Miracle-dz/UniFAR-FTUs huggingface dataset:
To stripe or not to stripe: The origin of a novel foliar pigmentation pattern in monkeyflowers (Mimulus).

The origin of phenotypic novelty is one of the most challenging problems in evolutionary biology. Although genetic regulatory network rewiring or co-option has been widely recognized as a major contributor, in most cases how such genetic rewiring/co-option happens is completely unknown. We have studied a novel foliar pigmentation pattern that evolved recently in the monkeyflower species Mimulus verbenaceus. Through genome-wide association tests using wild-collected samples, experimental crosses of laboratory inbred lines, gene expression analyses, and functional assays, we identified an anthocyanin-activating R2R3-MYB gene, STRIPY, as the causal gene triggering the emergence of the discrete, mediolateral anthocyanin stripe in the M. verbenaceus leaf. Chemical mutagenesis revealed the existence of upstream activators and repressors that form a "hidden" prepattern along the leaf proximodistal axis, potentiating the unique expression pattern of STRIPY. Population genomics analyses did not reveal signatures of positive selection, indicating that non-adaptive processes may be responsible for the establishment of this novel trait in the wild. This study demonstrates that the origin of phenotypic novelty requires at least two separate phases, potentiation and actualization. The foliar stripe pattern of M. verbenaceus provides an excellent platform to probe the molecular details of both processes in future studies.
Post-injury fear-avoidance and confidence in readiness to return to sport in Irish collegiate athletes

ABSTRACT Fear avoidance and low confidence in their ability to return to sport can impede athletes’ subsequent recovery and return to sport. This study aimed to identify fear avoidance and confidence in readiness to return to sport following injury in Irish collegiate athletes. Male (416) and female (256) athletes from 24 field-sport teams at one university were prospectively followed for one season. The Athlete Fear Avoidance Questionnaire (AFAQ) and pain on the Visual Analogue Scale was completed following a time-loss injury. Prior to return to sport, participants completed the AFAQ and the Injury Psychological Readiness to Return to Sport (I-PRRS). We recorded 179 injuries. Fear avoidance was evident post-injury (21.8 ± 6.7), and the mean total I-PRRS score was 48.4 ± 8.9. Those with severe injuries presented with higher fear avoidance and lower confidence. Higher pain was associated with greater fear avoidance following injury (r = 0.32, p < 0.0001), prior to return to sport (r = 0.38, p < 0.0001), and with lower confidence (r = −0.27, p < 0.0001). Low confidence and fear avoidance was identified, particularly in those with a severe injury or high pain levels. Identifying, and providing psychosocial support to athletes experiencing, these issues is recommended.
User Identification Across Multiple Social Networks Based on Naive Bayes Model

Recently, the problem of user identification across multiple social networks (UIAMSNs) has attracted considerable attention since it is a prerequisite for many downstream tasks and applications. Although substantial network feature-based approaches have been proposed to solve the UIAMSNs’ problem, the matching degree in most of the current works is given by experience, which lacks a solid theoretical basis. To alleviate the above predicament, we propose a user identification algorithm based on naive Bayes model (UI-NBM) within the network feature-based framework. First, a matching degree index is designed based on the naive Bayes model, which can accurately measure the contributions of different common matched node pairs (MNPs) to the connection probability of unmatched node pairs (UMNPs). Second, the matching degrees of all UMNPs are formulated as the product of matrices, giving rise to the great reduction of the time complexity and the compact expression; Finally, with the idea of recursion process, more UMNPs can be iteratively predicted even when only a small amount of prior information (i.e., a few number of MNPs) is known. The experimental results on the synthetic and real cross platforms demonstrate that the method outperforms the baseline methods within the feature-based framework.
Methionine oxidation and reduction of the ethylene signalling component MaEIL9 are involved in banana fruit ripening.

The ethylene insensitive 3/ethylene insensitive 3-like (EIN3/EIL) plays an indispensable role in fruit ripening. However, the regulatory mechanism that links posttranslational modification of EIN3/EIL to fruit ripening is largely unknown. Here, we studied the expression of 13 MaEIL genes during banana fruit ripening, among which MaEIL9 displayed higher enhancement particularly in the ripening stage. Consistent with its transcript pattern, abundance of MaEIL9 protein gradually increased during the ripening process, with maximal enhancement in the ripening. DAP-seq analysis revealed that MaEIL9 directly targets a subset of genes related to fruit ripening, such as the starch hydrolytic genes MaAMY3D and MaBAM1. Stably overexpressing MaEIL9 in tomato fruit hastened fruit ripening, whereas transiently silencing this gene in banana fruit retarded the ripening process, supporting a positive role of MaEIL9 in fruit ripening. Moreover, oxidation of methionines (Met-129, Met-130 and Met-282) in MaEIL9 resulted in the loss of its DNA-binding capacity and transcriptional activation activity. Importantly, we identified MaEIL9 as a potential substrate protein of methionine sulfoxide reductase A MaMsrA4, and oxidation of Met-129, Met-130 and Met-282 in MaEIL9 could be restored by MaMsrA4. Collectively, our findings reveal a novel regulatory network controlling banana fruit ripening, which involves MaMsrA4-mediated redox regulation of the ethylene signalling component MaEIL9. This article is protected by copyright. All rights reserved.
Two for the price of one: If moving beyond traditional single‐best discrete choice experiments, should we use best‐worst, best‐best or ranking for preference elicitation?

Abstract This study undertook a head‐to‐head comparison of best‐worst, best‐best and ranking discrete choice experiments (DCEs) to help decide which method to use if moving beyond traditional single‐best DCEs. Respondents were randomized to one of three preference elicitation methods. Rank‐ordered (exploded) mixed logit models and respondent‐reported data were used to compare methods and first and second choices. First choices differed from second choices and preferences differed between elicitation methods, even beyond scale and scale dynamics. First choices of best‐worst had good choice consistency, scale dynamics and statistical efficiency, but this method's second choices performed worst. Ranking performed best on respondent‐reported difficulty and preference; best‐best's second choices on statistical efficiency. All three preference elicitation methods improve efficiency of data collection relative to using first choices only. However, differences in preferences between first and second choices challenge moving beyond single‐best DCE. If nevertheless doing so, best‐best and ranking are preferred over best‐worst DCE.
Distal Humerus Morphological Analysis of Chinese Individuals: A Statistical Shape Modeling Approach

A detailed analysis of the morphology of distal humeral articulation can help in the creation of anatomic prostheses of hemiarthroplasty. This study used statistical shape modeling to evaluate the 3D morphology of the distal humerus in healthy Chinese individuals and to investigate the proper articular morphology differences.
COMPLEXITY OF PARTICIPATION POST-STROKE: LONGITUDINAL ASSESSMENT OF COMMUNITY PARTICIPATION, POSITIVE AFFECT, SOCIAL SUPPORT AND FUNCTIONAL INDEPENDENCE

Objective To investigate longitudinal, predictive associations between community participation, positive affect, social support, and functional independence for individuals post-stroke at 3 and 12 months post-discharge. Design Longitudinal design. Subjects Data from 544 participants were obtained from the Stroke Recovery in Underserved Populations (2005–06) database. Methods A cross-lagged panel network analysis to identify the complex set of predictive relationships between community participation, positive affect, social support, and functional independence 3 months post-discharge to 12 months post-discharge. Results Community participation, particularly religious/spiritual activities, was an early predictor of later motor (functional) independence and social support. In addition, positive affect was an early predictor of social support. Conclusion While participation in social situations is a desired outcome post-stroke, the results of this study indicate the importance of identifying early factors that influence community participation throughout the recovery process. LAY ABSTRACT The ability to participate in desired activities is a common outcome goal of rehabilitation for stroke survivors. However, there are many factors that influence success in achieving this goal, including support from family and friends, mental health, and functional independence. This study sought to understand how these factors influence each other and participation over time. The results indicate that early participation post-stroke is an important predictor of later motor independence and social support needs. The results indicate that, while participation is often viewed as a rehabilitation outcome, understanding participation as a predictor of other rehabilitation outcomes is also important.
Critical phenomena in hyperbolic space

In this paper we study the critical behavior of an $N$-component ${\phi}^{4}$-model in hyperbolic space, which serves as a model of uniform frustration. We find that this model exhibits a second-order phase transition with an unusual magnetization texture that results from the lack of global parallelism in hyperbolic space. Angular defects occur on length scales comparable to the radius of curvature. This phase transition is governed by a new strong curvature fixed point that obeys scaling below the upper critical dimension $d_{uc}=4$. The exponents of this fixed point are given by the leading order terms of the $1/N$ expansion. In distinction to flat space no order $1/N$ corrections occur. We conclude that the description of many-particle systems in hyperbolic space is a promising avenue to investigate uniform frustration and non-trivial critical behavior within one theoretical approach.
Testing for selection bias and moral hazard in private health insurance: Evidence from a mixed public‐private health system

Abstract Separating selection bias from moral hazard in private health insurance (PHI) markets has been a challenging task. We estimate selection bias and moral hazard in Australia's mixed public‐private health system, where PHI premiums are community‐rated rather than risk‐rated. Using longitudinal cohort data, with fine‐grained measures for medical services predominantly funded by PHI providers, we find consistent and robust estimates of advantageous selection among hospitalized cardiovascular disease (CVD) patients. Specifically, we show that in addition to their risk‐averse attributes, CVD patients who purchase PHI use fewer services that are not covered by PHI providers (e.g., general practitioners and emergency departments) and have fewer comorbidities. Finally, unlike previous studies, we show that ex‐post moral hazard exists in the use of specific “in‐hospital” medical services such as specialist and physician services, miscellaneous diagnostic procedures, and therapeutic treatments. From the perspective of PHI providers, the annual cost of moral hazard translates to a lower bound estimate of $707 per patient, equivalent to a 3.03% reduction in their annual profits.
Spectral ergodic Banach problem and flat polynomials

We exhibit a sequence of flat polynomials with coefficients $0,1$. We thus get that there exist a sequences of Newman polynomials that are $L^\alpha$-flat, $0 \leq \alpha<2$. This settles an old question of Littlewood. In the opposite direction, we prove that the Newman polynomials are not $L^\alpha$-flat, for $\alpha \geq 4$. We further establish that there is a conservative, ergodic, $\sigma$-finite measure preserving transformation with simple Lebesgue spectrum. This answer affirmatively a long-standing problem of Banach from the Scottish book. Consequently, we obtain a positive answer to Mahler's problem in the class of Newman polynomials, and this allows us also to answer a question raised by Bourgain on the supremum of the $L^1$-norm of $L^2$-normalized idempotent polynomials.
Probing Photon Statistics in Adaptive Optics Images with SCExAO/MEC

We present an experimental study of photon statistics for high-contrast imaging with the Microwave Kinetic Inductance Detector (MKID) Exoplanet Camera (MEC) located behind the Subaru Coronagraphic Extreme Adaptive Optics System at the Subaru Telescope. We show that MEC measures the expected distributions for both on-axis companion intensity and off-axis intensity, which manifests as quasi-static speckles in the image plane and currently limits high-contrast imaging performance. These statistics can be probed by any MEC observation due to the photon-counting capabilities of MKID detectors. Photon arrival time statistics can also be used to directly distinguish companions from speckles using a post-processing technique called Stochastic Speckle Discrimination (SSD). Here, we give an overview of the SSD technique and highlight the first demonstration of SSD on an extended source—the protoplanetary disk AB Aurigae. We then present simulations that provide an in-depth exploration as to the current limitations of an extension of the SSD technique called Photon-Counting SSD (PCSSD) to provide a path forward for transitioning PCSSD from simulations to on-sky results. We end with a discussion of how to further improve the efficacy of such arrival-time-based post-processing techniques applicable to both MKIDs, as well as other high-speed astronomical cameras.
Topological approach of characterizing optical Skyrmions and Skyrmion lattices

The Skyrmion number of paraxial optical Skyrmions can be defined solely via their polarization singularities and associated winding numbers, using a mathematical derivation that exploits Stokes's theorem. It is demonstrated that this definition provides a robust way to extract the Skyrmion number from experimental data, as illustrated for a variety of optical (N\'eel-type) Skyrmions and bimerons, and their corresponding lattices. This method generates not only an increase in accuracy, but also provides an intuitive geometrical approach to understanding the topology of such quasi-particles of light, and their robustness against smooth transformations.
A patch-based architecture for multi-label classification from single label annotations

In this paper, we propose a patch-based architecture for multi-label classification problems where only a single positive label is observed in images of the dataset. Our contributions are twofold. First, we introduce a light patch architecture based on the attention mechanism. Next, leveraging on patch embedding self-similarities, we provide a novel strategy for estimating negative examples and deal with positive and unlabeled learning problems. Experiments demonstrate that our architecture can be trained from scratch, whereas pre-training on similar databases is required for related methods from the literature.
Efficient Planar Pose Estimation via UWB Measurements

State estimation is an essential part of autonomous systems. Integrating the Ultra-Wideband (UWB) technique has been shown to correct the long-term estimation drift and bypass the complexity of loop closure detection. However, few works on robotics treat UWB as a stand-alone state estimation solution. The primary purpose of this work is to investigate planar pose estimation using only UWB range measurements. We prove the excellent property of a two-step scheme, which says we can refine a consistent estimator to be asymptotically efficient by one step of Gauss-Newton iteration. Grounded on this result, we design the GN-ULS estimator, which reduces the computation time significantly compared to previous methods and presents the possibility of using only UWB for real-time state estimation.
Complementary Ramsey Numbers

In this paper, we consider a variant of Ramsey numbers which we call complementary Ramsey numbers $\bR(m,t,s)$. We first establish their connections to pairs of Ramsey $(s,t)$-graphs. Using the classification of Ramsey $(s,t)$-graphs for small $s,t$, we determine the complementary Ramsey numbers $\bR(m,t,s)$ for $(s,t)=(4,4)$ and $(3,6)$.
TSFool: Crafting High-quality Adversarial Time Series through Multi-objective Optimization to Fool Recurrent Neural Network Classifiers

—Deep neural network (DNN) classiﬁers are vulnerable to adversarial attacks. Although the existing gradient-based attacks have achieved good performance in feed-forward model and image recognition tasks, the extension for time series classiﬁcation in the recurrent neural network (RNN) remains a dilemma, because the cyclical structure of RNN prevents direct model differentiation and the visual sensitivity to perturbations of time series data challenges the traditional local optimization objective to minimize perturbation. In this paper, an efﬁcient and widely applicable approach called TSFool for crafting high-quality adversarial time series for the RNN classiﬁer is proposed. We propose a novel global optimization objective named Cam-ouﬂage Coefﬁcient to consider how well the adversarial samples hide in class clusters, and accordingly redeﬁne the high-quality adversarial attack as a multi-objective optimization problem. We also propose a new idea to use intervalized weighted ﬁnite automata (IWFA) to capture deeply embedded vulnerable samples having otherness between features and latent manifold to guide the approximation to the optimization solution. Experiments on 22 UCR datasets are conducted to conﬁrm that TSFool is a widely effective, efﬁcient and high-quality approach with 93.22% less local perturbation, 32.33% better global camouﬂage, and 1.12 times speedup to existing methods.
DevNet: Self-supervised Monocular Depth Learning via Density Volume Construction

Self-supervised depth learning from monocular images normally relies on the 2D pixel-wise photometric relation between temporally adjacent image frames. However, they neither fully exploit the 3D point-wise geometric correspondences, nor effectively tackle the ambiguities in the photometric warping caused by occlusions or illumination inconsistency. To address these problems, this work proposes Density Volume Construction Network (DevNet), a novel self-supervised monocular depth learning framework, that can consider 3D spatial information, and exploit stronger geometric constraints among adjacent camera frustums. Instead of directly regressing the pixel value from a single image, our DevNet divides the camera frustum into multiple parallel planes and predicts the pointwise occlusion probability density on each plane. The final depth map is generated by integrating the density along corresponding rays. During the training process, novel regularization strategies and loss functions are introduced to mitigate photometric ambiguities and overfitting. Without obviously enlarging model parameters size or running time, DevNet outperforms several representative baselines on both the KITTI-2015 outdoor dataset and NYU-V2 indoor dataset. In particular, the root-mean-square-deviation is reduced by around 4% with DevNet on both KITTI-2015 and NYU-V2 in the task of depth estimation. Code is available at https://github.com/gitkaichenzhou/DevNet.
Free-Space Quasi-Phase Matching

Nonlinear optical effects based on birefringent phase matching and quasi-phase-matching (QPM) are an inherent part of contemporary photonics. Among the widely used nonlinear optical materials, numbering more than 80 compounds, only three (KTP, LN, LT) are suitable for creating commercially available periodically poled [1] (PP) structures. A number of approaches were used to create conditions for QPM in other materials. In [2], and [3] it was realized in Brewster-angle-stacked wafers (5 plates), made of GaAs and CdTe isotropic crystals. In [4] the QPM conditions were created in chalcogenide crystals by using specific packing defects-“twins”. Attempts were made to create a tight stack of multiple, bonded-together crystalline quartz plates [5]. But these methods did not receive further development. Here we propose a concept based on multiple passes of interacting beams through the same nonlinear crystal element placed inside a quasi-waveguide or multi-pass cell.
Look Before You Leap: Improving Text-based Person Retrieval by Learning A Consistent Cross-modal Common Manifold

The core problem of text-based person retrieval is how to bridge the heterogeneous gap between multi-modal data. Many previous approaches contrive to learning a latent common manifold mapping paradigm following a cross-modal distribution consensus prediction (CDCP) manner. When mapping features from distribution of one certain modality into the common manifold, feature distribution of the opposite modality is completely invisible. That is to say, how to achieve a cross-modal distribution consensus so as to embed and align the multi-modal features in a constructed cross-modal common manifold all depends on the experience of the model itself, instead of the actual situation. With such methods, it is inevitable that the multi-modal data can not be well aligned in the common manifold, which finally leads to a sub-optimal retrieval performance. To overcome this CDCP dilemma, we propose a novel algorithm termed LBUL to learn a Consistent Cross-modal Common Manifold (C3 M) for text-based person retrieval. The core idea of our method, just as a Chinese saying goes, is to 'san si er hou xing', namely, to Look Before yoU Leap (LBUL). The common manifold mapping mechanism of LBUL contains a looking step and a leaping step. Compared to CDCP-based methods, LBUL considers distribution characteristics of both the visual and textual modalities before embedding data from one certain modality into C3 M to achieve a more solid cross-modal distribution consensus, and hence achieve a superior retrieval accuracy. We evaluate our proposed method on two text-based person retrieval datasets CUHK-PEDES and RSTPReid. Experimental results demonstrate that the proposed LBUL outperforms previous methods and achieves the state-of-the-art performance.
Multiple View Performers for Shape Completion

We propose the Multiple View Performer (MVP) - a new architecture for 3D shape completion from a series of temporally sequential views. MVP accomplishes this task by using linear-attention Transformers called Performers. Our model allows the current observation of the scene to attend to the previous ones for more accurate infilling. The history of past observations is compressed via the compact associative memory approximating modern continuous Hopfield memory, but crucially of size independent from the history length. We compare our model with several baselines for shape completion over time, demonstrating the generalization gains that MVP provides. To the best of our knowledge, MVP is the first multiple view voxel reconstruction method that does not require registration of multiple depth views and the first causal Transformer based model for 3D shape completion.
Parallel Linear Algebra Methods

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