Current Representation Research Articles

Assessing the Future ODYSEA Satellite Mission for the Estimation of Ocean Surface Currents, Wind Stress, Energy Fluxes, and the Mechanical Coupling Between the Ocean and the Atmosphere

Over the past decade, several studies based on coupled ocean–atmosphere simulations have shown that the oceanic surface current feedback to the atmosphere (CFB) leads to a slow-down of the mean oceanic circulation and, overall, to the so-called eddy killing effect, i.e., a sink of kinetic energy from oceanic eddies to the atmosphere that damps the oceanic mesoscale activity by about 30%, with upscaling effects on large-scale currents. Despite significant improvements in the representation of western boundary currents and mesoscale eddies in numerical models, some discrepancies remain when comparing numerical simulations with satellite observations. These discrepancies include a stronger wind and wind stress response to surface currents and a larger air–sea kinetic energy flux from the ocean to the atmosphere in numerical simulations. However, altimetric gridded products are known to largely underestimate mesoscale activity, and the satellite observations operate at different spatial and temporal resolutions and do not simultaneously measure surface currents and wind stress, leading to large uncertainties in air–sea mechanical energy flux estimates. ODYSEA is a new satellite mission project that aims to simultaneously monitor total surface currents and wind stress with a spatial sampling interval of 5 km and 90% daily global coverage. This study evaluates the potential of ODYSEA to measure surface winds, currents, energy fluxes, and ocean–atmosphere coupling coefficients. To this end, we generated synthetic ODYSEA data from a high-resolution coupled ocean–wave–atmosphere simulation of the Gulf Stream using ODYSIM, the Doppler scatterometer simulator for ODYSEA. Our results indicate that ODYSEA would significantly improve the monitoring of eddy kinetic energy, the kinetic energy cascade, and air–sea kinetic energy flux in the Gulf Stream region. Despite the improvement over the current measurements, the estimates of the coupling coefficients between surface currents and wind stress may still have large uncertainties due to the noise inherent in ODYSEA, and also due to measurement capabilities related to wind stress. This study evidences that halving the measurement noise in surface currents would lead to a more accurate estimation of the surface eddy kinetic energy and wind stress coupling coefficients. Since measurement noise in surface currents strongly depends on the square root of the transmit power of the Doppler scatterometer antenna, noise levels can be reduced by increasing the antenna length. However, exploring other alternatives, such as the use of neural networks, could also be a promising approach. Additionally, the combination of wind stress estimation from ODYSEA with other satellite products and numerical simulations could improve the representation of wind stress in gridded products. Future efforts should focus on the assessment of the potential of ODYSEA in quantifying the production of eddy kinetic energy through horizontal energy fluxes and air–sea energy fluxes related to divergent and rotational motions.

Lived Experiences of LGBTQIA+ Community in Administrative Leadership of School Heads

This study explored the administrative leadership of LGBTQIA+ school heads in private high schools in the province of Quezon. The interview was the primary tool used in the form of semi-structured interview questions that were systematically crafted to give way to the target respondents based on their lived experience. Coding and thematic analysis were done to deeply analyze the responses from the participants. Based on the findings, the identity politics among school heads were described as having genderless and affirmative leadership. Creating inclusive environments, greater professional leadership, and identity reaffirmation and community acceptance were found useful in surpassing the challenges faced by LGBTQIA+ leaders. Reliving the unconventional leadership representations of LGBTQIA+ school heads focuses on reflecting transformative leadership and advocating inclusivity. In the private school setting, there is many recommendations formulated based on the responses of the participants. Though there are already existing policies on gender responsiveness, there is still room for modification to set clearer, more inclusive, and comprehensive policies. School leaders can make school safe and inclusive for all. Learning institutions can, therefore, foster effective and responsive school culture for LGBTQIA+ educators in their workplace. Undertaking this study discovered the identity politics, struggles, and representations of the LGBTQ+ Community. It explored their significant lived experiences, which became an eye-opener to the current status, experienced struggles, and representation of the LGBTQIA+ school heads, which could leave an impact on the target readers of the substantial contributions of these leaders in administrative leadership and gender studies. The result of the study can bring change among LGBTQIA+ school heads and teachers and encourage administrative change and integration of evidenced-based solutions that support the LBGTQIA+ community.

Establishment of a 3D multi-layered in vitro model of inflammatory bowel disease

Crohn's Disease and Ulcerative Colitis, the main types of Inflammatory Bowel Disease (IBD), are life-threatening gastrointestinal disorders with no definitive cure. The establishment of biorelevant in vitro models that closely recapitulate the IBD microenvironment is of utmost importance to validate newly developed IBD therapies. To address the existing flaws in the current representation of the IBD microenvironment, we propose a novel three-dimensional (3D) in vitro model comprising a multi-layered gastrointestinal tissue with functional immune responses under inflammatory conditions. The multi-layered architecture consists of a lamina propria-like hydrogel with human intestinal fibroblasts (HIF), supporting an epithelial layer composed of Caco-2 and HT29-MTX cells, along with an endothelial layer surrogating the absorptive capillary network. A collagen-alginate composite matrix was optimized for the lamina propria-like hydrogel, preserving HIF metabolic activity and morphology over time. To achieve immune competence, pre-differentiated THP-1-derived macrophages were incorporated into the epithelial barrier. Inflammation was induced through the optimization of an inflammatory cocktail consisting of E. coli O111:B4 lipopolysaccharide combined with a specialized cytokine array (tumor necrosis factor-α, interferon-γ, and interleukin-1β). This inflammation-inducing stimulus led to a significant upregulation of pro-inflammatory cytokines commonly associated with IBD onset, including CCL20, IL-6, CXCL9 and CXCL10. Altogether, this 3D in vitro model has the potential to accelerate the drug development pipeline by providing reliable permeability and efficacy outputs for emerging therapies, reducing unnecessary animal experiments. Moreover, it offers a valuable in vitro platform for studying IBD pathophysiology and cell interplay dynamics.

Word Vector Representation of Latin Cuengh Based on Root Feature Enhancement

The Latin Cuengh is a kind of language used in China’s minority areas. Due to its complex pronunciation and semantic system, it is difficult to spread widely. To deal with and protect this language further, this paper considers using the current word vector representation technology to study it. Word vector representation is the basic method and an important foundation of current research on natural language processing. It relies on a large number of data resources and is obtained through the paradigm of pre-training and feature learning. Due to the extreme lack of Latin Cuengh corpus resources, it is very difficult to obtain word vectors by relying on a large amount of data training. In this study, we propose a word vector representation method that combines the root features of Latin Cuengh words. Specifically, while training and learning the Latin Cuengh language corpus, this method uses the special word roots in the Latin Cuengh language to modify the training process, which can enhance the expression ability of the root features. The method uses the mask method based on BERT to mask the word roots after word segmentation and predict the masked word roots in the output layer of the model to obtain a better vector representation of Latin Cuengh words. The experimental results show that the word vector representation method proposed in this paper is effective and has the ability to express Latin Cuengh semantics. The accuracy rate of words semantic is nearly 2% points higher than that of BERT representation, and the judgment of the semantic similarity of words is more accurate.

Modeling Fibroblast-Cardiomyocyte Interactions: Unveiling the Role of Ion Currents in Action Potential Modulation.

Fibrotic cardiomyopathy represents a significant pathological condition characterized by the interaction between cardiomyocytes and fibroblasts in the heart, and it currently lacks an effective cure. In vitro platforms, such as engineered heart tissue (EHT) developed through the co-culturing of cardiomyocytes and fibroblasts, are under investigation to elucidate and manipulate these cellular interactions. We present the first integration of mathematical electrophysiological models that encapsulate fibroblast-cardiomyocyte interactions with experimental EHT studies to identify and modulate the ion channels governing these dynamics. Our findings resolve a long-standing debate regarding the effect of fibroblast coupling on cardiomyocyte action potential duration (APD). We demonstrate that these seemingly contradictory outcomes are contingent upon the specific properties of the cardiomyocyte to which the fibroblast is coupled, particularly the relative magnitudes of the fast Na+ and transient outward K+ currents within the cardiomyocyte. Our results emphasize the critical importance of detailed ionic current representation in cardiomyocytes for accurately predicting the interactions between cardiomyocytes and fibroblasts in EHT. Surprisingly, complex ion channel-based models of fibroblast electrophysiology did not outperform simplified resistance-capacitance models in this analysis. Collectively, our findings highlight the promising potential of synergizing in vitro and in silico approaches to identify therapeutic targets for cardiomyopathies.

Range contraction and expansion of the African buffalo under climate change scenarios

ABSTRACT Climate change is one of the leading causes of global biodiversity declines and extinctions. Yet knowledge of its impact on African buffalo distribution on the continent is lacking. In this study, we apply ensemble species distribution modelling to predict the distribution of two coarsely defined African buffalo functional groups under current and future climate scenarios (Shared Socioeconmic Pathway, SSP245 and SSP585 in 2100) and estimate habitat representation within protected areas. Model predictions indicate significant range contraction and expansion in the savanna and the forest buffalo functional groups, respectively. Also, the proportion of suitable habitats for both functional groups is represented poorly within protected areas, with the current coverage expected to decline further. Our findings reveal plausible implications of climate change on African buffalo conservation and zoonotic diseases. Overall, this climate-based approach could be applied to model the distribution of African buffalo subspecies at local geographical scales in countries with adequately sampled occurrence data to support conservation decision-making.

Comprehensive Geometric Parameterization and Computationally Efficient 3D Shape Matching Optimization of Realistic Stents

Abstract Flexible and compact shape representation schemes are essential for design optimization problems. Current shape representation schemes for coronary stent designs concern predominantly idealized or independent ring (IR) designs, which are outdated and only consider a small number of core design variables (such as strut width, height, and thickness) and ignore clinically critical design characteristics such as the number of connectors. No reports exist on the geometry parameterization of the latest helical stents (HS) that have more complex geometric designs than IR stents. Here, we present two new shape parameterization schemes to fully capture the 3D designs of contemporary IR and double-helix HS stents. We developed a 3D stent geometry builder based on 17 (IR) and 18 (HS) design variables, including strut width, thickness, height, number of connectors and rings, stent length, and strut centerline shape. The shape of the strut centerline was derived via a combination of NURBS, PARSEC, quarter circle, and straight line segments. Shape matching for complex 3D geometries, such as the contemporary stents within limited function evaluations, is not trivial and requires efficient parameterization and optimization algorithms. We used shape matching optimization with a limited function evaluation budget to test the proposed parameterization and two surrogate-assisted optimization algorithms relying on predictor believer and an expected improvement maximization formulation. The performance of these algorithms is objectively compared with a gradient-based optimization method to highlight their strengths. Our work paves the way for more realistic, full-fledged stent design optimization with structural and hemodynamic objectives in the future.

Culturally responsive strategies and practical considerations for live tissue studies in Māori participant cohorts.

Indigenous communities globally are inequitably affected by non-communicable diseases such as cancer and coronary artery disease. Increased focus on personalized medicine approaches for the treatment of these diseases offers opportunities to improve the health of Indigenous people. Conversely, poorly implemented approaches pose increased risk of further exacerbating current inequities in health outcomes for Indigenous peoples. The advancement of modern biology techniques, such as three-dimensional (3D) in vitro models and next generation sequencing (NGS) technologies, have enhanced our understanding of disease mechanisms and individualized treatment responses. However, current representation of Indigenous peoples in these datasets is lacking. It is crucial that there is appropriate and ethical representation of Indigenous peoples in generated datasets to ensure these technologies can be used to maximize the benefit of personalized medicine for Indigenous peoples. This project discusses the use of 3D tumor organoids and single cell/nucleus RNA sequencing to study cancer treatment responses and explore immune cell roles in coronary artery disease. Using key pillars from currently available Indigenous bioethics frameworks, strategies were developed for the use of Māori participant samples for live tissue and sequencing studies. These were based on extensive collaborations with local Māori community, scientific leaders, clinical experts, and international collaborators from the Broad Institute of MIT and Harvard. Issues surrounding the use of live tissue, genomic data, sending samples overseas and Indigenous data sovereignty were discussed. This paper illustrates a real-world example of how collaboration with community and the incorporation of Indigenous worldviews can be applied to molecular biology studies in a practical and culturally responsive manner, ensuring fair and equitable representation of Indigenous peoples in modern scientific data.

Factors influencing the inclusion of diverse volunteer patients within medical student primary care placements.

Research shows that medical students are graduating with inadequate teaching on diverse patients and insufficient experience of working with diverse patient groups. The inclusion of patients from diverse groups is necessary in healthcare teaching to ensure medical students are adequately prepared for practice. In this study, we explored the perspectives of General Practitioner (GP) tutors on the recruitment of diverse volunteer patients for medical student primary clinical care placements. In particular, we focused on the current representation of diverse volunteer patients, barriers affecting their inclusion and recommendations to help with this. Focus groups were carried out with GP tutors involved in the recruitment of volunteer patients from one region in the United Kingdom. Transcripts were analysed using Thematic Analysis. Participants acknowledged the importance of ensuring that medical students have clinical experience in assessing and managing patients from diverse populations, but most did not actively think about the diversity of the patients they were recruiting. Instead, recruitment was driven by the need to cover the curriculum and teaching requirements. To ensure that students' learning was not diminished and recognising time was a significant factor, participants automatically discounted certain patients from being a volunteer patient. They acknowledged that they did not feel comfortable identifying patients based on their demographics and were more likely to invite patients who had been volunteer patients before. Suggested solutions to overcome the factors affecting the recruitment of diverse patients are presented. Patient populations will continue to become more diverse, and therefore, medical schools must prepare their students for this and encourage GP tutors to make a conscious effort to recruit diverse patient volunteers for teaching.

Validating effectiveness of crown-of-thorns starfish control thresholds to limit coral loss throughout the Great Barrier Reef

Population irruptions of the Pacific crown-of-thorns starfish (CoTS, Acanthaster cf. solaris) are a key source of coral loss on Australia’s Great Barrier Reef (GBR). CoTS management currently involves their manual control (culling) to threshold densities below which net coral decline theoretically ceases based on analysis of a validated population dynamics model. Spatial variability in coral growth and community composition, and their predicted changes under continuing global warming, necessitate further consideration of current coral representation in CoTS models. Here, we consider the sensitivity of equilibrium coral-CoTS thresholds to coral growth rates and consider how the demographic composition of CoTS at a site may relate to culling thresholds. We found thresholds should be location-specific if the objective of CoTS control is coral recovery, but location-specific thresholds may not be needed if the objective is to limit coral cover loss based on coral growth and CoTS demography. The consequence of using a higher CPUE threshold than the analytical equilibrium coral-CoTS thresholds in terms of coral cover loss is suggested to be limited at coral cover < 40%, and varying control thresholds thereof may make little difference. Introducing a 0.06 CoTS.min−1 threshold for 40–60% coral cover may reduce coral loss at ~ 40% where it is likely greatest. With regional GBR coral cover averaging < 40%, this study validates the 0.04–0.08 CoTS.min−1 tiered threshold system for CoTS control and suggests methods developed from localised studies can be more broadly applicable and well-defined objectives (e.g. avoiding coral cover decline at a site) can help guide what thresholds are used and the sensitivity around these.

GrapheNet: a deep learning framework for predicting the physical and electronic properties of nanographenes using images.

In this work we introduce GrapheNet, a deep learning framework based on an Inception-Resnet architecture using image-like encoding of structural features for the prediction of the properties of nanographenes. The model is validated on datasets of computed structure/property data on graphene oxide and defected graphene nanoflakes. By exploiting the planarity of quasi-bidimensional systems and through encoding structures into images, and leveraging the flexibility and power of deep learning in image processing, Graphenet achieves significant accuracy in predicting the physicochemical properties of nanographenes. This approach is able to efficiently encode structures composed of hundreds of atoms, scaling efficiently with the size of the model and enabling the prediction of the properties of large systems, which contrasts with the limitations of current atomistic-level representations for deep learning applications. The approach proposed based on image encoding exhibit a significant numerical accuracy and outperforms the computational efficiency of current representations of materials at the atomistic level, with significant advantages especially in the representation of nanostructures and large planar systems.

A Depth Awareness and Learnable Feature Fusion Network for Enhanced Geometric Perception in Semantic Correspondence.

Deep learning is becoming the most widely used technology for multi-sensor data fusion. Semantic correspondence has recently emerged as a foundational task, enabling a range of downstream applications, such as style or appearance transfer, robot manipulation, and pose estimation, through its ability to provide robust correspondence in RGB images with semantic information. However, current representations generated by self-supervised learning and generative models are often limited in their ability to capture and understand the geometric structure of objects, which is significant for matching the correct details in applications of semantic correspondence. Furthermore, efficiently fusing these two types of features presents an interesting challenge. Achieving harmonious integration of these features is crucial for improving the expressive power of models in various tasks. To tackle these issues, our key idea is to integrate depth information from depth estimation or depth sensors into feature maps and leverage learnable weights for feature fusion. First, depth information is used to model pixel-wise depth distributions, assigning relative depth weights to feature maps for perceiving an object's structural information. Then, based on a contrastive learning optimization objective, a series of weights are optimized to leverage feature maps from self-supervised learning and generative models. Depth features are naturally embedded into feature maps, guiding the network to learn geometric structure information about objects and alleviating depth ambiguity issues. Experiments on the SPair-71K and AP-10K datasets show that the proposed method achieves scores of 81.8 and 83.3 on the percentage of correct keypoints (PCK) at the 0.1 level, respectively. Our approach not only demonstrates significant advantages in experimental results but also introduces the depth awareness module and a learnable feature fusion module, which enhances the understanding of object structures through depth information and fully utilizes features from various pre-trained models, offering new possibilities for the application of deep learning in RGB and depth data fusion technologies. We will also continue to focus on accelerating model inference and optimizing model lightweighting, enabling our model to operate at a faster speed.

Unifying approaches to understanding capacity in change detection.

To navigate changes within a highly dynamic and complex environment, it is crucial to compare current visual representations of a scene to previously formed representations stored in memory. This process of mental comparison requires integrating information from multiple sources to inform decisions about changes within the environment. In the present article, we combine a novel systems factorial technology change detection task (Blunden et al., 2022) with a set size manipulation. Participants were required to detect 0, 1, or 2 changes of low and high detectability between a memory and probe array of 1-4 spatially separated luminance discs. Analyses using systems factorial technology indicated that the processing architecture was consistent across set sizes but that capacity was always limited and decreased as the number of distractors increased. We developed a novel model of change detection based on the statistical principles of basic sampling theory (Palmer, 1990; Sewell et al., 2014). The sample size model, instantiated parametrically, predicts the architecture and capacity results a priori and quantitatively accounted for several key results observed in the data: (a) increasing set size acted to decrease sensitivity (d') in proportion to the square root of the number of items in the display; (b) the effect of redundancy benefited performance by a factor of the square root of the number of changes; and (c) the effect of change detectability was separable and independent of the sample size costs and redundancy benefits. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Enhancements in Clinical Practice in the Contemporary Landscape of Male Facial Attractiveness

The concept of gender has evolved significantly in recent decades, moving from a binary status to a multiplicity of gender types. In today’s world, the new reality of the concept of masculinity, as defined socially and historically, must be recognized and accepted by aesthetic medicine. Consequently, aesthetic doctors will have to adapt the views and treatment plans that they propose in their consultations to the ideals of beauty, as well as with the roles, behaviors, and attributes considered masculine within their society. Each facial feature suggests a personality characteristic that represents that individual. This expert opinion article aims to assess Caucasian male gender-related anatomical facial features and achieve an expert agreement on the association of specific features with the perception of attractiveness, masculinity, and personality traits in order to cover the wide range of current male representations, as well as to provide orientation for clinical practice improvement in the contemporary landscape of facial masculinization.

Reconceptualising graduate resilience – an integrated multi-level framework for future research

This paper draws on Bourdieusian social theory to reconceptualise graduate resilience in post-industrial societies to provide a fresh perspective on a concept that has gained increasing prominence in recent years. Through a review of sociological critiques of resilience, this paper argues that graduate resilience is a complex social phenomenon shaped by a range of factors, including material and social resources. In response, we propose an integrated multi-level framework that identifies different stages of graduate resilience, in the context of early transitions into the labour market, and how these stages are shaped at the micro, meso, and macro levels. This framework places resilience in the context of neo-liberalism and highlights structural barriers that hinder the building and signalling of graduate resilience. We argue that the framework enables current representations and understandings of graduate resilience within research, policy, and practice to be problematised and provides a critical starting point for advancing understanding.

Computational modelling of mouse atrio ventricular node action potential and automaticity.

The atrioventricular node (AVN) is a crucial component of the cardiac conduction system. Despite its pivotal role in regulating the transmission of electrical signals between atria and ventricles, a comprehensive understanding of the cellular electrophysiological mechanisms governing AVN function has remained elusive. This paper presents a detailed computational model of mouse AVN cell action potential (AP). Our model builds upon previous work and introduces several key refinements, including accurate representation of membrane currents and exchangers, calcium handling, cellular compartmentalization, dynamic update of intracellular ion concentrations, and calcium buffering. We recalibrated and validated the model against existing and unpublished experimental data. In control conditions, our model reproduces the AVN AP experimental features, (e.g. rate=175bpm, experimental range [121, 191]bpm). Notably, our study sheds light on the contribution of L-type calcium currents, through both Cav1.2 and Cav1.3 channels, in AVN cells. The model replicates several experimental observations, including the cessation of firing upon block of Cav1.3 or INa,r current. If block induces a reduction in beating rate of 11%. In summary, this work presents a comprehensive computational model of mouse AVN cell AP, offering a valuable tool for investigating pacemaking mechanisms and simulating the impact of ionic current blockades. By integrating calcium handling and refining formulation of ionic currents, our model advances understanding of this critical component of the cardiac conduction system, providing a platform for future developments in cardiac electrophysiology. KEY POINTS: This paper introduces a comprehensive computational model of mouse atrioventricular node (AVN) cell action potentials (APs). Our model is based on the electrophysiological data from isolated mouse AVN cells and exhibits an action potential and calcium transient that closely match the experimental records. By simulating the effects of blocking specific ionic currents, the model effectively predicts the roles of L-type Cav1.2 and Cav1.3 channels, T-type calcium channels, sodium currents (TTX-sensitive and TTX-resistant), and the funny current (If) in AVN pacemaking. The study also emphasizes the significance of other ionic currents, including IKr, Ito, IKur, in regulating AP characteristics and cycle length in AVN cells. The model faithfully reproduces the rate dependence of action potentials under pacing, opening the possibility of use in impulse propagation models. The population-of-models approach showed the robustness of this new AP model in simulating a wide spectrum of cellular pacemaking in AVN.

A - 77 A Systematic Review of Current Diversity Representation in Neuropsychological Research

Abstract Objective This study investigated current representation of diverse samples in research published in four leading neuropsychology journals. Methods We reviewed 367 articles published in 2023 from Archives of Clinical Neuropsychology, Neuropsychology, Journal of the International Neuropsychological Society, and The Clinical Neuropsychologist. 34 review articles and 4 editorial/announcements were excluded leaving 321 articles coded for age, education, gender/sex, race/ethnicity, and sample country. Results Overall, 89.41% of studies reported gender/sex and 50.46% reported race/ethnicity. Out of 63 original articles that included European populations (e.g., France, Sweden, Denmark, etc.), 93.65% reported gender/sex and 12.7% reported race/ethnicity. Out of 172 original articles that included United States (U.S.) samples, 95.93% reported gender/sex and 80.23% reported race/ethnicity. Studies of U.S. samples reported the following average sociodemographic variables: education 14.94 years (SD = 3.72), age 50.32 years (SD = 21.39), gender/sex 48.09% female and 51.90% male, and race/ethnicity was 65.11% White, 6.64% Black/African American, 2.18% Asian, 1.06% American Indian/Alaskan Native, 0.43% Native Hawaiian/Pacific Islander, and 24.57% unknown/other/mixed race. Additionally, 9.82% of participants were Hispanic/Latino. Conclusions Roughly half of studies did not report on race/ethnicity and this was particularly notable for European samples with more homogeneous populations. Studies with U.S. samples had equitable gender/sex representation but participants were predominantly White and older middle aged. Inconsistencies in how studies stratified race/ethnicity resulted in a large percentage of individuals being classified as unknown/other/mixed. This review highlights the need for more diverse representation in neuropsychological research as Hispanic/Latino, Black/African American, and Asian groups were represented at half their actual numbers in the U.S. population (18.5%, 12.2%, and 5.9% respectively).

Representing the Information of Multiplayer Online Battle Arena (MOBA) Video Games Using Convolutional Accordion Auto-Encoder (A2E) Enhanced by Attention Mechanisms

In this paper, we propose a representation of the visual information about Multiplayer Online Battle Arena (MOBA) video games using an adapted unsupervised deep learning architecture called Convolutional Accordion Auto-Encoder (Conv_A2E). Our study includes a presentation of current representations of MOBA video game information and why our proposal offers a novel and useful solution to this task. This approach aims to achieve dimensional reduction and refined feature extraction of the visual data. To enhance the model’s performance, we tested several attention mechanisms for computer vision, evaluating algorithms from the channel attention and spatial attention families, and their combination. Through experimentation, we found that the best reconstruction of the visual information with the Conv_A2E was achieved when using a spatial attention mechanism, deformable convolution, as its mean squared error (MSE) during testing was the lowest, reaching a value of 0.003893, which means that its dimensional reduction is the most generalist and representative for this case study. This paper presents one of the first approaches to applying attention mechanisms to the case study of MOBA video games, representing a new horizon of possibilities for research.

Surface Currents and Relative-Wind Stress in Coupled Ocean–Atmosphere Simulations of the Northern California Current System

Abstract The dependence of surface-current damping on the definition of surface current for the relative wind is examined in coupled ocean–atmosphere numerical simulations of the northern California Current System (nCCS) during March–October 2009. The model response is analyzed for wind stress computed from relative wind for six different choices of effective model surface velocity. Simulations without surface-current coupling are also considered. As a function of the geographically varying uppermost grid-level depth, the model uppermost grid-level velocity is found to have a wind-drift component with a log-layer structure. Mean geostrophic wind work is concentrated in the shelf and slope regions during March–May (MAM) and in the deep offshore region in June–September (JJAS). The surface-current damping effect on ocean kinetic energy depends more strongly on the parameterization of atmospheric planetary boundary layer (PBL) turbulence than on the surface-current coupling formulation: weaker PBL mixing gives stronger surface-current damping. The damping effect is stronger in the less energetic offshore region than in the more energetic region closer to the coast. During MAM, the changes in kinetic energy and geostrophic wind work in the shelf and slope regions are spatially correlated, while during JJAS, the changes in geostrophic wind work are strongly modulated by SST–stress coupling. The wind-drift-corrected surface-current formulations result in large changes in the effective wind work based on the product of stress and relative-wind surface current but result in only small changes in the kinetic energy of the circulation. Significance Statement Ocean currents and atmospheric winds are coupled by the exchange of momentum across the air–sea interface, the strength of which depends on the relative wind, the difference between the surface wind and surface current. The purpose of this work was to examine the dependence of a coupled ocean–atmosphere model of the northern California Current System (nCCS) on the representations of the relative-wind surface current and turbulent mixing in the lower atmosphere. The model surface current was found to have a shallow wind-drift response. The model ocean circulation was driven primarily by near-coast winds during the spring and by offshore winds during the summer. The ocean response depended more strongly on the atmospheric turbulent mixing than on the surface current representation.

Bayesian L 1/2 Regression

It is well known that Bridge regression enjoys superior theoretical properties when compared to traditional LASSO. However, the current latent variable representation of its Bayesian counterpart, based on the exponential power prior, is computationally expensive in higher dimensions. In this article, we show that the exponential power prior has a closed form scale mixture of normal decomposition for α = ( 1 2 ) γ , γ ∈ { 1 , 2 , … } . We call these types of priors L 1 2 prior for short. We develop an efficient partially collapsed Gibbs sampling scheme for computation using the L 1 2 prior and study theoretical properties when p > n . In addition, we introduce a non-separable Bridge penalty function inspired by the fully Bayesian formulation and a novel, efficient coordinate descent algorithm. We prove the algorithm’s convergence and show that the local minimizer from our optimization algorithm has an oracle property. Finally, simulation studies were carried out to illustrate the performance of the new algorithms. Supplementary materials for this article are available online.