Miracle-dz/UniFAR-FTUs huggingface dataset:
Parametric UMAP Embeddings for Representation and Semisupervised Learning

Abstract UMAP is a nonparametric graph-based dimensionality reduction algorithm using applied Riemannian geometry and algebraic topology to find low-dimensional embeddings of structured data. The UMAP algorithm consists of two steps: (1) computing a graphical representation of a data set (fuzzy simplicial complex) and (2) through stochastic gradient descent, optimizing a low-dimensional embedding of the graph. Here, we extend the second step of UMAP to a parametric optimization over neural network weights, learning a parametric relationship between data and embedding. We first demonstrate that parametric UMAP performs comparably to its nonparametric counterpart while conferring the benefit of a learned parametric mapping (e.g., fast online embeddings for new data). We then explore UMAP as a regularization, constraining the latent distribution of autoencoders, parametrically varying global structure preservation, and improving classifier accuracy for semisupervised learning by capturing structure in unlabeled data.1
Homotopy Covers of Graphs

We develop conditions for a graph cover to be a $\times$-homotopy cover, satisfying a $\times$-homotopy lifting property analogous to the homotopy lifting property of covers of topological spaces. We define a universal homotopy cover for graphs and show that homotopy covers as quotients of this universal cover by subgroups of the deck transformation group, which we identify with the fundamental groupoid defined in \cite{CS2}. We then explicitly compute the fundamental groups of cycle, wheel, complete and certain Kneser graphs, and use this to enumerate all connected homotopy covers of these graphs.
Action Sequence Augmentation for Early Graph-based Anomaly Detection

The proliferation of web platforms has created incentives for online abuse. Many graph-based anomaly detection techniques are proposed to identify the suspicious accounts and behaviors. However, most of them detect the anomalies once the users have performed many such behaviors. Their performance is substantially hindered when the users' observed data is limited at an early stage, which needs to be improved to minimize financial loss. In this work, we propose Eland, a novel framework that uses action sequence augmentation for early anomaly detection. Eland utilizes a sequence predictor to predict next actions of every user and exploits the mutual enhancement between action sequence augmentation and user-action graph anomaly detection. Experiments on three real-world datasets show that Eland improves the performance of a variety of graph-based anomaly detection methods. With Eland, anomaly detection performance at an earlier stage is better than non-augmented methods that need significantly more observed data by up to 15% on the Area under the ROC curve.
Deep Mining Generation of Lung Cancer Malignancy Models from Chest X-ray Images

Lung cancer is the leading cause of cancer death and morbidity worldwide. Many studies have shown machine learning models to be effective in detecting lung nodules from chest X-ray images. However, these techniques have yet to be embraced by the medical community due to several practical, ethical, and regulatory constraints stemming from the “black-box” nature of deep learning models. Additionally, most lung nodules visible on chest X-rays are benign; therefore, the narrow task of computer vision-based lung nodule detection cannot be equated to automated lung cancer detection. Addressing both concerns, this study introduces a novel hybrid deep learning and decision tree-based computer vision model, which presents lung cancer malignancy predictions as interpretable decision trees. The deep learning component of this process is trained using a large publicly available dataset on pathological biomarkers associated with lung cancer. These models are then used to inference biomarker scores for chest X-ray images from two independent data sets, for which malignancy metadata is available. Next, multi-variate predictive models were mined by fitting shallow decision trees to the malignancy stratified datasets and interrogating a range of metrics to determine the best model. The best decision tree model achieved sensitivity and specificity of 86.7% and 80.0%, respectively, with a positive predictive value of 92.9%. Decision trees mined using this method may be considered as a starting point for refinement into clinically useful multi-variate lung cancer malignancy models for implementation as a workflow augmentation tool to improve the efficiency of human radiologists.
Detection of the Mass-dependent Dual Type Transition of Galaxy Spins in IllustrisTNG Simulations

A numerical detection of the mass-dependent spin transition of the galaxies is presented. Analyzing a sample of the galaxies with stellar masses in the range of 109 < (M ⋆/M ⊙) ≤ 1011 from the IllustrisTNG300-1 simulations, we explore the alignment tendency between the galaxy baryon spins and the three eigenvectors of the linearly reconstructed tidal field as a function of M ⋆ and its evolution in the redshift range of 0 ≤ z ≤ 1.5. Detecting a significant signal of the occurrence of the mass-dependent transition of the galaxy spins, we show that the centrals differ from the satellites in their spin transition type. As M ⋆ increases beyond a certain threshold mass, the preferred directions of the central galaxy spins transit from the minor to the intermediate tidal eigenvectors (type two) at z = 0.5 and 1, while those of the satellites transit from the minor to the major tidal eigenvectors (type one) at z = 1 and 1.5. It is also shown that the mass range and type of the spin transition depend on the galaxy morphology, the degree of the alignments between the baryon and total spin vectors, and the environmental density. Meanwhile, the stellar spins of the galaxies are found to yield a weak signal of the T1 transitions at z = 0, whose strength and trend depend on the degree of the alignments between the stellar and baryon spins. The possible mechanisms responsible for the T1 and T2 spin transitions are discussed.
The Efficacy of Pyrotinib as a Third- or Higher-Line Treatment in HER2-Positive Metastatic Breast Cancer Patients Exposed to Lapatinib Compared to Lapatinib-Naive Patients: A Real-World Study

Background: Pyrotinib is a novel irreversible pan-ErbB receptor tyrosine kinase inhibitor. Evidence of the efficacy of pyrotinib-based treatments for HER2-positive metastatic breast cancer (MBC) in patients exposed to lapatinib is limited. Methods: Ninety-four patients who received pyrotinib as a third- or higher-line treatment for HER2-positive MBC were included in this retrospective study. The primary and secondary endpoints were overall survival (OS) and progression‐free survival (PFS). Propensity score matching (PSM) and inverse probability of treatment weighting (IPTW) analysis were implemented to balance important patient characteristics between groups. Results: Thirty (31.9%) patients were pretreated with lapatinib and subsequently received pyrotinib as an anti-HER2 treatment, and 64 (68.1%) patients did not receive this treatment. The OS and PFS indicated a beneficial trend in lapatinib-naive group compared to lapatinib-treated group in either the original cohort (PFS: 9.02 vs 6.36 months, p = 0.05; OS: 20.73 vs 14.35 months, p = 0.08) or the PSM (PFS: 9.02 vs 6.08 months, p = 0.07; OS: 19.07 vs 18.00 months, p = 0.61) or IPTW (PFS: 9.90 vs 6.17 months, p = 0.05; OS: 19.53 vs 15.10 months, p = 0.08) cohorts. Subgroup analyses demonstrated lapatinib treatment-related differences in PFS in the premenopausal subgroup and the no prior trastuzumab treatment subgroup, but no significant differences were observed in OS. Conclusion: Pyrotinib-based therapy demonstrated promising effects in HER2-positive MBC patients in a real-world study, especially in lapatinib-naive patients, and also some activity in lapatinib-treated patients.
Simultaneous identification of Chlamydia trachomatis, Neisseria gonorrhoeae, Mycoplasma genitalium, and Trichomonas vaginalis ‒ multicenter evaluation of the Alinity m STI assay

Abstract Objectives Accurate and rapid diagnosis of sexually transmitted infections (STIs) is essential for timely administration of appropriate treatment and reducing the spread of the disease. We examined the performance of the new Alinity m STI assay, a qualitative real-time multiplex PCR test for simultaneous identification of Chlamydia trachomatis (CT), Neisseria gonorrhoeae (NG), Mycoplasma genitalium (MG), and Trichomonas vaginalis (TV) run on the fully automated Alinity m platform. Methods This international, multicenter study evaluated the accuracy, reproducibility, and clinical performance of the Alinity m STI assay compared to commonly used STI assays in a large series of patient samples encountered in clinical practice. Results The Alinity m STI assay identified accurately and precisely single and mixed pathogens from an analytical panel of specimens. The Alinity m STI assay demonstrated high overall agreement rates with comparator STI assays (99.6% for CT [n=2,127], 99.2% for NG [n=2,160], 97.1% for MG [n=491], and 99.4% for TV [n=313]). Conclusions The newly developed Alinity m STI assay accurately detects the 4 sexually transmitted target pathogens in various collection devices across clinically relevant specimen types, regardless of single or mixed infection status.
Reassessing Passion and Perseverance as Dimensions of Individual Entrepreneurial Orientation: A Conceptual and Empirical Investigation into Theory and Measurement

Abstract Santos, G. , C. S. Marques, and J. J. Ferreira. 2020. “Passion and Perseverance as Two New Dimensions of an Individual Entrepreneurial Orientation Scale. ” Journal of Business Research 112: 190–9 proposed a five-dimensional conceptualization of individual entrepreneurial orientation (IEO), but more evidence is needed before authors can reliably apply their conceptualization and operationalization. We provide a theoretical critique of their conceptualization and continue their scale development process. Four of five dimensions converged with other scales of the same constructs, but their passion dimension did not. The factor structure and predictive validity evidence also suggested that passion should not be included within the scope of IEO, and we assert that Santos et al.’s conceptualization of IEO is better represented with a hierarchical structure. Proactiveness and perseverance as well as risk-taking and innovativeness load onto two separate second-order factors, and these two second-order factors load onto the broader factor of IEO. We suggest that the construct measured by Santos et al.’s passion dimension may instead be a mediator between IEO and entrepreneurial outcomes, and we provide further insights into the measurement of IEO.
A Murine Model With JAK2V617F Expression in Both Hematopoietic Cells and Vascular Endothelial Cells Recapitulates the Key Features of Human Myeloproliferative Neoplasm

The myeloproliferative neoplasms (MPNs) are characterized by an expansion of the neoplastic hematopoietic stem/progenitor cells (HSPC) and an increased risk of cardiovascular complications. The acquired kinase mutation JAK2V617F is present in hematopoietic cells in a majority of patients with MPNs. Vascular endothelial cells (ECs) carrying the JAK2V617F mutation can also be detected in patients with MPNs. In this study, we show that a murine model with both JAK2V617F-bearing hematopoietic cells and JAK2V617F-bearing vascular ECs recapitulated all the key features of the human MPN disease, which include disease transformation from essential thrombocythemia to myelofibrosis, extramedullary splenic hematopoiesis, and spontaneous cardiovascular complications. During aging and MPN disease progression, there was a loss of both HSPC number and HSPC function in the marrow while the neoplastic hematopoiesis was relatively maintained in the spleen, mimicking the advanced phases of human MPN disease. Different vascular niche of the marrow and spleen could contribute to the different JAK2V617F mutant stem cell functions we have observed in this JAK2V617F-positive murine model. Compared to other MPN murine models reported so far, our studies demonstrate that endothelial dysfunction plays an important role in both the hematologic and cardiovascular abnormalities of MPN. Key Points A murine model in which JAK2V617F is expressed in both hematopoietic cells and ECs recapitulated the key features of the human MPN disease Different vascular niche of the marrow and spleen could contribute to different JAK2V617F HSC functions during MPN disease progression
Best templates outperform homology models in predicting the impact of mutations on protein stability

Motivation Prediction of protein stability change upon mutation (ΔΔG) is crucial for facilitating protein engineering and understanding of protein folding principles. Robust prediction of protein folding free energy change requires the knowledge of protein three-dimensional (3D) structure. Unfortunately, protein 3D structure is not always available. In this case, one can still predict the protein stability change by constructing a homology model of the protein; however, the accuracy of homology model-based ΔΔG predictions is unknown. The perspectives of using 3D structures of the best templates are also unclear. Results To investigate these questions, we used the most popular and accurate publicly available tools: FoldX for stability change prediction and I-Tasser for homology modeling. We found that both homology models and best templates worsen the ΔΔG prediction, with best templates performing 1.5 times better than homology models. For AlphaFold models, we also found that the best templates seem to outperform protein models. Our findings imply using the 3D structures of the best templates for ΔΔG prediction if the 3D protein structure is unavailable. Contact d.ivankov@skoltech.ru
High Wind Speed Inversion Model of CYGNSS Sea Surface Data Based on Machine Learning

In response to the deficiency of the detection capability of traditional remote sensing means (scatterometer, microwave radiometer, etc.) for high wind speed above 25 m/s, this paper proposes a GNSS-R technique combined with a machine learning method to invert high wind speed at sea surface. The L1-level satellite-based data from the Cyclone Global Navigation Satellite System (CYGNSS), together with the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP) data, constitute the original sample set, which is processed and trained with Support Vector Regression (SVR), the combination of Principal Component Analysis (PCA) and SVR (PCA-SVR), and Convolutional Neural Network (CNN) methods, respectively, to finally construct a sea surface high wind speed inversion model. The three models for high wind speed inversion are certified by the test data collected during Typhoon Bavi in 2020. The results show that all three machine learning models can be used for high wind speed inversion on sea surface, among which the CNN method has the highest inversion accuracy with a mean absolute error of 2.71 m/s and a root mean square error of 3.80 m/s. The experimental results largely meet the operational requirements for high wind speed inversion accuracy.
Seasonal InSAR Displacements Documenting the Active Layer Freeze and Thaw Progression in Central-Western Spitsbergen, Svalbard

In permafrost areas, the active layer undergoes seasonal frost heave and thaw subsidence caused by ice formation and melting. The amplitude and timing of the ground displacement cycles depend on the climatic and ground conditions. Here we used Sentinel-1 Synthetic Aperture Radar Interferometry (InSAR) to document the seasonal displacement progression in three regions of Svalbard. We retrieved June–November 2017 time series and identified thaw subsidence maxima and their timing. InSAR measurements were compared with a composite index model based on ground surface temperature. Cyclic seasonal patterns are identified in all areas, but the timing of the displacement progression varies. The subsidence maxima occurred later on the warm western coast (Kapp Linné and Ny-Ålesund) compared to the colder interior (Adventdalen). The composite index model is generally able to explain the observed patterns. In Adventdalen, the model matches the InSAR time series at the location of the borehole. In Kapp Linné and Ny-Ålesund, larger deviations are found at the pixel-scale, but km or regional averaging improves the fit. The study highlights the potential for further development of regional InSAR products to represent the cyclic displacements in permafrost areas and infer the active layer thermal dynamics.
An Investigative Model of Adult Cyberbullying: A Court Case Analysis

Cyberbullying is a major social issue that is on the rise with a substantial potential to impact a large number of Internet users globally. The growth and rapid proliferation of the Internet and other ubiquitous technologies like social media and smart mobile devices have increased the propensity of cyberbullying, providing it with a wider audience and rapid access. This research developed an investigative model for cyberbullying, specifically developed for adults. Therein, the model considers the cyberbullying journey from conception of the bullying idea, identification of the target to the bullying as an action. The a-priori model is motivated by the General Theory of Crime and the Routine Activity Theory. The a-priori model is then validated using 20 cyberbullying court cases from Australia, Canada, the United States and Scotland.
The Importance of Subsurface Processes in Land Surface Modeling over a Temperate Region: An Analysis with SMAP, Cosmic Ray Neutron Sensing and Triple Collocation Analysis

Land surface models (LSMs) simulate water and energy cycles at the atmosphere–soil interface, however, the physical processes in the subsurface are typically oversimplified and lateral water movement is neglected. Here, a cross-evaluation of land surface model results (with and without lateral flow processes), the National Aeronautics and Space Administration (NASA) Soil Moisture Active/Passive (SMAP) mission soil moisture product, and cosmic-ray neutron sensor (CRNS) measurements is carried out over a temperate climate region with cropland and forests over western Germany. Besides a traditional land surface model (the Community Land Model (CLM) version 3.5), a coupled land surface-subsurface model (CLM-ParFlow) is applied. Compared to CLM stand-alone simulations, the coupled CLM-ParFlow model considered both vertical and lateral water movement. In addition to standard validation metrics, a triple collocation (TC) analysis has been performed to help understanding the random error variances of different soil moisture datasets. In this study, it is found that the three soil moisture datasets are consistent. The coupled and uncoupled model simulations were evaluated at CRNS sites and the coupled model simulations showed less bias than the CLM-standalone model (−0.02 cm3 cm−3 vs. 0.07 cm3 cm−3), similar random errors, but a slightly smaller correlation with the measurements (0.67 vs. 0.71). The TC-analysis showed that CLM-ParFlow reproduced better soil moisture dynamics than CLM stand alone and with a higher signal-to-noise ratio. This suggests that the representation of subsurface physics is of major importance in land surface modeling and that coupled land surface-subsurface modeling is of high interest.
Rupture Kinematics and Coseismic Slip Model of the 2021 Mw 7.3 Maduo (China) Earthquake: Implications for the Seismic Hazard of the Kunlun Fault

The 21 May 2021 Maduo earthquake was the largest event to occur on a secondary fault in the interior of the active Bayanhar block on the north-central Tibetan plateau in the last twenty years. A detailed kinematic study of the Maduo earthquake helps us to better understand the seismogenic environments of the secondary faults within the block, and its relationship with the block-bounding faults. In this study, firstly, SAR images are used to obtain the coseismic deformation fields. Secondly, we use a strain model-based method and steepest descent method (SDM) to resolve the three-dimensional displacement components and to invert the coseismic slip distribution constrained by coseismic displacement fields, respectively. The three-dimensional displacement fields reveal a dominant left-lateral strike-slip motion, local horizontal displacement variations and widely distributed near-fault subsidence/uplift deformation. We prefer a five-segment fault slip model, with well constrained fault geometry featuring different dip angles and striking, constrained by InSAR observations. The peak coseismic slip is estimated to be ~5 m near longitude 98.9°E at a depth of ~4–7 km. Overall, the distribution of the coseismic slip on the fault is highly correlated to the measured surface displacement offsets along the entire rupture. We observe the moderate shallow slip deficit and limited afterslip deformation following the Maduo earthquake, it may indicate the effects of off-fault deformation during the earthquake and stable interseismic creep on the fault. The occurrence of the Maduo earthquake on a subsidiary fault updates the importance and the traditional estimate of the seismic hazards for the Kunlun fault.
Exploring the relationship between local volunteering opportunities and the propensity to volunteer using a nationally representative survey of adults in Wales

ABSTRACT This study explored the respective importance of compositional (individual) and contextual (neighbourhood) factors associated with the propensity to engage in formal volunteering among a nationally representative sample of adults in Wales, UK. To date, while certain contextual characteristics of local communities have been found to be associated with the propensity to volunteer, compositional characteristics of residents tend to be stronger predictors. Few studies to date have specifically explored associations with local volunteering opportunities. To address such gaps, this study examined the extent to which such opportunities and broader neighbourhood factors such as urban/rural status and deprivation impacted upon propensities to volunteer, adjusting for important compositional predictors of voluntarism. In summary, while volunteering was marginally associated with a measure of local voluntary opportunities, hinting that the odds of formal volunteering are greater among those living in areas with more local voluntary organizations, this association was not retained following adjustment for other factors. This suggests that much of the area-level variance is explained by spatial variations in compositional factors. Further research is needed to examine determinants of volunteering behaviour at a range of spatial scales by drawing on wider measures of volunteering opportunities, before the factors at play can be fully understood.