Discrete Edge Research Articles

First record of Linichnus Jacobsen & Bromley, 2009, and Nihilichnus Mikuláš et al., 2006, in the Itaboraí Basin (early Eocene), in Southeastern Brazil: ichnological, morpho-anatomical, and paleobiological implications

We present the first records of Linichnus (cf. Linichnus serratus and L. bromleyi), as well as Nihilichnus sulcatus in the Itaboraí Basin, Brazil. The studied specimen, an incomplete left dentary of the metatherian Guggenheimia crocheti, displays a long, wide groove with a discrete serrated edge, suggestive of Linichnus serratus; a long, narrow, smooth-edged, Linichnus bromleyi, and three pairs of Nihilichnus sulcatus. The placement of the N. sulcatus perforations and grooves support tooth slippage as the mechanism for producing this ichnospecies. The studied ichnotaxa could only be produced by Epidolops ameghinoi, a polydolopimorphian metatherian characterized by plagiaulacoid third premolars, which are large teeth with a serrated longitudinal blade, hypertrophied and semi-procumbent lower incisors and lower canines, and complex chewing kinetics. Sebecids are ruled out due to the presence of N. sulcatus, a single shallow, long, ‘U-shaped’ groove, and a single, long L. bromleyi, plus the absence of Knethichnus parallelum, ‘V’ marks, Nihilichnus sicarius, and crushed bone. Consequently, the pattern of the studied specimen is opposite to that inferred to have been produced by sebecids. These traces represent the first record during the Paleogene of South America of Linichnus being produced by a land mammal like Polydolopimorphia. These observations support scavenging behaviour for E. ameghinoi.

Exploring the space of graphs with fixed discrete curvatures

Abstract Discrete curvatures are quantities associated to the nodes and edges of a graph that reflect the local geometry around them. These curvatures have a rich mathematical theory and they have recently found success as a tool to analyze networks across a wide range of domains. In this work, we consider the problem of constructing graphs with a prescribed set of discrete edge curvatures, and explore the space of such graphs. We address this problem in two ways: first, we develop an evolutionary algorithm to sample graphs with discrete curvatures close to a given set. We use this algorithm to explore how other network statistics vary when constrained by the discrete curvatures in the network. Second, we solve the exact reconstruction problem for the specific case of Forman–Ricci curvature. By leveraging the theory of Markov bases, we obtain a finite set of rewiring moves that connects the space of all graphs with a fixed discrete curvature.

Automatic analysis of the continuous edges of stone tools reveals fundamental handaxe variability

The edges of stone tools have significant technological and functional implications. The nature of these edges–their sharpness, whether they are concave or convex, and their asymmetry–reflect how they were made and how they could be used. Similarly, blunt portions of a tool’s perimeter hint at how they could have been grasped or hafted and in which directions force could be applied. However, due to the difficulty in accurately measuring the complex 3D geometry of tool edges with traditional methods, their attributes are often overlooked. When they are analyzed, they have traditionally been assessed with visual qualitative categories or unreliable physical measurements. We introduce new computational 3D methods for automatically and repeatably measuring key attributes of stone tool edges. These methods allow us to automatically identify the 3D perimeter of tools, segment this perimeter according to changes in edge angles, and measure these discrete edge segments with a range of metrics. We test this new computational toolkit on a large sample of 3D models of handaxes from the later Acheulean of the southern Levant. Despite these handaxes being otherwise technologically and morphologically similar, we find marked differences in the amount of knapped outline, edge angle, and the concavity of their edges. We find many handaxes possess blunt portions of perimeter, suitable for grasping, and some handaxes even possess more than one discrete sharp edge. Among our sample, sites with longer occupations and more diverse toolkits possessed handaxes with more diverse edges. Above all, this paper offers new methods for computing the complex 3D geometry of stone tool edges that could be applied to any number of artifact types. These methods are fully automated, allowing the analysis and visualization of entire assemblages.

On-Board Small-Scale Object Detection for Unmanned Aerial Vehicles (UAVs)

Object detection is a critical task that becomes difficult when dealing with onboard detection using aerial images and computer vision technique. The main challenges with aerial images are small target sizes, low resolution, occlusion, attitude, and scale variations, which affect the performance of many object detectors. The accuracy of the detection and the efficiency of the inference are always trade-offs. We modified the architecture of CenterNet and used different CNN-based backbones of ResNet18, ResNet34, ResNet50, ResNet101, ResNet152, Res2Net50, Res2Net101, DLA-34, and hourglass14. A comparison of the modified CenterNet with nine CNN-based backbones is conducted and validated using three challenging datasets, i.e., VisDrone, Stanford Drone dataset (SSD), and AU-AIR. We also implemented well-known off-the-shelf object detectors, i.e., YoloV1 to YoloV7, SSD-MobileNet-V2, and Faster RCNN. The proposed approach and state-of-the-art object detectors are optimized and then implemented on cross-edge platforms, i.e., NVIDIA Jetson Xavier, NVIDIA Jetson Nano, and Neuro Compute Stick 2 (NCS2). A detailed comparison of performance between edge platforms is provided. Our modified CenterNet combination with hourglass as a backbone achieved 91.62%, 75.61%, and 34.82% mAP using the validation sets of AU-AIR, SSD, and VisDrone datasets, respectively. An FPS of 40.02 was achieved using the ResNet18 backbone. We also compared our approach with the latest cutting-edge research and found promising results for both discrete GPU and edge platforms.

Discrete characteristic and edge effect during subsurface microhardness measurement of Ti-6Al-4V alloy

This paper investigates the discrete characteristic and edge effect of subsurface microhardness of Ti-6Al-4V alloy. The results show that the discrete degree of Ti-6Al-4V alloy microhardness decreases with increasing loading force. When the loading force is 200 gf, only five microhardness measurements are needed to reduce the interference of discrete characteristic. Importantly, the edge effect was found during measuring the subsurface microhardness of Ti-6Al-4V alloy. The edge effect means that the microhardness value is little when its indentation position is close to a workpiece edge. Finite element analysis reveals that the low support strength of workpiece edge is responsible for the edge effect. This study further clarifies that a Vickers indenter is not suitable for characterizing the machined subsurface microhardness on account of the limitations of edge effect and indenter size.

A model for fretting contact of layered materials with interfacial cracks

Surfaces of tightly fitted joints such as splines and dovetails in aero-engines are expected in nominally static contact but experience small amplitude oscillations in practice, leading to fretting fatigue due to nucleation and propagation of micro-cracks. Modern surface coatings are designed to improve the anti-friction/wear capabilities but result in severe stress concentrations across the bonding interfaces due to property mismatch, which may generate interfacial cracks and lead to delamination damage. In this study, a numerical model is developed to investigate the fretting behavior of layered materials with mixed-mode I and II cracks at the layer/layer/substrate interfaces in plane-strain conditions. The layer-type impurities are assumed as a bunch of inclusions, whilst the crack-type ones are simulated as discrete edge dislocations. The governing equations are established based on the analysis of stress state, and the impurity-induced displacement is taken into account for an accurate description of the fretting contact. The intensive interactions in the contact system are determined by a multi-loop algorithm, and the calculation efficiency is improved by using the Fast Fourier Transform (FFT) technique. The model can be easily handled without refining meshes near the crack tips, and the calculation time is far less than that of the finite element model to achieve comparable solutions. The model is also capable of simulating cracks with their faces in contact by disabling the loop that determines the mode I crack solution. Details are discussed on the surface traction and subsurface stress influenced by layer films and embedded cracks. The solutions to crack-face displacement are also presented to reveal the potential crack patterns enforced by the time-dependent contact loading. The conclusions are expected to provide insights into enhancing the damage resistance of surfaces suffering from fretting.

Improved compact model extraction of statistical variability in 5 nm nanosheet transistors and applied to SRAM simulations

In this paper, we look at how artificial neural networks (ANNs) may be used to improve compact model extraction of statistical variability in 5 nm nanosheet transistors (NSTs) and how it can be applied to 6NST-static random access memory (SRAM) simulations. To begin, both the TCAD simulation platform and compact model of 3D n-type and p-type NST have been rigorously validated against the experimental data. The transfer characteristics curves of 1104 NST samples generated by metal gate granularity, random discrete dopants and line edge roughness are used to extract the important figures of merit (FoM) including ON-current (I ON), OFF-current (I OFF), threshold voltage (V TH) and subthreshold slope. Meanwhile, we can collect the main compact model parameters of these NST samples using our automatic extraction technique. Furthermore, a multi-layer ANN engine is trained to anticipate the important compact model parameters by entering FoMs, which significantly speeds up the automatic extraction. When we compare the prediction results to the genuine values, we discover that their correlation coefficients are all larger than 0.99. Finally, we simulated the 6NST-SRAM circuit and obtained its stability variation, with the help of extracted NST variability by the aforementioned speedup techniques.

A Roundness Detection Algorithm of Non-Standard Discontinuous Circle Based on the Curvature Change of Discrete Edge Points

In the practical application scene of image detection, it is of great significance to detect the circle in the image and judge the roundness of the circle. Because the edges of real objects are often discontinuous due to occlusion, roundness detection of discontinuous circles is a common task. Traditional circle detection algorithms mainly include voting/clustering algorithm based on edge points, optimization algorithm and arc segment-based algorithm. For discontinuous circles, because the integrity of the circle edges is different, the voting results of edge points are difficult to be used as the basis for roundness detection. When the algorithms based on optimization and arc segment detect nonstandard circles, a large number of overlapping misjudgments are easy to occur, and the overlapping of objects before and after will lead to maximum suppression, which is difficult to achieve good results. In this paper, the geometric properties of the circle itself are used to classify the edge points, so that the roundness of the discontinuous circle can be calculated and the size of the circle can be judged. In this paper, taking the photos of mutually blocked round fruits as an example, an algorithm is constructed to detect the roundness of non-standard discontinuous round objects. The results show that this algorithm has a good effect on roundness detection of non-standard discontinuous circles.

Noise estimation in 2D MRI using DWT coefficients and optimized neural network

Robust estimation of noise strength in Magnetic Resonance Images (MRIs) is a task of great importance due to its extensive applications in image post-processing techniques. Many noise estimation algorithms have been proposed in the past to retrieve noise characteristics of the magnitude image. These algorithms rely on either the spatial or transform domain information of the image. In this research article, we propose a noise estimation algorithm that utilizes a hybrid Discrete Wavelet Transform (DWT) and edge information suppression based algorithm to estimate the strength of noise in magnitude MR images. The wavelet coefficients corresponding to spatial domain edges are eliminated using a down-sampled complement of the Sobel edge map. The distribution of average noise energy in spatial and transform domain which follows Parseval’s theorem is utilized for calculating the initial noise estimate. Further, the robustness of the proposed algorithm is enhanced using Feedforward Neural Network. The proposed algorithm is observed to be computationally fast and accurate. Results on synthetic and clinical T1- and T2-w brain MRIs following Gaussian or Rician distribution show better performance than the existing benchmark algorithms over a wide range of input noise.

Rapid prototyping of arbitrary 2D and 3D wireframe DNA origami.

Wireframe DNA origami assemblies can now be programmed automatically from the top-down using simple wireframe target geometries, or meshes, in 2D and 3D, using either rigid, six-helix bundle (6HB) or more compliant, two-helix bundle (DX) edges. While these assemblies have numerous applications in nanoscale materials fabrication due to their nanoscale spatial addressability and high degree of customization, no easy-to-use graphical user interface software yet exists to deploy these algorithmic approaches within a single, standalone interface. Further, top-down sequence design of 3D DX-based objects previously enabled by DAEDALUS was limited to discrete edge lengths and uniform vertex angles, limiting the scope of objects that can be designed. Here, we introduce the open-source software package ATHENA with a graphical user interface that automatically renders single-stranded DNA scaffold routing and staple strand sequences for any target wireframe DNA origami using DX or 6HB edges, including irregular, asymmetric DX-based polyhedra with variable edge lengths and vertices demonstrated experimentally, which significantly expands the set of possible 3D DNA-based assemblies that can be designed. ATHENA also enables external editing of sequences using caDNAno, demonstrated using asymmetric nanoscale positioning of gold nanoparticles, as well as providing atomic-level models for molecular dynamics, coarse-grained dynamics with oxDNA, and other computational chemistry simulation approaches.

Peierls-Nabarro barrier effect in nonlinear Floquet topological insulators

The Peierls-Nabarro barrier is a discrete effect that frequently occurs in discrete nonlinear systems. A signature of the barrier is the slowing and eventual stopping of discrete solitary waves. This work examines intense electromagnetic waves propagating through a periodic honeycomb lattice of helically driven waveguides, which serves as a paradigmatic Floquet topological insulator. Here it is shown that discrete topologically protected edge modes do not suffer from the typical slowdown associated with the Peierls-Nabarro barrier. Instead, as a result of their topological nature, the modes always move forward and redistribute their energy: a narrow (discrete) mode transforms into a wide effectively continuous mode. On the other hand, a discrete edge mode that is not topologically protected does eventually slow down and stop propagating. Topological modes that are initially narrow naturally tend to wide envelope states that are described by a generalized nonlinear Schrödinger equation. These results provide insight into the nature of nonlinear topological insulators and their application.

Digital Image Noise Estimation Using DWT Coefficients.

Noise type and strength estimation are important in many image processing applications like denoising, compression, video tracking, etc. There are many existing methods for estimation of the type of noise and its strength in digital images. These methods mostly rely on the transform or spatial domain information of images. We propose a hybrid Discrete Wavelet Transform (DWT) and edge information removal based algorithm to estimate the strength of Gaussian noise in digital images. The wavelet coefficients corresponding to spatial domain edges are excluded from noise estimate calculation using a Sobel edge detector. The accuracy of the proposed algorithm is further increased using polynomial regression. Parseval's theorem mathematically validates the proposed algorithm. The performance of the proposed algorithm is evaluated on a standard LIVE image dataset. Benchmarking results show that the proposed algorithm outperforms all other state of the art algorithms by a large margin over a wide range of noise.

A Safe and Fast Repulsion Method for GPU-based Cloth Self Collisions

Cloth dynamics and collision handling are the two most challenging topics in cloth simulation. While researchers have substantially improved the performances of cloth dynamics solvers recently, their success in fast collision detection and handling is rather limited. In this article, we focus our research on the safety, efficiency, and realism of the repulsion-based collision handling approach, which has demonstrated its potential in existing GPU-based simulators. Our first discovery is the necessary vertex distance conditions for cloth to enter self intersections, the negations of which can be viewed as vertex distance constraints continuous in time for sufficiently avoiding self collisions. Continuous constraints, however, cannot be enforced with ease. Our solution is to convert continuous constraints into three types of constraints: discrete edge length constraints, discrete vertex distance constraints, and vertex displacement constraints. Based on this solution, we develop a fast and safe collision handling process for enforcing constraints, a novel splitting method for integrating collision handling with dynamics solvers, and static and adaptive remeshing schemes to further improve the runtime performance. In summary, our cloth simulator is efficient, safe, robust, and parallelizable on a GPU. The experiment shows that it runs at least one order of magnitude faster than existing simulators.

Morphogenesis of limph nodes in Muscovy ducks during early posnatal onthogenesis

The dynamics of mass and linear parameters of lymph nodes of ducks is a direct reflection of structural and functional transformations of their lymphatic parenchyma. The peculiarities of quantitative dynamics of tissue components of peripheral lymphatic organs at early stages of postnatal ontogenesis are to a great extent determined by advanced growth rates of their absolute mass against the background of sharp increase of their antigenic stimulation intensity. Parenchyma of lymph nodes of newborn ducklings is characterized by relatively low degree of differentiation and is represented by diffuse lymphatic tissue with no pronounced signs of its division into separate functional zones. Internally, the lymphatic channel is represented by only two lymphatic sinuses - the central one, which is located in the central part of the organ and occupies a large relative area and a discrete edge sinus, which borders on the node capsule and has a much smaller relative area and, accordingly, is located at its periphery. The organ parenchyma is a diffuse cluster of stromal and lymphatic cells in the enlarged lymphatic vessel between the central (inner) and edge (outer) lymphatic sinuses, without signs of its division into cortical and brain matter. Among the lymphatic tissue of the lymph node, reticular stroma cells and the population of small lymphocytes have the largest relative amount.

Persistence-based resolution-independent meshes of superpixels

The over-segmentation problem is to split a pixel-based image into a smaller number of superpixels that can be treated as indecompasable regions to speed up higher level image processing such as segmentation or object detection. A traditional superpixel is a potentially disconnected union of square pixels, which can have complicated topology (with holes) and geometry (highly zigzag boundaries). This paper contributes to new resolution-independent superpixels modeled as convex polygons with straight-line edges and vertices with real coordinates not restricted to a fixed pixel grid. Any such convex polygon can be rendered at any resolution higher than in original images, hence superpixels are resolution-independent.The key difficulty in obtaining resolution-independent superpixels is to find continuous straight-line edges, while classical edge detection focuses on extracting only discrete edge pixels. The recent Persistent Line Segment Detector (PLSD) avoids intersections and small angles between line segments, which are hard to fix before a proper polygonal mesh can be constructed. The key novelty is an automatic selection of strongest straight-line segments by using the concept of persistence from Topological Data Analysis, which allows to rank segments by their strength. The PLSD performed well in comparison with the only past Line Segment Detector Algorithm (LSDA) on the Berkeley Segmentation Database of 500 real-life images. The PLSD is now extended to the Persistent Resolution-Independent Mesh (PRIM).

Blood vessel segmentation of retinal image using Clifford matched filter and Clifford convolution

The appearance and structure of blood vessels in retinal fundus image is a fundamental part of diagnosing different issues related with such as diabetes and hypertension. The proposed blood vessel segmentation in fundus image using Clifford Algebra approach is divided into three steps. Image vectorization as a first step helps to convert the image space into Clifford space. Next step introduces Clifford matched filter as a proposed mask which works for retinal blood vessel extraction. The third and final step of this method is Clifford convolution operation with the help of Clifford convolution. This mask generates edge points along the boundaries of the blood vessels. The edge points are represented as a Grade-0 vector or scalar unit. Discrete edge points along the boundary of blood vessels are the edge pixels instead of continuous edges. The output of this method differs in the representation of vessel tree compare to other existing methods. The output image can be defined as the edge point set. This method achieves blood vessel segmentation accuracy of 94.88% and 92.95% on two publicly available datasets STARE and DRIVE respectively in less than 0.5 s per image. The proposed matched filter and the segmentation technique opens many windows of reliable and faster processing for further image processing steps on retinal fundus images.

A field-effect approach to directly profiling the localized states in monolayer MoS2

A fundamental understanding of the charge transport mechanism in two-dimensional semiconductors (e.g., MoS2) is crucial for fully exploring their potential in electronic and optoelectronic devices. By using monolayer graphene as the barrier-free contact to MoS2, we show that the field-modulated conductivity can be used to probe the electronic structure of the localized states. A series of regularly distributed plateaus were observed in the gate-dependent transfer curves. Calculations based on the variable-range hopping theory indicate that such plateaus can be attributed to the discrete localized states near mobility edge. This method provides an effective approach to directly profiling the localized states in conduction channel with an ultrahigh resolution up to 1 meV.

Modeling of Electrically Triggered Tunable Magnetic Metamaterial Hat for Multifunctional Control in MRI Applications

Acquisition of images without surgical interposition into human body was possible due to magnetic resonance imaging (MRI) mechanism. We introduced noteworthy properties of specific combination of copper surface coils as tunable hybridized magnetic metamaterial hat (THMMH). In THMMH, some of the surface coil slots were loaded with capacitor elements and parallel merger of discrete edge ports as externally applied sinusoidal steady-state current source (IS). We highlighted the significance of IS, as it could make the design tunable and reconfigurable without any requirement of it being re-designed/re-fabricated. Efficiency comparison between THMMH and previously reported work (un-tunable hybridized magnetic metamaterial hat, HMMH) was analyzed. We concluded that THMMH exhibited better magnetic field (B-field) and SNR into region of interest (ROI) at the rat’s brain, as well as shown strong resonance in comparison to previously reported work on the rat’s brain imaging for 7-T MRI. In addition, THMMH excited two eigenmodes simultaneously, which exploited its properties as hybridized magnetic material. Furthermore, relative negative permeability, μr = − 3.5+j20.2 for THMMH as case I and μr = − 5.5+j36.3 for un-tunable HMMH as case II were achieved at 300 MHz for 7-T MRI and for comparison purpose.

Numerical analysis of the influence of inertial loading over morphing trailing edge devices

Larger and more flexible wind turbine blades are currently being manufactured. Those highly flexible blades suffer from loading of aeroelastic nature which increases the fatigue damage. Smart blade concepts are being developed to reduce the aerodynamic loading. The state of the art favors the discrete deformable trailing edge concept. Many authors have reported adequate performance of this type of actuators in reducing the blade vibrations. However, the question of whether the actuator can maintain its authority under strong external loading remains still answered. To solve this question, actuator models that include the loading produced by the blade vibration are required. In this article, a smart morphing trailing edge model is presented that includes the inertial forces produced by the blade dynamics. The model is applied to a commercial actuator and the influence of its parameters is analyzed. Finally, a simple estimation of the inertial loading produced by a 35-m wind turbine blade at the flutter instability condition is analyzed to understand the design requirements of this type of systems.

Consistency and convergence rate of phylogenetic inference via regularization.

It is common in phylogenetics to have some, perhaps partial, information about the overall evolutionary tree of a group of organisms and wish to find an evolutionary tree of a specific gene for those organisms. There may not be enough information in the gene sequences alone to accurately reconstruct the correct "gene tree." Although the gene tree may deviate from the "species tree" due to a variety of genetic processes, in the absence of evidence to the contrary it is parsimonious to assume that they agree. A common statistical approach in these situations is to develop a likelihood penalty to incorporate such additional information. Recent studies using simulation and empirical data suggest that a likelihood penalty quantifying concordance with a species tree can significantly improve the accuracy of gene tree reconstruction compared to using sequence data alone. However, the consistency of such an approach has not yet been established, nor have convergence rates been bounded. Because phylogenetics is a non-standard inference problem, the standard theory does not apply. In this paper, we propose a penalized maximum likelihood estimator for gene tree reconstruction, where the penalty is the square of the Billera-Holmes-Vogtmann geodesic distance from the gene tree to the species tree. We prove that this method is consistent, and derive its convergence rate for estimating the discrete gene tree structure and continuous edge lengths (representing the amount of evolution that has occurred on that branch) simultaneously. We find that the regularized estimator is "adaptive fast converging," meaning that it can reconstruct all edges of length greater than any given threshold from gene sequences of polynomial length. Our method does not require the species tree to be known exactly; in fact, our asymptotic theory holds for any such guide tree.