Miracle-dz/UniFAR-FTUs huggingface dataset:
Employing Partial Least Squares Regression with Discriminant Analysis for Bug Prediction

Forecasting defect proneness of source code has long been a major research concern. Having an estimation of those parts of a software system that most likely contain bugs may help focus testing efforts, reduce costs, and improve product quality. Many prediction models and approaches have been introduced during the past decades that try to forecast bugged code elements based on static source code metrics, change and history metrics, or both. However, there is still no universal best solution to this problem, as most suitable features and models vary from dataset to dataset and depend on the context in which we use them. Therefore, novel approaches and further studies on this topic are highly necessary. In this paper, we employ a chemometric approach - Partial Least Squares with Discriminant Analysis (PLS-DA) - for predicting bug prone Classes in Java programs using static source code metrics. To our best knowledge, PLS-DA has never been used before as a statistical approach in the software maintenance domain for predicting software errors. In addition, we have used rigorous statistical treatments including bootstrap resampling and randomization (permutation) test, and evaluation for representing the software engineering results. We show that our PLS-DA based prediction model achieves superior performances compared to the state-of-the-art approaches (i.e. F-measure of 0.44-0.47 at 90% confidence level) when no data re-sampling applied and comparable to others when applying up-sampling on the largest open bug dataset, while training the model is significantly faster, thus finding optimal parameters is much easier. In terms of completeness, which measures the amount of bugs contained in the Java Classes predicted to be defective, PLS-DA outperforms every other algorithm: it found 69.3% and 79.4% of the total bugs with no re-sampling and up-sampling, respectively.
Prediction of bacterial E3 ubiquitin ligase effectors using reduced amino acid peptide fingerprinting

Background Although pathogenic Gram-negative bacteria lack their own ubiquitination machinery, they have evolved or acquired virulence effectors that can manipulate the host ubiquitination process through structural and/or functional mimicry of host machinery. Many such effectors have been identified in a wide variety of bacterial pathogens that share little sequence similarity amongst themselves or with eukaryotic ubiquitin E3 ligases. Methods To allow identification of novel bacterial E3 ubiquitin ligase effectors from protein sequences we have developed a machine learning approach, the SVM-based Identification and Evaluation of Virulence Effector Ubiquitin ligases (SIEVE-Ub). We extend the string kernel approach used previously to sequence classification by introducing reduced amino acid (RED) alphabet encoding for protein sequences. Results We found that 14mer peptides with amino acids represented as simply either hydrophobic or hydrophilic provided the best models for discrimination of E3 ligases from other effector proteins with a receiver-operator characteristic area under the curve (AUC) of 0.90. When considering a subset of E3 ubiquitin ligase effectors that do not fall into known sequence based families we found that the AUC was 0.82, demonstrating the effectiveness of our method at identifying novel functional family members. Feature selection was used to identify a parsimonious set of 10 RED peptides that provided good discrimination, and these peptides were found to be located in functionally important regions of the proteins involved in E2 and host target protein binding. Our general approach enables construction of models based on other effector functions. We used SIEVE-Ub to predict nine potential novel E3 ligases from a large set of bacterial genomes. SIEVE-Ub is available for download at https://doi.org/10.6084/m9.figshare. 7766984.v1 or https://github.com/biodataganache/SIEVE-Ub for the most current version.
Unification of versions of photon quantum mechanics through Clifford spacetime algebra.

The Clifford spacetime algebraic description of Maxwell's equations is reviewed and shown to give a unified picture of recently published versions of photon quantum mechanics. Photon wave equations and a conserved four-current are derived from the complexified standard Lagrangian. The equations of motion and scalar product are found to be in good agreement with those obtained from Fourier transformation of momentum space wave function and scalar product [Phys. Rev. A 102, 042201 (2020)].
Instance based Generalization in Reinforcement Learning

Agents trained via deep reinforcement learning (RL) routinely fail to generalize to unseen environments, even when these share the same underlying dynamics as the training levels. Understanding the generalization properties of RL is one of the challenges of modern machine learning. Towards this goal, we analyze policy learning in the context of Partially Observable Markov Decision Processes (POMDPs) and formalize the dynamics of training levels as instances. We prove that, independently of the exploration strategy, reusing instances introduces significant changes on the effective Markov dynamics the agent observes during training. Maximizing expected rewards impacts the learned belief state of the agent by inducing undesired instance specific speedrunning policies instead of generalizeable ones, which are suboptimal on the training set. We provide generalization bounds to the value gap in train and test environments based on the number of training instances, and use insights based on these to improve performance on unseen levels. We propose training a shared belief representation over an ensemble of specialized policies, from which we compute a consensus policy that is used for data collection, disallowing instance specific exploitation. We experimentally validate our theory, observations, and the proposed computational solution over the CoinRun benchmark.
On the solutions of certain Lebesgue–Ramanujan–Nagell equations

We completely solve the Diophantine equation $x^2+2^k11^\ell19^m=y^n$ in integers $x,y\geq 1;~ k,\ell, m\geq 0~$ and $n\geq 3$ with $\gcd(x,y)=1$, and use this to recover some earlier results in this direction.
Unification of HDP and LDA Models for Optimal Topic Clustering of Subject Specific Question Banks

There has been an increasingly popular trend in Universities for curriculum transformation to make teaching more interactive and suitable for online courses. An increase in the popularity of online courses would result in an increase in the number of course-related queries for academics. This, coupled with the fact that if lectures were delivered in a video on demand format, there would be no fixed time where the majority of students could ask questions. When questions are asked in a lecture there is a negligible chance of having similar questions repeatedly, but asynchronously this is more likely. In order to reduce the time spent on answering each individual question, clustering them is an ideal choice. There are different unsupervised models fit for text clustering, of which the Latent Dirichlet Allocation model is the most commonly used. We use the Hierarchical Dirichlet Process to determine an optimal topic number input for our LDA model runs. Due to the probabilistic nature of these topic models, the outputs of them vary for different runs. The general trend we found is that not all the topics were being used for clustering on the first run of the LDA model, which results in a less effective clustering. To tackle probabilistic output, we recursively use the LDA model on the effective topics being used until we obtain an efficiency ratio of 1. Through our experimental results we also establish a reasoning on how Zeno's paradox is avoided.
End-to-End anti-spoofing with RawNet2

Spoofing countermeasures aim to protect automatic speaker verification systems from being manipulated by spoofed speech signals. While results from the most recent ASVspoof 2019 evaluation show great potential to detect most forms of attack, some continue to evade detection. This paper reports the first application of RawNet2 to anti-spoofing. RawNet2 ingests raw audio and has potential to learn cues that are not detectable using more traditional countermeasure solutions. We describe modifications made to the original RawNet2 architecture so that it can be applied to anti-spoofing. For A17 attacks, our RawNet2 systems results are the second-best reported, while the fusion of RawNet2 and baseline countermeasures gives the second-best results reported for the full ASVspoof 2019 logical access condition. Our results are reproducible with open source software.
Magnetic Braking of Accreting T Tauri Stars: Effects of Mass Accretion Rate, Rotation, and Dipolar Field Strength

The rotational evolution of an accreting pre-main-sequence star is influenced by its magnetic interaction with its surrounding circumstellar disk. Using the PLUTO code, we perform 2.5D magnetohydrodynamic, axisymmetric, time-dependent simulations of star–disk interaction—with an initial dipolar magnetic field structure, and a viscous and resistive accretion disk—in order to model the three mechanisms that contribute to the net stellar torque: accretion flow, stellar wind, and magnetospheric ejections (periodic inflation and reconnection events). We investigate how changes in the stellar magnetic field strength, rotation rate, and mass accretion rate (changing the initial disk density) affect the net stellar torque. All simulations are in a net spin-up regime. We fit semi-analytic functions for the three stellar torque contributions, allowing for the prediction of the net stellar torque for our parameter regime, as well as the possibility of investigating spin evolution using 1D stellar evolution codes. The presence of an accretion disk appears to increase the efficiency of stellar torques compared to isolated stars, for cases with outflow rates much smaller than accretion rates, because the star–disk interaction opens more of the stellar magnetic flux compared to that from isolated stars. In our parameter regime, a stellar wind with a mass-loss rate of ≈1% of the mass accretion rate is capable of extracting ≲50% of the accreting angular momentum. These simulations suggest that achieving spin equilibrium in a representative T Tauri case within our parameter regime, e.g., BP Tau, would require a wind mass-loss rate of ≈25% of the mass accretion rate.
Risk Bounds for Unsupervised Cross-Domain Mapping with IPMs

The recent empirical success of unsupervised cross-domain mapping algorithms, between two domains that share common characteristics, is not well-supported by theoretical justifications. This lacuna is especially troubling, given the clear ambiguity in such mappings. We work with adversarial training methods based on IPMs and derive a novel risk bound, which upper bounds the risk between the learned mapping $h$ and the target mapping $y$, by a sum of three terms: (i) the risk between $h$ and the most distant alternative mapping that was learned by the same cross-domain mapping algorithm, (ii) the minimal discrepancy between the target domain and the domain obtained by applying a hypothesis $h^*$ on the samples of the source domain, where $h^*$ is a hypothesis selectable by the same algorithm. The bound is directly related to Occam's razor and encourages the selection of the minimal architecture that supports a small mapping discrepancy and (iii) an approximation error term that decreases as the complexity of the class of discriminators increases and is empirically shown to be small. The bound leads to multiple algorithmic consequences, including a method for hyperparameters selection and for early stopping in cross-domain mapping GANs. We also demonstrate a novel capability for unsupervised learning of estimating confidence in the mapping of every specific sample.
RCC: Resilient Concurrent Consensus for High-Throughput Secure Transaction Processing

Recently, we saw the emergence of consensus-based database systems that promise resilience against failures, strong data provenance, and federated data management. Typically, these fully-replicated systems are operated on top of a primary-backup consensus protocol, which limits the throughput of these systems to the capabilities of a single replica (the primary). To push throughput beyond this single-replica limit, we propose concurrent consensus. In concurrent consensus, replicas independently propose transactions, thereby reducing the influence of any single replica on performance. To put this idea in practice, we propose our RCC paradigm that can turn any primary-backup consensus protocol into a concurrent consensus protocol by running many consensus instances concurrently. RCC is designed with performance in mind and requires minimal coordination between instances. Furthermore, RCC also promises increased resilience against failures. We put the design of RCC to the test by implementing it in ResilientDB, our high-performance resilient blockchain fabric, and comparing it with state-of-the-art primary-backup consensus protocols. Our experiments show that RCC achieves up to 2.75× higher throughput than other consensus protocols and can be scaled to 91 replicas.
A cross-sectional study of enteric disease risks associated with water quality and sanitation in Hyderabad City

This paper describes a cross-sectional, questionnaire-based study to estimate enteric disease risks from drinking water and poor sanitary conditions in Hyderabad City, India. Community incidence rates of enteric diseases potentially transmitted through water (acute gastrointestinal infections, typhoid, hepatitis A) obtained from questionnaire administration were found to be approximately 200-fold greater than estimates based on hospital incidence data. Community incidence rates were therefore used for comparison with a range of socio-economic and drinking water system parameters. The incidence of these diseases was found to be significantly higher in areas where sewage overflows were frequent and where sewage could accumulate around the home, where utensils were cleaned on the street, where water distribution pipelines leaked and where no residual chlorine was maintained. Surprisingly, socio-economic factors such as education and income were not strongly associated with waterborne diseases. Regression modelling also supported an association between health outcome and the following independent variables: rate of sewage overflow, % of cast iron pipe (as a measure of less leakage potential) and % samples without residual chlorine. The strength of the relationships suggests that improvement in sewage collection systems and water disinfection, and repair of water distribution system leakage should result in significant reduction in enteric diseases. This study provides a simple epidemiological approach to identify preliminary linkages between water quality and disease that can be used to affect policy decisions.
Multi-armed Bandits with Cost Subsidy

In this paper, we consider a novel variant of the multi-armed bandit (MAB) problem, MAB with cost subsidy, which models many real-life applications where the learning agent has to pay to select an arm and is concerned about optimizing cumulative costs and rewards. We present two applications, intelligent SMS routing problem and ad audience optimization problem faced by a number of businesses (especially online platforms) and show how our problem uniquely captures key features of these applications. We show that naive generalizations of existing MAB algorithms like Upper Confidence Bound and Thompson Sampling do not perform well for this problem. We then establish fundamental lower bound of $\Omega(K^{1/3} T^{2/3})$ on the performance of any online learning algorithm for this problem, highlighting the hardness of our problem in comparison to the classical MAB problem (where $T$ is the time horizon and $K$ is the number of arms). We also present a simple variant of explore-then-commit and establish near-optimal regret bounds for this algorithm. Lastly, we perform extensive numerical simulations to understand the behavior of a suite of algorithms for various instances and recommend a practical guide to employ different algorithms.
Dark matter admixed neutron star properties in the light of gravitational wave observations: a two fluid approach

We consider the effect of density dependent dark matter on the neutron star mass, radius, and tidal deformability. Nuclear matter (normal matter) as well as the fermionic dark matter sector is considered in a mean field model. We adopt the two fluid formalism to investigate the effect of dark matter on the neutron star properties. In the two fluid picture, there is no direct interaction between the dark matter and the nuclear matter. Rather these two sectors interact only through gravitational interaction. The nuclear matter sector is described by the $\sigma-\omega-\rho$ meson interaction in the "FSU2R" parameterization. In the dark matter sector, we use the Bayesian parameter optimization technique to fix the unknown parameters in the dark matter equation of state. In the two fluid picture, we solve the coupled Tolman-Oppenheimer-Volkoff (TOV) equations to obtain the mass and radius of dark matter admixed neutron stars (DANSs). We also estimate the effect of the density dependent dark matter sector on the tidal deformability of dark matter admixed neutron stars (DANSs).
Theory of dynamical stability for two- and three-dimensional Lennard-Jones crystals

The dynamical stability of three-dimensional (3D) Lennard-Jones (LJ) crystals has been studied for many years. The fcc and hcp structures are dynamically stable, while the bcc structure is stable only for long-range LJ potentials that are characterized by relatively small integer pairs $(m,n)$. Here, we study the dynamical stability of two-dimensional (2D) LJ crystals, where the planar hexagonal, the buckled honeycomb, and the buckled square structures are assumed. We demonstrate that the stability property of 2D and 3D LJ crystals can be classified into four groups depending on $(m,n)$. The instabilities of the planar hexagonal, the buckled square, and the bcc structures are investigated within analytical expressions. The structure-stability relationship between the LJ crystals and the elemental metals in the periodic table is also discussed.
The elephant in the bathtub: when the physics of star formation regulate the baryon cycle of galaxies

In simple models of galaxy formation and evolution, star formation is solely regulated by the amount of gas present in the galaxy. However, it has recently been shown that star formation can be suppressed by galactic dynamics in galaxies that contain a dominant spheroidal component and a low gas fraction. This 'dynamical suppression' is hypothesised to also contribute to quenching gas-rich galaxies at high redshift, but its impact on the galaxy population at large remains unclear. In this paper, we assess the importance of dynamical suppression in the context of gas regulator models of galaxy evolution through hydrodynamic simulations of isolated galaxies, with gas-to-stellar mass ratios of 0.01-0.20 and a range of galactic gravitational potentials from disc-dominated to spheroidal. Star formation is modelled using a dynamics-dependent efficiency per free-fall time, which depends on the virial parameter of the gas. We find that dynamical suppression becomes more effective at lower gas fractions and quantify its impact on the star formation rate as a function of gas fraction and stellar spheroid mass surface density. We combine the results of our simulations with observed scaling relations that describe the change of galaxy properties across cosmic time, and determine the galaxy mass and redshift range where dynamical suppression may affect the baryon cycle. We predict that the physics of star formation can limit and regulate the baryon cycle at low redshifts ($z \lesssim 1.4$) and high galaxy masses ($M_{\ast} \gtrsim 3 \times 10^{10}~M_{\odot}$), where dynamical suppression can drive galaxies off the star formation main sequence.
A generalization on derivations of Lie algebras

We initiate a study on a range of new generalized derivations of finite-dimensional Lie algebras over an algebraically closed field of characteristic zero. This new generalization of derivations has an analogue in the theory of associative prime rings and unites many well-known generalized derivations that have already appeared extensively in the study of Lie algebras and other nonassociative algebras. After exploiting fundamental properties, we introduce and analyze their interiors, especially focusing on the rationality of the corresponding Hilbert series. Applying techniques in computational ideal theory we develop an approach to explicitly compute these new generalized derivations for the three-dimensional special linear Lie algebra over the complex field.
Bayesian Workflow.

The Bayesian approach to data analysis provides a powerful way to handle uncertainty in all observations, model parameters, and model structure using probability theory. Probabilistic programming languages make it easier to specify and fit Bayesian models, but this still leaves us with many options regarding constructing, evaluating, and using these models, along with many remaining challenges in computation. Using Bayesian inference to solve real-world problems requires not only statistical skills, subject matter knowledge, and programming, but also awareness of the decisions made in the process of data analysis. All of these aspects can be understood as part of a tangled workflow of applied Bayesian statistics. Beyond inference, the workflow also includes iterative model building, model checking, validation and troubleshooting of computational problems, model understanding, and model comparison. We review all these aspects of workflow in the context of several examples, keeping in mind that in practice we will be fitting many models for any given problem, even if only a subset of them will ultimately be relevant for our conclusions.
You do (not) belong here: detecting DPI evasion attacks with context learning

As Deep Packet Inspection (DPI) middleboxes become increasingly popular, a spectrum of adversarial attacks have emerged with the goal of evading such middleboxes. Many of these attacks exploit discrepancies between the middlebox network protocol implementations, and the more rigorous/complete versions implemented at end hosts. These evasion attacks largely involve subtle manipulations of packets to cause different behaviours at DPI and end hosts, to cloak malicious network traffic that is otherwise detectable. With recent automated discovery, it has become prohibitively challenging to manually curate rules for detecting these manipulations. In this work, we propose CLAP, the first fully-automated, unsupervised ML solution to accurately detect and localize DPI evasion attacks. By learning what we call the packet context, which essentially captures inter-relationships across both (1) different packets in a connection; and (2) different header fields within each packet, from benign traffic traces only, CLAP can detect and pinpoint packets that violate the benign packet contexts (which are the ones that are specially crafted for evasion purposes). Our evaluations with 73 state-of-the-art DPI evasion attacks show that CLAP achieves an Area Under the Receiver Operating Characteristic Curve (AUCROC) of 0.963, an Equal Error Rate (EER) of only 0.061 in detection, and an accuracy of 94.6% in localization. These results suggest that CLAP can be a promising tool for thwarting DPI evasion attacks.