Miracle-dz/UniFAR-FTUs huggingface dataset:
Resonant Chains of Exoplanets: Libration Centers for Three-body Angles

Resonant planetary systems contain at least one planet pair with orbital periods librating at a near-integer ratio (2/1, 3/2, 4/3, etc.) and are a natural outcome of standard planetary formation theories. Systems with multiple adjacent resonant pairs are known as resonant chains and can exhibit three-body resonances—characterized by a critical three-body angle. Here we study three-body angles as a diagnostic of resonant chains through tidally damped N-body integrations. For each combination of the 2:1, 3:2, 4:3, and 5:4 mean motion resonances (the most common resonances in the known resonant chains), we characterize the three-body angle equilibria for several mass schemes, migration timescales, and initial separations. We find that under our formulation of the three-body angle, which does not reduce coefficients, 180° is the preferred libration center, and libration centers shifted away from 180° are associated with nonadjacent resonances. We then relate these angles to observables, by applying our general results to two transiting systems: Kepler-60 and Kepler-223. For these systems, we compare N-body models of the three-body angle to the zeroth order in e approximation accessible via transit phases, used in previous publications. In both cases, we find the three-body angle during the Kepler observing window is not necessarily indicative of the long-term oscillations and stress the role of dynamical models in investigating three-body angles. We anticipate our results will provide a useful diagnostic in the analysis of resonant chains.
Numerical simulations of macrospicule jets under energy imbalance conditions in the solar atmosphere

Using numerical simulations, we study the effects of thermal conduction and radiative cooling on the formation and evolution of solar jets with some macrospicules features. We initially assume that the solar atmosphere is rarely in equilibrium through energy imbalance. Therefore, we test whether the background flows resulting from an imbalance between thermal conduction and radiative cooling influence the jets’ behavior. In this particular scenario we trigger the formation of the jets by launching a vertical velocity pulse localized at the upper chromosphere for the following test cases: i) adiabatic case; ii) thermal conduction case; iii) radiative cooling case; iv) thermal conduction + radiative cooling case. According to the test results, the addition of the thermal conduction results in smaller and hotter jets than in the adiabatic case. On the other hand the radiative cooling dissipates the jet after reaching the maximum height (≈ 5.5 Mm), making it shorter and colder than in the adiabatic and thermal conduction cases. Besides, the flow generated by the radiative cooling is more substantial than the caused by the thermal conduction. Despite the energy imbalance of the solar atmosphere background, the simulated jet shows morphological features of macrospicules. Furthermore, the velocity pulse steepens into a shock that propagates upward into a solar corona that maintains its initial temperature. The shocks generate the jets with a quasi-periodical behavior that follows a parabolic path on time-distance plots consistent with macrospicule jets’ observed dynamics.
On Stochastic Moving-Average Estimators for Non-Convex Optimization

In this paper, we consider the widely used but not fully understood stochastic estimator based on moving average (SEMA), which only requires a general unbiased stochastic oracle . We demonstrate the power of SEMA on a range of stochastic non-convex optimization problems. In particular, we analyze various stochastic methods (existing or newly proposed) based on the variance recursion property of SEMA for three families of non-convex optimization, namely standard stochastic non-convex minimization, stochastic non-convex strongly-concave min-max optimization, and stochastic bilevel optimization. Our contributions include: (i) for standard stochastic non-convex minimization, we present a simple and intuitive proof of convergence for a family of Adam-style methods (including Adam, AMSGrad, AdaBound, etc.) with an increasing or large “momentum" parameter for the ﬁrst-order moment, which gives an alternative yet more natural way to guarantee Adam converge; (ii) for stochastic non-convex strongly-concave min-max optimization, we present a single-loop primal-dual stochastic momentum and adaptive methods based on the moving average estimators and establish its oracle complexity of O (1 /ǫ 4 ) without us-ing a large mini-batch size, addressing a gap in the literature; (iii) for stochastic bilevel optimization, we present a single-loop stochastic method based on the moving average estimators and establish its oracle complexity of e O (1 /ǫ 4 ) without computing the SVD of the Hessian matrix, improving state-of-the-art results. For all these problems, we also establish a variance diminishing result for the used stochastic gradient estimators.
Post-training deep neural network pruning via layer-wise calibration

We present a post-training weight pruning method for deep neural networks that achieves accuracy levels tolerable for the production setting and that is sufficiently fast to be run on commodity hardware such as desktop CPUs or edge devices. We propose a data-free extension of the approach for computer vision models based on automatically-generated synthetic fractal images. We obtain state-of-the-art results for data-free neural network pruning, with ~1.5% top@1 accuracy drop for a ResNet50 on ImageNet at 50% sparsity rate. When using real data, we are able to get a ResNet50 model on ImageNet with 65% sparsity rate in 8-bit precision in a post-training setting with a ~1% top@1 accuracy drop. We release the code as a part of the OpenVINOTM Post-Training Optimization tool1.
Asymptotic symmetries at null-infinity for the Rarita–Schwinger field with magnetic term

In this paper we study the magnetic charges of the free massless Rarita–Schwinger field in four dimensional asymptotically flat space-time. This is the first step towards extending the study of the dual BMS charges to supergravity. The magnetic charges appear due to the addition of a boundary term in the action. This term is similar to the theta term in Yang–Mills theory. At null-infinity an infinite dimensional algebra is discovered, both for the electric and magnetic charge.
Discovery of the Pure Polycyclic Aromatic Hydrocarbon Indene (c-C9H8) with GOTHAM Observations of TMC-1

Polycyclic aromatic hydrocarbons (PAHs) have long been invoked in the study of interstellar and protostellar sources, but the unambiguous identification of any individual PAH has proven elusive until very recently. As a result, the formation mechanisms for this important class of molecules remain poorly constrained. Here we report the first interstellar detection of a pure hydrocarbon PAH, indene (C9H8), as part of the GBT Observations of TMC-1: Hunting for Aromatic Molecules (GOTHAM) survey. This detection provides a new avenue for chemical inquiry, complementing the existing detections of CN-functionalized aromatic molecules. From fitting the GOTHAM observations, indene is found to be the most abundant organic ring detected in TMC-1 to date. And from astrochemical modeling with nautilus, the observed abundance is greater than the model’s prediction by several orders of magnitude, suggesting that current formation pathways in astrochemical models are incomplete. The detection of indene in relatively high abundance implies related species such as cyanoindene, cyclopentadiene, toluene, and styrene may be detectable in dark clouds.
(1 + 1 + 2)-generated lattices of quasiorders

A lattice is (1 + 1 + 2)-generated if it has a four-element generating set such that exactly two of the four generators are comparable. We prove that the lattice Quo( n ) of all quasiorders (also known as preorders) of an n -element set is (1 + 1 + 2)-generated for n = 3 (trivially), n = 6 (when Quo(6) consists of 209 527 elements), n = 11, and for every natural number n ≥ 13. In 2017, the second author and J. Kulin proved that Quo( n ) is (1 + 1 + 2)-generated if either n is odd and at least 13 or n is even and at least 56. Compared to the 2017 result, this paper presents twenty-four new numbers n such that Quo( n ) is (1 + 1 + 2)-generated. Except for Quo(6), an extension of Zádori’s method is used.
Chop Chop BERT: Visual Question Answering by Chopping VisualBERT's Heads

Vision-and-Language (VL) pre-training has shown great potential on many related downstream tasks, such as Visual Question Answering (VQA), one of the most popular problems in the VL field. All of these pre-trained models (such as VisualBERT, ViLBERT, LXMERT and UNITER) are built with Transformer, which extends the classical attention mechanism to multiple layers and heads. To investigate why and how these models work on VQA so well, in this paper we explore the roles of individual heads and layers in Transformer models when handling 12 different types of questions. Specifically, we manually remove (chop) heads (or layers) from a pre-trained VisualBERT model at a time, and test it on different levels of questions to record its performance. As shown in the interesting echelon shape of the result matrices, experiments reveal different heads and layers are responsible for different question types, with higher-level layers activated by higher-level visual reasoning questions. Based on this observation, we design a dynamic chopping module that can automatically remove heads and layers of the VisualBERT at an instance level when dealing with different questions. Our dynamic chopping module can effectively reduce the parameters of the original model by 50%, while only damaging the accuracy by less than 1% on the VQA task.
Analytical bounds on the local Lipschitz constants of ReLU networks

In this article, we determine analytical upper bounds on the local Lipschitz constants of feedforward neural networks with rectified linear unit (ReLU) activation functions. We do so by deriving Lipschitz constants and bounds for ReLU, affine-ReLU, and max-pooling functions and combining the results to determine a network-wide bound. Our method uses several insights to obtain tight bounds, such as keeping track of the zero elements of each layer and analyzing the composition of affine and ReLU functions. Furthermore, we employ a careful computational approach, which allows us to apply our method to large networks, such as AlexNet and VGG-16. We present several examples using different networks, which show how our local Lipschitz bounds are tighter than the global Lipschitz bounds. We also show how our method can be applied to provide adversarial bounds for classification networks. These results show that our method produces the largest known bounds on minimum adversarial perturbations for large networks, such as AlexNet and VGG-16.
On In-network learning. A Comparative Study with Federated and Split Learning

In this paper, we consider a problem in which distributively extracted features are used for performing inference in wireless networks. We elaborate on our proposed architecture, which we herein refer to as "in-network learning", provide a suitable loss function and discuss its optimization using neural networks. We compare its performance with both Federated- and Split learning; and show that this architecture offers both better accuracy and bandwidth savings.
Text2Video: Text-driven Talking-head Video Synthesis with Phonetic Dictionary

With the advance of deep learning technology, automatic video generation from audio or text has become an emerging and promising research topic. In this paper, we present a novel approach to synthesize video from the text. The method builds a phoneme-pose dictionary and trains a generative adversarial network (GAN) to generate video from interpolated phoneme poses. Compared to audio-driven video generation algorithms, our approach has a number of advantages: 1) It only needs a fraction of the training data used by an audio-driven approach; 2) It is more ﬂexible and not subject to vulnerability due to speaker variation; 3) It signiﬁcantly reduces the preprocessing, training and inference time. We perform extensive experiments to compare the proposed method with state-of-the-art talking face generation methods on a benchmark dataset and datasets of our own. The results demonstrate the effectiveness and superiority of our approach.
SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation

State-of-the-art semantic or instance segmentation deep neural networks (DNNs) are usually trained on a closed set of semantic classes. As such, they are ill-equipped to handle previously-unseen objects. However, detecting and localizing such objects is crucial for safety-critical applications such as perception for automated driving, especially if they appear on the road ahead. While some methods have tackled the tasks of anomalous or out-of-distribution object segmentation, progress remains slow, in large part due to the lack of solid benchmarks; existing datasets either consist of synthetic data, or suffer from label inconsistencies. In this paper, we bridge this gap by introducing the"SegmentMeIfYouCan"benchmark. Our benchmark addresses two tasks: Anomalous object segmentation, which considers any previously-unseen object category; and road obstacle segmentation, which focuses on any object on the road, may it be known or unknown. We provide two corresponding datasets together with a test suite performing an in-depth method analysis, considering both established pixel-wise performance metrics and recent component-wise ones, which are insensitive to object sizes. We empirically evaluate multiple state-of-the-art baseline methods, including several models specifically designed for anomaly / obstacle segmentation, on our datasets and on public ones, using our test suite. The anomaly and obstacle segmentation results show that our datasets contribute to the diversity and difficulty of both data landscapes.
Deep Learning Based Steel Pipe Weld Defect Detection

ABSTRACT Steel pipes are widely used in high-risk and high-pressure scenarios such as oil, chemical, natural gas, shale gas, etc. If there is some defect in steel pipes, it will lead to serious adverse consequences. Applying object detection in the field of deep learning to pipe weld defect detection and identification can effectively improve inspection efficiency and promote the development of industrial automation. Most predecessors used traditional computer vision methods applied to detect defects of steel pipe weld seams. However, traditional computer vision methods rely on prior knowledge and can only detect defects with a single feature, so it is difficult to complete the task of multi-defect classification, while deep learning is end-to-end. In this paper, the state-of-the-art single-stage object detection algorithm YOLOv5 is proposed to be applied to the field of steel pipe weld defect detection and compared with the two-stage representative object detection algorithm Faster R-CNN. The experimental results show that applying YOLOv5 to steel pipe weld defect detection can greatly improve the accuracy, complete the multi-classification task, and meet the criteria of real-time detection.
PointLIE: Locally Invertible Embedding for Point Cloud Sampling and Recovery

Point Cloud Sampling and Recovery (PCSR) is critical for massive real-time point cloud collection and processing since raw data usually requires large storage and computation. This paper addresses a fundamental problem in PCSR: How to downsample the dense point cloud with arbitrary scales while preserving the local topology of discarded points in a case-agnostic manner (i.e., without additional storage for point relationships)? We propose a novel Locally Invertible Embedding (PointLIE) framework to unify the point cloud sampling and upsampling into one single framework through bi-directional learning. Specifically, PointLIE decouples the local geometric relationships between discarded points from the sampled points by progressively encoding the neighboring offsets to a latent variable. Once the latent variable is forced to obey a pre-defined distribution in the forward sampling path, the recovery can be achieved effectively through inverse operations. Taking the recover-pleasing sampled points and a latent embedding randomly drawn from the specified distribution as inputs, PointLIE can theoretically guarantee the fidelity of reconstruction and outperform state-of-the-arts quantitatively and qualitatively.
Local well-posedness for the gKdV equation on the background of a bounded function

We prove the local well-posedness for the generalized Korteweg-de Vries equation in H(R), s > 1/2, under general assumptions on the nonlinearity f(x), on the background of an Lt,x-function Ψ(t, x), with Ψ(t, x) satisfying some suitable conditions. As a consequence of our estimates, we also obtain the unconditional uniqueness of the solution in H(R). This result not only gives us a framework to solve the gKdV equation around a Kink, for example, but also around a periodic solution, that is, to consider localized non-periodic perturbations of a periodic solution. As a direct corollary, we obtain the unconditional uniqueness of the gKdV equation in H(R) for s > 1/2. We also prove global existence in the energy space H(R), in the case where the nonlinearity satisfies that |f (x)| . 1.
On the finite element analysis of functionally graded sandwich curved beams via a new refined higher shear deformation theory

In the present paper, a new parabolic shear deformation beam theory is developed and applied to investigate the bending behavior of functionally graded (FG) sandwich curved beam. The present theory is exploited to satisfy parabolic variation of shear stress distribution along the thickness direction thereby obviating the use of any shear correction factors. The material properties of FG sandwich beam change continuously from one surface to another according to a power-law function. Three common configurations of FG beams are used for the study, namely: (a) single layer FG beam; (b) sandwich beam with FG face sheets and homogeneous core and (c) sandwich beams with homogeneous face sheets and FG core. The governing equations derived herein are solved by employing the finite element method using a two-noded beam element, developed for this purpose. The robustness and reliability of the developed finite element model are demonstrated by comparing its results with those available by other researchers in existing literature. The comparison studies show that the proposed model is: (a) accurate and comparable with the literature; b) of fast rate of convergence to the reference solution; c) excellent in terms of numerical stability and d) valid for FG sandwich curved beams. Moreover, comprehensive numerical results are presented and discussed in detail to investigate the effects of volume fraction index, radius of curvature, material distributions, length-to-thickness ratio, face-to-corethickness ratio, loadings and boundary conditions on the static response of FG curved sandwich beam. New referential results are reported which will be serve as a benchmark for future research.
Determinants of Exclusive Breastfeeding Practices of Female Healthcare Workers in Jakarta, Indonesia

The World Health Organization (WHO) recommended that mothers worldwide exclusively breastfeed their infants for the child's first six months to achieve optimal growth, development, and health. Indonesia had not fulfilled the global standard of breastfeeding rate so that there was low coverage of exclusive breastfeeding in East Jakarta, especially for female healthcare workers. This study aimed to determine the factors associated with exclusive breastfeeding practices of healthcare workers. This was a quantitative study using a cross-sectional design. The recruited sample consisted of 85 female public health center workers with infants aged 6-24 months. Data were collected using self-administered questionnaires. Logistic regression analysis was applied to identify factors associated with exclusive breastfeeding. The results showed that the proportion of exclusive breastfeeding was 54.1%. Variables associated with exclusive breastfeeding were knowledge, attitude, family support, co-worker support, and healthcare worker support. Multivariate analysis showed that knowledge and age were the dominant factors of exclusive breastfeeding practices with an odds ratio (OR) adjusted OR of 14 and 5, re¬spectively. Knowledge was an influential factor in the success of exclusive breastfeeding. Therefore, creating a training program related to breastfeeding would be expected to improve knowledge. Besides, a supportive policy such as providing breastfeeding facilities was needed.
Aging of aerosols emitted from biomass burning in northern Australia

There is a lack of knowledge of how biomass burning aerosols in the tropics age, including those in the fire-prone Northern Territory in Australia. This paper reports chemical characterization and aging of aerosols monitored during the one month long SAFIRED (Savannah Fires in the Early Dry Season) field study, with an emphasis on chemical signature and aging of organic aerosols. The campaign took place in June 2014 during the early dry season when the surface measurement 15 site, the Australian Tropical Atmospheric Research Station (ATARS), located in the Northern Territory, was heavily influenced by thousands of wild and prescribed bushfires. ATARS was equipped with a wide suite of instrumentation for gaseous and aerosol characterization. A compact time-of-flight aerosol mass spectrometer was deployed to monitor aerosol chemical composition. Approximately 80 % of submicron carbonaceous mass and 90 % of submicron non-refractory mass was composed of organic material. Ozone enhancement in biomass burning plumes indicated increased air mass 20 photochemistry and increased organic aerosol and particle diameter with the aging parameter (f44) suggested secondary organic aerosol formation. Diversity of biomass burning emissions was illustrated through variability in chemical signature (e.g. wide range in f44, from 0.06 to 0.13) for five intense fire events. The background particulate loading was characterized using Positive Matrix Factorization (PMF). A PMF-resolved BBOA (biomass burning organic aerosol) factor comprised 24 % of the submicron non-refractory organic aerosol mass, confirming the significance of fire sources. A dominant PMF 25 factor, OOA (oxygenated organic aerosol), made up 47 % of sampled aerosol fraction, illustrating the importance of aerosol aging in the Northern Territory. Biogenic IEPOX-SOA (isoprene epoxydiols-related secondary organic aerosol) was the third significant fraction of the background aerosol (28 %). Atmos. Chem. Phys. Discuss. , doi:10.5194/acp-2016-730, 2016 Manuscript under review for journal Atmos. Chem. Phys. Published: 15 August 2016 c © Author(s) 2016. CC-BY 3.0 License.