Miracle-dz/UniFAR-FTUs huggingface dataset:
Motives of moduli spaces of bundles on curves via variation of stability and flips

We study the rational Chow motives of certain moduli spaces of vector bundles on a smooth projective curve with additional structure (such as a parabolic structure or Higgs field). In the parabolic case, these moduli spaces depend on a choice of stability condition given by weights; our approach is to use explicit descriptions of variation of this stability condition in terms of simple birational transformations (standard flips/flops and Mukai flops) for which we understand the variation of the Chow motives. For moduli spaces of parabolic vector bundles, we describe the change in motive under wall‐crossings, and for moduli spaces of parabolic Higgs bundles, we show the motive does not change under wall‐crossings. Furthermore, we prove a motivic analogue of a classical theorem of Harder and Narasimhan relating the rational cohomology of moduli spaces of vector bundles with and without fixed determinant. For rank 2 vector bundles of odd degree, we obtain formulae for the rational Chow motives of moduli spaces of semistable vector bundles, moduli spaces of Higgs bundles and moduli spaces of parabolic (Higgs) bundles that are semistable with respect to a generic weight (all with and without fixed determinant).
Study of Oxygen Saturation by Pulse Oximetry and Arterial Blood Gas in ICU Patients: A Descriptive Cross-sectional Study

ABSTRACT Introduction: Pulse oximetery is expected to be an indirect estimation of arterial oxygen saturation. However, there often are gaps between SpO2 and SaO2. This study aims to study on arterial oxygen saturation measured by pulse oximetry and arterial blood gas among patients admitted in intensive care unit. Methods: It was a hospital-based descriptive cross-sectional study in which 101 patients meeting inclusion criteria were studied. SpO2 and SaO2 were measured simultaneously. Mean±SD of SpO2 and SaO2 with accuracy, sensitivity and specificity were measured. Results: According to SpO2 values, out of 101 patients, 26 (25.7%) were hypoxemic and 75 (74.25%) were non-hypoxemic. The mean±SD of SaO2 and SpO2 were 93.22±7.84% and 92.85±6.33% respectively. In 21 patients with SpO2<90%, the mean±SD SaO2 and SpO2 were 91.63±4.92 and 87.42±2.29 respectively. In 5 patients with SpO2 < 80%, the mean ± SD of SaO2 and SpO2 were: 63.40 ± 3.43 and 71.80±4.28, respectively. In non-hypoxemic group based on SpO2 values, the mean±SD of SpO2 and SaO2 were 95.773±2.19% and 95.654±3.01%, respectively. The agreement rate of SpO2 and SaO2 was 83.2%, and sensitivity and specificity of PO were 84.6% and 83%, respectively. Conclusions: Pulse Oximetry has high accuracy in estimating oxygen saturation with sp02>90% and can be used instead of arterial blood gas.
Impact of Aquatic-Based Physical Exercise Programs on Risk Markers of Cardiometabolic Diseases in Older People: A Study Protocol for Randomized-Controlled Trials

Cardiometabolic diseases are one of the primary causes of mortality and morbidity worldwide and sedentary lifestyles are contributing factors to these pathologies. Physical exercise has been recognized as an important tool in the prevention and treatment of these diseases. However, there are still some doubts about the efficacy of certain type of physical exercise programs for older participants. The main goal of this study is to assess the impact of different aquatic-based physical exercise programs on risk markers of cardiometabolic diseases in older people. The study group will consist of non-institutionalized individuals, within the age group of 65 or older. The sample will be randomly divided into four groups, three experimental groups (EG) and one control group (CG). Participants from the EGs will be exposed to three physical aquatic-based exercise programs for a period of 28 weeks (continuous aerobic, interval aerobic and combined). The evaluated parameters include anthropometry, physical functions, mental health, cognitive function, carotid arteries intima-media thickness, heart rate variability and biochemical markers. The results will allow an interpretation of the impact of different aquatic-based physical exercise programs on cardiometabolic diseases markers and can also be used as a tool for professionals to prescribe adequate and more efficient physical exercise programs.
Computational analysis of structural transformations in carbon nanostructures induced by hydrogenation

COMPUTATIONAL ANALYSIS OF STRUCTURAL TRANSFORMATIONS IN CARBON NANOSTRUCTURES INDUCED BY HYDROGENATION MAY 2011 ANDRE RODRIGUES MUNIZ B.E. , FEDERAL UNIVERSITY OF RIO GRANDE DO SUL, BRAZIL M.Sc., FEDERAL UNIVERSITY OF RIO GRANDE DO SUL, BRAZIL Ph.D., UNIVERSITY OF MASSACHUSETTS AMHERST Directed by: Professor Dimitrios Maroudas Carbon nanomaterials, such as carbon nanotubes and graphene, have attracted significant interest over the past several years due to their outstanding and unusual combination of physical properties. These properties can be modified in a controllable way by chemical functionalization in order to enable specific technological applications. One example is hydrogenation, achieved by the exposure of these materials to a source of atomic hydrogen. This process has been considered for hydrogen storage purposes and for the control of the band gap of these materials for applications in carbon-based electronics. Hydrogen atoms are chemisorbed onto the surface of these materials, introducing sp-hybridized C–C bonds in a structure originally formed by delocalized sp C–C bonding. This bonding transition causes severe structural and morphological changes to the graphene layers/walls. Also, it has been demonstrated that the exposure of multi-walled carbon nanotubes (MWCNTs) to a H2 plasma leads to the formation of diamond nanocrystals embedded within the nanotube walls.
Improved Semantic Role Labeling using Parameterized Neighborhood Memory Adaptation

Deep neural models achieve some of the best results for semantic role labeling. Inspired by instance-based learning that utilizes nearest neighbors to handle low-frequency context-specific training samples, we investigate the use of memory adaptation techniques in deep neural models. We propose a parameterized neighborhood memory adaptive (PNMA) method that uses a parameterized representation of the nearest neighbors of tokens in a memory of activations and makes predictions based on the most similar samples in the training data. We empirically show that PNMA consistently improves the SRL performance of the base model irrespective of types of word embeddings. Coupled with contextualized word embeddings derived from BERT, PNMA improves over existing models for both span and dependency semantic parsing datasets, especially on out-of-domain text, reaching F1 scores of 80.2, and 84.97 on CoNLL2005, and CoNLL2009 datasets, respectively.
Adaptive noise imitation for image denoising

The effectiveness of existing denoising algorithms typically relies on accurate pre-defined noise statistics or plenty of paired data, which limits their practicality. In this work, we focus on denoising in the more common case where noise statistics and paired data are unavailable. Considering that denoising CNNs require supervision, we develop a new \textbf{adaptive noise imitation (ADANI)} algorithm that can synthesize noisy data from naturally noisy images. To produce realistic noise, a noise generator takes unpaired noisy/clean images as input, where the noisy image is a guide for noise generation. By imposing explicit constraints on the type, level and gradient of noise, the output noise of ADANI will be similar to the guided noise, while keeping the original clean background of the image. Coupling the noisy data output from ADANI with the corresponding ground-truth, a denoising CNN is then trained in a fully-supervised manner. Experiments show that the noisy data produced by ADANI are visually and statistically similar to real ones so that the denoising CNN in our method is competitive to other networks trained with external paired data.
A Smoluchowski–Kramers approximation for an infinite dimensional system with state-dependent damping

We study the validity of a Smoluchowski-Kramers approximation for a class of wave equations in a bounded domain of $\mathbb{R}^n$ subject to a state-dependent damping and perturbed by a multiplicative noise. We prove that in the small mass limit the solution converges to the solution of a stochastic quasilinear parabolic equation where a noise-induced extra drift is created.
Locus: LiDAR-based Place Recognition using Spatiotemporal Higher-Order Pooling

Place Recognition enables the estimation of a globally consistent map and trajectory by providing non-local constraints in Simultaneous Localisation and Mapping (SLAM). This paper presents Locus, a novel place recognition method using 3D LiDAR point clouds in large-scale environments. We propose a method for extracting and encoding topological and temporal information related to components in a scene and demonstrate how the inclusion of this auxiliary information in place description leads to more robust and discriminative scene representations. Second-order pooling along with a non- linear transform is used to aggregate these multi-level features to generate a fixed-length global descriptor, which is invariant to the permutation of input features. The proposed method outperforms state-of-the-art methods on the KITTI dataset. Furthermore, Locus is demonstrated to be robust across several challenging situations such as occlusions and viewpoint changes in 3D LiDAR point clouds. The open-source implementation is available at: https://github.com/csiro-robotics/locus.
Audio, Speech, Language, & Signal Processing for COVID-19: A Comprehensive Overview

The Coronavirus (COVID-19) pandemic has been the research focus world-wide in the year 2020. Several efforts, from collection of COVID-19 patients' data to screening them for the virus's detection are taken with rigour. A major portion of COVID-19 symptoms are related to the functioning of the respiratory system, which in-turn critically influences the human speech production system. This drives the research focus towards identifying the markers of COVID-19 in speech and other human generated audio signals. In this paper, we give an overview of the speech and other audio signal, language and general signal processing-based work done using Artificial Intelligence techniques to screen, diagnose, monitor, and spread the awareness aboutCOVID-19. We also briefly describe the research related to detect accord-ing COVID-19 symptoms carried out so far. We aspire that this collective information will be useful in developing automated systems, which can help in the context of COVID-19 using non-obtrusive and easy to use modalities such as audio, speech, and language.
Modelling Verbal Morphology in Nen

Nen verbal morphology is particularly complex; a transitive verb can take up to 1,740 unique forms. The combined effect of having a large combinatoric space and a low-resource setting amplifies the need for NLP tools. Nen morphology utilises distributed exponence - a non-trivial means of mapping form to meaning. In this paper, we attempt to model Nen verbal morphology using state-of-the-art machine learning models for morphological reinflection. We explore and categorise the types of errors these systems generate. Our results show sensitivity to training data composition; different distributions of verb type yield different accuracies (patterning with E-complexity). We also demonstrate the types of patterns that can be inferred from the training data, through the case study of sycretism.
Role of Treatment Adherence, Doctor–Patient Trust, and Communication in Predicting Treatment Effects Among Tuberculosis Patients: Difference Between Urban and Rural Areas

Purpose China is the second highest tuberculosis (TB) burden in the world, and TB patients in the rural areas are about twice as many as urban patients. The purpose of present study was to explore the roles of medication adherence, doctor–patient trust and communication on treatment effects, and its inequality between urban and rural areas. Methods There were 564 eligible TB patients, from four tuberculosis hospitals in China, participating in this cross-sectional study. They filled out questionnaires regarding socio-demographic characteristics, medication adherence, treatment effect, doctor–patient trust, and communication. The structural equation model (SEM) was applied to explore the hypotheses in this study. All statistical analysis was done by SPSS 25.0 and Mplus 7.0 statistical software. Results This study included 267 (47.34%) urban and 297 (52.66%) rural eligible TB patients. The data fitted the research model well, and the urban TB patients reported better treatment effect than the rural ones (P=0.027). Overall, treatment adherence positively predicted treatment effect (Est.=0.353, P<0.001); doctor–patient communication positively influenced treatment adherence (Est.=0.214, P=0.002); and treatment adherence positively mediated the role of communication on treatment effect (Est.=0.076), 95% CI (0.026, 0.152). While in the grouping model, the urban patients’ treatment effect was only influenced by adherence (Est.=0.286, P=0.003); for the rural patients, treatment adherence (Est.=0.464, P<0.001) and doctor–patient trust (Est.=0.382, P=0.019) directly predicted treatment effects, and treatment adherence positively mediated the role of doctor–patient communication on treatment effect (Est.=0.175, P=0.006). Conclusion The treatment effect of TB patients, from urban and rural China, was influenced by a different mechanism, among which rural TB patients need not only improve the treatment adherence but also establish good doctor–patient trust and communication to improve treatment effects. These findings provided a theoretical guide on treatment and control for rural TB patients.
Occlusion Guided Scene Flow Estimation on 3D Point Clouds

3D scene flow estimation is a vital tool in perceiving our environment given depth or range sensors. Unlike optical flow, the data is usually sparse and in most cases partially occluded in between two temporal samplings. Here we propose a new scene flow architecture called OGSF-Net which tightly couples the learning for both flow and occlusions between frames. Their coupled symbiosis results in a more accurate prediction of flow in space. Unlike a traditional multi-action network, our unified approach is fused throughout the network, boosting performances for both occlusion detection and flow estimation. Our architecture is the first to gauge the occlusion in 3D scene flow estimation on point clouds. In key datasets such as Flyingthings3D and KITTI, we achieve the state-of-the-art results.1 2
Advancements in Radiofrequency Ablation of Cardiac Arrhythmias Open New Possibilities for Procedural Safety

The authors report no conflicts of interest for the published content. Address correspondence to: Rahul N. Doshi, MD, FHRS, Cardiac Arrhythmia Group, Cardiovascular Center of Excellence, HonorHealth, 10200 North 92nd Street Suite 150, Scottsdale, AZ 85258, USA. Email: rahuldoshimd@me.com. In this issue of the Journal of Innovations in Cardiac Rhythm Management, Zei et al. provide a comparison of the safety and efficacy of lowversus zero-fluoroscopy protocols for catheter ablation for atrial fibrillation (AF) using data from a multicenter, prospective registry.1 They report that no differences existed with respect to the primary outcomes of pulmonary isolation, safety, and procedural time between these two approaches. The data presented therefore provide food for thought—should we give up fluoroscopy completely?
Sideband transitions in a two-mode Josephson circuit driven beyond the rotating-wave approximation

Driving quantum systems periodically in time plays an essential role in the coherent control of quantum states. A good approximation for weak and nearly resonance driving fields, experiments often require large detuning and strong driving fields, for which the RWA may not hold. In this work, we experimentally, numerically, and analytically explore strongly driven two-mode Josephson circuits in the regime of strong driving and large detuning. Specifically, we investigate beam-splitter and two-mode squeezing interaction between the two modes induced by driving two-photon sideband transition. Using numerical simulations, we observe that the RWA is unable to correctly capture the amplitude of the sideband transition rates, which we verify using an analytical model based on perturbative corrections. Interestingly, we find that the breakdown of the RWA in the regime studied does not lead to qualitatively different dynamics, but gives the same results as the RWA theory at higher drive strengths, enhancing the coupling rates compared to what one would predict. Our work provides insight into the behavior of time-periodically driven systems beyond the RWA, and provides a robust theoretical framework for including these in the calculation and calibration of quantum protocols in circuit quantum electrodynamics.
Presence of concurrent sarcoid-like granulomas indicates better survival in cancer patients: a retrospective cohort study

Introduction An increased risk of sarcoidosis and sarcoid-like reactions in subjects with a history of malignancy has been suggested. We assessed the incidence and clinical characteristics of cancer patients with biopsies containing sarcoid-like granulomas on cancer metastasis and patient survival. Methods This is a retrospective, multicentre, observational study involving endobronchial ultrasound transbronchial needle aspiration and a melanoma patient dataset at the University of Miami, USA, and a sarcoidosis patient database at Chiba University, Japan. Subjects with a confirmed diagnosis of cancer and who subsequently developed granulomas in different organs were enrolled. The study was registered at Clinicaltrials.gov (NCT03844698). Results 133 patients met the study's criteria. The most common primary cancer sites were the skin (22.5%), breast (20.3%) and lymph node (12.8%). 24 (18%) patients developed sarcoid-like granulomas within 1 year of cancer diagnosis, 54 (40.6%) between 1 and 5 years and 49 (36.8%) after 5 years. Imaging showed possible sarcoid-like granulomas in lymph nodes in 51 cases (38.3%) and lung tissue and mediastinal lymph nodes in 73 cases (54.9%); some parenchymal reticular opacity and fibrosis was found in 5 (3.7%) and significant parenchymal fibrosis in 2 (1.5%) subjects. According to logistic regression analysis, the frequency of metastatic cancer was significantly lower in patients with sarcoid-like granulomas than in controls. Moreover, multivariate Cox proportional hazard analysis showed a significant survival advantage in those with sarcoid-like granuloma. Conclusion Sarcoid-like granulomas are uncommon pathology findings in cancer patients. There is a significant association between the presence of granulomas and reduced metastasis and increased survival. Further study is warranted to understand the protective mechanism involved. These findings suggest that patients with underlying malignancy who develop sarcoidosis and sarcoid-like reactions have a lower risk of stage 4 metastatic disease and better survival compared to patients who do not develop such granulomatous reactions https://bit.ly/2CNhc9e
Graphene on two-dimensional hexagonal BN, AlN, and GaN: Electronic, spin-orbit, and spin relaxation properties

We investigate the electronic band structure of graphene on a series of two-dimensional hexagonal nitride insulators $\mathrm{h}X\mathrm{N}, X=\text{B}$, Al, and Ga, with first-principles calculations. A symmetry-based model Hamiltonian is employed to extract orbital parameters and spin-orbit coupling (SOC) from the low-energy Dirac bands of the proximitized graphene. While commensurate hBN induces a staggered potential of about 10 meV into the Dirac band structure, less lattice-matched hAlN and hGaN disrupt the Dirac point much less, giving a staggered gap below 100 $\ensuremath{\mu}\mathrm{eV}$. Proximitized intrinsic SOC surprisingly does not increase much above the pristine graphene value of 12 $\ensuremath{\mu}\mathrm{eV}$; it stays in the window of 1--16 $\ensuremath{\mu}\mathrm{eV}$, depending strongly on stacking. However, Rashba SOC increases sharply when increasing the atomic number of the boron group, with calculated maximal values of 8, 15, and 65 $\ensuremath{\mu}\mathrm{eV}$ for B-, Al-, and Ga-based nitrides, respectively. The individual Rashba couplings also depend strongly on stacking, vanishing in symmetrically sandwiched structures, and can be tuned by a transverse electric field. The extracted spin-orbit parameters were used as input for spin transport simulations based on Chebyshev expansion of the time-evolution of the spin expectation values, yielding interesting predictions for the electron spin relaxation. Spin lifetime magnitudes and anisotropies depend strongly on the specific ($\mathrm{h}X\mathrm{N}$)/graphene/$\mathrm{h}X\mathrm{N}$ system, and they can be efficiently tuned by an applied external electric field as well as the carrier density in the graphene layer. A particularly interesting case for experiments is graphene/hGaN, in which the giant Rashba coupling is predicted to induce spin lifetimes of 1--10 ns, short enough to dominate over other mechanisms, and lead to the same spin relaxation anisotropy as that observed in conventional semiconductor heterostructures: 50%, meaning that out-of-plane spins relax twice as fast as in-plane spins.
ViDi: Descriptive Visual Data Clustering as Radiologist Assistant in COVID-19 Streamline Diagnostic

In the light of the COVID-19 pandemic, deep learning methods have been widely investigated in detecting COVID-19 from chest X-rays. However, a more pragmatic approach to applying AI methods to a medical diagnosis is designing a framework that facilitates human-machine interaction and expert decision making. Studies have shown that categorization can play an essential rule in accelerating real-world decision making. Inspired by descriptive document clustering, we propose a domain-independent explanatory clustering framework to group contextually related instances and support radiologists' decision making. While most descriptive clustering approaches employ domain-specific characteristics to form meaningful clusters, we focus on model-level explanation as a more general-purpose element of every learning process to achieve cluster homogeneity. We employ DeepSHAP to generate homogeneous clusters in terms of disease severity and describe the clusters using favorable and unfavorable saliency maps, which visualize the class discriminating regions of an image. These human-interpretable maps complement radiologist knowledge to investigate the whole cluster at once. Besides, as part of this study, we evaluate a model based on VGG-19, which can identify COVID and pneumonia cases with a positive predictive value of 95% and 97%, respectively, comparable to the recent explainable approaches for COVID diagnosis.
Retrospective Motion Correction of MR Images using Prior-Assisted Deep Learning

In MRI, motion artefacts are among the most common types of artefacts. They can degrade images and render them unusable for accurate diagnosis. Traditional methods, such as prospective or retrospective motion correction, have been proposed to avoid or alleviate motion artefacts. Recently, several other methods based on deep learning approaches have been proposed to solve this problem. This work proposes to enhance the performance of existing deep learning models by the inclusion of additional information present as image priors. The proposed approach has shown promising results and will be further investigated for clinical validity.