Miracle-dz/UniFAR-FTUs huggingface dataset:
Decision Tree-Based Adaptive Reconfigurable Cache Scheme

Applications have different preferences for caches, sometimes even within the different running phases. Caches with fixed parameters may compromise the performance of a system. To solve this problem, we propose a real-time adaptive reconfigurable cache based on the decision tree algorithm, which can optimize the average memory access time of cache without modifying the cache coherent protocol. By monitoring the application running state, the cache associativity is periodically tuned to the optimal cache associativity, which is determined by the decision tree model. This paper implements the proposed decision tree-based adaptive reconfigurable cache in the GEM5 simulator and designs the key modules using Verilog HDL. The simulation results show that the proposed decision tree-based adaptive reconfigurable cache reduces the average memory access time compared with other adaptive algorithms.
PODS: Policy Optimization via Differentiable Simulation

Current reinforcement learning (RL) methods use simulation models as simple black-box oracles. In this paper, with the goal of improving the performance exhibited by RL algorithms, we explore a systematic way of leveraging the additional information provided by an emerging class of differentiable simulators. Building on concepts established by Deterministic Policy Gradients (DPG) methods, the neural network policies learned with our approach represent deterministic actions. In a departure from standard methodologies, however, learning these policies does not hinge on approximations of the value function that must be learned concurrently in an actor-critic fashion. Instead, we exploit differentiable simulators to directly compute the analytic gradient of a pol-icy’s value function with respect to the actions it outputs. This, in turn, allows us to effciently perform locally optimal policy improvement iterations. Compared against other state-of-the-art RL methods, we show that with minimal hyper-parameter tuning our approach consistently leads to better asymptotic behavior across a set of pay-load manipulation tasks that demand a high degree of accuracy and precision.
Identification and characterization of nanosized tripuhyite in soil near Sb mine tailings

Abstract In soil near tailings from an antimony (Sb) mine, we found micro-grains coated with an antimonyrich layer. These grains were characterized in detail using multiple advanced analytical techniques such as micro-X‑ray absorption near edge structure (μ-XANES), micro-extended X‑ray absorption fine structure (μ-EXAFS), micro-X‑ray diffraction (μ-XRD), transmission electron microscope (TEM), and electron probe microanalysis (EPMA). The EPMA showed that one soil grain (grain A) locally accumulated a large amount of Sb in the secondary phases (40-61 wt% Sb2O5) with significant Fe (20-28 wt% Fe2O3). The spatial distribution of Sb in the grain was similar to that of iron. Both Fe μ-XANES and μ-XRD of the Sb hot spots in grain A consistently showed that the secondary products were dominantly composed of ferric antimonate, tripuhyite (FeSbO4). Fits to the Sb K-edge μ-EXAFS of this phase showed second-neighbor coordination numbers -30% smaller than in bulk tripuhyite, indicating that the tripuhyite included in grain A is nanoparticulate and/or has a high structural disorder. The TEM analysis suggests that the particle size of tripuhyite in grain A was around 10 nm, which is consistent with the size range indicated by μ-XRD and μ-EXAFS. This is the first report showing tripuhyite with nanocrystallinity in natural soil to date.
Anisotropy of Water Self-Diffusion in a Nafion Membrane under Traction

Measuring water self-diffusion in the oriented structure of stretched Nafion represents an effective way of studying the correlations between the stress-induced order and the transport properties of the membrane. In this work, we performed pulsed-field gradient NMR experiments to investigate water self-diffusion in a single membrane under traction. The data were collected with a high signal-to-noise ratio and small draw ratio steps using a specially design NMR probe together with a mini traction apparatus. The results demonstrate that the anisotropy of diffusion is much larger in a membrane under traction than in a stack of membranes that were uniaxially stretched at high temperature before the measurements. Arguments based on the mechanical properties of Nafion can explain qualitatively the observed differences by highlighting the important role of both applied stress and temperature in the stress-induced order. We used a simple, but realistic, deformation model to analyze the experimental data by extrac.
S2045796021000226jra 1..6

Aims. In March 2020, the UK government ordered mental health services to free up bed space to help manage the COVID-19 pandemic. This meant service users detained under the Mental Health Act were discharged at a higher rate than normal. We analysed whether this decision compromised the safety of this vulnerable group of service users. Methods. We utilised a cohort study design and allocated service users to either the pre-rapid discharge, rapid discharge or post-rapid discharge group. We conducted a recurrent event analysis to assess group differences in the risk of experiencing negative outcomes during the 61 days post-discharge. We defined negative outcomes as crisis service use, re-admission to a psychiatric ward, community incidents of violence or self-harm and death by suicide. Results. The pre-rapid discharge cohort included 258 service users, the rapid discharge cohort 127 and the post-rapid discharge cohort 76. We found no statistical association between being in the rapid discharge cohort and the risk of experiencing negative outcomes (HR: 1.14, 95% CI: 0.72–1.8, p = 0.58) but a trend towards statistical significance for service users in the postrapid discharge cohort (HR: 1.61, 95% CI: 0.91–2.83, p = 0.1). Conclusions. We did not find evidence that service users rapidly discharged from section experienced poorer outcomes. This raises the possibility that the Mental Health Act is applied in an overly restrictive manner, meaning that sections for some formally detained service users could be ended earlier without compromising safety.
Trefoil factor-2, an early predictor for acute gastrointestinal injury in patients with acute pancreatitis

Abstract Acute gastrointestinal injury (AGI) is commonly present in patients with acute pancreatitis (AP). It is often difficult to predict gastrointestinal function in the early stage due to lack of reliable markers. We aimed to assess whether early plasma trefoil factor 2 (TFF-2) is a potential predictor for AGI. Fifty one patients were included for the onset of AP (from developing abdominal pain) within 72 hours in this prospective observational single-center study from January 2013 to July 2015. Among them 23 patients were classified as mild, 17 as moderately severe, and 11 as severe according to 2012 Atlanta classification. Plasma samples were collected only once at admission to the ICU. Twenty samples of healthy adults were also collected as control. The TFF-2 levels were determined by using a human TFF-2 enzyme-linked immunoassay. AGI grades from 1st to 7th day after admission were observed. The plasma TFF-2 levels among AP patients in early stage were significantly higher than healthy controls (766.41 ng/mL vs 94.37 ng/mL, P < .0001). The correlations between TFF-2 levels and AGI grades from 1st to 4th day after admission were positive (r = 0.47, 0.43, 0.42, 0.40 respectively, P < .05). As a predictor of acute gastrointestinal failure, plasma TFF-2 was superior to others: Acute Physiology and Chronic Health Evaluation II, sequential organ failure assessment, procalcitonin, C-reactive protein, serum calcium. In addition, TFF-2 increased along with the severity of AP (r = 0.554, P < .0001) and associated with Acute Physiology and Chronic Health Evaluation II, sequential organ failure assessment, C-reactive protein, serum calcium. The plasma TFF-2 levels were increased in patients in early stage of AP and correlated with AGI grades and disease severity in our study. TFF-2 might be a potential predictor for acute gastrointestinal failure in patients with AP.
BCL7B is a potential novel diagnosis and prognosis biomarker for sarcomas using bioinformatics analysis

Abstract BCL7B plays a potential role in the progression of various cancers, while its role in sarcomas is unknown. We aimed to evaluate BCL7B's diagnostic and prognostic value, and potential BCL7B-related mechanisms in sarcomas based on The Cancer Genome Atlas (TCGA) database. We collected patients with sarcoma from TCGA. Wilcoxon rank sum test was used to compare the expression of BCL7B in sarcoma samples with different clinical-pathologic features. Univariate Cox regression analysis and multivariate Cox regression analysis were used to evaluate prognosis factors for sarcoma. Gene set enrichment analysis (GSEA) was conducted to elucidate the significant functions and pathways associated with BCL7B. BCL7B was a potential biomarker for distinguishing normal and tumor tissues with the analysis of ROC curve (AUC = 0.588). Low BCL7B expression was significantly correlated with tumor multifocal (OR = 0.39 for yes vs no), larger residual tumor (OR = 0.40 for R1,R2 vs RO), male gender (OR = 0.48 for male vs female) and White race (OR = 0.29 for White vs Asian, Black or African American). High BCL7B expression was correlated with leiomyosarcoma histological type (OR = 6.08 for leiomyosarcoma vs dedifferentiated liposarcoma, pleomorphic sarcoma). Univariate and multivariate Cox regression analysis showed that low BCL7B expression was independently associated with poor overall survival (P = .008). GSEA showed that GPCR (G protein-coupled receptors) ligand binding, secreted factors, class A1 rhodopsin-like receptors, extracellular matrix organization, core matrisome, Fc epsilon receptor I mediated NF-κB activation, and WNT signaling pathway were differentially enriched in BCL7B low expression phenotype (|NES| > 1, adjusted P value <.05, and FDR value <0.25). BCL7B may play an important role in sarcoma progression and may be a potential biomarker for prognosis and diagnosis in sarcomas.
Genome-Wide Expression Profiling of Small RNAs in Indian Strain of Rhizoctonia solani AG1-1A Reveals Differential Regulation of milRNAs during Pathogenesis and Crosstalk of Gene Regulation

Rhizoctonia solani AG1-1A is a necrotrophic fungus that causes sheath blight disease in rice. The reliable resistant source against this phytopathogenic fungus is not available in the gene pool of rice. Better understanding of pathogen genomics and gene regulatory networks are critical to devise alternate strategies for developing resistance against this noxious pathogen. In this study, miRNA-like RNAs (milRNAs) of an Indian strain of R. solani were identified by deep sequencing of small RNAs. We identified 128 known and 22 novel milRNAs from 20,963,123 sequence reads. These milRNAs showed 1725 target genes in the fungal genome which include genes associated with growth, development, pathogenesis and virulence of R. solani. Notably, these fungal milRNAs showed their target genes in host (rice) genome also which were later verified by qRT-PCR. The host target genes are associated with auxin metabolism, hypersensitive response, defense genes, and genes related to growth and development of rice. Osa-vacuolar-sorting receptor precursor: Rhi-milR-13, Osa-KANADI1:Rhi-milR-124, Osa-isoflavone reductase: Rhi-milR-135, Osa-nuclear transcription factor Y:Rhi-milR-131, Osa-NB-ARC domain containing protein: Rhi-milR-18, and Osa-OsFBX438: Rhi-milR-142 are notable potential regulons of host target genes: fungal milRNAs that need to be investigated for better understanding of the crosstalk of RNAi pathways between R. solani and rice. The detailed expression analysis of 17 milRNAs by qRT-PCR was analysed during infection at different time points of inoculation, at different growth stages of the host, in four different genotypes of the host, and also in four different strains of fungi which revealed differential regulation of milRNAs associated with pathogenesis and virulence. This study highlights several important findings on fungal milRNAs which need to be further studied and characterized to decipher the gene expression and regulation of this economically important phytopathogen.
Prototyping an app to assist game-based activities: co-design using a qualitative approach

Qualitative approaches have gained prominence in the scientific community being important to develop studies that use them, namely to assist prototyping stages. This article seeks to contribute to the deepening of knowledge in the field of cataloging and evaluating educational games and describes the process of designing an application's wireframes. This allowed the gathering of the app’s functional requirements, using a qualitative methodological approach through co-design: a focus group of eight experts from different fields such as geography, sociology, science, technology, engineering, art and mathematics was constituted. Considering the Communicative Design Paradigm and the Octalysis gamification model, the functional requirements were grouped, after analysis, into six domains – Games, News, Interaction, User, Evaluation and Language. The prototype design emerged from this process enabling the design of 47 wireframes.
Modeling of the Turkish Strait System Using a High Resolution Unstructured Grid Ocean Circulation Model

The Turkish Strait System, which is the only connection between the Black Sea and the Mediterranean Sea, is a challenging region for ocean circulation models due to topographic constraints and water mass structure. We present a newly developed high resolution unstructured finite element grid model to simulate the Turkish Strait System using realistic atmospheric forcing and lateral open boundary conditions. We find that the jet flowing from the Bosphorus Strait into the Marmara creates an anticyclonic circulation. The eddy kinetic energy field is high around the jets exiting from the Bosphorus Strait, Dardanelles Strait, and also the leeward side of the islands in the Marmara Sea. The model successfully captures the two-layer structure of the Sea of Marmara. The volume transport at the Bosphorus is around 120 km3/year which is consistent with the recent observations. The largest bias in the model is at the interface depth due to the shallower mixed layer.
Urban mobility performance indicators: a bibliometric analysis Indicadores de desempenho em mobilidade urbana: uma análise bibliométrica

s of the remaining 439 articles were read, with the purpose of selecting only those aligned with the objective of the work, that is, articles with a scope focused on discussing urban mobility indicators, for a city or for only one system/mode of transport. Articles that addressed only theoretical reflections or that indicated a solution, without explicitly a compilation or the use of indicators, were excluded. In this process, the list of articles reduced to 156. It is understood that the construction of knowledge is done through the contribution of several researchers, which generates a current of thought that is effective in the references of each work. Thus, an analysis of the citations of the selected articles was done with the intention of establishing which are the currents of thought on the theme, which interconnected 49 articles, being this the final selection of articles used. The third phase included the classification of articles in terms of structure and content. To do so, the spreadsheet with the articles selected was extended to highlight the main points of the publications. The parameters used in this systematization were as follows: author, country of authors, title, year of publication, periodical of publication, classification Figure 2. Phases of the methodological procedure.
Smarter Interoperability based on Automatic Schema Matching and Intelligence

The process of connecting enterprise systems and applications to digital platforms and ecosystems requires interoperability on different levels and eventually leads to the task of creating a schema mapping. At present, this task is carried out manually and prior research has proven this task difficult to automate. In this paper, we discuss the suitability of machine learning approaches to create an auto-mapping functionality, so different schemas and standards can (partially) be mapped automatically, and incorporate a symbiotic approach to improve the matching result. To the best of our knowledge, this is a new approach with potential to reduce the time needed for schema matching tasks. The main contribution of this paper is a reference architecture and prototype for smarter interoperability using a combination of automatic schema matching, based on machine learning, and intelligence amplification.
RF Power-Handling Performance for Direct Actuation of GeTe Switches

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. RF Power-Handling Performance for Direct Actuation of GeTe Switches Alexandre Leon, Bruno Reig, Etienne Perret, Florence Podevin, Damien Saint-Patrice, Vincent Puyal, Jose Lugo-Alvarez, Philippe Ferrari
Spectral recovery of binary censored block models

Community detection is the problem of identifying community structure in graphs. Often the graph is modeled as a sample from the Stochastic Block Model, in which each vertex belongs to a community. The probability that two vertices are connected by an edge depends on the communities of those vertices. In this paper, we consider a model of {\em censored} community detection with two communities, where most of the data is missing as the status of only a small fraction of the potential edges is revealed. In this model, vertices in the same community are connected with probability $p$ while vertices in opposite communities are connected with probability $q$. The connectivity status of a given pair of vertices $\{u,v\}$ is revealed with probability $\alpha$, independently across all pairs, where $\alpha = \frac{t \log(n)}{n}$. We establish the information-theoretic threshold $t_c(p,q)$, such that no algorithm succeeds in recovering the communities exactly when $tt_c(p,q)$, a simple spectral algorithm based on a weighted, signed adjacency matrix succeeds in recovering the communities exactly. While spectral algorithms are shown to have near-optimal performance in the symmetric case, we show that they may fail in the asymmetric case where the connection probabilities inside the two communities are allowed to be different. In particular, we show the existence of a parameter regime where a simple two-phase algorithm succeeds but any algorithm based on the top two eigenvectors of the weighted, signed adjacency matrix fails.
Feature Selection Via Random Subsets Of Uncorrelated Features Feature Selection Via Random Subsets Of Uncorrelated Features

The role of feature selection is crucial in many applications. A few of these include computational biology, image classiﬁcation and risk management. In biology, gene expression micro array data sets have been used extensively in many areas of research. These data sets typically suﬀer from an important problem: the ratio between the number of features over the number of examples is very high. This problem mainly aﬀects prediction accuracy because it is best to collect more labeled examples than features. A correlation based random subspace ensemble feature selector (CCC_RSM) was proposed to handle this problem [5]. In this approach, ﬁrst it determines the most relevant prediction features. Next, it groups these features based on their correlation to each other. Then, a feature is randomly chosen from each correlated group so that the selected features form a feature subset. The CCC_RSM algorithm repeats the previous step a pre-deﬁned number of times. The proposed algorithm’s performance is evaluated by combining either multiple decision trees or Support Vector Machines. Joining these models’ predictions together can signiﬁcantly increase the prediction accuracy. In ensembles of these models, each classiﬁer provides a vote and the majority vote is used to produce the ﬁnal class prediction. This design modiﬁes the random subspace method ensemble [13]. This study focuses on ﬁnding alternative feature selectors in the ﬁrst step so that the CCC_RSM algorithm can obtain good, or even better classiﬁcation performance. We used four micro array gene expression data sets in the experiments. Based on the original algorithm, we used the Gini Index in place of Relief-F. A detailed analysis of the alternative method’s outputs was considered: (1) overall and average accuracy, (2) Sensitivity, (3) Speciﬁcity, (4) F-measure.
IJOPM-07-2020-0482_proof 34..64

Purpose – Drawing on the theoretical concept of organisational fit, this paper questions the relevance of employees’ participation in the link between continuous improvement (CI) and operational performance. The literature has long emphasised that to be successful, CI implementation needs to rely on employees’ involvement as soon as its inception. This paper argues that this approach is not generalisable. Design/methodology/approach –Based on a database of 330 firms across 15 countries, regression analyses were used to hypothesise that the fit between CI and employee participation is positively associated with operational performance, and that the fit between CI and centralisation of authority is negatively associated with operational performance. The authors also ran a robustness check with polynomial regression analyses and the response surface methodology. Findings – CI–employee participation fit is positively associated with operational performance, suggesting that there is less need for employees to be involved when a firm has scarcely developed CI. Employee participation becomes gradually more relevant as CI progresses. Moreover, the results demonstrate that the CI–centralisation of authority fit is negatively associated with operational performance, suggesting that a top-down management approach with centralised authority is preferable when CI is low, whereas a bottom-up management approach is helpful when a firm has extensively developed CI. Originality/value – This research draws on the concept of organisational fit to explore the relationships between internal practices in the operationsmanagement literature. The authors suggest thatmanagers should dynamically balance the practices of employee participation and centralisation of authority as CI improves. This study highlights that CI has different evolutionary levels that require different managerial approaches and practices.
Deep Neural Transformer Model for Mono and Multi Lingual Machine Translation

In recent years, the Transformers have emerged as the most relevant deep architecture, especially machine translation. These models, which are based on attention mechanisms, outperformed previous neural machine translation architectures in several tasks. This paper proposes a new architecture based on the transformer model for the monolingual and multilingual translation system. The tests were carried out on the IWSLT 2015 and 2016 dataset. The Transformers attention mechanism increases the accuracy to more than 92% that we can quantify by more than 4 BLEU points (a performance metric used in machine translation systems).