Notes

A static correction: TGFβ-activation by simply dendritic tissues hard disks Th17 induction along with intestinal contractility and augments the particular expulsion of the parasite Trichinella spiralis in these animals.
The model achieves the accuracy of 97.28%, recall of 97.36%, and precision of 97.38% even with a limited number of samples. The experimental results demonstrate that, contrary to the transfer learning and deep feature extraction approaches, the proposed MHA-CoroCapsule has an encouraging performance with fewer trainable parameters and does not require pretraining and plenty of training samples.Modern graph neural networks (GNNs) learn node embeddings through multilayer local aggregation and achieve great success in applications on assortative graphs. However, tasks on disassortative graphs usually require non-local aggregation. In addition, we find that local aggregation is even harmful for some disassortative graphs. In this work, we propose a simple yet effective non-local aggregation framework with an efficient attention-guided sorting for GNNs. Based on it, we develop various non-local GNNs. MCC950 We perform thorough experiments to analyze disassortative graph datasets and evaluate our non-local GNNs. Experimental results demonstrate that our non-local GNNs significantly outperform previous state-of-the-art methods on seven benchmark datasets of disassortative graphs, in terms of both model performance and efficiency.Deep neural networks have enabled major progresses in semantic segmentation. However, even the most advanced neural architectures suffer from important limitations. First, they are vulnerable to catastrophic forgetting, i.e. they perform poorly when they are required to incrementally update their model as new classes are available. Second, they rely on large amount of pixel-level annotations to produce accurate segmentation maps. To tackle these issues, we introduce a novel incremental class learning approach for semantic segmentation taking into account a peculiar aspect of this task since each training step provides annotation only for a subset of all possible classes, pixels of the background class exhibit a semantic shift. Therefore, we revisit the traditional distillation paradigm by designing novel loss terms which explicitly account for the background shift. Additionally, we introduce a novel strategy to initialize classifiers parameters at each step in order to prevent biased predictions toward the background class. Finally, we demonstrate that our approach can be extended to point- and scribble-based weakly supervised segmentation, modeling the partial annotations to create priors for unlabeled pixels. We demonstrate the effectiveness of our approach with an extensive evaluation on the Pascal-VOC, ADE20K, and Cityscapes datasets, significantly outperforming state-of-the-art methods.As a challenging problem, few-shot class-incremental learning (FSCIL) continually learns a sequence of tasks, confronting the dilemma between slow forgetting of old knowledge and fast adaptation to new knowledge. link2 In this paper, we concentrate on this '`slow vs. fast'' (SvF) dilemma to determine which knowledge components to be updated in a slow fashion or a fast fashion, and thereby balance old-knowledge preservation and new-knowledge adaptation. We propose a multi-grained SvF learning strategy to cope with the SvF dilemma from two different grains intra-space (within the same feature space) and inter-space (between two different feature spaces). The proposed strategy designs a novel frequency-aware regularization to boost the intra-space SvF capability, and meanwhile develops a new feature space composition operation to enhance the inter-space SvF learning performance. With the multi-grained SvF learning strategy, our method outperforms the state-of-the-art approaches by a large margin.How can we efficiently find very large numbers of clusters C in very large datasets N of potentially high dimensionality D ? Here we address the question by using a novel variational approach to optimize Gaussian mixture models (GMMs) with diagonal covariance matrices. The variational method approximates expectation maximization (EM) by applying truncated posteriors as variational distributions and partial E-steps in combination with coresets. Run time complexity to optimize the clustering objective then reduces from O(NCD) per conventional EM iteration to for a variational EM iteration on coresets (with coreset size and truncation parameter ). Based on the strongly reduced run time complexity per iteration, which scales sublinearly with NC , we then provide a concrete, practically applicable, parallelized and highly efficient clustering algorithm. In numerical experiments on standard large-scale benchmarks we (A) show that also overall clustering times scale sublinearly with NC , and (B) observe substantial wall-clock speedups compared to already highly efficient recently reported results. The algorithm's sublinear scaling allows for applications at scales where alternative methods cease to be applicable. We demonstrate such very large-scale applicability using the YFCC100M benchmark, for which we realize with a GMM of up to 50.000 clusters an optimization of a data density model with up to 150 M parameters.Deep reinforcement learning (RL) agents are becoming increasingly proficient in a range of complex control tasks. However, the agent's behavior is usually difficult to interpret due to the introduction of black-box function, making it difficult to acquire the trust of users. Although there have been some interesting interpretation methods for vision-based RL, most of them cannot uncover temporal causal information, raising questions about their reliability. To address this problem, we present a temporal-spatial causal interpretation (TSCI) model to understand the agent's long-term behavior, which is essential for sequential decision-making. TSCI model builds on the formulation of temporal causality, which reflects the temporal causal relations between sequential observations and decisions of RL agent. Then a separate causal discovery network is employed to identify temporal-spatial causal features, which are constrained to satisfy the temporal causality. TSCI model is applicable to recurrent agents and can discover causal features with high efficiency once trained. The empirical results show that TSCI model can produce high-resolution and sharp attention masks to highlight task-relevant temporal-spatial information that constitutes most evidence about how RL agents make sequential decisions. In addition, we further demonstrate that our method can provide valuable causal interpretations for RL agents from the temporal perspective.Magnetic scaffolds have been investigated as promising tools for the interstitial hyperthermia treatment of bone cancers, to control local recurrence by enhancing radio- and chemotherapy effectiveness. The potential of magnetic scaffolds motivates the development of production strategies enabling tunability of the resulting magnetic properties. link3 Within this framework, deposition and drop-casting of magnetic nanoparticles on suitable scaffolds offer advantages such as ease of production and high loading, although these approaches are often associated with a non-uniform final spatial distribution of nanoparticles in the biomaterial. The implications and the influences of nanoparticle distribution on the final therapeutic application have not yet been investigated thoroughly. In this work, poly-caprolactone scaffolds are magnetized by loading them with synthetic magnetic nanoparticles through a drop-casting deposition and tuned to obtain different distributions of magnetic nanoparticles in the biomaterial. The physicochemical properties of the magnetic scaffolds are analyzed. The microstructure and the morphological alterations due to the reworked drop-casting process are evaluated and correlated to static magnetic measurements. THz tomography is used as an innovative investigation technique to derive the spatial distribution of nanoparticles. Finally, multiphysics simulations are used to investigate the influence on the loading patterns on the interstitial bone tumor hyperthermia treatment.It is necessary to control contact force through modulation of joint stiffness in addition to the position of our limb when manipulating an object. This is achieved by contracting the agonist muscles in an appropriate magnitude, as well as, balancing it with contraction of the antagonist muscles. Here we develop a decoding technique that estimates both the position and torque of a joint of the limb in interaction with an environment based on activities of the agonist-antagonistic muscle pairs using electromyography in real time. The long short-term memory (LSTM) network that is capable of learning time series of a longtime span with varying time lags is employed as the core processor of the proposed technique. We tested both the unidirectional LSTM network and bidirectional LSTM network. A validation was conducted on the wrist joint moving along a given trajectory under resistance generated by a robot. The decoding approach provided an agreement of greater than 93% in kinetics (i.e. torque) estimation and an agreement of greater than 83% in kinematics (i.e. angle) estimation, between the actual and estimated variables, during interactions with an environment. We found no significant differences in performance between the unidirectional LSTM and bidirectional LSTM as the learning device of the proposed decoding method.
A user-friendly steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) prefers no calibration for its target recognition algorithm, however, the existing calibration-free schemes perform still far behind their calibration-based counterparts. To tackle this issue, learning online from the subject's unlabeled data is investigated as a potential approach to boost the performance of the calibration-free SSVEP-based BCIs.

An online adaptation scheme is developed to tune the spatial filters using the online unlabeled data from previous trials, and then developing the online adaptive canonical correlation analysis (OACCA) method.

A simulation study on two public SSVEP datasets (Dataset I and II) with a total of 105 subjects demonstrated that the proposed online adaptation scheme can boost the CCA's averaged information transfer rate (ITR) from 94.60 to 158.87 bits/min in Dataset I and from 85.80 to 123.91 bits/min in Dataset II. Furthermore, in our online experiment it boosted the CCA's ITR from 55.81 bits/min to 95.73 bits/min. More importantly, this online adaptation scheme can be easily combined with any spatial filtering-based algorithms to achieve online learning.

By online adaptation, the proposed OACCA performed much better than the calibration-free CCA, and comparable to the calibration-based algorithms.

This work provides a general way for the SSVEP-based BCIs to learn online from unlabeled data and thus avoid calibration.
This work provides a general way for the SSVEP-based BCIs to learn online from unlabeled data and thus avoid calibration.Adolescent idiopathic scoliosis (AIS) is a common structural spinal deformity and is typically associated with altered muscle properties. However, it is still unclear how muscle activities and the underlying neuromuscular control are changed in the entire scoliotic zone, restricting the corresponding pathology investigation and treatment enhancements. In this study, high-density electromyogram (HD-EMG) was utilized to explore the neuromuscular synergy of back muscle activities. For each of ten AIS patients and ten healthy subjects for comparison, a high-density electrode array was placed on their back from T8 to L4 to record EMG signals when performing five spinal motions (flexion/extension, lateral bending, axial rotation, siting, and standing). From the HD-EMG recordings, muscle synergies were extracted using the non-negative matrix factorization method and the topographical maps of EMG root-mean-square were constructed. For both the AIS and healthy subjects, the experimental results indicated that two muscle synergy groups could explain over 90% of recorded muscle activities for all five motions.
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