Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Replicability and Reproducibility of Deep Learning in Software Engineering

Context: Deep learning (DL) techniques have gained significant popularity among software engineering (SE) researchers in recent years. This is because they can often solve many SE challenges without enormous manual feature engineering effort and complex domain knowledge. Objective: Although many DL studies have reported substantial advantages over other state-of-the-art models on effectiveness, they often ignore two factors: (1) reproducibility —whether the reported experimental results can be obtained by other researchers using authors’ artifacts (i.e., source code and datasets) with the same experimental setup; and (2) replicability —whether the reported experimental result can be obtained by other researchers using their re-implemented artifacts with a different experimental setup. We observed that DL studies commonly overlook these two factors and declare them as minor threats or leave them for future work. This is mainly due to high model complexity with many manually set parameters and the time-consuming optimization process, unlike classical supervised machine learning (ML) methods (e.g., random forest). This study aims to investigate the urgency and importance of reproducibility and replicability for DL studies on SE tasks. Method: In this study, we conducted a literature review on 147 DL studies recently published in 20 SE venues and 20 AI (Artificial Intelligence) venues to investigate these issues. We also re-ran four representative DL models in SE to investigate important factors that may strongly affect the reproducibility and replicability of a study. Results: Our statistics show the urgency of investigating these two factors in SE, where only 10.2% of the studies investigate any research question to show that their models can address at least one issue of replicability and/or reproducibility. More than 62.6% of the studies do not even share high-quality source code or complete data to support the reproducibility of their complex models. Meanwhile, our experimental results show the importance of reproducibility and replicability, where the reported performance of a DL model could not be reproduced for an unstable optimization process. Replicability could be substantially compromised if the model training is not convergent, or if performance is sensitive to the size of vocabulary and testing data. Conclusion: It is urgent for the SE community to provide a long-lasting link to a high-quality reproduction package, enhance DL-based solution stability and convergence, and avoid performance sensitivity on different sampled data.
Radiative cooling rates, ion fractions, molecule abundances, and line emissivities including self-shielding and both local and metagalactic radiation fields

We use the spectral synthesis code cloudy to tabulate the properties of gas for an extensive range in redshift (z = 0–9), temperature (log T[K] = 1–9.5), metallicity (log Z/Z⊙ = −4 – +0.5, Z = 0), and density ($\log n_{\mathrm{H}}[\, \mathrm{cm}^{-3}] = -8$ − +6). This therefore includes gas with properties characteristic of the interstellar, circumgalactic, and intergalactic media. The gas is exposed to a redshift-dependent UV/X-ray background, while for the self-shielded lower-temperature gas (i.e. ISM gas), an interstellar radiation field and cosmic rays are added. The radiation field is attenuated by a density- and temperature-dependent column of gas and dust. Motivated by the observed star formation law, this gas column density also determines the intensity of the interstellar radiation field and the cosmic ray density. The ionization balance, molecule fractions, cooling rates, line emissivities, and equilibrium temperatures are calculated self-consistently. We include dust, cosmic rays, and the interstellar radiation field step-by-step to study their relative impact. These publicly available tables are ideal for hydrodynamical simulations. They can be used stand alone or coupled to a non-equilibrium network for a subset of elements. The release includes a C routine to read in and interpolate the tables, as well as an easy-to-use python graphical user interface to explore the tables.
An $\ell_p$ theory of PCA and spectral clustering.

Principal Component Analysis (PCA) is a powerful tool in statistics and machine learning. While existing study of PCA focuses on the recovery of principal components and their associated eigenvalues, there are few precise characterizations of individual principal component scores that yield low-dimensional embedding of samples. That hinders the analysis of various spectral methods. In this paper, we first develop an $\ell_p$ perturbation theory for a hollowed version of PCA in Hilbert spaces which provably improves upon the vanilla PCA in the presence of heteroscedastic noises. Through a novel $\ell_p$ analysis of eigenvectors, we investigate entrywise behaviors of principal component score vectors and show that they can be approximated by linear functionals of the Gram matrix in $\ell_p$ norm, which includes $\ell_2$ and $\ell_\infty$ as special examples. For sub-Gaussian mixture models, the choice of $p$ giving optimal bounds depends on the signal-to-noise ratio, which further yields optimality guarantees for spectral clustering. For contextual community detection, the $\ell_p$ theory leads to a simple spectral algorithm that achieves the information threshold for exact recovery. These also provide optimal recovery results for Gaussian mixture and stochastic block models as special cases.
PREDICTIVE MODELS OF MOVEMENT BY SERENGETI GRAZERS

Many animal species are unevenly distributed across the landscape, in spatial patterns that continually shift over time. Such a shifting mosaic is thought to have profound implications for the persistence and stability of ecosystems. Management and conservation of natural systems would be enhanced if we could accurately predict movement. Such prediction has not yet been possible. Here we use an extensive set of field data on food abundance and quality, combined with experimentally derived measures of nutritional value, to predict the spatial distribution of Thomson's gazelles (Gazella thomsoni thomsoni Gunter) on the Serengeti Plains of East Africa. Twelve plausible models, based on alternate foraging objectives or movement rules, were assessed against field data on food and grazer abundance gathered at biweekly intervals (every two weeks) over the course of the wet seasons in two different years. Nomadic movements of gazelles closely tracked changes in the spatial distribution of short grass swards. Gazelles left short grass patches when local daily energy intake dropped below the expected intake averaged across the landscape. Subsequent redistribution of gazelles among neighboring patches was proportional to daily rates of energy intake in each patch. Thus, nomadic movements by Thomson's gazelles were predictable on the basis of local energy gain. This suggests that adaptive behavioral models can provide useful predictive tools for understanding the dynamics of complex natural systems.
A Novel and Reliable Deep Learning Web-Based Tool to Detect COVID-19 Infection from Chest CT-Scan

The corona virus is already spread around the world in many countries, and it has taken many lives. Furthermore, the world health organization (WHO) has announced that COVID-19 has reached the global epidemic stage. Early and reliable diagnosis using chest CT-scan can assist medical specialists in vital circumstances. In this work, we introduce a computer aided diagnosis (CAD) web service to detect COVID- 19 online. One of the largest public chest CT-scan databases, containing 746 participants was used in this experiment. A number of well-known deep neural network architectures consisting of ResNet, Inception and MobileNet were inspected to find the most efficient model for the hybrid system. A combination of the Densely connected convolutional network (DenseNet) in order to reduce image dimensions and Nu-SVM as an anti-overfitting bottleneck was chosen to distinguish between COVID-19 and healthy controls. The proposed methodology achieved 90.80% recall, 89.76% precision and 90.61% accuracy. The method also yields an AUC of 95.05%. Ultimately a flask web service is made public through ngrok using the trained models to provide a RESTful COVID-19 detector, which takes only 39 milliseconds to process one image. The source code is also available at this https URL. Based on the findings, it can be inferred that it is feasible to use the proposed technique as an automated tool for diagnosis of COVID-19.
Inferring median survival differences in general factorial designs via permutation tests

Factorial survival designs with right-censored observations are commonly inferred by Cox regression and explained by means of hazard ratios. However, in case of non-proportional hazards, their interpretation can become cumbersome; especially for clinicians. We therefore offer an alternative: median survival times are used to estimate treatment and interaction effects and null hypotheses are formulated in contrasts of their population versions. Permutation-based tests and confidence regions are proposed and shown to be asymptotically valid. Their type-1 error control and power behavior are investigated in extensive simulations, showing the new methods’ wide applicability. The latter is complemented by an illustrative data analysis.
QoE-Driven UAV-Enabled Pseudo-Analog Wireless Video Broadcast: A Joint Optimization of Power and Trajectory

The explosive demands for high quality mobile video services have caused heavy overload to the existing cellular networks. Although the small cell has been proposed to alleviate such a problem, the network operators may not be interested in deploying numerous base stations (BSs) due to expensive infrastructure construction and maintenance. The unmanned aerial vehicles (UAVs) can provide the low-cost and quick deployment, which can support high-quality line-of-sight communications and have become promising mobile BSs. In this paper, we propose a quality-of-experience (QoE)-driven UAV-enabled pseudo-analog wireless video broadcast scheme, which provides mobile video broadcast services for ground users (GUs). Due to limited energy available in UAV, the aim of the proposed scheme is to maximize the minimum peak signal-to-noise ratio (PSNR) of GUs’ video reconstruction quality by jointly optimizing the transmission power allocation strategy and the UAV trajectory. Firstly, the reconstructed video quality at GUs is defined under the constraints of the UAV's total energy and motion mechanism, and the proposed scheme is formulated as a complex non-convex optimization problem. Then, the optimization problem is simplified to obtain a tractable suboptimal solution with the help of the block coordinate descent model and the successive convex approximation model. Finally, the experimental results are presented to show the effectiveness of the proposed scheme. Specifically, the proposed scheme can achieve over 1.6 dB PSNR gains in terms of GUs’ minimum PSNR, compared with the state-of-the-art schemes, e.g., DVB, SoftCast, and SharpCast.
Examining the Effect of COVID-19 on Foreign Exchange Rate and Stock Market -- An Applied Insight into the Variable Effects of Lockdown on Indian Economy

Since March 25, 2020, India had been under a nation-wide lockdown announced as a response to the spread of SARS-CoV-2 and COVID-19 and has resorted to a process of 'unlocking' the lockdown over the past couple of months. This work attempts to examine the effect of novel coronavirus 2019 (COVID-19) and its resulting disease, the COVID-19, on the foreign exchange rates and stock market performances of India using secondary data over a span of 112 days spanning between March 11 and June 30, 2020. The study explores whether the causal relationships and directions among the growth rate of confirmed cases (GROWTHC), exchange rate (GEX) and SENSEX value (GSENSEX) are remaining the same across different pre and post-lockdown phases, attempting to capture any potential changes over time via the vector autoregressive (VAR) models. A positive correlation is found between the growth rate of confirmed cases and the growth rate of exchange rate, and a negative correlation between the growth rate of confirmed cases and the growth rate of SENSEX value. However, on applying a vector autoregressive (VAR) model, it is observed that an increase in the confirmed COVID-19 cases causes no significant change in the values of the exchange rate and SENSEX index. The result varies if the analysis is split across different time periods - before lockdown, the four phases of lockdown, and the first phase of unlock. Nuanced and sensible interpretations of the numeric results indicate significant variability across time in terms of the relation between the variables of interest. The detailed knowledge about the varying patterns of dependence could potentially help the policy makers and investors of India in order to develop their strategies to cope up with the situation.
The Effect of Optimization Methods on the Robustness of Out-of-Distribution Detection Approaches

Deep neural networks (DNNs) have become the de facto learning mechanism in different domains. Their tendency to perform unreliably on out-of-distribution (OOD) inputs hinders their adoption in critical domains. Several approaches have been proposed for detecting OOD inputs. However, existing approaches still lack robustness. In this paper, we shed light on the robustness of OOD detection (OODD) approaches by revealing the important role of optimization methods. We show that OODD approaches are sensitive to the type of optimization method used during training deep models. Optimization methods can provide different solutions to a non-convex problem and so these solutions may or may not satisfy the assumptions (e.g., distributions of deep features) made by OODD approaches. Furthermore, we propose a robustness score that takes into account the role of optimization methods. This provides a sound way to compare OODD approaches. In addition to comparing several OODD approaches using our proposed robustness score, we demonstrate that some optimization methods provide better solutions for OODD approaches.
The VLA-COSMOS 3 GHz Large Project: Evolution of Specific Star Formation Rates out to z ∼ 5

We provide a coherent, uniform measurement of the evolution of the logarithmic star formation rate (SFR)–stellar mass (M*) relation, called the main sequence (MS) of star-forming galaxies , for star-forming and all galaxies out to . We measure the MS using mean stacks of 3 GHz radio-continuum images to derive average SFRs for ∼ 200,000 mass-selected galaxies at z > 0.3 in the COSMOS field. We describe the MS relation by adopting a new model that incorporates a linear relation at low stellar mass (log(M*/M⊙) < 10) and a flattening at high stellar mass that becomes more prominent at low redshift (z < 1.5). We find that the SFR density peaks at 1.5 < z < 2, and at each epoch there is a characteristic stellar mass (M* = 1–4 × 1010M⊙) that contributes the most to the overall SFR density. This characteristic mass increases with redshift, at least to z ∼ 2.5. We find no significant evidence for variations in the MS relation for galaxies in different environments traced by the galaxy number density at 0.3 < z < 3, nor for galaxies in X-ray groups at z ∼ 0.75. We confirm that massive bulge-dominated galaxies have lower SFRs than disk-dominated galaxies at a fixed stellar mass at z < 1.2. As a consequence, the increase in bulge-dominated galaxies in the local star-forming population leads to a flattening of the MS at high stellar masses. This indicates that “mass quenching” is linked with changes in the morphological composition of galaxies at a fixed stellar mass.
The DIANOGA simulations of galaxy clusters: characterising star formation in protoclusters

Aims. We studied the star formation rate (SFR) in cosmological hydrodynamical simulations of galaxy (proto-)clusters in the redshift range 0 < z < 4, comparing them to recent observational studies; we also investigated the effect of varying the parameters of the star formation model on galaxy properties such as SFR, star-formation efficiency, and gas fraction. Methods. We analyse a set of zoom-in cosmological hydrodynamical simulations centred on 12 clusters. The simulations are carried out with the GADGET-3 Tree-PM smoothed-particle hydro-dynamics code which includes various subgrid models to treat unresolved baryonic physics, including AGN feedback. Results. Simulations do not reproduce the high values of SFR observed within protocluster cores, where the values of SFR are underpredicted by a factor ≳4 both at z ∼ 2 and z ∼ 4. The difference arises as simulations are unable to reproduce the observed starburst population and is greater at z ∼ 2 because simulations underpredict the normalisation of the main sequence (MS) of star forming galaxies (i.e. the correlation between stellar mass and SFR) by a factor of ∼3. As the low normalisation of the MS seems to be driven by an underestimated gas fraction, it remains unclear whether numerical simulations miss starburst galaxies due to overly underpredicted gas fractions or overly low star formation efficiencies. Our results are stable against varying several parameters of the star formation subgrid model and do not depend on the details of AGN feedback. Conclusions. The subgrid model for star formation, introduced to reproduce the self-regulated evolution of quiescent galaxies, is not suitable to describe violent events like high-redshift starbursts. We find that this conclusion holds, independently of the parameter choice for the star formation and AGN models. The increasing number of multi-wavelength high-redshift observations will help to improve the current star formation model, which is needed to fully recover the observed star formation history of galaxy clusters.
Benefits of a maize–bean–weeds mixed cropping system in Urubamba Valley, Peruvian Andes

Abstract In the Urubamba Valley, Peruvian Andes, we evaluated the abundance of insect pests and natural enemies in experimental plots where maize was grown either as monoculture, intercropped with beans, or intercropped with beans plus associated weeds. We also assessed the consequences of the cropping system for maize yield. The main insect pests recorded were Diabrotica spp. (Coleoptera: Chrysomelidae), Carpophilus sp. (Coleoptera: Nitidulidae) and Pagiocerus frontalis (Coleoptera: Scolytidae), and their overall abundance did not differ among crop diversity treatments. However, there was a significant adverse effect of crop diversity on the maximum abundance of both Carpophilus and Pagiocerus. The main beneficial arthropods were Paratriphleps sp. (Hemiptera: Anthocoridae), ladybirds and spiders, and their density did not differ among treatments. Maize yield did not decrease with increasing plant diversity. The reported benefits of intercropping, together with the associated efficiency in land use, make this traditional agricultural practice a valuable alternative to the use of pesticides, particularly for resource-poor Andean farmers.
Investor Emotions and Earnings Announcements

Armed with a decade of social media data, I explore the impact of investor emotions on earnings announcements. In particular, I test whether the emotional content of firm-specific messages posted on social media just prior to a firm's earnings announcement explains its earnings and announcement returns. I find that investors are typically excited about firms that end up exceeding expectations, yet their enthusiasm results in lower announcement returns. Specifically, a standard deviation increase in excitement is associated with an 8.9 basis points lower announcement return, which translates into an approximately 7.2% annualized loss. Motivated by this finding, I then construct a zero-cost portfolio leveraging social media emotions and opinions around earnings announcements, and show performance exceeding the market by a factor of 1.75 over the decade. My findings confirm that emotions and market dynamics are closely related and highlight the importance of considering investor emotions when assessing a firm's short-term value.
Gaussian approximation potentials for body-centered-cubic transition metals

We develop a set of machine-learning interatomic potentials for elemental V, Nb, Mo, Ta, and W using the Gaussian approximation potential framework. The potentials show good accuracy and transferability for elastic, thermal, liquid, defect, and surface properties. All potentials are augmented with accurate repulsive potentials, making them applicable to radiation damage simulations involving high-energy collisions. We study melting and liquid properties in detail and use the potentials to provide melting curves up to 400 GPa for all five elements.
Extension of the INFN Tier-1 on a HPC system

The INFN Tier-1 located at CNAF in Bologna (Italy) is a center of the WLCG e-Infrastructure, supporting the 4 major LHC collaborations and more than 30 other INFN-related experiments. After multiple tests towards elastic expansion of CNAF compute power via Cloud resources (provided by Azure, Aruba and in the framework of the HNSciCloud project), and building on the experience gained with the production quality extension of the Tier-1 farm on remote owned sites, the CNAF team, in collaboration with experts from the ALICE, ATLAS, CMS, and LHCb experiments, has been working to put in production a solution of an integrated HTC+HPC system with the PRACE CINECA center, located nearby Bologna. Such extension will be implemented on the Marconi A2 partition, equipped with Intel Knights Landing (KNL) processors. A number of technical challenges were faced and solved in order to successfully run on low RAM nodes, as well as to overcome the closed environment (network, access, software distribution, … ) that HPC systems deploy with respect to standard GRID sites. We show preliminary results from a large scale integration effort, using resources secured via the successful PRACE grant N. 2018194658, for 30 million KNL core hours.
Novel dynamic critical phenomena induced by superfluidity and the chiral magnetic effect in Quantum Chromodynamics

In this thesis, we study the novel dynamic critical phenomena induced by superfluidity and the chiral magnetic effect in Quantum Chromodynamics (QCD), respectively. In particular, we find the new dynamic universality class of the high-density QCD critical point between the nuclear and quark superfluid phases beyond the conventional Hohnenberg and Halperin's classification. We also consider the second-order chiral phase transition under an external magnetic field and find that the inclusion of the chiral magnetic effect changes the dynamic universality class from the model E into model A within the conventional classification.