A Novel Equivariant Self-Supervised Vector Network for Three-Dimensional Point Clouds

Increasingly higher demands are being made on the capacity‐limited cognitive capabilities of human operators as they strive to maintain situation awareness (i.e., understanding “what is going on”) and performance in complex tasks. In the current study we asked whether: (a) training administered via a mobile phone‐based app could improve multitasking and (b) improved multitasking in the app would generalize to improved performance and situation awareness in a simulated air traffic control task (ATC). Participants completed the ATC task before and after multiple sessions of app‐based multitasking training or control training. Multitasking on the app improved across training sessions. However, this did not lead to improved performance or situation awareness, or workload reduction, relative to control training on the ATC task. These outcomes indicate that app‐based multitasking training based on repetition of a single training task will not necessarily yield generalizable benefits to human performance in other complex dynamic tasks.

PurposeThe purpose of this study is to apply the motivation–opportunity–ability (MOA) framework to investigate the relationships between ambiguity tolerance (AT), reflective thinking (RT) and performance in a complex task to predict knowledge-sharing intent.Design/methodology/approachIn this study, 190 subjects performed a complex scheduling task in which they were randomly assigned to either participate in RT or not.FindingsResults show that factors of the MOA framework positively predicted knowledge-sharing intent. In addition, RT significantly increased intention to share for individuals with low performance or with low AT.Research limitations/implicationsMore research is needed to determine relationships between complex task performance and knowledge sharing, and the role of learning strategies, particularly self-directed ones such as RT. Future studies may use a larger sample size for more complex analysis.Practical implicationsRT may be used to create a sustainable and low-cost method of increasing knowledge sharing in complex tasks, without which those with low AT or low performance may not have participated.Originality/valueThe study supports the importance of contextual influences and points to how organizations can use RT in addition to individual motivation and ability to encourage knowledge sharing.

Evaluation of control strategies in a complex space-vehicle control task: Effects of training type

We investigated whether the surprisingly good memory performance of alcoholics may result from simple memory performance being spared while performance in complex memory tasks is impaired. Simple word span was contrasted with a complex word span task involving concurrent monitoring and re-organisation of items for recall. To control for disruption of rehearsal in the complex word span task, performance on two additional tasks with disrupted rehearsal but no additional processing components was studied. As hypothesised, the alcoholics showed a deficit in the complex but not the simple word span task. They were also impaired, compared to controls, on both tasks with disrupted rehearsal. The difference between groups remained in the complex span task when scores in simple span and either of the two other tasks were used as covariates. Thus, both executive processes necessary for coping with disrupted rehearsal and additional processes scheduling processing and storage in a complex task may play a role in accounting for working memory deficits found in alcoholics.

This study aimed to explore the influence of task complexity, prior knowledge, and gender on individual learning performance in the domains of knowledge and skills learning outcomes. The study examined task complexity (basic vs. advanced skills), and prior knowledge (less vs. more knowledgeable learners), involving 73 undergraduate students of varying genders. The findings indicated that male students outperformed female students in the skills domain for both low-complexity tasks (basic skills) and high-complexity tasks (advanced skills), but this was not the case in the knowledge-and-understanding (recall) domain. In these domains, females scored slightly higher across both complexity levels. In contrast, for high-complexity females' scores were lower, whereas males' scores remained unaffected. Furthermore, females' students with less knowledge generally did not score as highly. This decreased even more for high-complexity tasks. Conversely, males with less knowledge scored higher than their knowledge-and-understanding (recall) domain. Advanced prior knowledge promotes better performance in complex tasks, and there was gender difference.

A propositional structure is set forth that considers: (1) the role of various forms of feedback in eliciting differing motivational orientations toward performance in tasks that reflect ability; (2) the strength of arousal associated with these various motivational orientations as a function of repeated feedback and the age of the performer; and (3) the impact of differing strengths of arousal upon the performance of simple and complex tasks. On the basis of this propositional statement, a series of hypotheses are formulated and tested concerning predicted effects of various forms of feedback upon second, fourth and sixth grade Flemish boys performing simple and complex tasks in a classroom setting. In general, the results are consistent with predictions that the strength of competitive motives, induced by own and other performance feedback, as compared to individualistic motives induced by own feedback and a control condition of no feedback, increase both as a function of trials and the age of the performer, and that these increases in arousal lead to higher levels of performance in simple, but not complex tasks.

ABSTRACTVisual–Linguistic large models (V‐LLMs) have demonstrated strong semantic understanding capabilities in understanding visual open‐world scenes. However, there is limited research on how to effectively integrate these models with traditional deep learning networks for classify and segment 3D point clouds. This paper explores the joint training mechanisms of V‐VLLMs and Convolutional Neural Networks (CNNs), constructing a pluggable module that enhances 3D object classification and component segmentation using 2D images, 3D point clouds, and language descriptions. The idea of this pluggable module is to employ contrastive learning between the generalized features extracted by large models from text and image features extracted by the CNN to enhance the robustness and understanding accuracy of the original CNN‐based point cloud network. For experiments, we validated the effectiveness and accuracy of the proposed pluggable module using multiple point cloud classification frameworks, based on datasets such as ModelNet40, ScanObjectNN, and ShapeNetCore. The experimental results demonstrate that the multimodal contrastive learning pluggable module significantly enhances performance across various deep learning frameworks, resulting in significant enhancements in classification and segmentation precision. Additionally, for the multiscale feature PointNet++ framework, the proposed multiscale contrastive learning mechanism provides further performance gains. The Code is available at https://github.com/winbigqian/MCL‐3D‐VCNN.

Point cloud analysis is an important field for 3D scene understanding. It has applications in self driving cars and robotics (via LIDAR sensors), 3D graphics, and computer-aided design. Neural networks have recently achieved strong results on point cloud analysis problems such as classification and segmentation. Each point cloud network has the challenge of defining a convolution that can learn useful features on unstructured points. Some recent point cloud convolutions create separate weight matrices for separate directions like a CNN, but apply every weight matrix to every neighbor with soft assignments. This increases computational complexity and makes relatively small neighborhood aggregations expensive to compute. We propose Hard Directional Graph Networks (HDGN), a point cloud model that both learns directional weight matrices and assigns a single matrix to each neighbor, achieving directional convolutions at lower computational cost. HDGN's directional modeling achieves state-of-the-art results on multiple point cloud vision benchmarks.

Aneurysm segmentation in computed tomography angiography (CTA) images is essential for medical intervention aimed at preventing subarachnoid hemorrhages. However, most existing studies tend to overlook the topological characteristics of arteries related to aneurysms, often resulting in suboptimal performance in aneurysm segmentation. To address this challenge, we propose an artery-aware global-to-local framework for aneurysm segmentation (AASeg) using CTA images of head and neck. This framework consists of two key components: 1) a centerline graph network (CG-Net) for aneurysm global localization, and 2) a point cloud network (PC-Net) for local aneurysm segmentation. The centerline graph is generated by extracting artery centerline structures from vessel masks obtained through a pre-trained model for head and neck vessel segmentation. This representation serves as a high-level representation of the artery structure, allowing for analysis of aneurysms along the entire arteries. It facilitates aneurysm localization via aneurysm-segment graph classification along the arteries. Then, local region of aneurysm segment can be sampled from the vessel mask according to the aneurysm-segment graph. Subsequently, aneurysm segmentation is performed on the point cloud constructed from the aneurysm segment through the PC-Net. Extensive experiments show that the proposed framework achieves state-of-the-art performance in aneurysm localization on a main dataset and an external testing dataset, with Recall of 84.1% and 80.7%, false positives per case of 1.72 and 1.69, and segmentation DSC of 66.1% and 60.2%, respectively.

We propose using a statistical-relational model, the Markov Logic Network, for knowledge transfer in reinforcement learning. Our goal is to extract relational knowledge from a source task and use it to speed up learning in a related target task. We show that Markov Logic Networks are effective models for capturing both source-task Q-functions and source-task policies. We apply them via demonstration, which involves using them for decision making in an initial stage of the target task before continuing to learn. Through experiments in the RoboCup simulated-soccer domain, we show that transfer via Markov Logic Networks can significantly improve early performance in complex tasks, and that transferring policies is more effective than transferring Q-functions.

Several studies have demonstrated that error-related neuronal signatures can be successfully detected and used to improve the performance of brain-computer interfaces. However, this has been tested mainly in well-controlled environments and based on temporal features, such as the amplitude of event-related potentials. In this study, we propose a classification algorithm combining frequency features and a weighted SVM to detect the neuronal signatures of errors committed in a complex saccadic go/no-go task. We follow the hypothesis that frequency features yield better discrimination performance in complex tasks, generalize better, and require fewer pre-processing steps. When combining temporal and frequency features, we achieved a balanced classification accuracy of 75% - almost the same as using only frequency features. On the other hand, when using only temporal features, the balanced accuracy decreased to 66%. These findings show that subjects' performance can be automatically detected based on frequency features of error-related neuronal signatures. Additionally, our results revealed that features computed in the pre-response time contribute to the discrimination between correct and erroneous responses, which suggests the existence of error-related patterns even before response execution.

Substantial evidence suggests that test anxiety is associated with poor performance in complex tasks. Based on the differentiation of coordinative and sequential demands on working memory (Mayr & Kliegl, 1993), two studies examined the effects of sequential demands on the relationship between test anxiety and cognitive performance. Both studies found that high sequential demands had beneficial effects on the speed and accuracy of the performance of test-anxious participants. It is suggested that the more frequent memory updates associated with high sequential demands may represent external processing aids that compensate for the restricted memory capacity of individuals with high test anxiety.

In recent years, the field of image processing and computer vision has been deeply transformed by the advent of Convolutional Neural Network (CNN). These deep learning models, inspired by biological processes, have demonstrated remarkable success in tasks such as image classification, object detection, and semantic segmentation. A critical component of this success is feature extraction, where the pooling layer within CNN serves as a nonlinear down-sampling technique. This layer reduces the spatial size of the convolved feature map through operations like average or maximum pooling, significantly impacting the model’s performance in complex tasks. This research introduces a novel pooling algorithm, Right-Neighbor Deviation (RND), designed to enhance the performance of CNN. The RND pooling method aims to capture detailed spatial features and hierarchical representations more effectively, thereby improving the accuracy and resilience of deep learning models. A dataset of multi-class brain tumor classification, incorporating MRI images from the BR35H dataset, is utilized to explore the potential benefits of RND pooling compared to traditional methods. The effectiveness of the proposed method is evaluated using performance metrics including accuracy, precision, recall, and F1 score, achieving generalized values of 98.86%, 98.84%, 98.80%, and 98.80%, respectively. The results highlight the potential of RND pooling to significantly advance the state of the art in brain tumor classification using CNNs, potentially leading to improved diagnosis and treatment outcomes.

Team coordination—members of a group acting together rather than performing specific actions individually—is essential for success in many real-world tasks such as military missions, sports, workplace, or school interactions. However, team coordination is highly variable, which is one reason why its underlying neural processes are largely unknown. Here we used dual electroencephalography (EEG) in dyads to study the neurobehavioral dynamics of team coordination in an ecologically valid task that places intensive demands on joint performance. We present a novel conceptual framework to interpret neurobehavioral variability in terms of degeneracy, a fundamental property of complex biological systems said to enhance flexibility and robustness. We characterize degeneracy conceptually in terms of a manifold representing the geometric locus of the dynamics in the high dimensional state-space of neurobehavioral signals. The geometry and dimensionality of the manifold are determined by task constraints and team coordination requirements which restrict the manifold to trajectories that are conducive to successful task performance. Our results indicate that team coordination is associated with dimensionality reduction of the manifold as evident in increased inter-brain phase coherence of beta and gamma rhythms during critical phases of task performance where subjects exchange information. Team coordination was also found to affect the shape of the manifold manifested as a symmetry breaking of centro-parietal wavelet power patterns across subjects in trials with high team coordination. These results open a conceptual and empirical path to identifying the mechanisms underlying team performance in complex tasks.

Large language models (LLMs) have demon-strated excellent performance in complex tasks through Chain-of-Thought (CoT) reasoning. However, LLMs usually gener-ate long thinking content during reasoning, leading to high inference latency. Speculative decoding (SD) is a general acceleration method but has two limitations that weaken its acceleration effect in CoT reasoning scenarios. Therefore, in this study, we proposed the aggressive speculative decoding (ASD) method for efficient CoT reasoning, alleviating the two shortcomings. First, SD adopts a strict acceptance strategy to ensure the generated results are consistent with the target model. However, multiple feasible reasoning trajectories may lead to correct answers. Therefore, ASD adopts an aggressive acceptance strategy, which accepts more draft tokens based on the deviation between the probabilities. Second, SD only generates a small fixed number of draft tokens in each drafting stage. In contrast, ASD adopts an aggressive drafting strat-egy, which adaptively generates a suitable number of draft tokens by estimating the expected transformation value of the inference latency through confidence. Extensive experiments on different reasoning datasets and LLMs demonstrate the effectiveness of our method. In particular, ASD achieved a significantly better acceleration effect than SD on all datasets while ensuring accuracy.