Miracle-dz/UniFAR-FTUs huggingface dataset:
Run, Forest, Run? On Randomization and Reproducibility in Predictive Software Engineering

Machine learning (ML) has been widely used in the literature to automate software engineering tasks. However, ML outcomes may be sensitive to randomization in data sampling mechanisms and learning procedures. To understand whether and how researchers in SE address these threats, we surveyed 45 recent papers related to three predictive tasks: defect prediction (DP), predictive mutation testing (PMT), and code smell detection (CSD). We found that less than 50% of the surveyed papers address the threats related to randomized data sampling (via multiple repetitions); only 8% of the papers address the random nature of ML; and parameter values are rarely reported (only 18% of the papers). To assess the severity of these threats, we conducted an empirical study using 26 real-world datasets commonly considered for the three predictive tasks of interest, considering eight common supervised ML classifiers. We show that different data resamplings for 10-fold cross-validation lead to extreme variability in observed performance results. Furthermore, randomized ML methods also show non-negligible variability for different choices of random seeds. More worryingly, performance and variability are inconsistent for different implementations of the conceptually same ML method in different libraries, as also shown through multi-dataset pairwise comparison. To cope with these critical threats, we provide practical guidelines on how to validate, assess, and report the results of predictive methods.
Testing and Validating Two Morphological Flare Predictors by Logistic Regression Machine Learning

Whilst the most dynamic solar active regions (ARs) are known to flare frequently, predicting the occurrence of individual flares and their magnitude, is very much a developing field with strong potentials for machine learning applications. The present work is based on a method which is developed to define numerical measures of the mixed states of ARs with opposite polarities. The method yields compelling evidence for the assumed connection between the level of mixed states of a given AR and the level of the solar eruptive probability of this AR by employing two morphological parameters: 1) the separation parameter S l − f and 2) the sum of the horizontal magnetic gradient G S . In this work, we study the efficiency of S l − f and G S as flare predictors on a representative sample of ARs, based on the SOHO/MDI-Debrecen Data (SDD) and the SDO/HMI - Debrecen Data (HMIDD) sunspot catalogues. In particular, we investigate about 1,000 ARs in order to test and validate the joint prediction capabilities of the two morphological parameters by applying the logistic regression machine learning method. Here, we confirm that the two parameters with their threshold values are, when applied together, good complementary predictors. Furthermore, the prediction probability of these predictor parameters is given at least 70% a day before.
FMODetect: Robust Detection and Trajectory Estimation of Fast Moving Objects

We propose the first learning-based approach for detection and trajectory estimation of fast moving objects. Such objects are highly blurred and move over large distances within one video frame. Fast moving objects are associated with a deblurring and matting problem, also called deblatting. Instead of solving the complex deblatting problem jointly, we split the problem into matting and deblurring and solve them separately. The proposed method first detects all fast moving objects as a truncated distance function to the trajectory. Subsequently, a matting and fitting network for each detected object estimates the object trajectory and its blurred appearance without background. For the sharp appearance estimation, we propose an energy minimization based deblurring. The state-of-the-art methods are outperformed in terms of trajectory estimation and sharp appearance reconstruction. Compared to other methods, such as deblatting, the inference is of several orders of magnitude faster and allows applications such as real-time fast moving object detection and retrieval in large video collections.
Exponential Lower Bounds for Batch Reinforcement Learning: Batch RL can be Exponentially Harder than Online RL

Several practical applications of reinforcement learning involve an agent learning from past data without the possibility of further exploration. Often these applications require us to 1) identify a near optimal policy or to 2) estimate the value of a target policy. For both tasks we derive \emph{exponential} information-theoretic lower bounds in discounted infinite horizon MDPs with a linear function representation for the action value function even if 1) \emph{realizability} holds, 2) the batch algorithm observes the exact reward and transition \emph{functions}, and 3) the batch algorithm is given the \emph{best} a priori data distribution for the problem class. Furthermore, if the dataset does not come from policy rollouts then the lower bounds hold even if the action-value function of \emph{every} policy admits a linear representation. If the objective is to find a near-optimal policy, we discover that these hard instances are easily solved by an \emph{online} algorithm, showing that there exist RL problems where \emph{batch RL is exponentially harder than online RL} even under the most favorable batch data distribution. In other words, online exploration is critical to enable sample efficient RL with function approximation. A second corollary is the exponential separation between finite and infinite horizon batch problems under our assumptions. On a technical level, this work introduces a new `oracle + batch algorithm' framework to prove lower bounds that hold for every distribution, and automatically recovers traditional fixed distribution lower bounds as a special case. Finally this work helps formalize the issue known as \emph{deadly triad} and explains that the \emph{bootstrapping} problem \citep{sutton2018reinforcement} is potentially more severe than the \emph{extrapolation} issue for RL because unlike the latter, bootstrapping cannot be mitigated by adding more samples.
A case for new neural network smoothness constraints

How sensitive should machine learning models be to input changes? We tackle the question of model smoothness and show that it is a useful inductive bias which aids generalization, adversarial robustness, generative modeling and reinforcement learning. We explore current methods of imposing smoothness constraints and observe they lack the flexibility to adapt to new tasks, they don't account for data modalities, they interact with losses, architectures and optimization in ways not yet fully understood. We conclude that new advances in the field are hinging on finding ways to incorporate data, tasks and learning into our definitions of smoothness.
Do not repeat these mistakes - a critical appraisal of applications of explainable artificial intelligence for image based COVID-19 detection

The sudden outbreak and uncontrolled spread of COVID-19 disease is one of the most important global problems today. In a short period of time, it has led to the development of many deep neural network models for COVID-19 detection with modules for explainability. In this work, we carry out a systematic analysis of various aspects of proposed models. Our analysis revealed numerous mistakes made at different stages of data acquisition, model development, and explanation construction. In this work, we overview the approaches proposed in the surveyed ML articles and indicate typical errors emerging from the lack of deep understanding of the radiography domain. We present the perspective of both: experts in the field - radiologists, and deep learning engineers dealing with model explanations. The final result is a proposed a checklist with the minimum conditions to be met by a reliable COVID-19 diagnostic model.
GTA: Global Temporal Attention for Video Action Understanding

Self-attention learns pairwise interactions via dot products to model long-range dependencies, yielding great improvements for video action recognition. In this paper, we seek a deeper understanding of self-attention for temporal modeling in videos. In particular, we demonstrate that the entangled modeling of spatial-temporal information by flattening all pixels is sub-optimal, failing to capture temporal relationships among frames explicitly. We introduce Global Temporal Attention (GTA), which performs global temporal attention on top of spatial attention in a decoupled manner. Unlike conventional self-attention that computes an instance-specific attention matrix, GTA randomly initializes a global attention matrix that is intended to learn stable temporal structures to generalize across different samples. GTA is further augmented with a cross-channel multi-head fashion to exploit feature interactions for better temporal modeling. We apply GTA not only on pixels but also on semantically similar regions identified automatically by a learned transformation matrix. Extensive experiments on 2D and 3D networks demonstrate that our approach consistently enhances the temporal modeling and provides state-of-the-art performance on three video action recognition datasets.
The quantum cosmological tilt and the origin of dark matter

A promising candidate for cold dark matter is primordial black holes (PBH) formed from strong primordial quantum fluctuations. A necessary condition for the formation of PBH's is a change of sign in the tilt governing the anomalous scale invariance of the power spectrum from red at large scales into blue at small scales. Non-perturbative information on the dependence of the power spectrum tilt on energy scale can be extracted from the quantum Fisher information measuring the energy dependence of the quantum phases defining the de Sitter vacua. We show that this non-perturbative quantum tilt goes from a red tilted phase, at large scales, into a blue tilted phase at small scales converging to ns = 2 in the UV. This allows the formation of PBH's in the range of masses ≲ 1020 gr.
Invariant Feature Learning for Sensor-Based Human Activity Recognition

Wearable sensor-based human activity recognition (HAR) has been a research focus in the field of ubiquitous and mobile computing for years. In recent years, many deep models have been applied to HAR problems. However, deep learning methods typically require a large amount of data for models to generalize well. Significant variances caused by different participants or diverse sensor devices limit the direct application of a pre-trained model to a subject or device that has not been seen before. To address these problems, we present an invariant feature learning framework (IFLF) that extracts common information shared across subjects and devices. IFLF incorporates two learning paradigms: 1) meta-learning to capture robust features across seen domains and adapt to an unseen one with similarity-based data selection; 2) multi-task learning to deal with data shortage and enhance overall performance via knowledge sharing among different subjects. Experiments demonstrated that IFLF is effective in handling both subject and device diversion across popular open datasets and an in-house dataset. It outperforms a baseline model of up to 40 percent in test accuracy.
A Deep Multi-level Attentive Network for Multimodal Sentiment Analysis

Multimodal sentiment analysis has attracted increasing attention with broad application prospects. Most of the existing methods have focused on a single modality, which fails to handle social media data due to its multiple modalities. Moreover, in multimodal learning, most of the works have focused on simply combining the two modalities without exploring the complicated correlations between them. This resulted in dissatisfying performance for multimodal sentiment classification. Motivated by the status quo, we propose a Deep Multi-level Attentive network (DMLANet), which exploits the correlation between image and text modalities to improve multimodal learning. Specifically, we generate the bi-attentive visual map along the spatial and channel dimensions to magnify Convolutional neural network representation power. Then, we model the correlation between the image regions and semantics of the word by extracting the textual features related to the bi-attentive visual features by applying semantic attention. Finally, self-attention is employed to fetch the sentiment-rich multimodal features for the classification automatically. We conduct extensive evaluations on four real-world datasets, namely, MVSA-Single, MVSA-Multiple, Flickr, and Getty Images, which verify our method's superiority.
Torsion theory of coherent functors.

Let $C$ be an additive category with cokernels and let Mod($C$) be the category of additive functors from $C^{op}$ to the category Ab of abelian groups. Let mod($C$) be the full subcategory of Mod($C$) consisting of coherent functors. In this paper, we first study some basic properties of pseudo-kernel of morphisms in $C$. When $C$ has pseudo-kernels, mod($C$) is abelian and then, in this case, we study radical functors, half exact functors, left exact functors and injective objects in mod($C$). At last, we extend the results for Mod($C$).
Underestimation of COVID-19 mortality during the pandemic

Background There has been considerable international variation in mortality during the COVID-19 pandemic. The objective of this study was to investigate the differences between mortality registered as due to COVID-19 and the excess all-cause mortality reported in countries worldwide during the COVID-19 pandemic. Methods Ecological analysis of 22 countries compared 5-year historical all-cause mortality, reported all-cause mortality and expected all-cause mortality (calculated as historical mortality plus the reported deaths attributed to COVID-19). Data available from the first week of January 2020 to that most recently available were analysed. Results Compared to the preceding 5 years, there was an excess of 716 616 deaths, of which 64.3% were attributed to COVID-19. The proportion of deaths registered as COVID-19-related/excess deaths varied markedly between countries, ranging between 30% and 197% in those countries that had an excess of deaths during the period of observation. In most countries where a definite peak in COVID-19-related deaths occurred, the increase in reported all-cause mortality preceded the increase in COVID-19 reported mortality. During the latter period of observation, a few countries reported fewer all-cause deaths than the historical figures. Conclusion The increases in all-cause mortality preceded the increase in COVID-19 mortality in most countries that had definite spikes in COVID-19 mortality. The number of deaths attributed to COVID-19 was underestimated by at least 35%. Together these findings suggest that calculation of excess all-cause mortality is a better predictor of COVID-19 mortality than the reported rates, in those countries experiencing definite increases in mortality. In countries experiencing marked increases in all-cause mortality during the global pandemic, all-cause mortality more accurately estimates #COVID19 mortality and the onset of the pandemic than the reported COVID-19 mortality rates https://bit.ly/3fOtBJg
Effects of radish powder concentration and incubation time on the physicochemical characteristics of alternatively cured pork products

Abstract Previous research has indicated that radish powder could be a suitable replacement for chemical nitrite sources in alternatively cured meat products. However, the effects of radish powder level on the physicochemical properties of cured meat have not been systematically studied. In this study, we aimed to investigate the effects of varying concentrations of radish powder and incubation time on the physicochemical properties and cured meat pigments of alternatively cured meat products. We divided our experimental setup into seven groups with different radish powder concentrations and incubation times: control (0.01% sodium nitrite), treatment 1 (0.15% radish powder and 2 h incubation), treatment 2 (0.15% radish powder and 4 h incubation), treatment 3 (0.30% radish powder and 2 h incubation), treatment 4 (0.30% radish powder and 4 h incubation), treatment 5 (0.30% celery powder and 2 h incubation), and treatment 6 (0.30% celery powder and 4 h incubation). The cooking yield, CIE a* values (redness), and total pigment levels were not significantly different (p > 0.05) between any of the alternatively cured treatments and the control. However, when 0.30% radish powder or celery powder was added to the products, the CIE b* values increased significantly (p < 0.05) with incubation time. At the same vegetable concentration, the nitrite content, nitrosyl hemochrome, and curing efficiency also increased significantly (p < 0.05) as the incubation time increased from 2 to 4 h, regardless of the types of vegetable powder. Among the meat products cured with radish powder, treatment 4 showed the highest increase in residual nitrite content, nitrosyl hemochrome content, and curing efficiency, but showed decreased lipid oxidation. Our results suggest that increased concentrations of radish powder and longer incubation times would be more suitable for producing alternatively cured meat products comparable to traditionally cured products treated with synthetic nitrite.
2-methoxyestradiol sensitizes breast cancer cells to taxanes by targeting centrosomes

Centrosomes amplification is a hallmark of cancer. We hypothesize that 2-methoxyestradiol (2-ME) sensitizes breast cancer (BC) cells to taxanes by targeting amplified centrosomes. We assessed the extent by which 2-ME together with paclitaxel (PTX) induces centrosome alterations with subsequent mitotic catastrophe in different BC subtypes. 2-ME induced a significant reduction in PTX IC50 values in all cells tested ranging from 28–44% (P < 0.05). Treatment with both PTX and 2-ME significantly increased the number of misaligned metaphases compared to PTX alone (34%, 100% and 52% for MCF7, MDA-MB231 and SUM149, respectively; P < 0.05). The number of cells with multipolar spindle formation was significantly increased (81%, 220% and 285% for MCF7, MDA-MB231 and SUM 149, respectively; P < 0.05). PTX and 2-ME treatment significantly increased interphase declustering in cancer cells (56% for MCF7, 208% for MDA-MB231 and 218% for SUM149, respectively; P < 0.05) and metaphase declustering (1.4-fold, 1.56-fold and 2.48-fold increase for MCF7, MDA-MB231 and SUM149, respectively; P < 0.05). This report is the first to document centrosome declustering as a mechanism of action of 2-ME and provides a potential approach for reducing taxane toxicity in cancer treated patients.
Effect of Positive End-Expiratory Pressure and Proning on Ventilation and Perfusion in COVID-19 Acute Respiratory Distress Syndrome

ILD and COVID-19 symptoms. This confounding, however, would tend to bias our data toward the null by capturing patients with less severe disease. 4) Constrained geographic area potentially limits the generalizability of our conclusions. Ongoing larger international studies will help further elucidate the risk factors and outcomes of patients with ILD and COVID-19. In summary, in this multicenter case–control study, patients with ILD, particularly those of advanced age, had increased odds of severe disease and death from COVID-19. Patients with ILD should be counseled of their increased risk, with an emphasis on public health measures to prevent infection in this susceptible population. n
Impact of COVID-19 on COPD and asthma admissions, and the pandemic from a patient's perspective

The coronavirus disease 2019 (COVID-19) pandemic has led to an overall reduction in hospital admissions in the United Kingdom (UK) with national data showing a decline in emergency admissions for respiratory illness [1, 2]. There has been a significant reduction in all-cause and exacerbation-related #asthma and #COPD admissions during the #COVID19 pandemic. Patients also report a subjective decline in disease control and describe a negative impact on their mental health. https://bit.ly/2Kv0O0H
Magnitude of Streptococcus pneumoniae Among Under-Five Children with Symptom of Acute Respiratory Infection at Hiwot Fana Specialized University Hospital, Harar, Ethiopia: Associated Risk Factors and Antibacterial Susceptibility Patterns

Purpose Streptococcus pneumoniae is the major cause of pneumoniae infection among under-five children that leads to high morbidity and mortality. Thus, the aim of this study was to determine the magnitude of Streptococcus pneumoniae in under-five children of an acute respiratory infection, assess its antimicrobial susceptibility patterns, and define the associated factors. Methods An institutional-based cross-sectional study was conducted on a total of 384 under-five children of acute respiratory infection attending outpatient department of Hiwot Fana Specialized University Hospital, Harar, Ethiopia, from March 1 to 30, 2020. Socio-demographic and clinical data were collected from the study participants using a structured questionnaire. Sputum samples were collected and processed to identify Streptococcus pneumoniae pathogen using the culture and biochemical tests as per the standard procedures. The Kirby–Bauer disk diffusion method was used for antimicrobial susceptibility testing. Data were entered into Epi-data version 3.1 and analyzed by using Statistical Product and Service Solutions version 22. Results The proportion of Streptococcus pneumoniae in under-five children with acute respiratory infection was 11.2%. About 50% of isolated Streptococcus pneumoniae was resistant to tetracycline and cotrimoxazole, whereas more than 90% of it was susceptible to Ceftriaxone and amoxicillin-clavulanate. Children who lived in rural areas were 3.6 times more likely to have S. pneumoniae compared to children who lived in urban areas (AOR: 3.6, 95% CI: 1.2–11) and children with familysmokers in a house were 3 times at risk to be infected with S. pneumoniae (AOR: 3, 95% CI: 1.8–8.0). Conclusion High antimicrobial resistance of S. pneumoniae against tetracycline and cotrimoxazole was observed and children who lived in rural areas and live with a family of cigarette smoker are factors associated with Streptococcus pneumoniae. Therefore, providing health educations to the family of children rural residents and isolating smokers from the house where children lived are recommended actions to reduce bacteria caused by Streptococcus pneumoniae.
Population structure analysis of Yeonsan Ogye using microsatellite markers

Abstract The Yeonsan Ogye (YO) chicken is a natural heritage of Korea, characterized by black feathers, skin, bones, eyes, and comb. The purebred of YO population has been reared under the natural mating system with no systematic selection and breeding plan. The purpose of this study was to identify the genetic diversity and find the optimal number of population sub-division using 12 polymorphic microsatellite (MS) markers to construct a pedigree-based breeding plan for the YO population. A total of 509 YO birds were used for this study. Genetic diversity and population structure analysis were conducted based on the MS marker genotype information. The overall average polymorphic information content value and expected heterozygosity of the population were 0.586, and 0.642, respectively. The K-mean cluster analysis based on the genetic distance result confirmed that the current YO population can be divided into three ancestry groups. Individuals in each group were evaluated based on their genetic distance to identify the potential candidates for a future breeding plan. This study concludes that a future breeding plan with known pedigree information of selected founder animals, which holds high genetic diversity, could be the best strategy to ensure the conservation of the Korean YO chicken population.