Miracle-dz/UniFAR-FTUs huggingface dataset:
PiaNet: A pyramid input augmented convolutional neural network for GGO detection in 3D lung CT scans

This paper proposes a new convolutional neural network with multiscale processing for detecting ground-glass opacity (GGO) nodules in 3D computed tomography (CT) images, which is referred to as PiaNet for short. PiaNet consists of a feature-extraction module and a prediction module. The former module is constructed by introducing pyramid multiscale source connections into a contracting-expanding structure. The latter module includes a bounding-box regressor and a classifier that are employed to simultaneously recognize GGO nodules and estimate bounding boxes at multiple scales. To train the proposed PiaNet, a two-stage transfer learning strategy is developed. In the first stage, the feature-extraction module is embedded into a classifier network that is trained on a large data set of GGO and non-GGO patches, which are generated by performing data augmentation from a small number of annotated CT scans. In the second stage, the pretrained feature-extraction module is loaded into PiaNet, and then PiaNet is fine-tuned using the annotated CT scans. We evaluate the proposed PiaNet on the LIDC-IDRI data set. The experimental results demonstrate that our method outperforms state-of-the-art counterparts, including the Subsolid CAD and Aidence systems and S4ND and GA-SSD methods. PiaNet achieves a sensitivity of 91.75% with only one false positive per scan
Convex co-compact representations of 3-manifold groups.

A representation of a finitely generated group into the projective general linear group is called convex co-compact if it has finite kernel and its image acts convex co-compactly on a properly convex domain in real projective space. We prove that the fundamental group of a closed irreducible orientable 3-manifold can admit such a representation only when the manifold is geometric (with Euclidean, Hyperbolic, or Euclidean $\times$ Hyperbolic geometry) or when every component in the geometric decomposition is hyperbolic. In each case, we describe the structure of such examples.
Bonding few-layered graphene via collision with high-speed fullerenes

Graphene, as a typical two-dimensional material, is popular in the design of nanodevices. The interlayer relative sliding of graphene sheets can significantly affect the effective bending stiffness of the few-layered graphene. For restricting the relative sliding, we adopted the atomic shot peening method to bond the graphene sheets together by ballistic C60 fullerenes from its two surfaces. Collision effects are evaluated via molecular dynamics simulations. Results obtained indicate that the fullerenes’ incident velocity has an interval, in which the graphene sheet can be bonded after collision while no atoms on the fullerenes escaping from the graphene ribbon after collision. The limits of the interval increase with the layer number. Within a few picoseconds of collision, a stable carbon network is produced at an impacted area. The graphene sheets are bonded via the network and cannot slide relatively anymore. Conclusions are drawn to show the way of potential applications of the method in manufacturing a new graphene-based two-dimensional material that has a high out-of-plane bending stiffness.
Accurate and Intuitive Contextual Explanations using Linear Model Trees

With the ever-increasing use of complex machine learning models in critical applications within the finance domain, explaining the decisions of the model has become a necessity. With applications spanning from credit scoring to credit marketing, the impact of these models is undeniable. Among the multiple ways in which one can explain the decisions of these complicated models, local post hoc model agnostic explanations have gained massive adoption. These methods allow one to explain each prediction independent of the modelling technique that was used while training. As explanations, they either give individual feature attributions or provide sufficient rules that represent conditions for a prediction to be made. The current state of the art methods use rudimentary methods to generate synthetic data around the point to be explained. This is followed by fitting simple linear models as surrogates to obtain a local interpretation of the prediction. In this paper, we seek to significantly improve on both, the method used to generate the explanations and the nature of explanations produced. We use a Generative Adversarial Network for synthetic data generation and train a piecewise linear model in the form of Linear Model Trees to be used as the surrogate this http URL addition to individual feature attributions, we also provide an accompanying context to our explanations by leveraging the structure and property of our surrogate model.
Narratives and Needs: Analyzing Experiences of Cyclone Amphan Using Twitter Discourse

People often turn to social media to comment upon and share information about major global events. Accordingly, social media is receiving increasing attention as a rich data source for understanding people's social, political and economic experiences of extreme weather events. In this paper, we contribute two novel methodologies that leverage Twitter discourse to characterize narratives and identify unmet needs in response to Cyclone Amphan, which affected 18 million people in May 2020.
A Bayesian hierarchical model to estimate land surface phenology parameters with harmonized Landsat 8 and Sentinel-2 images

We develop a Bayesian Land Surface Phenology (LSP) model and examine its performance using Enhanced Vegetation Index (EVI) observations derived from the Harmonized Landsat Sentinel-2 (HLS) dataset. Building on previous work, we propose a double logistic function that, once couched within a Bayesian model, yields posterior distributions for all LSP parameters. We assess the efficacy of the Normal, Truncated Normal, and Beta likelihoods to deliver robust LSP parameter estimates. Two case studies are presented and used to explore aspects of the proposed model. The first, conducted over forested pixels within a HLS tile, explores choice of likelihood and space-time varying HLS data availability for long-term average LSP parameter point and uncertainty estimation. The second, conducted on a small area of interest within the HLS tile on an annual time-step, further examines the impact of sample size and choice of likelihood on LSP parameter estimates. Results indicate that while the Truncated Normal and Beta likelihoods are theoretically preferable when the vegetation index is bounded, all three likelihoods performed similarly when the number of index observations is sufficiently large and values are not near the index bounds. Both case studies demonstrate how pixel-level LSP parameter posterior distributions can be used to propagate uncertainty through subsequent analysis. As a companion to this article, we provide an open-source \R package \pkg{rsBayes} and supplementary data and code used to reproduce the analysis results. The proposed model specification and software implementation delivers computationally efficient, statistically robust, and inferentially rich LSP parameter posterior distributions at the pixel-level across massive raster time series datasets.
COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases From Chest CT Images

The coronavirus disease 2019 (COVID-19) pandemic continues to have a tremendous impact on patients and healthcare systems around the world. In the fight against this novel disease, there is a pressing need for rapid and effective screening tools to identify patients infected with COVID-19, and to this end CT imaging has been proposed as one of the key screening methods which may be used as a complement to RT-PCR testing, particularly in situations where patients undergo routine CT scans for non-COVID-19 related reasons, patients have worsening respiratory status or developing complications that require expedited care, or patients are suspected to be COVID-19-positive but have negative RT-PCR test results. Early studies on CT-based screening have reported abnormalities in chest CT images which are characteristic of COVID-19 infection, but these abnormalities may be difficult to distinguish from abnormalities caused by other lung conditions. Motivated by this, in this study we introduce COVIDNet-CT, a deep convolutional neural network architecture that is tailored for detection of COVID-19 cases from chest CT images via a machine-driven design exploration approach. Additionally, we introduce COVIDx-CT, a benchmark CT image dataset derived from CT imaging data collected by the China National Center for Bioinformation comprising 104,009 images across 1,489 patient cases. Furthermore, in the interest of reliability and transparency, we leverage an explainability-driven performance validation strategy to investigate the decision-making behavior of COVIDNet-CT, and in doing so ensure that COVIDNet-CT makes predictions based on relevant indicators in CT images. Both COVIDNet-CT and the COVIDx-CT dataset are available to the general public in an open-source and open access manner as part of the COVID-Net initiative. While COVIDNet-CT is not yet a production-ready screening solution, we hope that releasing the model and dataset will encourage researchers, clinicians, and citizen data scientists alike to leverage and build upon them.
Optimizing Convolutional Neural Network Architecture via Information Field

CNN architecture design has attracted tremendous attention of improving model accuracy or reducing model complexity. However, existing works either introduce repeated training overhead in the search process or lack an interpretable metric to guide the design. To clear the hurdles, we propose Information Field (IF), an explainable and easy-to-compute metric, to estimate the quality of a CNN architecture and guide the search process of designs. To validate the effectiveness of IF, we build a static optimizer to improve the CNN architectures at both the stage level and the kernel level. Our optimizer not only provides a clear and reproducible procedure but also mitigates unnecessary training efforts in the architecture search process. Experiments show that the models generated by our optimizer can achieve up to 5.47% accuracy improvement and up to 65.38% parameters deduction, compared with state-of-the-art CNN structures like MobileNet and ResNet.
Cubic phase gates are not suitable for non-Clifford operations on GKP states

With the Gottesman-Kitaev-Preskill (GKP) encoding, Clifford gates and error correction can be carried out using simple Gaussian operations. Still, non-Clifford gates, required for universality, require non-Gaussian elements. In their original proposal, GKP suggested a particularly simple method of using a single application of the cubic phase gate to perform the logical non-Clifford T-gate. Here we show that this cubic phase gate approach performs extraordinarily poorly, even for arbitrarily large amounts of squeezing in the GKP state. Thus, contrary to common belief, the cubic phase gate is not suitable for achieving universal fault-tolerant quantum computation with GKP states.
A Consecutive Lehmer Code for Parabolic Quotients of the Symmetric Group

In this article we define an encoding for parabolic permutations that distinguishes between parabolic $231$-avoiding permutations. We prove that the componentwise order on these codes realizes the parabolic Tamari lattice, and conclude a direct and simple proof that the parabolic Tamari lattice is isomorphic to a certain $\nu$-Tamari lattice, with an explicit bijection. Furthermore, we prove that this bijection is closely related to the map $\Theta$ used when the lattice isomorphism was first proved in (Ceballos, Fang and Mühle, 2020), settling an open problem therein.
Resveratrol modulates the apoptosis and autophagic death of human lung adenocarcinoma A549 cells via a p53-dependent pathway: Integrated bioinformatics analysis and experimental validation

Resveratrol (RSV) has been reported to exhibit cytotoxic activity in multiple types of malignant cells; however, the mechanisms underlying the antitumor effects of RSV in non-small-cell lung cancer (NSCLC) cells remain undetermined. Combining bioinformatics analysis with experimental validation, the present study aimed to examine the effects of RSV on the apoptosis and autophagy of A549 NSCLC cells, and to determine the potential underlying molecular mechanisms. Bioinformatics analysis was used to determine the differentially expressed genes (DEGs) and identify the enriched biological functions and pathways associated with these DEGs following RSV treatment. Cell viability was determined by MTT assay, and flow cytometry and TUNEL assay were used to evaluate cell apoptosis. Monodansylcadaverine staining combined with a transmission electron microscope were used to evaluate the extent of autophagy. The expression levels of apoptosis-, autophagy-, or pathway-associated molecular markers were measured by reverse transcription-quantitative PCR and/or western blot analysis. By bioinformatics analysis, a total of 1,031 DEGs were identified in the RSV-treated A549 cells, which were enriched in apoptosis-, or autophagy-related biological functions and the p53 signaling pathway. In validation experiments, RSV significantly reduced cell viability and initiated apoptosis, with an increase in the number of apoptotic cells; it also upregulated cleaved caspase-3 expression and Bax expression, and downregulated the Bcl-2 expression levels. Additionally, there was an increase in the accumulation of green dot-like structures, indicative of autophagic vesicles, observed under a fluorescence microscope, and an increase in the presence of autophagic vacuoles observed using a transmission electron microscope following RSV treatment. Furthermore, the expression levels of the autophagy-related proteins, LC3-II/LC3-I and Beclin-1, were increased and p62 expression was decreased. 3-methyladenine (3-MA), an inhibitor of autophagy, partially reversed the RSV-induced cytotoxic effects, but did not significantly alter the number of apoptotic cells. RSV elevated the p53 levels and decreased the phosphorylated (p-)Mdm2 and p-Akt levels. Pifithrin-α, an inhibitor of p53, partially reduced RSV-induced apoptosis and autophagy. On the whole, the results of the present study demonstrated that RSV initiates the apoptosis and autophagic death of A549 cells via the activation of the p53 signaling pathway, further highlighting the potential of RSV for the treatment of NSCLC.
Inhibition of DNA-PK by gefitinib causes synergism between gefitinib and cisplatin in NSCLC

Lung cancer has the highest incidence and mortality rates among the malignant tumor types world-wide. Platinum-based chemotherapy is the main treatment for advanced non-small-cell lung cancer (NSCLC), and epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) have greatly improved the survival of patients with EGFR-sensitive mutations. However, there is no standard therapy for treating patients who are EGFR-TKI resistant. Combining EGFR-TKIs and platinum-based chemotherapy is the most popular strategy in the clinical practice. However, the synergistic mechanism between EGFR-TKIs and platinum remains unknown. Therefore, the aim of the present study was to determine the synergistic mechanism of gefitinib (an EGFR-TKI) and cisplatin (a main platinum-based drug). MTT assay, apoptosis analysis, tumorsphere formation and an orthotropic xenograft mouse model were used to examine the combination effects of gefitinib and cisplatin on NSCLC. Co-immunoprecipitation and immunofluorescence were used to identify the underlying mechanism. It was found that gefi-tinib could selectively inhibit EGFR from entering the nucleus, decrease DNA-PK activity and enhance the cytotoxicity of cisplatin on NSCLC. Collectively, the results suggested that inhibition of DNA-dependent protein kinase by gefitinib may be due to the synergistic mechanism between gefitinib and cisplatin. Thus, the present study provides a novel insight into potential biomarkers for the selection of combination therapy of gefitinib and cisplatin.
A protocol for systematic review and meta analysis

Background: Post-stroke constipation is a major complication of stroke and increases the incidence of poor neurological outcomes and infectious complications and, therefore, warrants active and prompt treatment. In East Asian countries, several types of herbal medicines have been used for the treatment of post-stroke constipation because they are considered safer than existing pharmacotherapies. However, no systematic review has investigated the efficacy and safety of traditional East Asian herbal medicine in the treatment of post-stroke constipation. With this systematic review and meta-analysis, we aimed to evaluate the efficacy and safety of traditional East Asian herbal medicines for the treatment of post-stroke constipation. Methods and analysis: Eight electronic databases will be searched for relevant studies published from inception to April 2021. Only randomized controlled trials (RCTs) that assess the efficacy and safety of traditional East Asian herbal medicines for the treatment of post-stroke constipation will be included in this study. The methodological qualities, including the risk of bias, will be evaluated using the Cochrane risk of bias assessment tool. After screening the studies, a meta-analysis of the RCTs will be performed, if possible. Results:This study is expected to generate high-quality evidence of the efficacy and safety of herbal medicines to treat post-stroke constipation. Conclusion:Our systematic review will provide evidence to determine whether herbal medicines can be effective interventions for patients with post-stroke constipation. Ethics and dissemination: Ethical approval is not required, as this study was based on a review of published research. This review will be published in a peer-reviewed journal and disseminated electronically and in print. Trial registration number: Research registry reviewregistry1117 Abbreviations: CENTRAL = Cochrane Central Register of Controlled Trials, CiNii = Citation Information by Nii, Cis = Confidence intervals, CNKI = China National Knowledge Infrastructure Database, CRIS = Clinical Research Information Service, CSS = constipation scoring system, EMBASE = Excerpta Medica dataBASE, GRADE = Grading of Recommendations Assessment, Development and Evaluation, GVS = gas volume score, mBI = Modified Barthel Index, MD = Mean difference, mRS = Modified Rankin Scale, NDSL=National Digital Science Library, OASIS =Oriental Medicine Advanced Searching Integrated System, PRISMA = Preferred Reporting Items for Systematic Reviews and Meta-Analysis, QoL =Quality of Life, RCTs = Randomized controlled trials, RoB = Risk of bia, RR = Risk ratio.
Inferring Causal Dependencies between Chaotic Dynamical Systems from Sporadic Time Series

Discovering causal structures of processes is a major tool of scientific inquiry because it helps us better understand and explain the mechanisms driving a phenomenon of interest, thereby facilitating analysis, reasoning, and synthesis for such systems. However, accurately inferring causal structures within a phenomenon based on observational data only is still an open problem. In particular, this problem becomes increasingly difficult when it relies on data with missing values. In this article, we present a method to uncover causal relations between chaotic dynamical systems from sporadic time series (that is, incomplete observations at infrequent and irregular intervals), which builds upon Convergent Cross Mapping and recent advances in continuous time-series modeling (GRU-ODE-Bayes).
Deep Learning-Based Complaint Classification for Indonesia Telecommunication Company's Call Center

The preliminary research was held to utilize the call center conversations records from a broadband telecommunications company in Indonesia. There is a need from the company to classify customer's complaints automatically by a system to minimize human errors and at once streamline the business processes and resources. Natural Language Processing (NLP), as an integral part of artificial intelligence (AI), empower machines to understand human languages for performing beneficial tasks. The growth of deep learning is the main driver behind NLP for performing various practical applications and business, therefore deep learning is expected to overcome the problems encountered. This paper explains the methods used in designing the classification systems based on deep learning. A literature review is conducted to find the proper algorithm used in classifying problems. Furthermore, it also explains the stages performed in preparing the data and building the system model. From experiments conducted, it can be stated that the RNN algorithm can be used in the classification of customer complaints with the results shown by the accuracy value of the model.
Quasianalytic evaluation of stability of nonlinear dynamic systems oscillations

Abstract – The stability of nonlinear systems is studied by the method of partial discretization. The steady motion of a mechanical system is defined here as its motion without resonant oscillations. In order to avoid resonance oscillations in the operating modes of the system a quasianalytic evaluation of the behavior of the solution of the perturbed state equation is made. The equation is partially discretized in the class of generalized functions (Dirac delta function). An analytical solution characterizing the behavior of a small perturbation f  in time has been obtained. The efficiency of
Estimating uncertainty in pooled proxy time-series , including stable isotopes in tree-1 rings 2 3 4

Estimating uncertainty in pooled proxy time-series, including stable isotopes in tree1 rings 2 3 4 E.J. Woodley 1 , N.J. Loader 2 , D. McCarroll 2 , G.H. F. Young 2 , I. Robertson 2 , T.H. E. 5 Heaton 1 , M.H Gagen 2 6 7 8 1 NERC Isotope Geosciences Laboratory, Kingsley Dunham Centre, Keyworth, 9 Nottingham, NG12 5GG, UK 10 2 Department of Geography, Swansea University, Singleton Park, Swansea, SA2 8PP, 11 UK 12 13 Corresponding Author 14 15 Ewan J. Woodley 16 NERC Isotope Geosciences Laboratory 17 Kingsley Dunham Centre 18 Keyworth 19 Nottingham 20 NG12 5GG 21 22 Phone: +44 1159 363608 23 Fax: +44 1159 363302 24 Email: ewanwoodley@gmail.com 25 26 27
Study on Relationship between Election and Consensus in Asynchronous Distributed Systems

본 논문에서는 신뢰 할 수 없는 고장추적 장치로 구성 된 비동기적 분산 시스템 하에서 선출 (election) 문제와 합의(consensus) 문제의 관련성에 관하여 연구하고자 한다. 먼저 선출 문제는 합의 문제보다 더욱 어려운 문제임을 보인다. 합의 문제와는 대조적으로 선출 문제는 시스템 상에서 단 한 개의 노드가 죽은 경우에도 신뢰 할 수 없는 고장 추적 장치를 이용하여 선출 문제를 해결 할 수 없다. 보다 엄밀하게 표현하자면, 선출 문제를 해결하는데 필요한 가장 약한 고장 추적 장치는 완전한 고장 추적 장치이어야 하는 것으로, 이는 합의 문제를 해결하는데 필요한 가장 약한 고장 추적 장치보다 확실히 강한 것이다. 선출 문제가 합의 문제보다 어렵다는 것을 보이기 위해 본 논 문에서는 축소(reduction) 프로토클을 이용 한다.
Employing Best Input SVR Robust Lost Function with Nature-Inspired Metaheuristics in Wind Speed Energy Forecasting

—Wind power has been experiencing a quick improvement. Without a doubt, wind is a variable asset that is hard to forecast. For instance, traditionally time series, extra holds are distributed to deal with this uncertainty. This paper presents a comparison of the performance of various Support Vector Regression (SVR) applied to short-term wind power forecasting. The analogy with BORUTA and multivariate adaptive regression splines (MARS) as judge best input and employ genetic algorithm and particle swarm optimization to find best parameter in Support Vector Regression with robust lost function. We measure the accuracy of this models by Symmetric means absolute percentage error (sMAPE) and we get the best model BORUTA-SVR-PSO with sMAPE 2.07155%. Moreover, we measure the energy conversion using Feedback Linearization Control (FLC).