Miracle-dz/UniFAR-FTUs huggingface dataset:
Obstacles in Fully Automatic Program Repair: A survey

The current article is an interdisciplinary attempt to decipher automatic program repair processes. The review is done by the manner typical to human science known as diffraction. We attempt to spot a gap in the literature of self-healing and self-repair operations and further investigate the approaches that would enable us to tackle the problems we face. As a conclusion, we suggest a shift in the current approach to automatic program repair operations in order to attain our goals. The emphasis of this review is to achieve full automation. Several obstacles are shortly mentioned in the current essay but the main shortage that is covered is the overfitting obstacle, and this particular problem is investigated in the stream that is related to full automation of the repair process.
Pitfalls in Machine Learning Research: Reexamining the Development Cycle

Machine learning has the potential to fuel further advances in data science, but it is greatly hindered by an ad hoc design process, poor data hygiene, and a lack of statistical rigor in model evaluation. Recently, these issues have begun to attract more attention as they have caused public and embarrassing issues in research and development. Drawing from our experience as machine learning researchers, we follow the machine learning process from algorithm design to data collection to model evaluation, drawing attention to common pitfalls and providing practical recommendations for improvements. At each step, case studies are introduced to highlight how these pitfalls occur in practice, and where things could be improved.
The ALFALFA-SDSS Galaxy Catalog

We present a H i optical catalog of ∼30,000 galaxies based on the 100% complete Arecibo Legacy Fast Arecibo L-band Feed Array (ALFALFA) survey combined with data from the Sloan Digital Sky Survey (SDSS). Our goal is to facilitate the public use of the completed ALFALFA catalog by providing carefully determined matches to SDSS counterparts, including matches for ∼10,000 galaxies that do not have SDSS spectra. These identifications can provide a basis for further crossmatching with other surveys using SDSS photometric IDs as a reference point. We derive absolute magnitudes and stellar masses for each galaxy using optical colors combined with an internal reddening correction designed for small- and intermediate-mass galaxies with active star formation. We also provide measures of stellar masses and star formation rates based on infrared and/or ultraviolet photometry for galaxies that are detected by the Wide-field Infrared Survey Explorer and/or the Galaxy Evolution Explorer. Finally, we compare the galaxy population in the ALFALFA-SDSS sample with the populations in several other publicly available galaxy catalogs and confirm that ALFALFA galaxies typically have lower masses and bluer colors.
Multi-task learning for electronic structure to predict and explore molecular potential energy surfaces

We refine the OrbNet model to accurately predict energy, forces, and other response properties for molecules using a graph neural-network architecture based on features from low-cost approximated quantum operators in the symmetry-adapted atomic orbital basis. The model is end-to-end differentiable due to the derivation of analytic gradients for all electronic structure terms, and is shown to be transferable across chemical space due to the use of domain-specific features. The learning efficiency is improved by incorporating physically motivated constraints on the electronic structure through multi-task learning. The model outperforms existing methods on energy prediction tasks for the QM9 dataset and for molecular geometry optimizations on conformer datasets, at a computational cost that is thousand-fold or more reduced compared to conventional quantum-chemistry calculations (such as density functional theory) that offer similar accuracy.
Renormalized energies for unit-valued harmonic maps in multiply connected domains

In this article we derive the expression of \textit{renormalized energies} for unit-valued harmonic maps defined on a smooth bounded domain in \(\mathbb{R}^2\) whose boundary has several connected components. The notion of renormalized energies was introduced by Bethuel-Brezis-Helein in order to describe the position of limiting Ginzburg-Landau vortices in simply connected domains. We show here, how a non-trivial topology of the domain modifies the expression of the renormalized energies. We treat the case of Dirichlet boundary conditions and Neumann boundary conditions as well.
SOFIA/FIFI-LS Full-disk [C ii] Mapping and CO-dark Molecular Gas across the Nearby Spiral Galaxy NGC 6946

We present SOFIA/FIFI-LS observations of the [C ii] 158 μm cooling line across the nearby spiral galaxy NGC 6946. We combine these with UV, IR, CO, and H i data to compare [C ii] emission to dust properties, star formation rate (SFR), H2, and H i at 560 pc scales via stacking by environment (spiral arms, interarm, and center), radial profiles, and individual, beam-sized measurements. We attribute 73% of the [C ii] luminosity to arms, and 19% and 8% to the center and interarm region, respectively. [C ii]/TIR, [C ii]/CO, and [C ii]/PAH radial profiles are largely constant, but rise at large radii (≳ 8 kpc) and drop in the center (“[C ii] deficit”). This increase at large radii and the observed decline with the 70 μm/100 μm dust color are likely driven by radiation field hardness. We find a near proportional [C ii]–SFR scaling relation for beam-sized regions, though the exact scaling depends on methodology. [C ii] also becomes increasingly luminous relative to CO at low SFR (interarm or large radii), likely indicating more efficient photodissociation of CO and emphasizing the importance of [C ii] as an H2 and SFR tracer in such regimes. Finally, based on the observed [C ii] and CO radial profiles and different models, we find αCO to increase with radius, in line with the observed metallicity gradient. The low αCO (galaxy average ≲ 2 M⊙ pc−2 (K km s−1)−1) and low [C ii]/CO ratios (∼400 on average) imply little CO-dark gas across NGC 6946, in contrast to estimates in the Milky Way.
Short-Term Memory Optimization in Recurrent Neural Networks by Autoencoder-based Initialization

Training RNNs to learn long-term dependencies is difficult due to vanishing gradients. We explore an alternative solution based on explicit memorization using linear autoencoders for sequences, which allows to maximize the short-term memory and that can be solved with a closed-form solution without backpropagation. We introduce an initialization schema that pretrains the weights of a recurrent neural network to approximate the linear autoencoder of the input sequences and we show how such pretraining can better support solving hard classification tasks with long sequences. We test our approach on sequential and permuted MNIST. We show that the proposed approach achieves a much lower reconstruction error for long sequences and a better gradient propagation during the finetuning phase.
Quantum simulation and computing with Rydberg qubits

Arrays of optically trapped atoms excited to Rydberg states have recently emerged as a competitive physical platform for quantum simulation and computing, where high-fidelity state preparation and readout, quantum logic gates and controlled quantum dynamics of more than 50 qubits have all been demonstrated. These systems are now approaching the point where reliable quantum computations with hundreds of qubits and realistically thousands of multiqubit gates with low error rates should be within reach for the first time. In this article we give an overview of the Rydberg quantum toolbox, emphasizing the high degree of flexibility for encoding qubits, performing quantum operations and engineering quantum many-body Hamiltonians. We then review the state-of-the-art concerning high-fidelity quantum operations and logic gates as well as quantum simulations in many-body regimes. Finally, we discuss computing schemes that are particularly suited to the Rydberg platform and some of the remaining challenges on the road to general purpose quantum simulators and quantum computers.
Fast Rates for Contextual Linear Optimization

Incorporating side observations in decision making can reduce uncertainty and boost performance, but it also requires that we tackle a potentially complex predictive relationship. Although one may use off-the-shelf machine learning methods to separately learn a predictive model and plug it in, a variety of recent methods instead integrate estimation and optimization by fitting the model to directly optimize downstream decision performance. Surprisingly, in the case of contextual linear optimization, we show that the naïve plug-in approach actually achieves regret convergence rates that are significantly faster than methods that directly optimize downstream decision performance. We show this by leveraging the fact that specific problem instances do not have arbitrarily bad near-dual-degeneracy. Although there are other pros and cons to consider as we discuss and illustrate numerically, our results highlight a nuanced landscape for the enterprise to integrate estimation and optimization. Our results are overall positive for practice: predictive models are easy and fast to train using existing tools; simple to interpret; and, as we show, lead to decisions that perform very well. This paper was accepted by Hamid Nazerzadeh, data science.
Center-wise Local Image Mixture For Contrastive Representation Learning

Contrastive learning based on instance discrimination trains model to discriminate different transformations of the anchor sample from other samples, which does not consider the semantic similarity among samples. This paper proposes a new kind of contrastive learning method, named CLIM, which uses positives from other samples in the dataset. This is achieved by searching local similar samples of the anchor, and selecting samples that are closer to the corresponding cluster center, which we denote as center-wise local image selection. The selected samples are instantiated via an data mixture strategy, which performs as a smoothing regularization. As a result, CLIM encourages both local similarity and global aggregation in a robust way, which we find is beneficial for feature representation. Besides, we introduce \emph{multi-resolution} augmentation, which enables the representation to be scale invariant. We reach 75.5% top-1 accuracy with linear evaluation over ResNet-50, and 59.3% top-1 accuracy when fine-tuned with only 1% labels.
Spectral sets, extremal functions and exceptional matrices

ABSTRACT Let A be a square matrix and let Ω be an open set in the plane containing the spectrum of A. We consider the problem of maximizing the operator norm amongst all holomorphic functions f from Ω into the closed unit disk. If is extremal for this problem and if , then it turns out that the matrix has special properties, among them the fact that its principal left and right singular vectors are mutually orthogonal. We study this class of exceptional matrices . In particular, we are interested in the extent to which they are characterized by the aforementioned orthogonality property.
From Note-Level to Chord-Level Neural Network Models for Voice Separation in Symbolic Music

Music is often experienced as a progression of concurrent streams of notes, or voices. The degree to which this happens depends on the position along a voice-leading continuum, ranging from monophonic, to homophonic, to polyphonic, which complicates the design of automatic voice separation models. We address this continuum by defining voice separation as the task of decomposing music into streams that exhibit both a high degree of external perceptual separation from the other streams and a high degree of internal perceptual consistency. The proposed voice separation task allows for a voice to diverge to multiple voices and also for multiple voices to converge to the same voice. Equipped with this flexible task definition, we manually annotated a corpus of popular music and used it to train neural networks that assign notes to voices either separately for each note in a chord (note-level), or jointly to all notes in a chord (chord-level). The trained neural models greedily assign notes to voices in a left to right traversal of the input chord sequence, using a diverse set of perceptually informed input features. When evaluated on the extraction of consecutive within voice note pairs, both models surpass a strong baseline based on an iterative application of an envelope extraction function, with the chord-level model consistently edging out the note-level model. The two models are also shown to outperform previous approaches on separating the voices in Bach music.
Joint Optimisation of Privacy and Cost of in-App Mobile User Profiling and Targeted Ads

Online mobile advertising ecosystems provide advertising and analytics services that collect, aggregate, process, and trade a rich amount of consumers’ personal data and carry out interests-based ad targeting, which raised serious privacy risks and growing trends of users feeling uncomfortable while using internet services. In this paper, we address users’ privacy concerns by developing an optimal dynamic optimisation cost-effective framework for preserving user privacy for profiling, ads-based inferencing, temporal apps usage behavioral patterns, and interest-based ad targeting. A major challenge in solving this dynamic model is the lack of knowledge of time-varying updates during the profiling process. We formulate a mixed-integer optimisation problem and develop an equivalent problem to show that the proposed algorithm does not require knowledge of time-varying updates in user behavior. Following, we develop an online control algorithm to solve the equivalent problem and to overcome the difficulty of solving nonlinear programming by decomposing it into various cases and to achieve a trade-off between user privacy, cost, and targeted ads. We carry out extensive experimentations and demonstrate the proposed framework’s applicability by implementing its critical components using POC (Proof Of Concept) ‘System App’. We compare the proposed framework with other privacy protecting approaches and investigate whether it achieves better privacy and functionality for various performance parameters.
Nonparametric Variable Screening with Optimal Decision Stumps

Decision trees and their ensembles are endowed with a rich set of diagnostic tools for ranking and screening input variables in a predictive model. One of the most commonly used in practice is the Mean Decrease in Impurity (MDI), which calculates an importance score for a variable by summing the weighted impurity reductions over all non-terminal nodes split with that variable. Despite the widespread use of tree based variable importance measures such as MDI, pinning down their theoretical properties has been challenging and therefore largely unexplored. To address this gap between theory and practice, we derive rigorous finite sample performance guarantees for variable ranking and selection in nonparametric models with MDI for a single-level CART decision tree (decision stump). We find that the marginal signal strength of each variable and ambient dimensionality can be considerably weaker and higher, respectively, than state-of-the-art nonparametric variable selection methods. Furthermore, unlike previous marginal screening methods that attempt to directly estimate each marginal projection via a truncated basis expansion, the fitted model used here is a simple, parsimonious decision stump, thereby eliminating the need for tuning the number of basis terms. Thus, surprisingly, even though decision stumps are highly inaccurate for estimation purposes, they can still be used to perform consistent model selection.
Phase lag predicts nonlinear response maxima in liquid-sloshing experiments

Abstract Mass-spring models are essential for the description of sloshing resonances in engineering. By experimentally measuring the liquid's centre of mass in a horizontally oscillated rectangular tank, we show that low-amplitude sloshing obeys the Duffing equation. A bending of the response curve in analogy to a softening spring is observed, with growing hysteresis as the driving amplitude increases. At large amplitudes, complex wave patterns emerge (including wave breaking and run up at the tank walls), competition between flow states is observed and the dynamics departs progressively from Duffing. We also provide a quantitative comparison of wave shapes and response curves to the predictions of a multimodal model based on potential flow theory (Faltinsen & Timokha, Sloshing. Cambridge University Press, 2009) and show that it systematically overestimates the sloshing amplitudes and the hysteresis. We find that the phase lag between the liquid's centre of mass and the forcing is the key predictor of the nonlinear response maxima. The phase lag reflects precisely the onset of deviations from Duffing dynamics and – most importantly – at resonance the sloshing motion always lags the driving by $90^{\circ }$ (independently of the wave pattern). This confirms the theoretical $90^{\circ }$-phase-lag criterion (Cenedese & Haller, Proc. R. Soc. A, vol. 476, no. 2234, 2020, 20190494).
Predictive Process Model Monitoring using Recurrent Neural Networks

The field of predictive process monitoring focuses on modelling future characteristics of running business process instances, typically by either predicting the outcome of particular objectives (e.g. completion (time), cost), or next-in-sequence prediction (e.g. what is the next activity to execute). This paper introduces Processes-As-Movies (PAM), a technique that provides a middle ground between these predictive monitoring. It does so by capturing declarative process constraints between activities in various windows of a process execution trace, which represent a declarative process model at subsequent stages of execution. This high-dimensional representation of a process model allows the application of predictive modelling on how such constraints appear and vanish throughout a process' execution. Various recurrent neural network topologies tailored to high-dimensional input are used to model the process model evolution with windows as time steps, including encoder-decoder long short-term memory networks, and convolutional long short-term memory networks. Results show that these topologies are very effective in terms of accuracy and precision to predict a process model's future state, which allows process owners to simultaneously verify what linear temporal logic rules hold in a predicted process window (objective-based), and verify what future execution traces are allowed by all the constraints together (trace-based).
Two-gap time reversal symmetry breaking superconductivity in noncentrosymmetric 
LaNiC2

We report a $\mu$SR investigation of a non-centrosymmetric superconductor (LaNiC$_2$) in single crystal form. Compared to previous $\mu$SR studies of non-centrosymmetric superconducting polycrystalline and powder samples, the unambiguous orientation of single crystals enables a simultaneous determination of the absolute value of the magnetic penetration depth and the vortex core size from measurements that probe the magnetic field distribution in the vortex state. The magnetic field dependence of these quantities unambiguously demonstrates the presence of two nodeless superconducting energy gaps. In addition, we detect weak internal magnetic fields in the superconducting phase, confirming earlier $\mu$SR evidence for a time-reversal symmetry breaking superconducting state. Our results suggest that Cooper pairing in LaNiC$_2$ is characterized by the same interorbital equal-spin pairing model introduced to describe the pairing state in the centrosymmetric superconductor LaNiGa$_2$.
Assessing the impacts of several algae-based diets on cultured European abalone (Haliotis tuberculata)

The effects of different algal diets on the mortality, apparent ingestion, weight, length and conversion rates of the European abalone (Haliotis tuberculata ) maintained in a semi-closed seawater system throughout the year were compared. Various combinations of red algae (Palmaria palmata , Ceramium rubrum and Chondrus crispus cultured or harvested from the natural environment, as well as Porphyra spp . collected), brown algae (Laminaria digitata ) and green algae (fresh or frozen Ulva intestinalis ) were tested. The results showed that P. palmata , C. rubrum and U. intestinalis administered alone were associated with significantly higher weight growth rates than the other species of algae tested. However, some combinations of algae (i.e. different proportions of L. digitata in association with P. palmata ) were more favorable for weight increase when compared with the expected rates calculated for the diet based on L. digitata alone. Limiting the amount of any of these foods substantially reduced the conversion rate. Seasonal trends were apparent in both weight increase and food conversion rates, with the result that growth in weight on a diet of L. digitata was fastest in summer. Growth on P. palmata was faster in each season, and reached a maximum in early spring. The data collected allowed us to model weight increase and month-to-month food conversion rates for a diet based on P. palmata and L. digitata . The data obtained in this study were coupled with data regarding the availability of algae during the year, enabling us to suggest an optimal diet for each of the four seasons. Finally, the effects of different algae diets were investigated on hemocyte parameters, and the result suggested that P. palmata would reinforce the immune system of abalone.
Investigating Cell Signaling with Gene Expression Datasets

Modern molecular biology is a data- and computationally-intensive field with few instructional resources for introducing undergraduate students to the requisite skills and techniques for analyzing large data sets. This Lesson helps students: (i) build an understanding of the role of signal transduction in the control of gene expression; (ii) improve written scientific communication skills through engagement in literature searches, data analysis, and writing reports; and (iii) develop an awareness of the procedures and protocols for analyzing and making inferences from high-content quantitative molecular biology data. The Lesson is most suited to upper level biology courses because it requires foundational knowledge on cellular organization, protein structure and function, and the tenets of information flow from DNA to proteins. The first step lays the foundation for understanding cell signaling, which can be accomplished through assigned readings and presentations. In subsequent active learning sessions, data analysis is integrated with exercises that provide insight into the structure of scientific papers. The Lesson emphasizes the role of quantitative methods in research and helps students gain experience with functional genomics databases and data analysis, which are important skills for molecular biologists. Assessment is conducted through mini-reports designed to gauge students’ perceptions of the purpose of each step, their awareness of the possible limitations of the methods utilized, and the ability to identify opportunities for further investigation. Summative assessment is conducted through a final report. The modules are suitable for complementing wet-laboratory experiments and can be adapted for different courses that use molecular biology data.