Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of astaxanthin on neuron damage, inflammatory factors expressions and oxidative stress in mice with subarachnoid hemorrhage.

OBJECTIVE This study aimed to explore the effect of astaxanthin (ATX) on neuron damage, inflammatory factor expression and oxidative stress in mice with subarachnoid hemorrhage (SAH). METHODS Specific-pathogen-free, 'Institute of Cancer Research', male mice were randomly divided into four groups: SAH group, sham group, SAH + placebo group (SAH + Vehicle group) and SAH + ATX group. Neurological function was scored in each group. Brain water content, reactive oxygen species (ROS) content and inflammatory factor levels in the brain were detected by wet-dry weighting method, DCFH-DA fluorescent probe staining method and ELISA, respectively. Expression of NADPH oxidase 2 (NOX2), glial fibrillary acidic protein (GFAP) and apoptosis-related proteins Bax and Bcl-2 were detected by Western blot and quantitative real-time polymerase chain reaction. Neuronal apoptosis was detected by TUNEL staining. RESULTS Compared with sham group, neurological score, brain water content and ROS content in the other three groups increased significantly (all P<0.05). Neurological score, brain water content and ROS content in SAH + ATX group were lower than those in SAH group (all P<0.05). Compared with the sham group, there was increased expression of interleukin (IL)-6, IL-17 and tumor necrosis factor α (TNF-α), and increased neuronal apoptosis, as well as enhanced expression of NOX2, GFAP and Bax; while there was decreased IL-10 expression, and declined Bcl-2 expression, in the other three groups (all P<0.05). There was decreased expression of IL-6, IL-17 and TNF-α, declined expressions of NOX2, GFAP and Bax, and lowered neuronal apoptosis; while there was increased IL-10 expression, and enhanced Bcl-2 expression, in SAH + ATX group as compared to SAH group (all P<0.05). All indicators between SAH group and SAH + Vehicle group showed no significant differences (all P>0.05). CONCLUSION Astaxanthin can decrease neuron damage, inhibit inflammatory response, and improve oxidative stress in SAH mice. Thus, astaxanthin is a method for treating SAH.
Personalized Reminders for Immunization Using Short Messaging Systems to Improve Human Papillomavirus Vaccination Series Completion: Parallel-Group Randomized Trial

Background Completion rates among adolescents who initiate the human papillomavirus (HPV) vaccine 3-dose series are low. SMS text message vaccine reminders are effective, but less is known about the best types for HPV series completion or the ability to assess and target vaccine decision-making stage. Objective The aim of this study is to compare the effectiveness of HPV vaccine series completion in minority adolescents who received precision and educational versus conventional SMS text message reminders. Methods Enrolled parents of adolescents aged 9-17 years who received the first HPV vaccine dose at 1 of the 4 academic-affiliated community health clinics in New York City were randomized 1:1 to 1 of the 2 parallel, unblinded arms: precision SMS text messages (which included stage-targeted educational information, next dose due date, and site-specific walk-in hours) or conventional SMS text messages without educational information. Randomization was stratified according to gender, age, and language. The primary outcome was series completion within 12 months. In post hoc analysis, enrollees were compared with concurrent nonenrollees and historical controls. Results Overall, 956 parents were enrolled in the study. The precision (475 families) and conventional (481 families) SMS text message arms had similarly high series completion rates (344/475, 72.4% vs 364/481, 75.7%). A total of 42 days after the first dose, two-thirds of families, not initially in the preparation stage, moved to preparation or vaccinated stage. Those in either SMS text message arm had significantly higher completion rates than nonenrollees (708/1503, 47.1% vs 679/1503, 45.17%; P<.001). Even after removing those needing only 2 HPV doses, adolescents receiving any SMS text messages had higher completion rates than historical controls (337/2823, 11.93% vs 981/2823, 34.75%; P<.001). A population-wide effect was seen from 2014 to 2016, above historical trends. Conclusions SMS text message reminders led to timely HPV vaccine series completion in a low-income, urban, minority study population and also led to population-wide effects. Educational information did not provide an added benefit to this population. Trial Registration ClinicalTrials.gov NCT02236273; https://clinicaltrials.gov/ct2/show/NCT02236273
Relationship of parent-rated and objectively evaluated executive function to symptoms of posttraumatic stress and attention-deficit/hyperactivity disorder in homeless youth

ABSTRACT Compared to their stably housed peers, homeless, and highly mobile (HHM) youth experience disproportionately greater adversity and risk leading to a wide variety of poor developmental outcomes, and targeted interventions have the potential to mitigate such outcomes. A growing literature highlights the need for accurate diagnosis in high-risk populations given the considerable overlap between posttraumatic symptomology and behaviorally based disorders such as ADHD. Objective testing inferring neurobiological and circuit-based abnormalities in posttraumatic stress disorder (PTSD) and ADHD may provide a useful clinical tool to aid accurate diagnosis and treatment recommendations. This novel, exploratory study examined the relation between executive function (EF) as measured by objective testing and parent ratings with symptoms of posttraumatic stress and ADHD in 86 children (age 9 to 11) living in emergency homeless shelters. Parent-rated EF problems suggested broad impairment associated with ADHD symptoms but specific impairment in emotional/behavioral function associated with posttraumatic stress symptoms. While measures of inhibition and shifting EF were strongly associated with symptomology in bivariate correlations, they explained minimal variance in regression models. Internalizing behavior problems were associated with posttraumatic stress symptoms, while externalizing behavior problems were associated with ADHD symptoms. Implications for clinical practice and future research are discussed.
Optimal Variable Clustering for High-Dimensional Matrix Valued Data

Matrix valued data has become increasingly prevalent in many applications. Most of the existing clustering methods for this type of data are tailored to the mean model and do not account for the dependence structure of the features, which can be very informative, especially in high-dimensional settings or when mean information is not available. To extract the information from the dependence structure for clustering, we propose a new latent variable model for the features arranged in matrix form, with some unknown membership matrices representing the clusters for the rows and columns. Under this model, we further propose a class of hierarchical clustering algorithms using the difference of a weighted covariance matrix as the dissimilarity measure. Theoretically, we show that under mild conditions, our algorithm attains clustering consistency in the high-dimensional setting. While this consistency result holds for our algorithm with a broad class of weighted covariance matrices, the conditions for this result depend on the choice of the weight. To investigate how the weight affects the theoretical performance of our algorithm, we establish the minimax lower bound for clustering under our latent variable model in terms of some cluster separation metric. Given these results, we identify the optimal weight in the sense that using this weight guarantees our algorithm to be minimax rate-optimal. The practical implementation of our algorithm with the optimal weight is also discussed. Simulation studies show that our algorithm performs better than existing methods in terms of the adjusted Rand index (ARI). The method is applied to a genomic dataset and yields meaningful interpretations.
Network-Aware 5G Edge Computing for Object Detection: Augmenting Wearables to “See” More, Farther and Faster

Advanced wearable devices are increasingly incorporating high-resolution multi-camera systems. As state-of-the-art neural networks for processing the resulting image data are computationally demanding, there has been a growing interest in leveraging fifth generation (5G) wireless connectivity and mobile edge computing for offloading this processing closer to end-users. To assess this possibility, this paper presents a detailed simulation and evaluation of 5G wireless offloading for object detection in the case of a powerful, new smart wearable called VIS4ION, for the Blind-and-Visually Impaired (BVI). The current VIS4ION system is an instrumented book-bag with high-resolution cameras, vision processing, and haptic and audio feedback. The paper considers uploading the camera data to a mobile edge server to perform real-time object detection and transmitting the detection results back to the wearable. To determine the video requirements, the paper evaluates the impact of video bit rate and resolution on object detection accuracy and range. A new street scene dataset with labeled objects relevant to BVI navigation is leveraged for analysis. The vision evaluation is combined with a full-stack wireless network simulation to determine the distribution of throughputs and delays with real navigation paths and ray-tracing from new high-resolution 3D models in an urban environment. For comparison, the wireless simulation considers both a standard 4G-Long Term Evolution (LTE) sub-6-GHz carrier and high-rate 5G millimeter-wave (mmWave) carrier. The work thus provides a thorough and detailed assessment of edge computing for object detection with mmWave and sub-6-GHz connectivity in an application with both high bandwidth and low latency requirements.
On Dynamic Pricing with Covariates

We consider dynamic pricing with covariates under a generalized linear demand model: a seller can dynamically adjust the price of a product over a horizon of $T$ time periods, and at each time period $t$, the demand of the product is jointly determined by the price and an observable covariate vector $x_t\in\mathbb{R}^d$ through a generalized linear model with unknown co-efficients. Most of the existing literature assumes the covariate vectors $x_t$'s are independently and identically distributed (i.i.d.); the few papers that relax this assumption either sacrifice model generality or yield sub-optimal regret bounds. In this paper, we show that UCB and Thompson sampling-based pricing algorithms can achieve an $O(d\sqrt{T}\log T)$ regret upper bound without assuming any statistical structure on the covariates $x_t$. Our upper bound on the regret matches the lower bound up to logarithmic factors. We thus show that (i) the i.i. d. assumption is not necessary for obtaining low regret, and (ii) the regret bound can be independent of the (inverse) minimum eigenvalue of the covariance matrix of the $x_t$'s, a quantity present in previous bounds. Moreover, we consider a constrained setting of the dynamic pricing problem where there is a limited and unreplenishable inventory and we develop theoretical results that relate the best achievable algorithm performance to a variation measure with respect to the temporal distribution shift of the covariates. We also discuss conditions under which a better regret is achievable and demonstrate the proposed algorithms' performance with numerical experiments.
1.3 mu m GaInAsP near-travelling-wave laser amplifiers made by combination of angled facets and antireflection coatings

1. 3μm GaInAsP near-travelling-wave laser amplifiers have been realised by the combination of angled facets and antireflection coatings. Without in situ monitoring on the device itself during dielectric coatings, we can routinely obtain a low effective modal facet reflectivity of 5–8 × 10−4. The devices had an internal gain of 24 ± 1.5 dB for the TE mode and a single-mode fibre coupling loss of 5 dB/facet.
Electronic acoustic waves and bound states in the continuum in double-net metamaterials

Photonic bound states in the continuum are spatially localised modes with infinitely long lifetimes that exist within a radiation continuum at discrete energy levels. These states have been explored in various systems where their emergence is either guaranteed by crystallographic symmetries or due to topological protection. Their appearance at desired energy levels is, however, usually accompanied by non-BIC resonances, from which they cannot be disentangled. Here, we propose a new generic mechanism to realize bound states in the continuum that exist by first principles free of other resonances and are robust upon parameter tuning. The mechanism is based on the fundamental band in double-net metamaterials, which provides vanishing homogenized electromagnetic fields. We predict two new types of bound states in the continuum: i) generic modes confined to the metamaterial bulk, mimicking electronic acoustic waves in a hydrodynamic double plasma, and ii) topological surface bound states in the continuum.
Image Edge Restoring Filter

In computer vision, image processing and computer graphics, image smoothing filtering is a very basic and important task and to be expected possessing good edge-preserving smoothing property. Here we address the problem that the edge-preserving ability of many popular local smoothing filters needs to be improved. In this paper, we propose the image Edge Restoring Filter (ERF) to restore the blur edge pixels in the output of local smoothing filters to be clear. The proposed filter can been implemented after many local smoothing filter (such as Box filter, Gaussian filter, Bilateral Filter, Guided Filter and so on). The combinations of"original local smoothing filters + ERF"have better edge-preserving smoothing property than the original local smoothing filters. Experiments on image smoothing, image denoising and image enhancement demonstrate the excellent edges restoring ability of the proposed filter and good edgepreserving smoothing property of the combination"original local smoothing filters + ERF". The proposed filter would benefit a great variety of applications given that smoothing filtering is a high frequently used and fundamental operation.
The Curse of Low Task Diversity: On the Failure of Transfer Learning to Outperform MAML and Their Empirical Equivalence

Recently, it has been observed that a transfer learning solution might be all we need to solve many few-shot learning benchmarks -- thus raising important questions about when and how meta-learning algorithms should be deployed. In this paper, we seek to clarify these questions by 1. proposing a novel metric -- the diversity coefficient -- to measure the diversity of tasks in a few-shot learning benchmark and 2. by comparing Model-Agnostic Meta-Learning (MAML) and transfer learning under fair conditions (same architecture, same optimizer, and all models trained to convergence). Using the diversity coefficient, we show that the popular MiniImageNet and CIFAR-FS few-shot learning benchmarks have low diversity. This novel insight contextualizes claims that transfer learning solutions are better than meta-learned solutions in the regime of low diversity under a fair comparison. Specifically, we empirically find that a low diversity coefficient correlates with a high similarity between transfer learning and MAML learned solutions in terms of accuracy at meta-test time and classification layer similarity (using feature based distance metrics like SVCCA, PWCCA, CKA, and OPD). To further support our claim, we find this meta-test accuracy holds even as the model size changes. Therefore, we conclude that in the low diversity regime, MAML and transfer learning have equivalent meta-test performance when both are compared fairly. We also hope our work inspires more thoughtful constructions and quantitative evaluations of meta-learning benchmarks in the future.
Single-switch parallel power factor correction AC/DC converters with inherent load current feedback

Single-stage power factor correction (PFC) AC/DC converters usually suffer from high bulk capacitor voltage stress and extra switch current stress. Bulk capacitor voltage feedback with a coupled-winding structure can dramatically alleviate the stresses. However, the bulk capacitor voltage is still higher than 400 V and the switch has extra current stress. This paper presents a family of single-switch single-stage parallel PFC (PPFC) AC/DC converters with inherent load current feedback. The proposed converters combine the advantages of simple topology, low bulk capacitor voltage, and no extra current stress across the switch. The input current harmonics of an AC/DC converter with universal-line input and 5 V, 60 W output can meet IEC1000-3-2 Class D requirements with a 375 V maximum bulk capacitor voltage.
A formal approach to good practices in Pseudo-Labeling for Unsupervised Domain Adaptive Re-Identification

The use of pseudo-labels prevails in order to tackle Unsupervised Domain Adaptive (UDA) Re-Identification (re-ID) with the best performance. Indeed, this family of approaches has given rise to several UDA re-ID specific frameworks, which are effective. In these works, research directions to improve Pseudo-Labeling UDA re-ID performance are varied and mostly based on intuition and experiments: refining pseudo-labels, reducing the impact of errors in pseudo-labels. It can be hard to deduce from them general good practices, which can be implemented in any Pseudo-Labeling method, to consistently improve its performance. To address this key question, a new theoretical view on Pseudo-Labeling UDA re-ID is proposed. The contributions are threefold: (i) A novel theoretical framework for Pseudo-Labeling UDA re-ID, formalized through a new general learning upper-bound on the UDA re-ID performance. (ii) General good practices for Pseudo-Labeling, directly deduced from the interpretation of the proposed theoretical framework, in order to improve the target re-ID performance. (iii) Extensive experiments on challenging person and vehicle cross-dataset re-ID tasks, showing consistent performance improvements for various state-of-the-art methods and various proposed implementations of good practices.
Overview of Quantum Key Distribution Technique within IPsec Architecture

Quantum Key Distribution (QKD) is an approach for establishing symmetrical binary keys between distant users in an information-theoretically secure way. In this paper we provide an overview of existing solutions that integrate QKD within the most popular architecture for establishing secure communications in modern IP (Internet Protocol) networks - IPsec (Internet Protocol security). The provided overview can be used to further design the integration of QKD within the IPsec architecture striving for a standardized solution.
Fundamental analytic solutions for the Kulish-Sklyanin model with constant boundary conditions

In the present paper we analyze the construction of fundamental analytic solutions (FAS) for the generalized Kulish-Sklyanin models (KSM) for vanishing (VBC) and constant boundary conditions (CBC). Using FAS one can reduce the direct and inverse scattering problems for the Lax operator to a Riemann-Hilbert problem (RHP). For VBC we ﬁnd two FAS χ + ( x , t , λ ) and χ − ( x , t , λ ) analytic in the upper/lower C ± complex λ -plane. The RHP consists in: given the sewing function G ( x , t , λ ) to constructing both χ ± ( x , t , λ ) in their regions of analyticity. For CBC the problem becomes more complicated, because now the RHP must be formulated on a Riemannian surface of genus 1.
Nonlinear topological edge states: From dynamic delocalization to thermalization

Bertin Many Manda, 2 Rajesh Chaunsali, 3 Georgios Theocharis, and Charalampos Skokos LAUM, CNRS, Le Mans Université, Avenue Olivier Messiaen, 72085 Le Mans, France Nonlinear Dynamics and Chaos group, Department of Mathematics and Applied Mathematics, University of Cape Town, Rondebosch, 7701 Cape Town, South Africa Department of Aerospace Engineering, Indian Institute of Science, Bangalore 560012, India (Dated: December 28, 2021)
The behavior of essential dimension under specialization, II

Let $G$ be a linear algebraic group over a field. We show that, under mild assumptions, in a family of primitive generically free $G$-varieties over a base variety $B$ the essential dimension of the geometric fibers may drop on a countable union of Zariski closed subsets of $B$ and stays constant away from this countable union. We give several applications of this result.
Dipole dynamics in the point vortex model

At the very heart of turbulent fluid flows are many interacting vortices that produce a chaotic and seemingly unpredictable velocity field. Gaining new insight into the complex motion of vortices and how they can lead to topological changes of flows is of fundamental importance in our strive to understand turbulence. Our aim is form an understanding of vortex interactions by investigating the dynamics of point vortex dipoles interacting with a hierarchy of vortex structures using the idealized point vortex model. Motivated by its close analogy to the dynamics of quantum vortices in Bose–Einstein condensates, we present new results on dipole size evolution, stability properties of vortex clusters, and the role of dipole–cluster interactions in turbulent mixing in 2D quantum turbulence. In particular, we discover a mechanism of rapid cluster disintegration analogous to a time-reversed self-similar vortex collapse solution.
CUGE: A Chinese Language Understanding and Generation Evaluation Benchmark

Realizing general-purpose language intelligence has been a longstanding goal for natural language processing, where standard evaluation benchmarks play a fundamental and guiding role. We argue that for general-purpose language intelligence evaluation, the benchmark itself needs to be comprehensive and systematic. To this end, we propose CUGE, a Chinese Language Understanding and Generation Evaluation benchmark with the following features: (1) Hierarchical benchmark framework, where datasets are principally selected and organized with a language capability-task-dataset hierarchy. (2) Multi-level scoring strategy, where different levels of model performance are provided based on the hierarchical framework. To facilitate CUGE, we provide a public leaderboard that can be customized to support flexible model judging criteria. Evaluation results on representative pre-trained language models indicate ample room for improvement towards general-purpose language intelligence. CUGE is publicly available at cuge.baai. ac.cn.
Ornstein–Zernike behavior for Ising models with infinite-range interactions

We prove Ornstein-Zernike behavior for the large-distance asymptotics of the two-point function of the Ising model above the critical temperature under essentially optimal assumptions on the interaction. The main contribution of this work is that the interactions are not assumed to be of finite range. To the best of our knowledge, this is the first proof of OZ asymptotics for a nontrivial model with infinite-range interactions. Our results actually apply to the Green function of a large class of"self-repulsive in average"models, including a natural family of self-repulsive polymer models that contains, in particular, the self-avoiding walk, the Domb-Joyce model and the killed random walk. We aimed at a pedagogical and self-contained presentation.
The total coloring of $K_5$-minor-free graphs

A total k-coloring of a graph G is a coloring of V (G) ∪ E(G) using k colors such that no two adjacent or incident elements receive the same color. The total chromatic number χ′′(G) of G is the smallest integer k such that G has a total k-coloring. In the paper, it is proved that for any K5-minor-free graph G, χ ′′(G) ≤ ∆(G)+2 if ∆(G) ≥ 7. Moreover, χ′′(G) = ∆(G) + 1 if ∆(G) ≥ 10.