Patch Of Scales Research Articles

Two new species of genus Imachra Pascoe, 1874 (Coleoptera: Curculionidae) from Vietnam

Two new species of the genus Imachra Pascoe, 1874 are described from the Hòa Bình Province, Vietnam. Imachra (Orchestoides) zinchenkoi sp. n. differs from Imachra shirozui (Morimoto, 1964) in the wide pronotum, more robust elytra, black tibiae, femora and rostrum, densely punctate elytral interstriae, pronotum without three stripes of scales, elytra without postscutellar patch of scales and subbasal patches. Imachra (Orchestoides) colonnellii sp. n. differs from Imachra nipponica (Morimoto, 1964) in the black body, pronotum and elytra without patches and stripes of scales, longer rostrum and distinctly laterally curved apex of the aedeagus. It is the first record of the genus Imachra for Vietnam.

CrossFormer++: A Versatile Vision Transformer Hinging on Cross-Scale Attention.

While features of different scales are perceptually important to visual inputs, existing vision transformers do not yet take advantage of them explicitly. To this end, we first propose a cross-scale vision transformer, CrossFormer. It introduces a cross-scale embedding layer (CEL) and a long-short distance attention (LSDA). On the one hand, CEL blends each token with multiple patches of different scales, providing the self-attention module itself with cross-scale features. On the other hand, LSDA splits the self-attention module into a short-distance one and a long-distance counterpart, which not only reduces the computational burden but also keeps both small-scale and large-scale features in the tokens. Moreover, through experiments on CrossFormer, we observe another two issues that affect vision transformers' performance, i.e., the enlarging self-attention maps and amplitude explosion. Thus, we further propose a progressive group size (PGS) paradigm and an amplitude cooling layer (ACL) to alleviate the two issues, respectively. The CrossFormer incorporating with PGS and ACL is called CrossFormer++. Extensive experiments show that CrossFormer++ outperforms the other vision transformers on image classification, object detection, instance segmentation, and semantic segmentation tasks.

More than Olceclostera bifenestrata: New species and morphology of immature stages of Olceclostera Butler, 1879 (Lepidoptera: Bombycoidea, Apatelodidae)

Apatelodidae is a family of Neotropical bombycoids that still needs to be studied in several aspects, as many groups of species have not yet undergone a careful systematic revision. On the other hand, recent studies showed that some species known to be widely distributed in fact form species complexes. Until now, Olceclostera bifenestrata Schaus, 1912, described from Costa Rica, supposedly has a wide distribution, reaching the south of Brazil. We reviewed specimens from South America identified as O. bifenestrata. Using morphological data and DNA barcodes, we discovered that South American specimens belong to four new species: Olceclostera jairanasp. nov., Olceclostera quilombolasp. nov., Olceclostera xetasp. nov. and Olceclostera wayanasp. nov. The four species can be distinguished mainly by characters of genitalia, mostly in aedeagus structures. However, wing characters (number of hyaline spots) and abdomen characters (number of patches of larger scales) may be useful for classifying species groups in Olceclostera. Additionally, we illustrate and describe for the first time all the life stages of an Olceclostera species, including its chaetotaxy and life history, and provide the first SEM images of the egg of an Apatelodidae species. We present a discussion about the morphological characters of adults and immatures and their relevance to the systematics of Apatelodidae.

Three-dimensional Turbulent Reconnection within the Solar Flare Current Sheet

Solar flares can release coronal magnetic energy explosively and may impact the safety of near-Earth space environments. Their structures and properties on the macroscale have been interpreted successfully by the generally accepted 2D standard model, invoking magnetic reconnection theory as the key energy conversion mechanism. Nevertheless, some momentous dynamical features as discovered by recent high-resolution observations remain elusive. Here, we report a self-consistent high-resolution 3D magnetohydrodynamical simulation of turbulent magnetic reconnection within a flare current sheet. It is found that fragmented current patches of different scales are spontaneously generated with a well-developed turbulence spectrum at the current sheet, as well as at the flare loop-top region. The close coupling of tearing mode and Kelvin–Helmholtz instabilities plays a critical role in developing turbulent reconnection and in forming dynamical structures with synthetic observables in good agreement with realistic observations. The sophisticated modeling makes a paradigm shift from the traditional to a 3D turbulent reconnection model unifying flare dynamical structures of different scales.

Multiscale patch-based feature graphs for image classification

Deep learning architectures have demonstrated outstanding results in image classification in the last few years. However, applying sophisticated neural network architectures in small datasets remains challenging. In this context, transfer learning is a promising approach for dealing with this scenario. Generally, the available pre-trained architectures adopt a standard fixed input, which usually implies resizing and cropping the input images in the preprocessing phase, causing information loss. Besides, images present visual features in different scales in real-world scenarios, and most common approaches do not consider this fact. In this paper, we propose an approach that applies transfer learning for dealing with small datasets and leverages visual features extracted by pre-trained models from different scales. We based our approach on graph convolutional networks (GCN) that take graphs representing the images in different scales as input and whose nodes are characterized by features extracted by pre-trained models from regular image patches of different scales. Since GCN can deal with graphs with different numbers of nodes, our approach can deal naturally with images of heterogeneous sizes without discarding relevant information. We evaluated our approach in two datasets: a set of geological images and a publicly available dataset, both presenting characteristics that challenge traditional approaches. We tested our approach by adopting three different pre-trained models as feature extractors: two efficient pre-trained CNN models (DenseNet and ResNeXt) and one Vision Transformer (CLIP). We compared our approach with two conventional approaches for dealing with image classification. The experiments show that our approach achieves better results than the conventional approaches for this task.

Multi-Scale Human-Object Interaction Detector

Transformers are transforming the landscape of computer vision, especially for image-level recognition and instance-level detection tasks. Human-object interaction detection transformer (HOI-TR) is the first transformer-based end-to-end learning system for human-object interaction (HOI) detection; vision transformers build a simple multi-stage structure for multi-scale representation with single-scale patch and are the first patch-based transformer architecture for image-level recognition and instance-level detection. In this paper, we build a transformer-based multi-scale human-object interaction detector (MHOI), a novel method to integrate Vision and HOI detection Transformer, instead of directly incorporating two types of transformers, since the vision transformer lacks hierarchical architecture to handle the large variations in the scale of visual entities due to the single-scale patch partitioning. Specifically, MHOI embeds features of the same size (i.e., sequence length) with patches of variable scales simultaneously by utilizing overlapping convolutional patch embedding, then introduces an efficient transformer decoder that designs the query based on anchor points and essential auxiliary techniques to boost the HOI detection performance. Numerically, extensive experiments on several benchmarks demonstrate that our proposed framework outperforms prior existing methods coherently and achieves the impressive performance of 29.67 mAP on HICO-DET and 58.7 mAP on V-COCO, respectively.

Scaling patch analysis of planar turbulent mixing layers

Proper scales for the mean flow and Reynolds shear stress in planar turbulent mixing layers are determined from a scaling patch analysis of the mean continuity and momentum equations. By seeking an admissible scaling of the mean continuity equation, a proper scale for the mean transverse flow is determined as Vref=(dδ/dx)Uref, where dδ/dx is the growth rate of the mixing layer width and Uref=Uh−Ul is the difference between the velocity of the high speed stream Uh and the velocity of the low speed stream Ul. By seeking an admissible scaling for the mean momentum equation, a proper scale for the kinematic Reynolds shear stress is determined as Ruv,ref=UavgVref=[12Audδdx]Uref2, where Au=def(Uh−Ul)/(Uh+Ul) is the normalized velocity difference that emerges naturally in the admissible scaling of the mean momentum equation. Self-similar equations for the scaled mean transverse flow V* and Reynolds shear stress Ruv*=Ruv/Ruv,ref are derived from the mean continuity and mean momentum equations. Approximate equations for V* and Ruv* are developed and found to agree well with experimental data.

다중 패치를 이용한 트랜스포머 기반 장면 텍스트 인식

In this paper, we explore the application of Vision transformer (ViT) to the scene text recognition task. As a popular research direction in computer vision, Scene text recognition enables computers to recognize or read the text in natural scenes, such as object labels, text descriptions, and road text signs. At present, the traditional convolutional neural network-based model has better performance. Still, in the face of complex backgrounds and irregular scene text pictures, the performance of the convolutional neural network-based model is challenging to improve in curved text, diverse fonts, distortions, etc. With the application of transformers in computer vision, the model structure based on transformers has also significantly been developed. Although the current transformer-based model can obtain the performance of the model structure similar to CNN, it is currently in the early stage of application, and there is much room for research and improvement. We propose a multi-scale vertical rectangular patch model (MSVSTR) for transformer-based feature extractor to be more suitable for text images. By only arranging the patches in a single direction, when the image is cropped through the patch, it can be more suitable for the distribution form of the text in the text image. At the same time, to be suitable for different numbers of characters in other texts and more robust feature extraction, vertical rectangular patches of different scales are applied to crop the image. Our structure performs better through various ablation experiments than similar transformer-based STR models. At the same time, experiments show that our structure can perform seven benchmarks well.

Online tool wear monitoring by super-resolution based machine vision

The tool condition has been a major concern in modern computer numerical control (CNC) machining due to its direct effects on the quality of final product both in the surface and dimensional integrity. The conventional machine vision-based tool condition monitoring (TCM) approaches cannot meet the high precision requirement in micro machining, as the cutting parameters are in micro scale and the spindle works in high rotation speed which makes online tool wear measurement quite difficult. To meet these challenges, this study develops a single image super-resolution (SISR) approach for direct tool wear estimation in micro-milling. Motivated by the self-similarity of tool wear image morphology, this study proposes a sparse decomposition framework by learning dictionaries from the tool wear image pyramid. Based on their multi-scale invariant properties, the similar image patches of coarse scales can be retrieved from fine scales to reconstruct the high-resolution image fastly and in high quality. The reconstructed high-resolution image then can be conveniently applied to wear monitoring, and overcomes the image acquisition deficiencies of the conventional machine vision-based monitoring approaches. Experimental results validate this approach for the tool wear area estimation as well as its generalization of the wear width with regarding to the conventional manual measurements.

Scaling patch analysis of planar turbulent wakes

A scaling patch approach is used to investigate the proper scales in planar turbulent wakes. A proper scale for the mean axial flow is the well-known maximum velocity deficit Uref=U∞−Uctr, where U∞ is the free stream velocity and Uctr is the mean axial velocity at the wake centerline. From an admissible scaling of the mean continuity equation, a proper scale for the mean transverse flow is found as Vref=(dδ/dx)Uref, where dδ/dx is the growth rate of the wake width. From an admissible scaling of the mean momentum equation, a proper scale for the kinematic Reynolds shear stress is found as Ruv,ref=U∞Vref, which is a mixed scale of the free stream velocity and the mean transverse flow scale. Expressions are derived for the scaled mean transverse velocity and Reynolds shear stress in the far field of planar turbulent wakes. Using a Gaussian function for the mean axial velocity deficit, approximate functions for the scaled mean transverse velocity and Reynolds shear stress are developed and found to agree well with experimental and simulation data. This work reveals that the mean transverse flow, despite its small magnitude, plays an important role in the scaling and understanding of the planar turbulent wake.

Spatially adaptive multi-scale contextual attention for image inpainting

Image inpainting is the task to fill missing regions of an image. Recently, researchers have achieved a great performance by using convolutional neural networks (CNNs) with the conventional patch-matching method. Existing methods compute the attention scores, which are based on the similarity of patches between the known and missing regions. Considering that patches at different spatial positions can convey different levels of detail, we propose a spatially adaptive multi-scale attention score that uses the patches of different scales to compute scores for each pixel at different positions. Through experiments on the Paris Street View and Places datasets, our proposal shows slight improvement compared with some related methods on the quantitative evaluation metrics commonly used in the existing methods. Moreover, we found that these quantitative metrics are not appropriate enough considering the subjective impressions of the generated images. Therefore, we conducted subjective evaluation through user study for comparison, which shows that our proposal has superiority of performance generating much more detailed and subjectively plausible images.

Maxwelleus, a new genus of Curculionidae from the Himalayas, with two new species (Coleoptera: Curculionidae: Molytinae: Euderini).

The new genus Maxwelleus, with two new species, M. tibetanus (China, Tibet) and M. birmanus (Myanmar [Burma]), is described and illustrated. The new genus is immediately recognized by large body size (12.5-13.8 mm), pronotum with a distinct transverse groove or constriction behind apex and elytra with narrow but distinct transverse patches of yellow scales. A key to the species is given.

Egirdira, a new generic name for Pararhodeus niger Kosswig amp; Geldiay, 1952 (Teleostei: Leuciscidae).

Egirdira, new genus, is proposed for Pararhodeus niger from Central Anatolia based on its position in molecular phylogenetic analyses and its morphological characters. It is distinguished from all other genera of Leuciscidae except Delminichthys and Pelasgus by having the unbranched part of pectoral rays 23 thickened and slanted upward, with a conspicuous downward angle at the distal end of the thickened part, no or an incomplete lateral line, and the genital papilla of the female not protruding. An unambiguous, unique morphological character distinguishing Egirdira from Pelasgus has not been identified, except that Egirdira has numerous isolated silvery scales (or patches of such scales) on the flank. A combination of characters distinguishes Egirdira from individual species of Pelasgus. Phoxinellus egridiri is a junior synonym of Pararhodeus niger.

LncRNA MEG3 suppresses PI3K/AKT/mTOR signalling pathway to enhance autophagy and inhibit inflammation in TNF-α-treated keratinocytes and psoriatic mice.

Psoriasis is a common chronic inflammatory skin disorder that causes patches of thick red skin and silvery scales and affects 1-3% of the population, which reduces patient's quality of life. Understanding the pathogenesis of psoriasis is crucial for developing novel therapeutic strategies. HaCaT and NHEK cells were treated with TNF-α in vitro. A mouse model of psoriasis was established by topical imiquimod application on back skin. LncRNA MEG3 was cloned into the pcDNA3.1 vector and transfected in TNF-α-treated HaCaT and NHEK cells to overexpress its expression. Liposome-encapsulated pcDNA3.1-MEG3 was injected into imiquimod-treated mice via tail vein. RT-qPCR and western blot assays were used to examine the expression of lncRNA MEG3, IL-6, IL-8, IFN-γ, IL-1β, LC3, Beclin 1, p62, p-p65, p65, NLRP3, p-PI3K, PI3K, p-AKT, AKT, p-mTOR, mTOR respectively. The secretion of IL-6, IL-8, IFN-γ and IL-1β was determined using ELISA assay. Immunofluorescence and immunohistochemistry methods were performed for analyzing the expression of LC3 and NLRP3 in cells and skin tissues respectively. LY294002 was used to block the PI3K/AKT/mTOR signalling. MTT assay was applied to test the toxicity of LY294002 to HaCaT and NHEK cells. LncRNA MEG3 expression levels were downregulated in TNF-α-treated HaCaT and NHEK cells and skin tissues of psoriatic mice model. TNF-α treatment enhanced inflammation and suppressed autophagy in HaCaT and NHEK cells, which were largely reversed by overexpression of lncRNA MEG3. Autophagy puncta and NLRP3 inflammasome assembly showed the same patterns with the expression of inflammation and autophagy markers in TNF-α-treated HaCaT and NHEK cells with or without lncRNA MEG3 overexpression. TNF-α-induced activation of the PI3K/AKT/mTOR signalling was abolished by lncRNA MEG3 overexpression in HaCaT and NHEK cells. Blocking the PI3K/AKT/mTOR signalling inhibited TNF-α-induced inflammation and restored autophagy level in TNF-α-treated HaCaT and NHEK cells. Overexpression of lncRNA MEG3 suppressed inflammation, promoted autophagy and inhibited the activation of the PI3K/AKT/mTOR signalling in a mouse model of psoriasis. LncRNA MEG3 facilitates autophagy and suppresses inflammation in TNF-α-treated keratinocytes and psoriatic mice, which is dependent on the PI3K/AKT/mTOR signalling pathway. Our study enhances the understanding of psoriasis and provides potential therapeutic targets for psoriasis.

The Anopheles baileyi species complex (Diptera: Culicidae: Anophelinae) in Bhutan

This paper deals with three species (informal species A, B and C) of the Baileyi Complex of Anopheles subgenus Anopheles (Diptera: Culicidae) that were recently reported from Bhutan. Species A is An. baileyi sensu stricto, while species B and C are new species which are formally described and named herein as An. monticola Somboon, Namgay & Harbach and An. bhutanensis Somboon, Namgay & Harbach, respectively. Morphological characters of the adults, male genitalia, larval and pupal stages of the two new species, and the chaetotaxy of the immature stages, are provided and compared with An. baileyi from Bhutan. Although the patterns of wing spots of the adults and the pupal setae of the three species are very similar, significant differences in structures of the male genitalia distinguish them. Larvae of An. baileyi and An. monticola are similar, but differ from the larva of An. bhutanensis in the development of setae 3- and 13-T. The first antennal flagellomere of females of An. baileyi and An. bhutanensis has a distinct patch of pale scales, which is absent in An. monticola.

Multi-scale spatial-spectral fusion based on multi-input fusion calculation and coordinate attention for hyperspectral image classification

Recently, the deep learning method that integrates image features has gradually become a hot development trend in hyperspectral image classification. However, these studies did not fully consider the fusion of image features, and did not remove the interference to the classification process caused by the difference in the size of the objects. These factors hinder the further improvement of the classification effect. To eliminate these drawbacks, this paper proposes a more effective fusion scheme (MSF-MIF), which realizes the fusion from the perspective of location characteristics and channel characteristics through 3D convolution and spatial feature concatenation. In view of the size discrepancy of the objects to be classified, this method extracts features from several input patches of different scales and uses the novel calculation method proposed to fuse them, which minimizes the interference caused by size differences. In addition, this research also tried to quote the coordinate attention structure for the first time that combines spatial and spectral attention features to further improve the classification performance. Experimental results on three commonly used data sets prove that this framework has achieved a breakthrough in classification accuracy.

Layered structure of turbulent natural convection over a vertical flat plate

The layered structure of turbulent natural convection over a vertical flat plate (TNCVP) is first investigated qualitatively by dimensional analysis. The scaling patch approach is then applied to quantify the layered structure of TNCVP based on the characteristics of the mean momentum and energy equations. Fluid flow in TNCVP is divided into two layers: an inner layer and an outer layer, but heat transport is divided into four layers: a molecular diffusion sub-layer, a gradient balance layer, a meso layer and an outer layer. Scaling patch analysis identifies proper scales for the length, velocity, temperature, Reynolds shear stress, and turbulent heat flux in different layers, and develops a multi-scaling of the mean momentum and energy equations. Part of the novelty of the approach is that different regions incorporate unique scaling lengths, temperatures, turbulence temperature flux, and Reynolds stresses. The inner and outer scaled mean velocity, temperature, Reynolds shear stress and turbulent temperature flux profiles at four streamwise locations in the experiments of Tsuji and Nagano [1,2] merge onto a single curve in the inner and outer layers, respectively. This behavior supports the validity of the analysis. The layered structure of TNCVP is compared with three canonical turbulent flows: differentially heated turbulent flow in a vertical channel (DHVC), forced convection in turbulent boundary layer flows (TBL), and turbulent plumes. Striking similarities and distinctive differences among these turbulent flow and heat transport situations are elucidated. The inner layer of the mean momentum equation of TNCVP is similar to the inner layer of DHVC, but the outer layer of TNCVP is similar to that of a turbulent planar plume. The four layer structure for the mean energy equation of TNCVP is similar to that of pressure- or shear-driven turbulent boundary layers.

Remote monitoring of Cydia pomonella adults among an assemblage of nontargets in sex pheromone-kairomone-baited smart traps.

BACKGROUNDCaptures of codling moth, Cydia pomonella (L.), in traps are used to establish action thresholds and time insecticide sprays. The need for frequent trap inspections in often remote orchards has created a niche for remote sensing smart traps. A smart trap baited with a five‐component pheromone‐kairomone blend was evaluated for codling moth monitoring among an assemblage of other nontargets in apple and pear orchards.RESULTSCodling moth captures did not differ between the smart trap and a standard trap when both were checked manually. However, the correlation between automatic and manual counts of codling moth in the smart traps was low, R 2 = 0.66 ÷ 0.87. False‐negative identifications by the smart trap were infrequent <5%, but false‐positive identifications accounted for up to 67% of the count. These errors were primarily due to the misidentification of three moth species of fairly similar‐size to codling moth: apple clearwing moth Synanthedon myopaeformis (Borkhausen), oriental fruit moth Grapholita molesta (Busck), and carnation tortrix Cacoecimorpha pronubana (Hübner). Other false‐positive counts were less frequent and included the misidentifications of dipterans, other arthropods, patches of moth scales, and the double counting of some moths.CONCLUSIONCodling moth was successfully monitored remotely with a smart trap baited with a nonselective sex pheromone‐kairomone lure, but automatic counts were inflated in some orchards due to mischaracterizations of primarily similar‐sized nontarget moths. Improved image‐identification algorithms are needed for smart traps baited with less‐selective lures and with lure sets targeting multiple species.

Scaling patch analysis of turbulent planar plume

Proper scaling in turbulent planar plumes is investigated here using a scaling patch approach. Based on the scaled boundary conditions, a proper velocity scale for the mean axial flow is the plume centerline velocity Uref=Uctr, and a proper temperature scale for the temperature excess is Θref=Tctr−T∞, where Tctr is the plume centerline temperature and T∞ is the ambient fluid temperature. By seeking an admissible scaling, a key concept in the scaling patch approach, for the mean continuity, mean momentum, and mean energy equations, respectively, the following is found: (1) a proper scale for the mean transverse flow is Vref=(dδ/dx)Uctr, where dδ/dx is the growth rate of the plume width. (2) A proper scale for the Reynolds shear stress is Rvu,ref=UctrVref=(dδ/dx)Uctr2, a mix of the scales for the mean axial and transverse flows. (3) A proper scale for the turbulent heat flux is Rvθ,ref=VrefΘctr, a mix of the scales for the mean transverse flow and mean temperature excess. The mean transverse flow thus plays a critical role in the scaling of turbulent planar plumes. Approximate functions are developed for the scaled mean transverse flow, Reynolds shear stress, and turbulent temperature flux, and are found to agree favorably with experimental and numerical simulation data. The integral analysis of the mean momentum equation yields a Richardson number Ri, which remains invariant in the axial direction. The Richardson number is defined as Ri=defgβΘctrδt/(UctrVref)≈1/2, where g is the gravitational acceleration, β is the thermal expansion coefficient, and δt is the plume half-width based on the mean temperature profile. This Richardson number arises directly from the scaling patch analysis of the mean momentum equation, including both the streamwise and transverse velocity scales.

Scaling of the mean transverse flow and Reynolds shear stress in turbulent plane jet

Proper scaling for the mean transverse flow and Reynolds shear stress in a turbulent plane jet is determined using a scaling patch approach. By seeking an admissible scaling, a key concept in the scaling patch approach, for the mean continuity equation, a proper scale for the mean transverse flow in a turbulent plane jet is found as Vref=−δdUctr/dx, where δ is the jet half width and dUctr/dx is the decay rate of the mean axial velocity at the jet centerline. By seeking an admissible scaling for the mean axial momentum equation, a proper scale for the kinematic Reynolds shear stress is found as Ruv,ref=Uctr Vref, which is a mix of the velocity scales in the axial and transverse directions. Approximation functions for the scaled mean transverse flow and Reynolds shear stress are developed and found to agree well with experimental and numerical data. Similarities and differences between the scales of the mean transverse flow and Reynolds shear stress in turbulent plane jets and zero-pressure-gradient turbulent boundary layer flows are clarified.