Anchor Points Research Articles

CRIN: Rotation-Invariant Point Cloud Analysis and Rotation Estimation via Centrifugal Reference Frame

Various recent methods attempt to implement rotation-invariant 3D deep learning by replacing the input coordinates of points with relative distances and angles. Due to the incompleteness of these low-level features, they have to undertake the expense of losing global information. In this paper, we propose the CRIN, namely Centrifugal Rotation-Invariant Network. CRIN directly takes the coordinates of points as input and transforms local points into rotation-invariant representations via centrifugal reference frames. Aided by centrifugal reference frames, each point corresponds to a discrete rotation so that the information of rotations can be implicitly stored in point features. Unfortunately, discrete points are far from describing the whole rotation space. We further introduce a continuous distribution for 3D rotations based on points. Furthermore, we propose an attention-based down-sampling strategy to sample points invariant to rotations. A relation module is adopted at last for reinforcing the long-range dependencies between sampled points and predicts the anchor point for unsupervised rotation estimation. Extensive experiments show that our method achieves rotation invariance, accurately estimates the object rotation, and obtains state-of-the-art results on rotation-augmented classification and part segmentation. Ablation studies validate the effectiveness of the network design.

Tkachenko Methodology for building radio relay lines based on the use of geospatial information

When planning and organizing communication using modern military digital radios, a crucial and relevant task is to ensure the maximum possible communication range while providing the necessary speed and quality of information transmission. The main factors determining the possible communication range of modern military digital radios, in addition to their characteristics, are the terrain relief, buildings, and vegetation. Additionally, existing height objects in the area, such as communication operator towers, can be used to organize radio relay communication. An analysis showed that existing methods and specialized software can be used for certain calculations of signal levels, but they do not always take into account all necessary technical parameters of radio equipment and additional geospatial information. Moreover, the existing software is not designed to construct multi-interval radio relay lines while considering additional geospatial data. In constructing radio relay lines, there is often a situation where one interval cannot provide communication with the required quality. This situation calls for planning a radio relay line using relay stations. The choice of the location of relay stations depends on many factors, including the possibility of establishing communication, minimizing the number of relays, geospatial information regarding the terrain relief, and the presence of the necessary infrastructure (buildings, power supply, anchor points, towers, etc.) in the area. The article proposes a methodology for constructing radio relay communication lines based on calculations of signal levels while considering geospatial data. This methodology allows analyzing individual radio relay intervals for constructing multi-interval radio relay lines. The software implementation of the developed methodology allows creating software for calculating radio relay lines with the necessary input data and geospatial information regarding the terrain.

Triassic-Early Jurassic evaporites of the Saharan Platform, Algeria: Astronomical and geodynamic constraints on stratigraphy and sedimentation

The sequence stratigraphy of the Late Triassic - Early Jurassic evaporites of the Berkine Basin is described. Disconformities occur between all the major evaporitic units but lack of biostratigraphy (or other) chronostratigraphic control precludes their precise dating. The S4 and S3 Halites are predominantly non-marine halites deposited in low-lying salinas with a barrier to the north. The top of the S4 depositional sequence is marked by the D2, usually regarded as the Tr-J boundary in the Berkine Basin. Both the S3 and S4 salt deposits thin rapidly to the south-east and are thicker in the basin centre coincident with a subcrop of Carnian? aged volcanics. A combination of thermal cooling, rifting and reactivation of N–S lineaments parallel to the Hassi Messaoud-El Biod Arch controlled the (∼1500 m thick) depocenter.Time series analysis shows that astronomical forcing played a key role in the deposition of the S3 and S4 bedded halites. The prevailing climate was monsoonal with major replenishment of the basin indicated by long eccentricity cycles (405 kyr). Sedimentation rates were estimated using eCOCO analysis with average rates of 15 cm/kyr. Well to well comparisons shows that in marginal areas thinner sedimentary sequences relate to slower accumulation rates and periods of non-deposition or deflation. The pattern is similar in the S4 and S3 halite, but the reduced mud content and amalgamation of halite beds suggests a more arid climate in S3 times.The S1+S2 unit marks the first widespread deposition of sulphate in the basin. The lowermost anhydrite beds of the S1 + S2 rest unconformably on the underlying S3 and overstep the basin margins in the south-east; the sequence is capped by the B Horizon a basin-wide carbonate shelf deposit about 25 m thick indicating increased marine influence. Above there is a rapid return to thinly bedded mudstone-halite dominated sedimentation (Lias Salifère) which is overlain by the Lias Anhydritique an alternating sequence of halite and anhydrite deposits. Astronomical parameters of the whole sequence indicate an average sedimentation rate of ∼10 cm/kyr in this marine-influenced section, slower than the halite units. Although the time series analysis cannot provide precision dating of the evaporitic sequences the results indicate that there are important breaks in the depositional record. The combined S4 and S3 halites account for 4.75 Ma and the rest of the Liassic 9.4 Ma. It seems clear that much of the depositional record is missing.These Saharan Platform basins bear much in common with other western Mediterranean evaporite basins. Many show the same overall pattern of sedimentation with increased sulphate deposition above the Tr-J boundary. In late Triassic time they formed a contiguous low-lying zone flanked by cratonic highlands. This zone spanned the Gondwana-Laurussia boundary immediately prior to its break-up and Greater Adria formed a barrier between these basins and the developing Neotethys to the east. The major changes seen in the Saharan Platform are mirrored by the break-up of Adria and the separation of Gondwana and Laurasia and the ultimate connection of the western Mediterranean and the central Atlantic.Although the cyclostratigraphy can tell us much about the absolute duration of sedimentation, the lack of significant anchor points forbids the construction of a detailed chronostratigraphic framework. In particular, the precise location of the Tr-J boundary remains unclear. Although it is usually placed at the base of the D2 Horizon, biostratigraphical evidence from Southern Tunisia places the S3 in the Norian. The sharp contrast between the S4 and S3 halites with the later evaporite sequence suggests that positioning the boundary between the S3 halite and the S1 + S2 unconformity should be given consideration.

How Explainable Machine Learning Enhances Intelligence in Explaining Consumer Purchase Behavior: A Random Forest Model with Anchoring Effects

This study proposes a random forest model to address the limited explanation of consumer purchase behavior in search advertising, considering the influence of anchoring effects on rational consumer behavior. The model comprises two components: prediction and explanation. The prediction part employs various algorithms, including logistic regression (LR), adaptive boosting (ADA), extreme gradient boosting (XGB), multilayer perceptron (MLP), naive bayes (NB), and random forest (RF), for optimal prediction. The explanation part utilizes the SHAP explainable framework to identify significant indicators and reveal key factors influencing consumer purchase behavior and their relative importance. Our results show that (1) the explainable machine learning model based on the random forest algorithm performed optimally (F1 = 0.8586), making it suitable for analyzing and predicting consumer purchase behavior. (2) The dimension of product information is the most crucial attribute influencing consumer purchase behavior, with features such as sales level, display priority, granularity, and price significantly influencing consumer perceptions. These attributes can be considered by merchants to develop appropriate tactics for improving the user experience. (3) Consumers’ purchase intentions vary based on the presented anchor point. Specifically, high anchor information related to product quality ratings increases the likelihood of purchase, while price anchors prompted consumers to compare similar products and opt for the most economical option. Our findings provide guidance for optimizing marketing strategies and improving user experience while also contributing to a deeper understanding of the decision−making mechanisms and pathways in online consumer purchase behavior.

Dual-anchor anti-corrosion coating of copper foil for high-speed interconnects

In this experiment, two similar molecules with bifunctional groups have been characterized to prove the advantages of dual-anchor molecular coating in the interfacial modification of the copper foil. The physicochemical properties of the modified copper foils have been studied. The obtained results indicate that the anti-corrosion performance and peeling strength were significantly improved for the copper foil treated with (2-(Methylthio)pyrimidin-4-yl)methanamine (MET), whose bonding information was analyzed via the density functional theory calculation. It was found that the S-Cu and N-Cu bonds were both formed on the Cu (1 1 1) surface for MET coating, and the adsorption of the double anchor points improves the stability of the interface. The peeling test exhibits the function of MET coating as the adhesion promotor between low-surface profile copper and resin for high-speed signal transmission.

Probability-Based Two-Stage CNN for Pulmonary Embolism Detection in Computed Tomography Pulmonary Angiography

Pulmonary embolism (PE) can impede the normal flow of blood, which can result in reduced oxygen levels in vital organs and be life-threatening. Therefore, early detection and treatment of PE can effectively reduce mortality. Computed tomography pulmonary angiography (CTPA) is the clearest and most immediate tool available for PE diagnosis. With an increase in the CTPA image quality and number of slices, physician fatigue associated with interpreting these images during the diagnostic process has also increased. Therefore, this study proposes a modified two-stage convolutional neural network-based approach to automatically detect areas suspected of containing PE in CTPA images to reduce the burden of diagnosis on doctors. The main functions for eliminating false positives include principal component analysis, enlarged feature map (EFM), and probability-based anchor point extraction (PAE). Experimental results showed that although EFM can slightly improve the accuracy for small-object detection, it significantly increases the time required for training. PAE can reduce the training time and slightly improve detection accuracy. But the simultaneous use of EFM and PAE can significantly increase sensitivity (10.57%) with an increase of only 29 min in the training time.

Reflections on hypermobility: A study of business travelers during the COVID-19 pandemic

The temporary cessation of air travel imposed by the COVID-19 pandemic provided business travelers with an opportunity to mentally “zoom out” and reflect on their suspended hypermobile lifestyle. The present study used these circumstances to reexamine three key issues in the literature pre-pandemic: (a) the costs and benefits of a hypermobile lifestyle; (b) the justification of business trips given the option of online meetings; and (c) the role of tourism in shaping the business trip experience. An interpretive analysis of in-depth interviews with grounded business travelers conducted at the height of the pandemic revealed that the lack of frequent travel improved work-life balance but evoked a longing for tourism-related experiences, such as change, novelty, and pleasure. Grounded business travelers also proclaimed that the lack of physical proximity to colleagues caused by the suspension of travel was followed by difficulties in generating new business relationships and nurturing creativity at work. The study presented the costs and benefits of immobility as a reverse image of hypermobility, reinforcing the notion of hypermobility as a stressful but exciting lifestyle. It showed that physical proximity with colleagues cannot be fully replaced by online meetings, supporting the opinion that stresses the need for business trips. By suggesting that tourism-related experiences serve as anchor points for the reconstruction of memories and longing in the minds of grounded business travelers, the study confirmed that tourism-related experiences are important components of business trips.

Modeling the Topside Ionosphere Effective Scale Height through In Situ Electron Density Observations by Low-Earth-Orbit Satellites

In this work, we aim to characterize the effective scale height at the ionosphere F2-layer peak (H0) by using in situ electron density (Ne) observations by Langmuir Probes (LPs) onboard the China Seismo-Electromagnetic Satellite (CSES—01). CSES—01 is a sun-synchronous satellite orbiting at an altitude of ~500 km, with descending and ascending nodes at ~14:00 local time (LT) and ~02:00 LT, respectively. Calibrated CSES—01 LPs Ne observations for the years 2019–2021 provide information in the topside ionosphere, whereas the International Reference Ionosphere model (IRI) provides Ne values at the F2-layer peak altitude for the same time and geographical coordinates as CSES—01. CSES—01 and IRI Ne datasets are used as anchor points to infer H0 by assuming a linear scale height in the topside representation given by the NeQuick model. COSMIC/FORMOSAT—3 (COSMIC—1) radio occultation (RO) data are used to constrain the vertical gradient of the effective scale height in the topside ionosphere in the linear approximation. With the CSES—01 dataset, we studied the global behavior of H0 for daytime (~14:00 LT) and nighttime (~02:00 LT) conditions, different seasons, and low solar activity. Results from CSES—01 observations are compared with those obtained through Swarm B satellite Ne-calibrated measurements and validated against those from COSMIC—1 RO for similar diurnal, seasonal, and solar activity conditions. H0 values modeled by using CSES—01 and Swarm B-calibrated observations during daytime both agree with corresponding values obtained directly from COSMIC—1 RO profiles. Differently, H0 modeling for nighttime conditions deserves further investigation because values obtained from both CSES—01 and Swarm B-calibrated observations show remarkable and spatially localized differences compared to those obtained through COSMIC—1. Most of the H0 mismodeling for nighttime conditions can probably to be attributed to a sub-optimal spatial representation of the F2-layer peak density made by the underlying IRI model. For comparison, H0 values obtained with non-calibrated CSES—01 and Swarm B Ne observations are also calculated and discussed. The methodology developed in this study for the topside effective scale height modeling turns out to be applicable not only to CSES—01 satellite data but to any in situ Ne observation by low-Earth-orbit satellites orbiting in the topside ionosphere.

Comparison of Receptor-Ligand Restraint Schemes for Alchemical Absolute Binding Free Energy Calculations.

Alchemical absolute binding free energy calculations are of increasing interest in drug discovery. These calculations require restraints between the receptor and ligand to restrict their relative positions and, optionally, orientations. Boresch restraints are commonly used, but they must be carefully selected in order to sufficiently restrain the ligand and to avoid inherent instabilities. Applying multiple distance restraints between anchor points in the receptor and ligand provides an alternative framework without inherent instabilities which may provide convergence benefits by more strongly restricting the relative movements of the receptor and ligand. However, there is no simple method to calculate the free energy of releasing these restraints due to the coupling of the internal and external degrees of freedom of the receptor and ligand. Here, a method to rigorously calculate free energies of binding with multiple distance restraints by imposing intramolecular restraints on the anchor points is proposed. Absolute binding free energies for the human macrophage migration inhibitory factor/MIF180, system obtained using a variety of Boresch restraints and rigorous and nonrigorous implementations of multiple distance restraints are compared. It is shown that several multiple distance restraint schemes produce estimates in good agreement with Boresch restraints. In contrast, calculations without orientational restraints produce erroneously favorable free energies of binding by up to approximately 4 kcal mol-1. These approaches offer new options for the deployment of alchemical absolute binding free energy calculations.

Social classification and the changing boundaries of learning. A neopragmatic perspective on social sorting in digital education

ABSTRACT Allowing learners to move across learning contexts in novel ways, digital tools play an increasingly central role for the formation of learning trajectories and identities. They thus presumably also affect dynamics of social sorting in education. Against this background, this article introduces a conceptual framework for unravelling dynamics of social sorting in digital learning environments. Inspired by French pragmatic sociology, we propose classification as analytical anchor point for disentangling the intricate interplays between educational technologies, learning situations, and wider moral and social orders. We present a ‘speculative inquiry’ into current AIED to demonstrate the added value this analytical perspective. We identify a hiatus between ‘inspired’ and ‘industrial’ logics of classification in current digital learning tools and environments that are likely to yield unwanted social sorting effects. A classification lens helps foreground social dynamics underlying such patterns, thus furthering our understanding of persistent patterns of disadvantaging in (digital) education.

Competitive Analysis of Globalization Software Industry Development Using Weighted Sum Method

Software development refers to a range of computer engineering tasks involved in creating, deploying, and sustaining software. Software consists of instructions or programming that computers follow. It is hardware independent and enables programmability. System software, such as operating systems and disk management utilities, handles essential operational tasks. Software developers provide coding tools like text editors, compilers, linkers, and debuggers to programmers. Applications, or apps, are software programs that assist users in carrying out tasks. Examples include office suites, data management tools, music players, and antivirus programs. Applications can also refer to web and mobile platforms used for communication on Facebook or making purchases on Amazon.com. Software engineers employ engineering concepts when creating systems and software to address issues. Rather than simply providing a solution for one instance or customer, they often utilize process models or other tools to solve problems in a more general manner. With the advancement of microprocessors, sensors, and software, products have become more intelligent, increasing their capabilities. Software engineering needs to be integrated with the electrical and mechanical development work of products because they rely on it to differentiate themselves in the market. The weights are systematically changed, yielding different best-fit results. Approximations are made based on the obtained solutions. Weights with values of 0 serve as non-specific anchor points, and the most useful responses can be generated if there is any weak parity. Please note that the weighting method is configured for optimization by the pioneers of the sum system. The Alternative are Switzerland, Canada, Ireland, Greece, and India. Evaluation Preference is Project leader, business analyst, systems analyst, systems design, development programmer, support programmer, network analyst/designer, quality assurance specialist, database data analysis, metrics/process specialist, documentation/training staff, and test engineer. From the results, it can be seen that the development programmer is ranked first, while the quality assurance specialist has the lowest rank. The weighted sum method (WSM) demonstrates the value of the dataset for Software Industry Development, with the development programmer achieving the top ranking.

Fast and Robust Unsupervised Dimensionality Reduction with Adaptive Bipartite Graphs

Recent unsupervised dimension reduction algorithms use similarity graphs between data point pairs to preserve local structure while reducing dimension. However, the time complexity of these methods is proportional to the square of the number of samples, which limits their application to large-scale datasets. Moreover, the square Euclidean calculation criterion between sample point pairs will magnify the bad influence of outliers on the graph. In addition, these methods only preserve the local structure while losing other important structural information. To this end, we propose a fast adaptive unsupervised projection model termed Fast and Robust Unsupervised Dimensionality Reduction with Adaptive Bipartite Graph (FRUDR-ABG), which uses a few anchor points and sample points to build a bipartite graph to preserve the local geometric structure of the data to reduce the running time and improve efficiency. We propose a criterion based on the l2,1 norm to calculate the distance between anchor points and data points to reduce the negative influence of outliers on graph construction. A practical strategy is also proposed to realize joint learning of global and local structures. According to the characteristics of graph construction and dimensionality reduction adaptive learning in the algorithm, we design an iterative reweighting method to solve the model. Experimental results on several benchmark datasets show that FRUDR-ABG has higher efficiency and recognition performance than existing unsupervised dimensionality reduction methods.

Improving the Frost Resistance Performance of Hydrophilic Coatings by Modulating the Formation of Zwitterionic Polymers

In the past few years, less attention has been paid to hydrophilic materials in frost resistance studies due to the fact that the hydrated layer may not be able to resist frost formation under cold conditions. Zwitterionic polymers can tightly interact with water molecules through hydrogen bonding and electrostatic interactions, forming a hydrated layer that is stable at low temperatures. In this work, polyethyleneimine (PEI) and 1,3-propanesultone (PS) constitute the basis of a zwitterionic polymer, on which different catechol derivatives such as protocatechualdehyde (PA), protocatechuic acid (PCA), and 4-(chloroacetyl)catechol (CAC) are grafted. These coatings exhibit hydrophilicity with a water contact angle below 10°, where mussel mimetic catechol derivatives are used as anchor points to deposit the coating on the substrate. In addition, the relationship between the molecular structure and frost resistance capability was investigated by changing the type of catechol derivative, as well as revealing the frost resistance mechanism of the hydrophilic coating. In particular, the PEI-CAC-PS coating has strong hydrogen bonding due to the formation of a five-membered ring with amino and carbonyl groups attached to the same carbon, which, together with the electrostatic interaction, gives the coating a high content of nonfreezable water and thus excellent frost resistance. In addition to frost resistance properties, the hydrophilic zwitterionic polymer coating is multifunctional with antifogging and self-cleaning capabilities.

From Coastal Sites to Elevated Hinterland Locations in the Mesolithic – Discussing Human–Woodland Interaction in the Oslo Fjord Region, Southeast Norway

Abstract The coastal areas of Southeast Norway underwent dramatic environmental changes throughout the Mesolithic period. Continuous postglacial land uplift/isostatic rebound lead to retreating shorelines turning former seabed into coastal hinterland. While archaeological research lately has focused on the coastal and marine orientation of Mesolithic groups in the region, their interaction with the wooded coastal hinterland is little researched. This article explores shifting human–environment relations connected to these long-term changes. The starting points are nine archaeological sites in the Oslo fjord region, from which the artefact material shows that they were coast based in the earlier Mesolithic. They all have yielded radiocarbon dates from structures, mostly hearths, with later Mesolithic datings. At these later times, the sites had turned into elevated hinterland locations. First, the archaeological context and sample material of these later dates are discussed; on this basis, it is suggested that these later dates, earlier interpreted as impaction of forest fires, indicate the use of elevated hinterland locations in the Mesolithic. Second, this article explores the relevance of the wooded hinterland for the Mesolithic groups of the region, including animated beings, wayfinding practices and the possible function of these hinterland locations as potential practical and cosmological anchor points and strategic points in a communication system. It is argued that the study of the use of the hunter-fisher-gatherers’ interaction with the wooded and rocky coastal hinterland is needed to achieve a holistic understanding of their involvement and socializing with their world.

Adaptive Maximum Entropy Graph-Guided Fast Locality Discriminant Analysis.

Linear discriminant analysis (LDA) aims to find a low-dimensional space in which data points in the same class are to be close to each other while keeping data points from different classes apart. To improve the robustness of LDA to non-Gaussian distribution data, most existing discriminant analysis methods extend LDA by approximating the underlying manifold of data. However, these methods suffer from the following problems: 1) local affinity or reconstruction coefficients are learned on the basis of the relationships of all data pairs, which would lead to a sharp increase in the amount of computation and 2) they learn the manifold information in the original space, ignoring the interference of the noise and redundant features. Motivated by these challenges, this article represents a novel discriminant analysis model, called fast and adaptive locality discriminant analysis (FALDA), to improve the efficiency and robustness. First, with the anchor-based strategy, a bipartite graph of each class is constructed to characterize the local structure of data. Since the number of anchor points is far less than that of data points, learning of fuzzy membership relationships between data points and anchor points within each class can save training time. Second, a maximum entropy regularization is introduced to control the uniformity of the weights of graphs and avoid the trivial solution. Third, the above relationships are updated adaptively in the process of dimensionality reduction, which can suppress the interference of the noise and redundant features. Fourth, the whitening constraint is imposed on the projection matrix to remove the relevance between features and restrict the total scatter of data in the subspace. Last but not the least, data with complex distribution can be explicitly divided into sub-blocks according to the learned anchor points (or subclass center points). We test our proposed method on synthetic data, benchmark datasets, and imbalanced datasets. Promising experimental results demonstrate the success of this novel model.

Effects of Stimulus Frequency and Location on Vibrotactile Discrimination Performance Using Voice Coil Actuators on the Forearm

What are the effects of frequency variation of vibrotactile stimuli on localization acuity? The precise localization of vibrotactile stimuli is crucial for applications that are aimed at conveying vibrotactile information. In order to evaluate the ability to distinguish between vibrotactile stimuli based on their frequency and location on the forearm, we used a relative point localization method. Participants were presented with pairs of sequential vibrotactile stimuli at three possible locations on the forearm and asked to determine whether the second stimulation occurred at the same location as the first one in the pair or not. The stimulation frequency varied between 100 Hz, 150 Hz, 200 Hz and 250 Hz, which covers the range of frequencies that human observers are most sensitive to. The amplitude was kept constant. Our results revealed that the ability to discriminate between actuators remained unaffected by variations in the frequency of vibrotactile stimulation within the tested frequency range. The accuracy of the tactile discrimination task was heavily dependent on the location of the stimulation on the forearm, with the highest accuracy close to the wrist and elbow, locations that may serve as tactile anchor points. Our results highlight the critical role of stimulation location in precise vibrotactile localization and the importance of careful consideration of location in the design of forearm-mounted vibrotactile devices.

Anchor conditions for estimated unsaturated shear strength functions

Since the mid-1990s, several empirical unsaturated shear strength equations have been proposed for use in geotechnical engineering practice. Most of the equations have been based on information associated with the measured drying soil-water characteristic curve (SWCC) in combination with the effective shear strength parameters of the saturated soil. There have been few studies, however, undertaken on verifying and comparing the suitability of the proposed unsaturated shear strength equations. This study involves consideration of a wide range of possible drying SWCCs plotted as a relationship between the degree of saturation and suction (saturation soil-water characteristic curve ( S-SWCC)). The study forms the basis for comparing five methodologies that have been proposed for the estimation of the shear strength of unsaturated soils. The SWCCs are defined by the fitting parameters for the Fredlund and Xing (1994) sigmoidal equation. This study supports the use of unsaturated soil shear strength empirical estimation methods using the S-SWCC anchor points of air-entry value and residual suction of the soil.

The Misguided Veneration of Averageness in Clinical Neuroscience: A Call to Value Diversity over Typicality

Research and practice in clinical neurosciences often involve cognitive assessment. However, this has traditionally used a nomothetic approach, comparing the performance of patients to normative samples. This method of defining abnormality places the average test performance of neurologically healthy individuals at its center. However, evidence suggests that neurological ‘abnormalities’ are very common, as is the diversity of cognitive abilities. The veneration of central tendency in cognitive assessment, i.e., equating typicality with healthy or ideal, is, I argue, misguided on neurodiversity, bio-evolutionary, and cognitive neuroscientific grounds. Furthermore, the use of average performance as an anchor point for normal performance is unreliable in practice and frequently leads to the mischaracterization of cognitive impairments. Examples are explored of how individuals who are already vulnerable for socioeconomic reasons can easily be over-pathologized. At a practical level, by valuing diversity rather than typicality, cognitive assessments can become more idiographic and focused on change at the level of the individual. The use of existing methods that approach cognitive assessment ideographically is briefly discussed, including premorbid estimation methods and informant reports. Moving the focus away from averageness to valuing diversity for both clinical cognitive assessments and inclusion of diverse groups in research is, I argue, a more just and effective way forward for clinical neurosciences.

Reproducible target transfer from electroanatomic mapping to radiotherapy planning systems for cardiac radioablation - cross-validation for the RAVENTA trial

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – EU funding. Main funding source(s): RAVENTA is part of the EU-Horizon-2020 STOPSTORM project. Background Ventricular arrhythmias are a leading cause of morbidity and mortality in patients with structural heart disease. Non-invasive stereotactic single-fraction arrhythmia radioablation (STAR) with 25 Gy has been proposed as a bail-out strategy in patients where catheter-based ablation is either not feasible or has previously failed to control the arrhythmia. Defining the exact radiotherapy target, however, presents unique challenges, and manual transfer of locations from the electroanatomic map (EAM) into the cardiac CT results in a high degree of uncertainty. Different workflows have been developed to register EAM with the cardiac CT and allow for target definition in EAM space with semi-automated transfer of the target structure to CT coordinates. Validation of these approaches is presently lacking. Here we report the initial cross-validation results of two conceptually different software solutions for target transfer quality assurance in STAR. Methods Clinical case data of patients treated with STAR (EAM data, cardiac CT and screenshots of the map with marked target region) were collected retrospectively from centres participating in the RAdiosurgery for VENtricular TAchycardia (RAVENTA) trial. Two blinded investigators independently created a target volume in the cardiac CT based on the marked target region using either a 3D-3D registration approach (3D Slicer / EAMapReader) or a 2D-3D registration (CARDIO-RT), as described previously. The resulting target structures were analysed in a core lab for overlap and similarity by means of surface distance metrics and conformity. Results Three clinical data sets were analysed, one for each mapping system in widespread clinical use. Agreement between structures created with both approaches was good, with DICE coefficients of 0.88, 0.69 and 0.506 and median surface distances of 0 mm, 1.2 mm and 2.2 mm, respectively. The least agreement was seen in a case where no additional structures were mapped besides the LV, whereas additional anchor points such as the ascending aorta seem to reduce registration ambiguity. Conclusion Using two conceptually different approaches for the target transfer from EAM to cardiac CT we were able to show good agreement between both methods. Both techniques thus can be used for quality assurance and analysis in STAR. Further study in a larger sample of patient data sets is warranted to elucidate differences between transfer methods and predictors for improved registration.

An Individual Tree Segmentation Method That Combines LiDAR Data and Spectral Imagery

The dynamic monitoring of forest resources is an integral component of forest resource management and forest eco-system stability maintenance. In recent years, LiDAR (Light Detection and Ranging) has been increasingly utilized in precision forest surveys due to its great penetrating ability and capacity to detect forest vertical structure information. However, the present airborne LiDAR data individual tree segmentation algorithms are not highly adaptable to forest types, particularly in mixed coniferous and broad-leaved forest zones, where the accuracy of individual tree extraction is low, and trees are incorrectly recognized and missed. In order to address these issues, in this study, spectral images and LiDAR data of a red pine conifer–broadleaf mixed forest in the Changbai Mountain Nature Reserve in Jilin Province were chosen, and the normalized point cloud was segmented iteratively using the distance-threshold-based individual tree segmentation method to obtain the initial segmented individual tree vertices. For individual trees with deviations in the initial vertex identification position, and unidentified individual trees, identification anchor points of real tree vertices are added within the canopy of the trees. These identification anchor points have strong position directivity in LiDAR data, which can mark the individual trees whose vertices were misidentified or missed during the initial individual tree segmentation process and identify these two tuples. The tree vertices may be inserted precisely based on the 3D shape of the individual tree point cloud, and the seed-point-based individual tree segmentation method is used to segment the normalized point cloud and finish the extraction of individual trees in red pine mixed conifer forests. The results indicate that, compared to the previous individual tree segmentation approach based on the relative spacing between individual trees, this study enhances the accuracy of individual tree segmentation from 83% to 96%. The extremely high segmentation accuracy indicates that the proposed method can accurately identify individual trees based on remote sensing techniques to segment forest individual trees, can provide a data basis for subsequent individual tree information extraction, and has great potential in practical applications.