Background Layer Research Articles

Holocene extreme flood events in the middle reaches of the Lancang‒Mekong River basin recorded by a high-altitude lake in southwestern China

The Lancang‒Mekong River (LMR) basin is a region particularly susceptible to floods induced by extreme precipitation. However, owing to limited historical flood records, it is difficult to understand the processes and mechanisms of extreme floods in the LMR basin. In the present study, sediments from a high-altitude lake (Lake Baima, 4700 m above sea level) located in the upper-middle reaches of the LMR are used to reconstruct climate and hydrology variability since the last deglaciation based on a range of climate proxies and methods, including loss-on-ignition, grain size, X-ray diffraction, and X-ray fluorescence. Through the investigation of modern and historical floods in the LMR basin, we combine the climate proxies with hydrological frequency curves to reconstruct the paleoflood sequence during the Holocene. Our results suggest that the temperature and precipitation in southwestern China increased during the Holocene compared to the last deglaciation, indicating a response to changes in Northern Hemisphere summer solar insolation. In addition, Holocene sediments of Lake Baima are primarily composed of brown background layers and numerous light yellow/gray event layers. These event layers are further identified as flood events based on sedimentary facies, redness, and main exogenous elements content and their rate of change. The results show that extreme flood events in the LMR basin occurred more frequently during the early and late Holocene, with seven once-in-1000-year floods during the two intervals; while there were low-frequency flood periods during the mid-Holocene. The occurrence of Holocene floods may be related to external forcing, such as the abrupt changes in total solar irradiance (TSI) and volcanic activity, as well as the internal variability of the wider climate system including the Pacific Decadal Oscillation (PDO) and El Niño‒Southern Oscillation (ENSO). When the TSI was low, the Intertropical Convergence Zone (ITCZ) in the Northern Hemisphere shifted northward, and the Western Pacific Subtropical High (WPSH) moved eastward, accompanied with a negative phase of the PDO and La Niña-like phase of the ENSO. The air-sea interactions increased sea surface temperatures, leading to high precipitation and increasing flood risk in the LMR basin.

Influence of global/local processing on perceived colour transparency.

Our cognitive processing is flexible and affected by global/local dominance in prior cognitive tasks. Similar to cognitive processing, perceptual processing, especially colour perception related to global/local processing, may be affected by prior global/local dominance; however, this possibility has not yet been assessed. Here, we examined whether prior tasks involving global/local processing influenced colour perception related to global/local processing. As colour perception is related to global/local processing, we focused on perceived colour transparency, in which a transparent layer is perceived in front of a background layer, even though these stimuli are physically in the same layer. When viewing the colour transparency stimulus, we expected that the perceived colour of a specific region, when focusing on only the local region, would differ from that when focusing on the whole image. In our study, the participants completed a global or local Navon task, followed by a colour-matching task that assessed how they saw colours using colour transparency stimuli. The degree of optical illusion (i.e., perceived colour transparency) after the global Navon task was greater than that after the local Navon task. Thus, prior global/local processing, a flexible mode of cognitive processing, influenced colour perception. This study provides new insight into perceptual flexibility, especially in colour perception.

High Resolution Images of Human Meibum Spread on Saline.

Understanding of the role of the tear film lipid layer (TFLL) in evaporative dry eye requires knowledge of its structure. X-ray studies show 11.1-nm thick lamellae in meibum at tear film temperature (approximately 35°C), whereas below 30°C, 4.88-nm thick lamellae predominate. Here, high resolution microscopy of meibum spread on saline is studied as a function of temperature, to compare with x-ray results. A purpose-built high resolution color microscope, previously used to study the TFLL, was used to study meibum from 10 subjects. It was spread on buffered saline at near 40°C, and allowed to cool to room temperature. Analytical methods from previous studies were applied to measure meibum and lamellar thickness. Initially, an irregular "island" was formed, surrounded by a "background layer" of 7.8 ± 0.3 nm thickness. Dewetting of the meibum layer always occurred, leading to the formation of lens-shaped droplets. Below 30°C, the lenses start to emit "tails" having a multilamellar structure containing up to about 49 lamellae superimposed on the background layer, each lamella being 4.82 ± 0.13 nm thick. Below 30°C, meibum spread on saline shows a multilamellar structure like the 4.88 nm thickness in x-ray studies, demonstrating the ability to observe and measure tightly stacked lamellae. In contrast, above 30°C, the 11.1 nm lamellae were not observed as in x-ray studies, indicating that these lamellae were not tightly stacked but may be separated by disordered lipid. The role of these findings in evaporative dry eye is discussed.

RCDNet: An Interpretable Rain Convolutional Dictionary Network for Single Image Deraining.

As common weather, rain streaks adversely degrade the image quality and tend to negatively affect the performance of outdoor computer vision systems. Hence, removing rains from an image has become an important issue in the field. To handle such an ill-posed single image deraining task, in this article, we specifically build a novel deep architecture, called rain convolutional dictionary network (RCDNet), which embeds the intrinsic priors of rain streaks and has clear interpretability. In specific, we first establish a rain convolutional dictionary (RCD) model for representing rain streaks and utilize the proximal gradient descent technique to design an iterative algorithm only containing simple operators for solving the model. By unfolding it, we then build the RCDNet in which every network module has clear physical meanings and corresponds to each operation involved in the algorithm. This good interpretability greatly facilitates an easy visualization and analysis of what happens inside the network and why it works well in the inference process. Moreover, taking into account the domain gap issue in real scenarios, we further design a novel dynamic RCDNet, where the rain kernels can be dynamically inferred corresponding to input rainy images and then help shrink the space for rain layer estimation with few rain maps, so as to ensure a fine generalization performance in the inconsistent scenarios of rain types between training and testing data. By end-to-end training such an interpretable network, all involved rain kernels and proximal operators can be automatically extracted, faithfully characterizing the features of both rain and clean background layers and, thus, naturally leading to better deraining performance. Comprehensive experiments implemented on a series of representative synthetic and real datasets substantiate the superiority of our method, especially on its well generality to diverse testing scenarios and good interpretability for all its modules, compared with state-of-the-art single image derainers both visually and quantitatively. Code is available at https://github.com/hongwang01/DRCDNet.

Analysing the use of OpenAerialMap images for OpenStreetMap edits

ABSTRACT OpenAerialMap (OAM) is a crowdsourcing platform for uploading, hosting, sharing, displaying, searching, and downloading openly licensed Earth image data from around the world. OAM was launched with the primary goal to facilitate rapid disaster response mapping after natural events, such as floods or hurricanes. Images contributed to OAM can be used as a background layer in OpenStreetMap (OSM) editors to edit map features, such as buildings or facilities, which may have been affected by such events. This study analyzes how the provision of OAM images is associated with changes in underlying editing patterns of OSM features by comparing the number of edited OSM objects between 2 weeks before and 2 weeks after an image has been shared through OAM. The comparison also involves other aspects of OSM editing patterns, including geometry types and OSM primary feature types of edited objects, type of feature editing operations, edits per user, and contribution distribution across continents. Results show that the number of point features added to OSM more than quadrupled within the first 2 weeks after OAM image upload compared to that of 2 weeks before, and that the number of ways added almost doubled. This suggests that the OSM community utilizes provided OAM images for OSM map updates in the respective geographic areas. The study provides a showcase which demonstrates how information from one crowdsourcing platform (OAM) can be used to enhance the data quality of another (OSM) with regards to completeness and timeliness.

Simultaneous prediction of petrophysical properties and formation layered thickness from acoustic logging data using a modular cascading residual neural network (MCARNN) with physical constraints

Predicting petrophysical properties from logging data using deep neural networks (DNNs) improves the efficiency of subsurface exploration. Traditional methods, however, heavily rely on data pre-processing. Therefore, a novel Modular Cascading Residual Neural Network (MCARNN) is proposed to simultaneously retrieve layered thickness and petrophysical properties of porous media directly from raw acoustic logging data without pre-processing. The MCARNN mainly comprises two cascading modules: the first is tasked with retrieving layered thickness, while the second one further reconstructs petrophysical properties based on the thickness information. To more accurately simulate the uncertainties of the real world, we incorporate some known geophysical knowledge, including the distribution range of actual petrophysical parameters, physical constraints among porous medium parameters, configurations of logging instruments, and stratified geological models. During this process, all Biot's porous parameters are independently randomized to cover a wide range of geological rock types. Furthermore, to enhance MCARNN's generalization ability under different random noise distributions, we independently generate the training and testing datasets using distinct numerical algorithms, thus preventing the “inverse crime” phenomenon. Additionally, varying levels of Gaussian white noises are added to the training dataset, and the transmitter-receiver configuration of the logging instrument is varied to enhance the training dataset. Two numerical experiments are implemented to validate the reliability and superiority of the MCARNN. It is shown that the proposed MCARNN can not only accurately predict porous petrophysical parameters when the background layer configurations are known but also be able to simultaneously reconstruct layered thickness and petrophysical parameters with low model misfit. Notably, MCARNN successfully predicts the porous velocity dispersion curves within a broad frequency band based on limited frequency points—a task typically requiring auxiliary equations in traditional inversion methods, demonstrating the advanced predictive capability of the proposed MCARNN. Through this research, we not only address the deficiencies in existing real-world logging datasets in terms of sample volume and label reliability but also effectively integrate rock physics models with data-driven neural networks, providing a novel and effective pathway for reservoir parameter prediction in the field of geophysical logging.

Adhesion of Varnish Coatings as a Background for Analogue and Digital Printing Technologies

In analogue and digital printing technologies, from 3 up to 12 layers of lacquer products are applied. Technological parameters significantly influence the adhesion in the coating system. This article refers to the analysis of the influence of selected technological parameters, such as the number of layers, energy doses distributed by the radiators, and line speed, on the topography and adhesion of varnish coatings formed in the process of varnishing with rollers and UV-curing systems. The appropriately prepared surface can be used as a background layer for the analogue and digital printing technology. Manufacturers must adapt the production process to the particular varnish to obtain finished products with the best possible performance properties. The state of surface free energy and finally adhesion can be assessed by theoretically determining the possibility of an adhesive bonding between the product and the substrate, taking into account the assumptions of the adsorption theory of adhesion and measurement of the contact angle (Θ). An experimental confirmation of adhesion measurements included removing the coatings from the substrate via stamps glued to the coating.

Single‐image deraining algorithm based on multi‐stage recurrent network

AbstractThe damage caused by the rain streak attached to the rainy image to the background seriously affects the analysis of image information and subsequent research. Aiming at the problem that the current single image rain removal methods cannot extract the deep rain streak features, a single image deraining algorithm based on multi‐stage recurrent network is proposed, which is committed to separating the rain streak layer and the background layer. The network consists of three stages, each stage is mainly composed of upper and lower scale dilation blocks, the upper scale dilation block includes multi‐scale dilated convolution and scale fusion, the lower scale dilation block is set the same, and the stages are connected using improved gated recurrent units to facilitate parameter sharing between them. The experimental results show that the proposed algorithm outperforms the comparative algorithm in both quantitative indicators and qualitative analysis on synthetic and real datasets, and the restored image retains more detailed information.

Exemplar-Based Inpainting for 6DOF Virtual Reality Photos.

Multi-layer images are currently the most prominent scene representation for viewing natural scenes under full-motion parallax in virtual reality. Layers ordered in diopter space contain color and transparency so that a complete image is formed when the layers are composited in a view-dependent manner. Once baked, the same limitations apply to multi-layer images as to conventional single-layer photography, making it challenging to remove obstructive objects or otherwise edit the content. Object removal before baking can benefit from filling disoccluded layers with pixels from background layers. However, if no such background pixels have been observed, an inpainting algorithm must fill the empty spots with fitting synthetic content. We present and study a multi-layer inpainting approach that addresses this problem in two stages: First, a volumetric area of interest specified by the user is classified with respect to whether the background pixels have been observed or not. Second, the unobserved pixels are filled with multi-layer inpainting. We report on experiments using multiple variants of multi-layer inpainting and compare our solution to conventional inpainting methods that consider each layer individually.

A Low-Delay Dynamic Range Compression and Contrast Enhancement Algorithm Based on an Uncooled Infrared Sensor with Local Optimal Contrast

Real-time compression of images with a high dynamic range into those with a low dynamic range while preserving the maximum amount of detail is still a critical technology in infrared image processing. We propose a dynamic range compression and enhancement algorithm for infrared images with local optimal contrast (DRCE-LOC). The algorithm has four steps. The first involves blocking the original image to determine the optimal stretching coefficient by using the information of the local block. In the second, the algorithm combines the original image with a low-pass filter to create the background and detailed layers, compressing the background layer with a dynamic range of adaptive gain, and enhancing the detailed layer for the visual characteristics of the human eye. Third, the original image was used as input, the compressed background layer was used as a brightness-guided image, and the local optimal stretching coefficient was used for dynamic range compression. Fourth, an 8-bit image was created (from typical 14-bit input) by merging the enhanced details and the compressed background. Implemented on FPGA, it used 2.2554 Mb of Block RAM, five dividers, and a root calculator with a total image delay of 0.018 s. The study analyzed mainstream algorithms in various scenarios (rich scenes, small targets, and indoor scenes), confirming the proposed algorithm’s superiority in real-time processing, resource utilization, preservation of the image’s details, and visual effects.

Enhancing Income as a Starting Point for Designing Sustainable Health Product-Service Systems for Chronically Ill Elderly in Rural Areas

Insufficient income, inadequate healthcare resources, and insufficient chronic disease management awareness pose a fundamental obstacle to the sustained health maintenance of elderly individuals with chronic diseases in rural areas. In response to these challenges, this study adopted a research-through-design approach and introduced system design thinking by incorporating new stakeholders, experiential insights, and services. Initially, during the background research stage, a semi-structured interview was conducted to gain an understanding of the pains, needs, and potential opportunities of the target users. Subsequently, in the design model construction stage, desk research was carried out to refine and integrate the common components identified in various design models. In a focus group, these components were then incorporated into the development of a Sustainable Health Product-Service System Design Model tailored for rural elderly with chronic diseases after refining the pains, needs, and potential opportunities defined in the first stage. This design model encompassed elements including a background layer, a stakeholder layer, a funding layer, a product layer, a service layer, and an information layer. In the system design and design model validation stage, a design project aimed at health maintenance was launched within Liyang Town, with the design thinking method following a process of empathizing, defining, ideating, prototyping, and testing. Throughout the project, the established model guided the design process and facilitated the conceptualization and implementation of a Product-Service system. In conclusion, an investigation encompassing 15 elderly individuals was undertaken to analyze their income balance, health condition, health awareness, and their satisfaction with the health system. This analysis spanned the period both before and after their involvement in the project. The outcomes of this examination revealed notable enhancements across all three dimensions evaluated, highlighting the positive impact of the project on the elderly participants. Furthermore, the project’s social benefits were also comprehensively established.

Segmentation of Brain Metastases Using Background Layer Statistics (BLAST).

Accurate segmentation of brain metastases is important for treatment planning and evaluating response. The aim of this study was to assess the performance of a semiautomated algorithm for brain metastases segmentation using Background Layer Statistics (BLAST). Nineteen patients with 48 parenchymal and dural brain metastases were included. Segmentation was performed by 4 neuroradiologists and 1 radiation oncologist. K-means clustering was used to identify normal gray and white matter (background layer) in a 2D parameter space of signal intensities from postcontrast T2 FLAIR and T1 MPRAGE sequences. The background layer was subtracted and operator-defined thresholds were applied in parameter space to segment brain metastases. The remaining voxels were back-projected to visualize segmentations in image space and evaluated by the operators. Segmentation performance was measured by calculating the Dice-Sørensen coefficient and Hausdorff distance using ground truth segmentations made by the investigators. Contours derived from the segmentations were evaluated for clinical acceptance using a 5-point Likert scale. The median Dice-Sørensen coefficient was 0.82 for all brain metastases and 0.9 for brain metastases of ≥10 mm. The median Hausdorff distance was 1.4 mm. Excellent interreader agreement for brain metastases volumes was found with an intraclass correlation coefficient = 0.9978. The median segmentation time was 2.8 minutes/metastasis. Forty-five contours (94%) had a Likert score of 4 or 5, indicating that the contours were acceptable for treatment, requiring no changes or minor edits. We show accurate and reproducible segmentation of brain metastases using BLAST and demonstrate its potential as a tool for radiation planning and evaluating treatment response.

Robust implementation of foreground extraction and vessel segmentation for X-ray coronary angiography image sequence

The extraction of contrast-filled vessels from X-ray coronary angiography (XCA) image sequence has important clinical significance for intuitively diagnosis and therapy. In this study, the XCA image sequence is regarded as a 3D tensor input, the vessel layer is regarded as a sparse tensor, and the background layer is regarded as a low-rank tensor. Using tensor nuclear norm (TNN) minimization, a novel method for vessel layer extraction based on tensor robust principal component analysis (TRPCA) is proposed. Furthermore, considering the irregular movement of vessels and the low-frequency dynamic disturbance of surrounding irrelevant tissues, the total variation (TV) regularized spatial–temporal constraint is introduced to smooth the foreground layer. Subsequently, for vessel layer images with uneven contrast distribution, a two-stage region growing (TSRG) method is utilized for vessel enhancement and segmentation. A global threshold method is used as the preprocessing to obtain main branches, and the Radon-Like features (RLF) filter is used to enhance and connect broken minor segments. The final binary vessel mask is constructed by combining the two intermediate results. The visibility of TV-TRPCA algorithm for foreground extraction is evaluated on clinical XCA image sequences and third-party dataset, which can effectively improve the performance of commonly used vessel segmentation algorithms. Based on TV-TRPCA, the accuracy of TSRG algorithm for vessel segmentation is further evaluated. Both qualitative and quantitative results validate the superiority of the proposed method over existing state-of-the-art approaches.

Image deraining based on dual-channel component decomposition

Image deraining aims to remove rain streaks from images and reduce information loss in outdoor images caused by rain. As a fundamental task in image processing, image deraining not only enhances the visibility of images but also provides necessary image restoration for advanced vision tasks. Existing image deraining models mostly train end-to-end models by minimizing the similarity between the output image of the model and the rain-free ground truth. Although these methods have achieved significant results, they often perform poorly in the face of dense and changing rain streak scenes. In this paper, we propose a novel method, called Dual-Channel Component Decomposition Network (DCD-Net). The basic idea of DCD-Net is to leverage the separability prior of rainy images, treats the rain-free background layer and the rain streak mask layer as two parallel component extraction tasks. To this end, it builds a dual-branch parallel networks that extract the rain-free background image and decouple the reconstruction information of the rain streak mask, respectively. It finally applies a composite multi-level contrastive supervision to the output of the above dual-branch parallel network, thereby achieving rain streak removal. Extensive experiments on various datasets demonstrate that the proposed model outperforms existing methods in deraining dense rain streak images.

Mask-guided network for image captioning

Attention mechanisms have been widely adopted for image captioning because of their powerful performance. In this paper, we propose a Mask Captioning Network (MaC) consisting of an object layer and a background layer to capture the objects and scenes of an image to generate a sentence. To this end, we leverage the Mask RCNN to detect salient regions at the pixel level instead of a bounding box in the object layer. Meanwhile, in the background layer, a CNN model is used to encode the scene features. In addition, MaC is implemented in both LSTM-based and Transformer-based image captioning architectures. We introduce a mask-guided transformer encoder with additional features to enhance the model. Experimental results show that our model significantly outperforms (with a much richer sentence) baseline models and achieves comparable results with state-of-the-art methods on MSCOCO and Flickr30k datasets.

Real-Scene Reflection Removal With RAW-RGB Image Pairs

Most brands of modern consumer digital cameras nowadays are able to provide RAW-RGB image pairs conveniently, even in the automatic mode. RAW images store pixel intensities linearly related to the radiance, which could be beneficial for the image reflection removal (IRR) task. However, existing IRR solutions, usually directly restoring the background in the non-linear RGB domain, severely overlook the valuable information conveyed by readily-available RAW images. Such a negligence may limit the performance of IRR methods on real-scene images. To mitigate this deficiency, we propose a Cascaded RAW and RGB Restoration Network (CR3Net) by leveraging both the RGB images and their paired RAW versions. Specifically, we firstly separate background and reflection layers in the linear RAW domain, and then restore the two layers in the non-linear RGB format by converting RAW features into the RGB domain. A novel RAW-to-RGB module (RRM) is devised to upsample these features and mimic pointwise mappings in the camera image signal processor (ISP). In addition, we collect the first real-world dataset that contains paired RAW and RGB images for IRR. Compared with state-of-the-art approaches, our method achieves a significant performance gain of about 2.07dB in PSNR, 0.028 in SSIM, and 0.0123 in LPIPS tested on the captured dataset. The source code and dataset are available at https://github.com/NamecantbeNULL/RAW_RGB_RR.

Unpaired sonar image denoising with simultaneous contrastive learning

Implementing underwater sonar image denoising algorithms through unpaired clean and noisy images is feasible and meaningful, since compliant paired real underwater sonar data is nearly impractical. Nevertheless, acquiring high-performance denoising algorithm is challenging without pairs of data as learning objects. In addition, the wide domain gap between noisy and clean samples as well as the noise contained in underwater sonar images is significantly more complex than that involved in normal images. Therefore, directly utilizing current unpaired optimization algorithms (e.g. generative adversarial learning and circular consistency learning) in underwater sonar image denoising tasks is not sufficient to explore the essential connection from noisy inputs to clean outputs. In this paper, we propose a novel framework for unpaired sonar image denoising which probes the same features of unpaired samples through a dual contrast optimization approach in latent space, named as NCD-GAN. The designed algorithm is composed of two basic parts: Simultaneous Transition Structure (STS) and Separable Contrast Unit (SCU). Separately, STS adopts a cyclic structure of multi-domain transformation to generate valuable surface information, and mines potential feature distributions between different domains via the addition of bidirectional mappings. Meanwhile, SCU introduces additional constraints in layer domains to encourage closer background layers between adjacent domains, while increasing the distance between noise layers so as to better promote the removal of noise and assist in image restoration. Sufficient experiments confirm that our algorithm provides better performance than existing unpaired denoising algorithms on multiple publicly available sonar image datasets and exhibits excellent robustness in the processing of multiple noises. Compared to the latest denoising algorithms, there is at most a 26.43% improvement in the evaluation metrics. Furthermore, it gains a 22.75% mAP boost on the subsequent advanced visual tasks.

ILSGAN: Independent Layer Synthesis for Unsupervised Foreground-Background Segmentation

Unsupervised foreground-background segmentation aims at extracting salient objects from cluttered backgrounds, where Generative Adversarial Network (GAN) approaches, especially layered GANs, show great promise. However, without human annotations, they are typically prone to produce foreground and background layers with non-negligible semantic and visual confusion, dubbed "information leakage", resulting in notable degeneration of the generated segmentation mask. To alleviate this issue, we propose a simple-yet-effective explicit layer independence modeling approach, termed Independent Layer Synthesis GAN (ILSGAN), pursuing independent foreground-background layer generation by encouraging their discrepancy. Specifically, it targets minimizing the mutual information between visible and invisible regions of the foreground and background to spur interlayer independence. Through in-depth theoretical and experimental analyses, we justify that explicit layer independence modeling is critical to suppressing information leakage and contributes to impressive segmentation performance gains. Also, our ILSGAN achieves strong state-of-the-art generation quality and segmentation performance on complex real-world data.

Mean nutrient uptake depths of cereal crops change with compost incorporation into subsoil – evidence from 87Sr/86Sr ratios

Background and AimsRoot restricting layers often hinder crops from accessing the large reservoir of bioavailable mineral nutrients situated in subsoil. This study aims to explore changes in the mean nutrient uptake depth of cereal crops when removing root restricting layers through subsoil management.MethodsSubsoil management was performed by deep loosening, cultivation of lucerne as deep-rooting pre-crop, and their combination with compost incorporation. Management effects were evaluated by means of shoot biomass and element concentrations in shoots and soil compartments. The mean nutrient uptake depth was fingerprinted by graphically matching the 87Sr/86Sr ratios in shoots with the 87Sr/86Sr ratios in the exchangeable fraction in soil. Nutrient uplift from depth to topsoil was inferred from element concentrations in the exchangeable fraction in soil.ResultsShoot biomass remained constant in management and control plots. The mean nutrient uptake depth changed with subsoil management in the order: deep loosening < control < deep loosening with compost incorporation. The latter coincided with a reallocation of compost-derived Na and hence resulted in increased levels of bioavailable Na below the depth of compost incorporation, which may have led to an improved water use efficiency of the crops. Thus, Na relocation triggered the deepening of the mean uptake depth of water and nutrients. Moreover, nutrient uplift from depth to topsoil was evident 21 months after subsoiling.ConclusionSubsoil management by deep loosening with compost incorporation provides a sustainable use of soil resources because otherwise unused deep geogenic-derived nutrient reservoirs were additionally involved in crop nutrition.

As without, so within: how the brain's temporo-spatial alignment to the environment shapes consciousness.

Consciousness is constituted by a structure that includes contents as foreground and the environment as background. This structural relation between the experiential foreground and background presupposes a relationship between the brain and the environment, often neglected in theories of consciousness. The temporo-spatial theory of consciousness addresses the brain-environment relation by a concept labelled 'temporo-spatial alignment'. Briefly, temporo-spatial alignment refers to the brain's neuronal activity's interaction with and adaption to interoceptive bodily and exteroceptive environmental stimuli, including their symmetry as key for consciousness. Combining theory and empirical data, this article attempts to demonstrate the yet unclear neuro-phenomenal mechanisms of temporo-spatial alignment. First, we suggest three neuronal layers of the brain's temporo-spatial alignment to the environment. These neuronal layers span across a continuum from longer to shorter timescales. (i) The background layer comprises longer and more powerful timescales mediating topographic-dynamic similarities between different subjects' brains. (ii) The intermediate layer includes a mixture of medium-scaled timescales allowing for stochastic matching between environmental inputs and neuronal activity through the brain's intrinsic neuronal timescales and temporal receptive windows. (iii) The foreground layer comprises shorter and less powerful timescales for neuronal entrainment of stimuli temporal onset through neuronal phase shifting and resetting. Second, we elaborate on how the three neuronal layers of temporo-spatial alignment correspond to their respective phenomenal layers of consciousness. (i) The inter-subjectively shared contextual background of consciousness. (ii) An intermediate layer that mediates the relationship between different contents of consciousness. (iii) A foreground layer that includes specific fast-changing contents of consciousness. Overall, temporo-spatial alignment may provide a mechanism whose different neuronal layers modulate corresponding phenomenal layers of consciousness. Temporo-spatial alignment can provide a bridging principle for linking physical-energetic (free energy), dynamic (symmetry), neuronal (three layers of distinct time-space scales) and phenomenal (form featured by background-intermediate-foreground) mechanisms of consciousness.