Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards A Domain-Customized Automated Machine Learning Framework For Networks and Systems

Clouds gather a vast volume of telemetry from their networked systems which contain valuable information that can help solve many of the problems that continue to plague them. However, it is hard to extract useful information from such raw data. Machine Learning (ML) models are useful tools that enable operators to either leverage this data to solve such problems or develop intuition about whether/how they can be solved. Building practical ML models is time-consuming and requires experts in both ML and networked systems to tailor the model to the system/network (a.k.a "domain-customize" it). The number of applications we deploy exacerbates the problem. The speed with which our systems evolve and with which new monitoring systems are deployed (deprecated) means these models often need to be adapted to keep up. Today, the lack of individuals with both sets of expertise is becoming one of the bottlenecks for adopting ML in cloud operations. This paper argues it is possible to build a domain-customized automated ML framework for networked systems that can help save valuable operator time and effort.
Sequential Interpretability: Methods, Applications, and Future Direction for Understanding Deep Learning Models in the Context of Sequential Data

Deep learning continues to revolutionize an ever-growing number of critical application areas including healthcare, transportation, finance, and basic sciences. Despite their increased predictive power, model transparency and human explainability remain a significant challenge due to the "black box" nature of modern deep learning models. In many cases the desired balance between interpretability and performance is predominately task specific. Human-centric domains such as healthcare necessitate a renewed focus on understanding how and why these frameworks are arriving at critical and potentially life-or-death decisions. Given the quantity of research and empirical successes of deep learning for computer vision, most of the existing interpretability research has focused on image processing techniques. Comparatively, less attention has been paid to interpreting deep learning frameworks using sequential data. Given recent deep learning advancements in highly sequential domains such as natural language processing and physiological signal processing, the need for deep sequential explanations is at an all-time high. In this paper, we review current techniques for interpreting deep learning techniques involving sequential data, identify similarities to non-sequential methods, and discuss current limitations and future avenues of sequential interpretability research.
The Evolution of the Broadband Temporal Features Observed in the Black-hole Transient MAXI J1820+070 with Insight-HXMT

We study the evolution of the temporal properties of MAXI J1820+070 during the 2018 outburst in its hard state from MJD 58,190 to 58,289 with Insight-HXMT in a broad energy band 1–150 keV. We find different behaviors of the hardness ratio, the fractional rms and time lag before and after MJD 58,257, suggesting a transition occurred around this point. The observed time lags between the soft photons in the 1–5 keV band and the hard photons in higher energy bands, up to 150 keV, are frequency-dependent: the time lags in the low-frequency range, 2–10 mHz, are both soft and hard lags with a timescale of dozens of seconds but without a clear trend along the outburst; the time lags in the high-frequency range, 1–10 Hz, are only hard lags with a timescale of tens of milliseconds; they first increase until around MJD 58,257 and decrease after this date. The high-frequency time lags are significantly correlated to the photon index derived from the fit to the quasi-simultaneous NICER spectrum in the 1–10 keV band. This result is qualitatively consistent with a model in which the high-frequency time lags are produced by Comptonization in a jet.
The K supergiant runaway star HD 137071

Context. Extensive work exists on runaway massive stars with peculiar motions that are much higher than those typical of the extreme Population I to which they belong. Work on runaways has focused almost exclusively on O and B stars, most of which undergo a red supergiant phase before ending their lives as supernovae. Very few examples are known of red supergiant runaways, all of which descend from the more massive O-type precursors, but none from the lower mass B-type precursors, although runaway statistics of B-type stars suggest that K-type runaways must be relatively numerous. Aims. We study HD 137071, a star that has so far been considered to be a normal K-type red giant. Its parallax measured by Gaia and the derived luminosity suggest that it is a supergiant, whereas its derived distance to the Galactic plane and its spatial velocity of 54.1 km s−1 with respect to the local standard of rest suggest that it is also a runaway star. However, intrinsic limitations in determining the trigonometric parallaxes of cool supergiants, even in the Gaia era, require accurate spectral classifications for confirmation. Methods. We present visible spectroscopy obtained with the 2.2m telescope at Calar Alto Observatory and compare it with the spectra of MK standard stars to produce an accurate spectral classification, including the determination of its luminosity class. We complement this information with astrometric data from the Gaia DR2 catalog. Results. We reliably classify HD 137071 as a K4II star and establish its membership to the extreme Population I. This agrees with the luminosity derived using the Gaia DR2 parallax measurement. Kinematical data from the Gaia DR2 catalog confirm its high spatial velocity and runaway nature. By combining the spectral classification with astrometric information, recent Galactic potential models, and evolutionary models for high-mass stars, we trace the motion of HD 137071 back to the proximities of the Galactic plane and speculate which of the two proposed mechanisms for the production of runaway stars may be responsible for the high velocity of HD 137071. The available data favor the formation of HD 137071 in a massive binary system where the more massive companion underwent a supernova explosion about 32 Myr ago.
Uptake of carbonyl sulfide (COS) and emission of dimethyl sulfide (DMS) by plants

Higher plants represent a significant sink for atmospheric carbonyl sulfide (COS) and a potential source of dimethyl sulfide (DMS). In the present work, COS uptake was investigated on various plant species (Quercus robur, Juniperus excelsa, Hibiscus spec., Sorghum bicolor) differing in the activities of carbonic anhydrase (CA), the enzyme recognized responsible for COS consumption. COS uptake was observed for all plant species, and the range of COS consumption was 1.5-25 pmol m -2 s -1 (deposition velocity 1.2-10.6 mm s -1 ). The COS uptake was found to be light-independent, but was strongly under stomatal control. For the C 3 plant species the uptake rates were well correlated with the inherent capacity of CA, a fact that may confer a comfortable tool to model COS uptake by plants, and ultimately may help to decrease the uncertainty in estimates of the global COS sink strength of vegetation. S. bicolor, owing a C 4 metabolism and respective low CA activity, exhibited a relatively high COS uptake rate as compared to the C 3 plants. Potential reasons for this deviation are discussed. Emission of DMS was species-specific and was only observed in case of Hibiscus spec. under light conditions.
Turbulent drag reduction over curved walls

This work studies the effects of skin-friction drag reduction in a turbulent flow over a curved wall, with a view to understanding the relationship between the reduction of friction and changes to the total aerodynamic drag. Direct numerical simulations are carried out for an incompressible turbulent flow in a channel where one wall has a small bump; two bump geometries are considered, that produce mildly separated and attached flows. Friction drag reduction is achieved by applying streamwise-travelling waves of spanwise velocity (StTW). The local friction reduction produced by the StTW is found to vary along the curved wall, leading to a global friction reduction that, for the cases studied, is up to 10 % larger than that obtained in the plane wall case. Moreover, the modified skin friction induces non-negligible changes of pressure drag, which is favourably affected by StTW and globally reduces by up to 10 %. The net power saving, accounting for the power required to create the StTW, is positive and, for the cases studied, is one half larger than the net saving of the planar case. The study suggests that reducing friction at the surface of a body of complex shape induces further effects, a simplistic evaluation of which might lead to underestimating the total drag reduction.
Thermal spectra of thin accretion discs of finite thickness around Kerr black holes

The analysis of the thermal spectrum of geometrically thin and optically thick accretion disks of black holes, the so-called continuum-fitting method, is one of the leading techniques for measuring black hole spins. Current models normally approximate the disk as infinitesimally thin, while in reality the disk thickness is finite and increases as the black hole mass accretion rate increases. Here we present an XSPEC model to calculate the multi-temperature blackbody spectrum of a thin accretion disk of finite thickness around a Kerr black hole. We test our new model with an RXTE observation of the black hole binary GRS 1915+105. We find that the spin value inferred with the new model is slightly higher than the spin value obtained with a model with an infinitesimally thin disk, but the difference is small and the effect is currently subdominant with respect to other sources of uncertainties in the final spin measurement.
Feeding and feedback from little monsters: AGN in dwarf galaxies

Abstract Detecting the seed black holes from which quasars formed is extremely challenging; however, those seeds that did not grow into supermassive should be found as intermediate-mass black holes (IMBHs) of 100 – 105 M⊙ in local dwarf galaxies. The use of deep multiwavelength surveys has revealed that a population of actively accreting IMBHs (low-mass AGN) exists in dwarf galaxies at least out to z ˜3. The black hole occupation fraction of these galaxies suggests that the early Universe seed black holes formed from direct collapse of gas, which is reinforced by the possible flattening of the black hole-galaxy scaling relations at the low-mass end. This scenario is however challenged by the finding that AGN feedback can have a strong impact on dwarf galaxies, which implies that low-mass AGN in dwarf galaxies might not be the untouched relics of the early seed black holes. This has important implications for seed black hole formation models.
ColBERT: Using BERT Sentence Embedding for Humor Detection

Automatic humor detection has interesting use cases in modern technologies, such as chatbots and personal assistants. In this paper, we describe a novel approach for detecting humor in short texts using BERT sentence embedding. Our proposed model uses BERT to generate tokens and sentence embedding for texts. It sends embedding outputs as input to a two-layered neural network that predicts the target value. For evaluation, we created a new dataset for humor detection consisting of 200k formal short texts (100k positive, 100k negative). Experimental results show an accuracy of 98.1 percent for the proposed method, 2.1 percent improvement compared to the best CNN and RNN models and 1.1 percent better than a fine-tuned BERT model. In addition, the combination of RNN-CNN was not successful in this task compared to the CNN model.
Single Shot 6D Object Pose Estimation

In this paper, we introduce a novel single shot approach for 6D object pose estimation of rigid objects based on depth images. For this purpose, a fully convolutional neural network is employed, where the 3D input data is spatially discretized and pose estimation is considered as a regression task that is solved locally on the resulting volume elements. With 65 fps on a GPU, our Object Pose Network (OP-Net) is extremely fast, is optimized end-to-end, and estimates the 6D pose of multiple objects in the image simultaneously. Our approach does not require manually 6D pose-annotated real-world datasets and transfers to the real world, although being entirely trained on synthetic data. The proposed method is evaluated on public benchmark datasets, where we can demonstrate that state-of-the-art methods are significantly outperformed.
Deep learning black hole metrics from shear viscosity

Based on AdS/CFT correspondence, we build a deep neural network to learn black hole metrics from the complex frequency-dependent shear viscosity. The network architecture provides a discretized representation of the holographic renormalization group flow of the shear viscosity and can be applied to a large class of strongly coupled field theories. Given the existence of the horizon and guided by the smoothness of spacetime, we show that Schwarzschild and Reissner-Nordstr\"om metrics can be learned accurately. Moreover, we illustrate that the generalization ability of the deep neural network can be excellent, which indicates that by using the black hole spacetime as a hidden data structure, a wide spectrum of the shear viscosity can be generated from a narrow frequency range. These results are further generalized to an Einstein-Maxwell-dilaton black hole. Our work might not only suggest a data-driven way to study holographic transports but also shed some light on holographic duality and deep learning.
Positivity of vector bundles on homogeneous varieties

We study the following question: Given a vector bundle on a projective variety $X$ such that the restriction of $E$ to every closed curve $C \,\subset\, X$ is ample, under what conditions $E$ is ample? We first consider the case of an abelian variety $X$. If $E$ is a line bundle on $X$, then we answer the question in the affirmative. When $E$ is of higher rank, we show that the answer is affirmative under some conditions on $E$. We then study the case of $X \,=\, G/P$, where $G$ is a reductive complex affine algebraic group, and $P$ is a parabolic subgroup of $G$. In this case, we show that the answer to our question is affirmative if $E$ is $T$--equivariant, where $T\, \subset\, P$ is a fixed maximal torus. Finally, we compute the Seshadri constant for such vector bundles defined on $G/P$.
Experiments with LVT and FRE for Transformer model

In this paper, we experiment with Large Vocabulary Trick and Feature-rich encoding applied to the Transformer model for Text Summarization. We could not achieve better results, than the analogous RNN-based sequence-to-sequence model, so we tried more models to find out, what improves the results and what deteriorates them.
Hierarchical Multi Task Learning with Subword Contextual Embeddings for Languages with Rich Morphology

Morphological information is important for many sequence labeling tasks in Natural Language Processing (NLP). Yet, existing approaches rely heavily on manual annotations or external software to capture this information. In this study, we propose using subword contextual embeddings to capture the morphological information for languages with rich morphology. In addition, we incorporate these embeddings in a hierarchical multi-task setting which is not employed before, to the best of our knowledge. Evaluated on Dependency Parsing (DEP) and Named Entity Recognition (NER) tasks, which are shown to benefit greatly from morphological information, our final model outperforms previous state-of-the-art models on both tasks for the Turkish language. Besides, we show a net improvement of 18.86% and 4.61% F-1 over the previously proposed multi-task learner in the same setting for the DEP and the NER tasks, respectively. Empirical results for five different MTL settings show that incorporating subword contextual embeddings brings significant improvements for both tasks. In addition, we observed that multi-task learning consistently improves the performance of the DEP component.
Extremely Metal-poor Representatives Explored by the Subaru Survey (EMPRESS). I. A Successful Machine-learning Selection of Metal-poor Galaxies and the Discovery of a Galaxy with M* < 106 M⊙ and 0.016 Z⊙

We have initiated a new survey for local extremely metal-poor galaxies (EMPGs) with Subaru/Hyper Suprime-Cam (HSC) large-area (∼500 deg2) optical images reaching a 5σ limit of ∼26 mag, about 100 times deeper than the Sloan Digital Sky Survey (SDSS). To select Z/Z⊙ < 0.1 EMPGs from ∼40 million sources detected in the Subaru images, we first develop a machine-learning (ML) classifier based on a deep neural network algorithm with a training data set consisting of optical photometry of galaxy, star, and QSO models. We test our ML classifier with SDSS objects having spectroscopic metallicity measurements and confirm that our ML classifier accomplishes 86% completeness and 46% purity EMPG classifications with photometric data. Applying our ML classifier to the photometric data of the Subaru sources, as well as faint SDSS objects with no spectroscopic data, we obtain 27 and 86 EMPG candidates from the Subaru and SDSS photometric data, respectively. We conduct optical follow-up spectroscopy for 10 of our EMPG candidates with Magellan/LDSS-3+MagE, Keck/DEIMOS, and Subaru/FOCAS and find that the 10 EMPG candidates are star-forming galaxies at z = 0.007–0.03 with large Hβ equivalent widths of 104–265 Å, stellar masses of log( / ) = 5.0–7.1, and high specific star formation rates of ∼300 Gyr−1, which are similar to those of early galaxies at z ≳ 6 reported recently. We spectroscopically confirm that 3 out of 10 candidates are truly EMPGs with Z/Z⊙ < 0.1, one of which is HSC J1631+4426, the most metal-poor galaxy, with Z/Z⊙ = 0.016, ever reported.
On the importance of lensing for galaxy clustering in photometric and spectroscopic surveys

We study the importance of gravitational lensing in modelling the number counts of galaxies for the first time in spectroscopic surveys. We confirm previous results for photometric surveys, showing that lensing cannot be neglected in a survey like LSST since it would infer a significant shift of standard cosmological parameters. For a spectroscopic survey like SKA2, we find that neglecting lensing in the monopole, quadrupole and hexadecapole of the correlation function can also induce an important shift of cosmological parameters. For ΛCDM parameters, the shift is moderate, of the order of 0. 6σ or less. However, for a model-independent analysis, that measures the growth rate of structure in each redshift bins, neglecting lensing introduces a shift of up to 2. 3σ at high redshift. Since the growth rate is directly used to test the theory of gravity, such a strong shift would wrongly be interpreted as the breakdown of General Relativity. This shows the importance of including lensing in the analysis of future surveys. For a survey like DESI, we find on the other hand that lensing is not important, mainly due to the value of the magnification bias parameter of DESI, s(z), which strongly reduces the lensing contribution at high redshift. This result relies on our theoretical modelling of s(z) in DESI and should therefore be confirmed with measurements of s(z) in simulations. We also propose a way of improving the analysis of spectroscopic surveys, by including the cross-correlations between different redshift bins (which is neglected in spectroscopic surveys) from the spectroscopic survey or from a different photometric sample. We show that including the cross-correlations in the SKA2 analysis does not improve the constraints. On the other hand replacing the cross-correlations from SKA2 by cross-correlations measured with LSST improves the constraints by 10% to 20%. The marginal improvement is mainly due to the density correlations between nearby bins and, therefore, does not strongly depend on our knowledge of the magnification bias. Interestingly, for standard cosmological parameter estimation, the photometric survey LSST in its 12 redshift bin configuration and the spectroscopic SKA2 survey are highly complementary, since they are affected by different degeneracies between parameters: LSST yields the tightest constraints on Ωcdm, h and n s, while SKA2 better constrains Ωbaryon, A s and the bias.
Timely Estimation Using Coded Quantized Samples

The effects of quantization and coding on the estimation quality of a Gauss-Markov, namely Ornstein-Uhlenbeck, process are considered. Samples are acquired from the process, quantized, and then encoded for transmission using either infinite incremental redundancy or fixed redundancy coding schemes. A fixed processing time is consumed at the receiver for decoding and sending feedback to the transmitter. Decoded messages are used to construct a minimum mean square error (MMSE) estimate of the process as a function of time. This is shown to be an increasing functional of the age-of-information, defined as the time elapsed since the sampling time pertaining to the latest successfully decoded message. Such (age-penalty) functional depends on the quantization bits, codeword lengths and receiver processing time. The goal, for each coding scheme, is to optimize sampling times such that the long term average MMSE is minimized. This is then characterized in the setting of general increasing age-penalty functionals, not necessarily corresponding to MMSE, which may be of independent interest in other contexts.
Neural Topic Modeling with Bidirectional Adversarial Training

Recent years have witnessed a surge of interests of using neural topic models for automatic topic extraction from text, since they avoid the complicated mathematical derivations for model inference as in traditional topic models such as Latent Dirichlet Allocation (LDA). However, these models either typically assume improper prior (e.g. Gaussian or Logistic Normal) over latent topic space or could not infer topic distribution for a given document. To address these limitations, we propose a neural topic modeling approach, called Bidirectional Adversarial Topic (BAT) model, which represents the first attempt of applying bidirectional adversarial training for neural topic modeling. The proposed BAT builds a two-way projection between the document-topic distribution and the document-word distribution. It uses a generator to capture the semantic patterns from texts and an encoder for topic inference. Furthermore, to incorporate word relatedness information, the Bidirectional Adversarial Topic model with Gaussian (Gaussian-BAT) is extended from BAT. To verify the effectiveness of BAT and Gaussian-BAT, three benchmark corpora are used in our experiments. The experimental results show that BAT and Gaussian-BAT obtain more coherent topics, outperforming several competitive baselines. Moreover, when performing text clustering based on the extracted topics, our models outperform all the baselines, with more significant improvements achieved by Gaussian-BAT where an increase of near 6% is observed in accuracy.