Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of herpes zoster vaccination on incident dementia: A retrospective study in two patient cohorts

Background Herpes zoster (HZ) infection increases dementia risk, but it is not known if herpes zoster vaccination is associated with lower risk for dementia. We determined if HZ vaccination, compared to no HZ vaccination, is associated with lower risk for incident dementia. Methods and findings Data was obtained from Veterans Health Affairs (VHA) medical records (10/1/2008–9/30/2019) with replication in MarketScan® commercial and Medicare claims (1/1/2009-12/31/2018). Eligible patients were ≥65 years of age and free of dementia for two years prior to baseline (VHA n = 136,016; MarketScan n = 172,790). Two index periods (either start of 2011 or 2012) were defined, where patients either had or did not have a HZ vaccination. Confounding was controlled with propensity scores and inverse probability of treatment weighting. Competing risk (VHA) and Cox proportional hazard (MarketScan) models estimated the association between HZ vaccination and incident dementia in all patients and in age (65–69, 70–74, ≥75) and race (White, Black, Other) sub-groups. Sensitivity analysis measured the association between HZ vaccination and incident Alzheimer’s dementia (AD). HZ vaccination at index versus no HZ vaccination throughout follow-up. VHA patients mean age was 75.7 (SD±7.4) years, 4.0% were female, 91.2% white and 20.2% had HZ vaccination. MarketScan patients mean age was 69.9 (SD±5.7) years, 65.0% were female and 14.2% had HZ vaccination. In both cohorts, HZ vaccination compared with no vaccination, was significantly associated with lower dementia risk (VHA HR = 0.69; 95%CI: 0.67–0.72; MarketScan HR = 0.65; 95%CI:0.57–0.74). HZ vaccination was not related to dementia risk in MarketScan patients aged 65–69 years. No difference in HZ vaccination to dementia effects were found by race. HZ vaccination was associated with lower risk for AD. Conclusions HZ vaccination is associated with reduced risk of dementia. Vaccination may provide nonspecific neuroprotection by training the immune system to limit damaging inflammation, or specific neuroprotection that prevents viral cytopathic effects.
Awareness of Pregnancy Screening for Group B Streptococcus Infection Among Women of Reproductive Age and Physicians in Jeddah, Saudi Arabia

Background Group B Streptococcus is part of the normal flora of the female urogenital tract and rectum. Vaginal colonization and transmission of this bacteria during delivery can lead to neonatal life-threatening complications, which can be prevented by screening and the administration of intrapartum antibiotics. This study’s aim was to assess the level of awareness of antenatal screening of Group B Streptococcus among women and physicians in Jeddah, Saudi Arabia. Methods A cross-sectional study using an online survey from a previously published study was distributed among 767 participants in Jeddah from June to August 2020. The participants were family medicine or obstetrics and gynecology physicians and women of reproductive age. Results Our results revealed a good level of knowledge of the physicians, although almost half of them reported the need for training to correctly perform screenings. The level of the women’s knowledge was relatively poor, their mean knowledge was 50.7%, and the majority were unaware of this infection (85.3%). Conclusions This study found a low level of knowledge of Group B Streptococcus among women of reproductive age and physicians in obstetrics and gynecology and family medicine. These findings confirm the importance of increasing the awareness of Group B Streptococcus among these populations to avoid complications associated with this infection.
QGTC: Accelerating Quantized GNN via GPU Tensor Core

Over the most recent years, quantized graph neural network (QGNN) attracts lots of research and industry attention due to its high robustness and low computation and memory overhead. Unfortunately, the performance gains of QGNN have never been realized on modern GPU platforms. To this end, we propose the first Tensor Core (TC) based computing framework, QGTC 1 , to support any-bitwidth computation for QGNNs on GPUs. We introduce a novel quantized low-bit arithmetic design based on the low-bit data representation and bit-decomposed computation. We craft a novel TC-tailored CUDA kernel design by incorporating 3D-stacked bit compression, zero-tile jumping, and non-zero tile reuse technique to improve the performance systematically. We incorporate an effective bandwidth-optimized subgraph packing strategy to maximize the transferring efficiency be-tween CPU host and GPU device. We integrate QGTC with Pytorch for better programmability and extensibility. Extensive experiments demonstrate that QGTC achieves an average of 3.17 × speedup compared with the state-of-the-art Deep Graph Library framework across diverse settings.
The Prominence of Artificial Intelligence in COVID-19

In December 2019, a novel virus called COVID-19 had caused an enormous number of causalities to date. The battle with the novel Coronavirus is baffling and horrifying after the Spanish Flu 2019. While the front-line doctors and medical researchers have made significant progress in controlling the spread of the highly contiguous virus, technology has also proved its significance in the battle. Moreover, Artificial Intelligence has been adopted in many medical applications to diagnose many diseases, even baffling experienced doctors. Therefore, this survey paper explores the methodologies proposed that can aid doctors and researchers in early and inexpensive methods of diagnosis of the disease. Most developing countries have difficulties carrying out tests using the conventional manner, but a significant way can be adopted with Machine and Deep Learning. On the other hand, the access to different types of medical images has motivated the researchers. As a result, a mammoth number of techniques are proposed. This paper first details the background knowledge of the conventional methods in the Artificial Intelligence domain. Following that, we gather the commonly used datasets and their use cases to date. In addition, we also show the percentage of researchers adopting Machine Learning over Deep Learning. Thus we provide a thorough analysis of this scenario. Lastly, in the research challenges, we elaborate on the problems faced in COVID-19 research, and we address the issues with our understanding to build a bright and healthy environment.
Confronting cosmic ray electron and positron excesses with hybrid triplet Higgs portal dark matter

We perform a detailed study of scalar dark matter with triplet Higgs extensions of the Standard Model in order to explain the cosmic ray electron and positron excesses reported by AMS-02 and DAMPE. A detailed analysis of the AMS-02 positron excess reveals that for different orderings (normal, inverted, and quasi-degenerate) of neutrino mass, the hybrid triplet Higgs portal framework is more favored with respect to the single triplet Higgs portal for TeV scale dark matter. We also show that the resonant peak and continuous excess in DAMPE cosmic ray data can be well explained with the hybrid triplet Higgs portal dark matter when a dark matter sub-halo nearby is taken into account.
Improving Prediction-Based Lossy Compression Dramatically via Ratio-Quality Modeling

Error-bounded lossy compression is one of the most effective techniques for reducing scientific data sizes. However, the traditional trial-and-error approach used to configure lossy compressors for finding the optimal trade-off between reconstructed data quality and compression ratio is prohibitively expensive. To resolve this issue, we develop a general-purpose analytical ratio-quality model based on the prediction-based lossy compression framework, which can effectively foresee the reduced data quality and compression ratio, as well as the impact of lossy compressed data on post-hoc analysis quality. Our analytical model significantly improves the prediction-based lossy compression in three use-cases: (1) optimization of predictor by selecting the best-fit predictor; (2) memory compression with a target ratio; and (3) in-situ compression optimization by fine-grained tuning error-bounds for various data partitions. We evaluate our analytical model on 10 scientific datasets, demonstrating its high accuracy (93.47% accuracy on average) and low computational cost (up to 18.7x lower than the trial-and-error approach) for estimating the compression ratio and the impact of lossy compression on post-hoc analysis quality. We also verify the high efficiency of our ratio-quality model using different applications across the three use-cases. In addition, our experiment demonstrates that our modeling-based approach reduces the time to store the 3D RTM data with HDF5 by up to 3.4 x with 128 CPU cores over the traditional solution.
Monte Carlo Study of Electron and Positron Cosmic-Ray Propagation with the CALET Spectrum

Focusing on the electron and positron spectrum measured with the Calorimetric Electron Telescope (CALET), which shows characteristic structures, we calculate the flux contributions of cosmic rays that have escaped from randomly appearing supernova remnants. We adopt a Monte Carlo method to take into account the stochastic nature of the appearance of nearby sources. We find that without a complicated energy dependence of the diffusion coefficient, simple power-law diffusion coefficients can produce spectra similar to the CALET spectrum, even with a dispersion in the injection index. The positron component measured with AMS-02 is consistent with a bump-like structure around 300 GeV in the CALET spectrum. One to three nearby supernovae can contribute up to a few tens of percent of the CALET flux at 2–4 TeV, while ten or more unknown and distant (≳500 pc) supernovae account for the remaining several tens of percent of the flux. The CALET spectrum, showing a sharp drop at ∼1 TeV, allows for a contribution of cosmic rays from an extraordinary event that occurred ∼400 kyr ago. This type of event releases electrons/positrons with a total energy more than 10 times the average energy for usual supernovae, and its occurrence rate is lower than one three-hundredth of the usual supernova rate.
Smaller enterprises’ experiences with ISO 9000

Discusses the experience of small and medium sized enterprises (SMEs) with ISO 9000 series certification, based on the results of a Western Australian questionnaire survey. It explores issues ranging from why certification was sought, the benefits derived and disappointments. Also examines the problems encountered by SMEs and how they dealt with them. Comparisons are made between those SMEs perceiving more success with ISO 9000 series certification and those which do not. Findings suggest that SMEs are experiencing mixed results from ISO 9000 series certification. Some are reporting considerable internal benefits while many are not experiencing external benefits. An encouraging outcome is that a number of SMEs see ISO 9000 series certification as a useful first step in the quality journey with a view to moving forward in the direction of a broader quality management approach.
Metastable low energy states in TiS2, TiSe2, TiTe2 systems predicted with evolutionary algorithms

We report a systematic study of low energy metastable states in van der Waals semimetals TiS2, TiSe2 and TiTe2 within the DFT theory by means the evolutionary search algorithm. We find a big difference between TiSe2, TiS2 and TiTe2 in low energy metastable states. While several metastable states exist in TiSe2 and TiS2, no low energy metastable states were found in TiTe2. We show that some of the obtained metastable states can be identified as charge density wave (CDW). We argue that existence of the low energy metastable phases indicates that emergence CDW as a ground state in these compounds.
The Power of Selecting Key Blocks with Local Pre-ranking for Long Document Information Retrieval

Les réseaux neuronaux profonds et les modèles fondés sur les transformeurs comme BERT ont envahi le domaine de la recherche d’informations (RI) ces dernières années. Leur succès est lié au mécanisme d’auto-attention qui permet de capturer les dépendances entre les mots indépendamment de leur distance. Cependant, en raison de sa complexité quadratique dans le nombre de mots, ce mécanisme ne peut être directement utilisé sur de longues séquences, ce qui ne permet pas de déployer entièrement les modèles neuronaux sur des documents longs pouvant contenir des milliers de mots. Trois stratégies standard ont été adoptées pour contourner ce problème. La première consiste à tronquer les documents longs, la deuxième à segmenter les documents longs en passages plus courts et la dernière à remplacer le module d’auto-attention par des modules d’attention parcimonieux. Dans le premier cas, des informations importantes peuvent être perdues et le jugement de pertinence n’est fondé que sur une partie de l’information contenue dans le document. Dans le deuxième cas, une architecture hiérarchique peut être adoptée pour construire une représentation du document sur la base des représentations de chaque passage. Cela dit, malgré ses résultats prometteurs, cette stratégie reste coûteuse en temps, en mémoire et en énergie. Dans le troisième cas, les contraintes de parcimonie peuvent conduire à manquer des dépendances importantes et, in fine, à des résultats sous-optimaux. L’approche que nous proposons est légèrement différente de ces stratégies et vise à capturer, dans les documents longs, les blocs les plus importants permettant de décider du statut, pertinent ou non, de l’ensemble du document. Elle repose sur trois étapes principales : (a) la sélection de blocs clés (c’est-à-dire susceptibles d’être pertinents) avec un pré-classement local en utilisant soit des modèles de RI classiques, soit un module d’apprentissage, (b) l’apprentissage d’une représentation conjointe des requêtes et des blocs clés à l’aide d’un modèle BERT standard, et (c) le calcul d’un score de pertinence final qui peut être considéré comme une agrégation d’informations de pertinence locale. Dans cet article, nous menons tout d’abord une analyse qui révèle que les signaux de pertinence peuvent apparaître à différents endroits dans les documents et que de tels signaux sont mieux capturés par des relations sémantiques que par des correspondances exactes. Nous examinons ensuite plusieurs méthodes pour sélectionner les blocs pertinents et montrons comment intégrer ces méthodes dans les modèles récents de RI.
On the Size of Subsets of $\mathbb{F}_q^n$ Avoiding Solutions to Linear Systems with Repeated Columns

Consider a system of $m$ balanced linear equations in $k$ variables with coefficients in $\mathbb{F}_q$. If $k \geq 2m + 1$, then a routine application of the slice rank method shows that there are constants $\beta,\gamma \geq 1$ with $\gamma < q$ such that, for every subset $S \subseteq \mathbb{F}_q^n$ of size at least $\beta \cdot \gamma^n$, the system has a solution $(x_1,\ldots,x_k) \in S^k$ with $x_1,\ldots,x_k$ not all equal. Building on a series of papers by Mimura and Tokushige and on a paper by Sauermann, this paper investigates the problem of finding a solution of higher non-degeneracy; that is, a solution where $x_1,\ldots,x_k$ are pairwise distinct, or even a solution where $x_1,\ldots,x_k$ do not satisfy any balanced linear equation that is not a linear combination of the equations in the system. In this paper, we focus on linear systems with repeated columns. For a large class of systems of this type, we prove that there are constants $\beta,\gamma \geq 1$ with $\gamma < q$ such that every subset $S \subseteq \mathbb{F}_q^n$ of size at least $\beta \cdot \gamma^n$ contains a solution that is non-degenerate (in one of the two senses described above). This class is disjoint from the class covered by Sauermann's result, and captures the systems studied by Mimura and Tokushige into a single proof. Moreover, a special case of our results shows that, if $S \subseteq \mathbb{F}_p^n$ is a subset such that $S - S$ does not contain a non-trivial $k$-term arithmetic progression (with $p$ prime and $3 \leq k \leq p$), then $S$ must have exponentially small density.
DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing

This paper presents a new pre-trained language model, DeBERTaV3, which improves the original DeBERTa model by replacing mask language modeling (MLM) with replaced token detection (RTD), a more sample-efficient pre-training task. Our analysis shows that vanilla embedding sharing in ELECTRA hurts training efficiency and model performance. This is because the training losses of the discriminator and the generator pull token embeddings in different directions, creating the"tug-of-war"dynamics. We thus propose a new gradient-disentangled embedding sharing method that avoids the tug-of-war dynamics, improving both training efficiency and the quality of the pre-trained model. We have pre-trained DeBERTaV3 using the same settings as DeBERTa to demonstrate its exceptional performance on a wide range of downstream natural language understanding (NLU) tasks. Taking the GLUE benchmark with eight tasks as an example, the DeBERTaV3 Large model achieves a 91.37% average score, which is 1.37% over DeBERTa and 1.91% over ELECTRA, setting a new state-of-the-art (SOTA) among the models with a similar structure. Furthermore, we have pre-trained a multi-lingual model mDeBERTa and observed a larger improvement over strong baselines compared to English models. For example, the mDeBERTa Base achieves a 79.8% zero-shot cross-lingual accuracy on XNLI and a 3.6% improvement over XLM-R Base, creating a new SOTA on this benchmark. We have made our pre-trained models and inference code publicly available at https://github.com/microsoft/DeBERTa.
The Mass of the Milky Way from the H3 Survey

The mass of the Milky Way is a critical quantity that, despite decades of research, remains uncertain within a factor of two. Until recently, most studies have used dynamical tracers in the inner regions of the halo, relying on extrapolations to estimate the mass of the Milky Way. In this paper, we extend the hierarchical Bayesian model applied in Eadie & Juri to study the mass distribution of the Milky Way halo; the new model allows for the use of all available 6D phase-space measurements. We use kinematic data of halo stars out to 142 kpc, obtained from the H3 survey and Gaia EDR3, to infer the mass of the Galaxy. Inference is carried out with the No-U-Turn sampler, a fast and scalable extension of Hamiltonian Monte Carlo. We report a median mass enclosed within 100 kpc of M(<100kpc)=0.69−0.04+0.05×1012M⊙ (68% Bayesian credible interval), or a virial mass of M200=M(<216.2−7.5+7.5kpc)=1.08−0.11+0.12×1012M⊙ , in good agreement with other recent estimates. We analyze our results using posterior predictive checks and find limitations in the model’s ability to describe the data. In particular, we find sensitivity with respect to substructure in the halo, which limits the precision of our mass estimates to ∼15%.
Simulation of induced current densities in the human body at industrial induction heating frequencies

At industrial workplaces in the vicinity of induction heating and melting devices, workers are exposed to strong magnetic fields. Up until now, little knowledge about the coupling of external fields into the human body at low frequencies existed. This paper provides numerical investigations to clarify the ratio between external homogeneous magnetic fields and induced current densities inside the human body in the frequency range from 250 Hz up to 10 kHz. The finite-difference time-domain (FDTD) method is used to calculate the induced current density in a realistic inhomogeneous 1 cm resolution human body model with appropriate tissue parameters. The magnitude of the external magnetic field equals the reference value for occupational exposure in the current guideline of the International Commission on Nonionizing Radiation Protection (ICNIRP). It was found that the calculated maximum current densities inside the body may exceed the basic restrictions of the ICNIRP guideline at least up to a factor of two. Finally, the suitability of the human body model for dosimetric investigations is discussed in view of fine-resolution models presented in the literature.
Complex absorbing potential method for Stark resonances

We characterize the resonances of Stark Hamiltonians by the complex absorbing potential method. Namely, we prove that the Stark resonances are the limit points of complex eigenvalues of the Stark Hamiltonian with a quadratic complex absorbing potential when the absorbing coefficient tends to zero. The proof employs the complex distortion outside a cone introduced in the previous work by the author. Potentials with local singularities such as the Coulomb potential are allowed as perturbations.
Velocity monitoring of gamma-Cas stars reveals their binarity status

The binary status of W Cas stars has been discussed while theoretically examining the origin of their peculiar X-ray emission. However, except in two cases, no systematic radial velocity monitoring of these stars had been undertaken yet to clarify their status. We now fill this gap using TIGRE, CARMENES, and UVES high-resolution spectroscopy. Velocities were determined for 16 stars, revealing shifts and/or changes in line profiles. The orbit of six new binaries could be determined: the long periods (80–120d) and small velocity amplitudes (5–7 km s−1) suggest low mass companions (0.6–1M⊙). The properties of the known W Cas binaries appear similar to those of other Be systems, with no clear-cut separation between them. One of the new systems is a candidate for a rare case of quadruple system involving a Be star. Five additional W Cas stars display velocity variations compatible with the presence of companions, but no orbital solution could yet be formally established for them hence they only receive the status of “binary candidate”.
NetQASM—a low-level instruction set architecture for hybrid quantum–classical programs in a quantum internet

We introduce NetQASM, a low-level instruction set architecture for quantum internet applications. NetQASM is a universal, platform-independent and extendable instruction set with support for local quantum gates, powerful classical logic and quantum networking operations for remote entanglement generation. Furthermore, NetQASM allows for close integration of classical logic and communication at the application layer with quantum operations at the physical layer. This enables quantum network applications to be programmed in high-level platform-independent software, which is not possible using any other QASM variants. We implement NetQASM in a series of tools to write, parse, encode and run NetQASM code, which are available online. Our tools include a higher-level software development kit (SDK) in Python, which allows an easy way of programming applications for a quantum internet. Our SDK can be used at home by making use of our existing quantum simulators, NetSquid and SimulaQron, and will also provide a public interface to hardware released on a future iteration of Quantum Network Explorer.