Residual Map Research Articles

Outflow from the very massive Wolf-Rayet binary Melnick 34

Melnick 34 (Mk\,34) is one of the most massive binary systems known and is one of the brightest X-ray point sources in the 30 Doradus region. We investigated the impact of this massive system on the surrounding interstellar medium (ISM) using the optical spectroscopic capabilities of the narrow-field mode (NFM) of the Multi-Unit Spectroscopic Explorer (MUSE). MUSE-NFM spatially resolved the ISM in the vicinity of Mk\,34 with a resolution comparable to that of the HST. The analysis of the N ii \,lambda \,6583 and S ii \,lambda \,6717 emission lines reveals a cone-like structure apparently originating from Mk\,34 and extending southeast. Electron density maps and radial velocity measurements of the ISM lines further support an outflow scenario traced by these emissions. While no clear northwestern counterpart to this outflow was observed, we note increased extinction in that direction, towards the R136 cluster. The ISM material along the projected diagonal of the outflow on both sides of Mk\,34 shows similar properties in terms of the emission line ratios seen in the Baldwin-Phillips-Terlevich diagram. These results are consistent across two observational epochs. Additionally, we examined the residual maps within a 0.5" radius of Mk\,34 after modeling and subtracting the point spread function. The observed variations in the residuals could potentially be linked to Mk\,34's known periodic behavior. However, further observations with appropriate cadence are needed to fully monitor the 155\,day periodicity of Mk\,34's X-ray emissions.

Inhomogeneous strain partitioning in the Cenozoic Bohai Bay Basin controlled by pre-existing crustal fabrics and oblique subduction: insights from the Jizhong and Huanghua subbasins

The tectonic features and geodynamics of the Bohai Bay Basin (BBB) have been extensively studied over several decades. It is generally accepted that the Cenozoic BBB was subjected to a superimposed extensional and strike-slip stress field. However, the specific basin-forming mechanism remains ambiguous. Basin-scale studies with high spatial and temporal resolution reveal how strain migrates and localizes during multiphase rifting. Understanding these strain-related processes is crucial for deciphering the formation mechanisms and controlling factors of rift basins. This study investigates the rift-related evolution and strain partitioning in the Jizhong and Huanghua subbasins, the western half of the BBB. Using high-resolution 3D and 2D seismic datasets and well data, we present detailed geological sections, residual thickness maps, and fault systems, showing two distinct patterns of strain partitioning in the Cenozoic. In the Jizhong Subbasin, strain migrated inwards from the NE-trending boundary faults and finally became localized along the NE-trending rift axis; in the Huanghua Subbasin, strain migrated northeastwards from the southern part and then was largely localized along the near E–W-trending faults in the northeastern part. By integrating our results with previous studies of other subbasins, we identify two separate extensional domains within the Cenozoic BBB. The suborthogonal extensional domain in the Jizhong Subbasin is dominated by relatively stable NW–SE extension. In contrast, the extended areas to the east (i.e., the Huanghua, Bozhong, and Liaohe-Liaodongwan subbasins) comprise the oblique extensional domain, characterized by asymmetric transtensional pull-apart deformation and subjected to a clockwise-rotated extensional stress field from NW–SE to NNW–SSE. We suggest that the spatially inhomogeneous strain partitioning in the BBB has developed since the middle Eocene, and was controlled by the interaction of pre-existing crustal fabrics (e.g., the Tan-Lu Fault Zone) and the oblique subduction of the Pacific Plate. The boundary between the two domains may represent a transition zone where the margin-parallel residual velocity component of the oblique convergence decreased considerably landwards. Our study highlights the Cenozoic strain evolution and the associated geodynamics in the BBB, emphasizing the strain complexity within the back-arc rift basin under the oblique subduction background.

Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networks.

Graph neural networks (GNNs) tend to suffer from high computation costs due to the exponentially increasing scale of graph data and a large number of model parameters, which restricts their utility in practical applications. To this end, some recent works focus on sparsifying GNNs (including graph structures and model parameters) with the lottery ticket hypothesis (LTH) to reduce inference costs while maintaining performance levels. However, the LTH-based methods suffer from two major drawbacks: 1) they require exhaustive and iterative training of dense models, resulting in an extremely large training computation cost, and 2) they only trim graph structures and model parameters but ignore the node feature dimension, where vast redundancy exists. To overcome the above limitations, we propose a comprehensive graph gradual pruning framework termed CGP. This is achieved by designing a during-training graph pruning paradigm to dynamically prune GNNs within one training process. Unlike LTH-based methods, the proposed CGP approach requires no retraining, which significantly reduces the computation costs. Furthermore, we design a cosparsifying strategy to comprehensively trim all the three core elements of GNNs: graph structures, node features, and model parameters. Next, to refine the pruning operation, we introduce a regrowth process into our CGP framework, to reestablish the pruned but important connections. The proposed CGP is evaluated over a node classification task across six GNN architectures, including shallow models [graph convolutional network (GCN) and graph attention network (GAT)], shallow-but-deep-propagation models [simple graph convolution (SGC) and approximate personalized propagation of neural predictions (APPNP)], and deep models [GCN via initial residual and identity mapping (GCNII) and residual GCN (ResGCN)], on a total of 14 real-world graph datasets, including large-scale graph datasets from the challenging Open Graph Benchmark (OGB). Experiments reveal that the proposed strategy greatly improves both training and inference efficiency while matching or even exceeding the accuracy of the existing methods.

Volcanic weathered crust reservoir analog: Insights from fault-controlled fracture permeability in the trachytic rocks of the Pernambuco Basin, NE Brazil

Naturally fractured volcanic rocks represent a globally significant potential hydrocarbon reservoirs and can also be used for CO2 storage; however, the study of volcanic reservoirs in Brazil is limited. Although numerous volcanic reservoir types have been classified, relatively few studies are dedicated to the weathering of crust-like reservoirs, i.e., volcanic rocks undergoing subaerially alteration by weathering prior to deep burial. This study aims to characterize a volcanic weathered crust reservoir analog located in the coastal zone of the Pernambuco Basin, NE Brazil. We applied a multiscale approach motivated by the expressive potential of volcanic rocks as reservoirs in this marginal basin and considered their implications for CO2 geological storage projects. The deformational aspects observed in this analog have also been observed in other volcanic rocks that crop out in the coastal zone, thus, the data provided here can guide future characterization of both onshore and offshore reservoirs. We focused on an 80 m wide outcrop and employed geometric and topological analysis of two-dimensional fracture networks to investigate the influence of regional fault zone control on the fracture permeability tensors. The parameters of fracture systems were studied using terrestrial gravimetric data, orthophoto mosaic obtained by an unmanned aerial vehicle (UAV), petrography, and outcrop-based structural data. The investigated outcrop includes porphyritic trachytes with euhedral sanidine crystals and minor quartz phenocrysts, sourced from the Ipojuca Magmatic Suite. The residual Bouguer map indicates the influence of at least two regional rift fault zones, i.e., NW-SE (strike-slip) and NE-SW (normal) trending systems, in the studied rocks. These trachytic rocks exhibited a marked dissolution of sanidine phenocrysts and matrix, columnar disjunctions, and natural fractures, causing the porosity and permeability of the reservoir analog to increase. Geometric and topological analyses showed that the south-central sector of the outcrop exhibits heightened connectivity within the fracture network. Furthermore, the principal fracture permeability is controlled by the primary families of the fracture network and regional fault zones. Our results demonstrate that the fracture network observed in the volcanic reservoir analog was influenced by the post-rift tectonic reactivation of Cretaceous structures within the Pernambuco Basin. The secondary porosity resulting from weathering and subsequent dissolution of the primary constituents and fractures of the rocks studied demonstrates the potential for the exploration of these reservoirs as models for known occurrences of volcanic rocks in the onshore and offshore domains of the Pernambuco Basin.

Comparison of Land Surface and GGMPlus Satellite Gravity Data Results (Case Study: The Kalibening Basin)

As technology develops, many satellite gravity data with world coverage and high resolution have become available, one of which is GGMPlus. However, the quality of the resulting satellite gravity data is still doubtful, because the GGMPlus satellite data is the result of calculations. This research will compare satellite data with land surface data in the Kalibening Basin area to see the precision and correlation of satellite data with land surface data. Land surface data was obtained from field measurements using Scintrex CG-5 with a grid between stations of 500-1000m and GGMPlus satellite gravity data with a distance between points of 600m. The results obtained show that the residual anomaly maps have many similarities, while the regional anomalies provide quite significant differences between the two data. The slicing results show a density contrast that is similar to the two data and matches the geological boundaries of the Kalibening Basin. Based on the results obtained, GGMPlus data can be an alternative to fill the gaps in field data or as supporting data in disaster mitigation and exploration in general. The correlation between land surface data and GGMPlus is quite rational with a value of R2 = 0.95 and RMSE = 6.89mGal.

Sensing noise exposure and its inequality based on noise complaint data through vision-language hybrid method

This study seeks to reveal urban noise exposure patterns and inequalities using noise complaint data and vision-language hybrid method. By applying a natural language processing model to 17,243 noise complaint records, we uncovered distinct patterns of traffic, industrial, and living noise exposures across residential communities. Our analysis of street view images near complaint locations, utilizing a Residual Network (ResNet) model and Class Activation Mapping (CAM), identified the key environmental elements of different noise sources. Notably, our assessment of noise exposure inequality across 9791 communities yielded a counterintuitive finding: contrary to previous studies in Western contexts, rich communities in China experience higher and more unequal noise exposure compared to average communities, with Gini coefficients exceeding 0.8. This unexpected result likely stems from China's unique rapid urbanization process. Our use of crowdsourced complaint data aligns more closely with human subjective perceptions of noise, offering a novel perspective on noise exposure inequality. These findings challenge existing assumptions about the relationship between socioeconomic status and environmental quality in urban China, and have significant implications for urban planning and noise management strategies in rapidly developing cities.

Cascaded UNet for progressive noise residual prediction for structure-preserving video denoising

The prominence of high-quality video services has become so substantial that by 2030, it is estimated that approximately 80% of internet traffic will consist of videos. On the contrary, video denoising remains a relatively unexplored and intricate field, presenting more substantial challenges compared to image denoising. Many published deep learning video denoising algorithms typically rely on simple, efficient single encoder–decoder networks, but they have inherent limitations in preserving intricate image details and effectively managing noise information propagation for noise residue modelling. In response to these challenges, the proposed work introduces an innovative approach; in terms of utilization of cascaded UNets for progressive noise residual prediction in video denoising. This multi-stage encoder–decoder architecture is meticulously designed to accurately predict noise residual maps, thereby preserving the locally fine details within video content as represented by SSIM. The proposed network has undergone extensive end-to-end training from scratch without explicit motion compensation to reduce complexity. In terms of the more rigorous SSIM metric, the proposed network outperformed all video denoising methods while maintaining a comparable PSNR.

Geothermal energy appraisal and subsurface structural mapping of the Rafin Rewa warm spring region, Precambrian basement complex of Nigeria

This research work aims at evaluating the geothermal energy potentials of the Rafin Rewa warm spring (RRWS) of the Precambrian Basement Complex in Nigeria as an alternative energy source using integrated aeromagnetic geophysical techniques. Four aeromagnetic dataset were acquired, assemblage, analyzed, and interpreted using integrated geophysical processing techniques of spectral analysis and Euler deconvolution. Qualitative interpretation of the residual anomalous map reveals a distribution of positive anomalies (> 53 nT) majorly in the central and southeastern regions, which are traced to the granitic rocks, while the low anomalies (< − 1.5 nT) have been traced to the RRWS location emanating from the coastal plain sands of the Pliocene, Pleistocene, Oligocene, and Miocene ages. Quantitatively, the depth to the top (DTT) of the anomalous bodies reveals a depression that is almost intersecting with the Curie point depth (CPD) plot at the RRWS location, which indicates high heat flow in the RRWS region. The Spectral Analysis results reveal that the DTT and the CPD in this area ranges from 0.512 to 0.761 km and 6.504 to 10.582 km, respectively while the average CPD is 8.543 ± 0.325 km. It is observed that the DTT and CPD decrease as one move away from the RRWS region. The computed heat flow average was 160.76 ± 19.09 mW/m2 within the RRWS region. The Euler deconvolution result reveals the presence of geological structures, which were interpreted as faults and fractures. The major fractures trend in the east–west (E-W) directions, while the minor fractures trend northeast-southwest (NE-SW) directions. The geochemical result presented shows that iconic compositions impact the convective heat transfer processes associated with geothermal systems. It was conclusively believed that regions with comparable shallow CPDs could be viable for further geothermal energy investigations.

Far–field tectonic controls on deposition in the Appalachian Basin – A case study of Late Cambrian–Late Ordovician strata in Morrow County, Ohio

Far–field tectonics can often be overlooked when studying orogens, particularly when much of the deformation occurs near the loading loci. As stresses are propagated further from the loading into the craton, whether compressional, extensional, or shear, faults and fractures within the basement often act as pathways for stress transmission, traveling along these pre-existing planes of weakness, or creating new. The Appalachian Basin in the eastern United States is a classic foreland basin example that showcases far-field tectonic influence on deposition and structure. This study characterizes the structural architecture of the basement along the western Appalachian Basin margin by analyzing well log data from 2,662 wells to create structure, isopach, trend surface, and residual maps of Late Cambrian through Upper Ordovician strata (Knox through Black River). Fourteen lineaments interpreted as faults were delineated, creating two series of horsts and grabens. The northeast–southwest faults are related to structures that formed during Keweenawan extension, and the northwest–southeast faults are related to structures that formed during the Grenville Orogeny. Both sets of faults stem from the Precambrian basement and are shown to have been reactivated during the Blountian Tectophase of the Taconian Orogeny (Black Riverian deposition), creating localized thinning and thinning adjacent to the lineaments. Furthermore, these faults appear to have undergone additional reactivation since the deposition of these strata in a transtensional regime to create the observed horst and graben structures. Overall, far–field tectonics plays an important role in understanding the geologic history of a basin.

LPI radar waveform recognition model based on multiple feature image and quasi-residual attention module

Abstract Detecting and classifying the modulation type of the intercepted noisy LPI radar waveform has become a hot topic in the field of Electronic Countermeasures (ECM). In this paper, we propose a recognition model based on multiple feature images (MFI) and convolutional neural networks (CNN) with the quasi-residual attention module (QRAM). The core technologies of this model are divided into two parts. One is the MFI that combines the ambiguity function image (AFI) of the complete signal, covariance matrix image (CMI) of the reconstructed signal, and short-time Fourier transform image (STFTI) of the truncated signal into RGB color image in signal processing as the input for recognition network. The other is CNN with a QRAM recognition network, in which the input is sliced into two mappings called identity mapping and residual mapping to build the quasi-residual module, and the attention module is embedded in each mapping to denoise and enhance the feature. The performance of the MFI-QRAM model is demonstrated by recognizing 12 modulation types of the signals defined in this paper. The simulation experiments show that the model has strong robustness with the number of train sets and the variation of SNR. The overall probability of successful recognition (PSR) is 96.17% when the SNR is −6 dB.

Анализ эквивалентности трехмерных моделей при 3D-интерпретации данных аэроэлектроразведки

The article is devoted to the problem of the equivalence of three-dimensional geoelectric models obtained as a result of 3D-interpretation of aerial electrical survey data in medium where local conductive bodies are covered by a heterogeneous near-surface layer. To carry out the research, we used data from aerial electrical prospecting carried out in the Creighton area (Canada) when searching for polymetallic ores. A two-stage 3D-inversion was used to build the geoelectric model. The purpose of the first stage of 3D-inversion was to reconstruct near-surface structures based on survey data obtained at the early times. The second stage of 3D-inversion was based on the residual map constructed from the practical signals and the signals obtained after the first stage of 3D-inversion at later times. Using the residual map, the starting positions of local bodies under the overlying layer were specified, and their geometry and properties were obtained during local 3D-inversions. Based on the results of 3D-inversion, a local conductive body was identified and confirmed by drilling data. An equivalence analysis was carried out for a three-dimensional model of this body. Various options for initial models differing in the number of blocks describing the body were considered, as well as options for changing the size of blocks in the model obtained as a result of 3D-inversion. It was found that the main equivalence is related to the large distance between flight lines, which is several times greater than the distance between positions on the profile. Therefore, most equivalent models are characterized by a decrease in the size of blocks in the direction orthogonal to the profiles, due to an increase in their size along the profile and a change in resistivity. At the same time, according to the criterion for drilling wells, the resulting equivalent models were identical, i.e. from a geological point of view, no fundamental difference was found between the resulting equivalent models.

Edge-preserving image deraining network using cumulative feature aggregation

This study proposes an edge-preserving image deraining network using a wavelet feature aggregation method. Wavelet subbands re correlated with each other, and the high-frequency subband in the horizontal direction is the least affected by rain streak contamination. On this basis, we introduce a single image deraining network that cumulatively aggregates wavelet subband features according to their importance. The network architecture primarily comprises a wavelet feature aggregation block and a residue channel guide block. The aggregation of features with the cumulative wavelet feature aggregation block moves downward and upward, and a long short-term memory-based multiscale attentive rain streak removal block is developed to serve as the backbone for rain streak removal. We use a residual channel map based on the low-frequency subband to construct guide features that assist in rain streak removal. A repetitive image restoration framework that incorporates two proposed blocks is used to iteratively improve rainy images. We test the proposed network on various image datasets and compare the deraining performance with those of existing methods The experimental results demonstrate that the performance of the proposed scheme is superior to that of other tested deraining methods. https://github.com/SYChoi98/CWFANet/

Engineering attributes of ground motions from February 2023 Türkiye earthquake sequence

The 2023 Türkiye earthquake sequence includes the 6 February M7.81 mainshock followed approximately 9 h later by an M7.74 event, and many smaller aftershocks including M6.81 and M6.37 events on 6 and 20 February, respectively. These events occurred in a region near the plate boundary of the East Anatolian Fault, in the proximity of which numerous ground motion recording stations had been installed north of the Türkiye–Syria border. As a result, the events were well recorded both near the fault and at rupture distances up to 582 km. We describe the available recordings and component-specific data processing performed with the aim of optimizing usable bandwidth. The resulting database includes 337, 365, 284, and 229 usable three-component recordings from the M7.81, M7.74, M6.81, and M6.37 events, respectively. We also present source, path, and site metadata that were compiled according to uniform protocols. Comparisons to a global ground motion model (GMM) for active tectonic regions and a local, Türkiye-specific model demonstrate the existence of complex path effects that result in relatively poor fits between the GMMs and observed data at large distances (generally R JB &gt; 200 km). Under-predictions at some stations may be influenced by directivity and/or basin effects that affect the ground motions but that are not accounted for directly in the GMMs. We present analysis of spatial variability of several intensity measures. The residual maps produced from these analyses demonstrate that the global GMM overpredicts on the Anatolian block and underpredicts on the Arabian block, which is suggestive of distinct crustal attenuation features.

An object-based region-growing phase unwrapping method for mapping vertical displacement in permafrost landscapes

Terrain displacement due to the seasonal thaw of the active layer above permafrost can be sensitive to climate change; however, its accurate characterization remains a challenge. This study aimed to improve the measurement of the subsidence or vertical ground surface displacement using differential synthetic aperture radar interferometry (InSAR). Existing methods for reliable phase unwrapping are hindered by the decorrelation between time-series of SAR acquisitions that can result due to the heterogeneity and structural sensitivity of permafrost landscapes to external conditions. In this study, an advanced phase unwrapping method was proposed, in which three types of regions, namely non-residue, sparse residue, and dense residue objects, were obtained from wrapped interferogram and residue map using a segmentation method. Two variants of Polynomial-Based Region Growing Phase Unwrapping (PBRGPU) were developed, which are sparse-residue Object-based PBRGPU(SOP) and dense-residue Object-based PBRGPU(DOP). The results demonstrated that the proposed method outperformed the existing phase unwrapping methods by partially suppressing the decorrelation phase and enhancing robustness for complex terrain deformation in the absence of measured field data. Both the PBRGPU variants and segmentation strategies compose the object-based unwrapping method for permafrost, and also provide a new framework by combining the segmentations and scenarios for phase unwrapping for permafrost regions.

Sloshing and spiral structures breeding a putative radio mini-halo in the environment of a cool-core cluster, Abell 795

ABSTRACT Spiral structures and cold fronts in X-rays are frequently observed in cool-core galaxy clusters. However, studies on radio mini-haloes associated with such spirals and their physical connections are rare. Here, we present the detection of an extended diffuse radio emission entrained in the X-ray spiral structure in a known cool-core cluster, Abell 795. Though the cool core is a sign of the relaxed nature, our re-analysed 30-ks Chandra X-ray data of Abell 795 confirm the presence of an interesting log spiral structure of an X-ray deficit region complemented by an X-ray excess counter spiral in the residual map, exposing its dynamical activity. Our new analysis of 150- and 325-MHz Giant Metrewave Radio Telescope archival data confirms the detection of a ∼180-kpc ultra-steep (α ∼ −2.7) diffuse radio structure, previously reported as a candidate radio mini-halo from low-sensitive survey maps. This emission spans the entire spiral structure, enclosed by two previously reported cold fronts. Furthermore, optical spectra from the Sloan Digital Sky Survey Data Release 13 and far-ultraviolet data from the Galaxy Evolution Explorer show a considerably low total star formation rate of 2.52 M⊙ yr−1 with no significant variation in metallicity distribution. We argue that the two-phase (hot and cold) plasma at the core with differential velocity has plausibly caused the spiral formation and has redistributed the secondary electrons from the brightest cluster galaxy or the pre-accelerated electrons, which have been (re-)accelerated by the sloshing turbulence to form the observed candidate radio mini-halo structure. This is supported by a few previous studies indicating that spiral formation and sloshing turbulence quenches star formation and facilitates smooth metallicity distribution by mixing the gas in the core.

An attention mechanism and residual network based knowledge graph-enhanced recommender system

Recommender systems enhanced by a knowledge graph (KG) have attained widespread popularity and attention in recent years. However, traditional KG-based recommender systems encounter the challenge of gradient explosion as the network depth increases. Additionally, the abundance of unreliable paths in a KG has a detrimental impact on feature representation learning. In this article, we propose a KG-enhanced recommender system based on residual network and attention mechanism, which can capture high-order connectivity and long-range dependencies of the KG. Specifically, a resource allocation approach is employed to calculate the resource amount, which is subsequently utilized to evaluate the path reliability of the KG. After completing path extraction, we employ an attention mechanism to capture semantic correlations and structural information. To leverage the KG for enhancing recommender systems, we design a deep residual network with shortcut connections, effectively amalgamating advanced and abstract features using deep neural networks. The introduction of shortcut connections not only facilitates the fitting of residual mappings but also mitigates potential issues such as gradient explosion and convergence difficulties due to excessive network depth. Extensive experiments conducted on three standard datasets over baseline methods have demonstrated the superiority of our proposed recommender system.

Defect detection of printed circuit board based on adaptive key-points localization network

Many deep neural networks (DNNs) have been applied in the defect detection of products. Due to the irregular and small defects on printed circuit boards (PCB), it is difficult for the DNN-based defect detection models to achieve good detection performance. In this paper, a new DNN, adaptive key point localization network (AKPLNet) is proposed for PCB defect detection. Firstly, residual pyramid heat mapping network (RFHNet) that is composed of ResNet50_FPN and thermodynamic mechanism (TM), is used to perform multi-scale feature extraction and defect location. Secondly, an adaptive tree structure region proposal network (AT-RPN) based on tree structure Parzen estimation is proposed to obtain the predicted regions of the target, which reduces the need for large number of priori knowledge during the detection process. Finally, a key point regression algorithm is proposed to locate defects accurately. The defect detection performance of AKPLNet is validated on two PCB datasets. The mean average precision (mAP) of AKPLNet reaches 96.9% and 99.0% on PCB-Master dataset with the color images and DeepPCB-Master dataset with the grayscale images, improving 2.1% and 2.3% compared with Yolov7, respectively. The testing results demonstrate that AKPLNet achieves the better detection accuracy than those state-of-the-art methods.

Biochemical, Bioinformatic, and Structural Comparisons of Transketolases and Position of Human Transketolase in the Enzyme Evolution.

Transketolases (TKs) are key enzymes of the pentose phosphate pathway, regulating several other critical pathways in cells. Considering their metabolic importance, TKs are expected to be conserved throughout evolution. However, Tittmann et al. (J Biol Chem, 2010, 285(41): 31559-31570) demonstrated that Homo sapiens TK (hsTK) possesses several structural and kinetic differences compared to bacterial TKs. Here, we study 14 TKs from pathogenic bacteria, fungi, and parasites and compare them with hsTK using biochemical, bioinformatic, and structural approaches. For this purpose, six new TK structures are solved by X-ray crystallography, including the TK of Plasmodium falciparum. All of these TKs have the same general fold as bacterial TKs. This comparative study shows that hsTK greatly differs from TKs from pathogens in terms of enzymatic activity, spatial positions of the active site, and monomer-monomer interface residues. An ubiquitous structural pattern is identified in all TKs as a six-residue histidyl crown around the TK cofactor (thiamine pyrophosphate), except for hsTK containing only five residues in the crown. Residue mapping of the monomer-monomer interface and the active site reveals that hsTK contains more unique residues than other TKs. From an evolutionary standpoint, TKs from animals (including H. sapiens) and Schistosoma sp. belong to a distinct structural group from TKs of bacteria, plants, fungi, and parasites, mostly based on a different linker between domains, raising hypotheses regarding evolution and regulation.

Deep graph layer information mining convolutional network

Graph convolution network is a powerful method of deep learning of graph structure data. Existing methods usually adjust the neighborhood information aggregation mode or optimize the graph topology layer by layer for improving the graph convolution network. However, these methods seldom consider the discriminative information about hierarchical characteristics nodes (some special nodes only can be correctly classified in one layer and are the misclassification nodes in the other layers of deep graph convolutional networks) in the different layers for complementing the neighborhood topology information. To further find these information, a deep graph layer information mining convolutional network (GLIM) can alternately measure the neighborhood ranking information on topology structure and update the residual identity mapping node information on the different layers for enhancing the model classification performance. Moreover, GLIM can construct a unified framework with the various hyper-parameters for the different graph learning method based on graph convolution network. Experiments show GLIM outperforms the state-of-the-art methods for semi-supervised node classification in three cite datasets (Cora, CiteSeer,and PubMed) and three image datasets (MNIST, Cifar10 and Cifar100).

A cross-attention augmented model for event-triggered context-aware story generation

Despite recent advancements, existing story generation systems continue to encounter difficulties in effectively incorporating contextual and event features, which greatly influence the quality of generated narratives. To tackle these challenges, we introduce a novel neural generation model, EtriCA, that enhances the relevance and coherence of generated stories by employing a cross-attention mechanism to map context features onto event sequences through residual mapping. This feature capturing mechanism enables our model to exploit logical relationships between events more effectively during the story generation process. To further enhance our proposed model, we employ a post-training framework for knowledge enhancement (KeEtriCA) on a large-scale book corpus. This allows EtriCA to adapt to a wider range of data samples. This results in approximately 5% improvement in automatic metrics and over 10% improvement in human evaluation. We conduct extensive experiments, including comparisons with state-of-the-art (SOTA) baseline models, to evaluate the performance of our framework on story generation. The experimental results, encompassing both automated metrics and human assessments, demonstrate the superiority of our model over existing state-of-the-art baselines. These results underscore the effectiveness of our model in leveraging context and event features to improve the quality of generated narratives.