Stick-Breaking Policy Learning in Dec-POMDPs

Expectation maximization (EM) has recently been shown to be an efficient algorithm for learning finite-state controllers (FSCs) in large decentralized POMDPs (Dec-POMDPs). However, current methods use fixed-size FSCs and often converge to maxima that are far from optimal. This paper considers a variable-size FSC to represent the local policy of each agent. These variable-size FSCs are constructed using a stick-breaking prior, leading to a new framework called \emph{decentralized stick-breaking policy representation} (Dec-SBPR). This approach learns the controller parameters with a variational Bayesian algorithm without having to assume that the Dec-POMDP model is available. The performance of Dec-SBPR is demonstrated on several benchmark problems, showing that the algorithm scales to large problems while outperforming other state-of-the-art methods

Adaptive Temporal Compressive Sensing for Video

This paper introduces the concept of adaptive temporal compressive sensing (CS) for video. We propose a CS algorithm to adapt the compression ratio based on the scene's temporal complexity, computed from the compressed data, without compromising the quality of the reconstructed video. The temporal adaptivity is manifested by manipulating the integration time of the camera, opening the possibility to real-time implementation. The proposed algorithm is a generalized temporal CS approach that can be incorporated with a diverse set of existing hardware systems.Comment: IEEE Interonal International Conference on Image Processing (ICIP),201

Low-Cost Compressive Sensing for Color Video and Depth

A simple and inexpensive (low-power and low-bandwidth) modification is made to a conventional off-the-shelf color video camera, from which we recover {multiple} color frames for each of the original measured frames, and each of the recovered frames can be focused at a different depth. The recovery of multiple frames for each measured frame is made possible via high-speed coding, manifested via translation of a single coded aperture; the inexpensive translation is constituted by mounting the binary code on a piezoelectric device. To simultaneously recover depth information, a {liquid} lens is modulated at high speed, via a variable voltage. Consequently, during the aforementioned coding process, the liquid lens allows the camera to sweep the focus through multiple depths. In addition to designing and implementing the camera, fast recovery is achieved by an anytime algorithm exploiting the group-sparsity of wavelet/DCT coefficients.Comment: 8 pages, CVPR 201

Puerarin Alleviates Neuropathic Pain by Inhibiting Neuroinflammation in Spinal Cord

Neuropathic pain responds poorly to drug treatments, and partial relief is achieved in only about half of the patients. Puerarin, the main constituent of Puerariae Lobatae Radix, has been used extensively in China to treat hypertension and tumor. The current study examined the effects of puerarin on neuropathic pain using two most commonly used animal models: chronic constriction injury (CCI) and diabetic neuropathy. We found that consecutive intrathecal administration of puerarin (4–100 nM) for 7 days inhibited the mechanical and thermal nociceptive response induced by CCI and diabetes without interfering with the normal pain response. Meanwhile, in both models puerarin inhibited the activation of microglia and astroglia in the spinal dorsal horn. Puerarin also reduced the upregulated levels of nuclear factor-κB (NF-κB) and other proinflammatory cytokines, such as IL-6, IL-1β, and TNF-α, in the spinal cord. In summary, puerarin alleviated CCI- and diabetes-induced neuropathic pain, and its effectiveness might be due to the inhibition of neuroinflammation in the spinal cord. The anti-inflammation effect of puerarin might be related to the suppression of spinal NF-κB activation and/or cytokines upregulation. We conclude that puerarin has a significant effect on alleviating neuropathic pain and thus may serve as a therapeutic approach for neuropathic pain