Local Cues Research Articles

Landmarks, beacons, or panoramic views: What do pigeons attend to for guidance in familiar environments?

Birds and social insects represent excellent systems for understanding visually guided navigation. Both animal groups use surrounding visual cues for homing and foraging. Ants extract sufficient spatial information from panoramic views, which naturally embed all near and far spatial information, for successful homing. Although egocentric panoramic views allow for parsimonious explanations of navigational behaviors, this potential source of spatial information has been mostly neglected during studies of vertebrates. Here we investigate how distinct landmarks, a beacon, and panoramic views influence the reorientation behavior in pigeons (Columba livia). Pigeons were trained to search for a location characterized by a beacon and several distinct landmarks. Transformation tests manipulated aspects of the landmark configuration, allowing for a dissociation among navigational strategies. Quantitative image and path analyses provided support that the panoramic view was used by the pigeons. Although the results from some individuals support the use of beaconing, overall the pigeons relied predominantly on the panoramic view when spatial cues provided conflicting information regarding the goal location. Reorientation based on vector and bearing information derived from distinct landmarks as well as environmental geometry failed to account fully for the results. Thus, the results of our study support that pigeons can use panoramic views for reorientation in familiar environments. Given that the current model for landmark use by pigeons posits the use of different vectors from an object, a global panorama-matching strategy suggests a fundamental change in the theory of how pigeons use surrounding visual cues for localization.

Insights into the heterogeneity of iNKT cells: tissue-resident and circulating subsets shaped by local microenvironmental cues.

Invariant natural killer T (iNKT) cells are a distinct subpopulation of innate-like T lymphocytes. They are characterized by semi-invariant T cell receptors (TCRs) that recognize both self and foreign lipid antigens presented by CD1d, a non-polymorphic MHC class I-like molecule. iNKT cells play a critical role in stimulating innate and adaptive immune responses, providing an effective defense against infections and cancers, while also contributing to chronic inflammation. The functions of iNKT cells are specific to their location, ranging from lymphoid to non-lymphoid tissues, such as the thymus, lung, liver, intestine, and adipose tissue. This review aims to provide insights into the heterogeneity of development and function in iNKT cells. First, we will review the expression of master transcription factors that define subsets of iNKT cells and their production of effector molecules such as cytokines and granzymes. In this article, we describe the gene expression profiles contributing to the kinetics, distribution, and cytotoxicity of iNKT cells across different tissue types. We also review the impact of cytokine production in distinct immune microenvironments on iNKT cell heterogeneity, highlighting a recently identified circulating iNKT cell subset. Additionally, we explore the potential of exploiting iNKT cell heterogeneity to create potent immunotherapies for human cancers in the future.

Effect of inter-aural temporal envelope differences on inter-aural time difference thresholds for amplitude modulated noise.

The inter-aural time difference (ITD) and inter-aural level difference (ILD) are important acoustic cues for horizontal localization and spatial release from masking. These cues are encoded based on inter-aural comparisons of tonotopically matched binaural inputs. Therefore, binaural coherence or the interaural spectro-temporal similarity is a pre-requisite for encoding ITD and ILD. The modulation depth of envelope is an important envelope characteristic that helps in encoding the envelope-ITD. However, inter-aural difference in modulation depth can result in reduced binaural coherence and poor representation of binaural cues as in the case with reverberation, noise and compression in cochlear implants and hearing aids. This study investigated the effect of inter-aural modulation depth difference on the ITD thresholds for an amplitude-modulated noise in normal hearing young adults. An amplitude modulated high pass filtered noise with varying modulation depth differences was presented sequentially through headphones. In one ear, the modulation depth was retained at 90% and in the other ear it varied from 90% to 50%. The ITD thresholds for modulation frequencies of 8 Hz and 16 Hz were estimated as a function of the inter-aural modulation depth difference. The Friedman test findings revealed a statistically significant increase in the ITD threshold with an increase in the inter-aural modulation depth difference for 8 Hz and 16 Hz. The results indicate that the inter-aural differences in the modulation depth negatively impact ITD perception for an amplitude-modulated high pass filtered noise.

Label-Aware Dual Graph Neural Networks for Multi-Label Fundus Image Classification.

Fundus disease is a complex and universal disease involving a variety of pathologies. Its early diagnosis using fundus images can effectively prevent further diseases and provide targeted treatment plans for patients. Recent deep learning models for classification of this disease are gradually emerging as a critical research field, which is attracting widespread attention. However, in practice, most of the existing methods only focus on local visual cues of a single image, and ignore the underlying explicit interaction similarity between subjects and correlation information among pathologies in fundus diseases. In this paper, we propose a novel label-aware dual graph neural networks for multi-label fundus image classification that consists of population-based graph representation learning and pathology-based graph representation learning modules. Specifically, we first construct a population-based graph by integrating image features and non-image information to learn patient's representations by incorporating associations between subjects. Then, we represent pathologies as a sparse graph where its nodes are associated with pathology-based feature vectors and the edges correspond to probability of the co-occurrence of labels to generate a set of classifier scores by the propagation of multi-layer graph information. Finally, our model can adaptively recalibrate multi-label outputs. Detailed experiments and analysis of our results show the effectiveness of our method compared with state-of-the-art multi-label fundus image classification methods.

Addressing Challenges of Incorporating Appearance Cues Into Heuristic Multi-Object Tracker via a Novel Feature Paradigm.

In the field of Multi-Object Tracking (MOT), the incorporation of appearance cues into tracking-by-detection heuristic trackers using re-identification (ReID) features has posed limitations on its advancement. The existing ReID paradigm involves the extraction of coarse-grained object-level feature vectors from cropped objects at a fixed input size using a ReID model, and similarity computation through a simple normalized inner product. However, MOT requires fine-grained features from different object regions and more accurate similarity measurements to identify individuals, especially in the presence of occlusion. To address these limitations, we propose a novel feature paradigm. In this paradigm, we extract the feature map from the entire frame image to preserve object sizes and represent objects using a set of fine-grained features from different object regions. These features are sampled from adaptive patches within the object bounding box on the feature map to effectively capture local appearance cues. We introduce Mutual Ratio Similarity (MRS) to accurately measure the similarity of the most discriminative region between two objects based on the sampled patches, which proves effective in handling occlusion. Moreover, we propose absolute Intersection over Union (AIoU) to consider object sizes in feature cost computation. We integrate our paradigm with advanced motion techniques to develop a heuristic Motion-Feature joint multi-object tracker, MoFe. Within it, we reformulate the track state transition of tracklets to better model their life cycle, and firstly introduce a runtime recorder after MoFe to refine trajectories. Extensive experiments on five benchmarks, i.e., GMOT-40, BDD100k, DanceTrack, MOT17, and MOT20, demonstrate that MoFe achieves state-of-the-art performance in robustness and generalizability without any fine-tuning, and even surpasses the performance of fine-tuned ReID features.

Skeletal muscle niche, at the crossroad of cell/cell communications.

Skeletal muscle is composed of a variety of tissue and non-tissue resident cells that participate in homeostasis. In particular, the muscle stem cell niche is a dynamic system, requiring direct and indirect communications between cells, involving local and remote cues. Interactions within the niche must happen in a timely manner for the maintenance or recovery of the homeostatic niche. For instance, after an injury, pro-myogenic cues delivered too early will impact on muscle stem cell proliferation, delaying the repair process. Within the niche, myofibers, endothelial cells, perivascular cells (pericytes, smooth muscle cells), fibro-adipogenic progenitors, fibroblasts, and immune cells are in close proximity with each other. Each cell behavior, membrane profile, and secretome can interfere with muscle stem cell fate and skeletal muscle regeneration. On top of that, the muscle stem cell niche can also be modified by extra-muscle (remote) cues, as other tissues may act on muscle regeneration via the production of circulating factors or the delivery of cells. In this review, we highlight recent publications evidencing both local and remote effectors of the muscle stem cell niche.

Bilevel Fusion With Local and Global Cues for Point Cloud Upsampling

Bilevel Fusion With Local and Global Cues for Point Cloud Upsampling

SETA: Semantic-Aware Edge-Guided Token Augmentation for Domain Generalization.

Domain generalization (DG) aims to enhance the model robustness against domain shifts without accessing target domains. A prevalent category of methods for DG is data augmentation, which focuses on generating virtual samples to simulate domain shifts. However, existing augmentation techniques in DG are mainly tailored for convolutional neural networks (CNNs), with limited exploration in token-based architectures, i.e., vision transformer (ViT) and multi-layer perceptrons (MLP) models. In this paper, we study the impact of prior CNN-based augmentation methods on token-based models, revealing their performance is suboptimal due to the lack of incentivizing the model to learn holistic shape information. To tackle the issue, we propose the Semantic-aware Edge-guided Token Augmentation (SETA) method. SETA transforms token features by perturbing local edge cues while preserving global shape features, thereby enhancing the model learning of shape information. To further enhance the generalization ability of the model, we introduce two stylized variants of our method combined with two state-of-the-art (SOTA) style augmentation methods in DG. We provide a theoretical insight into our method, demonstrating its effectiveness in reducing the generalization risk bound. Comprehensive experiments on five benchmarks prove that our method achieves SOTA performances across various ViT and MLP architectures. Our code is available at https://github.com/lingeringlight/SETA.

Pyramid Scene Parsing Network for Driver Distraction Classification

In recent years, there has been a persistent increase in the number of road accidents worldwide. The US National Highway Traffic Safety Administration reports that distracted driving is responsible for approximately 45 percent of road accidents. In this study, we tackle the challenge of automating the detection and classification of driver distraction, along with the monitoring of risky driving behavior. Our proposed solution is based on the Pyramid Scene Parsing Network (PSPNet), which is a semantic segmentation model equipped with a pyramid parsing module. This module leverages global context information through context aggregation from different regions. We introduce a lightweight model for driver distraction classification, where the final predictions benefit from the combination of both local and global cues. For model training, we utilized the publicly available StateFarm Distracted Driver Detection Dataset. Additionally, we propose optimization techniques for classification to enhance the model’s performance.

Maize terpene synthase 1 impacts insect behavior via the production of monoterpene volatiles β-myrcene and linalool

Plant-derived volatiles are important mediators of plant-insect interactions as they can provide cues for host location and quality, or act as direct or indirect defense molecules. The volatiles produced by Zea mays (maize) include a range of terpenes, likely produced by several of the terpene synthases (TPS) present in maize. Determining the roles of specific terpene volatiles and individual TPSs in maize-insect interactions is challenging due to the promiscuous nature of TPSs in vitro and their potential for functional redundancy. In this study, we used metabolite GWAS of a sweetcorn diversity panel infested with Spodoptera frugiperda (fall armyworm) to identify genetic correlations between TPSs and individual volatiles. This analysis revealed a correlation between maize terpene synthase 1 (ZmTPS1) and emission of the monoterpene volatiles linalool and β-myrcene. Electroantennogram assays showed gravid S. frugiperda could detect both linalool and β-myrcene. Quantification of headspace volatiles in a maize tps1 loss-of-function mutant confirmed that ZmTPS1 is an important contributor to linalool and β-myrcene emission in maize. Furthermore, pairwise choice assays between tps1 mutant and wild-type plants showed that ZmTPS1, and by extension its volatile products, aid host location in the chewing insect S. frugiperda, yet repel the sap-sucking pest, Rhopalosiphum maidis (corn leaf aphid). On the other hand, ZmTPS1 had no impact on indirect defense via the recruitment of the parasitoid Cotesia marginiventris. ZmTPS1 is therefore an important mediator of the interactions between maize and its insect pests.

Blind people can actively manipulate virtual objects with a novel tactile device

Frequently in rehabilitation, visually impaired persons are passive agents of exercises with fixed environmental constraints. In fact, a printed tactile map, i.e. a particular picture with a specific spatial arrangement, can usually not be edited. Interaction with map content, instead, facilitates the learning of spatial skills because it exploits mental imagery, manipulation and strategic planning simultaneously. However, it has rarely been applied to maps, mainly because of technological limitations. This study aims to understand if visually impaired people can autonomously build objects that are completely virtual. Specifically, we investigated if a group of twelve blind persons, with a wide age range, could exploit mental imagery to interact with virtual content and actively manipulate it by means of a haptic device. The device is mouse-shaped and designed to jointly perceive, with one finger only, local tactile height and inclination cues of arbitrary scalar fields. Spatial information can be mentally constructed by integrating local tactile cues, given by the device, with global proprioceptive cues, given by hand and arm motion. The experiment consisted of a bi-manual task, in which one hand explored some basic virtual objects and the other hand acted on a keyboard to change the position of one object in real-time. The goal was to merge basic objects into more complex objects, like a puzzle. The experiment spanned different resolutions of the tactile information. We measured task accuracy, efficiency, usability and execution time. The average accuracy in solving the puzzle was 90.5%. Importantly, accuracy was linearly predicted by efficiency, measured as the number of moves needed to solve the task. Subjective parameters linked to usability and spatial resolutions did not predict accuracy; gender modulated the execution time, with men being faster than women. Overall, we show that building purely virtual tactile objects is possible in absence of vision and that the process is measurable and achievable in partial autonomy. Introducing virtual tactile graphics in rehabilitation protocols could facilitate the stimulation of mental imagery, a basic element for the ability to orient in space. The behavioural variable introduced in the current study can be calculated after each trial and therefore could be used to automatically measure and tailor protocols to specific user needs. In perspective, our experimental setup can inspire remote rehabilitation scenarios for visually impaired people.

Attention shifts in the spatial cueing paradigm reflect direct influences of experience and not top-down goals.

The spatial cueing effect (SCE) that is elicited by informative spatial cues serves as an empirical marker of attention shifts in the spatial cueing paradigm, and it has been widely interpreted to reflect a relatively pure form of top-down attention control. Contrary to this interpretation, the present study examined the extent to which the magnitude of the SCE could be due to learned associations between specific cues and shifts of attention to the corresponding location, while attempting to track potential changes in participants' task goals across the experiment. This was accomplished by using a novel two-choice, spatial cueing paradigm in which participants chose between two spatial validity contexts. One spatial validity context always involved a 25%-valid (uninformative) arrow cue - called the "test" context; whereas the alternate context - called the "inducing" context - was varied between groups. In particular, associations between specific cues and shifts of attention to the corresponding cued location were perfectly predictable in the "strong inducing" context (100%-valid arrow cues) and imperfectly predictable in the "weak inducing" context (70%-valid arrow cues). Consistent with the experience-dependent account, the results showed that the magnitude of the SCE observed in the test context increased as an individual's experience with the strong inducing context increased. Furthermore, these context effects were observed using both overlearned (arrow) and arbitrary (number) cues, as well as when eye movements were controlled. Altogether, these findings suggest that the magnitude of the SCE can be influenced directly by experience, and not by the top-down goals of the individual.

Self-Adaptive Perception of Object's Deformability with Multiple Deformation Attributes Utilizing Biomimetic Mechanoreceptors.

The perception of object's deformability in unstructured interactions relies on both kinesthetic and cutaneous cues to adapt the uncertainties of an object. However, the existing tactile sensors cannot provide adequate cutaneous cues to self-adaptively estimate the material softness, especially in non-standard contact scenarios where the interacting object deviates from the assumption of an elastic half-infinite body. This paper proposes an innovative design of a tactile sensor that integrates the capabilities of two slow-adapting mechanoreceptors within a soft medium, allowing self-decoupled sensing of local pressure and strain at specific locations within the contact interface. By leveraging these localized cutaneous cues, the sensor can accurately and self-adaptively measure the material softness of an object, accommodating variations in thicknesses and applied forces. Furthermore, when combined with a kinesthetic cue from the robot, the sensor can enhance tactile expression by the synergy of two relevant deformation attributes, including material softness and compliance.It is demonstrated that the biomimetic fusion of tactile information can fully comprehend the deformability of an object, hence facilitating robotic decision-making and dexterous manipulation.

A realistic computational model for the formation of a Place Cell

Hippocampal Place Cells (PCs) are pyramidal neurons showing spatially localized firing when an animal gets into a specific area within an environment. Because of their obvious and clear relation with specific cognitive functions, Place Cells operations and modulations are intensely studied experimentally. However, although a lot of data have been gathered since their discovery, the cellular processes that interplay to turn a hippocampal pyramidal neuron into a Place Cell are still not completely understood. Here, we used a morphologically and biophysically detailed computational model of a CA1 pyramidal neuron to show how, and under which conditions, it can turn into a neuron coding for a specific cue location, through the self-organization of its synaptic inputs in response to external signals targeting different dendritic layers. Our results show that the model is consistent with experimental findings demonstrating PCs stability within the same spatial context over different trajectories, environment rotations, and place field remapping to adapt to changes in the environment. To date, this is the only biophysically and morphologically accurate cellular model of PCs formation, which can be directly used in physiologically accurate microcircuits and large-scale model networks to study cognitive functions and dysfunctions at cellular level.

Transformer-Based Multi-Scale Feature Integration Network for Video Saliency Prediction

Most cutting-edge video saliency prediction models rely on spatiotemporal features extracted by 3D convolutions due to its local contextual cues acquirement ability. However, the shortage of 3D convolutions is that it cannot effectively capture long-term spatiotemporal dependencies in videos. To address this limitation, we propose a novel Transformer-based Multi-scale Feature Integration Network (TMFI-Net) for video saliency prediction, where the proposed TMFI-Net consists of a semantic-guided encoder and a hierarchical decoder. Firstly, embarking on the Transformer-based multi-level spatiotemporal features, the semantic-guided encoder enhances the features by inserting the high-level feature into each level feature via a top-down pathway and a longitudinal connection, which endows the multi-level spatiotemporal features with rich contextual information. In this way, the features are steered to give more concerns to saliency regions. Secondly, the hierarchical decoder employs a multi-dimensional attention (MA) module to elevate features along channel, temporal, and spatial dimensions jointly. Successively, the hierarchical decoder deploys a progressive decoding block to conduct an initial saliency prediction, which provides a coarse localization of saliency regions. Lastly, considering the complementarity of different saliency predictions, we integrate all initial saliency prediction results into the final saliency map. Comprehensive experimental results on four video saliency datasets firmly demonstrate that our model achieves superior performance when compared with the state-of-the-art video saliency models. The code is available at https://github.com/wusonghe/TMFI-Net.

Development and Human Factors Evaluation of a Portable Auditory Localization Training System.

To design and develop a Portable Auditory Localization Acclimation Training (PALAT) system capable of producing psychoacoustically accurate localization cues; evaluate the training effect against a proven full-scale, laboratory-grade system under three listening conditions; and determine if the PALAT system is sensitive to differences among electronic level-dependent hearing protection devices (HPDs). In-laboratory auditory localization training has demonstrated the ability to improve localization performance with the open (natural) ear, that is, unoccluded, and while wearing HPDs. The military requires a portable system capable of imparting similar training benefits as those demonstrated in laboratory experiments. In a full-factorial repeated measures design experiment, 12 audiometrically normal participants completed localization training and testing using an identical, optimized training protocol on two training systems under three listening conditions (open ear, TEP-100, and ComTac™ III). Statistical tests were performed on mean absolute accuracy score and front-back reversal errors. No statistical difference existed between the PALAT and laboratory-grade DRILCOM systems on two dependent localization accuracy measurements at all stages of training. In addition, the PALAT system detected the same localization performance differences among the three listening conditions. The PALAT system imparted similar training benefits as the DRILCOM system and was sensitive to HPD localization performance differences. The user-operable PALAT system and optimized training protocol can be employed by the military, law enforcement, and various industries, to improve auditory localization performance in conditions where auditory situation awareness is critical to safety.

Further investigation of horizontal sound localization in noise

The accuracy of sound localization suffers significantly in a noisy environment. Noise can have an impact on the binaural localization cues such as interaural time difference (ITD) and interaural level difference (ILD). This impact is not only related to the signal-to-noise ratio (SNR), but also to the noise level. Therefore, it's crucial to understand how to describe binaural localization cues in noise. This study investigates the effect of noise level (ranging from 80 dB to 95 dB) and SNR (ranging from -15 dB to -5 dB) on the localization of horizontal sound sources at different azimuths. By analyzing the interaction between different binaural cues and localization accuracy in noise, the results demonstrate that there is a strong association between localization accuracy and interaural partial loudness difference. A follow-up experiment with modified ILD confirmed that the interaural partial loudness difference might more accurately reflect the auditory localization properties of the horizontal sound source in noise.

A skin-integrated device for neck posture monitoring and correction

Cervical spondylosis is a common disease that is often caused by long-term abnormal cervical curvature due to activities such as reading books and using computers or smartphones. This paper explores building an untethered and skin-integrated device in an e-skin form factor to monitor and haptically correct neck posture. The proposed design features a multilayered structure that integrates all flexible electronic circuits and components into a compact skin space while being untethered and skin conformal. An accelerometer in the e-skin attaches to the neck for posture sensing, while four vibration actuators closely touch the neck skin to provide localized vibrotactile stimuli that encode four-direction correction cues of neck flexion pm alpha and lateral bending pm beta. To ensure the reliability of posture sensing and vibrotactile rendering during neck movement, it is necessary to prevent the e-skin device from shifting position. Thus, a hollow structure-based method is implemented for stably attaching the e-skin to the neck skin. Experiments validated the e-skin device’s sensing precision, skin-conformal compliance, stickiness, stability and effectiveness during the motion of neck postures, including its discrimination of localized four-direction vibrotactile cues. A user study verified the device’s performance for sensing and correcting different abnormal neck postures during activities such as using smartphones, reading books, and processing computer files. The proposed e-skin device may create opportunities for more convenient cervical spondylosis prevention and rehabilitation.

Experience-Dependent Plasticity in Nucleus Laminaris of the Barn Owl.

Interaural time differences (ITDs) are a major cue for sound localization and change with increasing head size. Since the barn owl's head width more than doubles in the month after hatching, we hypothesized that the development of their ITD detection circuit might be modified by experience. To test this, we raised owls with unilateral ear inserts that delayed and attenuated the acoustic signal, and then measured the ITD representation in the brainstem nucleus laminaris (NL) when they were adults. The ITD circuit is composed of delay line inputs to coincidence detectors, and we predicted that plastic changes would lead to shorter delays in the axons from the manipulated ear, and complementary shifts in ITD representation on the two sides. In owls that received ear inserts starting around P14, the maps of ITD shifted in the predicted direction, but only on the ipsilateral side, and only in those tonotopic regions that had not experienced auditory stimulation prior to insertion. The contralateral map did not change. Thus, experience-dependent plasticity of the ITD circuit occurs in NL, and our data suggest that ipsilateral and contralateral delays are independently regulated. As a result, altered auditory input during development leads to long-lasting changes in the representation of ITD.Significance Statement The early life of barn owls is marked by increasing sensitivity to sound, and by increasing ITDs. Their prolonged post-hatch development allowed us to examine the role of altered auditory experience in the development of ITD detection circuits. We raised owls with a unilateral ear insert and found that their maps of ITD were altered by experience, but only in those tonotopic regions ipsilateral to the occluded ear that had not experienced auditory stimulation prior to insertion. This experience-induced plasticity allows the sound localization circuits to be customized to individual characteristics, such as the size of the head, and potentially to compensate for imbalanced hearing sensitivities between the left and right ears.

External mechanical loading overrules cell-cell mechanical communication in sprouting angiogenesis during early bone regeneration.

Sprouting angiogenesis plays a key role during bone regeneration. For example, insufficient early revascularization of the injured site can lead to delayed or non-healing. During sprouting, endothelial cells are known to be mechano-sensitive and respond to local mechanical stimuli. Endothelial cells interact and communicate mechanically with their surroundings, such as outer-vascular stromal cells, through cell-induced traction forces. In addition, external physiological loads act at the healing site, resulting in tissue deformations and impacting cellular arrangements. How these two distinct mechanical cues (cell-induced and external) impact angiogenesis and sprout patterning in early bone healing remains however largely unknown. Therefore, the aim of this study was to investigate the relative role of externally applied and cell-induced mechanical signals in driving sprout patterning at the onset of bone healing. To investigate cellular self-organisation in early bone healing, an in silico model accounting for the mechano-regulation of sprouting angiogenesis and stromal cell organization was developed. Computer model predictions were compared to in vivo experiments of a mouse osteotomy model stabilized with a rigid or a semirigid fixation system. We found that the magnitude and orientation of principal strains within the healing region can explain experimentally observed sprout patterning, under both fixation conditions. Furthermore, upon simulating the selective inhibition of either cell-induced or externally applied mechanical cues, external mechanical signals appear to overrule the mechanical communication acting on a cell-cell interaction level. Such findings illustrate the relevance of external mechanical signals over the local cell-mediated mechanical cues and could be used in the design of fracture treatment strategies for bone regeneration.