Spatial View Research Articles

Imaging the large-scale and cellular response to focal traumatic brain injury in mouse neocortex.

Traumatic brain injury (TBI) affects neural function at the local injury site and also at distant, connected brain areas. However, the real-time neural dynamics in response to injury and subsequent effects on sensory processing and behavior are not fully resolved, especially across a range of spatial scales. We used in vivo calcium imaging in awake, head-restrained male and female mice to measure large-scale and cellular resolution neuronal activation, respectively, in response to a mild/moderate TBI induced by focal controlled cortical impact (CCI) injury of the motor cortex (M1). Widefield imaging revealed an immediate CCI-induced activation at the injury site, followed by a massive slow wave of calcium signal activation that traveled across the majority of the dorsal cortex within approximately 30 s. Correspondingly, two-photon calcium imaging in primary somatosensory cortex (S1) found strong activation of neuropil and neuronal populations during the CCI-induced traveling wave. A depression of calcium signals followed the wave, during which we observed atypical activity of a sparse population of S1 neurons. Longitudinal imaging in the hours and days after CCI revealed increases in the area of whisker-evoked sensory maps at early time points, in parallel to decreases in cortical functional connectivity and behavioral measures. Neural and behavioral changes mostly recovered over hours to days in our M1-TBI model, with a more lasting decrease in the number of active S1 neurons. Our results in unanesthetized mice describe novel spatial and temporal neural adaptations that occur at cortical sites remote to a focal brain injury.

Multiscale spatial‐temporal transformer with consistency representation learning for multivariate time series classification

SummaryMultivariate time series classification holds significant importance in fields such as healthcare, energy management, and industrial manufacturing. Existing research focuses on capturing temporal changes or calculating time similarities to accomplish classification tasks. However, as the state of the system changes, capturing spatial‐temporal consistency within multivariate time series is key to the ability of the model to classify accurately. This paper proposes the MSTformer model, specifically designed for multivariate time series classification tasks. Based on the Transformer architecture, this model uniquely focuses on multiscale information across both time and feature dimensions. The encoder, through a designed learnable multiscale attention mechanism, divides data into sequences of varying temporal scales to learn multiscale temporal features. The decoder, which receives the spatial view of the data, utilizes a dynamic scale attention mechanism to learn spatial‐temporal consistency in a one‐dimensional space. In addition, this paper proposes an adaptive aggregation mechanism to synchronize and combine the outputs of the encoder and decoder. It also introduces a multiscale 2D separable convolution designed to learn spatial‐temporal consistency in two‐dimensional space, enhancing the ability of the model to learn spatial‐temporal consistency representation. Extensive experiments were conducted on 30 datasets, where the MSTformer outperformed other models with an average accuracy rate of 85.6%. Ablation studies further demonstrate the reliability and stability of MSTformer.

Unraveling the spatial organization and development of human thymocytes through integration of spatial transcriptomics and single-cell multi-omics profiling

The structural components of the thymus are essential for guiding T cell development, but a thorough spatial view is still absent. Here we develop the TSO-his tool, designed to integrate multimodal data from single-cell and spatial transcriptomics to decipher the intricate structure of human thymus. Specifically, we characterize dynamic changes in cell types and critical markers, identifying ELOVL4 as a mediator of CD4+ T cell positive selection in the cortex. Utilizing the mapping function of TSO-his, we reconstruct thymic spatial architecture at single-cell resolution and recapitulates classical cell types and their essential co-localization for T cell development; additionally, previously unknown co-localization relationships such as that of CD8αα with memory B cells and monocytes are identified. Incorporating VDJ sequencing data, we also delineate distinct intermediate thymocyte states during αβ T cell development. Overall, these insights enhance our understanding of thymic biology and may inform therapeutic interventions targeting T cell-mediated immune responses.

The Spatial View

The Spatial View

SFormer: An end-to-end spatio-temporal transformer architecture for deepfake detection

Growing AI advancements are continuously pacing GAN enhancement that eventually facilitates the generation of deepfake media. Manipulated media poses serious risks pertaining court proceedings, journalism, politics, and many more where digital media have a substantial impact on society. State-of-the-art techniques for deepfake detection rely on convolutional networks for spatial analysis, and recurrent networks for temporal analysis. Since transformers are capable of recognizing wide-range dependencies with a global spatial view and along temporal sequence too, a novel approach called “SFormer” is proposed in this paper, utilizing a transformer architecture for both spatial and temporal analysis to detect deepfakes. Further, state-of-the-art techniques suffer from high computational complexity and overfitting which causes loss in generalizability. The proposed model utilized a Swin Transformer for spatial analysis that resulted in low complexity, thereby enhancing its generalization ability and robustness against the different manipulation types. Proposed end-to-end transformer based model, SFormer, is proven to be effective for numerous deepfake datasets, including FF++, DFD, Celeb-DF, DFDC and Deeper-Forensics, and achieved an accuracy of 100%, 97.81%, 99.1%, 93.67% and 100% respectively. Moreover, SFormer has demonstrated superior performance compared to existing spatio-temporal and transformer-based approaches for deepfake detection.

A ventromedial visual cortical ‘Where’ stream to the human hippocampus for spatial scenes revealed with magnetoencephalography

The primate including the human hippocampus implicated in episodic memory and navigation represents a spatial view, very different from the place representations in rodents. To understand this system in humans, and the computations performed, the pathway for this spatial view information to reach the hippocampus was analysed in humans. Whole-brain effective connectivity was measured with magnetoencephalography between 30 visual cortical regions and 150 other cortical regions using the HCP-MMP1 atlas in 21 participants while performing a 0-back scene memory task. In a ventromedial visual stream, V1–V4 connect to the ProStriate region where the retrosplenial scene area is located. The ProStriate region has connectivity to ventromedial visual regions VMV1–3 and VVC. These ventromedial regions connect to the medial parahippocampal region PHA1–3, which, with the VMV regions, include the parahippocampal scene area. The medial parahippocampal regions have effective connectivity to the entorhinal cortex, perirhinal cortex, and hippocampus. In contrast, when viewing faces, the effective connectivity was more through a ventrolateral visual cortical stream via the fusiform face cortex to the inferior temporal visual cortex regions TE2p and TE2a. A ventromedial visual cortical ‘Where’ stream to the hippocampus for spatial scenes was supported by diffusion topography in 171 HCP participants at 7 T.

Anticipatory declarations in obstetric care: a relational and spatial examination of patient empowerment, institutional impacts and temporal challenges.

Seeking an anticipatory declaration from the Court of Protection (CoP) to manage a risk of future loss of capacity in pregnant people during labour and delivery appears to be occurring more frequently. This article examines a growing case sample of recent CoP judgments in which anticipatory declarations have been sought and adopts a combined relational and spatial approach to question whether these types of anticipatory declarations empower patient autonomous choice, and to illuminate the complex web of relational, spatial, and temporal factors that hold influence over the way in which mental capacity law operates. Viewing such processes from both a patient and institutional perspective offers useful insights into the law's normative workings, boundaries, and constraints, and ultimately points to conclusions on the (in)effectiveness of anticipatory declarations as a legal mechanism for dealing with the risk of a patient losing capacity in the future. Moreover, however, taking a broader, spatial view signals the challenges posed by these cases to mental capacity legislation itself. The justifiability of the binary construct of capacity/incapacity has been challenged by some writers in this field, and this article offers further reflection on the integrity of this binary through its discussion of anticipatory orders for pregnant people.

Revisiting Bodies in Interior Design Practice

Addressing human bodies is inevitable in interior inquiries and practices. Integrating knowledge about human bodies into design practice can extend beyond the idea of bodies as merely static entities with fixed dimensions. The idea of bodies as the basis for spatial design practice views the human body as a living entity that dwells, occupies, moves, and engages in space. Knowledge about bodies as the basis of practice also needs to view bodies' presence as sociocultural entities. The emergence of the virtual environment provides another context with a shifting materiality of bodies and interior spaces. This issue of Interiority acknowledges the breadth of knowledge about bodies in spatial design disciplines. The articles demonstrate various ideas that reflect some challenges surrounding the integration of knowledge about bodies into design practice.

Global cloud optical depth daily variability based on DSCOVR/ EPIC observations

When investigating the potential effects of cloud changes on climate, the interday and intraday variabilities should be distinguished. Historically, studies have focused on the long-term cloud changes, and the intraday cycles of cloud properties have been mostly ignored partly owing to the limited availability of global datasets to study higher frequency variabilities. In this regard, DSCOVR’s vantage point at the Lagrange L1 point overcomes the temporal limitations of polar orbiters as well as the limited spatial views of geostationary satellites, allowing characterization of the daytime variability of cloud properties using a single sensor. In previous analyses, we used DSCOVR’s EPIC instrument to characterize the diurnal cycles of cloud height and cloud fraction; here, we expand on previous studies to additionally investigate the variability of cloud optical thickness. We observe a recurring diurnal pattern of cloud optical thicknesses for different latitudinal zones that reaches a maximum around noon regardless of the underlying surface. Once we separate the clouds based on their optical thickness into optically thin (0–3), intermediate (3–10), and thick (10–25), we find that these cloud classes follow different optical thickness diurnal cycles on the global scale. We further explore these differences by combining the evolution of cloud fraction and optical thickness of each group to obtain the diurnal evolution of cloud-fraction-weighted optical depths.

Primacy of vision shapes behavioral strategies and neural substrates of spatial navigation in marmoset hippocampus

The role of the hippocampus in spatial navigation has been primarily studied in nocturnal mammals, such as rats, that lack many adaptations for daylight vision. Here we demonstrate that during 3D navigation, the common marmoset, a new world primate adapted to daylight, predominantly uses rapid head-gaze shifts for visual exploration while remaining stationary. During active locomotion marmosets stabilize the head, in contrast to rats that use low-velocity head movements to scan the environment as they locomote. Pyramidal neurons in the marmoset hippocampus CA3/CA1 regions predominantly show mixed selectivity for 3D spatial view, head direction, and place. Exclusive place selectivity is scarce. Inhibitory interneurons are predominantly mixed selective for angular head velocity and translation speed. Finally, we found theta phase resetting of local field potential oscillations triggered by head-gaze shifts. Our findings indicate that marmosets adapted to their daylight ecological niche by modifying exploration/navigation strategies and their corresponding hippocampal specializations.

SIAM: A parameter-free, Spatial Intersection Attention Module

Attention mechanisms have been shown to play a crucial role in enhancing visual perception tasks. However, in most existing approaches, channel and spatial attention maps are estimated separately without considering the varying importance of each other. This results in a coarse attention weight for objects of interest from a holistic 3-D perspective. To address this issue, we propose a novel Parameter-free Spatial Intersection Attention Module (SIAM), which estimates 3D attention maps with spatial intersection using a parameter-free way. Specifically, SIAM first generates two independent mean queries from two spatial axes and views input as keys. Then, by computing a dot product between these mean queries and keys, SIAM generates two cross-dimension (channel and spatial) attention maps from two spatial directions and combines them into 3-D attention maps. By doing so, the produced attention maps reason important areas with spatial intersection, which can capture location-aware information to facilitate difficult objects’ location in the images. We evaluate our method in image classification, object detection, and object segmentation tasks. Extensive experimental results consistently demonstrate our approach is superior to its counterparts.

Object classification with aggregating multiple spatial views using a machine-learning approach

Abstract The article proposes a solution for object classification using multiple views generated from 3D data rendering and convolutional neural networks. For presentation purposes and easier verification of the solution, an application was developed to create views of 3D objects, classify them using the selected CNN, and evaluate the performance of the CNN. The evaluation is based on metrics and characteristics described in the article. Seven testing objects were used to verify the proposed solution; five CNNs were tested for each.

Multi-geometry embedded transformer for facial expression recognition in videos

Dynamic facial expressions in videos express more realistic emotional states, and recognizing emotions from in-the-wild facial expression videos is a challenging task due to the changeable posture, partial occlusion and various light conditions. Although current methods have designed transformer-based models to learn spatial–temporal features, they cannot explore useful local geometry structures from both spatial and temporal views to capture subtle emotional features for the videos with varied poses and facial occlusion. To this end, we propose a novel multi-geometry embedded transformer (MGET), which adapts multi-geometry knowledge into transformers and excavates spatial–temporal geometry information as complementary to learn effective emotional features. Specifically, from a new perspective, we first design a multi-geometry distance learning (MGDL) to capture emotion-related geometry structure knowledge under Euclidean and Hyperbolic spaces. Especially based on the advantages of hyperbolic geometry, it finds the more subtle emotional changes among local spatial and temporal features. Secondly, we combine MGDL with transformer to design spatial–temporal MGETs, which capture important spatial and temporal multi-geometry features to embed them into their corresponding original features, and then perform cross-regions and cross-frame interaction on these multi-level features. Finally, MGET gains superior performance on DFEW, FERV39k and AFEW datasets, where the unweighted average recall (UAR) and weighted average recall (WAR) are 58.65%/69.91%, 41.91%/50.76% and 53.23%/55.40%, respectively, and the gained improvements are 2.55%/0.66%, 3.69%/2.63% and 3.66%/1.14% compared to M3DFEL, Logo-Forme and EST methods.

Content-aware Nakagami morphing for incremental brain MRI

Within the carcinogenesis mechanism, from the initiation of the very first tumor cell to the preneoplastic and neoplastic cancer cell groups, cancer cells omnidirectionally and unpredictably proliferate in three-dimensional (3D) space during the promotion and progression steps. Whole tumors areas, consisting of edema, necrosis, enhancing and non-enhancing tumor subareas, could easily be identified by the typical sequences of magnetic resonance imaging (MRI), mostly by FLAIR, the fluid attenuated inversion recovery sequence. Depending on the increment value of the scanner, not every scan could be contiguous though, which might create some gaps between the slices and cause some crucial loss of information. Shape-based morphing by erosion and dilation operations might be inadequate when the source or target lesions are not found on the MRI slices, which might emerge due to unpredictable shape of tumors. Furthermore, the real trajectories and genuine shapes of the tumors in spatial view might not be seen in the planar MRI slices due to approximations during the voxel-to-pixel transformations executed in MRI devices. Therefore, we propose a novel content-aware morphing procedure to generate imaginary slices by m-parametric Nakagami imaging. The mathematical and parametric design in our framework not only leads to precise segmentation of the whole tumors according to the ground truth images; but also, to realistic morphing, based on the content of tumor footprints in MRI slices. For testing purposes, a total number of 5133 morphs are created using 1831 genuine images containing at least one lesion taken from the Brats 2012 dataset. Given the average dice score coefficients (DSC), we obtained an average of 88.44% DSC with our morphing framework outperforming the conventional dilation-based morphing result which is 83.25%. Besides being a novel morphing methodology, the outcomes of this research could also be very beneficial in smoother 3D reconstruction and more realistic volumetric calculations of lesions to help the experts working on assessment of lesions.

MVSTT: A Multiview Spatial-Temporal Transformer Network for Traffic-Flow Forecasting.

Accurate traffic-flow prediction remains a critical challenge due to complicated spatial dependencies, temporal factors, and unpredictable events. Most existing approaches focus on single-or dual-view learning and thus face limitations in systematically learning complex spatial-temporal features. In this work, we propose a novel multiview spatial-temporal transformer (MVSTT) network that can effectively learn complex spatial-temporal domain correlations and potential patterns from multiple views. First, we examine a temporal view and design a short-range gated convolution component from a short-term subview, and a long-range gated convolution component from a long-term subview. These two components effectively aggregate knowledge of the temporal domain at multiple granularities and mine patterns of node evolution across time steps. Meanwhile, in the spatial view, we design a dual-graph spatial learning module that captures fixed and dynamic spatial dependencies of nodes, as well as the evolution patterns of edges, from the static and dynamic graph subviews, respectively. In addition, we further design a spatial-temporal transformer to mine different levels of spatial-temporal features through multiview knowledge fusion. Extensive experiments on four real-world traffic datasets show that our method consistently outperforms the state-of-the-art baseline. The code of MVSTT is available at https://github.com/JianSoL/MVSTT.

Weakly Supervised Monocular 3D Object Detection by Spatial-Temporal View Consistency.

Monocular 3D object detection plays a crucial role In the field of self-driving cars, estimating the size and location of objects solely based on input images. However, a notable disparity exists between the training and inference of 3D object detectors. This discrepancy arises because during inference, monocular 3D detectors depend solely on images captured by cameras; while during training, these methods require 3D ground truths labeled on point cloud data, which is obtained using specialized devices like LiDAR. This discrepancy creates a break in the data loop, preventing the feedback data from production cars from being utilized to enhance the robustness of the detectors. To address this issue and establish a connection in the data loop, we present a weakly-supervised solution that trains monocular 3D object detectors solely using 2D labels, eliminating the requirement for 3D ground truths. Our approach considers two view consistency: spatial and temporal view consistency, which play a crucial role in regulating the prediction of 3D bounding boxes. Spatial view consistency is achieved by employing projection and multi-view consistency techniques to guide the optimization of the target's location and size. We leverage temporal viewpoint consistency to provide temporal multi-view image pairs, and we further introduce temporal movement consistency to tackle the challenge of dynamic scenes. With only 2D ground truths, our method achieves comparable performance to fully supervised methods. Additionally, our method can be employed as a pre-training method and achieves significant improvement when fine-tuned with a small proportion of fully supervised labels.

Trends in spatial planning of cities

The global trends of the modern development of urban planning activities are directly related to the theoretical and practical foundations of spatial planning of territories. In Russia today, the acute question is the interrelationships and contradictions of spatial and territorial views on the development of cities. There a lot of official discussions of the prospects for the implementation of such documents as a strategy or concept of spatial development in the professional research and scientific design community. The questions of master plan for the development of the territory, a comprehensive scheme for creating a comfortable environment are increasingly being raised also. The necessity and opportunity of replacing the master plan of the city with a spatial planning (or their symbiosis) is on the discussion at the federal level. The relevance of the research is determined by the need in the Russian theory and practice of urban planning to develop scientific and methodological foundations of spatial planning of urban development. The purpose of the work is to determine the types and role of spatial planning documents in the system of modern domestic urban planning activities.

Deep Reinforcement Learning for Voltage Control and Renewable Accommodation Using Spatial-Temporal Graph Information

Renewable energy resources (RERs) have been increasingly integrated into distribution networks (DNs) for decarbonization. However, the variable nature of RERs introduces uncertainties to DNs, frequently resulting in voltage fluctuations that threaten system security and hamper the further adoption of RERs. To incentivize more RER penetration, we propose a deep reinforcement learning (DRL)-based strategy to dynamically balance the trade-off between voltage fluctuation control and renewable accommodation. To further extract multi-time-scale spatial-temporal (ST) graphical information of a DN, our strategy draws on a multi-grained attention-based spatial-temporal graph convolution network (MG-ASTGCN), consisting of ST attention mechanism and ST convolution to explore the node correlations in the spatial and temporal views. The continuous decision-making process of balancing such a trade-off can be modeled as a Markov decision process optimized by the deep deterministic policy gradient (DDPG) algorithm with the help of the derived ST information. We validate our strategy on the modified IEEE 33, 69, and 118-bus radial distribution systems, with outcomes significantly outperforming the optimization-based benchmarks. Simulations also reveal that our developed MG-ASTGCN can to a great extent accelerate the convergence speed of DDPG and improve its performance in stabilizing node voltage in an RER-rich DN. Moreover, our method improves the DN's robustness in the presence of generator failures.

Spatial (in)accuracy of cell broadcast alerts in urban context: Feedback from the April 2023 Cannes tsunami trial

Since June 2022, France is equipped with cell broadcast technology which alerts individuals within a predefined area. Despite the proven effectiveness of this technology, few studies take a spatial view of cell broadcast alert at a local level. Trials carried out in France were assessed only on their technical success, without verifying the rate of reception of the message by individuals in the official alert area, or the gap between the official alert area and the actual broadcast area. This study focuses on a trial conducted in April 2023 in Cannes (France). Using a geo-located survey method and spatial analysis tools, we show how cell broadcasting is more imprecise than one might think at the local level. Reception rates depend on the telephone operators and a large and ragged edge effect is measured, which means that the message is broadcast far beyond the area defined by the authorities. A second approach was to check the reception of three cell broadcast messages sent within a 20-min interval at fixed points, which revealed the fluctuation of the broadcast area over time, making its spatial extent complex to predict. Similar works should be carried out in other urban and rural areas.

Path integration, rather than being suppressed, is used to update spatial views in familiar environments with constantly available landmarks

This project tested three hypotheses conceptualizing the interaction between path integration based on self-motion and piloting based on landmarks in a familiar environment with persistent landmarks. The first hypothesis posits that path integration functions automatically, as in environments lacking persistent landmarks (environment-independent hypothesis). The second hypothesis suggests that persistent landmarks suppress path integration (suppression hypothesis). The third hypothesis proposes that path integration updates the spatial views of the environment (updating-spatial-views hypothesis). Participants learned a specific object's location. Subsequently, they undertook an outbound path originating from the object and then indicated the object's location (homing). In Experiments 1&1b, there were landmarks throughout the first 9 trials. On some later trials, the landmarks were presented during the outbound path but unexpectedly removed during homing (catch trials). On the last trials, there were no landmarks throughout (baseline trials). Experiments 2–3 were similar but added two identical objects (the original one and a rotated distractor) during homing on the catch and baseline trials. Experiment 4 replaced two identical objects with two groups of landmarks. The results showed that in Experiments 1&1b, homing angular error on the first catch trial was significantly larger than the matched baseline trial, undermining the environment-independent hypothesis. Conversely, in Experiment 2–4, the proportion of participants who recognized the original object or landmarks was similar between the first catch and the matched baseline trial, favoring the updating-spatial-views hypothesis over the suppression hypothesis. Therefore, while mismatches between updated spatial views and actual views of unexpected removal of landmarks impair homing performance, the updated spatial views help eliminate ambiguous targets or landmarks within the familiar environment.