Miracle-dz/UniFAR-FTUs huggingface dataset:
CCID5: an implementation of the BBR congestion control algorithm for DCCP and its impact over multi-path scenarios

Providing multi-connectivity services is an important goal for next generation wireless networks, where multiple access networks are available and need to be integrated into a coherent solution that efficiently supports both reliable and non reliable traffic. Based on virtual network interfaces and per path congestion controlled tunnels, the MP-DCCP based multiaccess aggregation framework presents a novel solution that flexibly supports different path schedulers and congestion control algorithms as well as reordering modules. The framework has been implemented within the Linux kernel space and has been tested over different prototypes. Experimental results have shown that the overall performance strongly depends upon the congestion control algorithm used on the individual DCCP tunnels, denoted as CCID. In this paper, we present an implementation of the BBR (Bottleneck Bandwidth Round Trip propagation time) congestion control algorithm for DCCP in the Linux kernel. We show how BBR is integrated into the MP-DCCP multi-access framework and evaluate its performance over both single and multi-path environments. Our evaluation results show that BBR improves the performance compared to CCID2 (TCPlike Congestion Control) for multi-path scenarios due to the faster response to changes in the available bandwidth, which reduces latency and increases performance, especially for unreliable traffic. The MP-DCCP framework code, including the new CCID5 is available as OpenSource1.
Observation and calibration strategies for large-scale multi-beam velocity-resolved mapping of the [CII] emission in the Orion molecular cloud

Context. The [CII] 158 μm far-infrared fine-structure line is one of the dominant cooling lines of the star-forming interstellar medium. Hence [CII] emission originates in and thus can be used to trace a range of ISM processes. Velocity-resolved large-scale mapping of [CII] in star-forming regions provides a unique perspective of the kinematics of these regions and their interactions with the exciting source of radiation. Aims. We explore the scientific applications of large-scale mapping of velocity-resolved [CII] observations. With the [CII] observations, we investigate the effect of stellar feedback on the ISM. We present the details of observation, calibration, and data reduction using a heterodyne array receiver mounted on an airborne observatory. Methods. A 1.15 square degree velocity-resolved map of the Orion molecular cloud centred on the bar region was observed using the German REceiver for Astronomy at Terahertz Frequencies (upGREAT) heterodyne receiver flying on board the Stratospheric Observatory for Infrared Astronomy. The data were acquired using the 14 pixels of the German REceiver for Astronomy at Terahertz Frequencies that were observed in an on-the-fly mapping mode. 2.4 million spectra were taken in total. These spectra were gridded into a three-dimensional cube with a spatial resolution of 14.1 arcseconds and a spectral resolution of 0.3 km s−1. Results. A square-degree [CII] map with a spectral resolution of 0.3 km s−1 is presented. The scientific potential of this data is summarized with discussion of mechanical and radiative stellar feedback, filament tracing using [CII], [CII] opacity effects, [CII] and carbon recombination lines, and [CII] interaction with the large molecular cloud. The data quality and calibration is discussed in detail, and new techniques are presented to mitigate the effects of unavoidable instrument deficiencies (e.g. baseline stability) and thus to improve the data quality. A comparison with a smaller [CII] map taken with the Herschel/Heterodyne Instrument for the Far-Infrared spectrometer is presented. Conclusions. Large-scale [CII] mapping provides new insight into the kinematics of the ISM. The interaction between massive stars and the ISM is probed through [CII] observations. Spectrally resolving the [CII] emission is necessary to probe the microphysics induced by the feedback of massive stars. We show that certain heterodyne instrument data quality issues can be resolved using a spline-based technique, and better data correction routines allow for more efficient observing strategies.
Zombies in the Loop? People are Insensitive to the Transparency of AI-Powered Moral Advisors

Departing from the assumption that AI needs to be transparent to be trusted, we ﬁnd that users trustfully take ethical advice from a transparent and an opaque AI-powered algorithm alike. Even when transparency reveals information that warns against the algorithm, they continue to accept its advice. We conducted online experiments where the participants took the role of decision-makers who received AI-powered advice on how to deal with an ethical dilemma. We manipulated information about the algorithm to study its inﬂuence. Our ﬁndings suggest that AI is overtrusted rather than distrusted, and that users need digital literacy to beneﬁt from transparency.
Evidence of Accretion Burst: The Viscously Heated Inner Disk of the Embedded Protostar IRAS 16316-1540

Outbursts of young stellar objects occur when the mass accretion rate suddenly increases. However, such outbursts are difficult to detect for deeply embedded protostars due to their thick envelope and the rarity of outbursts. The near-IR spectroscopy is a useful tool to identify ongoing outburst candidates by the characteristic absorption features that indicate a disk origin. However, without high-resolution spectroscopy, the spectra of outburst candidates can be confused with the late-type stars since they have similar spectral features. For the protostar IRAS 16316-1540, the near-IR spectrum has line equivalent widths that are consistent with M-dwarf photospheres. However, our high-resolution IGRINS spectra reveal that the absorption lines have boxy and/or double-peaked profiles, as expected from a disk and not the star. The continuum emission source is likely the hot, optically thick disk, heated by viscous accretion. The projected disk rotation velocity of 41 ± 5 km s−1 corresponds to ∼0.1 au. Based on the result, we suggest IRAS 16316-1540 as an ongoing outburst candidate. Viscous heating of disks is usually interpreted as evidence for ongoing bursts, which may be more common than previously estimated from low-resolution near-IR spectra.
Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL

Despite a series of recent successes in reinforcement learning (RL), many RL algorithms remain sensitive to hyperparameters. As such, there has recently been interest in the field of AutoRL, which seeks to automate design decisions to create more general algorithms. Recent work suggests that population based approaches may be effective AutoRL algorithms, by learning hyperparameter schedules on the fly. In particular, the PB2 algorithm is able to achieve strong performance in RL tasks by formulating online hyperparameter optimization as time varying GP-bandit problem, while also providing theoretical guarantees. However, PB2 is only designed to work for continuous hyperparameters, which severely limits its utility in practice. In this paper we introduce a new (provably) efficient hierarchical approach for optimizing both continuous and categorical variables, using a new time-varying bandit algorithm specifically designed for the population based training regime. We evaluate our approach on the challenging Procgen benchmark, where we show that explicitly modelling dependence between data augmentation and other hyperparameters improves generalization.
Improving data-driven model-independent reconstructions and updated constraints on dark energy models from Horndeski cosmology

In light of the statistical performance of cosmological observations, in this work we present an improvement on the Gaussian reconstruction of the Hubble parameter data H(z) from Cosmic Chronometers, Supernovae Type Ia and Clustering Galaxies in a model-independent way in order to use them to study new constraints in the Horndeski theory of gravity. First, we have found that the prior used to calibrate the Pantheon supernovae data significantly affects the reconstructions, leading to a 13σ tension on the H 0 value. Second, according to the χ2-statistics, the reconstruction carried out by the Pantheon data calibrated using the H 0 value measured by The Carnegie-Chicago Hubble Program is the reconstruction which fits best the observations of Cosmic Chronometers and Clustering of Galaxies datasets. Finally, we use our reconstructions of H(z) to impose model-independent constraints in dark energy scenarios as Quintessence and K-essence from general cosmological viable Horndeski models, landscape in where we found that a Horndeski model of the K-essence type can reproduce the reconstructions of the late expansion of the universe within 2σ.
Research for Expression and Prognostic Value of GABRD in Colon Cancer and Coexpressed Gene Network Construction Based on Data Mining

Colon cancer is one of the top five cancers with the highest incidence rate in the world. In order to better understand the pathogenesis and progression of colon cancer, it is still necessary to investigate the abnormally expressed genes in cancer tissue. In this study, the Oncomine database was used for expression analysis, and it was found that the expression level of gamma-aminobutyric acid type A receptor subunit delta (GABRD) gene was upregulated in colon cancer tissue compared with that in normal tissue. UALCAN was used to analyze the expression of GABRD in different groups of age, gender, cancer stage, N stage, and histological subtype. Then, it was also found that the expression of GABRD in each subgroup of colon cancer tissue was all high compared with that in normal tissue. LinkedOmics was used to screen out the differentially expressed genes related to GABRD expression in colon cancer. GO annotation and KEGG pathway enrichment analyses found that the correlated genes may be related to breast cancer, human papillomavirus infection, Notch signaling pathway, and other pathways. Thereafter, GSEA was performed to obtain GABRD-related kinases, miRNAs, and transcription factors, and gene interaction networks were constructed. It was found that GABRD may be involved in cell cycle regulation. Finally, websites like GEPIA were used to detect the predictive ability of GABRD on the prognosis of patients with colon cancer. Kaplan-Meier analysis suggested that the upregulation of GABRD expression was related to the poor prognosis of patients with colon cancer. Overall, in this study, the potential role and prognostic ability of GABRD in colon cancer were explored through data mining, which can be a clue for further research on GABRD.
An Effective Method for Detecting and Classifying Diabetic Retinopathy Lesions Based on Deep Learning

Diabetic retinopathy occurs as a result of the harmful effects of diabetes on the eyes. Diabetic retinopathy is also a disease that should be diagnosed early. If not treated early, vision loss may occur. It is estimated that one third of more than half a million diabetic patients will have diabetic retinopathy by the 22nd century. Many effective methods have been proposed for disease detection with deep learning. In this study, unlike other studies, a deep learning-based method has been proposed in which diabetic retinopathy lesions are detected automatically and independently of datasets, and the detected lesions are classified. In the first stage of the proposed method, a data pool is created by collecting diabetic retinopathy data from different datasets. With Faster RCNN, lesions are detected, and the region of interests are marked. The images obtained in the second stage are classified using the transfer learning and attention mechanism. The method tested in Kaggle and MESSIDOR datasets reached 99.1% and 100% ACC and 99.9% and 100% AUC, respectively. When the obtained results are compared with other results in the literature, it is seen that more successful results are obtained.
A Systemic Review and Meta-analysis of the Leading Pathogens Causing Neonatal Sepsis in Developing Countries

Background Neonatal sepsis is one of the major public health problems globally, particularly, in developing countries. Klebsiella, Staphylococcus aureus, Coagulase-negative Staphylococcus, and Escherichia coli are the common pathogens for neonatal sepsis in developing countries. However, the pooled estimate of common pathogens causing neonatal sepsis in developing countries is still unknown. Therefore, this study is aimed at computing the pooled proportion of the leading cause of pathogens for neonatal sepsis in developing countries. Methods We strictly followed the Preferred Reporting Items for Systemic Reviews and Meta-analysis guidelines to report this systematic review and meta-analysis. PubMed, Cochrane Library, Web of Science, CINAHL, Science Direct, and other search engines such as Google Scholar, Africa Journals Online, and Hinari were used to obtain studies related to the leading cause of pathogens for neonatal sepsis in developing countries. The search was done from October 1 to December 30, 2018, by considering both published and gray literature. Studies were evaluated based on the PRISMA guideline checklist by using their titles, abstracts, and full texts. Studies were extracted using Microsoft Excel spreadsheets, and STATA software version 14 was used to analyze data. Heterogeneity between studies was checked based on Cochran's Q-test and the corresponding I2 statistic test. Results The pooled prevalence of the leading cause of pathogens of neonatal sepsis in developing countries were Klebsiella (26.36%), Staphylococcus aureus (23.22%), Coagulase-negative Staphylococcus (23.22%), and Escherichia coli (15.30%). Common pathogens were varied across regions; for instance, pooled isolated Coagulase-negative Staphylococcus was 25.98% in Africa, 16.62% in Asia, and 36.71% in Latin America, and Klebsiella was 29.80% in Africa, 23.21% in Asia, and 22.00% in Latin America. Also, Staphylococcus aureus was 27.87% in Africa and 18.28% in Asia, and Escherichia coli was 22.97% in Asia and 9.43% in Africa. Conclusions This study highlights that the more prevalent common isolated pathogens in developing countries were Klebsiella, Staphylococcus aureus, Coagulase-negative Staphylococcus, and Escherichia coli, Klebsiella, and Staphylococcus aureus pathogens were predominantly high in Africa as compared to other Asian and Latin American countries. At the same time, Coagulase-negative Staphylococcus was more prevalent in Latin America compared to other regions. Escherichia coli is more dominant in Asia as compared to Africa and Latin America.
Development of a novel hybrid cognitive model validation framework for implementation under COVID‐19 restrictions

The purpose of this study was to develop a method for validation of cognitive models consistent with the remote working situation arising from COVID‐19 restrictions in place in Spring 2020. We propose a framework for structuring validation tasks and applying a scoring system to determine initial model validity. We infer an objective validity level for cognitive models requiring no in‐person observations, and minimal reliance on remote usability and observational studies. This approach has been derived from the necessity of the COVID‐19 response, however, we believe this approach can lower costs and reduce timelines to initial validation in post‐Covid‐19 studies, enabling faster progress in the development of cognitive engineering systems. A three‐stage hybrid validation framework was developed based on existing validation methods and was adapted to enable compliance with the specific limitations derived from COVID‐19 response restrictions. This validation method includes elements of argument‐based validation combined with a cognitive walkthrough analysis, and reflexivity assessments. We conducted a case study of the proposed framework on a developmental cognitive model of cardiovascular surgery to demonstrate application of a real‐world validation task. This framework can be easily and quickly implemented by a small research team and provides a structured validation method to increase confidence in assumptions as well as to provide evidence to support validity claims in the early stages of model development.
Increased Global-Brain Functional Connectivity Is Associated with Dyslipidemia and Cognitive Impairment in First-Episode, Drug-Naive Patients with Bipolar Disorder

Objectives Previous researches have demonstrated that abnormal functional connectivity (FC) is associated with the pathophysiology of bipolar disorder (BD). However, inconsistent results were obtained due to different selections of regions of interest in previous researches. This study is aimed at examining voxel-wise brain-wide functional connectivity (FC) alterations in the first-episode, drug-naive patient with BD in an unbiased way. Methods A total of 35 patients with BD and 37 age-, sex-, and education-matched healthy controls underwent resting-state functional magnetic resonance imaging (rs-fMRI). Global-brain FC (GFC) was applied to analyze the image data. Support vector machine (SVM) was adopted to probe whether GFC abnormalities could be used to identify the patients from the controls. Results Patients with BD exhibited increased GFC in the left inferior frontal gyrus (LIFG), pars triangularis and left precuneus (PCu)/superior occipital gyrus (SOG). The left PCu belongs to the default mode network (DMN). Furthermore, increased GFC in the LIFG, pars triangularis was positively correlated with the triglycerides (TG) and low-density lipoprotein cholesterol (LDL-C) and negatively correlated with the scores of the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) coding test and Stroop color. Increased GFC values in the left PCu/SOG can be applied to discriminate patients from controls with preferable sensitivity (80.00%), specificity (75.68%), and accuracy (77.78%). Conclusions This study found increased GFC in the brain regions of DMN; LIFG, pars triangularis; and LSOG, which was associated with dyslipidemia and cognitive impairment in patients with BD. Moreover, increased GFC values in the left PCu/SOG may be utilized as a potential biomarker to differentiate patients with BD from controls.
Predictive Study of the Active Ingredients and Potential Targets of Codonopsis pilosula for the Treatment of Osteosarcoma via Network Pharmacology

Osteosarcoma (OS) is the most common type of primary bone tumor in children and adults. Dangshen (Codonopsis pilosula) is a traditional Chinese medicine commonly used in the treatment of OS worldwide. However, the molecular mechanisms of Dangshen in OS remain unclear. Hence, in this study, we aimed to systematically explore the underlying mechanisms of Dangshen in the treatment of OS. Our study adopted a network pharmacology approach, focusing on the identification of active ingredients, drug target prediction, gene collection, gene ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and other network tools. The network analysis identified 15 active compounds in Dangshen that were linked to 48 possible therapeutic targets related to OS. The results of the gene enrichment analysis show that Dangshen produces a therapeutic effect in OS likely by regulating multiple pathways associated with DNA damage, cell proliferation, apoptosis, invasion, and migration. Based on the network pharmacology approach, we successfully predicted the active compounds and their respective targets. In addition, we illustrated the molecular mechanisms that mediate the therapeutic effect of Dangshen in OS. These findings may aid in the development of novel targeted therapies for OS in the future.
Extending On-Chain Trust to Off-Chain – Trustworthy Blockchain Data Collection Using Trusted Execution Environment (TEE)

Blockchain creates a secure environment on top of strict cryptographic assumptions and rigorous security proofs. It permits on-chain interactions to achieve trustworthy properties such as traceability, transparency, and accountability. However, current blockchain trustworthiness is only confined to on-chain, creating a “trust gap” to the physical, off-chain environment. This is due to the lack of a scheme that can truthfully reflect the physical world in a real-time and consistent manner. Such an absence hinders further blockchain applications in the physical world, especially for the security-sensitive ones. In this paper, we propose a framework to extend blockchain trust from on-chain to off-chain, and take trustworthy vaccine tracing as an example scheme. Our scheme consists of 1) a Trusted Execution Environment (TEE)-enabled trusted environment monitoring system built with the Arm Cortex-M33 microcontroller that continuously senses the inside of a vaccine box through trusted sensors and generates anti-forgery data; and 2) a consistency protocol to upload the environment status data from the TEE system to blockchain in a truthful, real-time consistent, continuous and fault-tolerant fashion. Our security analysis indicates that no adversary can tamper with the vaccine in any way without being captured. We carry out an experiment to record the internal status of a vaccine shipping box during transportation, and the results indicate that the proposed system incurs an average latency of 84 ms in local sensing and processing followed by an average latency of 130 ms to have the sensed data transmitted to and been available in the blockchain.
Association between High-Density Lipoprotein Cholesterol to Apolipoprotein A-I Ratio and Nonalcoholic Fatty Liver Disease: A Cross-Sectional Study

Background This study aimed to explore the association between high-density lipoprotein cholesterol to apolipoprotein A-I ratio (HDL-C/apo A-I) and nonalcoholic fatty liver disease (NAFLD). Methods A total of 9025 Chinese adults were enrolled in this cross-sectional study, who presented their annual health checkups at Zhenhai Lianhua Hospital, Ningbo, during 2017. Results The NAFLD prevalence was 33.7%, and HDL-C/apo A-I was significantly decreased in NAFLD patients, as well as in lean NAFLD and in patients with NAFLD-related advanced fibrosis (all P < 0.001). The prevalence of NAFLD and components of metabolic syndrome are inversely associated with HDL-C/apo A-I (P < 0.001). Multivariate logistic regression analysis show that HDL-C/apo A-I is inversely associated with the risk of NAFLD (odds ratio: 0.353, 95% confidence interval: 0.257–0.486; P < 0.001). Conclusions Our results suggested that increased HDL-C/apo A-I is significantly associated with a decreased risk of NAFLD.