Many location-based services are supported by the moving k-nearest neighbour (k-NN) query, which continuously returns the k-nearest data objects for a query point. Most of existing approaches to this problem have focused on a centralized setting, which show poor scalability to work around massive-scale and distributed data sets. In this paper, we propose an efficient distributed solution for k-NN query over moving objects to tackle the increasingly large scale of data. This approach includes a new grid-based index called Block Grid Index (BGI), and a distributed k-NN query algorithm based on BGI. There are three advantages of our approach: (1) BGI can be easily constructed and maintained in a distributed setting; (2) the algorithm is able to return the results set in only two iterations. (3) the efficiency of k-NN query is improved. The efficiency of our solution is verified by extensive experiments with millions of nodes.

An efficient index structure for distributed k-nearest neighbours query processing

Biological motion perception plays a crucial role in understanding the actions of other animals, facilitating effective social interactions. While foundation of biological motion perception is rooted in bottom-up processes, as evidenced by point-light display studies, real-world complexities necessitate the involvement of top-down processes, such as attention and expectation. This study investigates the impact of expectations on biological motion perception using a cued individuation task with point-light display stimuli. We conducted three experiments, each providing advance information about distinct aspects of the subsequent biological motion stimuli – specifically information about action, emotion, and gender. Our results revealed a pattern in the action experiment, where participants demonstrated significantly slower response times for incongruent trials than congruent ones, but only under the 75% cue validity condition. This effect was notably absent in the emotion and gender experiments. Our exploration underscores the multi-faceted nature of biological motion perception, highlighting that while the brain adeptly harnesses prior cues to anticipate and interpret stimuli, the nature and reliability of these cues play a pivotal role on their effects. Specifically, action-related information stands out as an important modulator, possibly due to its evolutionary significance and unique neural processing pathway. These findings not only agree with the principles of predictive processing but also pave the way for future research, emphasizing the need to utilize naturalistic, complex stimuli together with neuroimaging methods to create more comprehensive models of biological motion perception.

Predictive processing in biological motion perception: Evidence from human behavior

Most vertebrates, including humans, are highly adept at detecting and encoding biological motion, even when it is portrayed by just a few point lights attached to the head and major joints. However, the function of subcortical regions in biological motion perception has been scarcely explored. Here, we investigate the role of the superior colliculus in local biological motion processing. Using high-field (3 T) and ultra-high-field (7 T) functional magnetic resonance imaging, we record the neural responses of the superior colliculus to scrambled point-light walkers (with local kinematics retained) in both humans and male macaque monkeys. Results show that the superior colliculus, especially the superficial layers, selectively responds to local biological motion. Furthermore, dynamic causal modeling analysis reveals a subcortical-cortical functional pathway that transmits local biological motion signals from the superior colliculus via the middle temporal visual complex to the posterior superior temporal sulcus in the human brain. These findings suggest the existence of a cross-species mechanism in the superior colliculus that facilitates the detection of local biological motion at the early stage of the visual processing stream. Using high field functional magnetic resonance imaging, the authors record the neural responses of the superior colliculus in both humans and macaque monkeys, and demonstrate that superior colliculus responds to local biological motion cues. 

Detecting biological motion signals in human and monkey superior colliculus: a subcortical-cortical pathway for biological motion perception

<title>Abstract</title> A facing-towards bias is commonly reported when observers are asked to judge the motion directions of others. However, it remains unclear just how accurately observers are able to estimate the motion direction of others. Here, we asked male and female participants to estimate the direction of a point-light walker (PLW) in three experiments. The gender of PLWs was neutral (Experiment 1, 96 participants), clearly male or female (Experiment 2, 72 participants), or more subtlety male or female (Experiment 3, 98 participants). We found that female PLWs showed a stronger reference repulsion bias (RRB) than male PLWs. That is, for female PLWs, the estimates of facing directions were biased away from the boundaries of facing-towards, facing-away, and lateral (left/right)-motion directions. Interestingly, RRBs differed depending on whether the observer was male or female. When the PLW gender difference was clear, the RRB was stronger for female participants than male participants; when the PLW gender difference was reduced, the trend disappeared or was reversed. Finally, the perceived PLW direction was biased towards the previously seen PLW direction, showing serial dependence that was not affected by the PLW and observer genders. In conclusion, the current study shows that observers can accurately estimate PLW directions, but that judgments are curiously affected by both the observers’ and PLWs’ genders. 

Estimations of biological motion walking direction are affected by observer and walker genders

Experts frequently demonstrate superior action anticipation within their domains. Although current hypothesis emphasis the role of kinematic invariants processing, the specific computational mechanisms remain unclear. In this study, table tennis experts and novices were recruited in two action anticipation tasks with normal and point-light displays. Using the kinematic coding framework, we established encoding and readout models to predicts actual action outcomes and subjects' choices, respectively. The results showed that experts consistently outperformed novices in both tasks. By analyzing the intersection between encoding and readout models, we found this superiority stems from experts' proficiency in identifying kinematic invariants and accurately mapping them with action outcomes. Importantly, the invariants mapping in experts showed consistency across display formats and was correlated with their professional training duration. Our work sheds light on the computational bases of the expert’s superiority of action anticipation, indicating the significant of internal models developed through sustained domain-specific experience. 

Computational Bases of Action Anticipation Superiority in Experts: Identifying and Mapping Kinematic Invariants

The human visual system can efficiently extract distinct physical, biological, and social attributes (e.g. facing direction, gender, and emotional state) from biological motion (BM), but how these attributes are encoded in the brain remains largely unknown. In the current study, we used functional magnetic resonance imaging to investigate this issue when participants viewed multidimensional BM stimuli. Using multiple regression representational similarity analysis, we identified distributed brain areas, respectively, related to the processing of facing direction, gender, and emotional state conveyed by BM. These brain areas are governed by a hierarchical structure in which the respective neural encoding of facing direction, gender, and emotional state is modulated by each other in descending order. We further revealed that a portion of the brain areas identified in representational similarity analysis was specific to the neural encoding of each attribute and correlated with the corresponding behavioral results. These findings unravel the brain networks for encoding BM attributes in consideration of their interactions, and highlight that the processing of multidimensional BM attributes is recurrently interactive.

Distributed and hierarchical neural encoding of multidimensional biological motion attributes in the human brain.

http://www.sersc.org/journals/IJDTA/vol10_no4/1.pdf

Grid-based k-Nearest Neighbor Queries over Moving Object Trajectories with MapReduce

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Distributed and hierarchical neural encoding of multidimensional biological motion attributes in the human brain.

Grid-based k-Nearest Neighbor Queries over Moving Object Trajectories with MapReduce