Medical studies have shown that EEG of Alzheimer′s disease (AD) patients is “slower” (i.e., contains more low‐frequency power) and is less complex compared to age‐matched healthy subjects. The relation between those two phenomena has not yet been studied, and they are often silently assumed to be independent. In this paper, it is shown that both phenomena are strongly related. Strong correlation between slowing and loss of complexity is observed in two independent EEG datasets: (1) EEG of predementia patients (a.k.a. Mild Cognitive Impairment; MCI) and control subjects; (2) EEG of mild AD patients and control subjects. The two data sets are from different patients, different hospitals and obtained through different recording systems. The paper also investigates the potential of EEG slowing and loss of EEG complexity as indicators of AD onset. In particular, relative power and complexity measures are used as features to classify the MCI and MiAD patients versus age‐matched control subjects. When combined with two synchrony measures (Granger causality and stochastic event synchrony), classification rates of 83% (MCI) and 98% (MiAD) are obtained. By including the compression ratios as features, slightly better classification rates are obtained than with relative power and synchrony measures alone.

Despite outstanding performance in image recognition, convolutional neural networks (CNNs) do not yet achieve the same impressive results on action recognition in videos. This is partially due to the inability of CNN for modeling long-range temporal structures especially those involving individual action stages that are critical to human action recognition. In this paper, we propose a novel action-stage (ActionS) emphasized spatiotemporal Vector of Locally Aggregated Descriptors (ActionS-STVLAD) method to aggregate informative deep features across the entire video according to adaptive video feature segmentation and adaptive segment feature sampling (AVFS-ASFS). In our ActionSST- VLAD encoding approach, by using AVFS-ASFS, the key frame features are chosen and the corresponding deep features are automatically split into segments with the features in each segment belonging to a temporally coherent ActionS. Then, based on the extracted key frame feature in each segment, a flow-guided warping technique is introduced to detect and discard redundant feature maps, while the informative ones are aggregated by using our exploited similarity weight. Furthermore, we exploit an RGBF modality to capture motion salient regions in the RGB images corresponding to action activity. Extensive experiments are conducted on four public benchmarks - HMDB51, UCF101, Kinetics and ActivityNet for evaluation. Results show that our method is able to effectively pool useful deep features spatiotemporally, leading to state-of-the-art performance for videobased action recognition.

In this paper, it is shown how (naive and structured) variational algorithms may be derived from a factor graph by mechanically applying generic message computation rules; in this way, one can bypass error-prone variational calculus. In prior work by Bishop et al., Xing et al., and Geiger, directed and undirected graphical models have been used for this purpose. The factor graph notation amounts to simpler generic variational message computation rules; by means of factor graphs, variational methods can straightforwardly be compared to and combined with various other message-passing inference algorithms, e.g., Kalman filters and smoothers, iterated conditional modes, expectation maximization (EM), gradient methods, and particle filters. Some of those combinations have been explored in the literature, others seem to be new. Generic message computation rules for such combinations are formulated.

This paper addresses the issue of video-based action recognition by exploiting an advanced multi-stream Convolutional Neural Network (CNN) to fully use semantics-derived multiple modalities in both spatial (appearance) and temporal (motion) domains, since the performance of the CNN-based action recognition methods heavily relate to two factors: semantic visual cues and the network architecture. Our work consists of two major parts. First, to extract useful human-related semantics accurately, we propose a novel spatiotemporal saliency based video object segmentation (STS-VOS) model. By fusing different distinctive saliency maps, which are computed according to object signatures of complementary object detection approaches, a refined spatiotemporal saliency maps can be obtained. In this way, various challenges in the realistic video can be handled jointly. Based on the estimated saliency maps, an energy function is constructed to segment two semantic cues: the actor and one distinctive acting part of the actor. Second, we modify the architecture of the two-stream network (TS-Net) to design a multi-stream network (MS-Net) that consists of three TS-Nets with respect to the extracted semantics, which is able to use deeper abstract visual features of multi-modalities in multi-scale spatiotemporally. Importantly, the performance of action recognition is significantly boosted when integrating the captured human-related semantics into our framework. Experiments on four public benchmarks JHMDB, HMDB51, UCF-Sports and UCF101 demonstrate that the proposed method outperforms the state of the art algorithms.

Crustal rock strength from outer space: The response of crustal rock to stresses is challenging to estimate yet vital for determining risks from events such as earthquakes. Moore et al. take advantage of the recent M _w 7.1 Kumamoto earthquake in Japan to determine the rheology of crustal rocks in the region. The observed inversion of the crustal strain rates demonstrates that certain areas have stiff rock and others (e.g., under the Aso volcanic complex) have much weaker rock. The results match up with expectations, which means that the method can successfully measure rock properties over a wide range of strength and large spatial and temporal scales. Science , this issue p. 163