Object Location Research Articles

Scene semantics affects allocentric spatial coding for action in naturalistic (virtual) environments

Interacting with objects in our environment requires determining their locations, often with respect to surrounding objects (i.e., allocentrically). According to the scene grammar framework, these usually small, local objects are movable within a scene and represent the lowest level of a scene’s hierarchy. How do higher hierarchical levels of scene grammar influence allocentric coding for memory-guided actions? Here, we focused on the effect of large, immovable objects (anchors) on the encoding of local object positions. In a virtual reality study, participants (n = 30) viewed one of four possible scenes (two kitchens or two bathrooms), with two anchors connected by a shelf, onto which were presented three local objects (congruent with one anchor) (Encoding). The scene was re-presented (Test) with 1) local objects missing and 2) one of the anchors shifted (Shift) or not (No shift). Participants, then, saw a floating local object (target), which they grabbed and placed back on the shelf in its remembered position (Response). Eye-tracking data revealed that both local objects and anchors were fixated, with preference for local objects. Additionally, anchors guided allocentric coding of local objects, despite being task-irrelevant. Overall, anchors implicitly influence spatial coding of local object locations for memory-guided actions within naturalistic (virtual) environments.

A spatial map: a propitious choice for constraining the binding problem.

Many studies have shown that the human visual system has two major functionally distinct cortical visual pathways: a ventral pathway, thought to be important for object recognition, and a dorsal pathway, thought to be important for spatial cognition. According to our and others previous studies, artificial neural networks with two segregated pathways can determine objects' identities and locations more accurately and efficiently than one-pathway artificial neural networks. In addition, we showed that these two segregated artificial cortical visual pathways can each process identity and spatial information of visual objects independently and differently. However, when using such networks to process multiple objects' identities and locations, a binding problem arises because the networks may not associate each object's identity with its location correctly. In a previous study, we constrained the binding problem by training the artificial identity pathway to retain relative location information of objects. This design uses a location map to constrain the binding problem. One limitation of that study was that we only considered two attributes of our objects (identity and location) and only one possible map (location) for binding. However, typically the brain needs to process and bind many attributes of an object, and any of these attributes could be used to constrain the binding problem. In our current study, using visual objects with multiple attributes (identity, luminance, orientation, and location) that need to be recognized, we tried to find the best map (among an identity map, a luminance map, an orientation map, or a location map) to constrain the binding problem. We found that in our experimental simulations, when visual attributes are independent of each other, a location map is always a better choice than the other kinds of maps examined for constraining the binding problem. Our findings agree with previous neurophysiological findings that show that the organization or map in many visual cortical areas is primarily retinotopic or spatial.

Glutamatergic signaling from melanin-concentrating hormone-producing neurons: A requirement for memory regulation, but not for metabolism control.

Melanin-concentrating hormone-producing neurons (MCH neurons), found mainly in the lateral hypothalamus and surrounding areas, play essential roles in various brain functions, including sleep and wakefulness, reward, metabolism, learning, and memory. These neurons coexpress several neurotransmitters and act as glutamatergic neurons. The contribution of glutamate from MCH neurons to memory- and metabolism-related functions has not been fully investigated. In a mouse model, we conditionally knocked out Slc17a6 gene, which encodes for vesicular glutamate transporter 2 (vGlut2), in the MCH neurons exclusively by using two different methods: the Cre recombinase/loxP system and in vivo genome editing using CRISPR/Cas9. Then, we evaluated several aspects of memory and measured metabolic rates using indirect calorimetry. We found that mice with MCH neuron-exclusive vGlut2 ablation had higher discrimination ratios between novel and familiar stimuli for novel object recognition, object location, and three-chamber tests. In contrast, there was no significant change in body weight, food intake, oxygen consumption, respiratory quotient, or locomotor activity. These findings suggest that glutamatergic signaling from MCH neurons is required to regulate memory, but its role in regulating metabolic rate is negligible.

A computational account of transsaccadic attentional allocation based on visual gain fields

Coordination of goal-directed behavior depends on the brain's ability to recover the locations of relevant objects in the world. In humans, the visual system encodes the spatial organization of sensory inputs, but neurons in early visual areas map objects according to their retinal positions, rather than where they are in the world. How the brain computes world-referenced spatial information across eye movements has been widely researched and debated. Here, we tested whether shifts of covert attention are sufficiently precise in space and time to track an object's real-world location across eye movements. We found that observers' attentional selectivity is remarkably precise and is barely perturbed by the execution of saccades. Inspired by recent neurophysiological discoveries, we developed an observer model that rapidly estimates the real-world locations of objects and allocates attention within this reference frame. The model recapitulates the human data and provides a parsimonious explanation for previously reported phenomena in which observers allocate attention to task-irrelevant locations across eye movements. Our findings reveal that visual attention operates in real-world coordinates, which can be computed rapidly at the earliest stages of cortical processing.

АЛГОРИТМ ПОИСКА ОПТИМАЛЬНОЙ СЕТИ ПОЛИГОНОВ СБОРА ТВЕРДЫХ КОММУНАЛЬНЫХ ОТХОДОВ

The aim of the study is to develop an algorithm for constructing a network of polygons for storing, recycling and neutralizing industrial and consumer waste in a certain region, taking into account the generated mass of solid communal waste and the existing transport network, allowing one to come to an economically optimal solution. The mathematical methods presented in the scientific literature are aimed at solving problems of determining the optimal location of logistics infrastructure objects, namely a manufacturing plant, a warehouse, a distribution centre, a retail outlet. The paper formulates and implements an algorithm for constructing an optimal network of polygons, which would minimize economic costs for logistics, construction, and operation of the polygons. The main factors taken into account are the mass of generated solid communal waste, the transportation cost from the temporary accumulation site to the solid communal waste polygon, the distance between administrative centres. The basic principle of searching for the logistics extremum is based on determining the main node, that is the one with the largest mass of solid communal waste along all available road junctions and choosing the polygon location based on the condition of the maximum difference in annual logistics costs. Using the proposed algorithm, the paper solves the problem of determining the number and location of solid communal waste polygons for the southern part of the Komi Republic.

Challenges of spatio-temporal trajectory datasets

ABSTRACT The rapid evolution of wireless location technologies has significantly increased the availability of mobility data, particularly spatio-temporal trajectory data, detailing object movements and locations over time. This data holds diverse applications such as predicting travel patterns, optimizing routes, analysing social interactions, and managing urban resources. However, leveraging trajectory datasets presents challenges, including data heterogeneity, volume, velocity requiring real-time processing, privacy concerns with sensitive information, and regulatory compliance. Traditional systematic reviews of literature may not fully capture industry challenges, as professionals often do not publish research. To address this gap, industry expert input is vital. Collaborating with academia, we propose using focus group discussions to explore these challenges comprehensively. Focus groups offer a qualitative platform for professionals and academics to identify practical issues and gaps in the literature. This combined approach will inform technological advancements in creating and utilizing spatio-temporal datasets. Additionally, integrating insights from focus groups with a literature review ensures a thorough analysis of data challenges. This methodology aims to provide a holistic understanding and foster innovation in managing and exploiting trajectory data effectively.

Choline supplementation in early life improves and low levels of choline can impair outcomes in a mouse model of Alzheimer's disease.

Maternal choline supplementation (MCS) improves cognition in Alzheimer's disease (AD) models. However, the effects of MCS on neuronal hyperexcitability in AD are unknown. We investigated the effects of MCS in a well-established mouse model of AD with hyperexcitability, the Tg2576 mouse. The most common type of hyperexcitability in Tg2576 mice are generalized EEG spikes (interictal spikes [IIS]). IIS also are common in other mouse models and occur in AD patients. In mouse models, hyperexcitability is also reflected by elevated expression of the transcription factor ∆FosB in the granule cells (GCs) of the dentate gyrus (DG), which are the principal cell type. Therefore, we studied ΔFosB expression in GCs. We also studied the neuronal marker NeuN within hilar neurons of the DG because reduced NeuN protein expression is a sign of oxidative stress or other pathology. This is potentially important because hilar neurons regulate GC excitability. Tg2576 breeding pairs received a diet with a relatively low, intermediate, or high concentration of choline. After weaning, all mice received the intermediate diet. In offspring of mice fed the high choline diet, IIS frequency declined, GC ∆FosB expression was reduced, and hilar NeuN expression was restored. Using the novel object location task, spatial memory improved. In contrast, offspring exposed to the relatively low choline diet had several adverse effects, such as increased mortality. They had the weakest hilar NeuN immunoreactivity and greatest GC ΔFosB protein expression. However, their IIS frequency was low, which was surprising. The results provide new evidence that a diet high in choline in early life can improve outcomes in a mouse model of AD, and relatively low choline can have mixed effects. This is the first study showing that dietary choline can regulate hyperexcitability, hilar neurons, ΔFosB, and spatial memory in an animal model of AD.

Выставочное пространство российских музеев как объект культуры

The article examines the exhibition space of Russian museums of the Soviet and post-Soviet periods as an object of culture. Emphasis is placed on its importance in shaping and reflecting social values, and the evolution of exhibition spaces, design elements, and the role they play in presenting, interpreting, and preserving cultural heritage are examined. The discussion covers the layout, interconnection and functionality of the various zones inside the museum. The tasks in designing architectural and artistic solutions for museum complexes are perhaps one of the most difficult in environmental design. Here it is necessary to take into account functionality, ergonomics, aesthetics, subordinating them to the general concept of the museum complex, considering the regularity of the location of objects, the movement of visitor flows, the zoning of space, taking into account the safety and preservation of cultural objects. The article highlights the design features of the museum space of the XX–XXI centuries, taking into account historical changes, political realities, stylistic trends and trends in art.

Accuracy analysis in determining the location of underground objects using GPR involving lidar data

Ground penetrating radar (GPR) is one of the most useful non-destructive techniques for locating underground objects. Advancements in this technology have facilitated the development of new sensors over the past decade. In this paper, an accuracy assessment of the location of underground objects using various GPR antennas is presented. To achieve the stated goals, measurements of 5 concrete slabs, reinforced with steel bars of various diameters and located at variable depths were taken. The experiment includes the usage of three GPR antennas to assess the format, characteristics, and differences of extracted data. This set of antennas from different manufacturers varied in terms of operating frequency. Additional lidar data from TLS (terrestrial laser scanning) was utilized in the methodology to provide precise surface measurements and therefore, external orientation of the surveyed data. The experiment allowed for the determination of vertical and horizontal accuracy for three tested antennas and the assessment of increasing errors value with greater depth of the measured items, which is important for surveying accuracy forecasting.

Towards zero-shot object counting via deep spatial prior cross-modality fusion

Existing counting models predominantly operate on a specific category of objects, such as crowds and vehicles. The recent emergence of multi-modal foundational models, e.g., Contrastive Language-Image Pre-training (CLIP), has facilitated class-agnostic counting. This involves counting objects of any given class from a single image based on textual instructions. However, CLIP-based class-agnostic counting models face two primary challenges. Firstly, the CLIP model lacks sensitivity to location information. It generally considers global content rather than the fine-grain location of objects. Therefore, adapting the CLIP model directly is suboptimal. Secondly, these models generally freeze pre-trained vision and language encoders, while neglecting the potential misalignment in the constructed hypothesis space. In this paper, we address these two issues in a unified framework termed Deep Spatial Prior Interaction (DSPI) network. The DSPI leverages the spatial-awareness ability of large-scale pre-trained object grounding models, i.e., Grounding DINO, to incorporate spatial location as an additional prior for a specific query class. This enables the network to be more specifically focused on the precise location of the objects. Additionally, to align the feature space across different modalities, we tailor a meta adapter that extracts textual information into an object query. This serves as an instruction for cross-modality matching. These two modules collaboratively ensure the alignment of multi-modal representations while preserving their discriminative nature. Comprehensive experiments conducted on a diverse set of benchmarks verify the superiority of the proposed model. The code is available at https://github.com/jinyongch/DSPI.

Structural Transformer with Region Strip Attention for Video Object Segmentation

Memory-based methods in semi-supervised video object segmentation (VOS) achieve competitive performance by performing feature similarity between the current frame and memory frames. However, this operation involves two challenges: 1) instances of occlusion caused by interaction, and 2) interference from similar objects or clutters in the background. In this work, we propose a Structural Transformer with Region Strip Attention (STRSA) approach to address these challenges. Specifically, we build a Structural Transformer (ST) architecture to decompose the feature similarity between the long-term memory frames and the current frame into two aspects: a time–space part and an object significance part. This allows us to investigate the spatio-temporal relationship of pixels and capture the salient features of the objects. Therefore, the differences between pixels and the specificity of objects are fully exploited. In addition, we leverage the object location information from the long-term memory masks and the strip pooling to design a Region Strip Attention (RSA) module, which boosts the attention for the foreground regions and suppresses the background clutters. Extensive experiments on DAVIS, YouTube-VOS, and MOSE benchmarks prove that our method achieves satisfactory results and outperforms the retrained baseline model.

Predatory Odor Exposure as a Potential Paradigm for Studying Emotional Modulation of Memory Consolidation-The Role of the Noradrenergic Transmission in the Basolateral Amygdala.

The pivotal role of the basolateral amygdala (BLA) in the emotional modulation of hippocampal plasticity and memory consolidation is well-established. Specifically, multiple studies have demonstrated that the activation of the noradrenergic (NA) system within the BLA governs these modulatory effects. However, most current evidence has been obtained by direct infusion of synthetic NA or beta-adrenergic agonists. In the present study, we aimed to investigate the effect of endogenous NA release in the BLA, induced by a natural aversive stimulus (coyote urine), on memory consolidation for a low-arousing, hippocampal-dependent task. Our experiments combined a weak object location task (OLT) version with subsequent mild predator odor exposure (POE). To investigate the role of endogenous NA in the BLA in memory modulation, a subset of the animals (Wistar rats) was treated with the non-selective beta-blocker propranolol at the end of the behavioral procedures. Hippocampal tissue was collected 90 min after drug infusion or after the OLT test, which was performed 24 h later. We used the obtained samples to estimate the levels of phosphorylated CREB (pCREB) and activity-regulated cytoskeleton-associated protein (Arc)-two molecular markers of experience-dependent changes in neuronal activity. The result suggests that POE has the potential to become a valuable behavioral paradigm for studying the interaction between BLA and the hippocampus in memory prioritization and selectivity.

Non-Uniform Spatial Partitions and Optimized Trajectory Segments for Storage and Indexing of Massive GPS Trajectory Data

The presence of abundant spatio-temporal information based on the location of mobile objects in publicly accessible GPS mobile devices makes it crucial to collect, analyze, and mine such information. Therefore, it is necessary to index a large volume of trajectory data to facilitate efficient trajectory retrieval and access. It is difficult for existing indexing methods that primarily rely on data-driven indexing structures (such as R-Tree) or space-driven indexing structures (such as Quadtree) to support efficient analysis and computation of data based on spatio-temporal range queries as a service basis, especially when applied to massive trajectory data. In this study, we propose a massive GPS data storage and indexing method based on uneven spatial segmentation and trajectory optimization segmentation. Primarily, the method divides GPS trajectories in a large spatio-temporal data space into multiple MBR sequences by greedy algorithm. Then, a hybrid indexing model for segmented trajectories is constructed to form a global spatio-temporal segmentation scheme, called HHBITS index, to achieve hierarchical organization of trajectory data. Eventually, a spatio-temporal range query processing method is proposed based on this index. This paper implements and evaluates the index in MongoDB and compares it with two other spatio-temporal composite indexes for performing spatio-temporal range queries efficiently. The experimental results show that the method in this paper has high performance in responding to spatio-temporal queries on large-scale trajectory data.

Comparative Evaluation of NeRF Algorithms on Single Image Dataset for 3D Reconstruction

Abstract. The reconstruction of three-dimensional scenes from a single image represents a significant challenge in computer vision, particularly in the context of cultural heritage digitisation, where datasets may be limited or of poor quality. This paper addresses this challenge by conducting a study of the latest and most advanced algorithms for single-image 3D reconstruction, with a focus on applications in cultural heritage conservation. Exploiting different single-image datasets, the research evaluates the strengths and limitations of various artificial intelligence-based algorithms, in particular Neural Radiance Fields (NeRF), in reconstructing detailed 3D models from limited visual data. The study includes experiments on scenarios such as inaccessible or non-existent heritage sites, where traditional photogrammetric methods fail. The results demonstrate the effectiveness of NeRF-based approaches in producing accurate, high-resolution reconstructions suitable for visualisation and metric analysis. The results contribute to advancing the understanding of NeRF-based approaches in handling single-image inputs and offer insights for real-world applications such as object location and immersive content generation.

Integrated Data Processing Architecture Applied to Learning Objects Repository for Educational Robotics: Proposing Approach

Educational Robotics has emerged as a multidisciplinary field integrating Information and Communication Technologies, Engineering, and Artificial Intelligence areas in education. Furthermore, Educational Robotics offers an innovative approach to teaching and learning by incorporating the construction and programming of robots. However, the small number of platforms for sharing Educational Robotics Learning Objects with a large amount of well-described metadata poses a challenge for efficient search and retrieval of relevant educational content. This study presents an architecture for data processing and ingestion in the RepositORE system, a Learning Objects Repository for Educational Robotics that stores educational content and associated metadata based on the Dublin Core standard. Based on microservices architecture, the proposed system aims to facilitate the location and reuse of learning objects by adapting the RepositORE system to work as a client service. The theoretical framework explores the concepts of learning objects, repositories, and the significance of Educational Robotics in education, and reviews related works. The proposed architecture and system can potentially enhance the accessibility and discoverability of Educational Robotics Learning Objects, fostering effective teaching and learning experiences.

Less spatial exploration is associated with poorer spatial memory in midlife adults.

Despite its importance for navigation, very little is known about how the normal aging process affects spatial exploration behavior. We aimed to investigate: (1) how spatial exploration behavior may be altered early in the aging process, (2) the relationship between exploration behavior and subsequent spatial memory, and (3) whether exploration behavior can classify participants according to age. Fifty healthy young (aged 18-28) and 87 healthy midlife adults (aged 43-61) freely explored a desktop virtual maze, learning the locations of nine target objects. Various exploration behaviors (object visits, distance traveled, turns made, etc.) were measured. In the test phase, participants navigated from one target object to another without feedback, and their wayfinding success (% correct trials) was measured. In the exploration phase, midlife adults exhibited less exploration overall compared to young adults, and prioritized learning target object locations over maze layout. In the test phase, midlife adults exhibited less wayfinding success when compared to the young adults. Furthermore, following principal components analysis (PCA), regression analyses indicated that both exploration quantity and quality components were associated with wayfinding success in the midlife group, but not the young adults. Finally, we could classify participants according to age with similar accuracy using either their exploration behavior or wayfinding success scores. Our results aid in the understanding of how aging impacts spatial exploration, and encourages future investigations into how pathological aging may affect spatial exploration behavior.

The attentive reconstruction of objects facilitates robust object recognition.

Humans are extremely robust in our ability to perceive and recognize objects-we see faces in tea stains and can recognize friends on dark streets. Yet, neurocomputational models of primate object recognition have focused on the initial feed-forward pass of processing through the ventral stream and less on the top-down feedback that likely underlies robust object perception and recognition. Aligned with the generative approach, we propose that the visual system actively facilitates recognition by reconstructing the object hypothesized to be in the image. Top-down attention then uses this reconstruction as a template to bias feedforward processing to align with the most plausible object hypothesis. Building on auto-encoder neural networks, our model makes detailed hypotheses about the appearance and location of the candidate objects in the image by reconstructing a complete object representation from potentially incomplete visual input due to noise and occlusion. The model then leverages the best object reconstruction, measured by reconstruction error, to direct the bottom-up process of selectively routing low-level features, a top-down biasing that captures a core function of attention. We evaluated our model using the MNIST-C (handwritten digits under corruptions) and ImageNet-C (real-world objects under corruptions) datasets. Not only did our model achieve superior performance on these challenging tasks designed to approximate real-world noise and occlusion viewing conditions, but also better accounted for human behavioral reaction times and error patterns than a standard feedforward Convolutional Neural Network. Our model suggests that a complete understanding of object perception and recognition requires integrating top-down and attention feedback, which we propose is an object reconstruction.

Machine Learning for the Visually Impaired: Benchmarking Object Detection Models

This research paper benchmarks object detection models, a form of machine learning, to determine which algorithm would be most beneficial for the visually impaired to locate objects that are used in everyday life. As most benchmarking experiments test object detection models with a variety of objects, it is essential to test the models using images of more relevant objects to find the most suitable algorithm. The models are tested using still images from the COCO database. Pretrained models employing five of the most popular object detection algorithms are used to process the images and find each model’s detection accuracy. To simulate real life scenarios, these objects may be partially hidden or at a distance. For each image, the models return a list of detections providing the names, confidence rating, and location of each object detected. These results will be filtered to remove detections with low confidence ratings as well as detections of irrelevant objects. The remaining results are compared to the dataset of object names and locations provided by the COCO database to calculate the distance between the predicted object locations and the true location. The algorithms will be ranked based on the number of failed detections, the time taken to analyze each image, and the accuracy of each object’s determined location.

Object Location and Weight Sensing System based on the Reaction Force Ratio of the Load Cell

Object Location and Weight Sensing System based on the Reaction Force Ratio of the Load Cell

Memory loss and aberrant neurogenesis in mice exposed to patient anti-N-methyl-d-aspartate receptor antibodies

ObjectiveAnti-N-methyl-d-aspartate receptor (anti-NMDAR) encephalitis results in chronic epilepsy and permanent cognitive impairment. One of the possible causes of cognitive impairment in anti-NMDAR could be aberrant neurogenesis, an established contributor to memory loss in idiopathic drug-resistant epilepsy. We developed a mouse model of anti-NMDAR encephalitis and showed that mice exposed to patient anti-NMDAR antibodies for 2 weeks developed seizures and memory loss. In the present study, we assessed the delayed effects of patient-derived antibodies on cognitive phenotype and examined the corresponding changes in hippocampal neurogenesis. MethodsMonoclonal anti-NMDAR antibodies or control antibodies were continuously infused into the lateral ventricle of male C56BL/6J mice (8–12 weeks) via osmotic minipumps for 2 weeks. The motor and anxiety phenotypes were assessed using the open field paradigm, and hippocampal memory and learning were assessed using the object location, Y maze, and Barnes maze paradigms during weeks 1 and 3–4 of antibody washout. The numbers of newly matured granule neurons (Prox-1+) and immature progenitor cells (DCX+) as well as their spatial distribution within the hippocampus were assessed at these time points. Bromodeoxyuridine (BrdU, 50 mg/kg, i.p., daily) was injected on days 2–12 of the infusion, and proliferating cell immunoreactivity was compared in antibody-treated mice and control mice during week 4 of the washout. ResultsMice infused with anti-NMDAR antibodies demonstrated spatial memory impairment during week 1 of antibody washout (p = 0.02, t-test; n = 9–11). Histological analysis of hippocampal sections from these mice revealed an increased ectopic displacement of Prox-1+ cells in the dentate hilus compared to the control-antibody-treated mice (p = 0.01; t-test). Mice exposed to anti-NMDAR antibodies also had an impairment of spatial memory and learning during weeks 3–4 of antibody washout (object location: p = 0.009; t-test; Y maze: p = 0.006, t-test; Barnes maze: p = 0.008, ANOVA; n = 8–10). These mice showed increased ratios of the low proliferating (bright) to fast proliferating (faint) BrdU+ cell counts and decreased number of DCX+ cells in the hippocampal dentate gyrus (p = 0.006 and p = 0.04, respectively; t-tests) suggesting ectopic migration and delayed cell proliferation. SignificanceThese findings suggest that memory and learning impairments induced by patient anti-NMDAR antibodies are sustained upon removal of antibodies and are accompanied by aberrant hippocampal neurogenesis. Interventions directed at the manipulation of neuronal plasticity in patients with encephalitis and cognitive loss may be protective and therapeutically relevant.