Variety Of Architectures Research Articles

Assessing the predictive efficacy of European-based systolic blood pressure polygenic risk scores in diverse Brazilian cohorts.

Despite the identification of numerous genetic variants affecting SBP in European populations, their applicability in admixed populations remains unclear. This study evaluates the predictive efficacy of a systolic blood pressure (SBP) polygenic risk score (PRS), derived from the UK Biobank data, in two Brazilian cohorts. We analyzed 944K genetic variants consistent across an independent UK Biobank dataset, Brazilian cohorts, and HapMap database. Results show a significant association between increased PRS and SBP, as well as hypertension, in each study groups analyzed. An increase of one standard deviation in the PRS showed a significant association with SBP (β [95% CI] (mmHg) = 5.2 [5.1-5.3], 2.8 [2.1-3.5] and 2.6 [2.2-3.0]) and hypertension (odds ratio (OR) [95% CI] = 1.56 [1.54-1.56], 1.28 [1.2-1.4] and 1.47 [1.3-1.6]) in an independent UKB dataset, Baependi, and Pelotas, respectively. The associations were weaker in the Brazilian samples and the reduced association was noticeable in the Pelotas vs. the UK comparison for hypertension stages 1 and 2 (OR [95% CI] = 2.1 [1.5-3.1] and 3.0 [1.9-4.7] vs. 2.5 [2.2-2.8] and 4.9 [4.4-5.6]), whereas the Baependi data showed no significance for stage 1 hypertension. This trend mirrors findings in homogeneous African and Asian populations with diverse genetic architecture, highlighting the limitations of European-based PRS also in admixed populations. These insights are crucial for developing tailored disease prevention and management strategies in ethnically diverse groups.

The recombination landscape of the barn owl, from families to populations.

Homologous recombination is a meiotic process that generates diversity along the genome and interacts with all evolutionary forces. Despite its importance, studies of recombination landscapes are lacking due to methodological limitations and limited data. Frequently used approaches include linkage mapping based on familial data that provides sex-specific broad-scale estimates of realized recombination and inferences based on population LD that reveal a more fine scale resolution of the recombination landscape, albeit dependent on the effective population size and the selective forces acting on the population. In this study, we use a combination of these two methods to elucidate the recombination landscape for the Afro-European barn owl (Tyto alba). We find subtle differences in crossover placement between sexes that leads to differential effective shuffling of alleles. LD based estimates of recombination are concordant with family-based estimates and identify large variation in recombination rates within and among linkage groups. Larger chromosomes show variation in recombination rates, while smaller chromosomes have a universally high rate which shapes the diversity landscape. We find that recombination rates are correlated with gene content, genetic diversity and GC content. We find no conclusive differences in the recombination landscapes between populations. Overall, this comprehensive analysis enhances our understanding of recombination dynamics, genomic architecture, and sex-specific variation in the barn owl, contributing valuable insights to the broader field of avian genomics.

Variation in animal architecture: Genes, environment, and culture.

A new study investigates how stingless bee colonies inherit one of two architecturally distinct types of comb and proposes the primary mechanism of inheritance as stigmergy: among-individual coordination in comb building informed by environmental cues (i.e., social artefacts). These findings highlight the importance of social information in creating and maintaining architectural variance among structures.

Diversifying Multi-Head Attention in the Transformer Model

Recent studies have shown that, due to redundancy, some heads of the Transformer model can be pruned without diminishing the efficiency of the model. In this paper, we propose a constrained optimization algorithm based on Hebbian learning, which trains specific layers in the Transformer architecture in order to enforce diversification between the different heads in the multi-head attention module. The diversification of the heads is achieved through a single-layer feed-forward neural network that is added to the Transformer architecture and is trained with the proposed algorithm. We utilize the algorithm in three different architectural variations of the baseline Transformer model. In addition to the diversification of the heads, the proposed methodology can be used to prune the heads that capture redundant information. Experiments on diverse NLP tasks, including machine translation, text summarization, question answering and large language modeling, show that our proposed approach consistently improves the performance of baseline Transformer models.

Synthesis of Clickable Isotactic Poly(vinyl ether)s through Organocatalytic Stereoselective Cationic Copolymerization.

By virtue of the high reliability of click chemistry, polymers with clickable groups provide a useful platform for the rapid synthesis of polymer materials with diverse functionalities and architectures. However, the polymerization of clickable vinyl monomers with a concurrent regulation on tacticity remains underdeveloped. Herein, we report the successful development of a stereoselective cationic copolymerization of C-C triple bond-containing vinyl ethers with simple alkyl vinyl ethers by employing confined Brønsted acid as catalyst, which allows for the synthesis of alkyne-functionalized vinyl ether copolymers with high isotacticity (up to 90% m), controlled molecular weight, and variable content of C-C triple bonds. Further transformation of the pendant clickable alkyne groups via thiol-yne click reactions and copper(I)-catalyzed alkyne-azide cycloaddition reaction enables a modular access to isotactic polymers with various functional groups.

Pixels relationship analysis for extracting building footprints

Abstract. Currently, remote sensing research is focused on developing an automated algorithm that can compete with empirical methods for mapping the contours of individual buildings. Despite facing numerous challenges related to suboptimal imaging conditions, diverse building architecture, and complex backgrounds, creating such an algorithm is essential for monitoring urban and natural areas, generating 3D city models, managing disasters, and estimating population density. Obtaining the polygonal boundary of a building and extracting a vectorized building mask as output for direct use is one of the current challenges in drawing building outlines. This work provides a comprehensive workflow for building extraction and improves their predicted area by regularization the boundaries of buildings. First, a convolutional neural network is used to train the binary semantic segmentation model and then regularization and vectorization processes are performed. The main difference from existing methods is a new regularization method based on compiling a neighborhood matrix for each point belonging to the “building” class. According to the experimental results, the algorithm shows high efficiency: IOU (intersection over union) = 91.2%, AP (average precision) = 64.1% and AR (average recall) = 75.1%, comparable to leading building boundary extraction methods such as PolyWorld.

Multi-source partial discharge pattern recognition in GIS based on Grabcut-MCNN

Partial discharge (PD) surveillance constitutes a pivotal methodology for diagnosing insulation failures in electrical equipment. Enhancing comprehensively the precision of identifying PD anomalies in Gas Insulated Switchgear (GIS) is of paramount significance for ensuring the steady functioning of power grids. This study introduces a novel framework that integrates Phase-Resolved PD Graph Segmentation (PRPD-Grabcut) with a tailored MobileNets-based Convolutional Neural Network (MCNN) to classify GIS-related PD issues. Leveraging image segmentation via PRPD-Grabcut, crucial features are extracted from PRPD diagrams, which then facilitate the construction of the MCNN model. This model employs depth-wise separable convolutions alongside inverted residual architectures to tackle the vanishing gradient dilemma inherent in Deep Convolutional Neural Networks (DCNNs) during GIS PD pattern discernment. Upon the model's subsequent training and validation, empirical evidence illustrates that the PRPD-Grabcut-MCNN hybrid significantly alleviates the computational load and storage requisites of the model, concurrently enhancing the recognition precision and expediting the training process of the neural network. Relative to diverse established lightweight neural network architectures, MCNN manifests superior performance in terms of recognition accuracy, reduced cross-entropy loss, and expedited training duration.

AI-Assisted Detection and Localization of Spinal Metastatic Lesions.

The integration of machine learning and radiomics in medical imaging has significantly advanced diagnostic and prognostic capabilities in healthcare. This study focuses on developing and validating an artificial intelligence (AI) model using U-Net architectures for the accurate detection and segmentation of spinal metastases from computed tomography (CT) images, addressing both osteolytic and osteoblastic lesions. Our methodology employs multiple variations of the U-Net architecture and utilizes two distinct datasets: one consisting of 115 polytrauma patients for vertebra segmentation and another comprising 38 patients with documented spinal metastases for lesion detection. The model demonstrated strong performance in vertebra segmentation, achieving Dice Similarity Coefficient (DSC) values between 0.87 and 0.96. For metastasis segmentation, the model achieved a DSC of 0.71 and an F-beta score of 0.68 for lytic lesions but struggled with sclerotic lesions, obtaining a DSC of 0.61 and an F-beta score of 0.57, reflecting challenges in detecting dense, subtle bone alterations. Despite these limitations, the model successfully identified isolated metastatic lesions beyond the spine, such as in the sternum, indicating potential for broader skeletal metastasis detection. The study concludes that AI-based models can augment radiologists' capabilities by providing reliable second-opinion tools, though further refinements and diverse training data are needed for optimal performance, particularly for sclerotic lesion segmentation. The annotated CT dataset produced and shared in this research serves as a valuable resource for future advancements.

Interpreting the space characteristics of everyday heritage gardens of Suzhou, China, through a space syntax approach

ABSTRACT China’s everyday heritage gardens, epitomizing social and spatial logic through classical layouts, offer a window into the nexus between Chinese culture and spatial design. Using space syntax and statistical methods, we examined 27 such gardens in Suzhou, categorizing them into four groups by spatial attributes. We first quantified building spatial traits, then delved into their syntactical relationships, guiding the final clustering. Cluster A, with 17 gardens, marries architectural diversity with spatial harmony, ensuring navigability. Cluster B’s seven gardens prioritize open landscapes, minimizing buildings for clarity. Cluster C, comprising two expansive gardens, blends architectural variety with spatial coherence, albeit with some navigational complexity. Unique, Cluster D, solely Yi Garden, presents a rich spatial and architectural tapestry, demanding deeper visitor engagement. This work sheds light on the intertwining of garden design and spatial structures, underscoring influences like ownership and cultural milieu, suggesting broader applicability to heritage gardens.

Systematic Analysis of Low-Precision Training in Deep Neural Networks: Factors Influencing Matrix Computations

As Deep Neural Networks (DNNs) continue to increase in complexity, the computational demands of their training have become a significant bottleneck. Low-precision training has emerged as a crucial strategy, wherein full-precision values are quantized to lower precisions, reducing computational overhead while aiming to maintain model accuracy. While prior research has primarily focused on minimizing quantization noise and optimizing performance for specific models and tasks, a comprehensive understanding of the general principles governing low-precision computations across diverse DNN architectures has been lacking. In this paper, we address this gap by systematically analyzing the factors that influence low-precision matrix computations, which are fundamental to DNN training. We investigate three critical factors—accumulation in matrix calculations, the frequency of element usage, and the depth of matrices within the model—and their impact on low-precision training. Through controlled experiments on standard models, as well as customized experiments designed to isolate individual factors, we derive several key insights: layers with higher accumulation and matrices with lower usage frequencies demonstrate greater tolerance to low-precision noise, without significantly compromising the stability of model training. Additionally, while the depth of matrices influences the stability of matrix operations to some extent, it does not have a noticeable effect on the overall training outcomes. Our findings contribute to the development of generalizable principles for low-precision training, offering a systematic framework applicable across various DNN architectures. We provide empirical evidence supporting the strategic allocation of training bit-widths based on the analyzed factors, thereby enhancing the efficiency and effectiveness of DNN training in resource-constrained environments.

Adaptation and innovation in darter fish cranial musculature (Etheostomatinae: Percidae): insights from diceCT

Abstract Fish skulls are often highly kinetic, with multiple linkage and lever systems powered by a diverse suite of muscles. Comparative analysis of the evolution of soft-tissue structures in the fish skull is often limited under traditional approaches, while new imaging techniques like diceCT (diffusible iodine-based contrast-enhanced computed tomography) allow for high-resolution imaging of muscles in situ. Darters (Percidae: Etheostomatinae) are a diminutive and species-rich clade of lotic freshwater fishes, which show diverse head shapes believed to be associated with different foraging strategies. We used diceCT to sample all major cranial adductors and abductors responsible for movement of the jaw, hyoid, operculum, and suspensorium from 29 species. We applied comparative phylogenetic approaches to analyse the evolutionary trends in muscle size across the clade. We found two major patterns: (i) darter cranial muscles show fundamental trade-offs relating to investment in musculature, as well as buccal expansion vs. biting attributes; early divergence in muscle size appears to be associated with shifts in habitat use and foraging; (ii) darter adductor mandibulae show high variation in architecture (fibre orientation, divisions). This study highlights how new imaging techniques can provide novel insights into the anatomy of even well-sampled/represented clades.

Investigation of the impact of internal acceptor and π-linker in 2, 5-di(2-thienyl)pyrrole based organic dye photosensitizer for DSSCs

The effect of π-linkers and internal acceptor group on the optoelectronic characteristics of D-π-A and D-A′-π-A dyes for dye-sensitized solar cells (DSSC) were examined. The DFT was used for estimating geometries and charge transport parameters, and the TD-DFT for calculating electronic excitations of these dyes. HOMO, LUMO, and HOMO-LUMO energy gap were also calculated for appreciating the suitable electron transfer, dye regeneration, and charge injection. For achieving the improved performance of dyes in DSSC, injection driving force (ΔGinj), short circuit current (JSC), the open circuit voltage (VOC), light harvesting efficiency (LHE), dye regeneration energy (ΔGreg), and Power conversion efficiency were also determined. For suitable Power conversion efficiency of the DSSCs, electron affinity (EA), hole and electron extraction potential, Ionization potential (IP), Total density of states, and Electrostatic potential (ESP) were also calculated. The result shows that the internal acceptor and π-linkers affect the photovoltaic parameters and a small variations in the dye architecture increase the efficiency of DSSCs. The dyes containing diphenyl π-linkers showed maximum power conversion efficiency for solar cell.

How might a planet between mars and jupiter influence the inner solar system? effects on orbital motion, obliquity, and eccentricity

The Kepler Space Telescope and the Transiting Exoplanet Satellite Survey have discovered a wide diversity of exoplanetary system architectures. In comparison to exoplanet systems, one peculiarity in the solar system is the distinct lack of a super-Earth analog, a planet archetype prevalent in the exoplanet population. Models suggest however, that the formation of an Earth-to-Super-Earth mass planet could have readily occurred in the inner regions of the solar system (<3 AU) without the migration of the Jovian worlds. In this study, we test the consequences of such a hypothesis using a three-dimensional (3D) N-Rigid-Body integrator. Previous work employed numerical models that assumed planets and interacting bodies as point-mass particles and neglected changes in spin evolution and obliquity. These neglects of one-dimensional models may be problematic when attempting to accurately track the orbital motion of a planet. With a 3D model in which the planet is modeled as a rigid body to account for its finite size and rotation, we investigate three inner terrestrial planets over 2 Myr periods and found that an additional super-Earth sized planet between 2 and 3.5 AU would have (i) destabilized Earth’s orbit over timescales of 1 Myr, (ii) increased Mars’s obliquity by ∼55°, and (iii) perturbed the eccentricity of Venus by up to e∼0.4. Our study explores an “alternate fate” of the terrestrial planets and our results suggest that if a super-Earth had formed in the inner solar system, orbital evolution histories of many terrestrial worlds would have looked very different.

Ancestral Variability in the Genetic Architecture of Urine Uromodulin

Ancestral Variability in the Genetic Architecture of Urine Uromodulin

Decoding Cortical Chronotopy - Comparing the Influence of Different Cortical Organizational Schemes

The brain's diverse intrinsic timescales enable us to perceive stimuli with varying temporal persistency. This study aimed to uncover the cortical organizational schemes underlying these variations, revealing the neural architecture for processing a wide range of sensory experiences. We collected resting-state fMRI, task-fMRI, and diffusion-weighted imaging data from 47 individuals. Based on this data, we extracted six organizational schemes: (1) the structural Rich Club (RC) architecture, shown to synchronize the connectome; (2) the structural Diverse Club architecture, as an alternative to the RC based on the network's module structure; (3) the functional uni-to-multimodal gradient, reflected in a wide range of structural and functional features; and (4) the spatial posterior/lateral-to-anterior/medial gradient, established for hierarchical levels of cognitive control. Also, we explored the effects of (5) structural graph theoretical measures of centrality and (6) cytoarchitectural differences. Using Bayesian model comparison, we contrasted the impact of these organizational schemes on (1) intrinsic resting-state timescales and (2) inter-subject correlation (ISC) from a task involving hierarchically nested digit sequences. As expected, resting-state timescales were slower in structural network hubs, hierarchically higher areas defined by the functional and spatial gradients, and thicker cortical regions. ISC analysis demonstrated hints for the engagement of higher cortical areas with more temporally persistent stimuli. Finally, the model comparison identified the uni-to-multimodal gradient as the best organizational scheme for explaining the chronotopy in both task and rest. Future research should explore the microarchitectural features that shape this gradient, elucidating how our brain adapts and evolves across different modes of processing.

Tomographic modeling and internal structure analysis of engineering textiles: A parametric approach

Tomographic modeling of textiles is attractive for numerical investigation of composites due to its ability to reveal the internal architecture. However, reliability is notably dependent on geometrical modeling accuracy, which is still challenging. This paper addresses existing issues using a parametric approach that relies on statistical resampling and spatial autocorrelated prediction. The proposed strategy involves three stages: first, an explicit representation of each fiber tow is derived from the segmented images through statistical resampling in the parametric domain, which is subsequently mapped back to the physical domain according to known spatial autocorrelation. It allows depicting tow surfaces with varying levels of detail. Second, the tow trajectory is parameterized and subjected to a smoothing process, thereby identifying the local fiber orientation as its tangent vector. Finally, the normal cross-sections of tows are solved as intersecting implicit planes with a parametric tubular surface thanks to the parametric representation. This allows evaluating the spatial architectural variability in tows with notable waviness. Examinations were performed by contrasting the proposed approach with existing techniques. This new approach demonstrated better consistency with the ground truth while bringing additional benefits to the geometry reconstruction.

Exploring the Impact of Architectural Variations in ResNet on CIFAR-100 Performance: An Investigation on Fully Connected Layers, Residual Blocks, and Kernel Size

ResNet is one of the leading neutral networks that has been widely applied in image classification. This study built a simple baseline network based on the concept of ResNet and then examines how variations in ResNet’s architecture affect its model performance to provide insights for optimizing network design. Firstly, this study investigates the number of fully connected layers, the results show that by reducing the number of fully connected layers significantly decreases the total number of trainable parameters, which in turn reduces the training time. However, this reduction does not lead to a noticeable improvement in the accuracy after convergence. In addition, increasing the number of fully connected layers not only greatly increases the training time but also leads to overfitting on the CIFAR-100 dataset, slightly reducing the training performance. Secondly, this study also analyzes the influence of reducing number of residual basic block. Analysis suggests that reducing the use of Residual Blocks has significantly negatively impacted both accuracy and training time. This may be because the use of Residual Blocks positively affects the network’s ability to learn features from the CIFAR-100 dataset. Finally, this study explores the effect of bigger kernel size of convolution layer in Residual Basic Block. The outcome demonstrates increasing the kernel size in the Residual Blocks significantly improves both training time and accuracy. Additionally, it was observed that in this variant experiment, a definitive convergence has not yet been clearly established, leaving the possibility that accuracy might continue to improve with more training epochs.

On a Simplified Approach to Achieve Parallel Performance and Portability Across CPU and GPU Architectures

This paper presents software advances to easily exploit computer architectures consisting of a multi-core CPU and CPU+GPU to accelerate diverse types of high-performance computing (HPC) applications using a single code implementation. The paper describes and demonstrates the performance of the open-source C++ matrix and array (MATAR) library that uniquely offers: (1) a straightforward syntax for programming productivity, (2) usable data structures for data-oriented programming (DOP) for performance, and (3) a simple interface to the open-source C++ Kokkos library for portability and memory management across CPUs and GPUs. The portability across architectures with a single code implementation is achieved by automatically switching between diverse fine-grained parallelism backends (e.g., CUDA, HIP, OpenMP, pthreads, etc.) at compile time. The MATAR library solves many longstanding challenges associated with easily writing software that can run in parallel on any computer architecture. This work benefits projects seeking to write new C++ codes while also addressing the challenges of quickly making existing Fortran codes performant and portable over modern computer architectures with minimal syntactical changes from Fortran to C++. We demonstrate the feasibility of readily writing new C++ codes and modernizing existing codes with MATAR to be performant, parallel, and portable across diverse computer architectures.

Genomic prediction of cereal crop architectural traits using models informed by gene regulatory circuitries from maize.

Plant architecture is a major determinant of planting density, which enhances productivity potential for crops per unit area. Genomic prediction is well positioned to expedite genetic gain of plant architectural traits since they are typically highly heritable. Additionally, the adaptation of genomic prediction models to query predictive abilities of markers tagging certain genomic regions could shed light on the genetic architecture of these traits. Here, we leveraged transcriptional networks from a prior study that contextually described developmental progression during tassel and leaf organogenesis in maize (Zea mays) to inform genomic prediction models for architectural traits. Since these developmental processes underlie tassel branching and leaf angle, 2 important agronomic architectural traits, we tested whether genes prioritized from these networks quantitatively contribute to the genetic architecture of these traits. We used genomic prediction models to evaluate the ability of markers in the vicinity of prioritized network genes to predict breeding values of tassel branching and leaf angle traits for 2 diversity panels in maize and diversity panels from sorghum (Sorghum bicolor) and rice (Oryza sativa). Predictive abilities of markers near these prioritized network genes were similar to those using whole-genome marker sets. Notably, markers near highly connected transcription factors from core network motifs in maize yielded predictive abilities that were significantly greater than expected by chance in not only maize but also closely related sorghum. We expect that these highly connected regulators are key drivers of architectural variation that are conserved across closely related cereal crop species.

Computational Study of SmI2-Catalyzed Intermolecular Couplings of Cyclopropyl Ketones: Links between the Structure and Reactivity.

SmI2-catalyzed intermolecular coupling reactions of cyclopropyl ketones with alkenes or alkynes offer an efficient strategy for furnishing diverse five-membered ring-containing molecular architectures. This study presents a systematic computational investigation to reveal the structure-reactivity relationships in these reactions. The reactivity of aryl cyclopropyl ketones is enhanced by the stabilized ketyl radical and cyclopropyl fragmentation, arising from the conjugation effect of the aryl ring, despite an obstacle emerging from the gauche styrene intermediate that elevates the energy barrier for radical trapping. By contrast, alkyl cyclopropyl ketones lack conjugation and exhibit high barriers for reduction and fragmentation but undergo facile radical trapping due to the minimal steric hindrance. Interestingly, ortho-substituted phenyl cyclopropyl ketones exhibit superior reactivity due to a balance between the moderate conjugation, promoting cyclopropyl fragmentation, and the pretwisted nature of the ortho-substituted phenyl that circumvents the hindrance posed by the gauche intermediate and facilitates the radical trapping. The markedly enhanced reactivity of bicyclo[1.1.0]butyl (BCB) ketones arises from facile fragmentation of the strained BCB motif. Bicyclo[2.1.0]pentyl (BCP) ketones, less strained than BCB ketones, are computationally verified to undergo efficient couplings with various partners, and this can be attributed to their stable fragmentation intermediates that facilitate radical trapping. Our findings provide insights that can aid in designing related reactions.