Miracle-dz/UniFAR-FTUs huggingface dataset:
Original Article

Objectives: This study sought to compare enamel cracks after orthodontic bracket debonding in the surfaces prepared with erbium, chromium: yttrium-scandium-galliumgarnet (Er,Cr:YSGG) laser and the conventional acid-etching technique. Materials and Methods: This in-vitro experimental study was conducted on 60 sound human premolars extracted for orthodontic purposes. The teeth were randomly divided into two groups (n=30). The teeth in group A were etched with 37% phosphoric acid gel, while the teeth in group B were subjected to Er,Cr:YSGG laser irradiation (gold handpiece, MZ8 tip, 50Hz, 4.5W, 60μs, 80% water and 60% air). Orthodontic brackets were bonded to the enamel surfaces and were then debonded in both groups. The samples were inspected under a stereomicroscope at ×38 magnification to assess the number and length of enamel cracks before bonding and after debonding. Independent-samples t-test was used to compare the frequency of enamel cracks in the two groups. Levene’s test was applied to assess the equality of variances. Results: No significant difference was noted in the frequency or length of enamel cracks between the two groups after debonding (P>0.05). Conclusions: Despite the same results of the frequency and length of enamel cracks in the two groups and by considering the side effects of acid-etching (demineralization and formation of white spot lesions), Er,Cr:YSGG laser may be used as an alternative to acidetching for enamel surface preparation prior to bracket bonding.
The Transient Responses of an Axisymmetric Tropical Cyclone to Instantaneous Surface Roughening and Drying

Inland tropical cyclone (TC) impacts due to high winds and rainfall-induced flooding depend strongly on the evolution of the wind field and precipitation distribution after landfall. However, research has yet to test the detailed response of a mature TC and its hazards to changes in surface forcing in idealized settings. This work tests the transient responses of an idealized hurricane to instantaneous transitions in two key surface properties associated with landfall: roughening and drying. Simplified axisymmetric numerical modeling experiments are performed in which the surface drag coefficient and evaporative fraction are each systematically modified beneath a mature hurricane. Surface drying stabilizes the eyewall and consequently weakens the overturning circulation, thereby reducing inward angular momentum transport that slowly decays the wind field only within the inner core. In contrast, surface roughening initially (~12 h) rapidly weakens the entire low-level wind field and enhances the overturning circulation dynamically despite the concurrent thermodynamic stabilization of the eyewall; thereafter the storm gradually decays, similar to drying. As a result, total precipitation temporarily increases with roughening but uniformly decreases with drying. Storm size decreases monotonically and rapidly with surface roughening, whereas the radius of maximum wind can increase with moderate surface drying. Overall, this work provides a mechanistic foundation for understanding the inland evolution of real storms in nature.
Generalized Picone inequalities and their applications to (p,q)-Laplace equations

Abstract We obtain a generalization of the Picone inequality which, in combination with the classical Picone inequality, appears to be useful for problems with the ( p , q ) (p,q) -Laplace-type operators. With its help, as well as with the help of several other known generalized Picone inequalities, we provide some nontrivial facts on the existence and nonexistence of positive solutions to the zero Dirichlet problem for the equation − Δ p u − Δ q u = f μ ( x , u , ∇ u ) -\hspace{-0.25em}{\text{Δ}}_{p}u-{\text{Δ}}_{q}u={f}_{\mu }(x,u,\nabla u) in a bounded domain Ω ⊂ ℝ N \text{Ω}\hspace{0.25em}\subset {{\mathbb{R}}}^{N} under certain assumptions on the nonlinearity and with a special attention to the resonance case f μ ( x , u , ∇ u ) = λ 1 ( p ) | u | p − 2 u + μ | u | q − 2 u {f}_{\mu }(x,u,\nabla u)={\lambda }_{1}(p)|u{|}^{p-2}u+\mu |u{|}^{q-2}u , where λ 1 ( p ) {\lambda }_{1}(p) is the first eigenvalue of the p-Laplacian.
Multi-Scale Aggregation Using Feature Pyramid Module for Text-Independent Speaker Verification

Currently, the most widely used approach for speaker veriﬁcation is the deep speaker embedding learning. In this approach, convolutional neural networks are mainly used as a frame-level feature extractor, and speaker embeddings are extracted from the last layer of the feature extractor. Multi-scale aggregation (MSA), which utilizes multi-scale features from different layers of the feature extractor, has recently been introduced into the approach and has shown improved performance for both short and long utterances. This paper improves the MSA by using a feature pyramid module, which enhances speaker-discriminative information of features at multiple layers via a top-down pathway and lateral connections. We extract speaker embeddings using the enhanced features that contain rich speaker information at different resolutions. Experiments on the VoxCeleb dataset show that the proposed module improves previous MSA methods with a smaller number of parameters, providing better performance than state-of-the-art approaches.
Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-Air

We study federated machine learning (ML) at the wireless edge, where power- and bandwidth-limited wireless devices with local datasets carry out distributed stochastic gradient descent (DSGD) with the help of a parameter server (PS). Standard approaches assume separate computation and communication, where local gradient estimates are compressed and transmitted to the PS over orthogonal links. Following this digital approach, we introduce D-DSGD, in which the wireless devices employ gradient quantization and error accumulation, and transmit their gradient estimates to the PS over a multiple access channel (MAC). We then introduce a novel analog scheme, called A-DSGD, which exploits the additive nature of the wireless MAC for over-the-air gradient computation, and provide convergence analysis for this approach. In A-DSGD, the devices first sparsify their gradient estimates, and then project them to a lower dimensional space imposed by the available channel bandwidth. These projections are sent directly over the MAC without employing any digital code. Numerical results show that A-DSGD converges faster than D-DSGD thanks to its more efficient use of the limited bandwidth and the natural alignment of the gradient estimates over the channel. The improvement is particularly compelling at low power and low bandwidth regimes. We also illustrate for a classification problem that, A-DSGD is more robust to bias in data distribution across devices, while D-DSGD significantly outperforms other digital schemes in the literature. We also observe that both D-DSGD and A-DSGD perform better with the number of devices, showing their ability in harnessing the computation power of edge devices.
Self-gravitating filament formation from shocked flows: velocity gradients across filaments

In typical environments of star-forming clouds, converging supersonic turbulence generates shock-compressed regions, and can create strongly magnetized sheet-like layers. Numerical magnetohydrodynamic simulations show that within these post-shock layers, dense filaments and embedded self-gravitating cores form via gathering material along the magnetic field lines. As a result of the preferred-direction mass collection, a velocity gradient perpendicular to the filament major axis is a common feature seen in simulations. We show that this prediction is in good agreement with recent observations from the CARMA Large Area Star Formation Survey (CLASSy), from which we identified several filaments with prominent velocity gradients perpendicular to their major axes. Highlighting a filament from the north-west part of Serpens South, we provide both qualitative and quantitative comparisons between simulation results and observational data. In particular, we show that the dimensionless ratio Cv ≡ Δvh2/(GM/L), where Δvh is half of the observed perpendicular velocity difference across a filament, and M/L is the filament’s mass per unit length, can distinguish between filaments formed purely due to turbulent compression and those formed due to gravity-induced accretion. We conclude that the perpendicular velocity gradient observed in the Serpens South north-west filament can be caused by gravity-induced anisotropic accretion of material from a flattened layer. Using synthetic observations of our simulated filaments, we also propose that a density-selection effect may explain observed subfilaments (one filament breaking into two components in velocity space) as reported in recent observations.
Effects of Different Cosmic Ray Transport Models on Galaxy Formation

Cosmic rays (CRs) with ∼ GeV energies can contribute significantly to the energy and pressure budget in the interstellar, circumgalactic, and intergalactic medium (ISM, CGM, IGM). Recent cosmological simulations have begun to explore these effects, but almost all studies have been restricted to simplified models with constant CR diffusivity and/or streaming speeds. Physical models of CR propagation/scattering via extrinsic turbulence and self-excited waves predict transport coefficients which are complicated functions of local plasma properties. In a companion paper, we consider a wide range of observational constraints to identify proposed physically-motivated cosmic-ray propagation scalings which satisfy both detailed Milky Way (MW) and extra-galactic γ-ray constraints. Here, we compare the effects of these models relative to simpler “diffusion+streaming” models on galaxy and CGM properties at dwarf through MW mass scales. The physical models predict large local variations in CR diffusivity, with median diffusivity increasing with galacto-centric radii and decreasing with galaxy mass and redshift. These effects lead to a more rapid dropoff of CR energy density in the CGM (compared to simpler models), in turn producing weaker effects of CRs on galaxy star formation rates (SFRs), CGM absorption profiles and galactic outflows. The predictions of the more physical CR models tend to lie “in between” models which ignore CRs entirely and models which treat CRs with constant diffusivity.
The life history of Larinus minutus, a biological control agent of invasive knapweeds, and its dispersal from release sites in Arkansas

Larinus minutus Gyllenhal, a biological control agent of invasive knapweeds, has become established in several states and provinces since initial North American introduction in 1991. In order to reduce growing spotted knapweed populations in Northwest Arkansas, Larinus minutus (a biological control agent of spotted knapweed) was released annually from 2008-2011. Little is known about the larval development of this species, although the widespread use of this insect has provided research describing detailed host range and generalized life history. The speed and extent of the spread of this weevil from release sites following introduction have not been reported. This research described the larval development of L. minutus and its spread from release sites. Overwintered adult weevils were field collected and allowed to mate for two days for larval development studies. Females were placed individually into a mesh cage attached to a capitulum and allowed ~24 hours to oviposit. Randomly-collected caged capitula were dissected biweekly and head capsule measurements recorded. Once a majority of larva pupated, alternate day observations were conducted on remaining caged capitula to determine average emergence date. Two cohorts (occurring at full and late-flower) were used to observe season-related development differences. Two larval instars were observed from head capsule data analyzed with Hcap, a computer program that analyzes frequency distributions to determine instar number, mean head capsule width, instar range, and optimal separation points. Compared to previously published observations, all developmental stages were accelerated and one fewer stage was observed. Release sites were surveyed with transect sampling in winter of 2011 and 2012 to describe average L. minutus spread following introduction. Sampling included collection of 100 capitula per quadrat along each transect for later dissection and timed visual observation to record positive infestation. GPS coordinates were recorded at each sample location to determine distance from a release site. Collected data were analyzed with a diffusion equation to describe the spread from a release site. This research shows two years post release, an annual increase of infested capitula, up to 21%, and spread from a release point, up to ~225 m can be expected. This thesis is approved for recommendation to the Graduate Council.
Pyramid Focusing Network for mutation prediction and classification in CT images

Predicting the mutation status of genes in tumors is of great clinical significance. Recent studies have suggested that certain mutations may be noninvasively predicted by studying image features of the tumors from Computed Tomography (CT) data. Currently, this kind of image feature identification method mainly relies on manual processing to extract generalized image features alone or machine processing without considering the morphological differences of the tumor itself, which makes it difficult to achieve further breakthroughs. In this paper, we propose a pyramid focusing network (PFNet) for mutation prediction and classification based on CT images. Firstly, we use Space Pyramid Pooling to collect semantic cues in feature maps from multiple scales according to the observation that the shape and size of the tumors are varied.Secondly, we improve the loss function based on the consideration that the features required for proper mutation detection are often not obvious in cross-sections of tumor edges, which raises more attention to these hard examples in the network. Finally, we devise a training scheme based on data augmentation to enhance the generalization ability of networks. Extensively verified on clinical gastric CT datasets of 20 testing volumes with 63648 CT images, our method achieves the accuracy of 94.90% in predicting the HER-2 genes mutation status of at the CT image.
Hierarchical Opacity Propagation for Image Matting

Natural image matting is a fundamental problem in computational photography and computer vision. Deep neural networks have seen the surge of successful methods in natural image matting in recent years. In contrast to traditional propagation-based matting methods, some top-tier deep image matting approaches tend to perform propagation in the neural network implicitly. A novel structure for more direct alpha matte propagation between pixels is in demand. To this end, this paper presents a hierarchical opacity propagation (HOP) matting method, where the opacity information is propagated in the neighborhood of each point at different semantic levels. The hierarchical structure is based on one global and multiple local propagation blocks. With the HOP structure, every feature point pair in high-resolution feature maps will be connected based on the appearance of input image. We further propose a scale-insensitive positional encoding tailored for image matting to deal with the unfixed size of input image and introduce the random interpolation augmentation into image matting. Extensive experiments and ablation study show that HOP matting is capable of outperforming state-of-the-art matting methods.
Effect of pebble flux-regulated planetesimal formation on giant planet formation

Context. The formation of gas giant planets by the accretion of 100 km diameter planetesimals is often thought to be inefficient. A diameter of this size is typical for planetesimals and results from self-gravity. Many models therefore use small kilometer-sized planetesimals, or invoke the accretion of pebbles. Furthermore, models based on planetesimal accretion often use the ad hoc assumption of planetesimals that are distributed radially in a minimum-mass solar-nebula way. Aims. We use a dynamical model for planetesimal formation to investigate the effect of various initial radial density distributions on the resulting planet population. In doing so, we highlight the directive role of the early stages of dust evolution into pebbles and planetesimals in the circumstellar disk on the subsequent planet formation. Methods. We implemented a two-population model for solid evolution and a pebble flux-regulated model for planetesimal formation in our global model for planet population synthesis. This framework was used to study the global effect of planetesimal formation on planet formation. As reference, we compared our dynamically formed planetesimal surface densities with ad hoc set distributions of different radial density slopes of planetesimals. Results. Even though required, it is not the total planetesimal disk mass alone, but the planetesimal surface density slope and subsequently the formation mechanism of planetesimals that enables planetary growth through planetesimal accretion. Highly condensed regions of only 100 km sized planetesimals in the inner regions of circumstellar disks can lead to gas giant growth. Conclusions. Pebble flux-regulated planetesimal formation strongly boosts planet formation even when the planetesimals to be accreted are 100 km in size because it is a highly effective mechanism for creating a steep planetesimal density profile. We find that this leads to the formation of giant planets inside 1 au already by pure 100 km planetesimal accretion. Eventually, adding pebble accretion regulated by pebble flux and planetesimal-based embryo formation as well will further complement this picture.
Entity Linking via Dual and Cross-Attention Encoders

Entity Linking has two main open areas of research: 1) generate candidate entities without using alias tables and 2) generate more contextual representations for both mentions and entities. Recently, a solution has been proposed for the former as a dual-encoder entity retrieval system (Gillick et al., 2019) that learns mention and entity representations in the same space, and performs linking by selecting the nearest entity to the mention in this space. In this work, we use this retrieval system solely for generating candidate entities. We then rerank the entities by using a cross-attention encoder over the target mention and each of the candidate entities. Whereas a dual encoder approach forces all information to be contained in the small, fixed set of vector dimensions used to represent mentions and entities, a crossattention model allows for the use of detailed information (read: features) from the entirety of each tuple. We experiment with features used in the reranker including different ways of incorporating document-level context. We achieve state-of-the-art results on TACKBP-2010 dataset, with 92.05% accuracy. Furthermore, we show how the rescoring model generalizes well when trained on the larger CoNLL-2003 dataset and evaluated on TACKBP-2010.
Cascaded Refinement Network for Point Cloud Completion

Point clouds are often sparse and incomplete. Existing shape completion methods are incapable of generating details of objects or learning the complex point distributions. To this end, we propose a cascaded refinement network together with a coarse-to-fine strategy to synthesize the detailed object shapes. Considering the local details of partial input with the global shape information together, we can preserve the existing details in the incomplete point set and generate the missing parts with high fidelity. We also design a patch discriminator that guarantees every local area has the same pattern with the ground truth to learn the complicated point distribution. Quantitative and qualitative experiments on different datasets show that our method achieves superior results compared to existing state-of-the-art approaches on the 3D point cloud completion task. Our source code is available at https://github.com/xiaogangw/cascaded-point-completion.git.
Ranking Computer Vision Service Issues using Emotion

Software developers are increasingly using machine learning APIs to implement 'intelligent' features. Studies show that incorporating machine learning into an application increases technical debt, creates data dependencies, and introduces uncertainty due to non-deterministic behaviour. However, we know very little about the emotional state of software developers who deal with such issues. In this paper, we do a landscape analysis of emotion found in 1,245 Stack Overflow posts about computer vision APIs. We investigate the application of an existing emotion classifier EmoTxt and manually verify our results. We found that the emotion profile varies for different question categories.
Multi-Task Learning Via Co-Attentive Sharing For Pedestrian Attribute Recognition

Learning to predict multiple attributes of a pedestrian is a multi-task learning problem. To share feature representation between two individual task networks, conventional methods like Cross-Stitch [1] and Sluice [2] network learn a linear combination of features or feature subspaces. However, linear combination rules out the complex interdependency between channels. Moreover, spatial information exchanging is less-considered. In this paper, we propose a novel Co-Attentive Sharing (CAS) module which extracts discriminative channels and spatial regions for more effective feature sharing in multi-task learning. The module consists of three branches, which leverage different channels for between-task feature fusing, attention generation and task-specific feature enhancing, respectively. Experiments on two pedestrian attribute recognition datasets show that our module outperforms the conventional sharing units and achieves superior results compared to the state-of-the-art approaches using many metrics.
Self-conjugate $(s,s+d,\dots,s+pd)$-core partitions and free rational Motzkin paths

A partition is called an $(s_1,s_2,\dots,s_p)$-core partition if it is simultaneously an $s_i$-core for all $i=1,2,\dots,p$. Simultaneous core partitions have been actively studied in various directions. In particular, researchers concerned with properties of such partitions when the sequence of $s_i$ is an arithmetic progression. In this paper, for $p\geq 2$ and relatively prime positive integers $s$ and $d$, we propose the $(s+d,d;a)$-abacus of a self-conjugate partition and establish a bijection between the set of self-conjugate $(s,s+d,\dots,s+pd)$-core partitions and the set of free rational Motzkin paths with appropriate conditions. For $p=2,3$, we give formulae for the number of self-conjugate $(s,s+d,\dots,s+pd)$-core partitions and the number of self-conjugate $(s,s+1,\dots,s+p)$-core partitions with $m$ corners.