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Further, results showed the potential to tailor rehabilitation exercises, or assess capacity for daily activity modifications, to optimally load knee tissue, especially when mechanically-induced cartilage degeneration and adaptation are of interest.Lower back pain and related injuries are prevalent and serious problems in various industries, and high compression force to the lumbosacral (L5/S1) region has been known as one of the key factors. Previous research on passive lower back exoskeletons focused on reducing lumbar muscle activation by providing an extensor moment. Additionally, lumbar traction forces can reduce the compression force, and is a common treatment method for lower back pain in clinics. In this paper, we propose a novel passive lower back exoskeleton that provides both extensor moment and lumbar traction force. The working principle of the exoskeleton, extending the coil springs during lumbar flexion, and its design criteria regarding the amount of each force element were provided. The kinematic model explained its operation, and the dynamic simulation estimated its performance and validated its satisfaction with the design criteria. The biomechanical model provided a brief insight into the expected exoskeleton's effect on the reduced lower back compression force. Ten subjects performed static holding and dynamic lifting tasks, and the generated force elements in two directions, parallel and perpendicular to the trunk, were evaluated using a force sensor and electromyography sensors, respectively. The experiment demonstrated a pulling force opposite to the direction of intradiscal pressure and reduced erector spinae activation. This implies the effect of wearing the exoskeleton to decrease the intervertebral pressure during static back bending or heavy lifting tasks.Robot-aided locomotor rehabilitation has proven challenging. To facilitate progress, it is important to first understand the neuro-mechanical dynamics and control of unimpaired human locomotion. Our previous studies found that human gait entrained to periodic torque pulses at the ankle when the pulse period was close to preferred stride duration. Moreover, synchronized gait exhibited a constant phase relation with the pulses so that the robot provided mechanical assistance. To test the generality of mechanical gait entrainment, this study characterized unimpaired human subjects' responses to periodic torque pulses during overground walking. The intervention was applied by a hip exoskeleton robot, Samsung GEMS-H. Gait entrainment was assessed based on the time-course of the phase at which torque pulses occurred within each stride. Experiments were conducted for two consecutive days to evaluate whether the second day elicited more entrainment. Whether entrainment was affected by the difference between pulse period and preferred stride duration was also assessed. Results indicated that the intervention evoked gait entrainment that occurred more often when the period of perturbation was closer to subjects' preferred stride duration, but the difference between consecutive days was insignificant. Entrainment was accompanied by convergence of pulse phase to a similar value across all conditions, where the robot maximized mechanical assistance. Clear evidence of motor adaptation indicated the potential of the intervention for rehabilitation. This study quantified important aspects of the nonlinear neuro-mechanical dynamics underlying unimpaired human walking, which will inform the development of effective approaches to robot-aided locomotor rehabilitation, exploiting natural dynamics in a minimally-encumbering way.We present the results of a scientometric analysis of 30 years of IEEE VIS publications between 1990-2020, in which we conducted a multifaceted analysis of interdisciplinary collaboration and gender composition among authors. To this end, we curated BiblioVIS, a bibliometric dataset that contains rich metadata about IEEE VIS publications, including 3032 papers and 6113 authors. One of the main factors differentiating BiblioVIS from similar datasets is the authors' gender and discipline data, which we inferred through iterative rounds of computational and manual processes. Our analysis shows that, by and large, inter-institutional and interdisciplinary collaboration has been steadily growing over the past 30 years. However, interdisciplinary research was mainly between a few fields, including Computer Science, Engineering and Technology, and Medicine and Health disciplines. Our analysis of gender shows steady growth in women's authorship. Despite this growth, the gender distribution is still highly skewed, with men dominating (~75%) of this space. Our predictive analysis of gender balance shows that if the current trends continue, gender parity in the visualization field will not be reached before the third quarter of the century (~2070). Our primary goal in this work is to call the visualization community's attention to the critical topics of collaboration, diversity, and gender. Our research offers critical insights through the lens of diversity and gender to help accelerate progress towards a more diverse and representative research community.In recent years, the community of object detection has witnessed remarkable progress with the development of deep neural networks. But the detection performance still suffers from the dilemma between complex networks and single-vector predictions. In this paper, we propose a novel approach to boost the object detection performance based on aggregating predictions. First, we propose a unified module with adjustable hyper-structure to generate multiple predictions from a single detection network. Second, we formulate the additive learning for aggregating predictions, which reduces the classification and regression losses by progressively adding the prediction values. Based on the gradient Boosting strategy, the optimization of the additional predictions is further modeled as weighted regression problems to fit the Newton-descent directions. By aggregating multiple predictions from a single network, we propose the BooDet approach which can Bootstrap the classification and bounding box regression for high-performance object Detection. In particular, we plug the BooDet into Cascade R-CNN for object detection. Extensive experiments show that the proposed approach is quite effective to improve object detection. We obtain a 1.3%~2.0% improvement over the strong baseline Cascade R-CNN on COCO val dataset. We achieve 56.5% AP on the COCO test-dev dataset with only bounding box annotations.Traditional image feature matching methods cannot obtain satisfactory results for multi-modal remote sensing images (MRSIs) in most cases because different imaging mechanisms bring significant nonlinear radiation distortion differences (NRD) and complicated geometric distortion. The key to MRSI matching is trying to weakening or eliminating the NRD and extract more edge features. This paper introduces a new robust MRSI matching method based on co-occurrence filter (CoF) space matching (CoFSM). Our algorithm has three steps (1) a new co-occurrence scale space based on CoF is constructed, and the feature points in the new scale space are extracted by the optimized image gradient; (2) the gradient location and orientation histogram algorithm is used to construct a 152-dimensional log-polar descriptor, which makes the multi-modal image description more robust; and (3) a position-optimized Euclidean distance function is established, which is used to calculate the displacement error of the feature points in the horM and MRSI datasets are published https//skyearth.org/publication/project/CoFSM/.Benefiting from the powerful expressive capability of graphs, graph-based approaches have been popularly applied to handle multi-modal medical data and achieved impressive performance in various biomedical applications. For disease prediction tasks, most existing graph-based methods tend to define the graph manually based on specified modality (e.g., demographic information), and then integrated other modalities to obtain the patient representation by Graph Representation Learning (GRL). However, constructing an appropriate graph in advance is not a simple matter for these methods. Meanwhile, the complex correlation between modalities is ignored. These factors inevitably yield the inadequacy of providing sufficient information about the patient's condition for a reliable diagnosis. To this end, we propose an end-to-end Multi-modal Graph Learning framework (MMGL) for disease prediction with multi-modality. To effectively exploit the rich information across multi-modality associated with the disease, modality-aware representation learning is proposed to aggregate the features of each modality by leveraging the correlation and complementarity between the modalities. Furthermore, instead of defining the graph manually, the latent graph structure is captured through an effective way of adaptive graph learning. It could be jointly optimized with the prediction model, thus revealing the intrinsic connections among samples. Our model is also applicable to the scenario of inductive learning for those unseen data. An extensive group of experiments on two disease prediction tasks demonstrates that the proposed MMGL achieves more favorable performance. The code of MMGL is available at https//github.com/SsGood/MMGL.The brains of many organisms are capable of complicated distributed computation underpinned by a highly advanced information processing capacity. Although substantial progress has been made towards characterising the information flow component of this capacity in mature brains, there is a distinct lack of work characterising its emergence during neural development. This lack of progress has been largely driven by the lack of effective estimators of information processing operations for spiking data. Here, we leverage recent advances in this estimation task in order to quantify the changes in transfer entropy during development. We do so by studying the changes in the intrinsic dynamics of the spontaneous activity of developing dissociated neural cell cultures. We find that the quantity of information flowing across these networks undergoes a dramatic increase across development. Moreover, the spatial structure of these flows exhibits a tendency to lock-in at the point when they arise. We also characterise the flow of information during the crucial periods of population bursts. We find that, during these bursts, nodes tend to undertake specialised computational roles as either transmitters, mediators, or receivers of information, with these roles tending to align with their average spike ordering. Further, we find that these roles are regularly locked-in when the information flows are established. Finally, we compare these results to information flows in a model network developing according to a spike-timing-dependent plasticity learning rule. Similar temporal patterns in the development of information flows were observed in these networks, hinting at the broader generality of these phenomena.Humans can often handle daunting tasks with ease by developing a set of strategies to reduce decision-making into simpler problems. The ability to use heuristic strategies demands an advanced level of intelligence and has not been demonstrated in animals. Here, we trained macaque monkeys to play the classic video game Pac-Man. The monkeys' decision-making may be described with a strategy-based hierarchical decision-making model with over 90% accuracy. The model reveals that the monkeys adopted the take-the-best heuristic by using one dominating strategy for their decision-making at a time and formed compound strategies by assembling the basis strategies to handle particular game situations. Selleck Merbarone With the model, the computationally complex but fully quantifiable Pac-Man behavior paradigm provides a new approach to understanding animals' advanced cognition.