Multi-Level Firing with Spiking DS-ResNet: Enabling Better and Deeper Directly-Trained Spiking Neural Networks

Spiking neural networks (SNNs) are bio-inspired neural networks with asynchronous discrete and sparse characteristics, which have increasingly manifested their superiority in low energy consumption. Recent research is devoted to utilizing spatio-temporal information to directly train SNNs by backpropagation. However, the binary and non-differentiable properties of spike activities force directly trained SNNs to suffer from serious gradient vanishing and network degradation, which greatly limits the performance of directly trained SNNs and prevents them from going deeper. In this paper, we propose a multi-level firing (MLF) method based on the existing spatio-temporal back propagation (STBP) method, and spiking dormant-suppressed residual network (spiking DS-ResNet). MLF enables more efficient gradient propagation and the incremental expression ability of the neurons. Spiking DS-ResNet can efficiently perform identity mapping of discrete spikes, as well as provide a more suitable connection for gradient propagation in deep SNNs. With the proposed method, our model achieves superior performances on a non-neuromorphic dataset and two neuromorphic datasets with much fewer trainable parameters and demonstrates the great ability to combat the gradient vanishing and degradation problem in deep SNNs.Comment: Accepted by the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI-22

Effective AER Object Classification Using Segmented Probability-Maximization Learning in Spiking Neural Networks

Address event representation (AER) cameras have recently attracted more attention due to the advantages of high temporal resolution and low power consumption, compared with traditional frame-based cameras. Since AER cameras record the visual input as asynchronous discrete events, they are inherently suitable to coordinate with the spiking neural network (SNN), which is biologically plausible and energy-efficient on neuromorphic hardware. However, using SNN to perform the AER object classification is still challenging, due to the lack of effective learning algorithms for this new representation. To tackle this issue, we propose an AER object classification model using a novel segmented probability-maximization (SPA) learning algorithm. Technically, 1) the SPA learning algorithm iteratively maximizes the probability of the classes that samples belong to, in order to improve the reliability of neuron responses and effectiveness of learning; 2) a peak detection (PD) mechanism is introduced in SPA to locate informative time points segment by segment, based on which information within the whole event stream can be fully utilized by the learning. Extensive experimental results show that, compared to state-of-the-art methods, not only our model is more effective, but also it requires less information to reach a certain level of accuracy.Comment: AAAI 2020 (Oral

Double-shell CeO2:Yb, Er@SiO2@Ag upconversion composite nanofibers as an assistant layer enhanced near-infrared harvesting for dye-sensitized solar cells

Double-shell CeO2:Yb,Er@SiO2@ Ag upconversion composite nanofibers are synthesized by electro- spinning and subsequent process. CeO2:Yb,Er@SiO2@ Ag nanofibers show high upconversion luminescence property due to the coating of amorphous SiO2 and the surface plasmon resonance effect of Ag nanoparticles. CeO2:Yb,Er@SiO2@ Ag nanofibers act as an assistant layer in dye-sensitized solar cells (DSSCs) and enhance the photoelectric conversion efficiency (PCE) to 8.17%. The photocurrent-voltage characteristic is obtained under 980 nm laser as illumination light source. In addition, the absorption of the incident photon-to-current conversion efficiency curve in 900-1000 nm near-infrared light confirms that the introduction of the upconversion nanomaterial broadens the absorption range, improves the utilization rate of the sunlight and increases the PCE of DSSCs. (C) 2018 Elsevier B.V. All rights reserved

Association between behavioral patterns and depression symptoms: dyadic interaction between couples

BackgroundBehavioral patterns are sometimes associated with depression symptoms; however, few studies have considered the intra-couple effects. This study examined the effect of a spouses’ behavioral patterns on depression symptoms within themself and in their spouse.MethodsA total of 61,118 childbearing age participants (30,559 husband-wife dyads) were surveyed. The depression symptoms were assessed using the nine-item Patient Health Questionnaire (PHQ-9). The behavioral patterns were identified by the latent class analysis. The effects of behavioral patterns on the couple’s own depression symptoms (actor effect) and their partner’s depression symptoms (partner effect) were analyzed using the Actor-Partner Interdependence Model (APIM).ResultsThree behavioral patterns were identified: low-risk group, moderate-risk group, and high-risk group. The high risk of these behavior patterns would be associated with a higher score on the PHQ-9; for both husbands and wives, their behavioral patterns were positively associated with PHQ-9 scores (βhusband = 0.53, P < 0.01; βwife = 0.58, P < 0.01). Wives’ behavioral patterns were also positively associated with their husbands’ PHQ-9 scores (β = 0.14, P < 0.01), but husbands’ behavioral patterns were not associated with their wives’ PHQ-9 scores.ConclusionsWives’ depression symptoms were affected only by their own behavioral patterns, whereas husbands’ depression symptoms were influenced by both their own and their spouses’ behavioral patterns

Is management of past and forecasted earnings by IPO firms associated with insider trading?

The objective of our paper is to investigate whether management of past or forecasted earnings by IPO firms in Singapore is associated with insider trading. We examine the reported earnings and forecasted earnings in IPO prospectuses and investigate the relationships between insider trading, earnings management, forecast issuance and forecast bias