Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Rise and Fall of Simple Stupid Bugs: a Life-Cycle Analysis of SStuBs

Bug detection and prevention is one of the most important goals of software quality assurance. Nowadays, many of the major problems faced by developers can be detected or even fixed fully or partially with automatic tools. However, recent works explored that there exists a substantial amount of simple yet very annoying errors in code-bases, which are easy to fix, but hard to detect as they do not hinder the functionality of the given product in a major way. Programmers introduce such errors accidentally, mostly due to inattention.Using the ManySStuBs4J dataset, which contains many simple, stupid bugs, found in GitHub repositories written in the Java programming language, we investigated the history of such bugs. We were interested in properties such as: How long do such bugs stay unnoticed in code-bases? Whether they are typically fixed by the same developer who introduced them? Are they introduced with the addition of new code or caused more by careless modification of existing code? We found that most of such stupid bugs lurk in the code for a long time before they get removed. We noticed that the developer who made the mistake seems to find a solution faster, however less then half of SStuBs are fixed by the same person. We also examined PMD’s performance when to came to flagging lines containing SStuBs, and found that similarly to SpotBugs, it is insufficient when it comes to finding these types of errors. Examining the life-cycle of such bugs allows us to better understand their nature and adjust our development processes and quality assurance methods to better support avoiding them.
TNM: A Tool for Mining of Socio-Technical Data from Git Repositories

Networks of collaboration between engineers are reflected in traces of developers’ activity in version control systems (VCSs). Extracting data from Git repositories is an essential task for researchers and practitioners working on socio-technical analysis, but it requires substantial engineering work. With increasing interest in analysing socio-technical data and applying it in practice, there are no flexible and easily reusable tools to retrieve socio-technical information from VCSs. With no common reusable toolkit existing for this task, the burden of mining diverts the focus of researchers from their core research questions.In this paper, we present TNM—an open-source tool for mining socio-technical data from Git repositories. TNM is fast, flexible, and easily extensible.TNM is available on GitHub: https://github.com/JetBrains-Research/tnm
Giant nonlinear anomalous Hall effect induced by spin-dependent band structure evolution

Anomalous Hall effect (AHE) is the key transport signature unlocking topological properties of magnetic materials. While AHE is usually proportional to the magnetization, the nonlinearity suggests the existence of complex magnetic and electron orders. Nonlinear AHE includes the topological Hall effect (THE) that has been widely used to identify the presence of spin chirality in real space. But it can in principle be induced by band structure evolution via Berry curvatures in the reciprocal space. This effect has been largely overlooked due to the intertwined mechanisms in both real and reciprocal spaces. Here, we observed a giant nonlinear AHE with the resistivity up to 383.5 μΩ ∙ cm , contributing unprecedentedly 97% of the total Hall response in EuCd2As2. Moreover, it can be further enhanced by tilting the magnetic field 30° away from [001] direction, reaching a large anomalous Hall angle up to 21%. Although it shows exactly the same double-peak feature as THE, our scaling analysis and first-principles calculations reveal that the Berry phase is extremely sensitive to the spin canting, and nonlinear AHE is a consequence of band structure evolution under the external magnetic fields. When the spins gradually tilt from the in-plane antiferromagnetic ground state to out-of-plane direction, band crossing and band inversion occur, introducing a bandgap at Γ point at a canting angle of 45°. That contributes to the enhancement of Berry curvature and consequently a large intrinsic Hall conductivity. Our results unequivocally reveal the sensitive dependence of band structures on spin tilting process under external magnetic fields and its pronounced influence on the transport properties, which also need to be taken into consideration in other magnetic materials.
Thermodynamic evolution of a sigmoidal active region with associated flares

Active regions often show S-shaped structures in the corona called sigmoids. These are highly sheared and twisted loops formed along the polarity inversion line. They are considered to be one of the best pre-eruption signatures for CMEs. Here, we investigate the thermodynamic evolution of an on-disc sigmoid observed during 2015 December 24–28. For this purpose, we have employed Emission Measure (EM) and filter-ratio techniques on the observations recorded by the Atmospheric Imaging Assembly (AIA) on-board the Solar Dynamics Observatory (SDO) and X-ray Telescope (XRT) on-board Hinode. The EM analysis showed multithermal plasma along the sigmoid and provided a peak temperature of ∼10–12.5 MK for all observed flares. The sigmoidal structure showed emission from Fe xviii (93.93 Å) and Fe xxi (128.75 Å) lines in the AIA 94 and 131 Å channels, respectively. Our results show that the hot plasma is often confined to very hot strands. The temperature obtained from the EM analysis was found to be in good agreement with that obtained using the XRT, AIA, and GOES filter-ratio methods. These results provide important constraints for the thermodynamic modelling of sigmoidal structures in the core of active regions. Moreover, this study also benchmarks different techniques available for temperature estimation in solar coronal structures.
Contrastive Learning of Musical Representations

While deep learning has enabled great advances in many areas of music, labeled music datasets remain especially hard, expensive, and time-consuming to create. In this work, we introduce SimCLR to the music domain and contribute a large chain of audio data augmentations to form a simple framework for self-supervised, contrastive learning of musical representations: CLMR. This approach works on raw time-domain music data and requires no labels to learn useful representations. We evaluate CLMR in the downstream task of music classification on the MagnaTagATune and Million Song datasets and present an ablation study to test which of our music-related innovations over SimCLR are most effective. A linear classifier trained on the proposed representations achieves a higher average precision than supervised models on the MagnaTagATune dataset, and performs comparably on the Million Song dataset. Moreover, we show that CLMR's representations are transferable using out-of-domain datasets, indicating that our method has strong generalisability in music classification. Lastly, we show that the proposed method allows data-efficient learning on smaller labeled datasets: we achieve an average precision of 33.1% despite using only 259 labeled songs in the MagnaTagATune dataset (1% of the full dataset) during linear evaluation. To foster reproducibility and future research on self-supervised learning in music, we publicly release the pre-trained models and the source code of all experiments of this paper.
Code Word Detection in Fraud Investigations using a Deep-Learning Approach

In modern litigation, fraud investigators often face an overwhelming number of documents that must be reviewed throughout a matter. In the majority of legal cases, fraud investigators do not know beforehand, exactly what they are looking for, nor where to find it. In addition, fraudsters may use deception to hide their behaviour and intentions by using code words. Effectively, this means fraud investigators are looking for a needle in the haystack without knowing what the needle looks like. As part of a larger research program, we use a framework to expedite the investigation process applying text-mining and machine learning techniques. We structure this framework using three well-known methods in fraud investigations: (i) the fraud triangle (ii) the golden ("W") investigation questions, and (iii) the analysis of competing hypotheses. With this framework, it is possible to automatically organize investigative data, so it is easier for investigators to find answers to typical investigative questions. In this research, we focus on one of the components of this framework: the identification of the usage of code words by fraudsters. Here for, a novel (annotated) synthetic data set is created containing such code words, hidden in normal email communication. Subsequently, a range of machine learning techniques are employed to detect such code words. We show that the state-of-the-art BERT model significantly outperforms other methods on this task. With this result, we demonstrate that deep neural language models can reliably (F1 score of 0.9) be applied in fraud investigations for the detection of code words.
RX J0529.8−6556: a BeXRB pulsar with an evolving optical period and out of phase X-ray outbursts

We report the results of eROSITA and NICER observations of the 2020 June outburst of the Be/X-ray binary pulsar RX J0529.8−6556 in the Large Magellanic Cloud, along with the analysis of archival X-ray and optical data from this source. We find two anomalous features in the system’s behaviour. First, the pulse profile observed by NICER during maximum luminosity is similar to that observed by XMM–Newton in 2000, despite the fact that the X-ray luminosity was different by two orders of magnitude. In contrast, a modest decrease in luminosity in the 2020 observations generated a significant change in pulse profile. Secondly, we find that the historical optical outbursts are not strictly periodic, as would be expected if the outbursts were triggered by periastron passage, as is generally assumed. The optical peaks are also not coincident with the X-ray outbursts. We suggest that this behaviour may result from a misalignment of the Be star disc and the orbital plane, which might cause changes in the timing of the passage of the neutron star through the disc as it precesses. We conclude that the orbital period of the source remains unclear.
Vertex deletion parameterized by elimination distance and even less

We study the parameterized complexity of various classic vertex-deletion problems such as Odd cycle transversal, Vertex planarization, and Chordal vertex deletion under hybrid parameterizations. Existing FPT algorithms for these problems either focus on the parameterization by solution size, detecting solutions of size k in time f(k) · nO(1), or width parameterizations, finding arbitrarily large optimal solutions in time f(w) · nO(1) for some width measure w like treewidth. We unify these lines of research by presenting FPT algorithms for parameterizations that can simultaneously be arbitrarily much smaller than the solution size and the treewidth. The first class of parameterizations is based on the notion of elimination distance of the input graph to the target graph class , which intuitively measures the number of rounds needed to obtain a graph in by removing one vertex from each connected component in each round. The second class of parameterizations consists of a relaxation of the notion of treewidth, allowing arbitrarily large bags that induce subgraphs belonging to the target class of the deletion problem as long as these subgraphs have small neighborhoods. Both kinds of parameterizations have been introduced recently and have already spawned several independent results. Our contribution is twofold. First, we present a framework for computing approximately optimal decompositions related to these graph measures. Namely, if the cost of an optimal decomposition is k, we show how to find a decomposition of cost kO(1) in time f(k) · nO(1). This is applicable to any class for which we can solve the so-called separation problem. Secondly, we exploit the constructed decompositions for solving vertex-deletion problems by extending ideas from algorithms using iterative compression and the finite state property. For the three mentioned vertex-deletion problems, and all problems which can be formulated as hitting a finite set of connected forbidden (a) minors or (b) (induced) subgraphs, we obtain FPT algorithms with respect to both studied parameterizations. For example, we present an algorithm running in time nO(1) + 2kO(1)·(n+m) and polynomial space for Odd cycle transversal parameterized by the elimination distance k to the class of bipartite graphs.
Inferred vs traditional personality assessment: are we predicting the same thing?

Machine learning methods are widely used by researchers to predict psychological characteristics from digital records. To find out whether automatic personality estimates retain the properties of the original traits, we reviewed 220 recent articles. First, we put together the predictive quality estimates from a subset of the studies which declare separation of training, validation, and testing phases, which is critical for ensuring the correctness of quality estimates in machine learning. Only 20% of the reviewed papers met this criterion. To compare the reported quality estimates, we converted them to approximate Pearson correlations. The credible upper limits for correlations between predicted and self-reported personality traits vary in a range between 0.42 and 0.48, depending on the specific trait. The achieved values are substantially below the correlations between traits measured with distinct self-report questionnaires. This suggests that we cannot readily interpret personality predictions as estimates of the original traits or expect predicted personality traits to reproduce known relationships with life outcomes regularly. Next, we complement quality estimates evaluation with evidence on psychometric properties of predicted traits. The few existing results suggest that predicted traits are less stable with time and have lower effective dimensionality than self-reported personality. The predictive text-based models perform substantially worse outside their training domains but stay above a random baseline. The evidence on the relationships between predicted traits and external variables is mixed. Predictive features are difficult to use for validation, due to the lack of prior hypotheses. Thus, predicted personality traits fail to retain important properties of the original characteristics. This calls for the cautious use and targeted validation of the predictive models.
Investigation of aflatoxins levels in commercial dried figs from western Turkey

The aim of the present work was to investigate aflatoxins in dried figs from Izmir, Turkey. A total of 1,973 samples of dried figs were obtained from retail shops and public outlets from 2014 to 2018. Aflatoxin contamination was determined by high-performance liquid chromatography coupled with fluorescence detection (HPLC-FLD) following clean-up by immunoaffinity (IAC). The obtained results were evaluated against the maximum limit set by the Turkish Regulation and European Commission for total aflatoxins (AFS) and aflatoxin B1 (AFB1). The number of samples exceeding the legal limit was 40 according to the Commission Regulation (EU) No 1058/2012 for the limit of aflatoxin B1 (6 μg/kg), and 42 samples exceeded the legal limit for the total aflatoxin content (10 μg/kg). Thirty-four and forty-two samples of dried figs exceeded the legal limit for AFB1 and AFS (8 and 10 μg/kg, respectively) based on the Turkish Food Codex.
Unusually Fast bis-Histidyl Coordination in a Plant Hemoglobin

The recently identified nonsymbiotic hemoglobin gene MtGlb1-2 of the legume Medicago truncatula possesses unique properties as it generates four alternative splice forms encoding proteins with one or two heme domains. Here we investigate the ligand binding kinetics of MtGlb1-2.1 and MtGlb1-2.4, bearing two hemes and one heme, respectively. Unexpectedly, the overall time-course of ligand rebinding was unusually fast. Thus, we complemented nanosecond laser flash photolysis kinetics with data collected with a hybrid femtosecond–nanosecond pump–probe setup. Most photodissociated ligands are rebound geminately within a few nanoseconds, which leads to rates of the bimolecular rebinding to pentacoordinate species in the 108 M−1s−1 range. Binding of the distal histidine to the heme competes with CO rebinding with extremely high rates (kh ~ 105 s−1). Histidine dissociation from the heme occurs with comparable rates, thus resulting in moderate equilibrium binding constants (KH ~ 1). The rate constants for ligation and deligation of distal histidine to the heme are the highest reported for any plant or vertebrate globin. The combination of microscopic rates results in unusually high overall ligand binding rate constants, a fact that contributes to explaining at the mechanistic level the extremely high reactivity of these proteins toward the physiological ligands oxygen, nitric oxide and nitrite.
Sperm Methylome Profiling Can Discern Fertility Levels in the Porcine Biomedical Model

A combined Genotyping By Sequencing (GBS) and methylated DNA immunoprecipitation (MeDIP) protocol was used to identify—in parallel—genetic variation (Genomic-Wide Association Studies (GWAS) and epigenetic differences of Differentially Methylated Regions (DMR) in the genome of spermatozoa from the porcine animal model. Breeding boars with good semen quality (n = 11) and specific and well-documented differences in fertility (farrowing rate, FR) and prolificacy (litter size, LS) (n = 7) in artificial insemination programs, using combined FR and LS, were categorized as High Fertile (HF, n = 4) or Low Fertile (LF, n = 3), and boars with Unknown Fertility (UF, n = 4) were tested for eventual epigenetical similarity with those fertility-proven. We identified 165,944 Single Nucleotide Polymorphisms (SNPs) that explained 14–15% of variance among selection lines. Between HF and LF individuals (n = 7, 4 HF and 3 LF), we identified 169 SNPs with p ≤ 0.00015, which explained 58% of the variance. For the epigenetic analyses, we considered fertility and period of ejaculate collection (late-summer and mid-autumn). Approximately three times more DMRs were observed in HF than in LF boars across these periods. Interestingly, UF boars were clearly clustered with one of the other HF or LF groups. The highest differences in DMRs between HF and LF experimental groups across the pig genome were located in the chr 3, 9, 13, and 16, with most DMRs being hypermethylated in LF boars. In both HF and LF boars, DMRs were mostly hypermethylated in late-summer compared to mid-autumn. Three overlaps were detected between SNPs (p ≤ 0.0005, n = 1318) and CpG sites within DMRs. In conclusion, fertility levels in breeding males including FR and LS can be discerned using methylome analyses. The findings in this biomedical animal model ought to be applied besides sire selection for andrological diagnosis of idiopathic sub/infertility.
Image Analysis of Defocused Single-Molecule Images for Three-Dimensional Molecule Orientation Studies

An efficient algorithm for pattern matching has been developed based on least-squares analysis of fitting a discrete set of master patterns against measured images. This algorithm has been applied to determine three-dimensional molecule orientations in defocused single-molecule images. The developed algorithm exploits the excellent agreement between electrodynamic calculations of single-molecule emission and experimentally measured images. The procedure is found to be reliable and simple and can be applied to any kind of pattern recognition where the patterns to be recognized are precisely known a priori. The procedure works well even for noisy and low-intensity signals as usually encountered in single-molecule experiments.
The Two-Faced Role of SIRT6 in Cancer

Simple Summary Cancer therapy relies on the employment of different strategies aimed at inducing cancer cell death through different mechanisms, including DNA damage and apoptosis induction. One of the key regulators of these pathways is the epigenetic enzyme SIRT6, which has been shown to have a dichotomous function in cell fate determination and, consequently, cancer initiation and progression. In this review, we aim to summarize the current knowledge on the role of SIRT6 in cancer. We show that it can act as both tumor suppressor and promoter, even in the same cancer type, depending on the biological context. We then describe the most promising modulators of SIRT6 which, through enzyme activation or inhibition, may impair tumor growth. These molecules can also be used for the elucidation of SIRT6 function, thereby advancing the current knowledge on this crucial protein. Abstract Sirtuin 6 (SIRT6) is a NAD+-dependent nuclear deacylase and mono-ADP-ribosylase with a wide spectrum of substrates. Through its pleiotropic activities, SIRT6 modulates either directly or indirectly key processes linked to cell fate determination and oncogenesis such as DNA damage repair, metabolic homeostasis, and apoptosis. SIRT6 regulates the expression and activity of both pro-apoptotic (e.g., Bax) and anti-apoptotic factors (e.g., Bcl-2, survivin) in a context-depending manner. Mounting evidence points towards a double-faced involvement of SIRT6 in tumor onset and progression since the block or induction of apoptosis lead to opposite outcomes in cancer. Here, we discuss the features and roles of SIRT6 in the regulation of cell death and cancer, also focusing on recently discovered small molecule modulators that can be used as chemical probes to shed further light on SIRT6 cancer biology and proposed as potential new generation anticancer therapeutics.
Tunable blood oxygenation in the vascular anatomy of a semi-anthropomorphic photoacoustic breast phantom

Abstract. Significance: Recovering accurate oxygenation estimations in the breast with quantitative photoacoustic tomography (QPAT) is not straightforward. Accurate light fluence models are required, but the unknown ground truth of the breast makes it difficult to validate them. Phantoms are often used for the validation, but most reported phantoms have a simple architecture. Fluence models developed in these simplistic objects are not accurate for application on the complex tissues of the breast. Aim: We present a sophisticated breast phantom platform for photoacoustic (PA) and ultrasound (US) imaging in general, and specifically for QPAT. The breast phantom is semi-anthropomorphic in distribution of optical and acoustic properties and contains wall-less channels with blood. Approach: 3D printing approaches are used to develop the solid 3D breast phantom from custom polyvinyl chloride plastisol formulations and additives for replicating the tissue optical and acoustic properties. A flow circuit was developed to flush the channels with bovine blood with a controlled oxygen saturation level. To showcase the phantom’s functionality, PA measurements were performed on the phantom with two oxygenation levels. Image reconstructions with and without fluence compensation from Monte Carlo simulations were analyzed for the accuracy of oxygen saturation estimations. Results: We present design aspects of the phantom, demonstrate how it is developed, and present its breast-like appearance in PA and US imaging. The oxygen saturations were estimated in two regions of interest with and without using the fluence models. The fluence compensation positively influenced the SO2 estimations in all cases and confirmed that highly accurate fluence models are required to minimize estimation errors. Conclusions: This phantom allows studies to be performed in PA in carefully controlled laboratory settings to validate approaches to recover both qualitative and quantitative features sought after in in-vivo studies. We believe that testing with phantoms of this complexity can streamline the transition of new PA technologies from the laboratory to studies in the clinic.