Miracle-dz/UniFAR-FTUs huggingface dataset:
NLTE carbon abundance determination in selected A- and B-type stars and the interpretation of C i emission lines

We constructed a comprehensive model atom for C\ione\ -- C\ii\ using the most up-to-date atomic data available and evaluated the non-local thermodynamic equilibrium (NLTE) line formation for C\ione\ and C\ii\ in classical 1D models representing the atmospheres of A and late B-type stars. Our NLTE calculations predict the emission that appears at effective temperature of 9250 to 10\,500~K depending on log~$g$ in the C\ione\ 8335, 9405\,\AA\ singlet lines and at \Teff~$>$~15\,000~K (log~$g$ = 4) in the C\ione\ 9061 -- 9111\,\AA\,, 9603 -- 9658\,\AA\, triplet lines. A prerequisite of the emission phenomenon is the overionization-recombination mechanism resulting in a depopulation of the lower levels of C\ione\ to a greater extent than the upper levels. Extra depopulation of the lower levels of the transitions corresponding to the near-infrared lines, is caused by photon loss in the UV lines C\ione\ 2479, 1930, and 1657\,\AA. We analysed the lines of C\ione\ and C\ii\ in Vega, HD~73666, Sirius, 21~Peg, $\pi$~Cet, HD~22136, and $\iota$ Her taking advantage of their observed high-resolution spectra. The C\ione\ emission lines were detected in the four hottest stars, and they were well reproduced in our NLTE calculations. For each star, the mean NLTE abundances from lines of the two ionization stages, C\ione\ and C\ii, including the C\ione\ emission lines, were found to be consistent. We show that the predicted C\ione\ emission phenomenon depends strongly on whether accurate or approximate electron-impact excitation rates are applied.
Analysing Diffusion-based Generative Approaches Versus Discriminative Approaches for Speech Restoration

Diffusion-based generative models have had a high impact on the computer vision and speech processing communities these past years. Besides data generation tasks, they have also been employed for data restoration tasks like speech enhancement and dereverberation. While discriminative models have traditionally been argued to be more powerful e.g. for speech enhancement, generative diffusion approaches have recently been shown to narrow this performance gap considerably. In this paper, we systematically compare the performance of generative diffusion models and discriminative approaches on different speech restoration tasks. For this, we extend our prior contributions on diffusion-based speech enhancement in the complex time-frequency domain to the task of bandwith extension. We then compare it to a discriminatively trained neural network with the same network architecture on three restoration tasks, namely speech denoising, dereverberation and bandwidth extension. We observe that the generative approach performs globally better than its discriminative counterpart on all tasks, with the strongest benefit for non-additive distortion models, like in dereverberation and bandwidth extension. Code and audio examples can be found online1.
Accelerated Projected Gradient Algorithms for Sparsity Constrained Optimization Problems

We consider the projected gradient algorithm for the nonconvex best subset selection problem that minimizes a given empirical loss function under an $\ell_0$-norm constraint. Through decomposing the feasible set of the given sparsity constraint as a finite union of linear subspaces, we present two acceleration schemes with global convergence guarantees, one by same-space extrapolation and the other by subspace identification. The former fully utilizes the problem structure to greatly accelerate the optimization speed with only negligible additional cost. The latter leads to a two-stage meta-algorithm that first uses classical projected gradient iterations to identify the correct subspace containing an optimal solution, and then switches to a highly-efficient smooth optimization method in the identified subspace to attain superlinear convergence. Experiments demonstrate that the proposed accelerated algorithms are magnitudes faster than their non-accelerated counterparts as well as the state of the art.
Evidence for the Disruption of a Planetary System During the Formation of the Helix Nebula

The persistence of planetary systems after their host stars evolve into their post-main-sequence phase is poorly constrained by observations. Many young white dwarf systems exhibit infrared excess emission and/or spectral absorption lines associated with a reservoir of dust (or planetesimals) and its accretion. However, most white dwarfs are too cool to sufficiently heat any circumstellar dust to detectable levels of emission. The Helix Nebula (NGC 7293) is a young, nearby planetary nebula; observations at mid- and far-infrared wavelengths have revealed excess emission associated with its central white dwarf (WD 2226-210). The origin of this excess is ambiguous. It could be a remnant planetesimal belt, a cloud of comets, or the remnants of material shed during the post-asymptotic giant branch (post-AGB) phase. Here we combine infrared (Stratospheric Observatory for Infrared Astronomy, Spitzer, Herschel) and millimeter (Atacama Large Millimeter/submillimeter Array) observations of the system to determine the origin of this excess using multiwavelength imaging and radiative transfer modeling. We find the data are incompatible with a compact remnant planetesimal belt or post-AGB disk, and conclude the dust most likely originates from deposition by a cometary cloud. The measured dust mass, and lifetime of the constituent grains, implies disruption of several thousand Hale–Bopp equivalent comets per year to fuel the observed excess emission around the Helix Nebula’s white dwarf.
UV R-CNN: Stable and efficient dense human pose estimation

As a dense prediction task aimed at instance-level human analysis, dense-pose estimation seeks to accurately map 2D pixels onto the 3D surface of the human body. Despite significant progress has been made, two major challenges continue to confront the research community: the first is training instability caused by a large number of surface points to be regressed; the second is the significant amount of time and computational resources to manually adjust multi-task loss weights. To overcome these challenges, we present a novel dense pose estimator, named UV R-CNN, which is based on a detailed analysis of the loss formulation used in existing algorithms. The proposed UV R-CNN first introduces a novel surface point regression loss, which serves to constrain the immense loss and stable the training progress, named Dense Points Loss (DP-Loss). Additionally, we incorporates a Balanced Weighting Strategy (BWS) that allows for the automatic adaptation of loss weights. Remarkably, without auxiliary supervision and external knowledge from other tasks, UV R-CNN can be trained with larger learning rate, achieving 65.0% A P _ g p s and 66.1% A P g p s m $AP_{gps^{m}}$ on the DensePose-COCO validation subset with ResNet-50-FPN as backbone, competitive to the state-of-the-art methods.
Linear bilevel programming with uncertain lower-level costs

We study a bilevel programming problem with linear data, whose lower-level objective is a random variable with a known distribution. We consider the case where this distribution is nonatomic, allowing to pose the problem of the leader as the optimization of his expected value. We prove that under suitable assumptions this formulation turns out to be piecewise aﬃne over the so-called chamber complex of the feasible region (of the high-point relaxation). We propose two algorithmic approaches to solve general problems enjoying this last property. The ﬁrst one is based on enumerating the vertices of the chamber complex and evaluating them. The second one is based on the fact that randomly drawn points of the domain should be in the interior of a full-dimensional chamber, where the problem (restricted to this chamber) can be reduced to a linear program.
Dealing with Abbreviations in the Slovenian Biographical Lexicon

Abbreviations present a significant challenge for NLP systems because they cause tokenization and out-of-vocabulary errors. They can also make the text less readable, especially in reference printed books, where they are extensively used. Abbreviations are especially problematic in low-resource settings, where systems are less robust to begin with. In this paper, we propose a new method for addressing the problems caused by a high density of domain-specific abbreviations in a text. We apply this method to the case of a Slovenian biographical lexicon and evaluate it on a newly developed gold-standard dataset of 51 Slovenian biographies. Our abbreviation identification method performs significantly better than commonly used ad-hoc solutions, especially at identifying unseen abbreviations. We also propose and present the results of a method for expanding the identified abbreviations in context.
Reaction-Induced Molecular Dancing and Boosted Diffusion of Enzymes

A novel mechanism of reaction-induced active molecular motion, not involving any kind of self-propulsion, is proposed and analyzed. Because of the momentum exchange with the surrounding solvent, conformational transitions in mechano-chemical enzymes are accompanied by motions of their centers of mass. As we show, in combination with rotational diffusion, such repeated reciprocal motions generate an additional random walk - or molecular dancing - and hence boost translational diffusion of an enzyme. A systematic theory of this phenomenon is developed, using as an example a simple enzyme model of a rigid two-state dumbbell. To support the analysis, numerical simulations are performed. Our conclusion is that the phenomenon of molecular dancing could underlie the observations of reaction-induced diffusion enhancement in enzymes. Major experimental findings, such as the occurrence of leaps, the anti-chemotaxis, the linear dependence on the reaction turnover rate and on the rate of energy supply, become thus explained. Moreover, the dancing behavior is possible in other systems, natural and synthetic, too. In the future, interesting biotechnology applications may be developed using such effects.
Fractal signature as a rotational modulation and stellar noise classifier based on the active kepler stars

In this study, we report on the analysis of 701 stars in a solar vicinity defined in three categories namely subsolar, solar, and supersolar with rotation periods between 1 and 70 days, based on rotational modulation signatures inferred from time series from the Kepler mission’s Public Archives. In our analysis, we performed an initial selection based on the rotation period and position in the period–H diagram, where H denotes the Hurst exponent extracted from fractal analysis. To refine our analysis, we applied a fractal approach known as the R/S method, taking into account the fluctuations of the features associated with photometric modulation at different time intervals and the fractality traces that are present in the time series of our sample. In this sense, we computed the so-called Hurst exponent for the referred stars and found that it can provide a strong discriminant of rotational modulation and background noise behavior, going beyond what can be achieved with solely the rotation period itself. Furthermore, our results emphasize that the rotation period of stars is scaled by the exponent H which increases following the increase in the rotation period. Finally, our approach suggests that the referred exponent may be a powerful rotational modulation and noise classifier.
Rethinking Storage Management for Data Processing Pipelines in Cloud Data Centers

Data processing frameworks such as Apache Beam and Apache Spark are used for a wide range of applications, from logs analysis to data preparation for DNN training. It is thus unsurprising that there has been a large amount of work on optimizing these frameworks, including their storage management. The shift to cloud computing requires optimization across all pipelines concurrently running across a cluster. In this paper, we look at one specific instance of this problem: placement of I/O-intensive temporary intermediate data on SSD and HDD. Efficient data placement is challenging since I/O density is usually unknown at the time data needs to be placed. Ad-ditionally, external factors such as load variability, job preemption, or job priorities can impact job completion times, which ultimately affect the I/O density of the temporary files in the workload. In this paper, we envision that machine learning can be used to solve this problem. We analyze production logs from Google’s data centers for a range of data processing pipelines. Our analysis shows that I/O density may be predictable. This suggests that learning-based strategies, if crafted carefully, could extract predictive features for I/O density of temporary files involved in various transformations, which could be used to improve the efficiency of storage management in data processing pipelines.
MultiWOZ-DF - A Dataflow implementation of the MultiWOZ dataset

In this paper we introduce MultiWOZ-DF, a dataﬂow implementation of the well known MultiWOZ dataset. The implementation demonstrates how to both convert and execute MultiWOZ dialogues as dataﬂow dialogues. Our goal is to encourage the research community to further investigate the dataﬂow dialogue paradigm.
Improving mean-field network percolation models with neighbourhood information and their limitations on highly modular, highly dispersed networks

Mean field theory models of percolation on networks provide analytic estimates of network robustness under node or edge removal. We introduce a new mean field theory model based on generating functions that includes information about the tree-likeness of each node's local neighbourhood. We show that our new model outperforms all other generating function models in prediction accuracy when testing their estimates on a wide range of real-world network data. We compare the new model's performance against the recently introduced message passing models and provide evidence that the standard version is also outperformed, while the `loopy' version is only outperformed on a targeted attack strategy. As we show, however, the computational complexity of our model implementation is much lower than that of message passing algorithms. We provide evidence that all discussed models are poor in predicting networks with highly modular structure with dispersed modules, which are also characterised by high mixing times, identifying this as a general limitation of percolation prediction models.
On the interplay between TLS certificates and QUIC performance

In this paper, we revisit the performance of the QUIC connection setup and relate the design choices for fast and secure connections to common Web deployments. We analyze over 1M Web domains with 272k QUIC-enabled services and find two worrying results. First, current practices of creating, providing, and fetching Web certificates undermine reduced round trip times during the connection setup since sizes of 35% of server certificates exceed the amplification limit. Second, non-standard server implementations lead to larger amplification factors than QUIC permits, which increase even further in IP spoofing scenarios. We present guidance for all involved stakeholders to improve the situation.
AntFuzzer: A Grey-Box Fuzzing Framework for EOSIO Smart Contracts

In the past few years, several attacks against the vulnerabilities of EOSIO smart contracts have caused severe financial losses to this prevalent blockchain platform. As a lightweight test-generation approach, grey-box fuzzing can open up the possibility of improving the security of EOSIO smart contracts. However, developing a practical grey-box fuzzer for EOSIO smart contracts from scratch is time-consuming and requires a deep understanding of EOSIO internals. In this work, we proposed AntFuzzer, the first highly extensible grey-box fuzzing framework for EOSIO smart contracts. AntFuzzer implements a novel approach that interfaces AFL to conduct AFL-style grey-box fuzzing on EOSIO smart contracts. Compared to black-box fuzzing tools, AntFuzzer can effectively trigger those hard-to-cover branches. It achieved an improvement in code coverage on 37.5% of smart contracts in our benchmark dataset. AntFuzzer provides unified interfaces for users to easily develop new detection plugins for continually emerging vulnerabilities. We have implemented 6 detection plugins on AntFuzzer to detect major vulnerabilities of EOSIO smart contracts. In our large-scale fuzzing experiments on 4,616 real-world smart contracts, AntFuzzer successfully detected 741 vulnerabilities. The results demonstrate the effectiveness and efficiency of AntFuzzer and our detection pl
Miko Team: Deep Learning Approach for Legal Question Answering in ALQAC 2022

We introduce efficient deep learning-based methods for legal document processing including Legal Document Retrieval and Legal Question Answering tasks in the Automated Legal Question Answering Competition (ALQAC 2022). In this competition, we achieve 1st place in the first task and 3rd place in the second task. Our method is based on the XLM-RoBERTa model that is pre-trained from a large amount of unlabeled corpus before fine-tuning to the specific tasks. The experimental results showed that our method works well in legal retrieval information tasks with limited labeled data. Besides, this method can be applied to other information retrieval tasks in low-resource languages.
Derived splinters in positive characteristic

Abstract This paper introduces the notion of a derived splinter. Roughly speaking, a scheme is a derived splinter if it splits off from the coherent cohomology of any proper cover. Over a field of characteristic 0, this condition characterises rational singularities, as suggested by the work of Kovács. Our main theorem asserts that over a field of characteristic p, derived splinters are the same as (underived) splinters, i.e. schemes that split off from any finite cover. Using this result, we answer some questions of Karen Smith concerning the extension of Serre/Kodaira-type vanishing results beyond the class of ample line bundles in positive characteristic; these are purely projective geometric statements independent of singularity considerations. In fact, we can prove ‘up to finite cover’ analogues in characteristic p of many vanishing theorems known in characteristic 0. All these results fit naturally in the study of F-singularities, and are motivated by a desire to understand the direct summand conjecture.
High-sensitivity VLBI observations of water masers in the Seyfert galaxy NGC 1068

We present observational results of water vapor maser emission with our high-sensitivity 22 GHz very long baseline interferometry (VLBI) imaging of the Seyfert galaxy NGC 1068. In this galaxy, there are the following four nuclear radio sources; NE, C, S1, and S2. Among them, the S1 component has been identified as the nucleus while the C component has been considered as attributed to the radio jet. In our VLBI observation, we find the following two types of water maser emission at the S1 component. One is a linearly aligned component that is considered as an edge-on disk with an inner radius of 0.62 pc. The dynamical mass enclosed within the inner radius was estimated to be 1.5 × 107 M⊙ by assuming the circular Keplerian motion. Note, however, that the best-fitting rotation curve shows a sub-Keplerian rotation (v ∝ r−0.24±0.10). The other is water maser emission distributed around the rotating disk component up to 1.5 pc from the S1 component, suggesting a bipolar outflow from the S1 component. Further, we detected water maser emission in the C component for the first time with VLBI, and discovered a ring-like distribution of water maser emission. It is known that a molecular cloud is associated with the C component (both HCN and HCO+ emission lines are detected by ALMA). Therefore, the ring-like maser emission can be explained by the jet collision to the molecular cloud. However, if these ring-like water masing clouds constitute a rotating ring around the C component, it is likely that the C component also has a supermassive black hole with a mass of ∼106 M⊙ that could be supplied from a past minor merger of a nucleated satellite galaxy.
Time Series Quantile Regression Using Random Forests

We discuss an application of Generalized Random Forests (GRF) proposed to quantile regression for time series data. We extended the theoretical results of the GRF consistency for i.i. d. data to time series data. In particular, in the main theorem, based only on the general assumptions for time series data and trees, we show that the tsQRF (time series Quantile Regression Forest) estimator is consistent. Compare with existing article, different ideas are used throughout the theoretical proof. In addition, a simulation and real data analysis were conducted. In the simulation, the accuracy of the conditional quantile estimation was evaluated under time series models. In the real data using the Nikkei Stock Average, our estimator is demonstrated to capture volatility more efficiently, thus preventing underestimation of uncertainty.
The “Problem” of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation

Human variation in labeling is often considered noise. Annotation projects for machine learning (ML) aim at minimizing human label variation, with the assumption to maximize data quality and in turn optimize and maximize machine learning metrics. However, thisconventional practice assumes that there exists a *ground truth*, and neglects that there exists genuine human variation in labeling due to disagreement, subjectivity in annotation or multiple plausible answers.In this position paper, we argue that this big open problem of human label variation persists and critically needs more attention to move our field forward. This is because human label variation impacts all stages of the ML pipeline: *data, modeling and evaluation*. However, few works consider all of these dimensions jointly; and existing research is fragmented. We reconcile different previously proposed notions of human label variation, provide a repository of publicly-available datasets with un-aggregated labels, depict approaches proposed so far, identify gaps and suggest ways forward. As datasets are becoming increasingly available, we hope that this synthesized view on the “problem” will lead to an open discussion on possible strategies to devise fundamentally new directions.
On fractional Bessel equation and the description of corneal topography

In this note we apply a modified fractional Bessel differential equation to the problem of describing corneal topography. We find the solution in terms of the power series. This solution has an interesting behavior at infinity which is a generalization of the classical results for modified Bessel function of order 0. Our model fits the real corneal geometry data with an error of order of a few per cent.