Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward Understanding the Impact of Staleness in Distributed Machine Learning

Many distributed machine learning (ML) systems adopt the non-synchronous execution in order to alleviate the network communication bottleneck, resulting in stale parameters that do not reflect the latest updates. Despite much development in large-scale ML, the effects of staleness on learning are inconclusive as it is challenging to directly monitor or control staleness in complex distributed environments. In this work, we study the convergence behaviors of a wide array of ML models and algorithms under delayed updates. Our extensive experiments reveal the rich diversity of the effects of staleness on the convergence of ML algorithms and offer insights into seemingly contradictory reports in the literature. The empirical findings also inspire a new convergence analysis of stochastic gradient descent in non-convex optimization under staleness, matching the best-known convergence rate of O(1/\sqrt{T}).
Circular RNAs as Therapeutic Agents and Targets

It has recently been reported that thousands of covalently linked circular RNAs (circRNAs) are expressed from human genomes. circRNAs emerge during RNA splicing. circRNAs are circularized in a reaction termed “backsplicing,” whereby the spliceosome fuses a splice donor site in a downstream exon to a splice acceptor site in an upstream exon. Although a young field of research, first studies indicate that backsplicing is not an erroneous reaction of the spliceosome. Instead, circRNAs are produced in cells with high cell-type specificity and can exert biologically meaningful and specific functions. These observations and the finding that circRNAs are stable against exonucleolytic decay are raising the question whether circRNAs may be relevant as therapeutic agents and targets. In this review, we start out with a short introduction into classification, biogenesis and general molecular mechanisms of circRNAs. We then describe reports, where manipulating circRNA abundance has been shown to have therapeutic value in animal disease models in vivo, with a focus on cardiovascular disease (CVD). Starting from existing approaches, we outline particular challenges and opportunities for future circRNA-based therapeutic approaches that exploit stability and molecular effector functions of native circRNAs. We end with considerations which designer functions could be engineered into artificial therapeutic circular RNAs.
Neuromuscular blocking agents and intubation: a reply

We thank Sorbello et al. for their thoughtful comments on our study comparing postoperative laryngeal morbidity and intubating condition using a McGRATH MAC video laryngoscope with or without neuromuscular blocking agents (NMBA) [1]. We did not use a depth of anaesthesia monitor or neuromuscular blockade monitor in this study. Such monitoring methods should be used in future dose– response studies using videolaryngoscopy without NMBAs. The dosage of rocuronium used in our study was not 6– 8 mg.kg 1 but 0.6–0.8 mg.kg ; this misprint was corrected immediately after the first online publication. We used smaller tracheal tubes than previous studies using direct laryngoscopy [2, 3]. We agree with Sorbello et al.’s comment that using a smaller tracheal tube reduces laryngeal complications. We used a stylet to make it easier for the tip of the tracheal tube to reach the glottis without abutting the pharynx or laryngeal tissues. We agree that videolaryngoscopy further reduces laryngeal complications during tracheal intubation compared with direct laryngoscopy. However, because other factors such as intubation time or extubation method can also affect laryngeal complications, we think that it is difficult to reduce the incidence of laryngeal complications beyond those reported in our research and other studies. We maintain that NMBAs are not always necessary for tracheal intubation when combining propofol, remifentanil and videolaryngoscopy, but agree that NMBAs should be used for patients with unstable haemodynamics and for surgery requiringmuscle relaxation.
Quantifying immediate carbon emissions from El Niño-mediated wildfires in humid tropical forests

Wildfires produce substantial CO2 emissions in the humid tropics during El Niño-mediated extreme droughts, and these emissions are expected to increase in coming decades. Immediate carbon emissions from uncontrolled wildfires in human-modified tropical forests can be considerable owing to high necromass fuel loads. Yet, data on necromass combustion during wildfires are severely lacking. Here, we evaluated necromass carbon stocks before and after the 2015–2016 El Niño in Amazonian forests distributed along a gradient of prior human disturbance. We then used Landsat-derived burn scars to extrapolate regional immediate wildfire CO2 emissions during the 2015–2016 El Niño. Before the El Niño, necromass stocks varied significantly with respect to prior disturbance and were largest in undisturbed primary forests (30.2 ± 2.1 Mg ha−1, mean ± s.e.) and smallest in secondary forests (15.6 ± 3.0 Mg ha−1). However, neither prior disturbance nor our proxy of fire intensity (median char height) explained necromass losses due to wildfires. In our 6.5 million hectare (6.5 Mha) study region, almost 1 Mha of primary (disturbed and undisturbed) and 20 000 ha of secondary forest burned during the 2015–2016 El Niño. Covering less than 0.2% of Brazilian Amazonia, these wildfires resulted in expected immediate CO2 emissions of approximately 30 Tg, three to four times greater than comparable estimates from global fire emissions databases. Uncontrolled understorey wildfires in humid tropical forests during extreme droughts are a large and poorly quantified source of CO2 emissions. This article is part of a discussion meeting issue ‘The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications’.
Human Dendritic Cells: Ontogeny and Their Subsets in Health and Disease

Dendritic cells (DCs) are a type of cells derived from bone marrow that represent 1% or less of the total hematopoietic cells of any lymphoid organ or of the total cell count of the blood or epithelia. Dendritic cells comprise a heterogeneous population of cells localized in different tissues where they act as sentinels continuously capturing antigens to present them to T cells. Dendritic cells are uniquely capable of attracting and activating naïve CD4+ and CD8+ T cells to initiate and modulate primary immune responses. They have the ability to coordinate tolerance or immunity depending on their activation status, which is why they are also considered as the orchestrating cells of the immune response. The purpose of this review is to provide a general overview of the current knowledge on ontogeny and subsets of human dendritic cells as well as their function and different biological roles.
Unifying Sum and Weighted Aggregations for Efficient Yet Effective Image Representation Computation

Embedding and aggregating a set of local descriptors (e.g., SIFT) into a single vector is normally used to represent images in image search. Standard aggregation operations include sum and weighted aggregations. While showing high efficiency, sum aggregation lacks discriminative power. In contrast, weighted aggregation shows promising retrieval performance but suffers extremely high time cost. In this paper, we present a general mixed aggregation method that unifies sum and weighted aggregation methods. Owing to its general formulation, our method is able to balance the trade-off between retrieval quality and image representation efficiency. Additionally, to improve query performance, we propose computing multiple weighting coefficients rather than one for each to be aggregated vector by partitioning them into several components with negligible computational cost. Extensive experimental results on standard public image retrieval benchmarks demonstrate that our aggregation method achieves state-of-the-art performance while showing over ten times speedup over baselines.
Long noncoding RNA GM12371 acts as a transcriptional regulator of synapse function

Significance Neuronal functions of long noncoding RNAs (lncRNAs) are poorly understood. Here we describe identification and function of lncRNA GM12371 in regulating synaptic transmission, synapse density, and dendritic arborization in primary hippocampal neurons. GM12371 expression is regulated by cAMP signaling and is critical for the activity regulated synaptic transmission. Importantly, GM12371 is associated with transcriptionally active chromatin and regulates expression of several genes involved in neuronal growth and development. Taken together, these results suggest that GM12371 acts as a transcriptional regulator of synapse function. Despite the growing evidence suggesting that long noncoding RNAs (lncRNAs) are critical regulators of several biological processes, their functions in the nervous system remain elusive. We have identified an lncRNA, GM12371, in hippocampal neurons that is enriched in the nucleus and necessary for synaptic communication, synapse density, synapse morphology, and dendritic tree complexity. Mechanistically, GM12371 regulates the expression of several genes involved in neuronal development and differentiation, as well as expression of specific lncRNAs and their cognate mRNA targets. Furthermore, we find that cAMP-PKA signaling up-regulates the expression of GM12371 and that its expression is essential for the activity-dependent changes in synaptic transmission in hippocampal neurons. Taken together, our data establish a key role for GM12371 in regulating synapse function.
A viral reckoning: viruses emerge as essential manipulators of global ecosystems.

Viruses are integral components and critical regulators of microbial ecosystems. In terms of numbers alone, virus-like particles seemingly outnumber microbial cells in every ecosystem (Weinbauer and Rassoulzadegan, 2004), with a virus-to-microbe ratio typically ranging from 1 to 100 (Wigington et al. , 2016). While challenging to assess, profound ecological and evolutionary impacts of virus-host interactions have nonetheless been uncovered across a broad range of ecosystems, from the bottom of the oceans to bubbling acidic hot springs, coral reefs, and thawing permafrost (Suttle, 2007; Rohwer and Vega Thurber, 2009; Dell’Anno et al. , 2015; Williamson et al. , 2017; Emerson et al. , 2018). Collectively, these studies highlighted multiple mechanisms by which viruses drive ecological and evolutionary processes in microbial ecosystems (Koskella and Brockhurst, 2014; Breitbart et al. , 2018). While the ecological importance of viruses is now undeniable, a thorough assessment of their influence on any microbial system remains elusive. Two of the current challenges are (i) comprehensively exploring and classifying environmental viral diversity, and (ii) establishing host linkages for uncultivated viruses. A number of recent methodological innovations suggest, however, that these hurdles may be overcome sooner rather than later (Mokili et al. , 2012; Dang and Sullivan, 2014; Brum and Sullivan, 2015; Sepulveda et al. , 2016; Sullivan et al. , 2017). Here, based on these latest advances in the field of viral ecology and genomics, we try to imagine how a comprehensive host-resolved mapping of the viral sequence space will enable researchers to address long-standing viral ecology questions in an unprecedented way. We present these as three stories relating how we picture (and/or wish) viral ecology research could be conducted ten years from now.
Comparative Analysis of B-Cell Receptor Repertoires Induced by Live Yellow Fever Vaccine in Young and Middle-Age Donors

Age-related changes can significantly alter the state of adaptive immune system and often lead to attenuated response to novel pathogens and vaccination. In present study we employed 5′RACE UMI-based full length and nearly error-free immunoglobulin profiling to compare plasma cell antibody repertoires in young (19–26 years) and middle-age (45–58 years) individuals vaccinated with a live yellow fever vaccine, modeling a newly encountered pathogen. Our analysis has revealed age-related differences in the responding antibody repertoire ranging from distinct IGH CDR3 repertoire properties to differences in somatic hypermutation intensity and efficiency and antibody lineage tree structure. Overall, our findings suggest that younger individuals respond with a more diverse antibody repertoire and employ a more efficient somatic hypermutation process than elder individuals in response to a newly encountered pathogen.
IMRCE: A Unity Toolkit for Virtual Co-Presence

In this paper we present the design, implementation, and evaluation of IMRCE, our immersive mixed reality collaborative environment toolkit. IMRCE is a lightweight, flexible, and robust Unity toolkit that allows designers and researchers to rapidly prototype mixed reality-mixed presence (MR-MP) environments that connect physical spaces, virtual spaces, and devices. IMRCE helps collaborators maintain group awareness of the shared collaborative environment by providing visual cues such as position indicators and virtual hands. At the same time IMRCE provides flexibility in how physical and virtual spaces are mapped, allowing work environments to be optimised for each collaborator while maintaining a sense of integration. The main contribution of the toolkit is its encapsulation of these features, allowing rapid development of MR-MP systems. We demonstrate IMRCE's features by linking a physical environment with tabletop and wall displays to a virtual replica augmented with support for direct 3D manipulation of shared work objects. We also conducted a comparative evaluation of IMRCE against a standard set of Unity libraries with complementary features with 10 developers, and found significant reductions in time taken, total LOC, errors, and requests for assistance.
Epidemiology of Carbapenem-Resistant Enterobacteriaceae at a Long-term Acute Care Hospital

Abstract Background Residents of long-term acute care hospitals (LTACHs) are considered important reservoirs of multidrug-resistant organisms, including Carbapenem-resistant Enterobacteriaceae (CRE). We conducted this study to define the characteristics of CRE-infected/colonized patients admitted to an LTACH and the molecular characteristics of the CRE isolates. Methods This retrospective study was conducted to collect information on demographic and comorbid conditions in CRE-colonized/infected patients admitted to a 77-bed LTACH in Detroit between January 2011 and July 2012. Data pertaining to hospital-related exposures were collected for 30 days before positive CRE culture. Polymerase chain reaction (PCR) gene amplification, repetitive sequence–based PCR, and multilocus sequence typing (MLST) were performed on 8 of the CRE isolates. Results The study cohort included 30 patients with CRE-positive cultures, 24 (80%) with infections, and 6 (20%) with colonization. The mean age of cohort was 69 ±12.41 years; 19 (63%) patients were ventilator-dependent, and 20 (67%) were treated with at least 1 antibiotic. Twenty-three (77%) patients had CRE detected following LTACH admission, and the median days from admission to CRE detection in these patients (interquartile range) was 25 (11–43). Seven more patients were already positive for CRE at the time of LTACH admission. Molecular genotyping and MLST of 8 CRE isolates demonstrated that all isolates belonged to the same strain type (ST258) and contained the blaKPC-3 sequence. Conclusions The majority of patients with CRE presented several days to weeks after LTACH admission, indicating possible organism acquisition in the LTACH itself. The genetic similarity of the CRE isolates tested could further indicate the occurrence of horizontal transmission in the LTACH or simply be representative of the regionally dominant strain.
Effect of Low Dose Docosahexaenoic Acid-Rich Fish Oil on Plasma Lipids and Lipoproteins in Pre-Menopausal Women: A Dose–Response Randomized Placebo-Controlled Trial

Omega-3 long chain polyunsaturated fatty acid (n-3 LCPUFA) supplementation has been shown to improve plasma lipid profiles in men and post-menopausal women, however, data for pre-menopausal women are lacking. The benefits of intakes less than 1 g/day have not been well studied, and dose–response data is limited. The aim of this study was to determine the effect of low doses of docosahexaenoic acid (DHA)-rich tuna oil on plasma triglyceride (TG) lowering in pre-menopausal women, and investigate if low dose DHA-rich tuna oil supplementation would increase the low-density lipoprotein (LDL) and high-density lipoprotein (HDL) particle sizes. A randomized, double-blind, placebo-controlled trial was conducted, in which 53 healthy pre-menopausal women with mildly elevated plasma TG levels consumed 0, 0.35, 0.7, or 1 g/day n-3 LCPUFA as HiDHA™ tuna oil or placebo (Sunola oil) capsules for 8 weeks. Supplementation with 1 g/day n-3 LCPUFA, but not lower doses, reduced plasma TG by 23% in pre-menopausal women. This was reflected in a dose-dependent reduction in very-low-density lipoprotein (VLDL)-TG (R2 = 0.20, p = 0.003). A weak dose-dependent shift in HDL (but not LDL) particle size was identified (R2 = 0.05, p = 0.04). The results of this study indicate that DHA-rich n-3 LCPUFA supplementation at a dose of 1 g/day is an effective TG-lowering agent and increases HDL particle size in pre-menopausal women.
Summary of a Literature Review in Scalability of QoS-aware Service Composition

This paper shows that authors have no consistent way to characterize the scalability of their solutions, and so consider only a limited number of scaling characteristics. This review aimed at establishing the evidence that the route for designing and evaluating the scalability of dynamic QoS-aware service composition mechanisms has been lacking systematic guidance, and has been informed by a very limited set of criteria. For such, we analyzed 47 papers, from 2004 to 2018.
Graph Classification with Geometric Scattering

One of the most notable contributions of deep learning is the application of convolutional neural networks (ConvNets) to structured signal classification, and in particular image classification. Beyond their impressive performances in supervised learning, the structure of such networks inspired the development of deep filter banks referred to as scattering transforms. These transforms apply a cascade of wavelet transforms and complex modulus operators to extract features that are invariant to group operations and stable to deformations. Furthermore, ConvNets inspired recent advances in geometric deep learning, which aim to generalize these networks to graph data by applying notions from graph signal processing to learn deep graph filter cascades. We further advance these lines of research by proposing a geometric scattering transform using graph wavelets defined in terms of random walks on the graph. We demonstrate the utility of features extracted with this designed deep filter bank in graph classification, and show its competitive performance relative to other methods, including graph kernel methods and geometric deep learning ones, on both social and biochemistry data.
Exploiting Contextual Information via Dynamic Memory Network for Event Detection

The task of event detection involves identifying and categorizing event triggers. Contextual information has been shown effective on the task. However, existing methods which utilize contextual information only process the context once. We argue that the context can be better exploited by processing the context multiple times, allowing the model to perform complex reasoning and to generate better context representation, thus improving the overall performance. Meanwhile, dynamic memory network (DMN) has demonstrated promising capability in capturing contextual information and has been applied successfully to various tasks. In light of the multi-hop mechanism of the DMN to model the context, we propose the trigger detection dynamic memory network (TD-DMN) to tackle the event detection problem. We performed a five-fold cross-validation on the ACE-2005 dataset and experimental results show that the multi-hop mechanism does improve the performance and the proposed model achieves best F1 score compared to the state-of-the-art methods.
Fully Homomorphic Image Processing

Fully homomorphic encryption has allowed devices to outsource computation to third parties while preserving the secrecy of the data being computed on. Many images contain sensitive information and are commonly sent to cloud services to encode images for different devices. We implement image processing homomorphically that ensures secrecy of the image while also providing reasonable overhead. We first present some previous related work, as well as the fully homomorphic encryption scheme we use. Then, we introduce our schemes for JPEG encoding and decoding, as well as schemes for bilinear and bicubic image resizing, as well as some data and analysis of our homomorphic schemes. Finally, we outline several issues with the homomorphic evaluation of proprietary algorithms, and how a third party can gain information on the algorithm through noise.
Identification of promoted eclipse unstable interfaces using clone detection technique

The Eclipse framework is a popular and widely used framework that has been evolving for over a decade. The framework provides both stable interfaces (APIs) and unstable interfaces (non-APIs). Despite being discouraged by Eclipse, client developers often use non-APIs which may cause their systems to fail when ported to new framework releases. To overcome this problem, Eclipse interface producers may promote unstable interfaces to APIs. However, client developers have no assistance to aid them to identify the promoted unstable interfaces in the Eclipse framework. We aim to help API users identify promoted unstable interfaces. We used the clone detection technique to identify promoted unstable interfaces as the framework evolves. Our empirical investigation on 16 Eclipse major releases presents the following observations. First, we have discovered that there exists over 60% non-API methods of the total interfaces in each of the analyzed 16 Eclipse releases. Second, we have discovered that the percentage of promoted non-APIs identified through clone detection ranges from 0.20% to 10.38%.
Evaluating the Effects of Feedback Type on Older Adults' Performance in Mid-Air Pointing and Target Selection

"Hands-free" pointing techniques used in mid-air gesture interaction require precise motor control and dexterity. Although being applied in a growing number of interaction contexts over the past few years, this input method can be challenging for older users (60+ years old) who experience natural decline in pointing abilities due to natural ageing process. We report the findings of a target acquisition experiment in which older adults had to perform "point-and-select" gestures in mid-air. The experiment investigated the effect of 6 feedback conditions on pointing and selection performance of older users. Our findings suggest that the bimodal combination of Visual and Audio feedback lead to faster target selection times for older adults, but did not lead to making less errors. Furthermore, target location on screen was found to play a more important role in both selection time and accuracy of point-and-select tasks than feedback type.