Correct Mapping Research Articles

Enhancing semantic mapping in text-to-image diffusion via Gather-and-Bind

Text-to-image synthesis is a challenging task that aims to generate realistic and diverse images from natural language descriptions. However, existing text-to-image diffusion models (e.g., Stable Diffusion) sometimes fail to satisfy the semantic descriptions of the users, especially when the prompts contain multiple concepts or modifiers such as colors. By visualizing the cross-attention maps of the Stable Diffusion model during the denoising process, we find that one of the concepts has a very scattered attention map, which cannot form a whole and gradually gets ignored. Moreover, the attention maps of the modifiers are hard to overlap with the corresponding concepts, resulting in incorrect semantic mapping. To address this issue, we propose a Gather-and-Bind method that intervenes in the cross-attention maps during the denoising process to alleviate the catastrophic forgetting and attribute binding problems without any pre-training. Specifically, we first use information entropy to measure the dispersion degree of the cross-attention maps and construct an information entropy loss to gather these scattered attention maps, which eventually captures all the concepts in the generated output. Furthermore, we construct an attribute binding loss that minimizes the distance between the attention maps of the attributes and their corresponding concepts, which enables the model to establish correct semantic mapping and significantly improves the performance of the baseline model. We conduct extensive experiments on public datasets and demonstrate that our method can better capture the semantic information of the input prompts. Code is available at https://github.com/huan085128/Gather-and-Bind.

CiteSpace-based Survey of International Advances in Chinese Traditional Culture Research in 1999-2019

With the globalization strategy and the initiative “One Belt and One Road” in China, Chinese traditional culture has a great impact on the world. This study, based on CiteSpace, explores the total numbers, hotspots, characteristics and tendency of international research on Chinese traditional culture, by retrieving 866 articles from SSCI and A&HCI in the Web of Science (Core Collection). The results of the study revealed that: there is a strong increases in total output from 1999 to 2019 and the research on the theme of Chinese traditional culture has become interdisciplinary; the most productive authors aren’t the ones in the most cited documents and the journals on which the most cited documents publish aren’t the prolific journals; most of the researchers are from Hong Kong, Taiwan, mainland of China, Canada and USA. In view of the results, it is obvious that it is a good way to spread Chinese traditional culture over the world. It is strongly recommended that we should do more analysis on the international literatures with different paradigms, methods and metrics in order to get a more comprehensive and correct domain visualization map of Chinese traditional culture research.

A semi-analytical transient undisturbed velocity correction scheme for wall-bounded two-way coupled Euler-Lagrange simulations

Force closure models employed in Euler-Lagrange (EL) point-particle simulations rely on the accurate estimation of the undisturbed fluid velocity at the particle center to evaluate the fluid forces on each particle. Due to the self-induced velocity disturbance of the particle in the fluid, two-way coupled EL simulations only have access to the disturbed velocity. The undisturbed velocity can be recovered if the particle-generated disturbance is estimated. In the present paper, we model the velocity disturbance generated by a regularized forcing near a planar wall, which, along with the temporal nature of the forcing, provides an estimate of the unsteady velocity disturbance of the particle near a planar wall. We use the analytical solution for a singular in-time transient Stokeslet near a planar wall (Felderhof [1]) and derive the corresponding time-persistent Stokeslets. The velocity disturbance due to a regularized forcing is then obtained numerically via a discrete convolution with the regularization kernel. The resulting Green's functions for parallel and perpendicular regularized forcing to the wall are stored as pre-computed temporal correction maps. By storing the time-dependent particle force on the fluid as fictitious particles, we estimate the unsteady velocity disturbance generated by the particle as a scalar product between the stored forces and the pre-computed Green's functions. Since the model depends on the analytical Green's function solution of the singular Stokeslet near a planar wall, the obtained velocity disturbance exactly satisfies the no-slip condition and does not require any fitted parameters to account for the rapid decay of the disturbance near the wall. The numerical evaluation of the convolution integral makes the present method suitable for arbitrary regularization kernels. Additionally, the generation of parallel and perpendicular correction maps enables to estimate the velocity disturbance due to particle motion in arbitrary directions relative to the local flow. The convergence of the method is studied on a fixed particle near a planar wall, and verification tests are performed in the Stokes regime on a settling particle parallel to a wall and a free-falling particle perpendicular to the wall.

Extending the C/C++ Memory Model with Inline Assembly

Programs written in C/C++ often include inline assembly: a snippet of architecture-specific assembly code used to access low-level functionalities that are impossible or expensive to simulate in the source language. Although inline assembly is widely used, its semantics has not yet been formally studied. In this paper, we overcome this deficiency by investigating the effect of inline assembly on the consistency semantics of C/C++ programs. We propose the first memory model of the C++ Programming Language with support for inline assembly for Intel's x86 including non-temporal stores and store fences. We argue that previous provably correct compiler optimizations and correct compiler mappings should remain correct under such an extended model and we prove that this requirement is met by our proposed model.

3D B1+ corrected simultaneous myocardial T1 and T1ρ mapping with subject-specific respiratory motion correction and water-fat separation.

To develop a 3D free-breathing cardiac multi-parametric mapping framework that is robust to confounders of respiratory motion, fat, and B1+ inhomogeneities and validate it for joint myocardial T1 and T1ρ mapping at 3T. An electrocardiogram-triggered sequence with dual-echo Dixon readout was developed, where nine cardiac cycles were repeatedly acquired with inversion recovery and T1ρ preparation pulses for T1 and T1ρ sensitization. A subject-specific respiratory motion model relating the 1D diaphragmatic navigator to the respiration-induced 3D translational motion of the heart was constructed followed by respiratory motion binning and intra-bin 3D translational and inter-bin non-rigid motion correction. Spin history B1+ inhomogeneities were corrected with optimized dual flip angle strategy. After water-fat separation, the water images were matched to the simulated dictionary for T1 and T1ρ quantification. Phantoms and 10 heathy subjects were imaged to validate the proposed technique. The proposed technique achieved strong correlation (T1: R2 = 0.99; T1ρ: R2 = 0.98) with the reference measurements in phantoms. 3D cardiac T1 and T1ρ maps with spatial resolution of 2 × 2 × 4 mm were obtained with scan time of 5.4 ± 0.5 min, demonstrating comparable T1 (1236 ± 59 ms) and T1ρ (50.2 ± 2.4 ms) measurements to 2D separate breath-hold mapping techniques. The estimated B1+ maps showed spatial variations across the left ventricle with the septal and inferior regions being 10%-25% lower than the anterior and septal regions. The proposed technique achieved efficient 3D joint myocardial T1 and T1ρ mapping at 3T with respiratory motion correction, spin history B1+ correction and water-fat separation.

Drude2019IDPC polarizable force field reveals structure-function relationship of insulin

Intrinsically disordered proteins (IDPs) lack stable tertiary structures under physiological conditions, yet play key roles in biological processes and associated with human complex diseases. Their conformational characteristics and high content of charged residues make the use of polarizable force fields an advantageous for simulating IDPs. The Drude2019IDP polarizable force field, previously introduced, has demonstrated comprehensive enhancements and improvements in dipeptides, short peptides, and IDPs, achieving a balanced sampling between IDPs and structured proteins. However, the performance in simulating 5 dipeptides was found to be underestimate. Therefore, we individually performed reweighting and grid-based energy correction map (CMAP) optimization for these 5 dipeptides, resulting in the enhanced Drude2019IDPC force field. The performance of Drude2019IDPC was evaluated with 5 dipeptides, 5 disordered short peptides, and a representative IDP. The results demonstrated a marked improvement comparing with original Drude2019IDP. To further substantiate the capabilities of Drude2019IDPC, MD simulation and Markov state model (MSM) were applied to wild type and mutant for insulin, to elucidate the difference of conformational characteristics and transition path. The findings reveal that mutation can maintain the monomorphic characteristics, providing insights for engineered insulin development. These results indicate that Drude2019IDPC could be used to reveal the structure-function relationship for other proteins.

High-precision and large-range deflection of light beams with fast steering mirrors.

Fast steering mirrors (FSMs) offer a potential alternative for large-range deflection of light beams. However, for a large-stroke FSM, its pointing precision is unacceptably deteriorated due to the actuator non-uniformity, mechanical axis coupling, and the coupling of line-of-sight (LOS) kinematics. This Letter proposes a comprehensive beam-pointing algorithm by decoupling the LOS kinematic model and establishing a two-dimensional correction mapping to compensate for the non-uniformity and mechanical coupling. Moreover, the incident angle is calibrated by a non-contact method to construct the LOS kinematic model accurately. The experimental results proved that the beam-pointing accuracy can achieve a sub-milliradian level within the square field of regard (FOR) of ±25° horizontally and ±14° vertically. A pointing error of 0.87 mrad can be guaranteed within the horizontal range of -30° to 36° and the vertical range of ±24°. Therefore, the proposed method can achieve high-precision beam pointing in a large FOR and contributes to the miniaturization of optical systems.

Retrospective motion correction for cardiac multi-parametric mapping with dictionary matching-based image synthesis and a low-rank constraint.

To develop a model-based motion correction (MoCo) method that does not need an analytical signal model to improve the quality of cardiac multi-parametric mapping. The proposed method constructs a hybrid loss that includes a dictionary-matching loss and a signal low-rankness loss, where the former registers the multi-contrast original images to a set of motion-free synthetic images and the latter forces the deformed images to be spatiotemporally coherent. We compared the proposed method with non-MoCo, a pairwise registration method (Pairwise-MI), and a groupwise registration method (pTVreg) via a free-breathing Multimapping dataset of 15 healthy subjects, both quantitatively and qualitatively. The proposed method achieved the lowest contour tracking errors (epicardium: 2.00 ± 0.39 mm vs 4.93 ± 2.29 mm, 3.50 ± 1.26 mm, and 2.61 ± 1.00 mm, and endocardium: 1.84 ± 0.34 mm vs 4.93 ± 2.40 mm, 3.43 ± 1.27 mm, and 2.55 ± 1.09 mm for the proposed method, non-MoCo, Pairwise-MI, and pTVreg, respectively; all p < 0.01) and the lowest dictionary matching errors among all methods. The proposed method also achieved the highest scores on the visual quality of mapping (T1: 4.74 ± 0.33 vs 2.91 ± 0.82, 3.58 ± 0.87, and 3.97 ± 1.05, and T2: 4.48 ± 0.56 vs 2.59 ± 0.81, 3.56 ± 0.93, and 4.14 ± 0.80 for the proposed method, non-MoCo, Pairwise-MI, and pTVreg, respectively; all p < 0.01). Finally, the proposed method had similar T1 and T2 mean values and SDs relative to the breath-hold reference in nearly all myocardial segments, whereas all other methods led to significantly different T1 and T2 measures and increases of SDs in multiple segments. The proposed method significantly improves the motion correction accuracy and mapping quality compared with non-MoCo and alternative image-based methods.

Dissipated correction map method with trapezoidal rule for the simulations of gravitational waves from spinning compact binary

ABSTRACT The correction map method means extended phase-space algorithm with correction map. In our research, we have developed a correction map method, specifically the dissipated correction map method with trapezoidal rule, for numerical simulations of gravitational waves from spinning compact binary systems. This new correction map method, denoted as $CM3$, has shown remarkable performance in various simulation results, such as phase-space distance, dissipated energy error, and gravitational waveform, closely resembling the high-order precision implicit Gaussian algorithm. When compared with the previously used mid-point map which is denoted as $C_2$, the $CM3$ consistently exhibits a closer alignment with the highly accurate Gaussian algorithm in waveform evolution and orbital trajectory analysis. Through detailed comparisons and analyses, it is evident that $CM3$ outperforms other algorithms, including $CM2$ and $C_2$ mentioned in this paper, in terms of accuracy and precision in simulating spinning compact binary systems. The incorporation of the trapezoidal rule and the optimization with a scale factor $\gamma$ have significantly enhanced the performance of $CM3$, making it a promising method for future numerical simulations in astrophysics. With the groundbreaking detection of gravitational waves by the LIGO/VIRGO collaboration, interest in this research domain has soared. Our work contributes valuable insights for the application of matched filtering techniques in the analysis of gravitational wave signals, enhancing the precision and reliability of these detection.

A Technological Framework to Support Asthma Patient Adherence Using Pictograms

Background: Low comprehension and adherence to medical treatment among the elderly directly and negatively affect their health. Many elderly patients forget medical instructions immediately after their appointments, misunderstand them, or fail to recall them altogether. Some identified causes include the short time slots allocated for appointments in the public health system in Chile, the complex terminology used by healthcare professionals, and the stress experienced by patients during appointments. One approach to improving patients’ adherence to medical treatment is to combine written and oral instructions with graphical elements such as pictograms. However, several challenges arise due to the ambiguity of natural language and the need for pictograms to accurately represent various medication combinations, doses, and frequencies. Objective: This study introduces SIMAP (System for Integrating Medical Instructions with Pictograms), a technological framework aimed at enhancing adherence among asthma patients through the delivery of pictograms via a computational system. SIMAP utilizes a collaborative and user-centered methodology, involving health professionals and patients in the construction and validation of its components. Methods: The technological framework presented in this study is composed of three parts. The first two are medical indications and pictograms related to the treatment of the disease. Both components were developed through a comprehensive and iterative methodology that incorporates both qualitative and quantitative approaches. This methodology includes the utilization of focus groups, interviews, paper and online surveys, as well as expert validation, ensuring a robust and thorough development. The core of SIMAP is the technological component that leveraged artificial intelligence methods for natural language processing to analyze, tokenize, and associate words and their context to a set of one or more pictograms, addressing issues such as the ambiguity in the text, the cultural factor that involves many ways of expressing the same indication, and typographical errors in the indications. Results: Firstly, we successfully validated 18 clinical indications along with their respective pictograms. Some of the pictograms were redesigned based on the validation results. However, in the final validation, the comprehension percentages of the pictograms exceeded 70%. Furthermore, we developed a software called SIMAP, which translates medical indications into previously validated pictograms. Our proposed software, SIMAP, achieves a correct mapping rate of 96.69%. Conclusions: SIMAP demonstrates great potential as a technological component for supplementing medical instructions with pictograms when tested in a laboratory setting. The use of artificial intelligence for natural language processing can successfully map medical instructions, both structured and unstructured, into pictograms. This integration of textual instructions and pictograms holds promise for enhancing the comprehension and adherence of elderly patients to their medical indications, thereby improving their long-term health.

Detecting outliers in local ionospheric model for GNSS precise positioning

Global Navigation Satellite System fast precise positioning can be achieved with accurate ionospheric corrections computed from an adequate number of GNSS stations in a local region. In low-latitude regions, the presence of electron density gradients over short distances can lead to outliers in the map of ionospheric corrections and decrease its accuracy. In this study, we explored outlier detection in ionospheric correction mapping through statistical residuals during a four-month test in 2021. Our findings indicate that the residuals of the local ionospheric model conform to the Laplace distribution. To determine outliers, we use an empirical rule for the Laplace distribution, setting thresholds at μ ± 3b, μ ± 3.5b, and μ ± 5.8b for data retention rates of 95%, 97%, and 99.7%, respectively. Here, μ represents the location parameter, which corresponds to the median of data, and b is the scale parameter, calculated as the medium absolute deviation. We found that while removing outliers can improve model accuracy, it may result in unavailable prediction due to a lack of data in a spare network. For example, applying a μ ± 3.5b threshold for outlier removal led to approximately 2.5% of recording time having no ionospheric corrections map in low-latitude regions, however, the local model has the potential to improve its mean accuracy by up to 50% for both low and mid-latitudes. Therefore, choosing the appropriate percentile threshold depends on the network configuration and the desired accuracy. Removing erroneous satellite data to improve ionospheric accuracy brings positive impacts on precise positioning.

Refined Bonded Terms in Coarse-Grained Models for Intrinsically Disordered Proteins Improve Backbone Conformations.

Coarse-grained models designed for intrinsically disordered proteins and regions (IDP/Rs) usually omit some bonded potentials (e.g., angular and dihedral potentials) as a conventional strategy to enhance backbone flexibility. However, a notable drawback of this approach is the generation of inaccurate backbone conformations. Here, we addressed this problem by introducing residue-specific angular, refined dihedral, and correction map (CMAP) potentials, derived based on the statistics from a customized coil database. These bonded potentials were integrated into the existing Mpipi model, resulting in a new model, denoted as the "Mpipi+" model. Results show that the Mpipi+ model can improve backbone conformations. More importantly, it can markedly improve the secondary structure propensity (SSP) based on the experimental chemical shift and, consequently, succeed in capturing transient secondary structures. Moreover, the Mpipi+ model preserves the liquid-liquid phase separation (LLPS) propensities of IDPs.

Dual NWP wind speed correction based on trend fusion and fluctuation clustering and its application in short-term wind power prediction

With the continuous growth of grid-connected installed capacity of wind power, the inevitable gap, volatility and randomness of wind power pose challenges to the stability of real-time operation of power system. Accurate wind power prediction (WPP) can effectively ensure the safe and stable operation of power system. At present, the main input of short-term power prediction based on data-driven model is numerical weather prediction (NWP), and its prediction accuracy leads to the failure to effectively improve the accuracy of short-term power prediction. To solve this problem, this paper proposes a dual NWP wind speed (WS) correction method based on trend fusion and fluctuation clustering. First, the WS trend is used to construct a new input feature, and the NWP WS error distribution is determined to establish a more correct and stable mapping relationship with the NWP WS error. Secondly, the error is decomposed by Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and the trend and fluctuation components are defined by Pearson coefficient. The trend curve features in the short 24h period were extracted from the two dimensions of time and value, and the trend and fluctuation curve databases under different scenarios were formed by K-Medoids clustering. Finally, the trend component is modified by the Attention-GRU model, and the corresponding trend component is searched and matched from the database to correct the fluctuation component. The NWP WS is modified by the superposition of the two components and the short-term WPP is made. Two wind farms in Mengxi, China were used for example analysis, and the prediction accuracy was improved by 10 % and 13.1 % respectively, which demonstrated the effectiveness of the proposed method.

Crowdsourcing RTK: a new GNSS positioning framework for building spatial high-resolution atmospheric maps based on massive vehicle GNSS data

High-quality spatial atmospheric delay correction information is essential for achieving fast integer ambiguity resolution (AR) in precise positioning. However, traditional real-time precise positioning frameworks (i.e., NRTK and PPP-RTK) depend on spatial low-resolution atmospheric delay correction through the expensive and sparsely distributed CORS network. This results in limited public appeal. With the mass production of autonomous driving vehicles, more cost-effective and widespread data sources can be explored to create spatial high-resolution atmospheric maps. In this study, we propose a new GNSS positioning framework that relies on dual base stations, massive vehicle GNSS data, and crowdsourced atmospheric delay correction maps (CAM). The map is easily produced and updated by vehicles equipped with GNSS receivers in a crowd-sourced way. Specifically, the map consists of between-station single-differenced ionospheric and tropospheric delays. We introduce the whole framework of CAM initialization for individual vehicles, on-cloud CAM maintenance, and CAM-augmented user-end positioning. The map data are collected and preprocessed in vehicles. Then, the crowdsourced data are uploaded to a cloud server. The massive data from multiple vehicles are merged in the cloud to update the CAM in time. Finally, the CAM will augment the user positioning performance. This framework forms a beneficial cycle where the CAM’s spatial resolution and the user positioning performance mutually improve each other. We validate the performance of the proposed framework in real-world experiments and the applied potency at different spatial scales. We highlight that this framework is a reliable and practical positioning solution that meets the requirements of ubiquitous high-precision positioning.

Detail-preserving noise suppression post-processing for low-light image enhancement

Low-light image enhancement is a challenging task for high-quality image display and visual applications. However, most of the existing methods are difficult to effectively improve brightness and suppress noise. To solve the problem of greatly amplifying noise in many low-light image enhancement methods, this paper presents a detail-preserving noise-suppressing post-processing method for low-light image enhancement. The main idea is that Gamma correction has good performance in noise suppression, by extending the single global parameter of Gamma correction to a Gamma correction map with the same size as the low-light image, we propose an improved domain transform recursive filter to optimize Gamma correction maps, aiming to smooth the Gamma correction map while preserving structural edges, thereby achieving the goal of suppressing noise levels in the enhanced image and preserving the details and contrast of the enhanced image. The proposed post-processing approach addresses the concern of enhancing noise when enhancing low-light images, and the results show that the proposed post-processing method has good detail preservation and noise suppression performance for different advanced low-light image enhancement methods.

LTA‐OM: Long‐term association LiDAR–IMU odometry and mapping

AbstractThis paper focuses on the Light Detection and Ranging (LiDAR)–Inertial Measurement Unit (IMU) simultaneous localization and mapping (SLAM) problem: How to fuse the sensor measurement from the LiDAR and IMU to online estimate robot's poses and build a consistent map of the environment. This paper presents LTA‐OM: an efficient, robust, and accurate LiDAR SLAM system. Employing fast direct LiDAR‐inertial odometry (FAST‐LIO2) and Stable Triangle Descriptor as LiDAR–IMU odometry and the loop detection method, respectively, LTA‐OM is implemented to be functionally complete, including loop detection and correction, false‐positive loop closure rejection, long‐term association (LTA) mapping, and multisession localization and mapping. One novelty of this paper is the real‐time LTA mapping, which exploits the direct scan‐to‐map registration of FAST‐LIO2 and employs the corrected history map to provide direct global constraints to the LIO mapping process. LTA mapping also has the notable advantage of achieving drift‐free odometry at revisit places. Besides, a multisession mode is designed to allow the user to store the current session's results, including the corrected map points, optimized odometry, and descriptor database for future sessions. The benefits of this mode are additional accuracy improvement and consistent map stitching, which is helpful for life‐long mapping. Furthermore, LTA‐OM has valuable features for robot control and path planning, including high‐frequency and real‐time odometry, driftless odometry at revisit places, and fast loop closing convergence. LTA‐OM is versatile as it is applicable to both multiline spinning and solid‐state LiDARs, mobile robots and handheld platforms. In experiments, we exhaustively benchmark LTA‐OM and other state‐of‐the‐art LiDAR systems with 18 data sequences. The results show that LTA‐OM steadily outperforms other systems regarding trajectory accuracy, map consistency, and time consumption. The robustness of LTA‐OM is validated in a challenging scene—a multilevel building having similar structures at different levels. To demonstrate our system, we created a video which can be found on https://youtu.be/DVwppEKlKps.

The internal transcribed spacer 1 sequence polymorphism brings updates to tsetse species distribution in the northern Cameroon: Importance in planning efficient vector control.

Vector control remains one of the best strategies to prevent the transmission of trypanosome infections in humans and livestock and, thus, a good way to achieve the elimination of human African trypanosomiasis and animal African trypanosomiasis. A key prerequisite for the success of any vector control strategy is the accurate identification and correct mapping of tsetse species. In this work, we updated the tsetse fly species identification and distribution in many geographical areas in Cameroon. Tsetse flies were captured from six localities in Cameroon, and their species were morphologically identified. Thereafter, DNA was extracted from legs of each tsetse fly and the length polymorphism of internal transcribed spacer-1 (ITS1) region of each fly was investigated using PCR. ITS1 DNA fragments of each tsetse species were sequenced. The sequences obtained were analysed and compared to those available in GenBank. This enabled to confirm/infirm results of the morphologic identification and then, to establish the phylogenetic relationships between tsetse species. Morphologic features allowed to clearly distinguish all the tsetse species captured in the South Region of Cameroon, that is, Glossina palpalis palpalis, G. pallicera, G. caliginea and G. nigrofusca. In the northern area, G. morsitans submorsitans could also be distinguished from G. palpalis palpalis, G. tachinoides and G. fuscipes, but these three later could not be distinguished with routine morphological characters. The ITS1 length polymorphism was high among most of the studied species and allowed to identify the following similar species with a single PCR, that is, G. palpalis palpalis with 241 or 242 bp and G. tachinoides with 221 or 222 bp, G. fuscipes with 236 or 237 bp. We also updated the old distribution of tsetse species in the areas assessed, highlighting the presence of G. palpalis palpalis instead of G. fuscipes in Mbakaou, or in sympatry with G. morsitans submorsitans in Dodeo (northern Cameroon). This study confirms the presence of G. palpalis palpalis in the Adamawa Region of Cameroon. It highlights the limits of using morphological criteria to differentiate some tsetse species. Molecular tools based on the polymorphism of ITS1 of tsetse flies can differentiate tsetse species through a simple PCR before downstream analyses or vector control planning.

Merger-tree-based Galaxy Matching: A Comparative Study across Different Resolutions

We introduce a novel halo/galaxy matching technique between two cosmological simulations with different resolutions, which utilizes the positions and masses of halos along their subhalo merger tree. With this tool, we conduct a study of resolution biases through the galaxy-by-galaxy inspection of a pair of simulations that have the same simulation configuration but different mass resolutions, utilizing a suite of IllustrisTNG simulations to assess the impact on galaxy properties. We find that, with the subgrid physics model calibrated for TNG100-1, subhalos in TNG100-1 (high resolution) have ≲0.5 dex higher stellar masses than their counterparts in the TNG100-2 (low resolution). It is also discovered that the subhalos with M gas ∼ 108.5 M ⊙ in TNG100-1 have ∼0.5 dex higher gas mass than those in TNG100-2. The mass profiles of the subhalos reveal that the dark matter masses of subhalos in TNG100-2 converge well with those from TNG100-1, except within 4 kpc of the resolution limit. The differences in stellar mass and hot gas mass are most pronounced in the central region. We exploit machine learning to build a correction mapping for the physical quantities of subhalos from low- to high-resolution simulations (TNG300-1 and TNG100-1), which enables us to find an efficient way to compile a high-resolution galaxy catalog even from a low-resolution simulation. Our tools can easily be applied to other large cosmological simulations, testing and mitigating the resolution biases of their numerical codes and subgrid physics models.

PETS-Nets: Joint Pose Estimation and Tissue Segmentation of Fetal Brains Using Anatomy-Guided Networks.

Fetal Magnetic Resonance Imaging (MRI) is challenged by fetal movements and maternal breathing. Although fast MRI sequences allow artifact free acquisition of individual 2D slices, motion frequently occurs in the acquisition of spatially adjacent slices. Motion correction for each slice is thus critical for the reconstruction of 3D fetal brain MRI. In this paper, we propose a novel multi-task learning framework that adopts a coarse-to-fine strategy to jointly learn the pose estimation parameters for motion correction and tissue segmentation map of each slice in fetal MRI. Particularly, we design a regression-based segmentation loss as a deep supervision to learn anatomically more meaningful features for pose estimation and segmentation. In the coarse stage, a U-Net-like network learns the features shared for both tasks. In the refinement stage, to fully utilize the anatomical information, signed distance maps constructed from the coarse segmentation are introduced to guide the feature learning for both tasks. Finally, iterative incorporation of the signed distance maps further improves the performance of both regression and segmentation progressively. Experimental results of cross-validation across two different fetal datasets acquired with different scanners and imaging protocols demonstrate the effectiveness of the proposed method in reducing the pose estimation error and obtaining superior tissue segmentation results simultaneously, compared with state-of-the-art methods.

Application of a novel geometric seismic attribute for enhancing fault visualization in areas of potential carbon capture and storage

Seismic fault interpretation is a critical task for any type of energy industry. Correct fault mapping can be crucial for the success of a project. Common geometric seismic attributes, such as coherence and curvature, are routinely employed to enhance fault visualization in seismic data. However, they can show limitations for subseismic faulting. In this study, we highlight the usefulness of including novel aberrancy attributes for fault identification in multiattribute analysis and unsupervised machine learning (ML) techniques. We compare broadband coherence, curvature, multispectral coherence, and aberrancy when trying to map faults in a potential CO2 storage location. We also compare self-organizing maps and generative topographic mapping techniques when including and excluding aberrancy attributes. Our results show that integrating aberrancy attributes during multiattribute analysis and ML steps considerably enhanced the visualization of lineaments with strikes similar to those of fracture sets seen only with well-log data and that were not clearly captured by the conventional seismic attributes and ML scenarios excluding aberrancy attributes. We demonstrate the potential of these novel geometric seismic attributes to map subseismic faults. We also provide an example that can encourage interpreters to include them in their interpretation workflows.