Notes

Considering Completeness regarding Under the radar Files upon Physical Operating for kids along with Cerebral Palsy in the Child Therapy Studying Health Method.
Class imbalance is a prevalent phenomenon in various real-world applications and it presents significant challenges to model learning, including deep learning. In this work, we embed ensemble learning into the deep convolutional neural networks (CNNs) to tackle the class-imbalanced learning problem. An ensemble of auxiliary classifiers branching out from various hidden layers of a CNN is trained together with the CNN in an end-to-end manner. To that end, we designed a new loss function that can rectify the bias toward the majority classes by forcing the CNN's hidden layers and its associated auxiliary classifiers to focus on the samples that have been misclassified by previous layers, thus enabling subsequent layers to develop diverse behavior and fix the errors of previous layers in a batch-wise manner. A unique feature of the new method is that the ensemble of auxiliary classifiers can work together with the main CNN to form a more powerful combined classifier, or can be removed after finished training the CNN and thus only acting the role of assisting class imbalance learning of the CNN to enhance the neural network's capability in dealing with class-imbalanced data. Comprehensive experiments are conducted on four benchmark data sets of increasing complexity (CIFAR-10, CIFAR-100, iNaturalist, and CelebA) and the results demonstrate significant performance improvements over the state-of-the-art deep imbalance learning methods.Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction toward cognition and human-level intelligence. In this survey, we provide a comprehensive review of the knowledge graph covering overall research topics about 1) knowledge graph representation learning; 2) knowledge acquisition and completion; 3) temporal knowledge graph; and 4) knowledge-aware applications and summarize recent breakthroughs and perspective directions to facilitate future research. We propose a full-view categorization and new taxonomies on these topics. Knowledge graph embedding is organized from four aspects of representation space, scoring function, encoding models, and auxiliary information. For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning are reviewed. We further explore several emerging topics, including metarelational learning, commonsense reasoning, and temporal knowledge graphs. To facilitate future research on knowledge graphs, we also provide a curated collection of data sets and open-source libraries on different tasks. In the end, we have a thorough outlook on several promising research directions.A state-of-the-art interdisciplinary survey on multi-modal radiogenomic approaches is presented, involving applications to the diagnosis and personalized management of gliomas, a common kind of brain tumours, through noninvasive imaging integrated with genomic information. It encompasses mining tumor radioimages, employing deep learning for the automated extraction of relevant features from the segmented volume of interest (VOI). Gene expression values, from surgically extracted tumor tissues, are often simultaneously analyzed to determine patient specific features. Association between genomic and radiomic features are also explored, in some cases, to determine the imaging surrogates. Deep learning and transfer learning are typically exploited for efficient knowledge discovery and decision-making. Some studies on survival prediction, ensemble learning, and interactive learning are also included. The literature mainly focuses on magnetic resonance imaging (MRI) data of the brain, for learning and validation, and generally involves the NIH TCIA and TCGA repositories as well as the BraTS Challenge databases.In this paper, we propose a bio-molecular algorithm with O( n2 + m ) biological operations, O( 2n ) DNA strands, O( n ) tubes and the longest DNA strand, O( n ), for solving the independent-set problem for any graph G with m edges and n vertices. Next, we show that a new kind of the straightforward Boolean circuit yielded from the bio-molecular solutions with m NAND gates, ( m +n × ( n + 1 )) AND gates and (( n × ( n + 1 ))/2) NOT gates can find the maximal independent-set(s) to the independent-set problem for any graph G with m edges and n vertices. We show that a new kind of the proposed quantum-molecular algorithm can find the maximal independent set(s) with the lower bound Ω ( 2n/2 ) queries and the upper bound O( 2n/2 ) queries. This work offers an obvious evidence for that to solve the independent-set problem in any graph G with m edges and n vertices, bio-molecular computers are able to generate a new kind of the straightforward Boolean circuit such that by means of implementing it quantum computers can give a quadratic speed-up. This work also offers one obvious evidence that quantum computers can significantly accelerate the speed and enhance the scalability of bio-molecular computers. Next, the element distinctness problem with input of n bits is to determine whether the given 2n real numbers are distinct or not. The quantum lower bound of solving the element distinctness problem is Ω ( 2n×(2/3) ) queries in the case of a quantum walk algorithm. We further show that the proposed quantum-molecular algorithm reduces the quantum lower bound to Ω (( 2n/2 )/( [Formula see text]) queries. Furthermore, to justify the feasibility of the proposed quantum-molecular algorithm, we successfully solve a typical independent set problem for a graph G with two vertices and one edge by carrying out experiments on the backend ibmqx4 with five quantum bits and the backend simulator with 32 quantum bits on IBM's quantum computer.Muscle networks describe the functional connectivity between muscles quantified through the decomposition of intermuscular coherence (IMC) to identify shared frequencies at which certain muscles are co-modulated by common neural input. Efforts have been devoted to characterizing muscle networks in healthy subjects but stroke-linked alterations to muscle networks remain unexplored. Muscle networks were assessed for eight key upper extremity muscles during isometric force generation in stroke survivors with mild, moderate, and severe impairment and compared against healthy controls to identify stroke-specificalterations in muscle connectivity. Coherence matrices were decomposed using non-negative matrix factorization. The variance accounted for thresholding was then assessed to identify the number of muscle networks. Results showed that the number of muscle networks decreased in stroke survivors compared to age-matched healthy controls (four networks in the healthy control group) as the severity of post-stroke motor impairment increased (three in the mild- and two in the moderate- and severe-strokegroups). Statistically significant reductions of IMC in the synergistic deltoid muscles in the alpha-band in stroke patients versus healthy controls ( p less then 0.05) were identified. This study represents the first effort, to the best of our knowledge, to assess stroke-linked alterations in functional intermuscular connectivity using muscle network analysis. The findings revealed a pattern of alterations to muscle networks in stroke survivors compared to healthy controls, as a result of the loss of brain function associated with the stroke. These alterations in muscle networks reflected underlying pathophysiology. These findings can help better understand the motor impairment and motor control in stroke and may advance rehabilitation efforts for stroke by identifying the impaired neuromuscular coordination among multiple muscles in the frequency domain.Alcohol Use Disorder (AUD) is a chronic relapsing brain disease characterized by excessive alcohol use, loss of control over alcohol intake, and negative emotional states under no alcohol consumption. The key factor in successful treatment of AUD is the accurate diagnosis for better medical and therapy management. Conventionally, for individuals to be diagnosed with AUD, certain criteria as outlined in the Diagnostic and Statistical Manual of Mental Disorders (DSM) should be met. However, this process is subjective in nature and could be misleading due to memory problems and dishonesty of some AUD patients. In this paper, an assessment scheme for objective diagnosis of AUD is proposed. For this purpose, EEG recording of 31 healthy controls and 31 AUD patients are used for the calculation of effective connectivity (EC) between the various regions of the brain Default Mode Network (DMN). The EC is estimated using partial directed coherence (PDC) which are then used as input to a 3D Convolutional Neural Network (CNN) for binary classification of AUD cases. Using 5-fold cross validation, the classification of AUD vs. HC effective connectivity matrices using the proposed 3D-CNN gives an accuracy of 87.85 ± 4.64 %. For further validation, 32 and 30 subjects are randomly selected for training and testing, respectively, giving 100% correct classification of all the testing subjects.The success of supervised learning-based single image depth estimation methods critically depends on the availability of large-scale dense per-pixel depth annotations, which requires both laborious and expensive annotation process. Therefore, the self-supervised methods are much desirable, which attract significant attention recently. However, depth maps predicted by existing self-supervised methods tend to be blurry with many depth details lost. To overcome these limitations, we propose a novel framework, named MLDA-Net, to obtain per-pixel depth maps with shaper boundaries and richer depth details. Selleckchem CCT245737 Our first innovation is a multi-level feature extraction (MLFE) strategy which can learn rich hierarchical representation. Then, a dual-attention strategy, combining global attention and structure attention, is proposed to intensify the obtained features both globally and locally, resulting in improved depth maps with sharper boundaries. Finally, a reweighted loss strategy based on multi-level outputs is proposed to conduct effective supervision for self-supervised depth estimation. Experimental results demonstrate that our MLDA-Net framework achieves state-of-the-art depth prediction results on the KITTI benchmark for self-supervised monocular depth estimation with different input modes and training modes. Extensive experiments on other benchmark datasets further confirm the superiority of our proposed approach.Inverse synthetic aperture radar (ISAR) imaging for the target with micro-motion parts is influenced by the micro-Doppler (m-D) effects. In this case, the radar echo is generally decomposed into the components from the main body and micro-motion parts of target, respectively, to remove the m-D effects and derive a focused ISAR image of the main body. For the sparse aperture data, however, the radar echo is intentionally or occasionally under-sampled, which defocuses the ISAR image by introducing considerable interference, and deteriorates the performance of signal decomposition for the removal of m-D effects. To address this issue, this paper proposes a novel m-D effects removed sparse aperture ISAR (SA-ISAR) imaging algorithm. Note that during a short interval of ISAR imaging, the range profiles of the main body of target from different pulses are similar, resulting in a low-rank matrix of range profile sequence of main body. For the range profiles of the micro-motion parts, they either spread in different range cells or glint in a single range cell, which results in a sparse matrix of range profile sequence.
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