Drive Video Analysis for the Detection of Traffic Near-Miss Incidents

Because of their recent introduction, self-driving cars and advanced driver assistance system (ADAS) equipped vehicles have had little opportunity to learn, the dangerous traffic (including near-miss incident) scenarios that provide normal drivers with strong motivation to drive safely. Accordingly, as a means of providing learning depth, this paper presents a novel traffic database that contains information on a large number of traffic near-miss incidents that were obtained by mounting driving recorders in more than 100 taxis over the course of a decade. The study makes the following two main contributions: (i) In order to assist automated systems in detecting near-miss incidents based on database instances, we created a large-scale traffic near-miss incident database (NIDB) that consists of video clip of dangerous events captured by monocular driving recorders. (ii) To illustrate the applicability of NIDB traffic near-miss incidents, we provide two primary database-related improvements: parameter fine-tuning using various near-miss scenes from NIDB, and foreground/background separation into motion representation. Then, using our new database in conjunction with a monocular driving recorder, we developed a near-miss recognition method that provides automated systems with a performance level that is comparable to a human-level understanding of near-miss incidents (64.5% vs. 68.4% at near-miss recognition, 61.3% vs. 78.7% at near-miss detection).Comment: Accepted to ICRA 201

Counterfactual Explanation of Brain Activity Classifiers Using Image-To-Image Transfer by Generative Adversarial Network

Deep neural networks (DNNs) can accurately decode task-related information from brain activations. However, because of the non-linearity of DNNs, it is generally difficult to explain how and why they assign certain behavioral tasks to given brain activations, either correctly or incorrectly. One of the promising approaches for explaining such a black-box system is counterfactual explanation. In this framework, the behavior of a black-box system is explained by comparing real data and realistic synthetic data that are specifically generated such that the black-box system outputs an unreal outcome. The explanation of the system's decision can be explained by directly comparing the real and synthetic data. Recently, by taking advantage of advances in DNN-based image-to-image translation, several studies successfully applied counterfactual explanation to image domains. In principle, the same approach could be used in functional magnetic resonance imaging (fMRI) data. Because fMRI datasets often contain multiple classes (e.g., multiple behavioral tasks), the image-to-image transformation applicable to counterfactual explanation needs to learn mapping among multiple classes simultaneously. Recently, a new generative neural network (StarGAN) that enables image-to-image transformation among multiple classes has been developed. By adapting StarGAN with some modifications, here, we introduce a novel generative DNN (counterfactual activation generator, CAG) that can provide counterfactual explanations for DNN-based classifiers of brain activations. Importantly, CAG can simultaneously handle image transformation among all the seven classes in a publicly available fMRI dataset. Thus, CAG could provide a counterfactual explanation of DNN-based multiclass classifiers of brain activations. Furthermore, iterative applications of CAG were able to enhance and extract subtle spatial brain activity patterns that affected the classifier's decisions. Together, these results demonstrate that the counterfactual explanation based on image-to-image transformation would be a promising approach to understand and extend the current application of DNNs in fMRI analyses

Functional Differentiation of Memory Retrieval Network in Macaque Posterior Parietal Cortex

SummaryHuman fMRI studies revealed involvement of the posterior parietal cortex (PPC) during memory retrieval. However, corresponding memory-related regions in macaque PPC have not been established. In this monkey fMRI study, comparisons of cortical activity during correct recognition of previously seen items and rejection of unseen items revealed two major PPC activation sites that were differentially characterized by a serial probe recognition paradigm: area PG/PGOp in inferior parietal lobule, along with the hippocampus, was more active for initial item retrieval, while area PEa/DIP in intraparietal sulcus was for the last item. Effective connectivity analyses revealed that connectivity from hippocampus to PG/PGOp, but not to PEa/DIP, increased during initial item retrieval. The two parietal areas with differential serial probe recognition profiles were embedded in two different subnetworks of the brain-wide retrieval-related regions. These functional dissociations in the macaque PPC imply the functional correspondence of retrieval-related PPC networks in macaques and humans

Fermi level tuning of Ag-doped Bi2Se3 topological insulator

The temperature dependence of the resistivity (rho) of Ag-doped Bi2Se3 (AgxBi2-xSe3) shows insulating behavior above 35 K, but below 35 K, rho suddenly decreases with decreasing temperature, in contrast to the metallic behavior for non-doped Bi2Se3 at 1.5-300 K. This significant change in transport properties from metallic behavior clearly shows that the Ag doping of Bi2Se3 can effectively tune the Fermi level downward. The Hall effect measurement shows that carrier is still electron in AgxBi2-xSe3 and the electron density changes with temperature to reasonably explain the transport properties. Furthermore, the positive gating of AgxBi2-xSe3 provides metallic behavior that is similar to that of non-doped Bi2Se3, indicating a successful upward tuning of the Fermi level

Mouse optical imaging for understanding resting-state functional connectivity in human fMRI

Resting-state functional connectivity (FC), which measures the temporal correlation of spontaneous hemodynamic activity between distant brain areas, is a widely accepted method in functional magnetic resonance imaging (fMRI) to assess the connectome of healthy and diseased human brains. A common assumption underlying FC is that it reflects the temporal structure of large-scale neuronal activity that is converted into large-scale hemodynamic activity. However, direct observation of such relationship has been difficult. In this commentary, we describe our recent progress regarding this topic. Recently, transgenic mice that express a genetically encoded calcium indicator (GCaMP) in neocortical neurons are enabling the optical recording of neuronal activity in large-scale with high spatiotemporal resolution. Using these mice, we devised a method to simultaneously monitor neuronal and hemodynamic activity and addressed some key issues related to the neuronal basis of FC. We propose that many important questions about human resting-state fMRI can be answered using GCaMP expressing transgenic mice as a model system

A method for reconstruction of interpretable brain networks from transient synchronization in resting-state BOLD fluctuations

Resting-state (rs) fMRI has been widely used to examine brain-wide large-scale spatiotemporal architectures, known as resting-state networks (RSNs). Recent studies have focused on the temporally evolving characteristics of RSNs, but it is unclear what temporal characteristics are reflected in the networks. To address this issue, we devised a novel method for voxel-based visualization of spatiotemporal characteristics of rs-fMRI with a time scale of tens of seconds. We first extracted clusters of dominant activity-patterns using a region-of-interest approach and then used these temporal patterns of the clusters to obtain voxel-based activation patterns related to the clusters. We found that activation patterns related to the clusters temporally evolved with a characteristic temporal structure and showed mutual temporal alternations over minutes. The voxel-based representation allowed the decoding of activation patterns of the clusters in rs-fMRI using a meta-analysis of functional activations. The activation patterns of the clusters were correlated with behavioral measures. Taken together, our analysis highlights a novel approach to examine brain activity dynamics during rest

Host Immunological Effects of Partial Splenic Embolization in Patients with Liver Cirrhosis

Purpose. Restoration of the balance between T lymphocyte subsets and between Th1/Th2 cytokines together with improvement of antitumor immunity has been reported after hepatosplenectomy in patients with liver cirrhosis (LC) and hepatocellular carcinoma (HCC). However, the detailed effects of partial splenic embolization (PSE) on host immunity are unknown. Accordingly, this study evaluated host immunity in patients with cirrhosis receiving PSE for thrombocytopenia. Methods. Twenty-three adult Japanese patients with cirrhosis and thrombocytopenia underwent PSE using straight coils at our hospital between 2010 and 2015. Blood samples were collected before PSE and 4 weeks after PSE. Results. The platelet counts were significantly higher 4 weeks after PSE compared with before PSE. The white blood cell count (neutrophils, lymphocytes, and monocytes) also increased significantly after PSE. Furthermore, Th1 cells and Th2 cells showed a significant increase at 4 weeks after PSE compared with before PSE, although there was no significant change of Treg cells. Moreover, serum levels of TNF-alpha, soluble TNF receptor I, and soluble Fas were significantly increased after PSE. There was no significant change of the Child-Pugh score. Conclusions. In patients with cirrhosis and thrombocytopenia, PSE not only promoted the recovery of leukopenia and thrombocytopenia but also induced activation of host immunity

On co-activation pattern analysis and non-stationarity of resting brain activity

The non-stationarity of resting-state brain activity has received increasing attention in recent years. Functional connectivity (FC) analysis with short sliding windows and coactivation pattern (CAP) analysis are two widely used methods for assessing the dynamic characteristics of brain activity observed with functional magnetic resonance imaging (fMRI). However, the statistical nature of the dynamics captured by these techniques needs to be verified. In this study, we found that the results of CAP analysis were similar for real fMRI data and simulated stationary data with matching covariance structures and spectral contents. We also found that, for both the real and simulated data, CAPs were clustered into spatially heterogeneous modules. Moreover, for each of the modules in the real data, a spatially similar module was found in the simulated data. The present results suggest that care needs to be taken when interpreting observations drawn from CAP analysis as it does not necessarily reflect non-stationarity or a mixture of states in resting brain activity