Miracle-dz/UniFAR-FTUs huggingface dataset:
Wearability and preference of mouthguard during sport in patients undergoing orthodontic treatment with fixed appliances: a randomized clinical trial

Summary Background Orthodontic patients wearing fixed appliances are susceptible to traumatic dental injuries during a wide range of sporting activities. This randomized clinical trial investigated wearability and preference of mouthguards during sporting activities in patients undergoing orthodontic treatment with fixed appliances. Methods A prospective three-arm crossover randomized clinical trial conducted in the UK. Thirty patients in active orthodontic treatment with fixed appliances undertaking at least 120 minutes of contact sport per 6–8-week observation period were randomly assigned to one of six mouthguard allocation sequences consisting of three mouthguard types: (MG1) custom-made laboratory constructed, (MG2) mouth-formed OPRO® Gold Braces, and (MG3) pre-fabricated Shock Doctor® Single Brace. Patients completed a nine-outcome 100-mm visual analogue scale (VAS) questionnaire relating to mouthguard wearability during sport. Once feedback was completed, subjects were allocated the next mouthguard in the sequence. At study-end, subjects were asked to identify their preferred mouthguard. Results Twenty-four patients (median age = 13; inter-quartile range 12–14.5 years) completed n = 72 follow-up questionnaires with most playing rugby union or field hockey. Considering VAS score as a continuous variable, for comfort, stability, hardness, ability to breathe, ability to not cause nausea, and inclination to chew, MG2 performed better than MG3. For categorization of VAS score into low (less than 80 mm) or high (at least 80 mm) wearability, for comfort, stability, ability to not cause nausea, and inclination to chew, MG1 and MG2 also rated superior to MG3. Patients preferred MG1 overall. Conclusions This randomized clinical trial found that during contact sport patients in fixed appliances reported superior wearability for custom-made and mouth-formed mouthguards in comparison to pre-fabricated. Overall, patients preferred custom-made mouthguards. Clinical trials registration ClinicalTrials.gov: NCT04588831.
Unemployment and cancer screening: Baseline estimates to inform health care delivery in the context of COVID‐19 economic distress

During the coronavirus disease 2019 pandemic, US unemployment rates rose to historic highs, and they remain nearly double those of prepandemic levels. Employers are the most common source of health insurance among nonelderly adults. Thus, job loss may lead to a loss of health insurance and reduce access to cancer screening. This study examined associations between unemployment, health insurance, and cancer screening to inform the pandemic's potential impacts on early cancer detection.
Temperature impact on the influence of penguin‐derived nutrients and mosses on non‐native grass in a simulated polar ecosystem

Human activity and climate change are increasing the spread of species across the planet, threatening biodiversity and ecosystem functions. Invasion engineers, such as birds, facilitate plant growth through manuring of soil, while native vegetation influences plant germination by creating suitable microhabitats which are especially valuable in cold and dry polar regions. Here we tested how penguin‐derived nitrogen, several common Antarctic moss species and warming affect seed germination and growth of the non‐native grass Agrostis capillaris under laboratory conditions. Experimental settings included a simulation of contemporary season‐specific Antarctic light and temperature (2°C) conditions and a +5°C warming scenario. Mosses (Andreaea depressinervis, A. regularis, Sanionia uncinata and Chorisodontium aciphyllum) incorporated a range of nitrogen content and isotopic nitrogen signatures (δ15N) due to variation in sampling proximity to penguin colonies. Moss species greatly affected time to germination with consequences for further growth under the simulated Antarctic conditions. Grass seeds germinated 10 days earlier among A. regularis compared to S. uncinata and C. aciphyllum and 26 days earlier compared to A. depressinervis. Moss‐specific effects are likely related to microclimatic differences within the moss canopy. Warming reduced this moss influence. Grass emerged on average 20 days earlier under warming, leading to increased leaf count (88%), plant height (112%) and biomass (145%). Positive correlations were identified between moss and grass nitrogen content (r = 0.377), grass biomass (r = 0.332) and height (r = 0.742) with stronger effects under the warming scenario. Transfer of nitrogen from moss to grass was confirmed by δ15N (r = 0.803). Overall, the results suggest a shift from temperature‐limited to N‐limited growth of invasive plants under increased warming in the maritime Antarctic.
Brain activation during cognitive reappraisal depending on regulation goals and stimulus valence

Abstract Neural bases of cognitive reappraisal may depend on the direction of regulation (up- or downregulation) and stimulus valence (positive or negative). This study aimed to examine this using a cognitive reappraisal task and conjunction analysis on a relatively large sample of 83 individuals. We identified regions in which activations were common for all these types of emotion regulation. We also investigated differences in brain activation between the ‘decrease’ and ‘increase’ emotional response conditions, and between the regulation of negative and positive emotions. The common activation across conditions involved mainly the prefrontal and temporal regions. Decreasing emotions was associated with stronger involvement of the dorsolateral prefrontal cortex, while increasing with activation of the amygdala and hippocampus. Regulation of negative emotions involved stronger activation of the lateral occipital cortex, while regulation of positive emotions involved stronger activation of the anterior cingulate cortex extending to the medial prefrontal cortex. This study adds to previous findings, not only by doing a conjunction analysis on both emotional valences and regulation goals, but also doing this in a bigger sample size. Results suggest that reappraisal is not a uniform process and may have different neural bases depending on regulation goals and stimulus valence.
Basophil-derived IL-4 promotes cutaneous Staphylococcus aureus infection

Superficial cutaneous Staphylococcus aureus (S. aureus) infection in humans can lead to soft tissue infection, an important cause of morbidity and mortality. IL-17A production by skin TCRγδ+ cells in response to IL-1 and IL-23 produced by epithelial and immune cells is important for restraining S. aureus skin infection. How S. aureus evades this cutaneous innate immune response to establish infection is not clear. Here we show that mechanical injury of mouse skin by tape stripping predisposed mice to superficial skin infection with S. aureus. Topical application of S. aureus to tape-stripped skin caused cutaneous influx of basophils and increased Il4 expression. This basophil-derived IL-4 inhibited cutaneous IL-17A production by TCRγδ+ cells and promoted S. aureus infection of tape-stripped skin. We demonstrate that IL-4 acted on multiple checkpoints that suppress the cutaneous IL-17A response. It reduced Il1 and Il23 expression by keratinocytes, inhibited IL-1+IL-23–driven IL-17A production by TCRγδ+ cells, and impaired IL-17A–driven induction of neutrophil-attracting chemokines by keratinocytes. IL-4 receptor blockade is shown to promote Il17a expression and enhance bacterial clearance in tape-stripped mouse skin exposed to S. aureus, suggesting that it could serve as a therapeutic approach to prevent skin and soft tissue infection.
Indications for surgical management of retained lens fragments

Purpose of review Retained lens fragments are a relatively rare occurrence after cataract surgery. While no definite guidelines for the surgical management or timing of surgery of this complication exist, surgery is indicated for patients with large lens fragments, persistent inflammation, secondary glaucoma, corneal edema, retinal tears or detachments, and associated endophthalmitis. The aim of this review is to summarize the current surgical management of retained lens material. Recent findings The Intelligent Research in Sight registry database of 2.26 million patients who underwent cataract surgery in the US indicated that 0.18% (1 in 563) had secondary removal of retained lens fragments in the anterior chamber in the operating room within 1 year of the original cataract surgery. The risk of returning to the operating room for retained lens material removal was greater among men, smokers, patients with Medicaid or military insurance, and those who had complex cataract surgery. Medical management with topical corticosteroids or observation may be considered for small lens fragments, but surgical removal remains the mainstay of the treatment for large lens fragments. Summary Retained lens fragments following cataract surgery may result in various vision-threatening complications. Understanding the risk factors, diagnosis, and surgical management of retained lens fragments are critical to preserving good visual outcomes. Vitrectomy is effective in patients with posterior nuclear fragments, retinal detachment, endophthalmitis, or uncontrolled glaucoma not responding to medical management. The best timing for surgery for retained lens fragments should be further investigated in a prospective study.
Clinical variability at the mild end of BRAT1‐related spectrum: Evidence from two families with genotype–phenotype discordance

Biallelic mutations in the BRAT1 gene, encoding BRCA1‐associated ATM activator 1, result in variable phenotypes, from rigidity and multifocal seizure syndrome, lethal neonatal to neurodevelopmental disorder, and cerebellar atrophy with or without seizures, without obvious genotype–phenotype associations. We describe two families at the mildest end of the spectrum, differing in clinical presentation despite a common genotype at the BRAT1 locus. Two siblings displayed nonprogressive congenital ataxia and shrunken cerebellum on magnetic resonance imaging. A third unrelated patient showed normal neurodevelopment, adolescence‐onset seizures, and ataxia, shrunken cerebellum, and ultrastructural abnormalities on skin biopsy, representing the mildest form of NEDCAS hitherto described. Exome sequencing identified the c.638dup and the novel c.1395G>A BRAT1 variants, the latter causing exon 10 skippings. The p53‐MCL test revealed normal ATM kinase activity. Our findings broaden the allelic and clinical spectrum of BRAT1‐related disease, which should be suspected in presence of nonprogressive cerebellar signs, even without a neurodevelopmental disorder.
Empirical Bayes Control of the False Discovery Exceedance

In sparse large-scale testing problems where the false discovery proportion (FDP) is highly variable, the false discovery exceedance (FDX) provides a valuable alternative to the widely used false discovery rate (FDR). We develop an empirical Bayes approach to controlling the FDX. We show that for independent hypotheses from a two-group model and dependent hypotheses from a Gaussian model fulﬁlling the exchangeability condition, an oracle decision rule based on ranking and thresholding the local false discovery rate ( lfdr ) is optimal in the sense that the power is maximized subject to FDX constraint. We propose a data-driven FDX procedure that emulates the oracle via carefully designed computational shortcuts. We investigate the empirical performance of the proposed method using simulations and illustrate the merits of FDX control through an application for identifying abnormal stock trading strategies.
NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient Framework

Pretrained language models have become the standard approach for many NLP tasks due to strong performance, but they are very expensive to train. We propose a simple and efficient learning framework, TLM, that does not rely on large-scale pretraining. Given some labeled task data and a large general corpus, TLM uses task data as queries to retrieve a tiny subset of the general corpus and jointly optimizes the task objective and the language modeling objective from scratch. On eight classification datasets in four domains, TLM achieves results better than or similar to pretrained language models (e.g., RoBERTa-Large) while reducing the training FLOPs by two orders of magnitude. With high accuracy and efficiency, we hope TLM will contribute to democratizing NLP and expediting its development.
Bilinear-biquadratic spin-1 model in the Haldane and dimerized phases

We study the low-lying spectrum of the bilinear-biquadratic Heisenberg model in the dimerized and Haldane phases using a tensor renormalization method. At the critical point θ = −π/4 the finitesize spectrum predicted by the Wess-Zumino-Witten (WZW) model can only partly be confirmed. We find a singlet-singlet gap which does not fit into the WZW systematics. The results obtained are compared to Bethe Ansatz, exact diagonalization, and DMRG calculations for specific parameters.
Focusing on Possible Named Entities in Active Named Entity Label Acquisition

Named entity recognition (NER) aims to identify mentions of named entities in an unstructured text and classify them into the predeﬁned named entity classes. Even though deep learning-based pre-trained language models achieve good predictive performances, many domain-speciﬁc NER tasks still require a sufﬁcient amount of labeled data. Active learning (AL), a general framework for the label acquisition problem, has been used for the NER tasks to minimize the annotation cost without sacriﬁcing model performance. However, heavily imbalanced class distribution of tokens introduces challenges in designing effective AL querying methods for NER. We propose AL sentence query evaluation functions which pay more attention to possible positive tokens, and evaluate these proposed functions with both sentence-based and token-based cost evaluation strategies. We also propose a better data-driven normalization approach to penalize too long or too short sentences. Our experiments on three datasets from different domains reveal that the proposed approaches reduce the number of annotated tokens while achieving better or comparable prediction performance with conventional methods.
Quasi-potential theory for escape problem: Quantitative sharpness effect on SGD's escape from local minima

We develop a quantitative theory on an escape problem of a stochastic gradient descent (SGD) algorithm and investigate the effect of sharpness of loss surfaces on the escape. Deep learning has achieved tremendous success in various domains, however, it has opened up various theoretical open questions. One of the typical questions is why an SGD can ﬁnd parameters that generalize well over non-convex loss surfaces. An escape problem is an approach to tackle this question, which investigates how efﬁciently an SGD escapes from local minima. In this paper, we develop a quasi-potential theory for the escape problem, by applying a theory of stochastic dynamical systems. We show that the quasi-potential theory can handle both geometric properties of loss surfaces and a covariance structure of gradient noise in a uniﬁed manner, while they have been separately studied in previous works. Our theoretical results imply that (i) the sharpness of loss surfaces contributes to the slow escape of an SGD, and (ii) the SGD’s noise structure cancels the effect and exponentially accelerates the escape. We also conduct experiments to empirically validate our theory using neural networks trained with real data.
Infinite families of higher torsion in the homotopy groups of Moore spaces

We give a refinement of the stable Snaith splitting of the double loop space of a Moore space and use it to construct infinite $v_1$-periodic families of elements of order $p^{r+1}$ in the homotopy groups of mod $p^r$ Moore spaces. For odd primes $p$, our splitting implies that the homotopy groups of the mod $p^{r+1}$ Moore spectrum are summands of the unstable homotopy groups of each mod $p^r$ Moore space.
A design of single‐ended to differential‐ended power divider for X band application

A five‐port power divider with single‐ended input and two differential‐ended outputs is proposed. The frequency ranges from 9 to 11 GHz with a central frequency of 10 GHz. A stepped impedance line with open stubs is used between differential pairs for broadening the bandwidth and miniaturization of the divider. The divider is fabricated and the measurement results comply with the simulation. A proper performance for differential mode signal is achieved. The insertion loss is within 1.8 dB, the isolation is more than 15 dB, and the return loss is more than 11 dB. In addition, the common mode suppression is reached as well. © 2015 Wiley Periodicals, Inc. Microwave Opt Technol Lett 55:2669–2673, 2015
Variance asymptotics and central limit theory for geometric functionals of Poisson cylinder processes

This paper deals with the union set of a stationary Poisson process of cylinders in $\mathbb{R}^n$ having an $(n-m)$-dimensional base and an $m$-dimensional direction space, where $m\in\{0,1,\ldots,n-1\}$ and $n\geq 2$. The concept simultaneously generalises those of a Boolean model and a Poisson hyperplane or $m$-flat process. Under very general conditions on the typical cylinder base a Berry-Esseen bound for the volume of the union set within a sequence of growing test sets is derived. Assuming convexity of the cylinder bases and of the window a similar result is shown for a broad class of geometric functionals, including the intrinsic volumes. In this context the asymptotic variance constant is analysed in detail, which in contrast to the Boolean model leads to a new degeneracy phenomenon. A quantitative central limit theory is developed in a multivariate set-up as well.
Optical and dielectric properties of MoO3 nanosheets for van der Waals heterostructures

Two-dimensional (2D) insulators are a key element in the design and fabrication of van der Waals heterostructures. They are vital as transparent dielectric spacers whose thickness can influence both the photonic, electronic, and optoelectronic properties of 2D devices. Simultaneously, they provide protection of the active layers in the heterostructure. For these critical roles, hexagonal Boron Nitride (hBN) is the dominant choice due to its large bandgap, atomic flatness, low defect density, and encapsulation properties. However, the broad catalogue of 2D insulators offers exciting opportunities to replace hBN in certain applications that require transparent thin layers with additional optical degrees of freedom. Here we investigate the potential of single-crystalline Molybdenum Oxide (MoO$_3$) as an alternative 2D insulator for the design of nanodevices that require precise adjustment of the light polarization at the nanometer scale. First, we measure the wavelength-dependent refractive indices of MoO$_3$ along its three main crystal axes and determine the in-plane and out-of-plane anisotropy of its optical properties. We find the birefringence in MoO$_3$ nanosheets compares favorably with other 2D materials that exhibit strong birefringence, such as black phosphorus, ReS$_2$, or ReSe$_2$, in particular in the visible spectral range where MoO$_3$ has the unique advantage of transparency. Finally, we demonstrate the suitability of MoO$_3$ for dielectric encapsulation by reporting linewidth narrowing and reduced inhomogeneous broadening of 2D excitons and optically active quantum emitters, respectively, in a prototypical monolayer transition-metal dichalcogenide semiconductor. These results show the potential of MoO$_3$ as a 2D dielectric layer for manipulation of the light polarization in vertical 2D heterostructures.
A note on the representations of $\text{SO}(1,d+1)$

SO(1, d+1) is the isometry group of (d+1)-dimensional de Sitter spacetime (dSd+1) and the conformal group of R. This note gives a pedagogical introduction to the representation theory of SO(1, d + 1), from the perspective of de Sitter quantum field theory and using tools from conformal field theory. Topics include (1) the construction and classification of all unitary irreducible representations (UIRs) of SO(1, 2) and SL(2,R), (2) the construction and classification of all UIRs of SO(1, d+1) that describe integer-spin fields in dSd+1, (3) a physical framework for understanding these UIRs, (4) the definition and derivation of Harish-Chandra group characters of SO(1, d+ 1), and (5) a comparison between UIRs of SO(1, d+ 1) and SO(2, d). 1 ar X iv :2 11 1. 04 59 1v 2 [ he pth ] 1 7 N ov 2 02 1
Convolutional Gated MLP: Combining Convolutions & gMLP

To the best of our knowledge, this is the first paper to introduce Convolutions to Gated MultiLayer Perceptron and contributes an implementation of this novel Deep Learning architecture. Google Brain introduced the gMLP in May 2021. Microsoft introduced Convolutions in Vision Transformer in Mar 2021. Inspired by both gMLP and CvT, we introduce convolutional layers in gMLP. CvT combined the power of Convolutions and Attention. Our implementation combines the best of Convolutional learning along with spatial gated MLP. Further, the paper visualizes how CgMLP learns. Visualizations show how CgMLP learns from features such as outline of a car. While Attention was the basis of much of recent progress in Deep Learning, gMLP proposed an approach that doesn't use Attention computation. In Transformer based approaches, a whole lot of Attention matrixes need to be learnt using vast amount of training data. In gMLP, the fine tunning for new tasks can be challenging by transfer learning with smaller datasets. We implement CgMLP and compares it with gMLP on CIFAR dataset. Experimental results explore the power of generaliza-tion of CgMLP, while gMLP tend to drastically overfit the training data. To summarize, the paper contributes a novel Deep Learning architecture and demonstrates the learning mechanism of CgMLP through visualizations, for the first time in literature.
Varuna: scalable, low-cost training of massive deep learning models

Systems for training massive deep learning models (billions of parameters) today assume and require specialized "hyperclusters": hundreds or thousands of GPUs wired with specialized high-bandwidth interconnects such as NV-Link and Infiniband. Besides being expensive, such dependence on hyperclusters and custom high-speed inter-connects limits the size of such clusters, creating (a) scalability limits on job parallelism; (b) resource fragmentation across hyperclusters. In this paper, we present Varuna a new system that enables training massive deep learning models on commodity networking. Varuna makes thrifty use of networking resources and automatically configures the user's training job to efficiently use any given set of resources. Therefore, Varuna is able to leverage "low-priority" VMs that cost about 5x cheaper than dedicated GPUs, thus significantly reducing the cost of training massive models. We demonstrate the efficacy of Varuna by training massive models, including a 200 billion parameter model, on 5x cheaper "spot VMs", while maintaining high training throughput. Varuna improves end-to-end training time for language models like BERT and GPT-2 by up to 18x compared to other model-parallel approaches and up to 26% compared to other pipeline parallel approaches on commodity VMs. The code for Varuna is available at https://github.com/microsoft/varuna.