Spatial Information Processing Research Articles

Revolutionizing vascular health through the temporal convolutional transformer for drug screening and model evolution

Vascular illnesses include a spectrum of disorders affecting the blood vessels and continue to be major global health challenges. Vascular disorders are major worldwide health concerns that require ongoing development of drug screening models in order to find efficient treatment approaches. The ever-changing field of vascular health demands novel ways to medication screening due to the enduring difficulties presented by a variety of illnesses. This abstract explores a paradigm shift, highlighting the innovative role of the Temporal Convolutional Transformer (TCT) in redefining drug screening techniques and promoting model evolution. With its seamless integration of transformer mechanisms and temporal convolutional layers, the TCT is a revolutionary hybrid algorithm. Because of this special combination, the model can simultaneously identify spatial subtleties and capture temporal dynamics in complicated biological information. The temporal convolutional layers of the TCT are particularly good at revealing the sequential dependencies that are present in the development of vascular disorders. The model ensures a detailed knowledge of the developing nature of these diseases by analyzing temporal dynamics with skill. Simultaneously, the TCT’s transformer mechanisms enable a thorough examination of long-range interdependence and global interconnections. The TCT’s dual capability establishes it as a comprehensive solution by offering a nuanced perspective on spatial patterns that are essential for identifying the complexity of vascular health. The TCT is hybrid in the sense that it can learn features at several scales, supporting different temporal and spatial resolutions. This flexibility guarantees that the model may extract pertinent information at various scales, enhancing the characterization of the complex nature of vascular illnesses. The TCT is positioned as a holistic solution since its transformer mechanisms allow for a thorough investigation of long-range dependencies and global interconnections at the same time. This combined capacity offers a sophisticated viewpoint on spatial patterns that is essential for understanding the intricacies of vascular health. Moreover, the remarkable flexibility of the TCT to dynamic temporal scales is consistent with the dynamic character of biological processes, providing a strong and adaptable framework for drug screening. The TCT is used in many different fields of vascular disease research. The model performs well in image-based screening, collecting sequential dependencies over time and processing spatial information from medical imaging datasets. The TCT’s capacity to identify long-range dependencies is essential for molecular interaction research since it helps to clarify the complex pathways associated with vascular disorders. Its adaptability also extends to personalized treatment approaches, which allow for the efficient modeling of patient-specific data, including genetic information. The TCT is a revolutionary tool that will continue to evolve as drug screening models do. By integrating temporal convolutional and transformer mechanisms, its all-encompassing methodology provides a sophisticated and comprehensive approach to deciphering the intricacies of vascular disorders. Across all evaluated metrics, the proposed method achieves near-perfect accuracy of 98.45%, sensitivity of 93.45%, specificity of 98.43%, precision of 84.25, AUC of 99 % and F1 score values 96.01%, respectively. This abstract sheds light on how the TCT has significantly changed drug development approaches, opening the door for more focused and effective therapies in the field of vascular health.

Effects of unilateral hippocampal surgical procedures needed for calcium imaging on mouse behavior and adult hippocampal neurogenesis

Hippocampus is essential for episodic memory formation, lesion studies demonstrating its role especially in processing spatial and temporal information. Further, adult hippocampal neurogenesis (AHN) in the dentate gyrus (DG) has also been linked to learning. To study hippocampal neuronal activity during events like learning, in vivo calcium imaging has become increasingly popular. It relies on the use of adeno-associated viral (AAV) vectors, which seem to lead to a decrease in AHN when applied on the DG. More notably, imaging requires the implantation of a relatively large lens into the tissue. Here, we examined how injection of an AAV vector and implantation of a 1-mm-diameter lens into the dorsal DG routinely used to image calcium activity impact the behavior of adult male C57BL/6 mice. To this aim, we conducted open-field, object-recognition and object-location tasks at baseline, after AAV vector injection, and after lens implantation. Finally, we determined AHN from hippocampal slices using a doublecortin-antibody. According to our results, the operations needed for in vivo imaging of the dorsal DG did not have adverse effects on behavior, although we noticed a decrease in AHN ipsilaterally to the operations. Thus, our results suggest that in vivo imaging can be safely used to, for example, correlate patterns of calcium activity with learned behavior. One should still keep in mind that the defects on the operated side might be functionally compensated by the (hippocampus in the) contralateral hemisphere.

Using unsupervised capsule neural network reveal spatial representations in the human brain.

Humans can extract high-level spatial features from visual signals, but spatial representations in the brain are complex and remain unclear. The unsupervised capsule neural network (U-CapsNet) is sensitive to the spatial location and relationship of the object, contains a special recurrent mechanism and uses a self-supervised generation strategy to represent images, which is similar to the computational principle in the human brain. Therefore, we hypothesized that U-CapsNet can help us understand how the human brain processes spatial information. First, brain activities were studied using functional magnetic resonance imaging during spatial working memory in which participants had to remember the locations of circles for a short time. Then, U-CapsNet served as a computational model of the brain to perform tasks that are identical to those performed by humans. Finally, the representational models were used to compare the U-CapsNet with the brain. The results showed that some human-defined spatial features naturally emerged in the latent space of U-CapsNet. Moreover, representations in U-CapsNet captured the response structure of two types of brain regions during different activity patterns, as well as important factors associated with human behavior. Together, our study not only provides a computationally feasible framework for modeling how the human brain encodes spatial features but also provides insights into the representational format and goals of the human brain.

A shallow 3D convolutional neural network for violence detection in videos

With the recent worldwide statistical rise in the amount of public violence, automated violence detection in surveillance cameras has become a matter of high importance. This work introduces an end-to-end, trainable 3D Convolutional Neural Network (3D CNN) for detecting violence in video footage. The proposed network is inherently capable of processing both spatial and temporal information, thereby obviating the need for additional models that would introduce higher computational requirements and complexity. This work has two main contributions: 1) developing a lightweight 3D CNN suitable for inference on edge devices as mobile systems, and 2) a comprehensive explanation of all components comprising a CNN model, thereby enhances model interpretability. Experiments were conducted to assess the performance of the proposed model using a consolidated dataset combining four benchmark datasets. The results of the experiments support the asserted contributions, which are discussed in detail.

Impairments in the early consolidation of spatial memories via group II mGluR agonism in the mammillary bodies

mGluR2 receptors are widely expressed in limbic brain regions associated with memory, including the hippocampal formation, retrosplenial and frontal cortices, as well as subcortical regions including the mammillary bodies. mGluR2/3 agonists have been proposed as potential therapeutics for neurological and psychiatric disorders, however, there is still little known about the role of these receptors in cognitive processes, including memory consolidation. To address this, we assessed the effect of the mGluR2/3 agonist, eglumetad, on spatial memory consolidation in both mice and rats. Using the novel place preference paradigm, we found that post-sample injections of eglumetad impaired subsequent spatial discrimination when tested 6 h later. Using the immediate early gene c-fos as a marker of neural activity, we showed that eglumetad injections reduced activity in a network of limbic brain regions including the hippocampus and mammillary bodies. To determine whether the systemic effects could be replicated with more targeted manipulations, we performed post-sample infusions of the mGluR2/3 agonist 2R,4R-APDC into the mammillary bodies. This impaired novelty discrimination on a place preference task and an object-in-place task, again highlighting the role of mGluR2/3 transmission in memory consolidation and demonstrating the crucial involvement of the mammillary bodies in post-encoding processing of spatial information.

Navigating Aged Care Services with GIS: Trends, Developments, and Future Directions

BackgroundWith the growing challenge of an aging population, emerging technologies are increasingly being integrated into the production, organization, and delivery of aged care services. Geographic Information System (GIS), a computer-based tool for spatial information analysis and processing, has made significant strides in the allocation of care recources and service delivery for older adults, a notably vulnerable group. Despite its growing importance, cross-disciplinary literature reviews on this theme are scare. This scoping review was conducted to encapsulate the advancements and discern the future trajectory of GIS applications in aged care services.MethodsA comprehensive search across nine databases yielded 5941 articles. Adhering to specific inclusion and exclusion criteria, 61 articles were selected for a detailed analysis.ResultsThe 61 articles span from 2003 to 2022, with a notable increase in publications since 2018, comprising 41 articles (67% of the total) published between 2018-2022. Developed countries contributed 66% of the papers, with 45% focusing on accessibility issues. In the domain of aged care services, GIS has been predominantly utilized for model construction, mapping, and site selection, with a growing emphasis on addressing the unique needs of different subgroups of older adults.ConclusionThe past two decades have seen substantial growth in the application of GIS in aged care services, reflecting its increasing importance in this field. This scoping review not only charts the historical development of GIS applications in aged care services but also underscores the need for innovative research approaches. Future directions should emphasize the integration of GIS with diverse methodologies to address the heterogeneous needs of older adults and improve the overall delivery of aged care services. Such advancements in GIS applications have the potential to significantly enhance the efficiency, accessibility, and quality of care for the aging population.

Research on the prediction of mine water inrush disasters based on multi-factor spatial game reconstruction

Water damage in mines poses a widespread challenge in the coal mining industry. Gaining a comprehensive understanding of the multi-factor spatial catastrophe evolution mechanism and process of floor water inrush is crucial, which will enable the achievement of dynamic, quantitative, and precise early warning systems. It holds significant theoretical guidance for implementing effective water prevention and control measures in coal mines. This study focuses on the issue of water inrush in the coal seam floor, specifically in the context of Pengzhuang coal mine. By utilizing a small sample of non-linear characteristics derived from drilling geological data, we adopt a multifactor spatial perspective that considers geological structure and hydrogeological conditions. In light of this, we propose a quantitative risk prediction model that integrates the coupled theoretical analysis, statistical analysis, and machine learning simulation methods. Firstly, the utilization of a quantification approach employing a triangular fuzzy number allows for the representation of a comparative matrix based on empirical values. Simultaneously, the networked risk transmission effect of underlying control risk factors is taken into consideration. The application of principal component analysis optimizes the entropy weight method, effectively reducing the interference caused by multifactor correlation. By employing game theory, the subjective and objective weight proportions of the control factors are reasonably allocated, thereby establishing a vulnerability index model based on a comprehensive weighting of subjective and objective factors. Secondly, the WOA-RF-GIS approach is employed to comprehensively explore the interconnectedness of water diversion channel data. Collaborative Kriging interpolation is utilized to enhance the dimensionality of the data and facilitate spatial information processing. Lastly, the representation of risk is coupled with necessary and sufficient condition layers, enabling the qualitative visualization of quantitative results. This approach aims to accurately predict disaster risk with limited sample data, ultimately achieving the goal of precise risk assessment. The research findings demonstrate that the reconstructed optimization model based on multi-factor spatial game theory exhibits high precision and generalization capability. This model effectively unveils the non-linear dynamic processes associated with floor water inrush, which are influenced by multiple factors, characterized by limited data volume, and governed by complex formation mechanisms. The identification of high-risk areas for water inrush is achieved with remarkable accuracy, providing invaluable technical support for the formulation of targeted water prevention and control measures, ultimately ensuring the safety of coal mining operations.

Temporal and Spatial Information Elicit Different Power and Connectivity Profiles during Working Memory Maintenance.

Working memory (WM) is the cognitive ability to store and manipulate information necessary for ongoing tasks. Although frontoparietal areas are involved in the retention of visually presented information, oscillatory neural activity differs for temporal and spatial WM processing. In this study, we corroborated previous findings describing the modulation of neural oscillations and expanded our investigation to the network organization underlying the cognitive processing of temporal and spatial information. We utilized MEG recordings during a Sternberg visual WM task. The spectral oscillatory activity in the maintenance phase revealed increased frontal theta (4-8 Hz) and parietal beta (13-30 Hz) in the temporal condition. Source level coherence analysis delineated the prominent role of parietal areas in all frequency bands during the maintenance of temporal information, whereas frontal and central areas showed major contributions in theta and beta ranges during the maintenance of spatial information. Our study revealed distinct spectral profiles of neural oscillations for separate cognitive subdomains of WM processing. The delineation of specific functional networks might have important implications for clinical applications, enabling the development of stimulation protocols targeting cognitive disabilities associated with WM impairments.

Fast Non-Rigid Radiance Fields from Monocularized Data.

The reconstruction and novel view synthesis of dynamic scenes recently gained increased attention. As reconstruction from large-scale multi-view data involves immense memory and computational requirements, recent benchmark datasets provide collections of single monocular views per timestamp sampled from multiple (virtual) cameras. We refer to this form of inputs as monocularized data. Existing work shows impressive results for synthetic setups and forward-facing real-world data, but is often limited in the training speed and angular range for generating novel views. This paper addresses these limitations and proposes a new method for full 360° inward-facing novel view synthesis of non-rigidly deforming scenes. At the core of our method are: 1) An efficient deformation module that decouples the processing of spatial and temporal information for accelerated training and inference; and 2) A static module representing the canonical scene as a fast hash-encoded neural radiance field. In addition to existing synthetic monocularized data, we systematically analyze the performance on real-world inward-facing scenes using a newly recorded challenging dataset sampled from a synchronized large-scale multi-view rig. In both cases, our method is significantly faster than previous methods, converging in less than 7 minutes and achieving real-time framerates at 1K resolution, while obtaining a higher visual accuracy for generated novel views. Our code and dataset are available online: https://github.com/MoritzKappel/MoNeRF.

The scalp time-varying network of auditory spatial attention in “cocktail-party” situations

Sound source localization in "cocktail-party" situations is a remarkable ability of the human auditory system. However, the neural mechanisms underlying auditory spatial attention are still largely unknown. In this study, the "cocktail-party" situations are simulated through multiple sound sources and presented through head-related transfer functions and headphones. Furthermore, the scalp time-varying network of auditory spatial attention is constructed using the high-temporal resolution electroencephalogram, and its network properties are measured quantitatively using graph theory analysis. The results show that the time-varying network of auditory spatial attention in "cocktail-party" situations is more complex and partially different than in simple acoustic situations, especially in the early- and middle-latency periods. The network coupling strength increases continuously over time, and the network hub shifts from the posterior temporal lobe to the parietal lobe and then to the frontal lobe region. In addition, the right hemisphere has a stronger network strength for processing auditory spatial information in "cocktail-party" situations, i.e., the right hemisphere has higher clustering levels, higher transmission efficiency, and more node degrees during the early- and middle-latency periods, while this phenomenon disappears and appears symmetrically during the late-latency period. These findings reveal different network patterns and properties of auditory spatial attention in "cocktail-party" situations during different periods and demonstrate the dominance of the right hemisphere in the dynamic processing of auditory spatial information.

Protracted neuronal recruitment in the temporal lobes of young children

The temporal lobe of the human brain contains the entorhinal cortex (EC). This region of the brain is a highly interconnected integrative hub for sensory and spatial information; it also has a key role in episodic memory formation and is the main source of cortical hippocampal inputs1–4. The human EC continues to develop during childhood5, but neurogenesis and neuronal migration to the EC are widely considered to be complete by birth. Here we show that the human temporal lobe contains many young neurons migrating into the postnatal EC and adjacent regions, with a large tangential stream persisting until the age of around one year and radial dispersal continuing until around two to three years of age. By contrast, we found no equivalent postnatal migration in rhesus macaques (Macaca mulatta). Immunostaining and single-nucleus RNA sequencing of ganglionic eminence germinal zones, the EC stream and the postnatal EC revealed that most migrating cells in the EC stream are derived from the caudal ganglionic eminence and become LAMP5+RELN+ inhibitory interneurons. These late-arriving interneurons could continue to shape the processing of sensory and spatial information well into postnatal life, when children are actively interacting with their environment. The EC is one of the first regions of the brain to be affected in Alzheimer’s disease, and previous work has linked cognitive decline to the loss of LAMP5+RELN+ cells6,7. Our investigation reveals that many of these cells arrive in the EC through a major postnatal migratory stream in early childhood.

The Cognition of Time Shaped by Linguistic Elements: Alphabetic Language and Ideographic Language

This study investigated the impact of language script on spatial cognition by comparing reaction times of native speakers of alphabetic languages and Chinese in response to horizontally and vertically arranged button groups. It is postulated that the linear, one-dimensional nature of alphabetic scripts may shape cognitive processing differently than the complex, two-dimensional Chinese characters. In the experiment, participants from both linguistic backgrounds were tasked with quickly responding to buttons arranged in both horizontal and vertical configurations. Results indicated that speakers of alphabetic languages exhibited significantly shorter reaction times for the horizontally oriented buttons compared to the Chinese speakers. Conversely, Chinese speakers demonstrated faster reaction times for vertically arranged buttons relative to the alphabetic language speakers. These results suggest that language script structure can influence spatial cognition. The linear, sequential arrangement inherent to alphabetic languages may predispose speakers to be more efficient in processing horizontally sequenced stimuli, while the two-dimensional structure of Chinese characters may confer an advantage in processing vertically presented information. Additionally, cultural factors, such as the traditional vertical writing and reading direction of Chinese script, may contribute to these differences. The findings provide preliminary support for the notion that language script can shape cognitive strategies for processing spatial information, with speakers of alphabetic languages and Chinese employing different strategies for horizontal and vertical spatial processing. Further research is needed to explore the underlying mechanisms, as well as the impact of early exposure to a specific script on spatial cognition.

The Digital Clock Drawing Test and Delayed Recall Predict Informant‐Rated Functional Impairment in Individuals with Cognitive Impairment

AbstractBackgroundNearly 6.7 million people aged 65 and older in the US are living with Alzheimer’s disease (AD). Early detection of dementia presents a difficult challenge, as the earliest signs of cognitive decline often go undetected. Thus, more sensitive and clinically meaningful cognitive screening tools are needed. Evaluating the patient’s dependence in activities of daily living is critical to detect progression toward dementia. The Linus Health DCTclock™ employs AI‐enabled analyses of the clock drawing process to evaluate cognitive and motor function. We evaluated the utility of four DCTclock composite scales including Spatial Reasoning, Drawing Efficiency, Information Processing, and Motor Skills, 22 DCTclock subscales, and the 3‐word Delayed Recall score from the Digital Clock and Recall (DCR™) to predict the informant‐rated functional activities questionnaire (FAQ) score in older adults ranging from cognitively unimpaired to individuals with mild cognitive impairment (MCI) and AD‐related dementia (ADRD).Method939 participants in a multisite study (age mean±SD = 72.1±6.7; 57.1% female; years of education mean±SD = 15.4±2.7; primary language English), classified as cognitively unimpaired (n = 406), MCI (n = 293), or ADRD (n = 238) were included. We conducted cross‐validated regularized logistic regressions with FAQ score ≥6 or ≥9 (mild or moderate functional impairment, respectively) as dependent variable and DCTclock composite scales, subscales, and Delayed Recall score as predictors, in addition to age, sex, and education.ResultCombination of DCTclock subscales and Delayed Recall predicted FAQ≥6 and ≥9 with AUCs = 0.79 and 0.81, respectively. DCTclock composite scales predicted FAQ≥ 6 and ≥9 with AUCs = 0.75 and 0.77, respectively. Combination of DCTclock composite scales and Delayed Recall predicted FAQ≥6 and ≥9 with AUCs = 0.77 and 0.79, respectively. DCTclock subscales by themselves predicted FAQ≥6 and ≥9 with AUCs = 0.77 and 0.79, respectively.ConclusionThe DCR, a brief digital cognitive assessment with automatic scoring that can be completed in primary‐care settings, can infer the effect of cognitive impairment on a patient’s functional activity with great accuracy and within a few minutes. This makes the DCR an empowering tool for primary‐care providers in practical evaluation of patients with cognitive impairment.

Improving indoor wayfinding with AR-enabled egocentric cues: A comparative study

Indoor wayfinding poses significant challenges, as navigating complex environments can be difficult and time-consuming. Augmented reality (AR) has emerged as a promising solution to address these challenges. However, understanding how to effectively leverage AR techniques for large-scale indoor wayfinding scenarios remains unclear. In particular, the implications of AR-enabled egocentric perspectives have not been thoroughly investigated. This study aims to explore the potential benefits of AR-based egocentric perspectives and their impact on indoor wayfinding performance. A human-subject experiment (N = 31) was conducted in a multi-story building, utilizing a self-developed AR system capable of providing both traditional exocentric and innovative egocentric perspectives. The results of the experiment confirmed the performance and cognitive benefits of the AR-enabled egocentric perspective. Participants using the egocentric perspective demonstrated improved wayfinding efficiency, reduced cognitive load, and enhanced spatial awareness. These findings suggest that the AR-based egocentric perspective offers a more intuitive and effective approach to indoor wayfinding, enabling users to better process spatial information and make informed decisions during wayfinding tasks. The findings are expected to provide empirical evidence on the efficacy of AR systems for wayfinding and inspire new user interface (UI) designs that would better leverage the advantages of AR techniques.

The effect of verbal instructions while using digital indoor wayfinding devices on gender, performance, and self-reported strategies

Studies indicate that verbal instructions may impact associations between gender and wayfinding performance (measured via relative direction pointing accuracy and walking pace). Following the increasing use of digital navigation applications in indoor environments, and their implications on acquiring and processing spatial information, the aim of this study is to evaluate the stability of previously established associations. The study included 34 participants (16 females) aged 24–34 and was conducted in an indoor hospital setting. In addition to using a navigation application, one of three types of verbal instructions (route, survey, or none) were given in each wayfinding scenario. Self-reported wayfinding strategies were also assessed. The findings indicate that male participants made fewer pointing accuracy errors and walked faster than females, regardless of the type of instructions given, implying that the impact of naturally employed wayfinding strategies by gender (route for females; survey for males) on wayfinding performance may be more dominant than that of navigational devices. In addition, when males and females were exposed to their unnatural wayfinding strategy, no significant differences were seen in either group's self-reported wayfinding strategies. These findings may suggest that applying survey knowledge to females may improve their indoor wayfinding.

A spatial design layout optimization model based on deep learning in the context of rural revitalization

In recent years, with the transformation of China's economy and social development, solving rural problems and achieving rural revitalization has become an urgent task. The development of information technology, specifically the advancements in deep learning technology, has provided new solutions for rural revitalization. This article aims to optimize the spatial structure of rural residential areas and investigate models for optimizing spatial design layouts within the context of rural revitalization utilizing deep learning techniques, specifically convolutional neural networks (DeepLab v3+). This article conducts in-depth research on the DeepLab v3+model and finds that it has good feature extraction ability in processing spatial information such as rural residential houses and ancillary land. By introducing an attention mechanism in the encoding region, we improved the feature extraction accuracy of the model. On the ImageNet and CoCo2017 datasets, the accuracy indicators PA, IoU, and F1 of the DeepLab v3+model performed well, reaching 98.25 %, 75.61 %, and 86.49 %, as well as 98.31 %, 74.91 %, and 85.49 %, respectively. Furthermore, it was discovered that as the training frequency approached 20, both ResNet50 and DeepLab v3-SE exhibited a generalization accuracy of 86.83 % and 89.26 %, correspondingly. This shows the effectiveness of our model in acquiring and utilizing knowledge during the training. The study further investigated the performance of the model in processing images with noise. When the noise standard deviation is 25, the mean SSIM of the O-DeepLab v3+network reaches 0.943, which is 0.53 % and 1.95 % higher than the DeepLab v3-SE network and traditional DeepLab v3+network, respectively. When the noise standard deviation is 50, the SSIM mean of the O-DeepLab v3+network reaches 0.82, which is 0.61 % and 1.49 % higher than the DeepLab v3-SE network and traditional DeepLab v3+network, respectively. This suggests that our model exhibits robustness when processing noisy images. In summary, the spatial design layout optimization model based on deep learning in the context of rural revitalization studied in this article has high accuracy and stability in processing spatial information such as rural residential houses and ancillary land. This is of great significance for optimizing rural land use, promoting urban-rural land allocation, and achieving rural revitalization. Therefore, this study has certain application value.

Construction of customer-aware grid interactive service system based on neural network model

Abstract Neural network models have advantages in spatial and temporal information processing, which provide new ideas for the construction of customer-aware grid interactive service system. In this paper, firstly, the spatio-temporal fusion deep neural network model based on the attention mechanism is used to extract spatial and temporal information by convolutional neural network and gated recurrent neural network, respectively. The attention mechanism is also introduced to assign weights to various types of features to improve prediction accuracy. Secondly, the global information of the attention mechanism of the channel on the feature map is fully utilized to solve the problem of inadequate extraction of feature information from a single model, and then the grid interactive service system is constructed. The results show that the model proposed in this paper has higher prediction accuracy with root mean square error, coefficient of determination, and prediction accuracy of 0.972, 1.742, and 0.935, respectively. To verify the performance of the cache-based curve service in handling customer-perceived grid interactions, minute samples are created, where the maximum number of minute sample records is 3.5 million. Thus, it is shown that by introducing the attention mechanism can effectively improve the computational efficiency of the model, get better prediction results, and meet the demand for efficient and stable customer-aware interactive service system.

Influence of background noise on spatial audio localization for helicopter pilot headsets

A helicopter pilot is subjected to high workloads. A high noise level in the cockpit further increases stress. This stress can be reduced through headsets for hearing protection including active noise control (ANC). An actual research topic is the improvement of the human-machine-interface by 3-D-audio systems. These systems can improve pilot’s situational awareness and, therefore, reduce stress. This paper presents a 3-D-audio system for headphones. This binaural system uses head-tracking to process spatial information about the location of the sound source. The spatial sensation is achieved by convolving the source signal with head-related transfer functions. This sensation is influenced by background noise, which can be reduced by ANC. A first listening test with physical sound sources provides information on the localizability of sound sources at different elevation angles. This is compared to a second listening test with virtual sound sources generated by the 3-D-audio system. In a third listening test, the virtual sound source is located at variable azimuth and elevation angles. The system is evaluated with respect to sound source localization and the influence of background noise and ANC. This study highlights the potential of the 3-D-audio system to improve the helicopter pilot’s situational awareness in spatial acoustic environments.

On learning distribution alignment for video-based visible-infrared person re-identification

This paper studies the matching problem of cross-modality video data from a discrete distribution alignment view. Central to this discussion is the visible-infrared person re-identification (VI-reID), a crucial feature that bolsters surveillance systems’ efficacy in monitoring individuals across diverse lighting conditions. Going beyond traditional image-to-image matching paradigms, a recent study shows that temporal information can bring richer cues to encode the pedestrian representation, improving the representation power of deep neural networks. However, this integration further complicates cross-modality data matching due to the joint processing of spatial and temporal information. This paper formulates the video data as a discrete distribution and aligns the cross-modality video representation by reducing the matching cost between the two distributions. To this end, a natural idea for aligning the videos is to reduce the divergence of distributions. Moreover, the powerful optimal transport (OT) scheme, which generates the optimal matching flows and establishes the relevance of two distributions, is also employed as a way to measure the distance of distributions. Nevertheless, we observe that endowing the OT in the advanced VI-reID feature extractor leads to a non-symmetric measurement. To mitigate this, the paper introduces a new metric, namely symmetric optimal transport (SOT), reformulating OT into a symmetric form. Thorough analyses and empirical studies affirm the superiority of the proposed SOT, which significantly outperforms the current state-of-the-art methods according to standard benchmarking evaluations.

Grid Cell Percolation.

Grid cells play a principal role in enabling cognitive representations of ambient environments. The key property of these cells-the regular arrangement of their firing fields-is commonly viewed as a means for establishing spatial scales or encoding specific locations. However, using grid cells' spiking outputs for deducing geometric orderliness proves to be a strenuous task due to fairly irregular activation patterns triggered by the animal's sporadic visits to the grid fields. This article addresses statistical mechanisms enabling emergent regularity of grid cell firing activity from the perspective of percolation theory. Using percolation phenomena for modeling the effect of the rat's moves through the lattices of firing fields sheds new light on the mechanisms of spatial information processing, spatial learning, path integration, and establishing spatial metrics. It is also shown that physiological parameters required for spiking percolation match the experimental range, including the characteristic 2/3 ratio between the grid fields' size and the grid spacing, pointing at a biological viability of the approach.