gaia DR2 data and applied our isochrone fitting code, updated as in Monteiro et al. (2020), toGBP andGRP Gaia DR2 data for member stars. In doing this we take into account the nominal errors in the data and derive distance, age, and extinction of each cluster. This work therefore provides parameters for 1743 open clusters and, as a byproduct, a list of likely not physical or dubious open clusters is provided as well for future investigations. Furthermore, it was possible to estimate the mean radial velocity of 831 clusters (198 of which are new and unpublished so far) using stellar radial velocities from Gaia DR2 catalog. By comparing the open cluster distances obtained from isochrone fitting with those obtained from a maximum likelihood estimate of individual member parallaxes, we found a systematic offset of (−0.05 ± 0.04)mas.
RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentation

Point clouds can be represented in many forms (views), typically, point-based sets, voxel-based cells or range-based images(i.e., panoramic view). The point-based view is geometrically accurate, but it is disordered, which makes it difficult to find local neighbors efficiently. The voxel-based view is regular, but sparse, and computation grows cubicly when voxel resolution increases. The range-based view is regular and generally dense, however spherical projection makes physical dimensions distorted. Both voxel-and range-based views suffer from quantization loss, especially for voxels when facing large-scale scenes. In order to utilize different view’s advantages and alleviate their own shortcomings in fine-grained segmentation task, we propose a novel range-point-voxel fusion network, namely RPVNet. In this network, we devise a deep fusion framework with multiple and mutual information interactions among these three views, and propose a gated fusion module (termed as GFM), which can adaptively merge the three features based on concurrent inputs. Moreover, the proposed RPV interaction mechanism is highly efficient, and we summarize it to a more general formulation. By leveraging this efficient interaction and relatively lower voxel resolution, our method is also proved to be more efficient. Finally, we evaluated the proposed model on two large-scale datasets, i.e., SemanticKITTI and nuScenes, and it shows state-of-the-art performance on both of them. Note that, our method currently ranks 1st on SemanticKITTI leaderboard without any extra tricks.
Mapping the Galactic centre’s dark cluster via resonant relaxation

Supermassive black holes in the centre of galaxies dominate the gravitational potential of their surrounding stellar clusters. In these dense environments, stars follow nearly Keplerian orbits, which get slowly distorted as a result of the potential fluctuations generated by the stellar cluster itself. In particular, stars undergo a rapid relaxation of their eccentricities through both resonant and non-resonant processes. An efficient implementation of the resonant diffusion coefficients allows for detailed and systematic explorations of the parameter space describing the properties of the stellar cluster. In conjunction with recent observations of the S-cluster orbiting SgrA*, this framework can be used to jointly constrain the distribution of the unresolved, old, background stellar cluster and the characteristics of a putative dark cluster. Specifically, we show how this can be used to estimate the typical mass and cuspide exponent of intermediate-mass black holes consistent with the relaxed state of the distribution of eccentricities in the observed S-cluster. This should prove useful in constraining supermassive black hole formation scenarios.
Variable Name Recovery in Decompiled Binary Code using Constrained Masked Language Modeling

Decompilation is the procedure of transforming binary programs into a high-level representation, such as source code, for human analysts to examine. While modern decompilers can reconstruct and recover much information that is discarded during compilation, inferring variable names is still extremely difficult. Inspired by recent advances in natural language processing, we propose a novel solution to infer variable names in decompiled code based on Masked Language Modeling, Byte-Pair Encoding, and neural architectures such as Transformers and BERT. Our solution takes \textit{raw} decompiler output, the less semantically meaningful code, as input, and enriches it using our proposed \textit{finetuning} technique, Constrained Masked Language Modeling. Using Constrained Masked Language Modeling introduces the challenge of predicting the number of masked tokens for the original variable name. We address this \textit{count of token prediction} challenge with our post-processing algorithm. Compared to the state-of-the-art approaches, our trained VarBERT model is simpler and of much better performance. We evaluated our model on an existing large-scale data set with 164,632 binaries and showed that it can predict variable names identical to the ones present in the original source code up to 84.15\% of the time.
Making It Normal for ‘New’ Enrollments: Effect of Institutional and Pandemic Influence on Selecting an Engineering Institution Under the COVID-19 Pandemic Situation

The COVID-19 pandemic has forced Indian engineering institutions (EIs) to bring their previous half-shut shades completely down. Fetching new admissions to EI campuses during the pandemic has become a ‘now or never’ situation for EIs. During crisis situations, institutions have struggled to return to the normal track. The pandemic has drastically changed students’ behavior and family preferences due to mental stress and the emotional life attached to it. Consequently, it becomes a prerequisite, and emergencies need to examine the choice characteristics influencing the selection of EI during the COVID-19 pandemic. The purpose of this study is to critically examine institutional influence and pandemic influence due to COVID-19 that affects students’ choice about an engineering institution (EI) and consequently to explore relationships between institutional and pandemic influence. The findings of this quantitative research, conducted through a self-reported survey, have revealed that institutional and pandemic influence have governed EI choice under the COVID-19 pandemic. Second, pandemic influence is positively affected by institutional influence. The study demonstrated that EIs will have to reposition themselves to normalize pandemic influence by tuning institutional characteristics that regulate situational influence and new enrollments. It can be yardstick for policy makers to attract new enrollments under pandemic situations.
Learning to Optimize: A Primer and A Benchmark

Learning to optimize (L2O) is an emerging approach that leverages machine learning to develop optimization methods, aiming at reducing the laborious iterations of hand engineering. It automates the design of an optimization method based on its performance on a set of training problems. This data-driven procedure generates methods that can efficiently solve problems similar to those in the training. In sharp contrast, the typical and traditional designs of optimization methods are theory-driven, so they obtain performance guarantees over the classes of problems specified by the theory. The difference makes L2O suitable for repeatedly solving a certain type of optimization problems over a specific distribution of data, while it typically fails on out-of-distribution problems. The practicality of L2O depends on the type of target optimization, the chosen architecture of the method to learn, and the training procedure. This new paradigm has motivated a community of researchers to explore L2O and report their findings. This article is poised to be the first comprehensive survey and benchmark of L2O for continuous optimization. We set up taxonomies, categorize existing works and research directions, present insights, and identify open challenges. We also benchmarked many existing L2O approaches on a few but representative optimization problems. For reproducible research and fair benchmarking purposes, we released our software implementation and data in the package Open-L2O at https://github.com/VITA-Group/Open-L2O.
Automated Mapping of Vulnerability Advisories onto their Fix Commits in Open Source Repositories

The lack of comprehensive sources of accurate vulnerability data represents a critical obstacle to studying and understanding software vulnerabilities (and their corrections). In this paper, we present an approach that combines heuristics stemming from practical experience and machine-learning (ML)—specifically, natural language processing (NLP)—to address this problem. Our method consists of three phases. First, we construct an advisory record object containing key information about a vulnerability that is extracted from an advisory, such those found in the National Vulnerability Database (NVD). These advisories are expressed in natural language. Second, using heuristics, a subset of candidate fix commits is obtained from the source code repository of the affected project, by filtering out commits that can be identified as unrelated to the vulnerability at hand. Finally, for each of the remaining candidate commits, our method builds a numerical feature vector reflecting the characteristics of the commit that are relevant to predicting its match with the advisory at hand. Based on the values of these feature vectors, our method produces a ranked list of candidate fixing commits. The score attributed by the ML model to each feature is kept visible to the users, allowing them to easily interpret the predictions. We implemented our approach and we evaluated it on an open data set, built by manual curation, that comprises 2,391 known fix commits corresponding to 1,248 public vulnerability advisories. When considering the top-10 commits in the ranked results, our implementation could successfully identify at least one fix commit for up to 84.03% of the vulnerabilities (with a fix commit on the first position for 65.06% of the vulnerabilities). Our evaluation shows that our method can reduce considerably the manual effort needed to search OSS repositories for the commits that fix known vulnerabilities.
Reynolds number trends in turbulent pipe flow: a DNS perspective

Sergio Pirozzoli1†, Joshua Romero2, Massimiliano Fatica2, Roberto Verzicco3,4, and Paolo Orlandi1 1Dipartimento di Ingegneria Meccanica e Aerospaziale, Sapienza Università di Roma, Via Eudossiana 18, 00184 Roma, Italy 2NVIDIA Corporation, 2701 San Tomas Expressway, Santa Clara, CA 95050, USA 3Dipartimento di Ingegneria Industriale, Università di Roma TorVergata, Viai del Politecnico 1, 00133 Roma, Italy 4Physics of Fluid Group, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands
Data Balancing Improves Self-Admitted Technical Debt Detection

A high imbalance exists between technical debt and non-technical debt source code comments. Such imbalance affects Self-Admitted Technical Debt (SATD) detection performance, and existing literature lacks empirical evidence on the choice of balancing technique. In this work, we evaluate the impact of multiple balancing techniques, including Data level, Classifier level, and Hybrid, for SATD detection in Within-Project and Cross-Project setup. Our results show that the Data level balancing technique SMOTE or Classifier level Ensemble approaches Random Forest or XGBoost are reasonable choices depending on whether the goal is to maximize Precision, Recall, F1, or AUC-ROC. We compared our best-performing model with the previous SATD detection benchmark (cost-sensitive Convolution Neural Network). Interestingly the top-performing XGBoost with SMOTE sampling improved the Within-project F1 score by 10% but fell short in Cross-Project set up by 9%. This supports the higher generalization capability of deep learning in Cross-Project SATD detection, yet while working within individual projects, classical machine learning algorithms can deliver better performance. We also evaluate and quantify the impact of duplicate source code comments in SATD detection performance. Finally, we employ SHAP and discuss the interpreted SATD features. We have included the replication package1 and shared a web-based SATD prediction tool2 with the balancing techniques in this study.
Interplay between transport and quantum coherences in free fermionic systems

We study the quench dynamics in free fermionic systems in the prototypical bipartitioning protocol obtained by joining two semi-infinite subsystems prepared in different states, aiming at understanding the interplay between quantum coherences in space in the initial state and transport properties. Our findings reveal that, under reasonable assumptions, the more correlated the initial state, the slower the transport is. Such statement is first discussed at qualitative level, and then made quantitative by introducing proper measures of correlations and transport ‘speed’. Moreover, it is supported for fermions on a lattice by an exact solution starting from specific initial conditions, and in the continuous case by the explicit solution for a wider class of physically relevant initial states. In particular, for this class of states, we identify a function, that we dub transition map, which takes the value of the stationary current as input and gives the value of correlation as output, in a protocol-independent way. As an aside technical result, in the discrete case, we give an expression of the full counting statistics in terms of a continuous kernel for a general correlated domain wall initial state, thus extending the recent results in Moriya et al (2019 J. Stat. Mech. 2019 063105) on the one-dimensional XX spin chain.
Variational study of the Kitaev-Heisenberg-Gamma model

Shang-Shun Zhang, Gábor B. Halász, Wei Zhu, and Cristian D. Batista Department of Physics and Astronomy, University of Tennessee, Knoxville, Tennessee 37996-1200, USA Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA Quantum Science Center, Oak Ridge, Tennessee 37831, USA 4 Key Laboratory for Quantum Materials of Zhejiang Province, School of Science, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China and Quantum Condensed Matter Division and Shull-Wollan Center, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
Designing All Graphdiyne Materials as Graphene Derivatives: Topologically Driven Modulation of Electronic Properties

Designing new 2D systems with tunable properties is an important subject for science and technology. Starting from graphene, we developed an algorithm to systematically generate 2D carbon crystals belonging to the family of graphdiynes (GDYs) and having different structures and sp/sp2 carbon ratios. We analyze how structural and topological effects can tune the relative stability and the electronic behavior, to propose a rationale for the development of new systems with tailored properties. A total of 26 structures have been generated, including the already known polymorphs such as α-, β-, and γ-GDY. Periodic density functional theory calculations have been employed to optimize the 2D crystal structures and to compute the total energy, the band structure, and the density of states. Relative energies with respect to graphene have been found to increase when the values of the carbon sp/sp2 ratio increase, following however different trends based on the peculiar topologies present in the crystals. These topologies also influence the band structure, giving rise to semiconductors with a finite band gap, zero-gap semiconductors displaying Dirac cones, or metallic systems. The different trends allow identifying some topological effects as possible guidelines in the design of new 2D carbon materials beyond graphene.
Weakly Supervised Instance Segmentation for Videos with Temporal Mask Consistency

Weakly supervised instance segmentation reduces the cost of annotations required to train models. However, existing approaches which rely only on image-level class labels predominantly suffer from errors due to (a) partial segmentation of objects and (b) missing object predictions. We show that these issues can be better addressed by training with weakly labeled videos instead of images. In videos, motion and temporal consistency of predictions across frames provide complementary signals which can help segmentation. We are the first to explore the use of these video signals to tackle weakly supervised instance segmentation. We propose two ways to leverage this information in our model. First, we adapt inter-pixel relation network (IRN) [1] to effectively incorporate motion information during training. Second, we introduce a new MaskConsist module, which addresses the problem of missing object instances by transferring stable predictions between neighboring frames during training. We demonstrate that both approaches together improve the instance segmentation metric AP50 on video frames of two datasets: Youtube-VIS and Cityscapes by 5% and 3% respectively.
Preparing Bethe Ansatz Eigenstates on a Quantum Computer

Several quantum many-body models in one dimension possess exact solutions via the Bethe ansatz method, which has been highly successful for understanding their behavior. Nevertheless, there remain physical properties of such models for which analytic results are unavailable, and which are also not well-described by approximate numerical methods. Preparing Bethe ansatz eigenstates directly on a quantum computer would allow straightforward extraction of these quantities via measurement. We present a quantum algorithm for preparing Bethe ansatz eigenstates of the spin-1/2 XXZ spin chain that correspond to real-valued solutions of the Bethe equations. The algorithm is polynomial in the number of T gates and circuit depth, with modest constant prefactors. Although the algorithm is probabilistic, with a success rate that decreases with increasing eigenstate energy, we employ amplitude amplification to boost the success probability. The resource requirements for our approach are lower than other state-of-the-art quantum simulation algorithms for small error-corrected devices, and thus may offer an alternative and computationally less-demanding demonstration of quantum advantage for physically relevant problems.
Wrinkle-resistant finishing of cotton fabric with BTCA - the effect of co-catalyst

Fabrics treated with dimethyloldihydroxylethyleneurea (DMDHEU) are susceptible to formaldehyde release. High concentrations of formaldehyde have been reported to cause cancer in animal studies. Due to the toxicity of DMDHEU, the polycarboxylic acid class of non-formaldehyde crosslinking agents used for wrinkle-resistant treatment has been introduced. Among various researches, 1,2,3,4-butanetetracarboxylic acid (BTCA) is a desirable reactant when catalysed with sodium hypophosphite (SHP). The characteristics of TiO 2 or nano-TiO2, acting as a co-catalyst in a BTCA wrinkle-resistant finishing system are examined in this study. Surface morphology and molecular structure of cotton specimens together with the wrinkle-resistant, UV protection, yellowing, tensile and tearing properties of the treated specimens are also evaluated.
Calculations based on measurements of charge deposited by a streamer on a PTFE surface

Surface charge on an insulator's surface is studied using 3-dimensional finite element modelling. The calculations are based on measurements of surface charge deposited by a streamer on a PTFE surface. The surface electric field strengths associated with uniform surface charge density distributions over a 1 mm/sup 2/ region are calculated for a range of different measured charge densities. The effect of reducing the area of the surface charge whilst keeping the total amount of charge in the region constant is observed. The surface charge is then modelled with a Gaussian distribution of charge and compared to that of a uniform distribution. By assuming a maximum allowable surface electric field strength an estimation of actual charged path diameter is made.
Fixed Point Networks: Implicit Depth Models with Jacobian-Free Backprop

A growing trend in deep learning replaces ﬁxed depth models by approximations of the limit as network depth approaches inﬁnity. This approach uses a portion of network weights to prescribe behavior by deﬁning a limit condition. This makes network depth implicit , varying based on the provided data and an error tolerance. Moreover, existing implicit models can be implemented and trained with ﬁxed memory costs in exchange for additional computational costs. In particular, backpropagation through implicit depth models requires solving a Jacobian-based equation arising from the implicit function theorem. We propose ﬁxed point networks (FPNs), a simple setup for implicit depth learning that guarantees convergence of forward propagation to a unique limit deﬁned by network weights and input data. Our key contribution is to provide a new Jacobian-free backpropagation (JFB) scheme that circumvents the need to solve Jacobian-based equations while maintaining ﬁxed memory costs. This makes FPNs much cheaper to train and easy to implement. Our numerical examples yield state of the art classiﬁcation results for implicit depth models and outperform corresponding explicit models. 2
Improved Estimation of Concentration Under $\ell_p$-Norm Distance Metrics Using Half Spaces

Concentration of measure has been argued to be the fundamental cause of adversarial vulnerability. Mahloujifar et al. presented an empirical way to measure the concentration of a data distribution using samples, and employed it to find lower bounds on intrinsic robustness for several benchmark datasets. However, it remains unclear whether these lower bounds are tight enough to provide a useful approximation for the intrinsic robustness of a dataset. To gain a deeper understanding of the concentration of measure phenomenon, we first extend the Gaussian Isoperimetric Inequality to non-spherical Gaussian measures and arbitrary $\ell_p$-norms ($p \geq 2$). We leverage these theoretical insights to design a method that uses half-spaces to estimate the concentration of any empirical dataset under $\ell_p$-norm distance metrics. Our proposed algorithm is more efficient than Mahloujifar et al.'s, and our experiments on synthetic datasets and image benchmarks demonstrate that it is able to find much tighter intrinsic robustness bounds. These tighter estimates provide further evidence that rules out intrinsic dataset concentration as a possible explanation for the adversarial vulnerability of state-of-the-art classifiers.
Representing Numbers in NLP: a Survey and a Vision

NLP systems rarely give special consideration to numbers found in text. This starkly contrasts with the consensus in neuroscience that, in the brain, numbers are represented differently from words. We arrange recent NLP work on numeracy into a comprehensive taxonomy of tasks and methods. We break down the subjective notion of numeracy into 7 subtasks, arranged along two dimensions: granularity (exact vs approximate) and units (abstract vs grounded). We analyze the myriad representational choices made by over a dozen previously published number encoders and decoders. We synthesize best practices for representing numbers in text and articulate a vision for holistic numeracy in NLP, comprised of design trade-offs and a unified evaluation.