Notes

Life span method setup throughout mental mastering: size and type associated with chaos usage.
In the context of nondestructive testing (NDT), this paper proposes an inverse problem approach for the reconstruction of high-resolution ultrasonic images from full matrix capture (FMC) datasets. We build a linear model that links the FMC data, i.e. the signals collected from all transmitter-receiver pairs of an ultrasonic array, to the discretized reflectivity map of the inspected object. In particular, this model includes the ultrasonic waveform corresponding to the transducers response. Despite the large amount of data, the inversion problem is illposed. Therefore, a regularization strategy is proposed, where the reconstructed image is defined as the minimizer of a penalized least-squares cost function. A mixed penalization function is considered, which simultaneously enhances the sparsity of the image (in NDT, the reflectivity map is mostly zero except at the flaw locations) and its spatial smoothness (flaws may have some spatial extension). The proposed method is shown to outperform two well-known imaging methods the Total Focusing Method (TFM) and Excitelet. Numerical simulations with two close reflectors show that the proposed method improves the resolution limit defined by the Rayleigh criterion by a factor of four. Such high-resolution imaging capability is confirmed by experimental results obtained with side drilled holes in an aluminum sample.Human brain development is a complex and dynamic process caused by several factors such as genetics, sex hormones, and environmental changes. A number of recent studies on brain development have examined functional connectivity (FC) defined by the temporal correlation between time series of different brain regions. We propose to add the directional flow of information during brain maturation. To do so, we extract effective connectivity (EC) through Granger causality (GC) for two different groups of subjects, i.e., children and young adults. The motivation is that the inclusion of causal interaction may further discriminate brain connections between two age groups and help to discover new connections between brain regions. The contributions of this study are threefold. First, there has been a lack of attention to EC-based feature extraction in the context of brain development. To this end, we propose a new kernel-based GC (KGC) method to learn nonlinearity of complex brain network, where a reduced Sine hyperbolic polynomial (RSP) neural network was used as our proposed learner. Second, we used causality values as the weight for the directional connectivity between brain regions. Our findings indicated that the strength of connections was significantly higher in young adults relative to children. In addition, our new EC-based feature outperformed FC-based analysis from Philadelphia neurocohort (PNC) study with better discrimination of different age groups. Moreover, the fusion of these two sets of features (FC + EC) improved brain age prediction accuracy by more than 4%, indicating that they should be used together for brain development studies.When more than two elemental materials are present in a given object, material quantification may not be robust and accurate when the routine two-material decomposition scheme in current dual energy CT imaging is employed. In this work, we present an innovative scheme to accomplish accurate three-material decomposition with measurements from a dual energy differential phase contrast CT (DE-DPC-CT) acquisition. A DE-DPC-CT system was constructed using a grating interferometer and a photon counting CT imaging system with two energy bins. The DE-DPC-CT system can simultaneously measure both the imaginary and the real part of the complex refractive index to enable a three-material decomposition. Physical phantom with 21 material inserts were constructed and measured using DE-DPC-CT system. Results demonstrates excellent accuracy in elemental material quantification. For example, relative root-mean-square errors of 4.5% for calcium and 5.2% for iodine have been achieved using the proposed three-material decomposition scheme. Biological tissues with iodine inserts were used to demonstrate the potential utility of the proposed spectral CT imaging method. Experimental results showed that the proposed method correctly differentiates the bony structure, iodine, and the soft tissue in the biological specimen samples. A triple spectra CT scan was also performed to benchmark the performance of the DE-DPC-CT scan. Results demonstrated that the material decomposition from the DE-DPCCT has a much lower quantification noise than that from the triple spectra CT scan.Serious games are receiving increasing attention in the field of Cultural Heritage (CH) applications. A special field of CH and education is Intangible Cultural Heritage and particularly dance. Machine learning (ML) tools are necessary elements for the success of a serious game platform since they introduce intelligence in processing and analysis of users' interactivity. ML provides intelligent scoring and monitoring capabilities of the user's progress in a serious game platform. In this paper, we introduce a deep learning model for motion primitive classification. The model combines a convolutional processing layer with a bi-directional analysis module. This way, RGB information is efficiently handled by the hierarchies of convolutions, while the bi-directional properties of an LSTM model are retained. The resulting Convolutionally Enhanced Bi-directional LSTM (CEBi-LSTM) architecture is less sensitive to skeleton errors, occurring using low-cost sensors, such as Kinect, while simultaneously handling the high amount of detail when using RGB visual information.Bayesian networks are a class of popular graphical models that encode causal and conditional independence relations among variables by directed acyclic graphs (DAGs). We propose a novel structure learning method, annealing on regularized Cholesky score (ARCS), to search over topological sorts, or permutations of nodes, for a high-scoring Bayesian network. Our scoring function is derived from regularizing Gaussian DAG likelihood, and its optimization gives an alternative formulation of the sparse Cholesky factorization problem from a statistical viewpoint. We combine simulated annealing over permutation space with a fast proximal gradient algorithm, operating on triangular matrices of edge coefficients, to compute the score of any permutation. Combined, the two approaches allow us to quickly and effectively search over the space of DAGs without the need to verify the acyclicity constraint or to enumerate possible parent sets given a candidate topological sort. Through extensive numerical comparisons, we show that ARCS outperformed existing methods by a substantial margin, demonstrating its great advantage in structure learning of Bayesian networks from both observational and experimental data. We also establish the consistency of our scoring function in estimating topological sorts and DAG structures in the large-sample limit. Source code of ARCS is available at https//github.com/yeqiaoling/arcs_bn.Augmenting neural networks with skip connections, as introduced in the so-called ResNet architecture, surprised the community by enabling the training of networks of more than 1,000 layers with significant performance gains. This paper deciphers ResNet by analyzing the effect of skip connections, and puts forward new theoretical results on the advantages of identity skip connections in neural networks. We prove that the skip connections in the residual blocks facilitate preserving the norm of the gradient, and lead to stable back-propagation, which is desirable from optimization perspective. We also show that, perhaps surprisingly, as more residual blocks are stacked, the norm-preservation of the network is enhanced. Our theoretical arguments are supported by extensive empirical evidence. Can we push for extra norm-preservation? We answer this question by proposing an efficient method to regularize the singular values of the convolution operator and making the ResNet's transition layers extra norm-preserving. Our numerical investigations demonstrate that the learning dynamics and the classification performance of ResNet can be improved by making it even more norm preserving. Our results and the introduced modification for ResNet, referred to as Procrustes ResNets, can be used as a guide for training deeper networks and can also inspire new deeper architectures.This paper addresses the problem of multiple graph matching (MGM) in terms of both offline batch mode and online setting. We explore the concept of cycle-consistency over pairwise matchings and formulate the problem as finding optimal composition path on the supergraph, whose nodes refer to graphs and edge weights denote score function regarding consistency and affinity. By some theoretical study we show that the offline and online MGM on supergraph can be converted to finding all pairwise shortest paths and single-source shortest paths respectively. We adopt the Floyd algorithm [1] and shortest path faster algorithm (SPFA) [2], [3] to effectively find the optimal path. Extensive experimental results show our methods surpass two state-of-the-art MGM methods CAO-C [4] and IMGM [5], for offline and online settings respectively. Source code will be made publicly available.OBJECTIVE Alzheimer's disease is a neurodegenerative disorder that initially presents with memory loss in the presence of underlying neurofibrillary tangle and amyloid plaque pathology. This period offers an early window for detecting subtle cognitive impairment prior to progressive decline and dementia. We recently developed the Visuospatial Memory Eye-Tracking Test (VisMET), a passive task capable of predicting cognitive impairment in AD in under five minutes. Here we describe the development of a mobile version of VisMET to enable efficient and widespread administration of the task. METHODS We delivered VisMET on iPad devices and used a transfer learning approach to train a deep neural network to track eyegaze. Eye movements were used to extract memory features to assess cognitive status in a population of 250 individuals. RESULTS Mild to severe cognitive impairment was identifiable with a test accuracy of 70%. By enforcing a minimal eye tracking calibration error of 2cm, we achieved an accuracy of 76% which is equivalent to the accuracy obtained using commercial hardware for eye-tracking. CONCLUSION This work demonstrates a mobile version of VisMET capable of predicting the severity of cognitive impairment. SIGNIFICANCE Given the ubiquity of tablet devices, our approach has the potential to scale globally.OBJECTIVE The efficacy of deep brain stimulation (DBS) depends on accurate placement of electrodes. Although stereotactic frames enable co-registration of image-based surgical planning and the operative field, the accuracy of electrode placement can be degraded by intra-operative brain shift. CP21 cell line In this study, we adapted a biomechanical model to estimate whole brain displacements from which we deformed preoperative CT (preCT) to generate an updated CT (uCT) that compensates for brain shift. METHODS We drove the deformation model using displacement data derived from deformation in the frontal cortical surface that occurred during the DBS intervention. We evaluated 15 patients, retrospectively, who underwent bilateral DBS surgery, and assessed the accuracy of uCT in terms of target registration error (TRE) relative to a CT acquired post-placement (postCT). We further divided subjects into large (Group L) and small (Group S) deformation groups based on a TRE threshold of 1.6mm. Anterior commissure (AC), posterior commissure (PC) and pineal gland (PG) were identified on preCT and postCT and used to quantify TREs in preCT and uCT.
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