Whetstone Trained Spiking Deep Neural Networks to Spiking Neural Networks

A deep neural network is a non-spiking artificial neural network which uses multiple structured layers to extract features from the input. Spiking neural networks are another type of artificial neural network which closely mimic biology with time dependent pulses to transmit information. Whetstone is a training algorithm for spiking deep neural networks. It modifies the back propagation algorithm, typically used in deep learning, to train a spiking deep neural network, by converting the activation function found in deep neural networks into a threshold used by a spiking neural network. This work converts a spiking deep neural network trained from Whetstone to a traditional spiking neural network in the TENNLab framework. This conversion decomposes the dot product operation found in the convolutional layer of spiking deep neural networks to synapse connections between neurons in traditional spiking neural networks. The conversion also redesigns the neuron and synapse structure in the convolutional layer to trade time for space. A new architecture is created in the TENNLab framework using traditional spiking neural networks, which behave the same as the spiking deep neural network trained by Whetstone before conversion. This new architecture verifies the converted spiking neural network behaves the same as the original spiking deep neural network. This work can convert networks to run on other architectures from TENNLab, and this allows networks from those architectures to be trained with back propagation from Whetstone. This expands the variety of training techniques available to the TENNLab architectures

Media as Other Information for Fundamental Valuation

The media is an important information intermediary. We investigate the informational role of the media by examining whether media content, measured by the sentiment of news articles, contains information about a firm’s fundamental value beyond that conveyed in earnings, book value, and analyst forecasts. We show that incorporating media content into Ohlson’s (1995) residual income model generally improves its ability to predict future residual income, explain current stock prices, and predict future stock prices. Our results are strengthened when media coverage is higher and when media sentiment is more disperse

Emerging smart design of electrodes for micro-supercapacitors: a review

Owing to high power density and long cycle life, micro-supercapacitors (MSCs) are regarded as a prevalent energy storage unit for miniaturized electronics in modern life. A major bottleneck is achieving enhanced energy density without sacrificing both power density and cycle life. To this end, designing electrodes in a “smart” way has emerged as an effective strategy to achieve a trade-off between the energy and power densities of MSCs. In the past few years, considerable research efforts have been devoted to exploring new electrode materials for high capacitance, but designing clever configurations for electrodes has rarely been investigated from a structural point of view, which is also important for MSCs within a limited footprint area, in particular. This review article categorizes and arranges these “smart” design strategies of electrodes into three design concepts: layer-by-layer, scaffold-assisted and rolling origami. The corresponding strengths and challenges are comprehensively summarized, and the potential solutions to resolve these challenges are pointed out. Finally, the smart design principle of the electrodes of MSCs and key perspectives for future research in this field are outlined

CCD photometric study of the W UMa-type binary II CMa in the field of Berkeley 33

The CCD photometric data of the EW-type binary, II CMa, which is a contact star in the field of the middle-aged open cluster Berkeley 33, are presented. The complete R light curve was obtained. In the present paper, using the five CCD epochs of light minimum (three of them are calculated from Mazur et al. (1993)'s data and two from our new data), the orbital period P was revised to 0.22919704 days. The complete R light curve was analyzed by using the 2003 version of W-D (Wilson-Devinney) program. It is found that this is a contact system with a mass ratio $q=0.9$ and a contact factor $f=4.1$. The high mass ratio ($q=0.9$) and the low contact factor ($f=4.1$) indicate that the system just evolved into the marginal contact stage

Wetting equilibrium in a rectangular channel

When a capillary channel with corners is wetted by a fluid, there are regions where the fluid fills the whole cross-section and regions where only the corners are filled by the fluid. The fluid fraction of the partially-filled region, $s^*$, is an important quantity related to the capillary pressure. We calculate the value of $s^*$ for channels with a cross-section slightly deviated from a rectangle: the height is larger in the center than those on the two short sides. We find that a small change in the cross-section geometry leads to a huge change of $s^*$. This result is consistent with experimental observations.Comment: 23 pages, 8 figures, submitted to Soft Matte

Multi-Tenant Provisioning for Quantum Key Distribution Networks with Heuristics and Reinforcement Learning: A Comparative Study

Quantum key distribution (QKD) networks are potential to be widely deployed in the immediate future to provide long-term security for data communications. Given the high price and complexity, multi-tenancy has become a cost-effective pattern for QKD network operations. In this work, we concentrate on addressing the online multi-tenant provisioning (On-MTP) problem for QKD networks, where multiple tenant requests (TRs) arrive dynamically. On-MTP involves scheduling multiple TRs and assigning non-reusable secret keys derived from a QKD network to multiple TRs, where each TR can be regarded as a high-security-demand organization with the dedicated secret-key demand. The quantum key pools (QKPs) are constructed over QKD network infrastructure to improve management efficiency for secret keys. We model the secret-key resources for QKPs and the secret-key demands of TRs using distinct images. To realize efficient On-MTP, we perform a comparative study of heuristics and reinforcement learning (RL) based On-MTP solutions, where three heuristics (i.e., random, fit, and best-fit based On-MTP algorithms) are presented and a RL framework is introduced to realize automatic training of an On-MTP algorithm. The comparative results indicate that with sufficient training iterations the RL-based On-MTP algorithm significantly outperforms the presented heuristics in terms of tenant-request blocking probability and secret-key resource utilization