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It is highly desirable to adapt the reference vectors to unknown Pareto fronts (PFs) in decomposition-based evolutionary many-objective optimization. While adapting the reference vectors enhances the diversity of the achieved solutions, it often decelerates the convergence performance. To address this dilemma, we propose to adapt the reference vectors and the scalarizing functions in a coordinated way. On the one hand, the adaptation of the reference vectors is based on a local angle threshold, making the adaptation better tuned to the distribution of the solutions. On the other hand, the weights of the scalarizing functions are adjusted according to the local angle thresholds and the reference vectors' age, which is calculated by counting the number of generations in which one reference vector has at least one solution assigned to it. Such coordinated adaptation enables the algorithm to achieve a better balance between diversity and convergence, regardless of the shape of the PFs. Experimental studies on MaF, DTLZ, and DPF test suites demonstrate the effectiveness of the proposed algorithm in solving problems with both regular and irregular PFs.

The primate scapula has been studied widely since its shape has been shown to correlate with how the forelimb is used in daily activities. In this study, we expand on the existing literature and use an image-based methodology that was originally developed for orthopaedic practice to quantify and compare the three-dimensional (3D) morphology of the scapula across humans and great apes. We expect that this image-based approach will allow us to identify differences between great apes and humans that can be related to differences in mobility and loading regime of the shoulder. We hypothesize that gorillas and chimpanzees will have a similar scapular morphology, geared towards stability and weight-bearing in knuckle-walking, whilst the scapular morphology of orangutans is expected to be more similar to that of humans given their high glenohumeral mobility associated with their suspensory lifestyle. We made 3D reconstructions of computed tomography scans of 69 scapulae from four hominid genera (Pongo, Gorilla, Pan and Homo). On these 3D bone meshes, the inferior glenoid plane was determined, and subsequently, a set of bony landmarks on the scapular body, coracoid, and acromion were defined. These landmarks allowed us to measure a set of functionally relevant angles which represent acromial overhang, subacromial space and coracoacromial space. The angles that were measured are: the delto-fulcral triangle (DFT), comprising the alpha, beta, and delta angle, the acromion-glenoid angle (AGA), the coracoid-glenoid centre-posterior acromial angle (CGA), the anterior tilt (TA CGA) and the posterior tilt of the CGA (PT CGA). Three observers placed the landmarks on the 3D bone meshes, allowing us to calculate the inter-observer error. The main differences in the DFT were found between humans and the great apes, with small differences between the great apes. The DFT of humans was significantly lower compared to that of the great apes, with the smallest alpha (32.7 degrees), smallest delta (45.7 degrees) and highest beta angle (101.6 degrees) of all genera. The DFT of chimpanzees was significantly higher compared to that of humans (p < 0.01), with a larger alpha (37.6 degrees) and delta angle (54.5 degrees) and smaller beta angle (87.9 degrees). The mean AGA of humans (59.1 degrees) was significantly smaller (p < 0.001) than that of gorillas (68.8 degrees). The mean CGA of humans (110.1 degrees) was significantly higher (p < 0.001) than in orangutans (92.9 degrees). Humans and gorillas showed mainly a posterior tilt of their coracoacromial complex whilst chimpanzees showed mainly an anterior tilt. The coracoacromial complex of the orangutans was not tilted anteriorly or posteriorly. With our image-based method, we were able to identify morphological features of the scapula that differed significantly between hominid genera. However, we did not find an overall dichotomy in scapular morphology geared towards high stability (Pan/Gorilla) or high mobility (Homo/Pongo). Further research is needed to investigate the functional implications of these differences in scapular morphology.

The robotic manipulation of composite rigid-deformable objects (i.e., those with mixed nonhomogeneous stiffness properties) is a challenging problem with clear practical applications that, despite the recent progress in the field, it has not been sufficiently studied in the literature. To deal with this issue, in this article, we propose a new visual servoing method that has the capability to manipulate this broad class of objects (which varies from soft to rigid) with the same adaptive strategy. To quantify the object's infinite-dimensional configuration, our new approach computes a compact feedback vector of 2-D contour moments features. A sliding mode control scheme is then designed to simultaneously ensure the finite-time convergence of both the feedback shape error and the model estimation error. The stability of the proposed framework (including the boundedness of all the signals) is rigorously proved with Lyapunov theory. Detailed simulations and experiments are presented to validate the effectiveness of the proposed approach. To the best of the author's knowledge, this is the first time that contour moments along with finite-time control have been used to solve this difficult manipulation problem.

Non-Rigid Structure-from-Motion (NRSfM) reconstructs a deformable 3D object from the correspondences established between monocular 2D images. Current NRSfM methods lack statistical robustness, which is the ability to cope with correspondence errors.This prevents one to use automatically established correspondences, which are prone to errors, thereby strongly limiting the scope of NRSfM. We propose a three-step automatic pipeline to solve NRSfM robustly by exploiting isometry. Step 1 computes the optical flow from correspondences, step 2 reconstructs each 3D point's normal vector using multiple reference images and integrates them to form surfaces with the best reference and step 3 rejects the 3D points that break isometry in their local neighborhood. Importantly, each step is designed to discard or flag erroneous correspondences. Our contributions include the robustification of optical flow by warp estimation, new fast analytic solutions to local normal reconstruction and their robustification, and a new scale-independent measure of 3D local isometric coherence. Experimental results show that our robust NRSfM method consistently outperforms existing methods on both synthetic and real datasets., Accepted in TPAMI 2021

This paper studies formation stabilization and maneuvering of mobile agents governed by double-integrator dynamics. The desired formation is described by a set of triple-agent angles. A carefully chosen such set of angle constraints guarantees that the desired formation is angle rigid. To achieve the desired angle rigid formation, a stabilization control law is proposed using only local velocity and direction measurements. We show that the closed-loop dynamics of the formation, when each agent is modeled by a double-integrator, are closely related to the corresponding one in single-integrator agent dynamics. Sufficient conditions are constructed to guarantee the closed-loop stability for identical and distinct velocity damping gains, respectively. To guide an angle rigid formation to move with the desired translational velocity, orientation and scale, formation maneuvering laws are then proposed. Simulation examples are also provided to validate the results.

Topology optimization (TO) has been formulated as a quadratic unconstrained binary optimization (QUBO) problem that can be solved by Digital Annealer (DA) and quantum computers, which can realize massive parallelization. It is shown that the 3-D TO of a permanent magnet (PM), with 600 unknowns, is successfully performed by the DA with the aid of the finite element method (FEM) for field computations. Moreover, it is shown to perform TO of a magnetic core, with 758 unknowns, in such a way that the QUBO and field problems are iteratively solved to determine the optimal solution in which the magnetic field and optimal core structure are self-consistent.

A novel automatic design method for permanent magnet (PM) motors using a Monte Carlo tree search is presented. The optimal motor structures are determined through a tree search, in which the motors with different numbers of poles, current phase angles, PM configurations, and numbers of PMs are simultaneously considered. At the leaf nodes, parameter and topology optimizations are performed to obtain the optimal material shape and distribution. The proposed method was applied to the optimization of a 24-slot motor. It was shown to be effective in finding the optimal motor structure and geometry to maximize the average torque while considering iron loss. The proposed method can be applied not only to the design of PM motors but also to many types of electric apparatus and other systems.

In molecular analysis, Spatial Distribution Functions (SDF) are fundamental instruments in answering questions related to spatial occurrences and relations of atomic structures over time. Given a molecular trajectory, SDFs can, for example, reveal the occurrence of water in relation to particular structures and hence provide clues of hydrophobic and hydrophilic regions. For the computation of meaningful distribution functions, the definition of molecular reference structures is essential. Therefore we introduce the concept of an internal frame of reference (IFR) for labeled point sets that represent selected molecular structures, and we propose an algorithm for tracking the IFR over time and space using a variant of Kabschs algorithm. This approach lets us generate a consistent space for the aggregation of the SDF for molecular trajectories and molecular ensembles. We demonstrate the usefulness of the technique by applying it to temporal molecular trajectories as well as ensemble datasets. The examples include different docking scenarios with DNA, insulin, and aspirin. Funding agencies:This work was supported through grants from the ExcellenceCenter at Link¨oping and Lund in Information Technology(ELLIIT) and the Swedish e-Science Research Centre(SeRC). The authors thanks the Swedish National Infrastructurefor Computing (SNIC) for providing computingresources.

DNA oligomers are known to serve as stabilizing ligands for silver nanodusters (Ag-N-DNAs) with rod-like nanoduster geometries and nanosecond-lived fluorescence. Here, we report two Ag-N-DNAs that possess distinctly different structural properties and are the first to exhibit only microsecond-lived luminescence. These emitters are characterized by significant broadband downconversion from the ultraviolet/visible to the near-infrared region. Circular dichroism spectroscopy shows that the structures of these two Ag-N-DNAs differ significantly from previously reported Ag-N-DNAs. We find that these nanoclusters contain eight valence electrons, making them the first reported DNA-stabilized luminescent quasi-spherical superatoms. This work demonstrates the important role that nanoduster composition and geometry play in dictating luminescence properties of Ag-N-DNAs and significantly expands the space of structure-property relations that can be achieved for Ag-N-DNAs.

BACKGROUND: The second-generation cryoballoon (CB) has proven to be a highly effective ablative strategy in patients with symptomatic atrial fibrillation (AF). This study sought to investigate the anatomical characteristics of pulmonary veins (PVs) and the relationship between their size, ovality, and late reconnections in a large cohort of patients undergoing repeat ablation for recurrence of atrial arrhythmias. METHODS AND RESULTS: A total of 152 consecutive patients (98 males, 64.5%; mean age 64.9 ± 9.6 years) underwent a repeat ablation for recurrent atrial tachyarrhythmias after a median time of 6.5 months [IQR 11] from the index CB ablation. All repeat ablations were performed using a 3-dimensional electro-anatomical mapping system. Among all 593 PVs, 134 (22.6%) showed a late PV reconnection in 95 patients (0.88 per patient), at the time of repeat ablation procedure. There was a significant difference in ovality between left- and right-sided PVs (p

Cam-follower mechanisms are key in various mechatronic applications to convert rotary to linear reciprocating motions. The dynamic behavior of these systems relies on the design parameters such as the cam shape and follower mass. It appears that for some combinations of system parameters, continuous contact between the cam and follower cannot be assured, leading to harmful periodic impacts. This research presents a data-driven approach to predict the influence of parameter settings on the system dynamics by learning from a limited data set of nominal operating conditions. More specifically, we present a hybrid model architecture encompassing an ordinary differential equation, consisting of a close interconnection of neural and physics-based network layers. Due to an increased generalization established by the physical laws, these physics-based neural network models exhibit enhanced extrapolation capabilities compared to their black-box counterparts. Consequently, the presented models can accurately simulate the system behavior for parameter settings far beyond the nominal values included in the training data. This way, starting from a limited set of nominal time-series data, we could accurately estimate the set of critical system parameters that lead to hazardous jump phenomena in cam-follower systems.

In this paper, atomistic molecular dynamics simulations are performed for the systems consisting of functionalized gold nanoparticles (NPs) in a toluene medium. Gold NPs are coated with ligand molecules that have different terminal groups, that is, polar carboxyl (COOH), hydroxyl (OH), amine (NH2), and nonpolar methyl (CH3). These functional groups are selected to understand the relation between the aggregation behavior of functionalized gold NPs in toluene and the polarity of terminal groups of ligand molecules. The center-of-mass distances between NP pairs, the radial distribution functions, the mean square displacements, the radius of gyration, and the number of hydrogen bonds (H-bond) between ligand molecules are computed. Our simulation results indicate that functionalized gold NPs exhibit different aggregation/dispersion behaviors depending upon the terminal group of ligands., TUBITAK (Turkish Agency) [117F246], This work has been supported by TUBITAK (Turkish Agency) under Research Project number 117F246. The numerical calculations reported in this paper were fully performed at the TUBITAK ULAKBIM, High Performance and Grid Computing Center (TRUBA resources).

International audience; Accurate and robust cephalometric image analysis plays an essential role in orthodontic diagnosis, treatment assessment and surgical planning. This paper proposes a novel landmark localization method for cephalometric analysis using multiscale image patch-based graph convolutional networks. In detail, image patches with the same size are hierarchically sampled from the Gaussian pyramid to well preserve multiscale context information. We combine local appearance and shape information into spatialized features with an attention module to enrich node representations in graph. The spatial relationships of landmarks are built with the incorporation of three-layer graph convolutional networks, and multiple landmarks are simultaneously updated and moved toward the targets in a cascaded coarse-to-fine process. Quantitative results obtained on publicly available cephalometric X-ray images have exhibited superior performance compared with other state-of-the-art methods in terms of mean radial error and successful detection rate within various precision ranges. Our approach performs significantly better especially in the clinically accepted range of 2 mm and this makes it suitable in cephalometric analysis and orthognathic surgery.

Elongated plasmas lead to improved performance in tokamaks but make the plasma prone to vertical instability, which requires active feedback control, a critical issue for future fusion reactors. Vertical control was optimized for the TCV tokamak by applying modern control theory to electromagnetic models for the plasma-vessel-coils dynamics. Two different optimal combinations of poloidal field coils for vertical control actuation are derived from linear plasma response models and used on different timescales for controlling the plasma vertical position. On fast timescales, the priority is input minimization, while on long timescales position control is designed to be compatible with shape control. A structured H-infinity design extending classical H-infinity to fixed-structure control systems was subsequently applied to obtain an optimized controller using all available coils for position control. Closed-loop performance improvement was demonstrated in dedicated TCV experiments, showing a reduction of input requirement for stabilizing the same plasma, thus reducing the risk of power supply saturation and consequent loss of vertical control. This novel algorithm is adaptable to different plasma equilibria as it is designed for model-based automated coil selection and controller tuning, thus avoiding extensive experimental gain scans when performing plasma discharges in TCV. The presented technique is general and can be applied to any present tokamak with independent coils or for the design of future tokamak magnetic control systems.

The eyebrow is a complex structure of the human face, which has both functional (communication) and aesthetic aspects and contributes to a persons’ individual appearance. Ptosis of the eyebrow is a common condition, especially among the elderly and smokers. Facial appearance is disrupted by eyebrow ptosis, and in many cases, correction and treatment are needed. No specific type of browlift has been shown to be superior to another, and since there is a wide variation in treatment preference between patients, it requires an individual approach to each specific patient. This review provides an overview of the history of the browlift, non-surgical treatment and current surgical techniques available.Level of evidence: Not ratable.

Efficient coding provides a concise account of key early visual properties, but can it explain higher-level visual function such as shape perception? If curvature is a key primitive of local shape representation, efficient shape coding predicts that sensitivity of visual neurons should be determined by naturally-occurring curvature statistics, which follow a scale-invariant power-law distribution. To assess visual sensitivity to these power-law statistics, we developed a novel family of synthetic maximum-entropy shape stimuli that progressively match the local curvature statistics of natural shapes, but lack global structure. We find that humans can reliably identify natural shapes based on 4th and higher-order moments of the curvature distribution, demonstrating fine sensitivity to these naturally-occurring statistics. What is the physiological basis for this sensitivity? Many V4 neurons are selective for curvature and analysis of population response suggests that neural population sensitivity is optimized to maximize information rate for natural shapes. Further, we find that average neural response in the foveal confluence of early visual cortex increases as object curvature converges to the naturally-occurring distribution, reflecting an increased upper bound on information rate. Reducing the variance of the curvature distribution of synthetic shapes to match the variance of the naturally-occurring distribution impairs the linear decoding of individual shapes, presumably due to the reduction in stimulus entropy. However, matching higher-order moments improves decoding performance, despite further reducing stimulus entropy. Collectively, these results suggest that efficient coding can account for many aspects of curvature perception.

A system can change shape. In biology, shape changes lead to the wonderful variety of forms that surround us; in engineering, they are a tool to design soft robots and deployable structures. In this thesis, we identify and explore three fundamental paths that lead to shape change: elastic instabilities, spontaneous deformations, and the combination of these two. Classic elastic instabilities occur when a progressive load is applied to a system which initially responds progressively, but at a critical threshold suddenly changes response and, importantly, shape. Here, we study a classic example of large strain instability — ballooning – and the process that leads to longitudinal phase separation in solids. We show that near a critical point, phase separation is described by a universal and simple energy which allows us to understand shape evolution analytically. Typical examples of the second path towards shape change — via spontaneous defor- mations — are biological growth or muscle contraction. They sculpt the shape of flowers or power the beating of a heart. Outside of the realm of biology, the swelling of gels and the anisotropic deformations in liquid crystal elastomers (LCEs) are two excellent examples of spontaneous deformations that can be patterned, allowing scientists to program shape changes, mimic biology and design soft robots. A typical example of patterned deformation is the morphing of flat sheets into curved surfaces. Here, we show that an LCE sheet can be morphed into a variety of shapes in a reprogrammable way by patterning the activation strength of the LCE via light modulation. Moreover, we show that patterned shape changes are not limited to 2D and discuss the origin of the 3D deformation that causes twist and contraction in LCE fibres. Finally, we recognise the last and mechanically most rich path towards shape change where elastic instabilities and spontaneous deformations combine. This new mode of defor- mation underpins, for instance, patterns in biology such as the corrugations on the mammal brain or the folding of pollen grains. In the case of soft active materials, we show that the ballooning instability and phase separation can be triggered and amplified by the spontaneous deformation of LCEs; that a coiling instability can be induced in spontaneous twisting fibres; and discuss how curvature loads govern the instabilities in thin shells.

Most nanoparticles’ parameters affect their interactions with cells. To date, all the parameters studied are basically static (e.g., size, shape, ligands, and charge). This is unfortunate, because proteins have structural dynamics that most nanoparticles do not possess. Here, we study single-chain polymeric nanoparticles (SCPNs), whose structures undergo dynamic changes. We produced multiple sets of particles from identical polymer chains via a supramolecular reshuffling approach that allowed iterative reshuffling between a compact/static and a sparse/dynamic form. These particles are topological isomers because they have identical molecular formulas differing in connectivity and thus structural dynamics. We show that cell uptake discriminates between these SCPN topological isomers. Through different endocytic pathways, the sparse/dynamic isomers are uptaken more, but the compact/static isomers access the cytosol more efficiently, as evidenced by a glucocorticoid translocation assay. These results highlight the importance of structural dynamics’ role in cellular interactions.

Activation of the innate immune STimulator of INterferon Genes (STING) pathway potentiates antitumour immunity, but systemic delivery of STING agonists to tumours is challenging. We conjugated STING-activating cyclic dinucleotides (CDNs) to PEGylated lipids (CDN-PEG-lipids; PEG, polyethylene glycol) via a cleavable linker and incorporated them into lipid nanodiscs (LNDs), which are discoid nanoparticles formed by self-assembly. Compared to state-of-the-art liposomes, intravenously administered LNDs carrying CDN-PEG-lipid (LND-CDNs) exhibited more efficient penetration of tumours, exposing the majority of tumour cells to STING agonist. A single dose of LND-CDNs induced rejection of established tumours, coincident with immune memory against tumour rechallenge. Although CDNs were not directly tumoricidal, LND-CDN uptake by cancer cells correlated with robust T-cell activation by promoting CDN and tumour antigen co-localization in dendritic cells. LNDs thus appear promising as a vehicle for robust delivery of compounds throughout solid tumours, which can be exploited for enhanced immunotherapy.

In an effort to find a novel way to enhance the attractiveness of healthy food, this article proposes surface mimicry—that is, designing a product to visually resemble another product—as an effective intervention to communicate property information to consumers. Specifically, it advances the notion that exposure to surface mimicry primes property mapping, a thinking style that leads consumers to transfer property information from one product onto another. To this end, three studies show that exposure to a target food product (e.g., kiwifruit) mimicking visual characteristics of another, modifier food product (e.g., popsicle) induces a transfer of attribute values of the modifier onto the target product for salient, alignable attributes on which the products differ (e.g., tastiness). A fourth study points to the activation of a property-mapping mindset as the underlying process. Finally, the effect is shown to persist, but it attenuates when the difference in belief(s) about the target and mimicked product is substantial (e.g., the taste expectations for Brussels sprouts and popsicles).

Nowadays, with the advances and use of technological possibilities and devices, the number of digital images is increasing gradually. Computer-aided classification of image types is widely applied in many applications such as medicine, security, and automation. The feature extraction and selection stages have great importance in terms of improving the classification performance as sub-stages of the pattern recognition process. Researchers apply different feature extraction methods for their works due to the requirements. In this study, a novel pattern recognition framework combining diverse and large-scale handcrafted feature extraction methods (shape-based and texture-based) and the selection stage on images is developed. Genetic algorithms are also used for feature selection. In the experimental studies, Flavia leaf recognition, Caltech101 object classification image datasets, and five supervised classification models (random forest, ECOC-SVM, k-nearest neighbor, AdaBoost, classification tree) with different parameters' values are used. The experimental results show that the proposed method achieves 98.39% and 82.77% accuracy rates on Flavia and Caltech101 datasets with the ECOC-SVM model, respectively. The proposed framework is also competitive with the existing state-of-the-art methods in the related literature., Scientific and Technological Research Council of Turkey (TUBITAK) 2211 National Graduate Scholarship Program, The author Erdal TASCI has been supported by the Scientific and Technological Research Council of Turkey (TUBITAK) 2211 National Graduate Scholarship Program.

This paper is motivated by the renewed interest in space exploration and the need to provide structurally sound and resource-efficient shielding solutions for valuable assets and future habitable modules. We present, implement and test a methodology for the preliminary design and assessment of optimal arch forms subjected to self-weight as well as seismically induced loads. The numerical framework, built around a limit thrust-line analysis, previously published by the authors, is summarized first. This is followed by a detailed account of the form-finding algorithm for arches of variable thickness. Special attention is placed on the physical feasibility of our assumptions and the justification of the engineering inputs adopted. The newly form-found arches achieve material efficiencies between 10% and 50% in comparison with their constant minimum-thickness circular or elliptical counterparts, depending on the relative intensity of the seismic action. The influence of the initial input geometry and the stabilising presence of additional shielding material against extreme radiation are also evaluated with emphasis on the effects of low-gravity conditions. Finally, a case study is presented and Discrete Element Models of constant and varying thickness arches (VTAs) are assessed under a set of representative ground-motions on a lunar setting. The significant over-conservatism of constant thickness arches (CTAs) is made manifest and potential improvements of the optimally found arch shape are highlighted. Although developed with extraterrestrial applications in mind, the results and methods we present herein are also applicable to terrestrial conditions when material efficiency is of utmost concern.

ispartof: IEEE ACCESS vol:10 pages:23450-23462 status: published

Scene text detection has attracted increasing concerns with the rapid development of deep neural networks in recent years. However, existing scene text detectors may overfit on the public datasets due to the limited training data, or generate inaccurate localization for arbitrary-shape scene texts. This paper presents an arbitrary-shape scene text detection method that can achieve better generalization ability and more accurate localization. We first propose a Scale-Aware Data Augmentation (SADA) technique to increase the diversity of training samples. SADA considers the scale variations and local visual variations of scene texts, which can effectively relieve the dilemma of limited training data. At the same time, SADA can enrich the training minibatch, which contributes to accelerating the training process. Furthermore, a Shape Similarity Constraint (SSC) technique is exploited to model the global shape structure of arbitrary-shape scene texts and backgrounds from the perspective of the loss function. SSC encourages the segmentation of text or non-text in the candidate boxes to be similar to the corresponding ground truth, which is helpful to localize more accurate boundaries for arbitrary-shape scene texts. Extensive experiments have demonstrated the effectiveness of the proposed techniques, and state-of-the-art performances are achieved over public arbitrary-shape scene text benchmarks (e.g., CTW1500, Total-Text, and ArT).

In this Letter, the Nyquist plot for linear, continuous-time MIMO systems under odd frequency transformations is dealt with. Given a system $\mathrm {G}(s)$ , a frequency transformation of the type $s\leftarrow F(s)$ allows one to obtain a transformed system $\tilde {\mathrm {G}}(s)=\mathrm {G}(F(s))$ with useful properties. For instance, in analog filter design this operation allows to obtain multi-bandpass/bandstops filters from lossless frequency transformations applied to prototype lowpass filters. We prove that under these transformations the Nyquist plot is transformed into a locus having the same shape of that of the original system. As the number of encirclements of any point in the complex plane performed by the curves in the Nyquist plot can be also related to that of the original system, we conclude that closed-loop stability of the transformed system can be inferred from the original system.

This article presents a structured light codification technique called frequency shift method which is especially designed for scenes containing strong interreflections. The method works by projecting multiple sine wave patterns having different frequencies. While the phase shift method associates a phase to each column of the projector, in the proposed codification, each column of the projector is uniquely encoded by the frequencies used and explicitly decoded using the discrete Fourier transform. This encoding allows the method to recover all the projector columns that contribute to a camera pixel’s observed intensity. An energy minimization is then used to separate direct and indirect illuminations. Objects presenting diffuse and specular interreflections are considered, and our experimental results illustrate the efficiency of our method compared with previously proposed methods.

Image noise removal is a common problem with many proposed solutions. The current standard is set by learning-based approaches, however these are not appropriate in all scenarios, perhaps due to lack of training data or the need for predictability in novel circumstances. The bitonic filter is a non-learning-based filter for removing noise from signals, with a mathematical morphology (ranking) framework in which the signal is postulated to be locally bitonic (having only one minimum or maximum) over some domain of finite extent. A novel version of this filter is developed in this paper, with a domain that is locally-adaptive to the signal, and other adjustments to allow application to real image sensor noise. These lead to significant improvements in noise reduction performance at no cost to processing times. The new bitonic filter performs better than the block-matching 3D filter for high levels of additive white Gaussian noise. It also surpasses this and other more recent non-learning-based filters for two public data sets containing real image noise at various levels. This is despite an additional adjustment to the block-matching filter, which leads to significantly better performance than has previously been cited on these data sets. The new bitonic filter has a signal-to-noise ratio 2.4dB lower than the best learning-based techniques when they are optimally trained. However, the performance gap is closed completely when these techniques are trained on data sets not directly related to the benchmark data. This demonstrates what can be achieved with a predictable, explainable, entirely local technique, which makes no assumptions of repeating patterns either within an image or across images, and hence creates residual images which are well behaved even in very high noise. Since the filter does not require training, it can still be used in situations where training is either difficult or inappropriate.

This paper proposes a new adaptive framework for tracking multiple objects in the presence of data association uncertainty and heavy clutter, either with or without knowledge of the measurement rates and/or target shapes. Built upon an online Gibbs sequential Markov chain Monte Carlo sampling scheme, the adaptive tracker is Bayesian optimal and robust, requiring no additional approximations or measurement partition steps. With a non-homogeneous Poisson process measurement model, our tracker can tackle the data association task with linear computational complexity. Meanwhile, we study generalised inverse Gaussian and inverse Wishart distributions for modelling Poisson rates and object shapes, respectively; these prior models ensure closed-form full conditionals in our online Gibbs sampling steps, under which object states and shapes can be jointly estimated with associations and Poisson rates in a parallel fashion. Furthermore, a fast Rao-Blackwellisation scheme for linear Gaussian dynamics is designed and demonstrated to significantly improve both tracking efficiency and accuracy. We validate the efficacy of our method on real and simulated data.

Soft continuum robots are highly flexible and adapt- able, making them ideal for unstructured environments such as the human body and agriculture. However, their high compliance and manoeuvrability make them difficult to model, sense, and control. Current control strategies focus on Cartesian space control of the end-effector, but few works have explored full-body control. This study presents a novel image-based deep learning approach for closed-loop kinematic shape control of soft continuum robots. The method combines a local inverse kinematics formulation in the image-space with deep convolutional neural networks for accurate shape control that is robust to feedback noise and mechanical changes in the continuum arm. The shape controller is fast and straightforward to implement; it takes only a few hours to generate training data, train the network, and deploy, requiring only a web camera for feedback. This method offers an intuitive and user-friendly way to control the robot’s 3D shape and configuration through teleoperation using only 2D hand-drawn images of the desired target state without the need for further user instruction or consideration of the robot’s kinematics.

The purpose of this investigation was to analyze the scientific literature concerning the influence of grain physical characteristics on functional value of poultry feed. The review considers the role of grain feed particle size and its shape in ensuring optimal development and functioning of digestive tract and increasing productivity of birds. The results showed that compared to the feeding with smaller particles, the use of coarse feed increased stomach weight in broilers, enhanced enzymatic degradation of feed in digestive tract and improved nutrient bioavailability. The study found that increase in the level of coarse corn in the diet increased the number of Lactobacillus spp. and Bifidobacteria spp. beneficial bacteria in digestive tract. Meanwhile, number of opportunistic pathogenic microorganisms decreased. In birds which were given granular feed we found insufficient development of stomach, mainly due to the lack of mechanical stimulation. We assume that in order to improve stomach function and health of birds, it is necessary to apply a source of dietary fiber to their diet. The results of studies suggest that adding 2-3% of insoluble source of fiber to the routine diets of young chickens based on high-protein soy flour and corn can improve their digestive tract development and growth characteristics.

When RTL designers define the hierarchy of a system, they exploit their knowledge about the conceptual abstractions devised during the design and the functional interactions between the logical components. This valuable information is often lost during physical synthesis. This paper proposes HiDaP, a novel multi-level algorithm that uses RTL information and analytical methods for the macro placement problem of modern designs dominated by multi-cycle connection pipelines. By taking advantage of the hierarchy tree, the netlist is divided into blocks containing macros and standard cells, and their dataflow affinity is inferred considering the register latency and flow width of their interaction. The layout is represented using slicing structures and generated with a top-down algorithm capable of handling blocks with both hard and soft components. An adaptive multi-objective cost function is used to simultaneously minimize wirelength, timing, overlap and distance to preferred locations, which can be user-defined or generated by analytic methods (spectral and force-directed). These techniques have been applied to a set of large industrial circuits and compared against state-of-theart commercial and academic placers, and also to handcrafted floorplans generated by expert back-end engineers. The proposed approach outperforms previous algorithmic methods and can produce solutions with better wirelength and timing than the best handcrafted floorplans. Post-routing layouts are almost brought to timing closure and DRC cleanness with minimal engineer modification, showing that the generated floorplans provide an excellent starting point for the physical design flow and contribute to reduce turn-around time significantly. This work has been partially supported by a grant from Inphi Corporation and funds from the Spanish Ministry for Economy and Competitiveness and the European Union (FEDER funds) under grant TIN2017-86727-C2-1-R, and the Generalitat de Catalunya (2017 SGR 786).

We present a field-programmable gate array (FPGA)-based technique for the on-line identification of highly ionizing particles in a liquid xenon (LXe) detector. The method was developed and successfully exploited to select $\alpha $ particles emitted by 241Am sources submerged in LXe in an overwhelming, mostly beam-related, $\gamma $ -ray background. After revising the main features of xenon and other liquid noble gases as ultra-violet (UV) scintillating media, we describe the algorithm idea and its firmware implementation. We then present the results in terms of efficiency and background suppression for the real-time $\alpha $ -particle tagging and the limits of the MEG trigger configuration. Finally, we show that in MEG II we are going to overcome the main issues and further improve the performances.

The connections in a graph generate a structure that is independent of a coordinate system. This visual metaphor allows creating a more flexible representation of data than a two-dimensional scatterplot. In this work, we present STAD (Simplified Topological Abstraction of Data), a parameter-free dimensionality reduction method that projects high-dimensional data into a graph. STAD generates an abstract representation of high-dimensional data by giving each data point a location in a graph which preserves the approximate distances in the original high-dimensional space. The STAD graph is built upon the Minimum Spanning Tree (MST) to which new edges are added until the correlation between the distances from the graph and the original dataset is maximized. Additionally, STAD supports the inclusion of additional functions to focus the exploration and allow the analysis of data from new perspectives, emphasizing traits in data which otherwise would remain hidden. We demonstrate the effectiveness of our method by applying it to two real-world datasets: traffic density in Barcelona and temporal measurements of air quality in Castile and Leon in ´ Spain. The authors wish to thank Danai Kafetzaki for valuable feedback and proofreading. This work was supported in part by the IWT/SBO 150056 project ”ACquiring CrUcial Medical information Using LAnguage TEchnology” (ACCUMULATE), and by the Flanders AI Impulse Program (“Onderzoeksprogramma Artificiele Intelligentie (AI) ¨ Vlaanderen”).

We use galaxies from the IllustrisTNG, MassiveBlack-II and Illustris hydrodynamic simulations to investigate the behaviour of large scale galaxy intrinsic alignments. Our analysis spans four redshift slices over the approximate range of contemporary lensing surveys $z=0-1$. We construct comparable weighted samples from the three simulations, which we then analyse using an alignment model that includes both linear and quadratic alignment contributions. Our data vector includes galaxy-galaxy, galaxy-shape and shape-shape projected correlations, with the joint covariance matrix estimated analytically. In all of the simulations, we report non-zero IAs at the level of several $\sigma$. For a fixed lower mass threshold, we find a relatively strong redshift dependence in all three simulations, with the linear IA amplitude increasing by a factor of $\sim 2$ between redshifts $z=0$ and $z=1$. We report no significant evidence for non-zero values of the tidal torquing amplitude, $A_2$, in TNG, above statistical uncertainties, although MassiveBlack-II favours a moderately negative $A_2\sim-2$. Examining the properties of the TATT model as a function of colour, luminosity and galaxy type (satellite or central), our findings are consistent with the most recent measurements on real data. We also outline a novel method for constraining the TATT model parameters directly from the pixelised tidal field, alongside a proof of concept exercise using TNG. This technique is shown to be promising, although the comparison with previous results obtained via other methods is non-trivial., Comment: 28 pages, 18 Figures; submitted to MNRAS

This article presents a fully automated algorithm for the extraction of parameterized macromodels from frequency responses. The reference framework is based on a frequency-domain rational approximation combined with a parameter-space expansion into Gaussian radial basis functions (RBFs). An iterative least-squares fitting with positivity constraints is used to optimize model coefficients, with a guarantee of uniform stability over the parameter space. The main novel contribution of this work is a set of algorithms, supported by strong theoretical arguments with associated proofs, for the automated determination of all the hyperparameters that define model orders, placement, and width of RBFs. With respect to standard approaches, which tune these parameters using time-consuming tentative model extractions following a trial-and-error strategy, the presented technique allows much faster tuning of the model structure. The numerical results show that models with up to ten independent parameters are easily extracted in few minutes.

The mammalian order Carnivora is characterized by a broad taxonomic and ecological diversity. By using a large sample of extant species, we tested the impact of ecological factors on carnivoran skull (cranium and mandible) morphology, taking advantage of a combined geometric morphometrics and comparative method approach. We implemented several evolutionary models to account for different tempo and mode of evolution in size and shape data. These models validated the association between skull morphology and diet at the interspecific scale. The functional distinction between pinniped (aquatic) and fissiped (mostly terrestrial) taxa was found valid only in mandible shape and cranial size. High levels of morphological disparity and evolutionary rates were identified in specialized dietary groups, and positive association between rates and disparity was found for skull size. Cranium and mandible showed consistent patterns of covariation that reflect constrained functional processes, which stabilize the ecomorphological evolution of Carnivora. Aquatic adaptations allowed carnivorans to invade and persist within novel regions of the mandibular morphospace. This ecological shift did not increase morphological disparity but occurred at a faster rate than in terrestrial species. Those species exhibit a stronger level of cranio-mandibular covariation due to constraints imposed by more demanding masticatory adaptations.

Obstructive sleep apnea is a common disorder that leads to sleep fragmentation and is potentially bidirectionally related to a variety of comorbidities, including an increased risk of heart failure and stroke. It is often considered a consequence of anatomical abnormalities, especially in the head and neck, but its pathophysiology is likely to be multifactorial in origin. With geometric morphometrics, and a large sample of adults from the Study for Health in Pomerania, we explore the association of craniofacial morphology to the apnea-hypopnea index used as an estimate of obstructive sleep apnea severity. We show that craniofacial size and asymmetry, an aspect of morphological variation seldom analysed in obstructive sleep apnea research, are both uncorrelated to apnea-hypopnea index. In contrast, as in previous analyses, we find evidence that brachycephaly and larger nasal proportions might be associated to obstructive sleep apnea severity. However, this correlational signal is weak and completely disappears when age-related shape variation is statistically controlled for. Our findings suggest that previous work might need to be re-evaluated, and urge researchers to take into account the role of confounders to avoid potentially spurious findings in association studies.

Redden and Hoch (2009) found that variety in a set of items robustly decreased the perceived quantity of the sum of these items across multiple studies. For example, a set of multicolored M&M’s was estimated to contain fewer M&M’s than an equally large set of single-colored M&M’s (e.g., Redden & Hoch, 2009, Study 3). We conducted six close replication studies of the studies reported by Redden and Hoch and did not find this effect in any of them. A meta-analysis of the four original studies and 6 replication studies (N = 1,383) revealed no evidence for the phenomenon that variety reduces perceived quantity.

The distinction between bones of sheep and bones of goats is a difficult issue in zooarchaeology. Several studies undertaken in the past to facilitate this task have relied upon both qualitative criteria and osteometry. Geometric morphometrics has proved to be a powerful tool to evaluate morphological differences in a rigorous and detailed manner. This study aims to analyse variations in the morphology of the calcaneum among sheep and goats based upon two-dimensional geometric morphometrics (GM). Twenty landmarks were selected on the surfaces of 79 calcanea (47 sheep and 32 goats) to calculate the principal components of shape variations among these specimens. Clear interspecific differences in the morphology of this bone were extracted. Most are located on the calcaneal tuber and neck, the sustentacular tali region, the articular surfaces of both the malleolus and the cubonavicular. Furthermore, the use of GM methods has enabled us to assess small but significant amounts of geometric variation that are difficult to measure using traditional morphometric techniques. They provide a new and useful perspective to what is already known in the published literature. Our results shed new light upon the possibility of the existence of qualitative features that may help to distinguish caprine breeds.

The flavor of fresh onion and its processed products is an important index with which to evaluate its quality. In this study, the highly volatile compounds of onion with different fresh-cut styles (bulb, ring, and square) and different storage temperatures (4 °C, 20 °C, and 25 °C) were characterized at the molecular level, focusing in particular on the volatile sulfur compounds. Headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) and headspace solid-phase microextraction-gas chromatography−mass spectrometry (HS-SPME-GC-MS) were employed. A total of 14 highly volatile compounds were identified in onion samples by HS-GC-IMS, and the square sample contained more volatile components. (E,E)-2,4-heptadianal, ethyl acetate, 2-methyl-1-pentanol, 2-pentylfuran, propyl acetate, and 2,6-dimethylpyrazine were produced in the ring and square samples when stored at higher temperatures, while pentanal, 2-heptenal, hexanal were decreased after cutting. Simultaneously, 16 sulfur compounds were identified in onions by HS-SPME-GC-MS. The sulfur compounds profile of the bulbs was significantly different from that of the rings and squares at any temperature. When stored at a low temperature (4 °C), cutting onions into a ring or square shape produced more sulfur. However, at higher temperatures (20 °C and 25 °C), fresh-cutting decreased the sulfur concentration. The total content of sulfur compounds was higher in the same cut style stored at higher temperatures (20 °C or 25 °C). 2-Mercapto-3,4-dimethyl-2,3-dihydrothiophene and 2,4-dimethylthiophene were formed during storage; however, (E)-1-(prop-1-en-1-yl)-3-propyltrisulfane, 1-(1-(methylthio)propyl)-2-propyldisulfane, (Z)-1-(1-propenyldithio)propyl disulfide, dipropyl trisulfide, and methyl 1-(1-propenylthio)propyl disulfide were lost from all samples after storage.

Large, fine-grained image segmentation datasets, annotated at pixel-level, are difficult to obtain, particularly in medical imaging, where annotations also require expert knowledge. Weakly-supervised learning can train models by relying on weaker forms of annotation, such as scribbles. Here, we learn to segment using scribble annotations in an adversarial game. With unpaired segmentation masks, we train a multi-scale GAN to generate realistic segmentation masks at multiple resolutions, while we use scribbles to learn their correct position in the image. Central to the model's success is a novel attention gating mechanism, which we condition with adversarial signals to act as a shape prior, resulting in better object localization at multiple scales. Subject to adversarial conditioning, the segmentor learns attention maps that are semantic, suppress the noisy activations outside the objects, and reduce the vanishing gradient problem in the deeper layers of the segmentor. We evaluated our model on several medical (ACDC, LVSC, CHAOS) and non-medical (PPSS) datasets, and we report performance levels matching those achieved by models trained with fully annotated segmentation masks. We also demonstrate extensions in a variety of settings: semi-supervised learning; combining multiple scribble sources (a crowdsourcing scenario) and multi-task learning (combining scribble and mask supervision). We release expert-made scribble annotations for the ACDC dataset, and the code used for the experiments, at https://vios-s.github.io/multiscale-adversarial-attention-gates, Paper accepted for publication at: IEEE Transaction on Medical Imaging - Project page: https://vios-s.github.io/multiscale-adversarial-attention-gates

The interaction between morphology, performance, and ecology has long been studied in order to explain variation in the natural world. Within arboreal salamanders, diversification in foot morphology and microhabitat use are thought to be linked by the impact of foot size and shape on clinging and climbing performance, resulting in an ability to access new habitats. We examine whether various foot shape metrics correlate with stationary cling performance and microhabitat to explicitly quantify this performance gradient across 14 species of salamander, including both arboreal and nonarboreal species. Clinging performance did not correlate with foot shape, as quantified by landmark‐based geometric morphometrics, nor with microhabitat use. Mass‐corrected foot centroid size and foot contact area, on the other hand, correlated positively with clinging performance on a smooth substrate. Interestingly, these foot variables correlated negatively with clinging performance on rough substrates, suggesting the use of multiple clinging mechanisms dependent upon the texture of the surface. These findings demonstrate that centroid size and foot contact area are more functionally relevant for clinging in salamanders than foot shape, suggesting that foot shape need not converge in order to achieve convergent performance. More broadly, our results provide an example of how the quantification of the performance gradient can provide the appropriate lens through which to understand the macroevolution of morphology and ecology., Arboreal salamanders have confounded herpetologists for years. We test the relationship between form and function of foot shape with respect to arboreality, showing no dependence of clinging performance on foot shape or microhabitat use. Rather, mass‐corrected foot centroid size and foot contact area do confer differences in cling performance.

Some of the most impressive functional specializations in the human brain are found in the occipitotemporal cortex (OTC), where several areas exhibit selectivity for a small number of visual categories, such as faces and bodies, and spatially cluster based on stimulus animacy. Previous studies suggest this animacy organization reflects the representation of an intuitive taxonomic hierarchy, distinct from the presence of face- and body-selective areas in OTC. Using human functional magnetic resonance imaging, we investigated the independent contribution of these two factors-the face-body division and taxonomic hierarchy-in accounting for the animacy organization of OTC and whether they might also be reflected in the architecture of several deep neural networks that have not been explicitly trained to differentiate taxonomic relations. We found that graded visual selectivity, based on animal resemblance to human faces and bodies, masquerades as an apparent animacy continuum, which suggests that taxonomy is not a separate factor underlying the organization of the ventral visual pathway.SIGNIFICANCE STATEMENT Portions of the visual cortex are specialized to determine whether types of objects are animate in the sense of being capable of self-movement. Two factors have been proposed as accounting for this animacy organization: representations of faces and bodies and an intuitive taxonomic continuum of humans and animals. We performed an experiment to assess the independent contribution of both of these factors. We found that graded visual representations, based on animal resemblance to human faces and bodies, masquerade as an apparent animacy continuum, suggesting that taxonomy is not a separate factor underlying the organization of areas in the visual cortex. ispartof: JOURNAL OF NEUROSCIENCE vol:41 issue:33 pages:7103-7119 ispartof: location:United States status: published

Soft object manipulation has recently gained popularity within the robotics community due to its potential applications in many economically important areas. Although great progress has been recently achieved in these types of tasks, most state-of-the-art methods are case-specific; They can only be used to perform a single deformation task (e.g. bending), as their shape representation algorithms typically rely on "hard-coded" features. In this paper, we present LaSeSOM, a new feedback latent representation framework for semantic soft object manipulation. Our new method introduces internal latent representation layers between low-level geometric feature extraction and high-level semantic shape analysis; This allows the identification of each compressed semantic function and the formation of a valid shape classifier from different feature extraction levels. The proposed latent framework makes soft object representation more generic (independent from the object's geometry and its mechanical properties) and scalable (it can work with 1D/2D/3D tasks). Its high-level semantic layer enables to perform (quasi) shape planning tasks with soft objects, a valuable and underexplored capability in many soft manipulation tasks. To validate this new methodology, we report a detailed experimental study with robotic manipulators., Comment: 12 pages, 14 figures, 2 tables

Landmark labeling in 3D head surfaces is an important and routine task in clinical practice to evaluate head shape, namely to analyze cranial deformities or growth evolution. However, manual labeling is still applied, being a tedious and time-consuming task, highly prone to intra-/inter-observer variability, and can mislead the diagnose. Thus, automatic methods for anthropometric landmark detection in 3D models have a high interest in clinical practice. In this paper, a novel framework is proposed to accurately detect landmarks in 3D infant’s head surfaces. The proposed method is divided into two stages: (i) 2D representation of the 3D head surface; and (ii) landmark detection through a deep learning strategy. Moreover, a 3D data augmentation method to create shape models based on the expected head variability is proposed. The proposed framework was evaluated in synthetic and real datasets, achieving accurate detection results. Furthermore, the data augmentation strategy proved its added value, increasing the method’s performance. Overall, the obtained results demonstrated the robustness of the proposed method and its potential to be used in clinical practice for head shape analysis., This work was funded by projects “NORTE-01-0145-FEDER-024300” and “NORTE-01-0145-FEDER-000045”, supported by Northern Portugal Regional Operational Programme (Norte2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER). It was also funded by national funds, through the FCT – Fundação para a Ciência e Tecnologia and FCT/MCTES in the scope of the project UIDB/05549/2020 and UIDP/05549/2020. The authors also acknowledge support from FCT and the European Social Found, through Programa Operacional Capital Humano (POCH), in the scope of the PhD grant SFRH/BD/136670/2018, SFRH/BD/136721/2018, and SFRH/BD/131545/2017.