Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting Uncorrectable Memory Errors from the Correctable Error History: No Free Predictors in the Field

Being the major causes of hardware failures in datacenters, uncorrectable memory errors result in server crashes. In this paper, we address the problem of predicting uncorrectable errors (UEs) using the historical correctable error (CE) information. We first establish a new UE prediction framework of inferring latent memory faulty status from CE observations and correlating the inferred faulty status with the UE occurrences for prediction. We then design several predictors based on different memory fault modes and examine their performance on 4 datasets of memory errors from contemporary servers in datacenters of 3 top-tier technology companies. While in existing work, UE prediction is studied in a single environment only, this is the first comparative study on the prediction performance across datasets from different environments. Through the cross-dataset study, we demonstrate that predictors performing relatively well in some environments do not perform well in some other environments. The prediction performance are highly impacted by different characteristics in different environments and no free predictors are universally applicable. Finally, in order to capture the characteristics specific to each environment in UE prediction, we propose a properly designed learning process to induce a weighted ensemble of the predictors from the data and show that the ensemble predictor learned consistently outperforms the individual predictors within each dataset.
A Classification Strategy for Internet of Things Data Based on the Class Separability Analysis of Time Series Dynamics

This article proposes TSCLAS, a time series classification strategy for the Internet of Things (IoT) data, based on the class separability analysis of their temporal dynamics. Given the large number and incompleteness of IoT data, the use of traditional classification algorithms is not possible. Thus, we claim that solutions for IoT scenarios should avoid using raw data directly, preferring their transformation to a new domain. In the ordinal patterns domain, it is possible to capture the temporal dynamics of raw data to distinguish them. However, to be applied to this challenging scenario, TSCLAS follows a strategy for selecting the best parameters for the ordinal patterns transformation based on maximizing the class separability of the time series dynamics. We show that our method is competitive compared to other classification algorithms from the literature. Furthermore, TSCLAS is scalable concerning the length of time series and robust to the presence of missing data gaps on them. By simulating missing data gaps as long as 50% of the data, our method could beat the accuracy of the compared classification algorithms. Besides, even when losing in accuracy, TSCLAS presents lower computation times for both training and testing phases.
Dietary diversity score (DDS) and odds of colorectal cancer and adenoma: a case–control study

Abstract Despite mounting evidence that dietary factors might have a protective role against risk of cancer, few studies have assessed the relationship between diet diversity with colorectal cancer (CRC) and colorectal adenoma (CRA). Thus, we examined the relationship between dietary diversity score (DDS) and the odds of CRC and CRA. Overall, 129 CRC diagnosed patients, 130 CRA diagnosed cases and 240 healthy hospitalised controls were studied. DDS was calculated based on information on the usual diet that was assessed by a valid and reliable food frequency questionnaire (FFQ). Multivariate logistic regression was used to estimate the relationship between DDS and odds of colorectal cancer and adenoma. After adjusting for potential confounders, the diversity of grains is associated with the increased odds of CRC (ORgrains: 2·96 (1·05–8·32); P = 0·032), while the diversity of vegetables and fruits are associated with decreased odds of CRC (ORvegetables: 0·31 (0·16–0·62); P = 0·001, ORfruits: 0·37 (0·23–0·61); P < 0·001). The diversity of vegetables, fruits and dairy are inversely associated with odds of CRA (ORvegetables: 0·41 (0·21–0·78); P = 0·007, ORfruits: 0·58 (0·36–0·93); P = 0·021, ORdairies: 0·56 (0·37–0·83); P = 0·004). Also, higher DDS was related to decreased odds of both CRC (OR: 0·41 (0·23–0·72); P for trend = 0·002) and CRA (OR: 0·36 (0·21–0·65); P for trend = 0·001). Our results indicated that higher dietary diversity and particularly a diet varied in fruits and vegetables may reduce the odds of CRC and CRA. Also, the consumption of dairy products may decrease the odds of CRC, whereas the consumption of grains may increase the odds of CRC.
Employees’ Satisfaction with Supervisor Communication and Personal Feedback

Abstract The subject of the research in this paper was the examination of satisfaction with the internal communication dimensions that have the strongest correlations with job satisfaction, which are: supervisor communication and personal feedback. Therefore, if employees are satisfied with these communication dimensions, their job satisfaction will be at a satisfactory level. The goal of the research was to determine satisfaction with the observed communication dimensions based on the employees’ attitudes. Managers’ and non-managerial employees’ attitudes towards the observed communication dimensions are presented, also. On the sample of 72 respondents, using descriptive statistics, the results have shown that the employees are not satisfied with the observed dimensions, also, the non-managerial employees are less satisfied than the managerial ones. The results are useful since they provide a description of the observed dimensions and findings of the previous studies, especially the ones which investigated the relationship between satisfaction with the observed communication dimensions and job satisfaction. The results may be useful for (Serbian) managers in the process of managing the observed communication dimensions. The sample size limits the generalizability of the result.
Dewlap colour variation in Anolis sagrei is maintained among habitats within islands of the West Indies

Animal signals evolve in an ecological context. Locally adapting animal sexual signals can be especially important for initiating or reinforcing reproductive isolation during the early stages of speciation. Previous studies have demonstrated that dewlap colour in Anolis lizards can be highly variable between populations in relation to both biotic and abiotic adaptive drivers at relatively large geographical scales. Here, we investigated differentiation of dewlap coloration among habitat types at a small spatial scale, within multiple islands of the West Indies, to test the hypothesis that similar local adaptive processes occur over smaller spatial scales. We explored variation in dewlap coloration in the most widespread species of anole, Anolis sagrei, across three characteristic habitats spanning the Bahamas and the Cayman Islands, namely beach scrub, primary coppice forest and mangrove forest. Using reflectance spectrometry paired with supervised machine learning, we found significant differences in spectral properties of the dewlap between habitats within small islands, sometimes over very short distances. Passive divergence in dewlap phenotype associated with isolation‐by‐distance did not seem to explain our results. On the other hand, these habitat‐specific dewlap differences varied in magnitude and direction across islands, and thus, our primary test for adaptation—parallel responses across islands—was not supported. We suggest that neutral processes or selection could be involved in several ways, including sexual selection. Our results shed new light on the scale at which signal colour polymorphism can be maintained in the presence of gene flow, and the relative role of local adaptation and other processes in driving these patterns of dewlap colour variation across islands.
Identifying New Classes of High Temperature Superconductors With Convolutional Neural Networks

Applying machine learning to aid the search for high temperature superconductors has recently been a topic of significant interest due to the broad applications of these materials but is challenging due to the lack of a quantitative microscopic model. Here we analyze over 33,000 entries from the Superconducting Materials Database, maintained by the National Institute for Materials Science of Japan, assigning crystal structures to each entry by correlation with Materials project and other structural databases. These augmented inputs are combined with material-specific properties, including critical temperature, to train convolutional neural networks (CNNs) to identify superconductors. Classification models achieve accuracy >95% and regression models trained to predict critical temperature achieve R2 >0.92 and mean absolute error ≈ 5.6 K. A crystal-graph representation whereby an undirected graph encodes atom sites (graph vertices) and their bonding relationships (graph edges), is used to represent materials’ periodic crystal structure to the CNNs. Trained networks are used to search though 130,000 crystal structures in the Materials Project for high temperature superconductor candidates and predict their critical temperature; several materials with model-predicted T C >30 K are proposed, including rediscovery of the recently explored infinite layer nickelates.
Exploitation of the IPHAS to Investigate Planetary Nebulae

Similar to other classes of astronomical objects, there is a large discrepancy between the total count of theoretically predicted planetary nebulae (PNe) and the number of those actually observed. This discrepancy introduces bias in our attempt to globally understand and characterize the PNe population. Major efforts have been made to find the missing PNe. In particular, the INT Photometric Hα Survey (IPHAS) has, since its debut, provided a whelm of new (candidate) PNe, some of which have been studied in depth using various methodologies such as deep imaging and low- and high-resolution spectroscopy. Here, we present the outcome of the analysis of a first group of these well-investigated IPHAS PNe with a focus on the extended ones. We show that, in general, the missing objects that were expected to be unveiled by the survey (low density, evolved, and distant) are indeed discovered, but the survey also allows the retrieval of “simply” overlooked PNe.
ASSESSING AND IMPROVING THE RELIABILITY AND SECURITY OF CIRCUITS AFFECTED BY NATURAL AND INTENTIONAL FAULTS

The reliability and security vulnerability of modern electronic systems have emerged as concerns due to the increasing natural and intentional interferences. Radiation of high-energy charged particles generated from space environment or packaging materials on the substrate of integrated circuits results in natural faults. As the technology scales down, factors such as critical charge, voltage supply, and frequency change tremendously that increase the sensitivity of integrated circuits to natural faults even for systems operating at sea level. An attacker is able to simulate the impact of natural faults and compromise the circuit or cause denial of service. Therefore, instead of utilizing different approaches to counteract the effect of natural and intentional faults, a unified countermeasure is introduced. The unified countermeasure thwarts the impact of both reliability and security threats without paying the price of more area overhead, power consumption, and required time. This thesis first proposes a systematic analysis method to assess the probability of natural faults propagating the circuit and eventually being latched. The second part of this work focuses on the methods to thwart the impact of intentional faults in cryptosystems. We exploit a power-based side-channel analysis method to analyze the effect of the existing fault detection methods for natural faults on fault attack. Countermeasures for different security threats on cryptosystems are investigated separately. Furthermore, a new microarchitecture is proposed to thwart the combination of fault attacks and side-channel attacks, reducing the fault bypass rate and slowing down the key retrieval speed. The third contribution of this thesis is a unified countermeasure to thwart the impact of both natural faults and attacks. The unified countermeasure utilizes dynamically alternated multiple generator polynomials for the cyclic redundancy check (CRC) codec to resist the reverse engineering attack.
Ophthalmology and Artificial Intelligence: Present or Future? A Diabetic Retinopathy Screening Perspective of the Pursuit for Fairness

Computers that simulate human thought were first described in 1950, with the first artificial intelligence (AI) publication in 1943 describing a computer model that generated an autonomous binary output inspired by a human neuron (1). Since then, advances in machine learning and deep learning have expanded AI and created new paradigms in computer science. In healthcare, automatic processes facilitate diagnosis prediction, medical care, smart medical devices implementation, workflow improvement, electronic medical records interpretation, and screening programs (2, 3). Convolutional neural networks are examples of deep learning analytics applied to image processing; they simulate interconnected neurons and provide output after multiple interconnected information layers (2). Machine learning algorithms could be unsupervised, supervised, or reinforced; in every learning method, the construction of datasets is a critical step (3). More than twenty thousand articles have been published regarding AI in the last five years, with more than 1000 pertaining to ophthalmology. Regarding the retina subspecialty, AI has been applied in disease screening for diabetic retinopathy (DR), age-related macular degeneration (AMD), and retinopathy of prematurity (4). AI has already been applied in the IDx-DR system, the first FDA-approved device, with good results in Caucasian, North African, and Sub-Saharan populations (5). In the European Union, EyeArt has been used to exclude low-quality images, estimate DR progression, and recommend referral (5). Other algorithms, including Google’s, Singapore’s SERI-NUS, the Bosch DR Algorithm, and Retinalyze, have been developed (5). Algorithms have also been developed to support decision-making, e.g., in the anti-angiogenic treatment of AMD (6). Despite technological advances, many challenges hamper real-world implementation of AI, such as variability in algorithm performance, patient acceptance in automated processes, and ethical conflicts. Therefore, this article’s objective was to compare characteristics of open-access retinal fundus photos datasets, implementations of AI in ophthalmology, and challenges to AI application in ophthalmology.
Work-From-Home During COVID-19 Lockdown: When Employees’ Well-Being and Creativity Depend on Their Psychological Profiles

With the COVID-19 pandemic, governments implemented successive lockdowns that forced employees to work from home (WFH) to contain the spread of the coronavirus. This crisis raises the question of the effects of mandatory work from home on employees’ well-being and performance, and whether these effects are the same for all employees. In the present study, we examined whether working at home may be related to intensity, familiarity with WFH, employees’ well-being (loneliness at work, stress, job satisfaction, and work engagement) and creativity (‘subjective’ and ‘objective’). We also examined whether the psychological profile of employees, combining preference for solitude and associated personality variables from the Big Five, may influence the effects of WFH. The data were collected via an online survey from November 13th to December 15th 2020 among 946 employees from various organizations during the second lockdown in France. In addition to identifying two distinctive psychological profiles for employees having to WFH, results revealed that those with a “Solitary” profile reported higher loneliness at work, higher levels of stress, and lower levels of job satisfaction and work engagement than those with an “Affiliative” profile. It was also found that employees with a “Solitary” profile perceived themselves as less creative and produced objectively fewer ideas than individuals with an “Affiliative” profile. The present study suggests the necessity to distinguish the profiles of teleworkers and to offer a stronger support for the less affiliative employees when working from home.
Comprehensive Volumetric Analysis of Mecp2-Null Mouse Model for Rett Syndrome by T2-Weighted 3D Magnetic Resonance Imaging

Rett syndrome (RTT) is a severe progressive neurodevelopmental disorder characterized by various neurological symptoms. Almost all RTT cases are caused by mutations in the X-linked methyl-CpG-binding protein 2 (MeCP2) gene, and several mouse models have been established to understand the disease. However, the neuroanatomical abnormalities in each brain region of RTT mouse models have not been fully understood. Here, we investigated the global and local neuroanatomy of the Mecp2 gene-deleted RTT model (Mecp2-KO) mouse brain using T2-weighted 3D magnetic resonance imaging with different morphometry to clarify the brain structural abnormalities that are involved in the pathophysiology of RTT. We found a significant reduction in global and almost all local volumes in the brain of Mecp2-KO mice. In addition, a detailed comparative analysis identified specific volume reductions in several brain regions in the Mecp2-deficient brain. Our analysis also revealed that the Mecp2-deficient brain shows changes in hemispheric asymmetry in several brain regions. These findings suggest that MeCP2 affects not only the whole-brain volume but also the region-specific brain structure. Our study provides a framework for neuroanatomical studies of a mouse model of RTT.
Multiple Levels of Heuristic Reasoning Processes in Scientific Model Construction

Science historians have recognized the importance of heuristic reasoning strategies for constructing theories, but their extent and degree of organization are still poorly understood. This paper first consolidates a set of important heuristic strategies for constructing scientific models from three books, including studies in the history of genetics and electromagnetism, and an expert think-aloud study in the field of mechanics. The books focus on qualitative reasoning strategies (processes) involved in creative model construction, scientific breakthroughs, and conceptual change. Twenty four processes are examined, most of which are field-general, but all are heuristic in not being guaranteed to work. An organizing framework is then proposed as a four-level hierarchy of nested reasoning processes and subprocesses at different size and time scales, including: Level (L4) Several longer-time-scale Major Modeling Modes, such as Model Evolution and Model Competition; the former mode utilizes: (L3) Modeling Cycle Phases of Model Generation, Evaluation, and Modification under Constraints; which can utilize: (L2) Thirteen Tactical Heuristic Processes, e.g., Analogy, Infer new model feature (e.g., by running the model), etc.; many of which selectively utilize: (L1) Grounded Imagistic Processes, namely Mental Simulations and Structural Transformations. Incomplete serial ordering in the framework gives it an intermediate degree of organization that is neither anarchistic nor fully algorithmic. Its organizational structure is hypothesized to promote a difficult balance between divergent and convergent processes as it alternates between them in modeling cycles with increasingly constrained modifications. Videotaped think-aloud protocols that include depictive gestures and other imagery indicators indicate that the processes in L1 above can be imagistic. From neurological evidence that imagery uses many of the same brain regions as actual perception and action, it is argued that these expert reasoning processes are grounded in the sense of utilizing the perceptual and motor systems, and interconnections to and possible benefits for reasoning processes at higher levels are examined. The discussion examines whether this grounding and the various forms of organization in the framework may begin to explain how processes that are only sometimes useful and not guaranteed to work can combine successfully to achieve innovative scientific model construction.
PANDORA: A Fast, Anchor-Restrained Modelling Protocol for Peptide: MHC Complexes

Deeper understanding of T-cell-mediated adaptive immune responses is important for the design of cancer immunotherapies and antiviral vaccines against pandemic outbreaks. T-cells are activated when they recognize foreign peptides that are presented on the cell surface by Major Histocompatibility Complexes (MHC), forming peptide:MHC (pMHC) complexes. 3D structures of pMHC complexes provide fundamental insight into T-cell recognition mechanism and aids immunotherapy design. High MHC and peptide diversities necessitate efficient computational modelling to enable whole proteome structural analysis. We developed PANDORA, a generic modelling pipeline for pMHC class I and II (pMHC-I and pMHC-II), and present its performance on pMHC-I here. Given a query, PANDORA searches for structural templates in its extensive database and then applies anchor restraints to the modelling process. This restrained energy minimization ensures one of the fastest pMHC modelling pipelines so far. On a set of 835 pMHC-I complexes over 78 MHC types, PANDORA generated models with a median RMSD of 0.70 Å and achieved a 93% success rate in top 10 models. PANDORA performs competitively with three pMHC-I modelling state-of-the-art approaches and outperforms AlphaFold2 in terms of accuracy while being superior to it in speed. PANDORA is a modularized and user-configurable python package with easy installation. We envision PANDORA to fuel deep learning algorithms with large-scale high-quality 3D models to tackle long-standing immunology challenges.
Hsa_circ_0062270 Promotes Tumorigenesis of Melanoma by Stabilizing the Linear Transcript Cell Division Cycle Protein 45

Background: To elucidate the potential biological function of hsa_circ_0062270 in the malignant process of melanoma and its potential target. Methods: Quantitative real-time polymerase chain reaction (qRT-PCR) was conducted to examine relative level of hsa_circ_0062270 in melanoma tissues and normal skin tissues. The diagnostic and prognostic potentials of hsa_circ_0062270 in melanoma were evaluated. The regulatory effect of hsa_circ_0062270 on the expression of linear transcript Cell division cycle protein 45 (CDC45) was also examined. Results: Hsa_circ_0062270 was up-regulated in melanoma samples and cell lines, which displayed certain diagnostic and prognostic potentials in melanoma. Inhibition of hsa_circ_0062270 attenuated the proliferative, migratory and invasive functions. Hsa_circ_0062270 could stabilize the expression of linear transcript CDC45, and thus participated in the malignant process of melanoma. Conclusion: Hsa_circ_0062270 promotes proliferative, migratory and invasive functions of melanoma cells via stabilizing the linear transcript CDC45. Hsa_circ_0062270 can be used to diagnosis and treatment of melanoma.