Miracle-dz/UniFAR-FTUs huggingface dataset:
Global well-posedness of 3-D inhomogeneous Navier-Stokes equations with ill-prepared initial data

In this paper, we investigate the global well-posedness of 3-D incompressible inhomogeneous Navier-Stokes equations with ill-prepared large initial data which are slowly varying in one space variable, that is, initial data of the form $\bigl(1+\e^{\be}a_0(x_{\rm h},\e x_3),(\ve^{1-\al} v^{\rm h}_0, \ve^{-\al}v_0^3)(x_{\rm h},\e x_3)\bigr)$ for any $\al\in ]0,1/3[,$ $\be>2\al,$ and $\ve$ being sufficiently small. We remark that initial data of this type do not satisfy the smallness conditions in \cite{c-p-z,HPZ3} no matter how small $\e$ is. In particular, this result greatly improves the global well-posedness result in \cite{PZZ3} with the so-called well-prepared initial data.
The Prognostic Value of Periostin Expression in Non-Small Cell Lung Cancer: A Meta-Analysis

Background Non-small cell lung cancer (NSCLC) is one of the leading causes of cancer-related death in the world and its poor prognosis is a major concern. Periostin was found to be associated with the prognosis of NSCLC. However, the research results were inconsistent. This meta-analysis evaluated the correlation between periostin expression and the prognosis of NSCLC. Material/Methods A meta-analysis was performed on data acquired from PubMed, EMBASE, the Cochrane Library, China National Knowledge Infrastructure (CNKI), and Wanfang Database from inception to 18 June 2022. Published and unpublished studies investigating the correlation between periostin expression and the prognosis of NSCLC were included in this meta-analysis. Eligible studies reported at least 1 of the following clinical outcome measures: overall survival, progression-free survival, cancer-specific survival, relapse-free survival, disease-free survival, or other clinical parameters of prognosis. Pooled hazard ratios (HR) with 95% confidence interval (CI) were calculated using the random-effects model. Sensitivity and subgroup analyses and assessment of publication bias were also conducted. Results This meta-analysis enrolled 2504 NSCLC cases from 12 eligible studies. The hazard ratio for the overall survival was 1.761 (95% CI: 1.022–3.033, P=0.041). Heterogeneity was significant among the studies, but publication bias was lacking. Subgroup analyses were performed based on different issues, such as districts, antibodies and methods for periostin detection. Conclusions Overexpression of periostin is a negative prognostic factor and is associated with worse overall survival (OS) in NSCLC patients. Periostin may serve as a prognostic biomarker for NSCLC patients.
Characteristics, treatment and delirium incidence of older adults hospitalized with COVID-19: a multicentre retrospective cohort study

Background: The COVID-19 pandemic has affected older adults disproportionately, and delirium is a concerning consequence; however, the relationship between delirium and corticosteroid use is uncertain. The objective of the present study was to describe patient characteristics, treatments and outcomes among older adults hospitalized with COVID-19, with a focus on dexamethasone use and delirium incidence. Methods: We completed this retrospective cohort study at 7 sites (including acute care, rehabilitation and long-term care settings) in Toronto, Ontario, Canada. We included adults aged 65 years or older, consecutively hospitalized with confirmed SARS-CoV-2 infection, between Mar. 11, 2020, and Apr. 30, 2021. We abstracted patient characteristics and outcomes from charts and analyzed them descriptively. We used a logistic regression model to determine the association between dexamethasone use and delirium incidence. Results: During the study period, 927 patients were admitted to the acute care hospitals with COVID-19. Patients’ median age was 79.0 years (interquartile range [IQR] 72.0–87.0), and 417 (45.0%) were female. Most patients were frail (61.9%), based on a Clinical Frailty Scale score of 5 or greater. The prevalence of delirium was 53.6%, and the incidence was 33.1%. Use of restraints was documented in 20.4% of patients. In rehabilitation and long-term care settings (n = 115), patients’ median age was 86.0 years (IQR 78.5–91.0), 72 (62.6%) were female and delirium occurred in 17 patients (14.8%). In patients admitted to acute care during wave 2 of the pandemic (Aug. 1, 2020, to Feb. 20, 2021), dexamethasone use had a nonsignificant association with delirium incidence (adjusted odds ratio 1.38, 95% confidence interval 0.77–2.50). Overall, in-hospital death occurred in 262 (28.4%) patients in acute care settings and 28 (24.3%) patients in rehabilitation or long-term care settings. Interpretation: In-hospital death, delirium and use of restraints were common in older adults admitted to hospital with COVID-19. Further research should be directed to improving the quality of care for this population with known vulnerabilities during continued waves of the COVID-19 pandemic.
Asymptotic Numerical Method for Nonlinear Constitutive Laws

ABSTRACT This paper deals with the application of the asymptomatic numerical method (ANM) to problems involving nonlinear constitutive laws. We are interested in the deformation theory of plasticity which does not take into account the elastic unloading. We show how to obtain a quadratic form of the problem, what allows us to apply easily the perturbation techniques and to obtain the fastest algorithm. Three constitutive behaviors will be analyzed and some examples will be presented to assess the interest of the proposed algorithm as compared with the classical iterative method of Newton-Raphson.
Effects of exercise anticipation on cardiorespiratory coherence

In this study, we explored the role of feedforward mechanisms in triggering cardiorespiratory adjustments before the onset of exercise. To isolate the feedforward aspects, we examined the effect of exercise anticipation on cardiorespiratory coherence. Twenty‐nine healthy males (age = 18.8 [0.96] years) were subjected to bicycle (BE) and handgrip exercise (H) at two different intensities, viz., low and high. Bicycle exercise was performed in a unilateral (left‐ and right‐sided) or bilateral mode, whereas handgrip was performed only in a unilateral mode. Single‐lead ECG and respiratory rhythm, measured in the 5 min of anticipation phase before the onset of exercise, were used for analysis. Coherence was computed between ECG‐derived instantaneous heart rate and respiratory signal. Average coherence in the high‐frequency band (0.15–0.4 Hz) was used to estimate respiratory sinus arrhythmia (RSA). We found that coherence decreased with the anticipation of exercise relative to baseline (baseline = 0.54 [0.16], BE = 0.41 [0.12], H = 0.39 [0.12], p < 0.001). The decrease was greater for high intensity exercise (low = 0.42 [0.11], high = 0.37 [0.1], p < 0.001). The fall of coherence with intensity was stronger for bicycle exercise (BE: low = 0.44 [0.12], high = 0.37 [0.12], H: low = 0.4 [0.12], high = 0.37 [0.12], p = 0.00433). The expectation of bilateral exercise resulted in lower coherence compared to unilateral exercise (right‐sided = 0.45 [0.16], left‐sided = 0.4 [0.16], bilateral = 0.36 [0.15], unilateral vs. bilateral: p < 0.001), and the left‐sided exercise had lower coherence compared to that of the right (left‐sided vs. right‐sided: p = 0.00925). Handgrip exercise showed similar trend (right‐sided = 0.4 [0.15], left‐sided = 0.37 [0.14], p = 0.0056). In conclusion, feedforward RSA adjustments in anticipation of exercise covaried with subsequent exercise‐related features like intensity, muscle mass (unilateral vs. bilateral), and the exercise side (left vs. right). The left versus the right difference in coherence indicates autonomic asymmetry. Feedforward changes in RSA are like those seen during actual exercise and might facilitate the rapid phase transition between rest and exercise.
Quantitative 7T sodium magnetic resonance imaging of the human brain using a 32‐channel phased‐array head coil: Application to patients with secondary progressive multiple sclerosis

Apparent tissue sodium concentrations (aTSCs) determined by 23Na brain magnetic resonance imaging (MRI) have the potential to serve as a biomarker in pathologies such as multiple sclerosis (MS). However, the quantification is hindered by the intrinsically low signal‐to‐noise ratio of 23Na MRI. The purpose of this study was to improve the accuracy and reliability of quantitative 23Na brain MRI by implementing a dedicated postprocessing pipeline and to evaluate the applicability of the developed approach for the examination of MS patients. 23Na brain MRI measurements of 13 healthy volunteers and 17 patients with secondary progressive multiple sclerosis (SPMS) were performed at 7 T using a dual‐tuned 23Na/1H birdcage coil with a receive‐only 32‐channel phased array. The aTSC values were determined for normal appearing white matter (NAWM) and normal appearing gray matter (NAGM) in healthy subjects and SPMS patients. Signal intensities were normalized using the mean cerebrospinal fluid (CSF) sodium concentration determined in 37 separate patients receiving a spinal tap for routine diagnostic purposes. Five volunteers underwent MRI examinations three times in a row to assess repeatability. Coefficients of variation (CoVs) were used to quantify the repeatability of the proposed method. aTSC values were compared regarding brain regions and subject cohort using the paired‐samples Wilcoxon rank‐sum test. Laboratory CSF sodium concentration did not differ significantly between patients without and with MS (p = 0.42). The proposed quantification workflow for 23Na MRI was highly repeatable with CoVs averaged over all five volunteers of 1.9% ± 0.9% for NAWM and 2.2% ± 1.6% for NAGM. Average NAWM aTSC was significantly higher in patients with SPMS compared with the control group (p = 0.009). Average NAGM aTSC did not differ significantly between healthy volunteers and MS patients (p = 0.98). The proposed postprocessing pipeline shows high repeatability and the results can serve as a baseline for further studies establishing 23Na brain MRI as a biomarker in diseases such as MS.
An improved, simple and field‐deployable CRISPR‐Cas12a assay for the detection of SARS‐CoV‐2

The RT‐PCR is the most popular confirmatory test for SARS‐CoV‐2. It is sensitive, but high instrumentation cost makes it difficult for use outside routine clinical setup. This has necessitated the development of alternative methods such as CRISPR‐based DETECTR method which uses lateral flow technology. Although accurate and sensitive, this method is limited by complex steps and recurrent cost of high‐quality lateral flow strips. The main goal of this study was to improve the Cas12a‐based SARS‐CoV‐2 DETECTR method and develop a portable and field‐deployable system to reduce the recurring consumable cost.
Exact spectral norm error of sample covariance

Let $X_1,\ldots,X_n$ be i.i. d. centered Gaussian vectors in $\mathbb{R}^p$ with covariance $\Sigma$, and let $\hat{\Sigma}\equiv n^{-1}\sum_{i=1}^n X_iX_i^\top$ be the sample covariance. A central object of interest in the non-asymptotic theory of sample covariance is the spectral norm error $||\hat{\Sigma}-\Sigma||$ of the sample covariance $\hat{\Sigma}$. In the path-breaking work of Koltchinskii and Lounici [KL17a], the `zeroth-order' magnitude of $||\hat{\Sigma}-\Sigma||$ is characterized by the dimension-free two-sided estimate $\mathbb{E} \{||\hat{\Sigma}-\Sigma||/||\Sigma||\}\asymp \sqrt{r(\Sigma)/n}+r(\Sigma)/n $, using the so-called effective rank $r(\Sigma)\equiv \mathrm{tr}(\Sigma)/||\Sigma||$. The goal of this paper is to provide a dimension-free first-order characterization for $||\hat{\Sigma}-\Sigma||$. We show that \begin{equation*} \bigg|\frac{\mathbb{E} \{||\hat{\Sigma}-\Sigma||/||\Sigma||\} }{\mathbb{E}\sup_{\alpha \in [0,1]}\{(\alpha+n^{-1/2}\mathscr{G}_{\Sigma}(h;\alpha))^2-\alpha^2\}}- 1\bigg| \leq \frac{C}{\sqrt{r(\Sigma)} }, \end{equation*} where $\{\mathscr{G}_{\Sigma}(h;\alpha): \alpha \in [0,1]\}$ are (stochastic) Gaussian widths over spherical slices of the (standardized) $\Sigma$-ellipsoid, playing the role of a first-order analogue to the zeroth-order characteristic $r(\Sigma)$. As an immediate application of the first-order characterization, we obtain a version of the Koltchinskii-Lounici bound with optimal constants. In the more special context of spiked covariance models, our first-order characterization reveals a new phase transition of $||\hat{\Sigma}-\Sigma||$ that exhibits qualitatively different behavior compared to the BBP phase transitional behavior of $||\hat{\Sigma}||$. A similar phase transition is also proved for the associated eigenvector.
Early Results from GLASS-JWST. IV. Spatially Resolved Metallicity in a Low-mass z ∼ 3 Galaxy with NIRISS

We report the first gas-phase metallicity map of a distant galaxy measured with the James Webb Space Telescope (JWST). We use the NIRISS slitless spectroscopy acquired by the GLASS Early Release Science program to spatially resolve the rest-frame optical nebular emission lines in a gravitationally lensed galaxy at z = 3.06 behind the A2744 galaxy cluster. This galaxy (dubbed GLASS-Zgrad1) has stellar mass ∼108.6 M ⊙, instantaneous star formation rate ∼8.6 M ⊙ yr−1 (both corrected for lensing magnification), and global metallicity one-fourth solar. From its emission-line maps ([O iii], Hβ, Hγ, [Ne iii], and [O ii]), we derive its spatial distribution of gas-phase metallicity using a well-established forward-modeling Bayesian inference method. The exquisite resolution and sensitivity of JWST/NIRISS, combined with lensing magnification, enable us to resolve this z ∼ 3 dwarf galaxy in ≳50 resolution elements with sufficient signal, an analysis hitherto not possible. We find that the radial metallicity gradient of GLASS-Zgrad1 is strongly inverted (i.e., positive): Δlog(O/H)/Δr = 0.165 ± 0.023 dex kpc−1. This measurement is robust at ≳4−σ confidence level against known systematics. This positive gradient may be due to tidal torques induced by a massive nearby (∼15 kpc projected) galaxy, which can cause inflows of metal-poor gas into the central regions of GLASS-Zgrad1. These first results showcase the power of JWST wide-field slitless spectroscopic modes to resolve the mass assembly and chemical enrichment of low-mass galaxies in and beyond the peak epoch of cosmic star formation (z ≳ 2). Reaching masses ≲ 109 M ⊙ at these redshifts is especially valuable to constrain the effects of galactic feedback and environment and is possible only with JWST’s new capabilities.
Two-Stream UNET Networks for Semantic Segmentation in Medical Images

Recent advances of semantic image segmentation greatly beneﬁt from deeper and larger Convolutional Neural Network (CNN) models. Compared to image segmentation in the wild, properties of both medical images themselves and of existing medical datasets hinder training deeper and larger models because of overﬁtting. To this end, we propose a novel two-stream UNET architecture for automatic end-to-end medical image segmentation, in which intensity value and gradient vector ﬂow (GVF) are two inputs for each stream, respectively. We demonstrate that two-stream CNNs with more low-level features greatly beneﬁt semantic segmentation for imperfect medical image datasets. Our proposed two-stream networks are trained and evaluated on the popular medical image segmentation benchmarks, and the results are competitive with the state of the art. The code will be released soon.
Constraining resonant dark matter self-interactions with strong gravitational lenses

We devise a method to constrain self-interacting dark matter (SIDM) from observations of quadruply-imaged quasars, and apply it to five self-interaction potentials with a long-range dark force. We consider several SIDM models with an attractive potential that allows for the formation of quasi-bound states, giving rise to resonant features in the cross section localized at particular velocities below $50 \ \rm{km} \ \rm{s^{-1}}$. We propose these resonances, which amplify or suppress the cross section amplitude by over an order of magnitude, accelerate or delay the onset of core collapse in low-mass dark matter halos, and derive constraints on the timescale for core collapse for the five interaction potentials we consider. Our data strongly disfavors scenarios in which a majority of halos core collapse, with the strongest constraints obtained for cross section strengths exceeding $100 \ \rm{cm^2} \rm{g}^{-1}$ at relative velocities below $30 \ \rm{km} \ \rm{s^{-1}}$. This work opens a new avenue to explore the vast landscape of possible SIDM theories.
FP4: Line-rate Greybox Fuzz Testing for P4 Switches

Compared to fixed-function switches, the flexibility of programmable switches comes at a cost, as programmer mistakes frequently result in subtle bugs in the network data plane. In this paper, we present the design and implementation of FP4, a fuzz-testing framework for P4 switches that achieves high expressiveness, coverage, and scalability. FP4 directly tests running switches by generating semi-random input packets and observing their resulting execution in the data plane. To achieve high coverage and scalability, at runtime, FP4 leverages P4 itself with another"tester"switch that generates and mutates billions of test packets per second entirely in the dataplane. Because testing some program branches requires navigating complex semantic input requirements, FP4 additionally leverages the programmability of P4 by instrumenting the tested program to pass coverage information back to the tester through the packet header. We present case studies showing that FP4 can validate both safety and stateful properties, improves efficiency over existing random packet generation baselines, and reaches 100% coverage in under a minute on a wide range of examples.
SAC-AP: Soft Actor Critic based Deep Reinforcement Learning for Alert Prioritization

Intrusion detection systems (IDS) generate a large number of false alerts which makes it difficult to inspect true positives. Hence, alert prioritization plays a crucial role in deciding which alerts to investigate from an enormous number of alerts that are generated by IDS. Recently, deep reinforcement learning (DRL) based deep deterministic policy gradient (DDPG) off-policy method has shown to achieve better results for alert prioritization as compared to other state-of-the-art methods. However, DDPG is prone to the problem of overfitting. Additionally, it also has a poor exploration capability and hence it is not suitable for problems with a stochastic environment. To address these limitations, we present a soft actor-critic based DRL algorithm for alert prioritization (SAC-AP), an off-policy method, based on the maximum entropy reinforcement learning framework that aims to maximize the expected reward while also maximizing the entropy. Further, the interaction between an adversary and a defender is modeled as a zero-sum game and a double oracle framework is utilized to obtain the approximate mixed strategy Nash equilibrium (MSNE). SAC-AP finds robust alert investigation policies and computes pure strategy best response against opponent's mixed strategy. We present the overall design of SAC-AP and evaluate its performance as compared to other state-of-the art alert prioritization methods. We consider defender's loss, i.e., the defender's inability to investigate the alerts that are triggered due to attacks, as the performance metric. Our results show that SAC-AP achieves up to 30% decrease in defender's loss as compared to the DDPG based alert prioritization method and hence provides better protection against intrusions. Moreover, the benefits are even higher when SAC-AP is compared to other traditional alert prioritization methods including Uniform, GAIN, RIO and Suricata.
XADLiME: eXplainable Alzheimer's Disease Likelihood Map Estimation via Clinically-guided Prototype Learning

Diagnosing Alzheimer's disease (AD) involves a deliberate diagnostic process owing to its innate traits of irreversibility with subtle and gradual progression. These characteristics make AD biomarker identification from structural brain imaging (e.g., structural MRI) scans quite challenging. Furthermore, there is a high possibility of getting entangled with normal aging. We propose a novel deep-learning approach through eXplainable AD Likelihood Map Estimation (XADLiME) for AD progression modeling over 3D sMRIs using clinically-guided prototype learning. Specifically, we establish a set of topologically-aware prototypes onto the clusters of latent clinical features, uncovering an AD spectrum manifold. We then measure the similarities between latent clinical features and well-established prototypes, estimating a"pseudo"likelihood map. By considering this pseudo map as an enriched reference, we employ an estimating network to estimate the AD likelihood map over a 3D sMRI scan. Additionally, we promote the explainability of such a likelihood map by revealing a comprehensible overview from two perspectives: clinical and morphological. During the inference, this estimated likelihood map served as a substitute over unseen sMRI scans for effectively conducting the downstream task while providing thorough explainable states.
Future Unruptured Intracranial Aneurysm Growth Prediction using Mesh Convolutional Neural Networks

The growth of unruptured intracranial aneurysms (UIAs) is a predictor of rupture. Therefore, for further imaging surveillance and treatment planning, it is important to be able to predict if an UIA is likely to grow based on an initial baseline Time-of-Flight MRA (TOF-MRA). It is known that the size and shape of UIAs are predictors of aneurysm growth and/or rupture. We perform a feasibility study of using a mesh convolutional neural network for future UIA growth prediction from baseline TOF-MRAs. We include 151 TOF-MRAs, with 169 UIAs where 49 UIAs were classified as growing and 120 as stable, based on the clinical definition of growth (>1 mm increase in size in follow-up scan). UIAs were segmented from TOF-MRAs and meshes were automatically generated. We investigate the input of both UIA mesh only and region-of-interest (ROI) meshes including UIA and surrounding parent vessels. We develop a classification model to predict UIAs that will grow or remain stable. The model consisted of a mesh convolutional neural network including additional novel input edge features of shape index and curvedness which describe the surface topology. It was investigated if input edge mid-point co-ordinates influenced the model performance. The model with highest AUC (63.8%) for growth prediction was using UIA meshes with input edge mid-point co-ordinate features (average F1 score = 62.3%, accuracy = 66.9%, sensitivity = 57.3%, specificity = 70.8%). We present a future UIA growth prediction model based on a mesh convolutional neural network with promising results.
Contextual Information and Commonsense Based Prompt for Emotion Recognition in Conversation

Emotion recognition in conversation (ERC) aims to detect the emotion for each utterance in a given conversation. The newly proposed ERC models have leveraged pre-trained language models (PLMs) with the paradigm of pre-training and fine-tuning to obtain good performance. However, these models seldom exploit PLMs' advantages thoroughly, and perform poorly for the conversations lacking explicit emotional expressions. In order to fully leverage the latent knowledge related to the emotional expressions in utterances, we propose a novel ERC model CISPER with the new paradigm of prompt and language model (LM) tuning. Specifically, CISPER is equipped with the prompt blending the contextual information and commonsense related to the interlocutor's utterances, to achieve ERC more effectively. Our extensive experiments demonstrate CISPER's superior performance over the state-of-the-art ERC models, and the effectiveness of leveraging these two kinds of significant prompt information for performance gains. To reproduce our experimental results conveniently, CISPER's sourcecode and the datasets have been shared at https://github.com/DeqingYang/CISPER.