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In this work, we thoroughly analyze the rate-region provided by the asynchronous transmission in multiple access channels (MACs). We derive the corresponding capacity-regions, applicable to a wide range of pulse shaping methods. We analytically prove that asynchronous transmission enlarges the capacity-region of MACs. Although successive interference cancellation (SIC) can achieve the optimal sum-rate for the conventional uplink non-orthogonal multiple access (NOMA) methods, it is unable to achieve the boundary of the capacity-region for the asynchronous transmission. We demonstrate that for the asynchronous transmission, the optimal SIC decoding order to achieve the maximum sum-rate is based on the users' channel strengths. This optimal ordering is in contrast to the conventional uplink NOMA, where various decoding orders can result in the maximum sum-rate. Furthermore, we provide practical transceiver designs to approach the capacity-region. The memory induced by asynchronous transmission enables the use of the trellis-based detection methods which improves the performance. In addition, we propose a transceiver design, based on channel diagonalization to exploit the frequency-selectivity introduced by timing offsets. The proposed transceiver design, joint with the turbo principle, enables us to achieve a rate pair that is not achievable by the synchronous transmission.

RNA molecules can fold into complex two- and three-dimensional shapes that are critical for their function. Chemical probes have long been utilized to interrogate RNA structure and are now considered invaluable resources in the goal of relating structure to function. Recently, the power of deep sequencing and careful chemical probe design have merged, permitting researchers to obtain a holistic understanding of how RNA structure can be utilized to control RNA biology transcriptome-wide. Within this review, we outline the recent advancements in chemical probe design for interrogating RNA structures inside cells and discuss the recent advances in our understanding of RNA biology through the lens of chemical probing.

Abstract Background There have been reports of unique dental morphological features amongst Latin American and Hispanic populations, and this might invalidate the use of current orthodontic diagnostic tools within this population. There are no tooth size/tooth ratio normative standards for the Hispanic population, despite overwhelming evidence about differences in tooth size between racial groups. Objective This study aimed to determine whether there are significant differences in 3-D tooth shape between patients with Angle Class I, Class II, and Class III dental malocclusion in the Hispanic population. Methodology Orthodontic study models representing Hispanic orthodontic patients with Angle Class I, II, and III dental malocclusions scanned using an intra-oral scanner. The scanned models were digitized and transferred to a geometric morphometric system. Tooth size shape were determined, quantified, and visualized using contemporary geometric morphometric computational tools using MorphoJ software. General Procrustes Analysis (GPA) and canonical variates analysis (CVA) used to delineate the features of shape that are unique to each group. Result The study revealed differences in tooth shape between the different dental malocclusion groups on all twenty-eight teeth that were studied; the pattern of shape differences varied between the teeth and the dental malocclusions. The MANOVA test criteria, F approximations, and P-values show that shape in all the groups was significantly different

Study Purpose. Road cycling activities are closely related to the aerodynamics and stability of the bicycle movement when used by the user. Drop bars are one of the elements of a road bike and help control speed movements while driving. Drop bars, or what is commonly known as road bike handlebars, are designed to ride at high speeds without sacrificing bike stability. Materials and methods. The research carried out aims to analyze the shape and position of the hands when riding a bicycle, the results of which can be used to maximize the potential of the bicycle drop bar object. This is because cycling has become a community activity nowadays and the use of drop bars on bicycles is often an option for cycling. By using descriptive qualitative research method the shape of the drop bar can be associated with driving stability. Results. The research was conducted by observing a literature study that describes the shape, position of the user, and speed of the bicycle against the drop bar. The drop bar form is made by adjusting the needs and desires of its users. The shape of the drop bar on a racing bicycle can affect the speed, agility, stability, and direction of the bicycle while riding. Conclusion. The comfort generated by the position of the user's hands on the drop bar while riding has an important role in directing and balancing the bicycle so that it does not fall and maintains a constant speed in other words the bicycle is in a stable state.

Abstract Ligand-based virtual screening is a widespread method in modern drug design. It allows for a rapid screening of large compound databases in order to identify similar structures. Here we report an open-source command line tool which includes a substructure-, fingerprint- and shape-based virtual screening. Most of the implemented features fully rely on the RDKit cheminformatics framework. VSFlow accepts a wide range of input file formats and is highly customizable. Additionally, a quick visualization of the screening results as pdf and/or pymol file is supported. Graphical Abstract

Remodeling in adults with repaired tetralogy of Fallot (rToF) may occur due to chronic pulmonary regurgitation, but may also be related to altered flow patterns, including vortices. We aimed to correlate and quantify relationships between vorticity and ventricular shape derived from atlas-based analysis of biventricular shape. Adult rToF (n = 12) patients underwent 4D flow and cine MRI imaging. Vorticity in the RV was computed after noise reduction using a neural network. A biventricular shape atlas built from 95 rToF patients was used to derive principal component modes, which were associated with vorticity and pulmonary regurgitant volume (PRV) using univariate and multivariate linear regression. Univariate analysis showed that indexed PRV correlated with 3 modes (r = -0.55,-0.50, and 0.6, all p < 0.05) associated with RV dilatation and an increase in basal bulging, apical bulging and tricuspid annulus tilting with more severe regurgitation, as well as a smaller LV and paradoxical movement of the septum. RV outflow and inflow vorticity were also correlated with these modes. However, total vorticity over the whole RV was correlated with two different modes (r = -0.62,-0.69, both p < 0.05). Higher vorticity was associated with both RV and LV shape changes including longer ventricular length, a larger bulge beside the tricuspid valve, and distinct tricuspid tilting. RV flow vorticity was associated with changes in biventricular geometry, distinct from associations with PRV. Flow vorticity may provide additional mechanistic information in rToF remodeling. Both LV and RV shapes are important in rToF RV flow patterns.

Automatic Feature Recognition (AFR) is considered as the key connection technique of the integration of Computer Aided Design (CAD) and Computer Aided Process Planning (CAPP). At present, there is a lack of a systematic method to identify and evaluate the local features of 3D CAD models. The process information such as topological structure, shape and size, tolerance and surface roughness should be considered. Therefore, a novel Model Based Definition (MBD) based on 3D CAD model AFR and similarity evaluation are proposed in this paper. A Multi-Dimensional Attributed Adjacency Matrix (MDAAM) based on MBD is established based on the fully consideration of the topological structure, shape and size, surface roughness, tolerance and other process information of the B-rep model. Based on the MDAAM, a two-stage model local feature similarity evaluation method is proposed, which combines the methods of optimal matching and adjacency judgment. First, the faces of source feature and target model are used as independent sets to construct a bipartite graph. Secondly, supplement the vertices in the independent set of source feature to make the number of vertices in two independent sets equal. Thirdly, based on MDAAM data, the weighted complete bipartite graph is constructed with the face similarity between two independent sets as the weight. Fourthly, Kuhn-Munkres algorithm is used to calculate the optimal matching between the faces of source feature and target model. Fifthly, the adjacency between matching faces in target model is judged. Finally, the similarity between matching faces of the two models is calculated, which is used as the similarity evaluation result. The effectiveness of this method is verified by three applications.

Surface finishing of hard-alloy parts is investigated. The best conditions for preliminary and final finishing are established. The grid density of the resultant raster trajectory is q = 0.5–3.0 mm–1 for preliminary finishing and q = 5–7 mm–1 for final finishing. [ABSTRACT FROM AUTHOR]

In the present work, the impact that the longitudinal shape of channels has on the current produced in the flow of a magneto-hydrodynamic microgenerator (MHDMG) is studied. The goal is to find the micro-channel geometry via modeling to maximize the current output for low Reynolds and Mach regimes. To carry out this study, a 3D dynamic numerical tool relying on the finite volume method was handled with the open-source software OpenFOAM. It is the base model to study the impact of intricate geometries on the ability to produce energy. An additional steady-state 2D analytical model was also developed to check some basic modeling assumptions. Both models have been experimentally validated on the simplest flow system having a constant square cross-section throughout. The results produced by both models cross-check very well and compare favorably with respect to experimental data. Hence, using the validated numerical tool, three shapes have been further investigated, namely, progressive (linear decrease of the cross-section), arc (parabolic decrease of the cross-section), and wavy (sinusoidal shape). It was found that the arc channel provides the greatest current output for the same volumetric flow. It is therefore the preferred choice for developing high current gain and more efficient MHDMG used in micro-scaled actuators and sensors.

Otoliths are acellular apposition structures composed of approximately 90% of calcium carbonate and other inorganic salts, which develop over a protein matrix located in the inner ear of bony fishes playing an essential role in the senses of balance and hearing. These structures grow throughout a fish’s lifetime. Owing to their species-specific shape, otoliths are an important tool for the identification of fish species in the diet of predators when collected from stomachs and, due to their low water content, are helpful for archaeological, paleontological and anthropological studies for its presence in fossilized remains. This atlas is aimed at the identification of abundant or frequent bony fishes otoliths in the diet of predators and in fossilized remains from the continental shelf and upper continental slope of southern Brazil between Santa Marta Grande cape (28° S) and Chuí (34° S) on the border with Uruguay. It includes the otolith description of 136 bony fishes species, including most of the abundant and frequent species in the region. Easy to use graphics are provided to infer fish size based on otolith measurements. Vouchers of different sizes of otolith of all species included in this atlas are deposited and available for consultation at the Demersal Fisheries Resources and Cephalopods Laboratory (IO-FURG).

The introduction of the invasive tick Rhipicephalus (Boophilus) microplus in Côte d'Ivoire has led to a change in the population of ticks of the genus Rhipicephalus (Boophilus). Thus, this study aims to characterize morphologically the tick species of the genus Rhipicephalus (B.) by geometric morphometry. It consisted of collecting all ticks from cattle in 74 farms in the Savanes District. The collected ticks were identified in the laboratory and, after identification, only non-gorged adult ticks of the genus Rhipicephalus (B.) were retained for morphometric characterisation. A total of 394 ticks were examined under a digital microscope, images were digitised using the online program XYOM and the data were analysed. This study revealed that males of each species have a smaller average size than females. It also showed a variation in mean size in all species except for the females of Rhipicephalus (B.) decoloratus and Rhipicephalus (B.) geigyi whose mean sizes did not differ. Geometric morphometry allowed the separation of Rhipicephalus (B.) annulatus males from other males. In females, Rhipicephalus (B.) geigyi and Rhipicephalus (B.) microplus were distinguished from each other and from other females.

During its 320 Myr evolution, dragon- and damselfly (Odonata) wing morphology underwent intense modifications. The resulting diversity prompted comparative analyses focusing on phylogeny. However, homoplasy proved to plague wing-related characters. Concurrently, limited benefits were obtained from considering fossil taxa, similarly impacted. Herein, we investigate two aspects particularly affected by convergence, namely the acquisition of vein-like structuring elements derived from regular cross-venation, termed conamina; and the evolution of butter knife wing shape. Conamen implementation is found to be consistently linked with vein curvature sharpening, itself generating potential breaking points. Conamina therefore likely evolved to address wing integrity issues during ever-more-demanding flight performance. Moreover, an existing conamen is likely to trigger the acquisition of further, associated conamina. As for butter knife shape, previously documented in the extinct Archizygoptera and among damselflies, we report a new, 315 Ma occurrence with the rare species Haidilaozhen cuiae gen. et sp. nov. (family Haidilaozhenidae fam. nov.), from the Xiaheyan locality (China). The repeated acquisition of butter knife-shaped wing can be related to slow speed flight and, in turn, predator avoidance. In both cases of iterated regularities, the unique ‘network-and-membrane’ wing design proper to insects is found to compose a strong, constraining factor.

Introduction: Adults possess a natural inclination to associate sensory cues derived from distinct modalities, such as the pairing of sweet with pink. However, studies exploring crossmodal correspondences in children, particularly in the sensory pairing of visual features and tastes, are scant, leaving unanswered questions regarding the developmental trajectory of crossmodal correspondences. The present study investigates whether Japanese preschool children demonstrate specific biases in shape-color, shape-taste, and color-taste associations. Methods: In a series of in-person experiments, 92 children between 3 to 6 years of age completed matching tasks utilizing paper stimuli. Results: Children exhibit crossmodal correspondences in shape-color (circle-red and asymmetrical star-yellow), shape-taste (triangle-salty and circle-sweet), and color-taste (yellow-sour, black-bitter, and pink-sweet) associations. Moreover, children's choices are not influenced by their individual preferences. Discussion: The crossmodal correspondences observed in this study have been observed in previous research on adults from the same (Japanese) culture, although adults showed more crossmodal correspondences than the children in this study (e.g., pink-circle, triangle-sour, and green-bitter). Thus, while some crossmodal correspondences emerge during childhood, others may require additional time to develop, thereby highlighting the importance of understanding the cognitive mechanisms underlying crossmodal correspondences from an ontogenic perspective. [ABSTRACT FROM AUTHOR]

Paraffin wax stores energy in the form of latent heat at a nearly constant temperature during melting and releases this energy during solidification. This effect is used in industrial energy storage. At the same time, the possible deformation of even small volumes of material as a result of phase change is insufficiently studied. In this paper, the physical nature of such deformation, probably for the first time, is studied on the example of a droplet of paraffin wax. An unusual change in the shape of a melted droplet of paraffin wax placed on a relatively cold glass plate was observed in the laboratory experiments. As the droplet solidifies, its upper surface becomes nearly flat, and a dimple is formed in the center of this surface, making the droplet look like a fruit (pumpkins are more commonly shaped like this, but the authors prefer apples). A series of experiments, as well as physical and numerical modeling of the droplet's thermal state, taking into account the formation of a mushy zone between liquidus and solidus, made it possible to understand the role of gravity and gradual increase in viscosity and density of paraffin wax on changing the droplet shape and, in particular, to clarify the mechanism of formation of the dimple on its upper. It was shown that the mushy zone between the liquidus and solidus of the paraffin wax is responsible for the dimple formation. [ABSTRACT FROM AUTHOR]

The selection of a suitable shape representation model is an essential component to consider in order to achieve satisfactory results in shape recognition. However, this selection is highly dependent on the specific case of study. In this paper, a comparison of outline-based shape descriptors for the alphanumeric character typeface used on vehicle license plates is reported. The shape of each character is described by the polygonal curve that connects the set of points uniformly distributed around its image outline. Formally, two descriptors are analyzed: mesh distance and circle mesh distance. However, considering that, from a practical point of view, rotation of characters can be discarded due to the standard vertical alignment of characters in vehicle plates, parametric functions of coordinates (x and y) are also considered in this study. To exploit and collect all the geometric information provided by the outline points, piecewise linear functions are used. The main contribution of this work is precisely to determine, among the set of shape descriptors considered in the present study, the best choice to identify shape similarity (with the aid of piecewise linear functions) in alphanumeric characters with the typography style used in vehicle license plates. Experimental results reveal that for this case of study, the best shape signature is the circle mesh distance, and the most descriptive information about shape can be found in the parametric function of x. [ABSTRACT FROM AUTHOR]

Ivanov introduced the shape of a Majorana algebra as a record of the 2-generated subalgebras arising in that algebra. As a broad generalisation of this concept and to free it from the ambient algebra, we introduce the concept of an axet and shapes on an axet. A shape can be viewed as an algebra version of a group amalgam. Just like an amalgam, a shape leads to a unique algebra completion which may be non-trivial or it may collapse. Then for a natural family of shapes of generalised Monster type we classify all completion algebras and discover that a great majority of them collapse, confirming the observations made in an earlier paper [12]. [ABSTRACT FROM AUTHOR]

Selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE) is a widely used technique for RNA structure analysis using N-methylisatoic anhydride (NMIA) treatment that has been proven to be applicable to different types of RNA templates. In this study, we performed the structural analysis of the viral satellite RNA of cucumber mosaic virus TA-Tb (satCMV TA-Tb) using the SHAPE method. In the preliminary experiment, we optimized the protocol of SHAPE method for analyzing satCMV TA-Tb by determining a suitable quantity of template RNA. This optimization effectively reduced the appearance of a large number of intense bands in the NMIA-untreated sample lane, along with a strong overall background signal that prevented the clear elucidation of the RNA structure. SHAPE analysis indicated the presence of non-structured, single-stranded flexible regions throughout satCMV TA-Tb with prominent flexible stretches located around nucleotide positions 145 to 200. The positions of these flexible regions were particularly consistent with a secondary structure of satCMV TA-Tb predicted by mfold software v.2.3, which consisted of five 5′- and 3′-proximal stem-loops and one internal large multi-branched stem-loop. Sequence alignment and secondary RNA structure prediction of other satCMV sequences that are phylogenetically the same group with satCMV TA-Tb also suggested the presence of 5′- and 3′-proximal stem-loop structures. Our data provide the structural basis for elucidating the mechanism by which satCMV TA-Tb regulates the pathogenicity and replication of its helper virus. [ABSTRACT FROM AUTHOR]

The particle size distribution provides important information about soil engineering properties. Finer particles are usually determined by classical sedimentation methods such as hydrometer tests. These methods are based on simplifying assumptions and despite operational complexities, the obtained results are not precise. At this time, Image Processing methods were used in different industries to obtain various information about size distributions and particle shape analysis. The present research suggests the image processing method based on the analysis of microscopic images of stable particles (static method) for determining the size and shape distribution of the fine-grained fraction of the soil. The images of fine-grained soil particles prepared with optical microscopes were analyzed to generate information about particle size and shape. By comparing gradation curves obtained with image processing and hydrometric methods, we found that there is more adaptation in the range of 40 to 70 microns of particle sizes. Investigations have shown that in this range, the shape of the particles has less diversity and is closer to the perfect sphere form. This study found that image processing while reducing costs, could provide more comprehensive information and would be expected to be used in geotechnical laboratories. [ABSTRACT FROM AUTHOR]

In semiconductor wafer manufacturing process, the height of random defects provides an important physical parameter to understand their impact and root cause. In this work, we demonstrate a novel method of defect height estimation based on SEM images without using ground truth or any external reference data of target defect types. Our method is applicable in conditions such as absence of shadows, self-obstruction by defects, lack of precise knowledge of SEM detector geometry and does not require sample tilting or imaging at different working distances, thereby removing major limitations of previous work. The error in our height estimation is $\sim 3$ % compared with $\sim 11$ % for current manual method on same dataset. In addition to improving accuracy and precision, our method reduces the time and manual effort required and can be easily extended to past and new defects for trend monitoring purpose, thus satisfying some key requirements of manufacturing environment. [ABSTRACT FROM AUTHOR]

We present our automated real-time socket inspection system capable of detecting an assortment of defects including metallic and liquid staining, loose capacitors and pins, and other debris and foreign material (FM). Our test tools pick and place manufactured units into sockets for electrical testing. Any debris accumulated inside the test sockets will likely damage subsequent units until the defective socket is replaced. To quickly capture in-situ defects and mitigate further damage, we equipped each pick-and-place arm with a new vision system designed to fit within the existing tool. The tight footprint constraints required a highly compact imaging system which resulted in a variety of image artifacts, creating several unique challenges. Our inspection algorithm utilizes a variety of advanced computer vision and machine learning techniques to normalize images, extract and match features, register the images, suppress unwanted artifacts, and detect defects. The detected changes are then sent to a deep learning classifier to further filter between true defects and natural socket deterioration. The flagged socket images can be manually dispositioned by the user and the socket can be sent for repair or cleaning as needed. [ABSTRACT FROM AUTHOR]

Introduction: Mandible also known as the largest and inferior, primary facial bone of the face giving a curved shape to it. It changes its shape and gives variations to the bony structure of the face from birth till older age. Mental foramen is known as the vintage of the mandible and is an important mark of the face for carrying out many diagnostics and surgical processes along with aesthetic procedures of the face. Thus, the present study is designed with an aim to get insight knowledge of position and shape of mental foramen in dry adult human mandible. Material and Method: Totally, 350 bones were studied in 2 years duration. The study was carried out in the Department of Anatomy, Index Medical College Hospital & Research Center over a period of 2 year. The position of mental foramen was studied using an instrument known as digital Vernier Calliper (in mm) while shape was analysed visually. Position of mental foramen was calculated using Statistical package for social sciences (SPSS) software. Results: In our study, the mean and standard deviation of Distance from alveolar crest to upper margin of mental foramen was found to be (13.14±0.42) on right side and (12.42±0.31) on left side. In our study, the mean and standard deviation of Distance from lower border of mandible to lower margin of mental foramen was found to be (16.23±0.53) on right side and (15.41±0.46) on left side. In our study, the mean and standard deviation of Distance from alveolar crest to lower border of mandible was found to be (31.42±0.64) on right side and (30.35±0.57) on left side. In our study, the mean and standard deviation of Vertical diameter of foramen was found to be (2.65±0.31) on right side and (2.42±0.13) on left side. Conclusion: Hence, mental foramen plays a pivotal role in performing major facial surgeries and is an important landmark for several facial procedures performed. [ABSTRACT FROM AUTHOR]

The motion of droplets in non‐Newtonian fluids is widely present in ubiquitous industrial processes. Understanding the droplet motion characteristics is important for optimizing the design of related processes. Here the progress in the research on the droplet motion characteristics in non‐Newtonian fluids is reviewed. The mechanism of droplet deformation in non‐Newtonian fluids were elucidated, the influence of different rheological properties on droplet shape was discussed, and the empirical correlations of droplet aspect ratio were summarized. Moreover, the dimensionless correlations of drag coefficient were discussed. There are relatively few drag coefficient correlations of droplets in non‐Newtonian fluids, while the correlation of drag coefficients of bubbles in non‐Newtonian fluids can provide some reference. Finally, the possible prospects for future studies of the subject were proposed. [ABSTRACT FROM AUTHOR]

The purpose of this study is to test the feasibility for deep CNN–based artificial intelligence methods for automatic classification of the mass margin and shape, while radiomic feature–based machine learning methods were also implemented in this study as baseline and for comparison study. In this retrospective study, 596 patients with breast mass that underwent mammography from 4 hospitals were enrolled from January 2012 to October 2019. Margin and shape of each mass were annotated according to BI-RADS by 2 experienced radiologists. Deep CNN–based AI was implemented for the classification task based on Resnet50. Balanced sampler and CBAM were also used to improve the performance of the Deep CNNs. As comparison, image texture features were extracted and then dimensionality reduction methods (such as PCA, ICA) and classical classifiers (such as SVM, DT, KNN) were used for classification task. Based on Python programming software, accuracy (ACC) was used to evaluate the performance of the model, and the model with the highest ACC value was selected. Deep CNN based on Resnet50 with balanced sampler and CBAM achieved the best performance for both margin and shape classification, with ACC of 0.838 and 0.874, respectively. For the radiomics-based machine learning, the best performance for margin was achieved as 0.676 by the combination of FA + RF, while the best performance for shape was 0.802 by the combination of PCA + MLP. The feasibility for automatic classification with coarse labeling of the mass shape and margin was testified with the deep CNN–based AI methods, while radiomic feature–based machine learning methods achieved inferior classification results. [ABSTRACT FROM AUTHOR]

Microplastics can cause physical, chemical, biological, and structural problems in soil. In this study, microplastics were identified in the soil near two landfills where contamination by microplastics was expected. Pretreatment was performed to remove organic matter and to separate microplastics from the soil samples. FT-IR microscope analysis was performed to confirm the quantity and types of microplastics. The colors and shapes of microplastics in the soil were analyzed using a digital microscope. Averages of 73.4 MPs(ea)/kg and 97.8 MPs(ea)/kg of microplastics were identified in the soil at the two landfills. The main shapes of microplastics were fragments, fibers, and films, and it was confirmed that secondary plastics were found at a high rate. The major plastic types were identified as PP and PE, ranging from 62.5 to 65.3% in proportion, followed by PET, PS, nylon, PMMA, and PVC. As for the colors of microplastics, black had the highest percentage, while other microplastics were identified as being white, blue, transparent, gray, green, red, and yellow. These results can be taken as important data indicating that microplastics in the soil around landfills can be affected by landfill waste. [ABSTRACT FROM AUTHOR]

A growing body of experimental research now demonstrates that neurologically normal individuals associate different taste qualities with design features such as curvature, symmetry, orientation, texture and movement. The form of everything from the food itself through to the curvature of the plateware on which it happens to be served, and from glassware to typeface, not to mention the shapes of/on food product packaging have all been shown to influence people's taste expectations, and, on occasion, also their taste/food experiences. Although the origins of shape–taste and other form–taste crossmodal correspondences have yet to be fully worked out, it would appear that shape qualities are occasionally elicited directly. However, more often, there may be a metaphorical attempt to translate the temporal qualities of taste sensations into a spatial analogue. At the same time, emotional mediation may sometimes also play a role in the affinity people experience between shape properties and taste. And finally, it should be acknowledged that associative learning of the relation between packaging shapes, glassware shapes, logos, labels and iconic food forms that commonly co-occur with specific taste properties (i.e., in the case of branded food products) may also play an important role in determining the nature of shape–taste correspondences. Ultimately, however, any attempt to use such shape–taste correspondences to nudge people's behaviour/perception in the real world is made challenging due to the fact that shape properties are associated with multiple qualities, and not just taste. [ABSTRACT FROM AUTHOR]

Metal oxide nanoparticles are considered to be good alternatives as fungicides for plant disease control. To date, numerous metal oxide nanoparticles have been produced and evaluated as promising antifungal agents. Consequently, a detailed and critical review on the use of mono-, bi-, and tri-metal oxide nanoparticles for controlling phytopathogenic fungi is presented. Among the studied metal oxide nanoparticles, mono-metal oxide nanoparticles—particularly ZnO nanoparticles, followed by CuO nanoparticles —are the most investigated for controlling phytopathogenic fungi. Limited studies have investigated the use of bi- and tri-metal oxide nanoparticles for controlling phytopathogenic fungi. Therefore, more studies on these nanoparticles are required. Most of the evaluations have been carried out under in vitro conditions. Thus, it is necessary to develop more detailed studies under in vivo conditions. Interestingly, biological synthesis of nanoparticles has been established as a good alternative to produce metal oxide nanoparticles for controlling phytopathogenic fungi. Although there have been great advances in the use of metal oxide nanoparticles as novel antifungal agents for sustainable agriculture, there are still areas that require further improvement. [ABSTRACT FROM AUTHOR]

Deep Neural Networks (DNNs) are vulnerable to adversarial patch attacks, which raises security concerns for face recognition systems using DNNs. Previous attack methods focus on the perturbation texture and generate adversarial patches with fixed shapes at random or pre-designed locations, which causes poor adversarial transferability. This paper proposes a Spatial Mutable Adversarial Patch (SMAP) method to generate a dynamic mutable patch to be injected into the face. In the proposed SMAP, the texture, position and shape of the patch are optimized simultaneously and the patch generation pipeline is end-to-end differentiable. Specifically, a Patch Location Selection Scheme is designed to find the critical patch position with the most significant influence on the target identity by the step-based gradient search. By innovatively bridging the pre-defined mask and the dynamic update of the patch, the patch position and shape are changed based on the affine transformation and sampling mechanism in each iteration, which maintains the importance of the injected patch to the adversarial objective. To evaluate the vulnerability of face recognition models, we explore more threatening impersonation attacks under the black-box setting and design a strict evaluation metric that aligns with the real-world scenario. Extensive experiments show that the proposed SMAP improves attack performance across various face recognition models and datasets. Moreover, SMAP achieves better transferability on commercial face recognition systems than existing methods. [ABSTRACT FROM AUTHOR]

For a number of machine learning problems, an exogenous variable can be identified such that it heavily influences the appearance of the different classes, and an ideal classifier should be invariant to this variable. An example of such exogenous variable is identity if facial expression recognition (FER) is considered. In this paper, we propose a dual exogenous/endogenous representation. The former captures the exogenous variable whereas the second one models the task at hand (e.g., facial expression). We design a prediction layer that uses a tree-gated deep ensemble conditioned by the exogenous representation. We also propose an exogenous dispelling loss to remove the exogenous information from the endogenous representation. Thus, the exogenous information is used two times in a throwable fashion, first as a conditioning variable for the target task, and second to create invariance within the endogenous representation. We call this method THIN, standing for THrowable Information Networks. We experimentally validate THIN in several contexts where an exogenous information can be identified, such as digit recognition under large rotations and shape recognition at multiple scales. We also apply it to FER with identity as the exogenous variable. We demonstrate that THIN significantly outperforms state-of-the-art approaches on several challenging datasets. [ABSTRACT FROM AUTHOR]

Binary stochastic neurons (BSNs) are excellent activators for machine learning. An ideal platform for implementing them is low- or zero-energy-barrier nanomagnets (LBMs) possessing in-plane anisotropy (e.g., circular or slightly elliptical disks) whose fluctuating magnetization encodes a probabilistic (p-) bit. Here, we show that such a BSN's activation function, the pinning current (which pins the output to a particular binary state), and the response time – all exhibit strong sensitivity to very slight geometric variations in the LBM's cross-section. A mere 1% change in the diameter of a circular nanomagnet in any arbitrary direction can alter the response time by a factor of ∼4 at room temperature and a 10% change can alter the pinning current by a factor of ∼2. All this causes large device-to-device variation which is detrimental to integration. we also show that the energy dissipation is lowered but the response time is increased by replacing a circular cross-section with a slightly elliptical one and then encoding the p-bit in the magnetization component along the major axis. Encoding the p-bit in the magnetization component along the minor axis has the opposite effect. The energy-delay-product, however, is relatively independent of whether the cross-section is a circle or an ellipse and which magnetization component encodes the p-bit in the case of the ellipse. [ABSTRACT FROM AUTHOR]

The characterization of connectedness by coverings i.e. chain connectedness led to some new concepts. For example chain components coincide with quasicomponents. We present some important results on chain connectedness and on chain components. [ABSTRACT FROM AUTHOR]

With the development of modern weapons such as UAV swarms and multiwarhead missiles, infrared (IR) cluster small target detection technology has become increasingly important. However, the difficulty in characterizing cluster multitargets leads to poor detection performance of existing methods. On the one hand, this letter proposes improved DBSCAN (IDBSCAN) to accurately extract the features of cluster multitargets with unknown numbers and distribution. On the other hand, an IDBSCAN-based difference measure (IDBSCAN-DM) is proposed, which fuses saliency and distribution features to further enhance cluster multitargets. Specifically, we first design the multiscale sliding window to quickly extract candidate targets. Then, the IDBSCAN-based local window is constructed and IDBSCAN-DM is computed for better target enhancement and background suppression. Finally, adaptive threshold segmentation is performed on the IDBSCAN-DM map to detect real targets. Extensive comparative experiments demonstrate that the proposed method achieves better target enhancement and a higher probability of detection. [ABSTRACT FROM AUTHOR]

Land cover change detection (LCCD) with bitemporal remote sensing images has been widely used in practical applications. However, when the bitemporal images are multimodal remote sensing images (MRSIs) which are acquired with different sensors, the change detection performance may be unsatisfactory, because MRSIs cannot be compared directly to generate a change magnitude and obtain a change detection map. Here a novel approach is proposed to overcome this problem, i.e., the enhanced UNet (E-UNet) which learns deep shared features from MRSIs to achieve change detection with MRSIs. First, a preevent image to postevent image (P2P) transformation module based on classical cycle-consistent generation adversarial network (CGAN) is suggested to embed at the head of the proposed E-UNet to translate the preevent image to a P2P one. Then, multiscale convolutions are added at each encoding layer to capture the various shapes and sizes of ground targets. Finally, a polarized self-attention (PSA) module is employed before beginning the decoding progress of E-UNet with an aim to pay extra attention to changed areas. Compared with five typical state-of-the-art methods, experimental results based on two pairs of MRSIs well demonstrated the feasibility and advantages of the proposed E-UNet for LCCD with MRSIs in terms of visual observations and quantitative evaluations. For example, the improvement is 4.19% and 4.75% in terms of the overall accuracy for the Sardinia dataset and California dataset, respectively. The code of the proposed approach can be found at https://github.com/ImgSciGroup/E-UNet. [ABSTRACT FROM AUTHOR]

Robust and accurate parameter estimation of K-distributed clutter plays an important role in target detection for marine radars. The estimation performance of the existing estimators is limited by only two moments or percentiles. To break this limit, a self-learning estimator using gate recurrent unit (GRU) network is proposed to estimate shape parameter by a well-designed feature vector composed of ten moment ratios and 13 percentile ratios. It is theoretically proved that the feature vector is independent of scale parameter. Then, the scale parameter is determined by a shape-parameter-dependent percentile. Finally, it is verified by simulated data and measured data that the proposed estimator is suitable for the complicated and various clutter environments. [ABSTRACT FROM AUTHOR]

Residential area (RA) segmentation is of great significance in the remote-sensing (RS) field. Training the segmentation network with image-level weakly annotated data (WAD) has become a research hot spot due to the easy access to classification labels. The quality of class activation maps (CAMs) is crucial for obtaining accurate segmentation results. Limited to irregular shapes, tortuous boundaries, and greatly varied scales of targets in RS images, generating high-quality CAMs is still a great challenge. To solve these problems, a novel weakly annotated RA segmentation model based on attention redistribution and co-learning (ARC) is proposed in this letter. We develop aggregate-and-distribute-based feature coupling (ADFC) to achieve the redistribution of attention on channel and spatial dimensions, which deals with multilevel features at the same time and makes them fully embedded together. Such an arrangement can effectively capture the shape characteristic of targets and filter out complicated backgrounds. To mitigate the impact of ambiguous regions like surroundings of boundaries and potential scattered houses, a confusion co-learning (CCL) strategy is designed to jointly explore the class-specific features and refine the cross-class features through a two-stream classifier with sharing weights, which helps generate sharper edges and discover ignored targets. Experimental results on GeoEye-1, SPOT5, and Landsat8 datasets reveal that our proposal outperforms the competing methods by a large margin in both subjective and objective assessments. [ABSTRACT FROM AUTHOR]

In view of the current situation of low visibility at night and increasing poaching activities in wildlife reserves, the use of unmanned aerial vehicle (UAV) monitoring equipped with thermal infrared (TIR) cameras has become a trend. To overcome the difficulties of small objects and low resolution in infrared images, this letter proposes a nonsalient object detection algorithm in infrared images based on deep learning. Based on Grid R-CNN, we designed a feature extraction network, improved the region proposal network by guided anchoring (GA-RPN), and introduced the slice inference mechanism. Our method makes the network selectively fuse multiscale features to generate high-quality proposals and improve detection accuracy. Experimental results show our method is superior to other object detection algorithms for nonsalient elephant images on the BIRDSAI dataset. [ABSTRACT FROM AUTHOR]

Existing point cloud modeling datasets primarily express the modeling precision by pose or trajectory precision rather than the point cloud modeling effect itself. Under this demand, we first independently construct a set of LiDAR system with an optical stage, and then we build an HPMB dataset based on the constructed LiDAR system, a High-Precision, Multi-Beam, real-world dataset. Second, we propose a modeling evaluation method based on HPMB for object-level modeling to overcome this limitation. In addition, the existing point cloud modeling methods tend to generate continuous skeletons of the global environment, hence lacking attention to the shape of complex objects. To tackle this challenge, we propose a novel learning-based joint framework, DSMNet, for high-precision 3-D surface modeling from sparse point cloud frames. DSMNet comprises density-aware point cloud registration (PCR) and geometry-aware point cloud sampling (PCS) to effectively learn the implicit structure feature of sparse point clouds. Extensive experiments demonstrate that DSMNet outperforms the state-of-the-art methods in PCS and PCR on the Multi-View Partial Point Cloud (MVP) database. Furthermore, the experiments on the open-source KITTI and our proposed HPMB datasets show that DSMNet can be generalized as a postprocessing of simultaneous localization and mapping (SLAM), thereby improving modeling precision in environments with sparse point clouds. [ABSTRACT FROM AUTHOR]

Automatic registration of point clouds is a fundamental research problem in 3-D computer vision. In this letter, a sketch-based registration framework is proposed targeting 3-D scenarios. It consists of two major modules: pairwise alignment and multiview alignment. For the pairwise alignment, a point cloud is first abstracted into a sketch which greatly preserves the contour information in the scene; then an entropy point pair feature (EPPF) method that integrates contour shape features and point pair geometric features is applied to estimate transformation. For the multiview alignment, the key is to combine the voting-based pairwise method with simultaneous localization and mapping (SLAM) system, which ensures the robustness of the proposed framework in different scenarios. Experiments show that the proposed sketch-based method clearly outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

During various operations of military and civil importance, the vehicles need to move in different off-road conditions. Several soil parameters that differ in spatial and temporal domains impact the possibility of vehicular movement. Conventional evaluation of the prevalent state of soil being cumbersome and time-consuming, the information extracted from the rut profile of the vehicle is used. Several techniques ranging from manual to advanced laser scanners are there to monitor this profile. The laser scan contains a vast point cloud dataset. It brings in the data storage and retrieval issue for an efficient computational framework for real-time mobility guidance. Little research exists for addressing the storage issue of this sort of rut profile data. Here, rut profile data captured manually in different field running conditions are used for studying the optimization aspects. Various mathematical formulations are analyzed, and nonlinear regression analysis is attempted on the commonly observed rut shapes. Using the proposed modified bell-shaped function on these common shapes, the $R2$ value moved above 0.98. It could fetch an additional compression of over 80% on straight patches and 71% on turnings. Although complex scenarios could be dealt with separately, an optimal representation of the most common rut shapes can aid the development of an efficient decision support system. [ABSTRACT FROM AUTHOR]

This letter presents an analytical study of the performance of the maximum-likelihood gamma constant false alarm rate (MLG-CFAR) detector operating over a homogenous gamma-distributed (GM) clutter. To accomplish this task, a novel closed-form expression for the probability of false alarm and a highly accurate approximation for the probability of detection are derived. Specifically, this proposed approximation is also given in a closed form. These expressions, which can be of great interest to the radar community, are given in terms of the Gauss hypergeometric function and the Tricomi function. Besides, the proposed expressions can be easily implemented and computed using most popular scientific software packages such as MATLAB, Maple, and Mathematica. Our propositions are then tested and validated using numerical computing and Monte Carlo simulations. The obtained results demonstrate a high accuracy and significant computational advantages of the proposed expressions compared to the alternative ones. Moreover, the detection performance of the MLG-CFAR detector is also conducted assuming a homogenous clutter with a known shape parameter. [ABSTRACT FROM AUTHOR]

Wideband waveform design of multiple-input multiple-output (MIMO) radar with high resolution in complex electromagnetic environment is a challenging problem. To address the issue, this letter proposes a waveform optimization method based on multiple criteria, including beam pattern formation, frequency–space power distribution (FSPD) matching, and discrete ambiguity function (DAF) shaping. Considering the constant modulus constraint and Range-Doppler resolution requirement, the continuous phase code with a “thumbtack” ambiguity function (AF) is selected as the waveform form to be designed, and then, the waveform phase matrix becomes a variable to be optimized. Suppose a multitarget and bandwidth resource sharing scenario; under the premise of signal time-domain discreteness, both the space and bandwidth are divided into many discrete units, and a time–frequency–space joint optimization model with respect to the waveform phase matrix is established, so as to realize the beam pattern, FSPD, and DAF in compliance with expectations. Furthermore, aiming to solve the nonconvex optimization problem, a calculating approach based on conjugate gradient (CG) method is employed to achieve the closed-form solution of the multidimensional variable after simplifying the objective function. Compared with the traditional methods, the experiments verify that the proposed method can not only approximate the required multibeam shape and FSPD but also have good range-Doppler tolerance and sensitivity. [ABSTRACT FROM AUTHOR]

The shape reconstruction of buildings based on LiDAR point clouds is extremely significant in remote sensing. In recent years, reconstruction methods based on the implicit network have been widely used in object-level shape reconstruction. However, the incompleteness and sparsity of airborne LiDAR scanning point clouds will lead to poor reconstruction results. To solve this problem, GEOP-Net: an implicit modeling framework embedded with high-dimensional geometric features, is proposed in this letter. First, the geometric encoding (GE) module added to extract high-dimensional features enhances the feature extraction ability of the network to the detailed structures. The point clouds of buildings in Zurich are collected and used to evaluate the performance of the proposed method. The experimental results show that the proposed method has better accuracy than the existing methods, so it provides a new research idea for building reconstruction. [ABSTRACT FROM AUTHOR]

Learning performance is unsatisfactory when training deep-learning networks without prior-knowledge guidance. In this letter, a multiscale change detection neural network guided by a change gradient image (CGI) was proposed. First, a multiscale information attentional module was embedded in the backbone of UNet to achieve a multiscale information fusion task of bitemporal images. Second, the position channel attention module (PCAM) was promoted to make the neural network pay more attention to the spectral and spatial information in the multiscale fused feature map. Finally, a change gradient guide module (CGGM) was proposed to optimize backpropagation and overcome the negative effects of pseudo-change. Compared with seven state-of-the-art methods using three pairs of real remote sensing images, the proposed approach could smoothen the salt-and-pepper noise from the detection maps and improve the detection accuracy. The quantitative improvements are about 1.67% and 3.00% in terms of overall accuracy (OA) and kappa coefficient, respectively, thus confirming the feasibility and superiority of the proposed approach for detecting land cover change with remotely sensed images. Code: https://github.com/ImgSciGroup/MACGGNet.git. [ABSTRACT FROM AUTHOR]

Accurately extracting buildings from high-resolution remote sensing images is important to obtain urban information, and promote the development of smart cities. At present, the knowledge-driven building extraction methods mostly rely on building prior knowledge of manual design, resulting in low automation and poor versatility. The data-driven methods usually rely too much on training samples, resulting in insufficient pertinence for the features of building extraction and low generalization ability of the model. Therefore, this study proposes a novel buildings edge-aware refined network (BEARNet) for building extraction from high-resolution remote sensing images. The network takes the building edge as a priori knowledge, learns the building edge features by decoupling the building body and edge, and further optimizes the network by combining the multiobjective loss function to strengthen the pertinence of building edge features extraction. Experimental results show that on the WHU building dataset, which is less difficult to extract buildings, compared with other methods, BEARNet has the highest Precision, F1, IoU, and OA values of 97.70%, 97.42%, 95.3%, and 98.67%. On the Massachusetts building dataset, where building extraction is difficult, BEARNet has the highest Precision, Recall, F1, IoU, and OA compared to other methods, with values of 84.92%, 85.27%, 85.09%, 75.82%, and 93.99%, respectively. Our proposed method is more accurate in extracting complex shapes and dense small-scale buildings, and the building edges are more refined and complete. [ABSTRACT FROM AUTHOR]

Substantial progress has been made in detecting ships of arbitrary orientation in synthetic aperture radar (SAR) images. However, the mainstream method is still limited by the horizontal bounding box (HBB) boundary, which cannot provide scaling information for the length and width of the oriented bounding box (OBB) in an intuitive way. In this study, we propose a novel encode representation to describe the OBB by breaking through the border restriction of the HBB. Specifically, we derive an inclination factor from two left-top point offsets (LTPO), which enables us to directly infer the coordinates of the four OBB vertices and obtain an oriented rectangular proposal. To obtain high-quality oriented semantic features, we utilize a feature adaptive module (FAM) to learn the shape and orientation implied by arbitrary-oriented ships through spatial transformation. Our comparative experiments demonstrate that our proposed method achieves superior performance and detection accuracy on two commonly-used benchmark datasets for oriented SAR ship detection dataset named SSDD and high-resolution SAR images dataset (HRSID). [ABSTRACT FROM AUTHOR]

The uncertainty of crop phenological cycle is an important issue in crop classification with time series polarimetric synthetic aperture radar (PolSAR) data. The time series alignment algorithm represented by dynamic time warping (DTW) can supply a potential solution, which realigns curves based on shape matching, dealing with the distortion of feature curves caused by uncertain crop phenological development. However, previous studies mainly focused on shape characteristics of time-varying feature curves, which is hard to comprehensively evaluate the similarity degree of crop phenological cycles. Furthermore, it ignored the differences in scattering signal and polarimetric statistical distribution of crops, which limited the accuracy of crop classification. In this letter, a novel crop classification method based on phenology alignment is proposed. First, the dual-branch time series alignment method is proposed, including the time-weighted DTW (TWDTW) alignment and the Wishart distance-based TWDTW (WD-TWDTW) alignment, which combines the feature curve characteristics and the polarimetric statistical information to correctly describe the similarity degree of phenological cycles. Second, a multi-similarity measure (including shape similarity, feature similarity, and polarimetric similarity) is defined to improve capacity of crop discrimination. The multisimilarity measure can describe the differences of crop types from three aspects, including crop growth trend, growth status, and statistical distribution. The proposed method is evaluated with time series full-polarization Radarsat-2 data in Flevoland area. The results show that our method is superior to traditional method with single TWDTW alignment and shape similarity, and the corresponding overall accuracy (OA) is improved by 6%. [ABSTRACT FROM AUTHOR]

Sea clutter modeling and parameter estimation/prediction are important basis of system design, performance assessment, and target detection of maritime radars. In addition to parameter estimation from measured data, parameter prediction from radar parameters, sea state, and the viewing geometry of the radar is an alternative. In this letter, a dual-polarimetric full-recorded sea clutter database measured by an island-based X-band experimental radar on the Yellow Sea of China is introduced. On this database, the model selection reveals that the compound-Gaussian model with inverse Gaussian (CGIG) texture is suited for HH high-resolution sea clutter data, the generalized Pareto (GP) intensity distributions are for HH moderate-resolution data, and the $K$ amplitude distributions are for high and moderate-resolution VV polarized data. Further, three five-parametric empirical formulas are constructed to predict the shape parameter of the CGIG, GP, and $K$ distributions of sea clutter from the area of spatial resolution cell, the grazing angle, the significant wave height (SWH), the average wave period, and the wave direction relative to the sight line of radar. The new empirical formulas attain more accurate prediction than existing empirical formula on the database. [ABSTRACT FROM AUTHOR]

The short-time Fourier transform (STFT) is widely used in the analysis of non-stationary signals because of its mature principle and simple implementation, as well as in the multi-target tracking based on Doppler through-wall radar (TWR). However, due to the limitation of the uncertainty principle, when the frequencies of several echo signals are close to each other, there will be aliasing in the spectrum obtained by STFT, which will seriously degrade the accuracy of multi-target tracking. To overcome this limitation, we proposed an improved Ferguson-Hough algorithm, which combines the asymmetric Ferguson parameter model with the Hough transform and can fit the instantaneous frequency curve of the echo signal in the frequency ambiguous area, thus compensating for the loss of tracking accuracy. Experiments show that the method can effectively fit the frequencies in the frequency-ambiguous region, thus making the localization results approximate to the real data, and achieving better localization accuracy than other similar algorithms. [ABSTRACT FROM AUTHOR]