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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. [https://www.selleckchem.com/products/agi-24512.html read more] 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. 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. RESULTS In the group of large brain deformation, average TREs for uCT were 1.11±0.13 and 1.07±0.38 mm at AC and PC, respectively, compared to 1.85±0.17 and 0.92±0.52 mm for preCT. The model updating approach improved AC localization but did not alter TREs at PC. CONCLUSION This preliminary study suggests that our image updating method may compensate for brain shift around surgical targets of importance during DBS surgery, although further investigation is warranted before conclusive evidence will be available. SIGNIFICANCE With further development and evaluation, our model-based image updating method using intraoperative sparse data may compensate for brain shift in DBS surgery efficiently, and have utility in updating targeting coordinates.Infection by the parasite of malaria is a serious healthcare problem for populations residing primarily in tropical and subtropical countries. Early detection of the disease is essential to reduce both the mortality rate and spreading of the disease in the infected areas. Current methods for malaria diagnosis still rely on microscopic analysis of blood smears, which is a time-consuming and expensive process, in addition of requiring trained examiners to perform the analysis. In this paper, we introduce a novel fast screening tool for malaria based on a portable blood impedance analyzer. The simultaneous multi-tone injection of current and voltage detection of the device allow reducing the screening time (order of seconds) while enhancing the differences in impedance signal registered among frequencies to increase parasitemia level discrimination. We went further to demonstrate the possibility of applying directly the device on blood samples collected from volunteers to distinguish between infected and non-infected samples.