Miracle-dz/UniFAR-FTUs huggingface dataset:
A Memory-Sensitive Classification Model of Errors in Early Second Language Learning

In this paper, we explore a variety of linguistic and cognitive features to better understand second language acquisition in early users of the language learning app Duolingo. With these features, we trained a random forest classifier to predict errors in early learners of French, Spanish, and English. Of particular note was our finding that mean and variance in error for each user and token can be a memory efficient replacement for their respective dummy-encoded categorical variables. At test, these models improved over the baseline model with AUROC values of 0.803 for English, 0.823 for French, and 0.829 for Spanish.
Gesture-Visualise-Simulate: a framework to support the teaching of computational thinking in primary school classrooms

The Gesture-Visualise-Simulate framework describes an ordered progression over three distinct conceptual representations and methods of interaction. The sequence decreases in perceptual detail as recommended by Bruner’s modes of representation, and is influenced by recent attempts to apply theories of grounded cognition in an educational setting. The framework will be used to guide the development of an interactive learning environment for use within primary school classrooms. In a computational thinking context, the framework may increase the learner’s ability to interpret written code – a crucial component of debugging, refactoring, and extending algorithms – by promoting engagement with perceptual symbols that ground abstract concepts in sensorimotor information.
Fully Abstract Models of the Probabilistic lambda-calculus

We compare three models of the probabilistic λ-calculus: the probabilistic Bohm trees of Leventis, the probabilistic concurrent games of Winskel et al., and the weighted relational model of Ehrhard et al. Probabilistic Bohm trees and probabilistic strategies are shown to be related by a precise correspondence theorem, in the spirit of existing work for the pure λ-calculus. Using Leventis' theorem (probabilistic Bohm trees characterise observational equivalence), we derive a full abstraction result for the games model. Then, we relate probabilistic strategies to the weighted relational model, using an interpretation-preserving functor from the former to the latter. We obtain that the relational model is also fully abstract.
Aggregating Incomplete Judgments : Axiomatisations for Scoring Rules

We propose a model of judgment aggregation in which agents can choose to provide a judgment for only some of the issues at stake. A natural class of aggregation rules in this context are scoring rules for which an agent’s weight in the decision making process is determined by the number of issues she provides a judgment for. We formulate several appealing axioms for aggregating incomplete judgments and show how they characterise specific rules within this class of scoring rules.
Year round measurements of O3 and CO at a rural site near Beijing: variations in their correlations

We examine seasonal variations of carbon monoxide (CO), ozone (O3), and their relationships observed over the course of 3 yr (2005–2007) at Miyun, a rural site 100 km north of Beijing. Monthly mean afternoon mixing ratios of CO have broad maxima in winter and a secondary peak in June. Monthly mean afternoon O3 shows a clear seasonal pattern with a major peak in June (85 ppb), a secondary peak in September (65 ppb) and minimum in winter (50–55 ppb). The seasonal cycles of O3 and CO are associated with seasonal changes in dominant synoptic pattern. Substantial interannual variability is found for CO which is attributed to the interannual variability of meteorology and emissions from biomass burning. The seasonality and magnitude of background CO and O3 derived at Miyun are consistent with observations at upwind remote continental sites. The O3–CO correlation slope is about 0.07 ppb ppb−1 on average in summer, significantly lower than the typical slope of 0.3 ppb ppb−1 reported for developed countries. The O3–CO correlation slope shows large gradients for different types of air masses (0.133 ± 0.017 ppb ppb−1 in aged urban pollution plumes and 0.047 ± 0.008 ppb ppb−1 in biomass burning plumes), suggesting that the conventional method of direct scaling the mean O3–CO slope by CO emissions to deduce O3 production rate is subject to large uncertainties if applied for China.
An Explainable Adversarial Robustness Metric for Deep Learning Neural Networks

Deep Neural Networks(DNN) have excessively advanced the field of computer vision by achieving state of the art performance in various vision tasks. These results are not limited to the field of vision but can also be seen in speech recognition and machine translation tasks. Recently, DNNs are found to poorly fail when tested with samples that are crafted by making imperceptible changes to the original input images. This causes a gap between the validation and adversarial performance of a DNN. An effective and generalizable robustness metric for evaluating the performance of DNN on these adversarial inputs is still missing from the literature. In this paper, we propose Noise Sensitivity Score (NSS), a metric that quantifies the performance of a DNN on a specific input under different forms of fix-directional attacks. An insightful mathematical explanation is provided for deeply understanding the proposed metric. By leveraging the NSS, we also proposed a skewness based dataset robustness metric for evaluating a DNN's adversarial performance on a given dataset. Extensive experiments using widely used state of the art architectures along with popular classification datasets, such as MNIST, CIFAR-10, CIFAR-100, and ImageNet, are used to validate the effectiveness and generalization of our proposed metrics. Instead of simply measuring a DNN's adversarial robustness in the input domain, as previous works, the proposed NSS is built on top of insightful mathematical understanding of the adversarial attack and gives a more explicit explanation of the robustness.
Improving rewards in overloaded real-time systems

Competitive analysis of online algorithms has commonly been applied to understand the behaviour of real-time systems during overload conditions. While competitive analysis provides insight into the behaviour of certain algorithms, it is hard to make inferences about the performance of those algorithms in practice. Other approaches to dealing with overload resort to heuristics that seem to perform well but are hard to prove as being good. Further, most work on handling overload in real-time systems does not consider using information regarding the distribution of arrival rates of jobs and execution times to make scheduling decisions. We present an scheduling policy (obtained through stochastic approximation, and using information about the workload) to handle overload in real-time systems and improve the revenue earned when each successful job completion results in revenue accrual. We prove that the policy we outline does lead to increased revenue when compared to a class of scheduling policies that make static resource allocations to different service classes. We also use empirical evidence to underscore the fact that this policy performs better than a variety of other scheduling policies. The ideas presented can be applied to several soft real-time systems, specifically systems with multiple service classes.
Energy-efficient localised rollback after failures via data flow analysis

Exascale systems will suffer failures hourly. HPC programmers rely mostly on application-level checkpoint and a global rollback to recover. In recent years, techniques reducing the number of rolling back processes have been implemented via message logging. However, the log-based approaches have weaknesses, such as being dependent on complex modifications within an MPI implementation, and the fact that a full restart may be required in the general case. To address the limitations of all log-based mechanisms, we return to checkpoint-only mechanisms, but advocate data-flow-driven recovery (DFR), a fundamentally different approach relying on analysis of the data flow of iterative codes, and the well-known concept of data-flow graphs. We demonstrate the effectiveness of DFR for an MPI stencil code to optimise rollback and reduce the overall energy consumption by 10-12 % on idling nodes during localised rollback. We also provide large-scale estimates for the energy savings of DFR compared to global rollback, which for stencil codes increase as n square for a process count n.
Prognostic value of Midkine expression in patients with solid tumors: a systematic review and meta-analysis

Background Accumulated studies have shown the important role of Midkine (MDK) protein in various solid tumors and indicated its correlation with patients’ survival. This meta-analysis was performed to further explore the prognostic value of MDK expression in solid tumors. Materials and Methods We collected the literatures through searching PubMed, Embase and the Cochrane Library (last up to April 10, 2017) to assess the effect of MDK on survival in solid tumor patients. The STATA 12.0 software was used for the meta-analysis. Fixed-effects models or random-effects models were used to estimate the pooled hazard ratios (HRs) for overall survival (OS). Results A total of 2097 patients from 17 observational studies were summarized. High expression of MDK was notably associated with worse OS in solid tumor patients. (pooled HR = 1.96; 95% CI = 1.67–2.31). The subgroup analysis of tumor type demonstrated negative impact of elevated MDK on OS in most solid tumor patients (P < 0.05), while MDK had no relevance with OS in the patients with OSCC (pooled HR = 1.68; 95% CI = 0.84–3.36; P = 0.145) or HNSCC (pooled HR = 1.56; 95% CI = 0.96–2.51; P = 0.075). Conclusions The present meta-analysis clarifies that MDK is a potential prognostic biomarker in solid tumor patients. Future large-scale prospective clinical trials are needed to determine the prognostic value of MDK in solid tumor patients.
Automatic Vector-based Road Structure Mapping Using Multi-beam LiDAR

In this paper, we studied a SLAM method for vector-based road structure mapping using multi-beam LiDAR. We proposed to use the polyline as the primary mapping element instead of grid cell or point cloud, because the vector-based representation is precise and lightweight, and it can directly generate vector-based High-Definition (HD) driving map as demanded by autonomous driving systems. We explored: 1) the extraction and vectorization of road structures based on local probabilistic fusion. 2) the efficient vector-based matching between frames of road structures. 3) the loop closure and optimization based on the pose-graph. In this study, we took a specific road structure, the road boundary, as an example. We applied the proposed matching method in three different scenes and achieved the average absolute matching error of 0.07 m. We further applied the vector-based mapping system to a road with the length of 860 meters and achieved an average global accuracy of 0.466 m without the aid of GPS.
Comparative Genomics Reveals Evidence of Genome Reduction and High Extracellular Protein Degradation Potential in Kangiella

The genus Kangiella has recently been proposed within the family Kangiellaceae, belonging to order Oceanospirillales. Here, we report the complete genome sequence of a novel strain, Kangiella profundi FT102, which is the only Kangiella species isolated from a deep sea sediment sample. Furthermore, gaps in the publicly available genome scaffold of K. aquimarina DSM 16071 (NCBI Reference Sequence: NZ_ARFE00000000.1) were also filled using polymerase chain reaction (PCR) and Sanger sequencing. A comparative genomic analysis of five Kangiella and 18 non-Kangiella strains revealed insights into their metabolic potential. It was shown that low genomic redundancy and Kangiella-lineage-specific gene loss are the key reasons behind the genome reduction in Kangiella compared to that in any other free-living Oceanospirillales strain. The occurrence of relatively diverse and more frequent extracellular protease-coding genes along with the incomplete carbohydrate metabolic pathways in the genome suggests that Kangiella has high extracellular protein degradation potential. Growth of Kangiella strains has been observed using amino acids as the only carbon and nitrogen source and tends to increase with additional tryptone. Here, we propose that extracellular protein degradation and amino acid utilization are significant and prominent features of Kangiella. Our study provides more insight into the genomic traits and proteolytic metabolic capabilities of Kangiella.
Exploring Feature Reuse in DenseNet Architectures

Densely Connected Convolutional Networks (DenseNets) have been shown to achieve state-of-the-art results on image classification tasks while using fewer parameters and computation than competing methods. Since each layer in this architecture has full access to the feature maps of all previous layers, the network is freed from the burden of having to relearn previously useful features, thus alleviating issues with vanishing gradients. In this work we explore the question: To what extent is it necessary to connect to all previous layers in order to reap the benefits of feature reuse? To this end, we introduce the notion of local dense connectivity and present evidence that less connectivity, allowing for increased growth rate at a fixed network capacity, can achieve a more efficient reuse of features and lead to higher accuracy in dense architectures.
Clinical review of user engagement with mental health smartphone apps: evidence, theory and improvements

The potential of smartphone apps to improve quality and increase access to mental health care is increasingly clear. Yet even in the current global mental health crisis, real-world uptake of smartphone apps by clinics or consumers remains low. To understand this dichotomy, this paper reviews current challenges surrounding user engagement with mental health smartphone apps. While smartphone engagement metrics and reporting remains heterogeneous in the literature, focusing on themes offers a framework to identify underlying trends. These themes suggest that apps are not designed with service users in mind, do not solve problems users care most about, do not respect privacy, are not seen as trustworthy and are unhelpful in emergencies. Respecting these current issues surrounding mental health app engagement, we propose several solutions and highlight successful examples of mental health apps with high engagement. Further research is necessary to better characterise engagement with mental health apps and identify best practices for design, testing and implementation.
Dynamic capabilities related implementation skills for Internet of Things solutions in the digital economy

The digital economy is shaped by the increasing implementation of Internet of Things (IoT) solutions. These solutions enable the vertical integration of smart objects into existing information systems, thereby realizing the vision that every physical object obtains a digital identity. However, dynamic characterizes the technologically driven IoT market and requires related capabilities from enterprises, aiming to provide IoT solutions. Therefore, the purpose of this study is to explore which DC are sufficient for the implementation of effective IoT solutions by taking a DC perspective. Based upon an empirical survey of IoT solution integrators and an exploratory fuzzy-set qualitative comparative analysis (fsQCA), our results show that the combination of differentiation strategy, technological and entrepreneurial orientation enables the implementation of effective IoT solutions. The results further provide a theoretical contribution for a DC discussion in the IoT research area and, offer implementation recommendations for enterprises about how to manage IoT solution implementation.
Cell-intrinsic regulation of murine epidermal Langerhans cells by protein S

Significance Langerhans cells (LCs) are the exclusive antigen-presenting cells of the epidermis, capable of mounting immunity and tolerance. LCs maintain themselves locally by self-renewing throughout life, a process that is regulated by both LCs and keratinocytes. Nevertheless, the mechanisms underlying this process are not clearly understood. Using targeted genetic ablation, we demonstrate that lack of protein S (PROS1) in keratinocytes, but not in LCs, results in reduced numbers of terminally developed LCs. This is due to increased apoptosis of LCs and associated with altered expression of cytokines involved in tissue homeostasis. Furthermore, PROS1 also down-regulates LC differentiation from bone marrow precursors. Together, these identify PROS1 as a regulator of LC development and homeostasis. AXL, a member of the TYRO3, AXL, and MERTK (TAM) receptor tyrosine kinase family, has been shown to play a role in the differentiation and activation of epidermal Langerhans cells (LCs). Here, we demonstrate that growth arrest-specific 6 (GAS6) protein, the predominant ligand of AXL, has no impact on LC differentiation and homeostasis. We thus examined the role of protein S (PROS1), the other TAM ligand acting primarily via TYRO3 and MERTK, in LC function. Genetic ablation of PROS1 in keratinocytes resulted in a typical postnatal differentiation of LCs; however, a significant reduction in LC frequencies was observed in adult mice due to increased apoptosis. This was attributed to altered expression of cytokines involved in LC development and tissue homeostasis within keratinocytes. PROS1 was then excised in LysM+ cells to target LCs at early embryonic developmental stages, as well as in adult monocytes that also give rise to LCs. Differentiation and homeostasis of LCs derived from embryonic precursors was not affected following Pros1 ablation. However, differentiation of LCs from bone marrow (BM) precursors in vitro was accelerated, as was their capability to reconstitute epidermal LCs in vivo. These reveal an inhibitory role for PROS1 on BM-derived LCs. Collectively, this study highlights a cell-specific regulation of LC differentiation and homeostasis by TAM signaling.
Hepatocyte-Specific Deletion of TIPARP, a Negative Regulator of the Aryl Hydrocarbon Receptor, Is Sufficient to Increase Sensitivity to Dioxin-Induced Wasting Syndrome

The aryl hydrocarbon receptor (AHR) mediates the toxic effects of dioxin (2, 3, 7, 8-tetrachlorodibenzo-p-dioxin; TCDD), which includes thymic atrophy, steatohepatitis, and a lethal wasting syndrome in laboratory rodents. Although the mechanisms of dioxin toxicity remain unknown, AHR signaling in hepatocytes is necessary for dioxin-induced liver toxicity. We previously reported that loss of TCDD-inducible poly(adenosine diphosphate [ADP]-ribose) polymerase (TIPARP/PARP7/ARTD14), an AHR target gene and mono-ADP-ribosyltransferase, increases the sensitivity of mice to dioxin-induced toxicities. To test the hypothesis that TIPARP is a negative regulator of AHR signaling in hepatocytes, we generated Tiparpfl/fl mice in which exon 3 of Tiparp is flanked by loxP sites, followed by Cre-lox technology to create hepatocyte-specific (Tiparpfl/flCreAlb) and whole-body (Tiparpfl/flCreCMV; TiparpEx3-/-) Tiparp null mice. Tiparpfl/flCreAlb and TiparpEx3-/- mice given a single injection of 10 μg/kg dioxin did not survive beyond days 7 and 9, respectively, while all Tiparp+/+ mice survived the 30-day treatment. Dioxin-exposed Tiparpfl/flCreAlb and TiparpEx3-/- mice had increased steatohepatitis and hepatotoxicity as indicated by greater staining of neutral lipids and serum alanine aminotransferase activity than similarly treated wild-type mice. Tiparpfl/flCreAlb and TiparpEx3-/- mice exhibited augmented AHR signaling, denoted by increased dioxin-induced gene expression. Metabolomic studies revealed alterations in lipid and amino acid metabolism in liver extracts from Tiparpfl/flCreAlb mice compared with wild-type mice. Taken together, these data illustrate that TIPARP is an important negative regulator of AHR activity, and that its specific loss in hepatocytes is sufficient to increase sensitivity to dioxin-induced steatohepatitis and lethality.
Threshold regulation and stochasticity from the MecA/ClpCP proteolytic system in Streptococcus mutans competence

Many bacterial species use the MecA/ClpCP proteolytic system to block entry into genetic competence. In Streptococcus mutans, MecA/ClpCP degrades ComX (also called SigX), an alternative sigma factor for the comY operon and other late competence genes. Although the mechanism of MecA/ClpCP has been studied in multiple Streptococcus species, its role within noisy competence pathways is poorly understood. S. mutans competence can be triggered by two different peptides, CSP and XIP, but it is not known whether MecA/ClpCP acts similarly for both stimuli, how it affects competence heterogeneity, and how its regulation is overcome. We have studied the effect of MecA/ClpCP on the activation of comY in individual S. mutans cells. Our data show that MecA/ClpCP is active under both XIP and CSP stimulation, that it provides threshold control of comY, and that it adds noise in comY expression. Our data agree quantitatively with a model in which MecA/ClpCP prevents adventitious entry into competence by sequestering or intercepting low levels of ComX. Competence is permitted when ComX levels exceed a threshold, but cell-to-cell heterogeneity in MecA levels creates variability in that threshold. Therefore MecA/ClpCP provides a stochastic switch, located downstream of the already noisy comX, that enhances phenotypic diversity.
Segmentation of visual motion by minimizing convex non-quadratic functionals

A minimization problem is proposed to combine smoothing of locally computed motion data (e.g. normal flow) with the detection of motion boundaries. The continuous formulation of the cost functional allows one to incorporate arbitrary continuity-equations which locally determine apparent motion, and a nonlinear smoothing term adapts to the magnitude of the flow-gradient or to its components divergence, rotation, and shear. The approach has been designed such that gradient descent converges to a unique solution.
Development of a Highly Sensitive FcMito qPCR Assay for the Quantification of the Toxigenic Fungal Plant Pathogen Fusarium culmorum

Fusarium culmorum is a ubiquitous, soil-borne fungus (ascomycete) causing foot and root rot and Fusarium head blight on cereals. It is responsible for yield and quality losses as well as grain contamination with mycotoxins, which are a potential health hazard. An extremely sensitive mitochondrial-based qPCR assay (FcMito qPCR) for quantification of F. culmorum was developed in this study. To provide specificity, the FcMito assay was successfully validated against 85 F. culmorum strains and 53 isolates of 30 other fungal species. The assay efficiency and sensitivity were evaluated against different F. culmorum strains with various amounts of pure fungal DNA and in the presence of background wheat DNA. The results demonstrated the high efficiency of the assay (97.2–106.0%, R2-values > 0.99). It was also shown that, in the presence of background DNA, 0.01 pg of fungal template could be reliably quantified. The FcMito assay was used to quantify F. culmorum DNA using 108 grain samples with different trichothecene levels. A significant positive correlation was found between fungal DNA quantity and the total trichothecene content. The obtained results showed that the sensitivity of the FcMito assay was much higher than the nuclear-based qPCR assay for F. culmorum.