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User-level localization outputs derived from GNSS often exhibit residual localization drift in dense urban environments where signal degradation coexists with legitimate dynamic movements, creating ambiguity between localization errors and true motion. To address this post-fusion, coordinate-level issue, we propose a threshold-agnostic drift identification paradigm using a hybrid BERT–LSTM architecture. The method synergizes Attention’s contextual awareness for spatiotemporal pattern extraction from 30-step sequences with LSTM’s sequential dynamics modeling, eliminating reliance on empirical kinematic thresholds. Validation across 654,855 real-world points from consumer-device deployments demonstrates a 92.2% F1-score in drift-detection accuracy, surpassing pure BERT by 5.9% and LSTM baselines by 18.0%, while reducing false positives by 38.7% versus standalone transformers. The framework successfully preserves trajectory integrity amid rapid transitions where residual coordinate drift coexists with legitimate dynamics, overcoming limitations of context-insensitive Transformers and limited-context LSTMs. The approach operates directly on post-fusion coordinate sequences rather than raw GNSS observations. This study establishes a robust foundation for reliable user-level localization assessment in challenging environments where multipath effects and dynamic movements interact.

Many everyday activities require coordination and monitoring of multiple deadlines. One way to handle these temporal demands might be to represent future goals and deadlines as a pattern of spatial relations. We examined the hypothesis that spatial ability, in addition to executive functioning, contributes to individual differences in multitasking. In two studies, participants completed a multitasking session in which they monitored four digital clocks running at different rates. In Study 1, we found that individual differences in spatial ability and executive functions were independent predictors of multiple-task performance. In Study 2, we found that individual differences in specific spatial abilities were selectively related to multiple-task performance, as only coordinate spatial processing, but not categorical, predicted multitasking, even beyond executive functioning and numeracy. In both studies, males outperformed females in spatial ability and multitasking and in Study 2 these sex differences generalized to a simulation of everyday multitasking. Menstrual changes moderated the effects on multitasking, in that sex differences in coordinate spatial processing and multitasking were observed between males and females in the luteal phase of the menstrual cycle, but not between males and females at menses. Overall, these findings suggest that multiple-task performance reflects independent contributions of spatial ability and executive functioning. Furthermore, our results support the distinction of categorical versus coordinate spatial processing, and suggest that these two basic relational processes are selectively affected by female sex hormones and differentially effective in transforming and handling temporal patterns as spatial relations in the context of multitasking.

Goal-directed movements require mapping of target information to patterns of muscular activation. While visually acquired information about targets is initially encoded in extrinsic, object-centered coordinates, muscular activation patterns are encoded in intrinsic, body-related coordinates. Intermanual transfer of movements previously learned with one hand is accomplished by the recall of unmodified extrinsic coordinates if the task is performed in original orientation. Intrinsic coordinates are retrieved in case of mirror-reversed orientation. In contrast, learned extrinsic coordinates are modified during the mirror movement and intrinsic coordinates during the originally oriented task. To investigate the neural processes of recall and modification, electroencephalogram (EEG) recording was employed during the performance of a figure drawing task previously trained with the right hand in humans. The figure was reproduced with the right hand (Learned-task) and with the left hand in original (Normal-task) and mirror orientations (Mirror-task). Prior to movement onset, beta-power and alpha- and beta-coherence decreased during the Normal-task as compared with the Learned-task. Negative amplitudes over fronto-central sites during the Normal-task exceeded amplitudes manifested during the Learned-task. In comparison to the Learned-task, coherences between fronto-parietal sites increased during the Mirror-task. Results indicate that intrinsic coordinates are processed during the pre-movement period. During the Normal-task, modification of intrinsic coordinates was revealed by cerebral activation. Decreased coherences appeared to reflect suppressed inter-regional information flow associated with utilization of intrinsic coordinates. During the Mirror-task, modification of extrinsic coordinates induced activation of cortical networks.

Antarctica, abundant in resources and of exceptional scientific value, is a focus of research. Unmanned aerial vehicle (UAV) photogrammetry, as a low-cost and efficient method for geographic information acquisition, plays an important role in Antarctic studies. However, due to the unique conditions of Antarctica, conventional UAV photogrammetry processing methods are inadequate, and relevant research in this area is limited. This paper uses a photogrammetric coordinate system for the Antarctic environment and derives the conversion formulas between geodetic coordinates and the coordinates. This paper also discusses methods for image matching to accelerate image retrieval speed and improve retrieval efficiency in the harsh natural conditions in Antarctica. The results of photogrammetric production using real data confirm that the proposed planar coordinate system is suitable for Antarctic image processing, and the proposed retrieval method effectively addresses the challenges in Antarctic image matching. This provides valuable insights for UAV photogrammetry research in Antarctica.

The human posterior cingulate, retrosplenial, and medial parietal cortex are involved in memory and navigation. The functional anatomy underlying these cognitive functions was investigated by measuring the effective connectivity of these Posterior Cingulate Division (PCD) regions in the Human Connectome Project‐MMP1 atlas in 171 HCP participants, and complemented with functional connectivity and diffusion tractography. First, the postero‐ventral parts of the PCD (31pd, 31pv, 7m, d23ab, and v23ab) have effective connectivity with the temporal pole, inferior temporal visual cortex, cortex in the superior temporal sulcus implicated in auditory and semantic processing, with the reward‐related vmPFC and pregenual anterior cingulate cortex, with the inferior parietal cortex, and with the hippocampal system. This connectivity implicates it in hippocampal episodic memory, providing routes for “what,” reward and semantic schema‐related information to access the hippocampus. Second, the antero‐dorsal parts of the PCD (especially 31a and 23d, PCV, and also RSC) have connectivity with early visual cortical areas including those that represent spatial scenes, with the superior parietal cortex, with the pregenual anterior cingulate cortex, and with the hippocampal system. This connectivity implicates it in the “where” component for hippocampal episodic memory and for spatial navigation. The dorsal–transitional–visual (DVT) and ProStriate regions where the retrosplenial scene area is located have connectivity from early visual cortical areas to the parahippocampal scene area, providing a ventromedial route for spatial scene information to reach the hippocampus. These connectivities provide important routes for “what,” reward, and “where” scene‐related information for human hippocampal episodic memory and navigation. The midcingulate cortex provides a route from the anterior dorsal parts of the PCD and the supracallosal part of the anterior cingulate cortex to premotor regions. First, the postero‐ventral parts of the Posterior Cingulate Division of the Human Connectome Project Multimodal Parcellation atlas (PCD; 31pd, 31pv, 7m, d23ab, and v23ab) have effective connectivity with the temporal pole, inferior temporal visual cortex, cortex in the superior temporal sulcus implicated in auditory and semantic processing, with the reward‐related vmPFC and pregenual anterior cingulate cortex, with the inferior parietal cortex, and with the hippocampal system; and this connectivity implicates these regions in hippocampal episodic memory, providing routes for “what,” reward, and semantic schema‐related information to access the hippocampus. Second, the antero‐dorsal parts of the PCD (especially 31a and 23d, PCV, and also RSC) have connectivity with early visual cortical areas including those that represent spatial scenes, with the superior parietal cortex, with the pregenual anterior cingulate cortex, and with the hippocampal system. This connectivity implicates them in the “where” component for hippocampal episodic memory and for spatial navigation. Third, the dorsal–transitional–visual (DVT) and ProStriate regions are where the retrosplenial scene area is located, have connectivity from early cortical visual areas, connect to the parahippocampal scene area, and provide a ventromedial cortex route for scene information to reach the hippocampal system.

Image processing plays a vital role in artificial visual systems, which have diverse applications in areas such as biomedical imaging and machine vision. In particular, optical analog image processing is of great interest because of its parallel processing capability and low power consumption. Here, we present ultra-compact metasurfaces performing all-optical geometric image transformations, which are essential for image processing to correct image distortions, create special image effects, and morph one image into another. We show that our metasurfaces can realize binary image transformations by modifying the spatial relationship between pixels and converting binary images from Cartesian to log-polar coordinates with unparalleled advantages for scale- and rotation-invariant image preprocessing. Furthermore, we extend our approach to grayscale image transformations and convert an image with Gaussian intensity profile into another image with flat-top intensity profile. Our technique will potentially unlock new opportunities for various applications such as target tracking and laser manufacturing. Metasurfaces enable all-optical geometric coordinate transformations, converting images with altered pixel spatial relations, which can facilitate fast, energy-efficient preprocessing for tasks like object tracking, or aid in laser manufacturing.

The concept of coding metasurface makes a link between physically metamaterial particles and digital codes, and hence it is possible to perform digital signal processing on the coding metasurface to realize unusual physical phenomena. Here, this study presents to perform Fourier operations on coding metasurfaces and proposes a principle called as scattering‐pattern shift using the convolution theorem, which allows steering of the scattering pattern to an arbitrarily predesigned direction. Owing to the constant reflection amplitude of coding particles, the required coding pattern can be simply achieved by the modulus of two coding matrices. This study demonstrates that the scattering patterns that are directly calculated from the coding pattern using the Fourier transform have excellent agreements to the numerical simulations based on realistic coding structures, providing an efficient method in optimizing coding patterns to achieve predesigned scattering beams. The most important advantage of this approach over the previous schemes in producing anomalous single‐beam scattering is its flexible and continuous controls to arbitrary directions. This work opens a new route to study metamaterial from a fully digital perspective, predicting the possibility of combining conventional theorems in digital signal processing with the coding metasurface to realize more powerful manipulations of electromagnetic waves. Convolutions are operated on 2‐bit coding metasurfaces to reach the steering of scattering pattern to an arbitrarily predesigned direction. The radiation angle can be continuously designed in the entire upper‐half space by simply combining two or multiple gradient coding sequences from a 2‐bit coding metasurface which has only four different coding digits.

Underwater object detection is a key technology in the development of intelligent underwater vehicles. Object detection faces unique challenges in underwater applications: blurry underwater images; small and dense targets; and limited computational capacity available on the deployed platforms. To improve the performance of underwater object detection, we proposed a new object detection approach that combines a new detection neural network called TC-YOLO, an image enhancement technique using an adaptive histogram equalization algorithm, and the optimal transport scheme for label assignment. The proposed TC-YOLO network was developed based on YOLOv5s. Transformer self-attention and coordinate attention were adopted in the backbone and neck of the new network, respectively, to enhance feature extraction for underwater objects. The application of optimal transport label assignment enables a significant reduction in the number of fuzzy boxes and improves the utilization of training data. Our tests using the RUIE2020 dataset and ablation experiments demonstrate that the proposed approach performs better than the original YOLOv5s and other similar networks for underwater object detection tasks; moreover, the size and computational cost of the proposed model remain small for underwater mobile applications.

Coordinate-based meta-analyses (CBMA) are very useful for summarizing the large number of voxel-based neuroimaging studies of normal brain functions and brain abnormalities in neuropsychiatric disorders. However, current CBMA methods do not conduct common voxelwise tests, but rather a test of convergence, which relies on some spatial assumptions that data may seldom meet, and has lower statistical power when there are multiple effects. Here we present a new algorithm that can use standard voxelwise tests and, importantly, conducts a standard permutation of subject images (PSI). Its main steps are: a) multiple imputation of study images; b) imputation of subject images; and c) subject-based permutation test to control the familywise error rate (FWER). The PSI algorithm is general and we believe that developers might implement it for several CBMA methods. We present here an implementation of PSI for seed-based d mapping (SDM) method, which additionally benefits from the use of effect sizes, random-effects models, Freedman-Lane-based permutations and threshold-free cluster enhancement (TFCE) statistics, among others. Finally, we also provide an empirical validation of the control of the FWER in SDM-PSI, which showed that it might be too conservative. We hope that the neuroimaging meta-analytic community will welcome this new algorithm and method. •We present a new algorithm for coordinate-based meta-analyses (CBMA) methods.•Opposed to current methods, it conducts common permutation tests.•It may be implemented in several CBMA methods.•We detail and validate its implementation for seed-based d mapping (SDM).

This paper is conceived as a tutorial on rotation averaging, summarizing the research that has been carried out in this area; it discusses methods for single-view and multiple-view rotation averaging, as well as providing proofs of convergence and convexity in many cases. However, at the same time it contains many new results, which were developed to fill gaps in knowledge, answering fundamental questions such as radius of convergence of the algorithms, and existence of local minima. These matters, or even proofs of correctness have in many cases not been considered in the Computer Vision literature. We consider three main problems: single rotation averaging, in which a single rotation is computed starting from several measurements; multiple-rotation averaging, in which absolute orientations are computed from several relative orientation measurements; and conjugate rotation averaging, which relates a pair of coordinate frames. This last is related to the hand-eye coordination problem and to multiple-camera calibration.